Updates

.github/workflows/claude-dependabot.yml

@@ -80,7 +80,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/claude.yml

@@ -44,12 +44,6 @@ jobs:
         with:
           fetch-depth: 1
 
-      - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.1
-        with:
-          large-packages: false  # slow
-          docker-images: false  # limited benefit
-
       # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -96,7 +90,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/copilot-setup-steps.yml

@@ -78,7 +78,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -299,4 +299,4 @@ jobs:
           echo "✅ AutoGPT Platform development environment setup complete!"
           echo "🚀 Ready for development with Docker services running"
           echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"
+          echo "🌐 Frontend server: pnpm dev (port 3000)"

.github/workflows/platform-backend-ci.yml

@@ -32,9 +32,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # Use Python 3.13 to match Docker image (see backend/Dockerfile)
-        # ClamAV tests moved to platform-backend-security-ci.yml (runs on merge to master)
-        python-version: ["3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
     runs-on: ubuntu-latest
 
     services:
@@ -50,6 +48,23 @@ jobs:
         env:
           RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
           RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
+      clamav:
+        image: clamav/clamav-debian:latest
+        ports:
+          - 3310:3310
+        env:
+          CLAMAV_NO_FRESHCLAMD: false
+          CLAMD_CONF_StreamMaxLength: 50M
+          CLAMD_CONF_MaxFileSize: 100M
+          CLAMD_CONF_MaxScanSize: 100M
+          CLAMD_CONF_MaxThreads: 4
+          CLAMD_CONF_ReadTimeout: 300
+        options: >-
+          --health-cmd "clamdscan --version || exit 1"
+          --health-interval 30s
+          --health-timeout 10s
+          --health-retries 5
+          --health-start-period 180s
 
     steps:
       - name: Checkout repository
@@ -131,6 +146,35 @@ jobs:
         # outputs:
         # DB_URL, API_URL, GRAPHQL_URL, ANON_KEY, SERVICE_ROLE_KEY, JWT_SECRET
 
+      - name: Wait for ClamAV to be ready
+        run: |
+          echo "Waiting for ClamAV daemon to start..."
+          max_attempts=60
+          attempt=0
+          
+          until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
+            echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
+            sleep 5
+            attempt=$((attempt+1))
+          done
+          
+          if [ $attempt -eq $max_attempts ]; then
+            echo "ClamAV failed to start after $((max_attempts*5)) seconds"
+            echo "Checking ClamAV service logs..."
+            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
+            exit 1
+          fi
+          
+          echo "ClamAV is ready!"
+          
+          # Verify ClamAV is responsive
+          echo "Testing ClamAV connection..."
+          timeout 10 bash -c 'echo "PING" | nc localhost 3310' || {
+            echo "ClamAV is not responding to PING"
+            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
+            exit 1
+          }
+
       - name: Run Database Migrations
         run: poetry run prisma migrate dev --name updates
         env:

.github/workflows/platform-backend-security-ci.yml

@@ -1,145 +0,0 @@
-name: AutoGPT Platform - Backend Security CI
-
-# This workflow runs ClamAV-dependent security tests.
-# It only runs on merge to master to avoid the 3-5 minute ClamAV startup time on every PR.
-
-on:
-  push:
-    branches: [master]
-    paths:
-      - "autogpt_platform/backend/**/file*.py"
-      - "autogpt_platform/backend/**/scan*.py"
-      - "autogpt_platform/backend/**/virus*.py"
-      - "autogpt_platform/backend/**/media*.py"
-      - ".github/workflows/platform-backend-security-ci.yml"
-
-concurrency:
-  group: ${{ format('backend-security-ci-{0}', github.sha) }}
-  cancel-in-progress: false
-
-defaults:
-  run:
-    shell: bash
-    working-directory: autogpt_platform/backend
-
-jobs:
-  security-tests:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    services:
-      redis:
-        image: redis:latest
-        ports:
-          - 6379:6379
-      clamav:
-        image: clamav/clamav-debian:latest
-        ports:
-          - 3310:3310
-        env:
-          CLAMAV_NO_FRESHCLAMD: false
-          CLAMD_CONF_StreamMaxLength: 50M
-          CLAMD_CONF_MaxFileSize: 100M
-          CLAMD_CONF_MaxScanSize: 100M
-          CLAMD_CONF_MaxThreads: 4
-          CLAMD_CONF_ReadTimeout: 300
-        options: >-
-          --health-cmd "clamdscan --version || exit 1"
-          --health-interval 30s
-          --health-timeout 10s
-          --health-retries 5
-          --health-start-period 180s
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      - name: Set up Python 3.13
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.13"
-
-      - name: Setup Supabase
-        uses: supabase/setup-cli@v1
-        with:
-          version: 1.178.1
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          HEAD_POETRY_VERSION=$(python ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Using Poetry version ${HEAD_POETRY_VERSION}"
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-
-      - name: Install Python dependencies
-        run: poetry install
-
-      - name: Generate Prisma Client
-        run: poetry run prisma generate
-
-      - id: supabase
-        name: Start Supabase
-        working-directory: .
-        run: |
-          supabase init
-          supabase start --exclude postgres-meta,realtime,storage-api,imgproxy,inbucket,studio,edge-runtime,logflare,vector,supavisor
-          supabase status -o env | sed 's/="/=/; s/"$//' >> $GITHUB_OUTPUT
-
-      - name: Wait for ClamAV to be ready
-        run: |
-          echo "Waiting for ClamAV daemon to start..."
-          max_attempts=60
-          attempt=0
-
-          until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
-            echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
-            sleep 5
-            attempt=$((attempt+1))
-          done
-
-          if [ $attempt -eq $max_attempts ]; then
-            echo "ClamAV failed to start after $((max_attempts*5)) seconds"
-            exit 1
-          fi
-
-          echo "ClamAV is ready!"
-
-      - name: Run Database Migrations
-        run: poetry run prisma migrate dev --name updates
-        env:
-          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
-
-      - name: Run security-related tests
-        run: |
-          poetry run pytest -v \
-            backend/util/virus_scanner_test.py \
-            backend/util/file_test.py \
-            backend/server/v2/store/media_test.py \
-            -x
-        env:
-          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
-          SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
-          SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
-          JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
-          REDIS_HOST: "localhost"
-          REDIS_PORT: "6379"
-          ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw="
-          CLAMAV_SERVICE_HOST: "localhost"
-          CLAMAV_SERVICE_PORT: "3310"
-          CLAMAV_SERVICE_ENABLED: "true"
-
-    env:
-      CI: true
-      PLAIN_OUTPUT: True
-      RUN_ENV: local
-      PORT: 8080

.github/workflows/platform-frontend-ci.yml

@@ -12,10 +12,6 @@ on:
       - "autogpt_platform/frontend/**"
   merge_group:
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
   run:
     shell: bash
@@ -34,7 +30,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -66,7 +62,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -101,7 +97,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -142,7 +138,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -154,78 +150,35 @@ jobs:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
-      # Docker image tar caching - loads images from cache in parallel for faster startup
-      - name: Set up Docker image cache
-        id: docker-cache
+      - name: Cache Docker layers
         uses: actions/cache@v4
         with:
-          path: ~/docker-cache
-          key: docker-images-frontend-${{ runner.os }}-${{ hashFiles('autogpt_platform/docker-compose.yml') }}
+          path: /tmp/.buildx-cache
+          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
           restore-keys: |
-            docker-images-frontend-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-          )
-
-          # Check if any cached tar files exist
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-
-          echo "Docker images ready for use"
+            ${{ runner.os }}-buildx-frontend-test-
 
       - name: Run docker compose
         run: |
           NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
         env:
           DOCKER_BUILDKIT: 1
+          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
+          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
+
+      - name: Move cache
+        run: |
+          rm -rf /tmp/.buildx-cache
+          if [ -d "/tmp/.buildx-cache-new" ]; then
+            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
+          fi
 
       - name: Wait for services to be ready
         run: |
           echo "Waiting for rest_server to be ready..."
-          timeout 30 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
+          timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
           echo "Waiting for database to be ready..."
-          timeout 30 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
+          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
 
       - name: Create E2E test data
         run: |
@@ -264,27 +217,9 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      # Playwright browser caching - saves 30-60s when cache hits
-      - name: Get Playwright version
-        id: playwright-version
-        run: |
-          echo "version=$(pnpm list @playwright/test --json | jq -r '.[0].dependencies["@playwright/test"].version')" >> $GITHUB_OUTPUT
-
-      - name: Cache Playwright browsers
-        uses: actions/cache@v4
-        id: playwright-cache
-        with:
-          path: ~/.cache/ms-playwright
-          key: playwright-${{ runner.os }}-${{ steps.playwright-version.outputs.version }}
-
-      - name: Install Playwright browsers
-        if: steps.playwright-cache.outputs.cache-hit != 'true'
+      - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 
-      - name: Install Playwright deps only (when cache hit)
-        if: steps.playwright-cache.outputs.cache-hit == 'true'
-        run: pnpm playwright install-deps chromium
-
       - name: Run Playwright tests
         run: pnpm test:no-build
 

.github/workflows/platform-fullstack-ci.yml

@@ -12,10 +12,6 @@ on:
       - "autogpt_platform/**"
   merge_group:
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
   run:
     shell: bash
@@ -34,7 +30,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -70,7 +66,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "22.18.0"
+          node-version: "21"
 
       - name: Enable corepack
         run: corepack enable
@@ -83,66 +79,6 @@ jobs:
         run: |
           cp ../backend/.env.default ../backend/.env
 
-      # Docker image tar caching - loads images from cache in parallel for faster startup
-      - name: Set up Docker image cache
-        id: docker-cache
-        uses: actions/cache@v4
-        with:
-          path: ~/docker-cache
-          key: docker-images-fullstack-${{ runner.os }}-${{ hashFiles('autogpt_platform/docker-compose.yml') }}
-          restore-keys: |
-            docker-images-fullstack-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-          )
-
-          # Check if any cached tar files exist
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-
-          echo "Docker images ready for use"
-
       - name: Run docker compose
         run: |
           docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
@@ -164,9 +100,9 @@ jobs:
       - name: Wait for services to be ready
         run: |
           echo "Waiting for rest_server to be ready..."
-          timeout 30 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
+          timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
           echo "Waiting for database to be ready..."
-          timeout 30 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
+          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
 
       - name: Generate API queries
         run: pnpm generate:api:force

.github/workflows/repo-close-stale-issues.yml

@@ -11,7 +11,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v10
+      - uses: actions/stale@v9
         with:
           # operations-per-run: 5000
           stale-issue-message: >

.github/workflows/repo-pr-label.yml

@@ -61,6 +61,6 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@v6
+      - uses: actions/labeler@v5
         with:
           sync-labels: true

.gitignore

@@ -178,4 +178,3 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-/autogpt_platform/backend/logs

autogpt_platform/CLAUDE.md

@@ -192,8 +192,6 @@ Quick steps:
 Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
 ex: do the inputs and outputs tie well together?
 
-If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
-
 **Modifying the API:**
 
 1. Update route in `/backend/backend/server/routers/`

autogpt_platform/Makefile

@@ -1,4 +1,4 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
 
 # Run just Supabase + Redis + RabbitMQ
 start-core:
@@ -42,10 +42,7 @@ run-frontend:
 
 test-data:
 	cd backend && poetry run python test/test_data_creator.py
-
-load-store-agents:
-	cd backend && poetry run load-store-agents
-
+	
 help:
 	@echo "Usage: make <target>"
 	@echo "Targets:"
@@ -57,5 +54,4 @@ help:
 	@echo "  migrate - Run backend database migrations"
 	@echo "  run-backend - Run the backend FastAPI server"
 	@echo "  run-frontend - Run the frontend Next.js development server"
-	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  test-data - Run the test data creator"

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,10 +1,5 @@
 from .config import verify_settings
-from .dependencies import (
-    get_optional_user_id,
-    get_user_id,
-    requires_admin_user,
-    requires_user,
-)
+from .dependencies import get_user_id, requires_admin_user, requires_user
 from .helpers import add_auth_responses_to_openapi
 from .models import User
 
@@ -13,7 +8,6 @@ __all__ = [
     "get_user_id",
     "requires_admin_user",
     "requires_user",
-    "get_optional_user_id",
     "add_auth_responses_to_openapi",
     "User",
 ]

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -4,53 +4,11 @@ FastAPI dependency functions for JWT-based authentication and authorization.
 These are the high-level dependency functions used in route definitions.
 """
 
-import logging
-
 import fastapi
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 
 from .jwt_utils import get_jwt_payload, verify_user
 from .models import User
 
-optional_bearer = HTTPBearer(auto_error=False)
-
-# Header name for admin impersonation
-IMPERSONATION_HEADER_NAME = "X-Act-As-User-Id"
-
-logger = logging.getLogger(__name__)
-
-
-def get_optional_user_id(
-    credentials: HTTPAuthorizationCredentials | None = fastapi.Security(
-        optional_bearer
-    ),
-) -> str | None:
-    """
-    Attempts to extract the user ID ("sub" claim) from a Bearer JWT if provided.
-
-    This dependency allows for both authenticated and anonymous access. If a valid bearer token is
-    supplied, it parses the JWT and extracts the user ID. If the token is missing or invalid, it returns None,
-    treating the request as anonymous.
-
-    Args:
-        credentials: Optional HTTPAuthorizationCredentials object from FastAPI Security dependency.
-
-    Returns:
-        The user ID (str) extracted from the JWT "sub" claim, or None if no valid token is present.
-    """
-    if not credentials:
-        return None
-
-    try:
-        # Parse JWT token to get user ID
-        from autogpt_libs.auth.jwt_utils import parse_jwt_token
-
-        payload = parse_jwt_token(credentials.credentials)
-        return payload.get("sub")
-    except Exception as e:
-        logger.debug(f"Auth token validation failed (anonymous access): {e}")
-        return None
-
 
 async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
@@ -74,44 +32,16 @@ async def requires_admin_user(
     return verify_user(jwt_payload, admin_only=True)
 
 
-async def get_user_id(
-    request: fastapi.Request, jwt_payload: dict = fastapi.Security(get_jwt_payload)
-) -> str:
+async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
     """
     FastAPI dependency that returns the ID of the authenticated user.
 
-    Supports admin impersonation via X-Act-As-User-Id header:
-    - If the header is present and user is admin, returns the impersonated user ID
-    - Otherwise returns the authenticated user's own ID
-    - Logs all impersonation actions for audit trail
-
     Raises:
         HTTPException: 401 for authentication failures or missing user ID
-        HTTPException: 403 if non-admin tries to use impersonation
     """
-    # Get the authenticated user's ID from JWT
     user_id = jwt_payload.get("sub")
     if not user_id:
         raise fastapi.HTTPException(
             status_code=401, detail="User ID not found in token"
         )
-
-    # Check for admin impersonation header
-    impersonate_header = request.headers.get(IMPERSONATION_HEADER_NAME, "").strip()
-    if impersonate_header:
-        # Verify the authenticated user is an admin
-        authenticated_user = verify_user(jwt_payload, admin_only=False)
-        if authenticated_user.role != "admin":
-            raise fastapi.HTTPException(
-                status_code=403, detail="Only admin users can impersonate other users"
-            )
-
-        # Log the impersonation for audit trail
-        logger.info(
-            f"Admin impersonation: {authenticated_user.user_id} ({authenticated_user.email}) "
-            f"acting as user {impersonate_header} for requesting {request.method} {request.url}"
-        )
-
-        return impersonate_header
-
     return user_id

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -4,10 +4,9 @@ Tests the full authentication flow from HTTP requests to user validation.
 """
 
 import os
-from unittest.mock import Mock
 
 import pytest
-from fastapi import FastAPI, HTTPException, Request, Security
+from fastapi import FastAPI, HTTPException, Security
 from fastapi.testclient import TestClient
 from pytest_mock import MockerFixture
 
@@ -46,7 +45,6 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    @pytest.mark.asyncio
     async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -60,7 +58,6 @@ class TestAuthDependencies:
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    @pytest.mark.asyncio
     async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
@@ -76,7 +73,6 @@ class TestAuthDependencies:
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
@@ -86,7 +82,6 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
@@ -95,7 +90,6 @@ class TestAuthDependencies:
             await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
@@ -111,7 +105,6 @@ class TestAuthDependencies:
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -121,7 +114,6 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
@@ -129,40 +121,31 @@ class TestAuthDependencies:
         with pytest.raises(KeyError):
             await requires_admin_user(jwt_payload)
 
-    @pytest.mark.asyncio
     async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
         """Test get_user_id extracts user ID correctly."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user_id = await get_user_id(request, jwt_payload)
+        user_id = await get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    @pytest.mark.asyncio
     async def test_get_user_id_missing_sub(self):
         """Test get_user_id with missing user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_get_user_id_none_sub(self):
         """Test get_user_id with None user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
         jwt_payload = {"sub": None, "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -187,7 +170,6 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    @pytest.mark.asyncio
     async def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
@@ -202,7 +184,6 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    @pytest.mark.asyncio
     async def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
@@ -222,7 +203,6 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
-    @pytest.mark.asyncio
     async def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
@@ -260,7 +240,6 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    @pytest.mark.asyncio
     async def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
@@ -284,7 +263,6 @@ class TestAuthDependenciesEdgeCases:
         admin = await requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    @pytest.mark.asyncio
     async def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
@@ -298,7 +276,6 @@ class TestAuthDependenciesEdgeCases:
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    @pytest.mark.asyncio
     async def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
@@ -313,7 +290,6 @@ class TestAuthDependenciesEdgeCases:
         assert user.user_id == "user-123"
         assert user.email is None
 
-    @pytest.mark.asyncio
     async def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
@@ -338,7 +314,6 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    @pytest.mark.asyncio
     async def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
@@ -350,7 +325,6 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
-    @pytest.mark.asyncio
     async def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
@@ -359,196 +333,3 @@ class TestAuthDependenciesEdgeCases:
         # Valid case
         user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
         assert user.user_id == "user"
-
-
-class TestAdminImpersonation:
-    """Test suite for admin user impersonation functionality."""
-
-    @pytest.mark.asyncio
-    async def test_admin_impersonation_success(self, mocker: MockerFixture):
-        """Test admin successfully impersonating another user."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger to verify audit logging
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the impersonated user ID
-        assert user_id == "target-user-123"
-
-        # Should log the impersonation attempt
-        mock_logger.info.assert_called_once()
-        log_call = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_call
-        assert "admin@example.com" in log_call
-        assert "target-user-123" in log_call
-
-    @pytest.mark.asyncio
-    async def test_non_admin_impersonation_attempt(self, mocker: MockerFixture):
-        """Test non-admin user attempting impersonation returns 403."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "regular-user",
-            "role": "user",
-            "email": "user@example.com",
-        }
-
-        # Mock verify_user to return regular user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="regular-user", email="user@example.com", role="user"
-        )
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
-
-        assert exc_info.value.status_code == 403
-        assert "Only admin users can impersonate other users" in exc_info.value.detail
-
-    @pytest.mark.asyncio
-    async def test_impersonation_empty_header(self, mocker: MockerFixture):
-        """Test impersonation with empty header falls back to regular user ID."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": ""}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_missing_header(self, mocker: MockerFixture):
-        """Test normal behavior when impersonation header is missing."""
-        request = Mock(spec=Request)
-        request.headers = {}  # No impersonation header
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_audit_logging_details(self, mocker: MockerFixture):
-        """Test that impersonation audit logging includes all required details."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "victim-user-789"}
-        jwt_payload = {
-            "sub": "admin-999",
-            "role": "admin",
-            "email": "superadmin@company.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-999", email="superadmin@company.com", role="admin"
-        )
-
-        # Mock logger to capture audit trail
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Verify all audit details are logged
-        assert user_id == "victim-user-789"
-        mock_logger.info.assert_called_once()
-
-        log_message = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_message
-        assert "superadmin@company.com" in log_message
-        assert "victim-user-789" in log_message
-
-    @pytest.mark.asyncio
-    async def test_impersonation_header_case_sensitivity(self, mocker: MockerFixture):
-        """Test that impersonation header is case-sensitive."""
-        request = Mock(spec=Request)
-        # Use wrong case - should not trigger impersonation
-        request.headers = {"x-act-as-user-id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to admin's own ID (header case mismatch)
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_with_whitespace_header(self, mocker: MockerFixture):
-        """Test impersonation with whitespace in header value."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "  target-user-123  "}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should strip whitespace and impersonate successfully
-        assert user_id == "target-user-123"
-        mock_logger.info.assert_called_once()

autogpt_platform/backend/.env.default

@@ -134,6 +134,13 @@ POSTMARK_WEBHOOK_TOKEN=
 # Error Tracking
 SENTRY_DSN=
 
+# Cloudflare Turnstile (CAPTCHA) Configuration
+# Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
+# This is the backend secret key
+TURNSTILE_SECRET_KEY=
+# This is the verify URL
+TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
+
 # Feature Flags
 LAUNCH_DARKLY_SDK_KEY=
 

autogpt_platform/backend/agents/StoreAgent_rows.csv

@@ -1,242 +0,0 @@
-listing_id,storeListingVersionId,slug,agent_name,agent_video,agent_image,featured,sub_heading,description,categories,useForOnboarding,is_available
-6e60a900-9d7d-490e-9af2-a194827ed632,d85882b8-633f-44ce-a315-c20a8c123d19,flux-ai-image-generator,Flux AI Image Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg""]",false,Transform ideas into breathtaking images,"Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!","[""creative""]",false,true
-f11fc6e9-6166-4676-ac5d-f07127b270c1,c775f60d-b99f-418b-8fe0-53172258c3ce,youtube-transcription-scraper,YouTube Transcription Scraper,https://youtu.be/H8S3pU68lGE,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg""]",false,Fetch the transcriptions from the most popular YouTube videos in your chosen topic,"Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content.","[""writing""]",false,true
-17908889-b599-4010-8e4f-bed19b8f3446,6e16e65a-ad34-4108-b4fd-4a23fced5ea2,business-ownerceo-finder,Decision Maker Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg""]",false,Contact CEOs today,"Find the key decision-makers you need, fast.
-
-This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses you’re looking for and where, and it will:
-
-* Search the area and gather public information
-* Return names, roles, and contact details when available
-* Provide smart Google search suggestions if details aren’t found
-
-Perfect for:
-
-* B2B sales teams seeking verified leads
-* Recruiters sourcing local talent
-* Researchers looking to connect with business leaders
-
-Save hours of manual searching and get straight to the people who matter most.","[""business""]",true,true
-72beca1d-45ea-4403-a7ce-e2af168ee428,415b7352-0dc6-4214-9d87-0ad3751b711d,smart-meeting-brief,Smart Meeting Prep,https://youtu.be/9ydZR2hkxaY,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png""]",true,Business meeting briefings delivered daily,"Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls.
-
-How It Works
-1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day.
-2. It reviews recent email threads with each participant to surface key relationship history and communication context.
-3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data.
-4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points.","[""personal""]",true,true
-9fa5697a-617b-4fae-aea0-7dbbed279976,b8ceb480-a7a2-4c90-8513-181a49f7071f,automated-support-ai,Automated Support Agent,https://youtu.be/nBMfu_5sgDA,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png""]",true,Automate up to 80 percent of inbound support emails,"Overview:
-Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution.
-
-How it Works:
-New support emails are routed to the agent.
-The agent checks internal documentation for answers.
-It measures confidence in the answer found and either replies directly or escalates to a human.
-
-Business Value:
-Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times.","[""business""]",false,true
-2bdac92b-a12c-4131-bb46-0e3b89f61413,31daf49d-31d3-476b-aa4c-099abc59b458,unspirational-poster-maker,Unspirational Poster Maker,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg""]",false,Because adulting is hard,"This witty AI agent generates hilariously relatable ""motivational"" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to ""get it together."" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.","[""creative""]",false,true
-9adf005e-2854-4cc7-98cf-f7103b92a7b7,a03b0d8c-4751-43d6-a54e-c3b7856ba4e3,ai-shortform-video-generator-create-viral-ready-content,AI Video Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png""]",false,Create Viral-Ready Shorts Content in Seconds,"OVERVIEW
-Transform any trending headline or broad topic into a polished, vertical short-form video in a single run.  
-The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags.
-
-HOW IT WORKS
-1. Input a topic or an exact news headline.  
-2. The agent fetches live search results and selects the most engaging related story.  
-3. Key facts are summarised into concise research notes.  
-4. Claude writes a 30–35 second script with visual cues, a three-second hook, tension loops, and a call-to-action.  
-5. GPT-4o generates an eye-catching title and one or two discoverability hashtags.  
-6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”).  
-   – All voice, style and resolution settings can be adjusted in the Builder before you press ""Run"".  
-7. Output delivered: Title, Script, Hashtags, Video URL.
-
-KEY USE CASES
-- Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”).  
-- Real-time newsjacking with a specific breaking headline.  
-- Product-launch spotlights and quick event recaps while interest is high.  
-
-BUSINESS VALUE
-- One-click speed: from idea to finished video in minutes.  
-- Consistent brand look: Revid presets keep voice, style and aspect ratio on spec.  
-- No-code workflow: marketers create social video without design or development queues.  
-- Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys.  
-  Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once.
-
-IMPORTANT NOTES
-- The agent outputs exactly one video per execution. Run it again for additional shorts.  
-- Video rendering time varies; AI-generated footage may take several minutes.","[""writing""]",false,true
-864e48ef-fee5-42c1-b6a4-2ae139db9fc1,55d40473-0f31-4ada-9e40-d3a7139fcbd4,automated-blog-writer,Automated SEO Blog Writer,https://youtu.be/nKcDCbDVobs,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg""]",true,"Automate research, writing, and publishing for high-ranking blog posts","Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility.
-
-How it works:
-
-1. Share your pitch, website, and values.
-2. The agent studies your site and uncovers proven SEO opportunities.
-3. It spends two hours researching and drafting each post.
-4. You set the cadence—publishing runs on autopilot.
-
-Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most.
-
-Use cases:
-• Founders: Keep your blog active with no time drain.
-• Agencies: Deliver scalable SEO content for clients.
-• Strategists: Automate execution, focus on strategy.
-• Marketers: Drive steady organic growth.
-• Local businesses: Capture nearby search traffic.","[""writing""]",false,true
-6046f42e-eb84-406f-bae0-8e052064a4fa,a548e507-09a7-4b30-909c-f63fcda10fff,lead-finder-local-businesses,Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp""]",false,Auto-Prospect Like a Pro,"Turbo-charge your local lead generation with the AutoGPT Marketplace’s top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching.
-
-**WHAT IT DOES**
-• Searches Google Maps via the official API (no scraping)
-• Prompts like “dentists in Chicago” or “coffee shops near me”
-• Returns: Name, Website, Rating, Reviews, **Phone & Address**
-• Exports instantly to your CRM, sheet, or outreach workflow
-
-**WHY YOU’LL LOVE IT**
-✓ Hyper-targeted leads in minutes
-✓ Unlimited searches & locations
-✓ Zero CAPTCHAs or IP blocks
-✓ Works on AutoGPT Cloud or self-hosted (with your API key)
-✓ Cut prospecting time by 90%
-
-**PERFECT FOR**
-— Marketers & PPC agencies
-— SEO consultants & designers
-— SaaS founders & sales teams
-
-Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit.
-
-→ Click *Add to Library* and own your market today.","[""business""]",true,true
-f623c862-24e9-44fc-8ce8-d8282bb51ad2,eafa21d3-bf14-4f63-a97f-a5ee41df83b3,linkedin-post-generator,LinkedIn Post Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png""]",false,Auto‑craft LinkedIn gold,"Create research‑driven, high‑impact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed.  
-
-FEATURES  
-• Automated YouTube research – discovers and analyses top‑ranked videos so you don’t have to  
-• AI‑curated synthesis – combines multiple transcripts into one authoritative narrative  
-• Full creative control – adjust style, tone, objective, opinion, clarity, target word count and number of videos  
-• LinkedIn‑optimised output – hook, 2‑3 key points, CTA, strategic line breaks, 3‑5 hashtags, no markdown  
-• One‑click publish – returns a ready‑to‑post text block (≤1 300 characters)  
-
-HOW IT WORKS  
-1. Enter a topic and your preferred writing parameters.  
-2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs.  
-3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet.  
-4. The model writes a concise, engaging post designed for maximum LinkedIn engagement.  
-
-USE CASES  
-• Thought‑leadership updates backed by fresh video research  
-• Rapid industry summaries after major events, webinars, or conferences  
-• Consistent LinkedIn content for busy founders, marketers, and creators  
-
-WHY YOU’LL LOVE IT  
-Save hours of manual research, avoid surface‑level hot‑takes, and publish posts that showcase real expertise—without the heavy lift.","[""writing""]",true,true
-7d4120ad-b6b3-4419-8bdb-7dd7d350ef32,e7bb29a1-23c7-4fee-aa3b-5426174b8c52,youtube-to-linkedin-post-converter,YouTube to LinkedIn Post Converter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png""]",false,Transform Your YouTube Videos into Engaging LinkedIn Posts with AI,"WHAT IT DOES: 
-This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn.
-
-HOW IT WORKS:
-- You provide the URL to the YouTube video (required)
-- You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.)
-- You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.)
-- The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model
-- The models extract key insights, memorable quotes, and the main points from the video
-- You’ll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement
-
-INPUTS:
-- Source YouTube Video – Provide the URL to the YouTube video
-- Structure – Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.)
-- Content – Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.)
-- Tone – Select the tone for the post (e.g., Conversational, Inspirational, etc.)
-
-OUTPUT:
-- LinkedIn Post – A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences
-
-Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding.","[""writing""]",false,true
-c61d6a83-ea48-4df8-b447-3da2d9fe5814,00fdd42c-a14c-4d19-a567-65374ea0e87f,personalized-morning-coffee-newsletter,Personal Newsletter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png""]",false,Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood.,"This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained.
-
-
-How It Works
-1. Enter your favorite topics, industries, or areas of interest.
-2. Choose your tone—professional, casual, or humorous.
-3. Set your preferred delivery cadence: daily or weekly.
-4. The agent scans top sources and compiles 3–5 engaging stories, insights, and fun facts into a conversational newsletter.
-
-Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day.
-
-
-Use Cases
-• Executives: Get a daily digest of market updates and leadership insights.
-• Marketers: Receive curated creative trends and campaign inspiration.
-• Entrepreneurs: Stay updated on your industry without information overload.","[""research""]",true,true
-e2e49cfc-4a39-4d62-a6b3-c095f6d025ff,fc2c9976-0962-4625-a27b-d316573a9e7f,email-address-finder,Email Scout - Contact Finder Assistant,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg""]",false,Find contact details from name and location using AI search,"Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information.
-
-Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like ""Tim Cook, USA"" or ""Sarah Smith, London"" and let the AI assistant do the work of finding potential contact details.
-
-Key Features:
-- Quick search from just name and location
-- Scans multiple public sources
-- Automated AI-powered search process
-- Easy to use with simple inputs
-
-Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact.
-
-Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible.","[""""]",false,true
-81bcc372-0922-4a36-bc35-f7b1e51d6939,e437cc95-e671-489d-b915-76561fba8c7f,ai-youtube-to-blog-converter,YouTube Video to SEO Blog Writer,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png""]",false,One link. One click. One powerful blog post.,"Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts.
-
-Your videos deserve a second life—in writing.  
-Make your content work twice as hard by repurposing it into engaging, searchable articles.
-
-Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest.
-
-FEATURES
-
-• CONTENT ANALYSIS  
-  Extracts key points from the video while preserving your message and intent.
-
-• CUSTOMIZABLE OUTPUT  
-  Select a tone that fits your audience: casual, professional, educational, or formal.
-
-• SEO OPTIMIZATION  
-  Automatically creates engaging titles and structured subheadings for better search visibility.
-
-• USER-FRIENDLY  
-  Repurpose your videos into written content to expand your reach and improve accessibility.
-
-Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless.
-","[""writing""]",true,true
-5c3510d2-fc8b-4053-8e19-67f53c86eb1a,f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9,ai-webpage-copy-improver,AI Webpage Copy Improver,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg""]",false,Boost Your Website's Search Engine Performance,"Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver.","[""marketing""]",true,true
-94d03bd3-7d44-4d47-b60c-edb2f89508d6,b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0,domain-name-finder,Domain Name Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg""]",false,Instantly generate brand-ready domain names that are actually available,"Overview:
-Finding a domain name that fits your brand shouldn’t take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable.
-
-How It Works
-1. Input your product pitch, company name, or core keywords.
-2. The agent analyzes brand tone, audience, and industry context.
-3. It generates a list of unique, memorable domains that match your criteria.
-4. All names are pre-filtered for real-time availability, so you can register immediately.
-
-
-Business Value
-Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains.
-
-
-Key Use Cases
-• Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands.
-• Marketers: Test name options across campaigns with instant availability data.
-• Entrepreneurs: Validate ideas faster with instant domain options.","[""business""]",false,true
-7a831906-daab-426f-9d66-bcf98d869426,516d813b-d1bc-470f-add7-c63a4b2c2bad,ai-function,AI Function,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png""]",false,Never Code Again,"AI FUNCTION MAGIC  
-Your AI‑powered assistant for turning plain‑English descriptions into working Python functions.  
-
-HOW IT WORKS  
-1. Describe what the function should do.  
-2. Specify the inputs it needs.  
-3. Receive the generated Python code.  
-
-FEATURES  
-- Effortless Function Generation: convert natural‑language specs into complete functions.  
-- Customizable Inputs: define the parameters that matter to you.  
-- Versatile Use Cases: simulate data, automate tasks, prototype ideas.  
-- Seamless Integration: add the generated function directly to your codebase.  
-
-EXAMPLE  
-Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.”  
-Input parameter: number_of_people (default 20)  
-Result: a list of dictionaries such as  
-[  
-  { ""name"": ""Emma Martinez"",  ""date_of_birth"": ""1992‑11‑03"", ""job_title"": ""Data Analyst"",        ""age"": 32 },  
-  { ""name"": ""Liam O’Connor"",  ""date_of_birth"": ""1985‑07‑19"", ""job_title"": ""Marketing Manager"",  ""age"": 39 },  
-  …18 more entries…  
-]","[""development""]",false,true

autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json

@@ -1,590 +0,0 @@
-{
-  "id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-  "version": 29,
-  "is_active": false,
-  "name": "Unspirational Poster Maker",
-  "description": "This witty AI agent generates hilariously relatable \"motivational\" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clich\u00e9s and embrace our collective struggles to \"get it together.\" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
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-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Generated Image",
-        "description": "The resulting generated image ready for you to review and post."
-      },
-      "metadata": {
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-          "y": 80.49068076698347
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-          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
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-          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
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-          "source_name": "result",
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autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json

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autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json

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-{
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autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json

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-  "created_at": "2024-12-20T19:47:22.036Z",
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-      "Homepage URL": {
-        "advanced": false,
-        "secret": false,
-        "title": "Homepage URL",
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-    "title": "AIWebpageCopyImproverCredentialsInputSchema",
-    "type": "object"
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-}

autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json

@@ -1,615 +0,0 @@
-{
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-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "claude-sonnet-4-5-20250929",
-        "prompt": "<business_website>\n{{WEBSITE_CONTENT}}\n</business_website>\n\nExtract the Contact Email of {{BUSINESS_NAME}}.\n\nIf no email that can be used to contact {{BUSINESS_NAME}} is present, output `N/A`.\nDo not share any emails other than the email for this specific entity.\n\nIf multiple present pick the likely best one.\n\nRespond with the email (or N/A) inside <email></email> tags.\n\nExample Response:\n\n<thoughts_or_comments>\nThere were many emails present, but luckily one was for {{BUSINESS_NAME}} which I have included below.\n</thoughts_or_comments>\n<email>\nexample@email.com\n</email>",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 2774.879259081777,
-          "y": 243.3102035752969
-        }
-      },
-      "input_links": [
-        {
-          "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_BUSINESS_NAME",
-          "is_static": true
-        },
-        {
-          "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-          "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "results",
-          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-          "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "source_name": "response",
-          "sink_name": "text",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-      "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "source_name": "response",
-      "sink_name": "text",
-      "is_static": false
-    },
-    {
-      "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "source_name": "negative",
-      "sink_name": "values_#_Result",
-      "is_static": false
-    },
-    {
-      "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-      "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "output",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_NAME",
-      "is_static": true
-    },
-    {
-      "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "positive",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_BUSINESS_NAME",
-      "is_static": true
-    },
-    {
-      "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-      "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "source_name": "output",
-      "sink_name": "query",
-      "is_static": false
-    },
-    {
-      "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-      "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_ADDRESS",
-      "is_static": true
-    },
-    {
-      "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-      "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "results",
-      "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2025-01-03T00:46:30.244Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Address": {
-        "advanced": false,
-        "secret": false,
-        "title": "Address",
-        "default": "USA"
-      },
-      "Business Name": {
-        "advanced": false,
-        "secret": false,
-        "title": "Business Name",
-        "default": "Tim Cook"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Email": {
-        "advanced": false,
-        "secret": false,
-        "title": "Email"
-      }
-    },
-    "required": [
-      "Email"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "jina_api_key_credentials": {
-        "credentials_provider": [
-          "jina"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "jina",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "anthropic_api_key_credentials": {
-        "credentials_provider": [
-          "anthropic"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "anthropic",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
-          "google/gemini-2.0-flash-lite-001": "open_router",
-          "google/gemini-2.5-flash": "open_router",
-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
-          "google/gemini-2.5-pro-preview-03-25": "open_router",
-          "google/gemini-3-pro-preview": "open_router",
-          "gpt-3.5-turbo": "openai",
-          "gpt-4-turbo": "openai",
-          "gpt-4.1-2025-04-14": "openai",
-          "gpt-4.1-mini-2025-04-14": "openai",
-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "claude-sonnet-4-5-20250929"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "anthropic_api_key_credentials"
-    ],
-    "title": "EmailAddressFinderCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/backend/blocks/agent.py

@@ -11,7 +11,7 @@ from backend.data.block import (
     BlockType,
     get_block,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
+from backend.data.execution import ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
@@ -72,9 +72,9 @@ class AgentExecutorBlock(Block):
         input_data: Input,
         *,
         graph_exec_id: str,
-        execution_context: ExecutionContext,
         **kwargs,
     ) -> BlockOutput:
+
         from backend.executor import utils as execution_utils
 
         graph_exec = await execution_utils.add_graph_execution(
@@ -83,9 +83,7 @@ class AgentExecutorBlock(Block):
             user_id=input_data.user_id,
             inputs=input_data.inputs,
             nodes_input_masks=input_data.nodes_input_masks,
-            execution_context=execution_context.model_copy(
-                update={"parent_execution_id": graph_exec_id},
-            ),
+            parent_graph_exec_id=graph_exec_id,
         )
 
         logger = execution_utils.LogMetadata(

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -1,4 +1,3 @@
-import asyncio
 from enum import Enum
 from typing import Literal
 
@@ -20,26 +19,11 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import MediaFileType, store_media_file
+from backend.util.file import MediaFileType
 
 
 class GeminiImageModel(str, Enum):
     NANO_BANANA = "google/nano-banana"
-    NANO_BANANA_PRO = "google/nano-banana-pro"
-
-
-class AspectRatio(str, Enum):
-    MATCH_INPUT_IMAGE = "match_input_image"
-    ASPECT_1_1 = "1:1"
-    ASPECT_2_3 = "2:3"
-    ASPECT_3_2 = "3:2"
-    ASPECT_3_4 = "3:4"
-    ASPECT_4_3 = "4:3"
-    ASPECT_4_5 = "4:5"
-    ASPECT_5_4 = "5:4"
-    ASPECT_9_16 = "9:16"
-    ASPECT_16_9 = "16:9"
-    ASPECT_21_9 = "21:9"
 
 
 class OutputFormat(str, Enum):
@@ -84,11 +68,6 @@ class AIImageCustomizerBlock(Block):
             default=[],
             title="Input Images",
         )
-        aspect_ratio: AspectRatio = SchemaField(
-            description="Aspect ratio of the generated image",
-            default=AspectRatio.MATCH_INPUT_IMAGE,
-            title="Aspect Ratio",
-        )
         output_format: OutputFormat = SchemaField(
             description="Format of the output image",
             default=OutputFormat.PNG,
@@ -112,7 +91,6 @@ class AIImageCustomizerBlock(Block):
                 "prompt": "Make the scene more vibrant and colorful",
                 "model": GeminiImageModel.NANO_BANANA,
                 "images": [],
-                "aspect_ratio": AspectRatio.MATCH_INPUT_IMAGE,
                 "output_format": OutputFormat.JPG,
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
@@ -137,25 +115,11 @@ class AIImageCustomizerBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         try:
-            # Convert local file paths to Data URIs (base64) so Replicate can access them
-            processed_images = await asyncio.gather(
-                *(
-                    store_media_file(
-                        graph_exec_id=graph_exec_id,
-                        file=img,
-                        user_id=user_id,
-                        return_content=True,
-                    )
-                    for img in input_data.images
-                )
-            )
-
             result = await self.run_model(
                 api_key=credentials.api_key,
                 model_name=input_data.model.value,
                 prompt=input_data.prompt,
-                images=processed_images,
-                aspect_ratio=input_data.aspect_ratio.value,
+                images=input_data.images,
                 output_format=input_data.output_format.value,
             )
             yield "image_url", result
@@ -168,14 +132,12 @@ class AIImageCustomizerBlock(Block):
         model_name: str,
         prompt: str,
         images: list[MediaFileType],
-        aspect_ratio: str,
         output_format: str,
     ) -> MediaFileType:
         client = ReplicateClient(api_token=api_key.get_secret_value())
 
         input_params: dict = {
             "prompt": prompt,
-            "aspect_ratio": aspect_ratio,
             "output_format": output_format,
         }
 

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -60,14 +60,6 @@ SIZE_TO_RECRAFT_DIMENSIONS = {
     ImageSize.TALL: "1024x1536",
 }
 
-SIZE_TO_NANO_BANANA_RATIO = {
-    ImageSize.SQUARE: "1:1",
-    ImageSize.LANDSCAPE: "4:3",
-    ImageSize.PORTRAIT: "3:4",
-    ImageSize.WIDE: "16:9",
-    ImageSize.TALL: "9:16",
-}
-
 
 class ImageStyle(str, Enum):
     """
@@ -106,7 +98,6 @@ class ImageGenModel(str, Enum):
     FLUX_ULTRA = "Flux 1.1 Pro Ultra"
     RECRAFT = "Recraft v3"
     SD3_5 = "Stable Diffusion 3.5 Medium"
-    NANO_BANANA_PRO = "Nano Banana Pro"
 
 
 class AIImageGeneratorBlock(Block):
@@ -270,20 +261,6 @@ class AIImageGeneratorBlock(Block):
                 )
                 return output
 
-            elif input_data.model == ImageGenModel.NANO_BANANA_PRO:
-                # Use Nano Banana Pro (Google Gemini 3 Pro Image)
-                input_params = {
-                    "prompt": modified_prompt,
-                    "aspect_ratio": SIZE_TO_NANO_BANANA_RATIO[input_data.size],
-                    "resolution": "2K",  # Default to 2K for good quality/cost balance
-                    "output_format": "jpg",
-                    "safety_filter_level": "block_only_high",  # Most permissive
-                }
-                output = await self._run_client(
-                    credentials, "google/nano-banana-pro", input_params
-                )
-                return output
-
         except Exception as e:
             raise RuntimeError(f"Failed to generate image: {str(e)}")
 

autogpt_platform/backend/backend/blocks/airtable/_api.py

@@ -1371,7 +1371,7 @@ async def create_base(
     if tables:
         params["tables"] = tables
 
-    logger.debug(f"Creating Airtable base with params: {params}")
+    print(params)
 
     response = await Requests().post(
         "https://api.airtable.com/v0/meta/bases",

autogpt_platform/backend/backend/blocks/codex.py

@@ -1,224 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Literal
-
-from openai import AsyncOpenAI
-from openai.types.responses import Response as OpenAIResponse
-from pydantic import SecretStr
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    NodeExecutionStats,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-
-
-@dataclass
-class CodexCallResult:
-    """Structured response returned by Codex invocations."""
-
-    response: str
-    reasoning: str
-    response_id: str
-
-
-class CodexModel(str, Enum):
-    """Codex-capable OpenAI models."""
-
-    GPT5_1_CODEX = "gpt-5.1-codex"
-
-
-class CodexReasoningEffort(str, Enum):
-    """Configuration for the Responses API reasoning effort."""
-
-    NONE = "none"
-    LOW = "low"
-    MEDIUM = "medium"
-    HIGH = "high"
-
-
-CodexCredentials = CredentialsMetaInput[
-    Literal[ProviderName.OPENAI], Literal["api_key"]
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="e2fcb203-3f2d-4ad4-a344-8df3bc7db36b",
-    provider="openai",
-    api_key=SecretStr("mock-openai-api-key"),
-    title="Mock OpenAI API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def CodexCredentialsField() -> CodexCredentials:
-    return CredentialsField(
-        description="OpenAI API key with access to Codex models (Responses API).",
-    )
-
-
-class CodeGenerationBlock(Block):
-    """Block that talks to Codex models via the OpenAI Responses API."""
-
-    class Input(BlockSchemaInput):
-        prompt: str = SchemaField(
-            description="Primary coding request passed to the Codex model.",
-            placeholder="Generate a Python function that reverses a list.",
-        )
-        system_prompt: str = SchemaField(
-            title="System Prompt",
-            default=(
-                "You are Codex, an elite software engineer. "
-                "Favor concise, working code and highlight important caveats."
-            ),
-            description="Optional instructions injected via the Responses API instructions field.",
-            advanced=True,
-        )
-        model: CodexModel = SchemaField(
-            title="Codex Model",
-            default=CodexModel.GPT5_1_CODEX,
-            description="Codex-optimized model served via the Responses API.",
-            advanced=False,
-        )
-        reasoning_effort: CodexReasoningEffort = SchemaField(
-            title="Reasoning Effort",
-            default=CodexReasoningEffort.MEDIUM,
-            description="Controls the Responses API reasoning budget. Select 'none' to skip reasoning configs.",
-            advanced=True,
-        )
-        max_output_tokens: int | None = SchemaField(
-            title="Max Output Tokens",
-            default=2048,
-            description="Upper bound for generated tokens (hard limit 128,000). Leave blank to let OpenAI decide.",
-            advanced=True,
-        )
-        credentials: CodexCredentials = CodexCredentialsField()
-
-    class Output(BlockSchemaOutput):
-        response: str = SchemaField(
-            description="Code-focused response returned by the Codex model."
-        )
-        reasoning: str = SchemaField(
-            description="Reasoning summary returned by the model, if available.",
-            default="",
-        )
-        response_id: str = SchemaField(
-            description="ID of the Responses API call for auditing/debugging.",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="86a2a099-30df-47b4-b7e4-34ae5f83e0d5",
-            description="Generate or refactor code using OpenAI's Codex (Responses API).",
-            categories={BlockCategory.AI, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=CodeGenerationBlock.Input,
-            output_schema=CodeGenerationBlock.Output,
-            test_input=[
-                {
-                    "prompt": "Write a TypeScript function that deduplicates an array.",
-                    "credentials": TEST_CREDENTIALS_INPUT,
-                }
-            ],
-            test_output=[
-                ("response", str),
-                ("reasoning", str),
-                ("response_id", str),
-            ],
-            test_mock={
-                "call_codex": lambda *_args, **_kwargs: CodexCallResult(
-                    response="function dedupe<T>(items: T[]): T[] { return [...new Set(items)]; }",
-                    reasoning="Used Set to remove duplicates in O(n).",
-                    response_id="resp_test",
-                )
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-        self.execution_stats = NodeExecutionStats()
-
-    async def call_codex(
-        self,
-        *,
-        credentials: APIKeyCredentials,
-        model: CodexModel,
-        prompt: str,
-        system_prompt: str,
-        max_output_tokens: int | None,
-        reasoning_effort: CodexReasoningEffort,
-    ) -> CodexCallResult:
-        """Invoke the OpenAI Responses API."""
-        client = AsyncOpenAI(api_key=credentials.api_key.get_secret_value())
-
-        request_payload: dict[str, Any] = {
-            "model": model.value,
-            "input": prompt,
-        }
-        if system_prompt:
-            request_payload["instructions"] = system_prompt
-        if max_output_tokens is not None:
-            request_payload["max_output_tokens"] = max_output_tokens
-        if reasoning_effort != CodexReasoningEffort.NONE:
-            request_payload["reasoning"] = {"effort": reasoning_effort.value}
-
-        response = await client.responses.create(**request_payload)
-        if not isinstance(response, OpenAIResponse):
-            raise TypeError(f"Expected OpenAIResponse, got {type(response).__name__}")
-
-        # Extract data directly from typed response
-        text_output = response.output_text or ""
-        reasoning_summary = (
-            str(response.reasoning.summary)
-            if response.reasoning and response.reasoning.summary
-            else ""
-        )
-        response_id = response.id or ""
-
-        # Update usage stats
-        self.execution_stats.input_token_count = (
-            response.usage.input_tokens if response.usage else 0
-        )
-        self.execution_stats.output_token_count = (
-            response.usage.output_tokens if response.usage else 0
-        )
-        self.execution_stats.llm_call_count += 1
-
-        return CodexCallResult(
-            response=text_output,
-            reasoning=reasoning_summary,
-            response_id=response_id,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        **_kwargs,
-    ) -> BlockOutput:
-        result = await self.call_codex(
-            credentials=credentials,
-            model=input_data.model,
-            prompt=input_data.prompt,
-            system_prompt=input_data.system_prompt,
-            max_output_tokens=input_data.max_output_tokens,
-            reasoning_effort=input_data.reasoning_effort,
-        )
-
-        yield "response", result.response
-        yield "reasoning", result.reasoning
-        yield "response_id", result.response_id

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -1,9 +1,8 @@
 import base64
 import io
 import mimetypes
-from enum import Enum
 from pathlib import Path
-from typing import Any, Literal, cast
+from typing import Any
 
 import discord
 from pydantic import SecretStr
@@ -34,19 +33,6 @@ TEST_CREDENTIALS = TEST_BOT_CREDENTIALS
 TEST_CREDENTIALS_INPUT = TEST_BOT_CREDENTIALS_INPUT
 
 
-class ThreadArchiveDuration(str, Enum):
-    """Discord thread auto-archive duration options"""
-
-    ONE_HOUR = "60"
-    ONE_DAY = "1440"
-    THREE_DAYS = "4320"
-    ONE_WEEK = "10080"
-
-    def to_minutes(self) -> int:
-        """Convert the duration string to minutes for Discord API"""
-        return int(self.value)
-
-
 class ReadDiscordMessagesBlock(Block):
     class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
@@ -1180,211 +1166,3 @@ class DiscordChannelInfoBlock(Block):
             raise ValueError(f"Login error occurred: {login_err}")
         except Exception as e:
             raise ValueError(f"An error occurred: {e}")
-
-
-class CreateDiscordThreadBlock(Block):
-    class Input(BlockSchemaInput):
-        credentials: DiscordCredentials = DiscordCredentialsField()
-        channel_name: str = SchemaField(
-            description="Channel ID or channel name to create the thread in"
-        )
-        server_name: str = SchemaField(
-            description="Server name (only needed if using channel name)",
-            advanced=True,
-            default="",
-        )
-        thread_name: str = SchemaField(description="The name of the thread to create")
-        is_private: bool = SchemaField(
-            description="Whether to create a private thread (requires Boost Level 2+) or public thread",
-            default=False,
-        )
-        auto_archive_duration: ThreadArchiveDuration = SchemaField(
-            description="Duration before the thread is automatically archived",
-            advanced=True,
-            default=ThreadArchiveDuration.ONE_WEEK,
-        )
-        message_content: str = SchemaField(
-            description="Optional initial message to send in the thread",
-            advanced=True,
-            default="",
-        )
-
-    class Output(BlockSchemaOutput):
-        status: str = SchemaField(description="Operation status")
-        thread_id: str = SchemaField(description="ID of the created thread")
-        thread_name: str = SchemaField(description="Name of the created thread")
-
-    def __init__(self):
-        super().__init__(
-            id="e8f3c9a2-7b5d-4f1e-9c6a-3d8e2b4f7a1c",
-            input_schema=CreateDiscordThreadBlock.Input,
-            output_schema=CreateDiscordThreadBlock.Output,
-            description="Creates a new thread in a Discord channel.",
-            categories={BlockCategory.SOCIAL},
-            test_input={
-                "channel_name": "general",
-                "thread_name": "Test Thread",
-                "is_private": False,
-                "auto_archive_duration": ThreadArchiveDuration.ONE_HOUR,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("status", "Thread created successfully"),
-                ("thread_id", "123456789012345678"),
-                ("thread_name", "Test Thread"),
-            ],
-            test_mock={
-                "create_thread": lambda *args, **kwargs: {
-                    "status": "Thread created successfully",
-                    "thread_id": "123456789012345678",
-                    "thread_name": "Test Thread",
-                }
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def create_thread(
-        self,
-        token: str,
-        channel_name: str,
-        server_name: str | None,
-        thread_name: str,
-        is_private: bool,
-        auto_archive_duration: ThreadArchiveDuration,
-        message_content: str,
-    ) -> dict:
-        intents = discord.Intents.default()
-        intents.guilds = True
-        intents.message_content = True  # Required for sending messages in threads
-        client = discord.Client(intents=intents)
-
-        result = {}
-
-        @client.event
-        async def on_ready():
-            channel = None
-
-            # Try to parse as channel ID first
-            try:
-                channel_id = int(channel_name)
-                try:
-                    channel = await client.fetch_channel(channel_id)
-                except discord.errors.NotFound:
-                    result["status"] = f"Channel with ID {channel_id} not found"
-                    await client.close()
-                    return
-                except discord.errors.Forbidden:
-                    result["status"] = (
-                        f"Bot does not have permission to view channel {channel_id}"
-                    )
-                    await client.close()
-                    return
-            except ValueError:
-                # Not an ID, treat as channel name
-                # Collect all matching channels to detect duplicates
-                matching_channels = []
-                for guild in client.guilds:
-                    # Skip guilds if server_name is provided and doesn't match
-                    if (
-                        server_name
-                        and server_name.strip()
-                        and guild.name != server_name
-                    ):
-                        continue
-                    for ch in guild.text_channels:
-                        if ch.name == channel_name:
-                            matching_channels.append(ch)
-
-                if not matching_channels:
-                    result["status"] = f"Channel not found: {channel_name}"
-                    await client.close()
-                    return
-                elif len(matching_channels) > 1:
-                    result["status"] = (
-                        f"Multiple channels named '{channel_name}' found. "
-                        "Please specify server_name to disambiguate."
-                    )
-                    await client.close()
-                    return
-                else:
-                    channel = matching_channels[0]
-
-            if not channel:
-                result["status"] = "Failed to resolve channel"
-                await client.close()
-                return
-
-            # Type check - ensure it's a text channel that can create threads
-            if not hasattr(channel, "create_thread"):
-                result["status"] = (
-                    f"Channel {channel_name} cannot create threads (not a text channel)"
-                )
-                await client.close()
-                return
-
-            # After the hasattr check, we know channel is a TextChannel
-            channel = cast(discord.TextChannel, channel)
-
-            try:
-                # Create the thread using discord.py 2.0+ API
-                thread_type = (
-                    discord.ChannelType.private_thread
-                    if is_private
-                    else discord.ChannelType.public_thread
-                )
-
-                # Cast to the specific Literal type that discord.py expects
-                duration_minutes = cast(
-                    Literal[60, 1440, 4320, 10080], auto_archive_duration.to_minutes()
-                )
-
-                # The 'type' parameter exists in discord.py 2.0+ but isn't in type stubs yet
-                # pyright: ignore[reportCallIssue]
-                thread = await channel.create_thread(
-                    name=thread_name,
-                    type=thread_type,
-                    auto_archive_duration=duration_minutes,
-                )
-
-                # Send initial message if provided
-                if message_content:
-                    await thread.send(message_content)
-
-                result["status"] = "Thread created successfully"
-                result["thread_id"] = str(thread.id)
-                result["thread_name"] = thread.name
-
-            except discord.errors.Forbidden as e:
-                result["status"] = (
-                    f"Bot does not have permission to create threads in this channel. {str(e)}"
-                )
-            except Exception as e:
-                result["status"] = f"Error creating thread: {str(e)}"
-            finally:
-                await client.close()
-
-        await client.start(token)
-        return result
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            result = await self.create_thread(
-                token=credentials.api_key.get_secret_value(),
-                channel_name=input_data.channel_name,
-                server_name=input_data.server_name or None,
-                thread_name=input_data.thread_name,
-                is_private=input_data.is_private,
-                auto_archive_duration=input_data.auto_archive_duration,
-                message_content=input_data.message_content,
-            )
-
-            yield "status", result.get("status", "Unknown error")
-            if "thread_id" in result:
-                yield "thread_id", result["thread_id"]
-            if "thread_name" in result:
-                yield "thread_name", result["thread_name"]
-
-        except discord.errors.LoginFailure as login_err:
-            raise ValueError(f"Login error occurred: {login_err}")

autogpt_platform/backend/backend/blocks/email_block.py

@@ -1,6 +1,4 @@
 import smtplib
-import socket
-import ssl
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from typing import Literal
@@ -50,7 +48,9 @@ def SMTPCredentialsField() -> SMTPCredentialsInput:
 
 
 class SMTPConfig(BaseModel):
-    smtp_server: str = SchemaField(description="SMTP server address")
+    smtp_server: str = SchemaField(
+        default="smtp.example.com", description="SMTP server address"
+    )
     smtp_port: int = SchemaField(default=25, description="SMTP port number")
 
     model_config = ConfigDict(title="SMTP Config")
@@ -67,7 +67,10 @@ class SendEmailBlock(Block):
         body: str = SchemaField(
             description="Body of the email", placeholder="Enter the email body"
         )
-        config: SMTPConfig = SchemaField(description="SMTP Config")
+        config: SMTPConfig = SchemaField(
+            description="SMTP Config",
+            default=SMTPConfig(),
+        )
         credentials: SMTPCredentialsInput = SMTPCredentialsField()
 
     class Output(BlockSchemaOutput):
@@ -117,7 +120,7 @@ class SendEmailBlock(Block):
         msg["Subject"] = subject
         msg.attach(MIMEText(body, "plain"))
 
-        with smtplib.SMTP(smtp_server, smtp_port, timeout=30) as server:
+        with smtplib.SMTP(smtp_server, smtp_port) as server:
             server.starttls()
             server.login(smtp_username, smtp_password)
             server.sendmail(smtp_username, to_email, msg.as_string())
@@ -127,59 +130,10 @@ class SendEmailBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: SMTPCredentials, **kwargs
     ) -> BlockOutput:
-        try:
-            status = self.send_email(
-                config=input_data.config,
-                to_email=input_data.to_email,
-                subject=input_data.subject,
-                body=input_data.body,
-                credentials=credentials,
-            )
-            yield "status", status
-        except socket.gaierror:
-            yield "error", (
-                f"Cannot connect to SMTP server '{input_data.config.smtp_server}'. "
-                "Please verify the server address is correct."
-            )
-        except socket.timeout:
-            yield "error", (
-                f"Connection timeout to '{input_data.config.smtp_server}' "
-                f"on port {input_data.config.smtp_port}. "
-                "The server may be down or unreachable."
-            )
-        except ConnectionRefusedError:
-            yield "error", (
-                f"Connection refused to '{input_data.config.smtp_server}' "
-                f"on port {input_data.config.smtp_port}. "
-                "Common SMTP ports are: 587 (TLS), 465 (SSL), 25 (plain). "
-                "Please verify the port is correct."
-            )
-        except smtplib.SMTPNotSupportedError:
-            yield "error", (
-                f"STARTTLS not supported by server '{input_data.config.smtp_server}'. "
-                "Try using port 465 for SSL or port 25 for unencrypted connection."
-            )
-        except ssl.SSLError as e:
-            yield "error", (
-                f"SSL/TLS error when connecting to '{input_data.config.smtp_server}': {str(e)}. "
-                "The server may require a different security protocol."
-            )
-        except smtplib.SMTPAuthenticationError:
-            yield "error", (
-                "Authentication failed. Please verify your username and password are correct."
-            )
-        except smtplib.SMTPRecipientsRefused:
-            yield "error", (
-                f"Recipient email address '{input_data.to_email}' was rejected by the server. "
-                "Please verify the email address is valid."
-            )
-        except smtplib.SMTPSenderRefused:
-            yield "error", (
-                "Sender email address defined in the credentials that where used"
-                "was rejected by the server. "
-                "Please verify your account is authorized to send emails."
-            )
-        except smtplib.SMTPDataError as e:
-            yield "error", f"Email data rejected by server: {str(e)}"
-        except Exception as e:
-            raise e
+        yield "status", self.send_email(
+            config=input_data.config,
+            to_email=input_data.to_email,
+            subject=input_data.subject,
+            body=input_data.body,
+            credentials=credentials,
+        )

autogpt_platform/backend/backend/blocks/exa/_test.py

@@ -1,22 +0,0 @@
-"""
-Test credentials and helpers for Exa blocks.
-"""
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="exa",
-    api_key=SecretStr("mock-exa-api-key"),
-    title="Mock Exa API key",
-    expires_at=None,
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}

autogpt_platform/backend/backend/blocks/exa/answers.py

@@ -1,55 +1,52 @@
-from typing import Optional
-
-from exa_py import AsyncExa
-from exa_py.api import AnswerResponse
-from pydantic import BaseModel
-
 from backend.sdk import (
     APIKeyCredentials,
+    BaseModel,
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
     CredentialsMetaInput,
-    MediaFileType,
+    Requests,
     SchemaField,
 )
 
 from ._config import exa
 
 
-class AnswerCitation(BaseModel):
-    """Citation model for answer endpoint."""
+class CostBreakdown(BaseModel):
+    keywordSearch: float
+    neuralSearch: float
+    contentText: float
+    contentHighlight: float
+    contentSummary: float
 
-    id: str = SchemaField(description="The temporary ID for the document")
-    url: str = SchemaField(description="The URL of the search result")
-    title: Optional[str] = SchemaField(description="The title of the search result")
-    author: Optional[str] = SchemaField(description="The author of the content")
-    publishedDate: Optional[str] = SchemaField(
-        description="An estimate of the creation date"
-    )
-    text: Optional[str] = SchemaField(description="The full text content of the source")
-    image: Optional[MediaFileType] = SchemaField(
-        description="The URL of the image associated with the result"
-    )
-    favicon: Optional[MediaFileType] = SchemaField(
-        description="The URL of the favicon for the domain"
-    )
 
-    @classmethod
-    def from_sdk(cls, sdk_citation) -> "AnswerCitation":
-        """Convert SDK AnswerResult (dataclass) to our Pydantic model."""
-        return cls(
-            id=getattr(sdk_citation, "id", ""),
-            url=getattr(sdk_citation, "url", ""),
-            title=getattr(sdk_citation, "title", None),
-            author=getattr(sdk_citation, "author", None),
-            publishedDate=getattr(sdk_citation, "published_date", None),
-            text=getattr(sdk_citation, "text", None),
-            image=getattr(sdk_citation, "image", None),
-            favicon=getattr(sdk_citation, "favicon", None),
-        )
+class SearchBreakdown(BaseModel):
+    search: float
+    contents: float
+    breakdown: CostBreakdown
+
+
+class PerRequestPrices(BaseModel):
+    neuralSearch_1_25_results: float
+    neuralSearch_26_100_results: float
+    neuralSearch_100_plus_results: float
+    keywordSearch_1_100_results: float
+    keywordSearch_100_plus_results: float
+
+
+class PerPagePrices(BaseModel):
+    contentText: float
+    contentHighlight: float
+    contentSummary: float
+
+
+class CostDollars(BaseModel):
+    total: float
+    breakDown: list[SearchBreakdown]
+    perRequestPrices: PerRequestPrices
+    perPagePrices: PerPagePrices
 
 
 class ExaAnswerBlock(Block):
@@ -62,21 +59,31 @@ class ExaAnswerBlock(Block):
             placeholder="What is the latest valuation of SpaceX?",
         )
         text: bool = SchemaField(
-            description="Include full text content in the search results used for the answer",
-            default=True,
+            default=False,
+            description="If true, the response includes full text content in the search results",
+            advanced=True,
+        )
+        model: str = SchemaField(
+            default="exa",
+            description="The search model to use (exa or exa-pro)",
+            placeholder="exa",
+            advanced=True,
         )
 
     class Output(BlockSchemaOutput):
         answer: str = SchemaField(
             description="The generated answer based on search results"
         )
-        citations: list[AnswerCitation] = SchemaField(
-            description="Search results used to generate the answer"
+        citations: list[dict] = SchemaField(
+            description="Search results used to generate the answer",
+            default_factory=list,
         )
-        citation: AnswerCitation = SchemaField(
-            description="Individual citation from the answer"
+        cost_dollars: CostDollars = SchemaField(
+            description="Cost breakdown of the request"
+        )
+        error: str = SchemaField(
+            description="Error message if the request failed", default=""
         )
-        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -90,24 +97,26 @@ class ExaAnswerBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+        url = "https://api.exa.ai/answer"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
 
-        # Get answer using SDK (stream=False for blocks) - this IS async, needs await
-        response = await aexa.answer(
-            query=input_data.query, text=input_data.text, stream=False
-        )
+        # Build the payload
+        payload = {
+            "query": input_data.query,
+            "text": input_data.text,
+            "model": input_data.model,
+        }
 
-        # this should remain true as long as they don't start defaulting to streaming only.
-        # provides a bit of safety for sdk updates.
-        assert type(response) is AnswerResponse
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()
 
-        yield "answer", response.answer
+            yield "answer", data.get("answer", "")
+            yield "citations", data.get("citations", [])
+            yield "cost_dollars", data.get("costDollars", {})
 
-        citations = [
-            AnswerCitation.from_sdk(sdk_citation)
-            for sdk_citation in response.citations or []
-        ]
-
-        yield "citations", citations
-        for citation in citations:
-            yield "citation", citation
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/code_context.py

@@ -1,118 +0,0 @@
-"""
-Exa Code Context Block
-
-Provides code search capabilities to find relevant code snippets and examples
-from open source repositories, documentation, and Stack Overflow.
-"""
-
-from typing import Union
-
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-class CodeContextResponse(BaseModel):
-    """Stable output model for code context responses."""
-
-    request_id: str
-    query: str
-    response: str
-    results_count: int
-    cost_dollars: str
-    search_time: float
-    output_tokens: int
-
-    @classmethod
-    def from_api(cls, data: dict) -> "CodeContextResponse":
-        """Convert API response to our stable model."""
-        return cls(
-            request_id=data.get("requestId", ""),
-            query=data.get("query", ""),
-            response=data.get("response", ""),
-            results_count=data.get("resultsCount", 0),
-            cost_dollars=data.get("costDollars", ""),
-            search_time=data.get("searchTime", 0.0),
-            output_tokens=data.get("outputTokens", 0),
-        )
-
-
-class ExaCodeContextBlock(Block):
-    """Get relevant code snippets and examples from open source repositories."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        query: str = SchemaField(
-            description="Search query to find relevant code snippets. Describe what you're trying to do or what code you're looking for.",
-            placeholder="how to use React hooks for state management",
-        )
-        tokens_num: Union[str, int] = SchemaField(
-            default="dynamic",
-            description="Token limit for response. Use 'dynamic' for automatic sizing, 5000 for standard queries, or 10000 for comprehensive examples.",
-            placeholder="dynamic",
-        )
-
-    class Output(BlockSchemaOutput):
-        request_id: str = SchemaField(description="Unique identifier for this request")
-        query: str = SchemaField(description="The search query used")
-        response: str = SchemaField(
-            description="Formatted code snippets and contextual examples with sources"
-        )
-        results_count: int = SchemaField(
-            description="Number of code sources found and included"
-        )
-        cost_dollars: str = SchemaField(description="Cost of this request in dollars")
-        search_time: float = SchemaField(
-            description="Time taken to search in milliseconds"
-        )
-        output_tokens: int = SchemaField(description="Number of tokens in the response")
-
-    def __init__(self):
-        super().__init__(
-            id="8f9e0d1c-2b3a-4567-8901-23456789abcd",
-            description="Search billions of GitHub repos, docs, and Stack Overflow for relevant code examples",
-            categories={BlockCategory.SEARCH, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExaCodeContextBlock.Input,
-            output_schema=ExaCodeContextBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/context"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        payload = {
-            "query": input_data.query,
-            "tokensNum": input_data.tokens_num,
-        }
-
-        response = await Requests().post(url, headers=headers, json=payload)
-        data = response.json()
-
-        context = CodeContextResponse.from_api(data)
-
-        yield "request_id", context.request_id
-        yield "query", context.query
-        yield "response", context.response
-        yield "results_count", context.results_count
-        yield "cost_dollars", context.cost_dollars
-        yield "search_time", context.search_time
-        yield "output_tokens", context.output_tokens

autogpt_platform/backend/backend/blocks/exa/contents.py

@@ -1,9 +1,3 @@
-from enum import Enum
-from typing import Optional
-
-from exa_py import AsyncExa
-from pydantic import BaseModel
-
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -12,45 +6,12 @@ from backend.sdk import (
     BlockSchemaInput,
     BlockSchemaOutput,
     CredentialsMetaInput,
+    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import (
-    CostDollars,
-    ExaSearchResults,
-    ExtrasSettings,
-    HighlightSettings,
-    LivecrawlTypes,
-    SummarySettings,
-)
-
-
-class ContentStatusTag(str, Enum):
-    CRAWL_NOT_FOUND = "CRAWL_NOT_FOUND"
-    CRAWL_TIMEOUT = "CRAWL_TIMEOUT"
-    CRAWL_LIVECRAWL_TIMEOUT = "CRAWL_LIVECRAWL_TIMEOUT"
-    SOURCE_NOT_AVAILABLE = "SOURCE_NOT_AVAILABLE"
-    CRAWL_UNKNOWN_ERROR = "CRAWL_UNKNOWN_ERROR"
-
-
-class ContentError(BaseModel):
-    tag: Optional[ContentStatusTag] = SchemaField(
-        default=None, description="Specific error type"
-    )
-    httpStatusCode: Optional[int] = SchemaField(
-        default=None, description="The corresponding HTTP status code"
-    )
-
-
-class ContentStatus(BaseModel):
-    id: str = SchemaField(description="The URL that was requested")
-    status: str = SchemaField(
-        description="Status of the content fetch operation (success or error)"
-    )
-    error: Optional[ContentError] = SchemaField(
-        default=None, description="Error details, only present when status is 'error'"
-    )
+from .helpers import ContentSettings
 
 
 class ExaContentsBlock(Block):
@@ -58,70 +19,22 @@ class ExaContentsBlock(Block):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="The Exa integration requires an API Key."
         )
-        urls: list[str] = SchemaField(
-            description="Array of URLs to crawl (preferred over 'ids')",
-            default_factory=list,
-            advanced=False,
-        )
         ids: list[str] = SchemaField(
-            description="[DEPRECATED - use 'urls' instead] Array of document IDs obtained from searches",
-            default_factory=list,
-            advanced=True,
+            description="Array of document IDs obtained from searches"
         )
-        text: bool = SchemaField(
-            description="Retrieve text content from pages",
-            default=True,
-        )
-        highlights: HighlightSettings = SchemaField(
-            description="Text snippets most relevant from each page",
-            default=HighlightSettings(),
-        )
-        summary: SummarySettings = SchemaField(
-            description="LLM-generated summary of the webpage",
-            default=SummarySettings(),
-        )
-        livecrawl: Optional[LivecrawlTypes] = SchemaField(
-            description="Livecrawling options: never, fallback (default), always, preferred",
-            default=LivecrawlTypes.FALLBACK,
-            advanced=True,
-        )
-        livecrawl_timeout: Optional[int] = SchemaField(
-            description="Timeout for livecrawling in milliseconds",
-            default=10000,
-            advanced=True,
-        )
-        subpages: Optional[int] = SchemaField(
-            description="Number of subpages to crawl", default=0, ge=0, advanced=True
-        )
-        subpage_target: Optional[str | list[str]] = SchemaField(
-            description="Keyword(s) to find specific subpages of search results",
-            default=None,
-            advanced=True,
-        )
-        extras: ExtrasSettings = SchemaField(
-            description="Extra parameters for additional content",
-            default=ExtrasSettings(),
+        contents: ContentSettings = SchemaField(
+            description="Content retrieval settings",
+            default=ContentSettings(),
             advanced=True,
         )
 
     class Output(BlockSchemaOutput):
-        results: list[ExaSearchResults] = SchemaField(
-            description="List of document contents with metadata"
+        results: list = SchemaField(
+            description="List of document contents", default_factory=list
         )
-        result: ExaSearchResults = SchemaField(
-            description="Single document content result"
+        error: str = SchemaField(
+            description="Error message if the request failed", default=""
         )
-        context: str = SchemaField(
-            description="A formatted string of the results ready for LLMs"
-        )
-        request_id: str = SchemaField(description="Unique identifier for the request")
-        statuses: list[ContentStatus] = SchemaField(
-            description="Status information for each requested URL"
-        )
-        cost_dollars: Optional[CostDollars] = SchemaField(
-            description="Cost breakdown for the request"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -135,91 +48,23 @@ class ExaContentsBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        if not input_data.urls and not input_data.ids:
-            raise ValueError("Either 'urls' or 'ids' must be provided")
+        url = "https://api.exa.ai/contents"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
 
-        sdk_kwargs = {}
+        # Convert ContentSettings to API format
+        payload = {
+            "ids": input_data.ids,
+            "text": input_data.contents.text,
+            "highlights": input_data.contents.highlights,
+            "summary": input_data.contents.summary,
+        }
 
-        # Prefer urls over ids
-        if input_data.urls:
-            sdk_kwargs["urls"] = input_data.urls
-        elif input_data.ids:
-            sdk_kwargs["ids"] = input_data.ids
-
-        if input_data.text:
-            sdk_kwargs["text"] = {"includeHtmlTags": True}
-
-        # Handle highlights - only include if modified from defaults
-        if input_data.highlights and (
-            input_data.highlights.num_sentences != 1
-            or input_data.highlights.highlights_per_url != 1
-            or input_data.highlights.query is not None
-        ):
-            highlights_dict = {}
-            highlights_dict["numSentences"] = input_data.highlights.num_sentences
-            highlights_dict["highlightsPerUrl"] = (
-                input_data.highlights.highlights_per_url
-            )
-            if input_data.highlights.query:
-                highlights_dict["query"] = input_data.highlights.query
-            sdk_kwargs["highlights"] = highlights_dict
-
-        # Handle summary - only include if modified from defaults
-        if input_data.summary and (
-            input_data.summary.query is not None
-            or input_data.summary.schema is not None
-        ):
-            summary_dict = {}
-            if input_data.summary.query:
-                summary_dict["query"] = input_data.summary.query
-            if input_data.summary.schema:
-                summary_dict["schema"] = input_data.summary.schema
-            sdk_kwargs["summary"] = summary_dict
-
-        if input_data.livecrawl:
-            sdk_kwargs["livecrawl"] = input_data.livecrawl.value
-
-        if input_data.livecrawl_timeout is not None:
-            sdk_kwargs["livecrawl_timeout"] = input_data.livecrawl_timeout
-
-        if input_data.subpages is not None:
-            sdk_kwargs["subpages"] = input_data.subpages
-
-        if input_data.subpage_target:
-            sdk_kwargs["subpage_target"] = input_data.subpage_target
-
-        # Handle extras - only include if modified from defaults
-        if input_data.extras and (
-            input_data.extras.links > 0 or input_data.extras.image_links > 0
-        ):
-            extras_dict = {}
-            if input_data.extras.links:
-                extras_dict["links"] = input_data.extras.links
-            if input_data.extras.image_links:
-                extras_dict["image_links"] = input_data.extras.image_links
-            sdk_kwargs["extras"] = extras_dict
-
-        # Always enable context for LLM-ready output
-        sdk_kwargs["context"] = True
-
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-        response = await aexa.get_contents(**sdk_kwargs)
-
-        converted_results = [
-            ExaSearchResults.from_sdk(sdk_result)
-            for sdk_result in response.results or []
-        ]
-
-        yield "results", converted_results
-
-        for result in converted_results:
-            yield "result", result
-
-        if response.context:
-            yield "context", response.context
-
-        if response.statuses:
-            yield "statuses", response.statuses
-
-        if response.cost_dollars:
-            yield "cost_dollars", response.cost_dollars
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()
+            yield "results", data.get("results", [])
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/helpers.py

@@ -1,150 +1,51 @@
-from enum import Enum
-from typing import Any, Dict, Literal, Optional, Union
+from typing import Optional
 
-from backend.sdk import BaseModel, MediaFileType, SchemaField
+from backend.sdk import BaseModel, SchemaField
 
 
-class LivecrawlTypes(str, Enum):
-    NEVER = "never"
-    FALLBACK = "fallback"
-    ALWAYS = "always"
-    PREFERRED = "preferred"
-
-
-class TextEnabled(BaseModel):
-    discriminator: Literal["enabled"] = "enabled"
-
-
-class TextDisabled(BaseModel):
-    discriminator: Literal["disabled"] = "disabled"
-
-
-class TextAdvanced(BaseModel):
-    discriminator: Literal["advanced"] = "advanced"
-    max_characters: Optional[int] = SchemaField(
-        default=None,
+class TextSettings(BaseModel):
+    max_characters: int = SchemaField(
+        default=1000,
         description="Maximum number of characters to return",
         placeholder="1000",
     )
     include_html_tags: bool = SchemaField(
         default=False,
-        description="Include HTML tags in the response, helps LLMs understand text structure",
+        description="Whether to include HTML tags in the text",
         placeholder="False",
     )
 
 
 class HighlightSettings(BaseModel):
     num_sentences: int = SchemaField(
-        default=1,
+        default=3,
         description="Number of sentences per highlight",
-        placeholder="1",
-        ge=1,
+        placeholder="3",
     )
     highlights_per_url: int = SchemaField(
-        default=1,
+        default=3,
         description="Number of highlights per URL",
-        placeholder="1",
-        ge=1,
-    )
-    query: Optional[str] = SchemaField(
-        default=None,
-        description="Custom query to direct the LLM's selection of highlights",
-        placeholder="Key advancements",
+        placeholder="3",
     )
 
 
 class SummarySettings(BaseModel):
     query: Optional[str] = SchemaField(
-        default=None,
-        description="Custom query for the LLM-generated summary",
-        placeholder="Main developments",
-    )
-    schema: Optional[dict] = SchemaField(  # type: ignore
-        default=None,
-        description="JSON schema for structured output from summary",
-        advanced=True,
-    )
-
-
-class ExtrasSettings(BaseModel):
-    links: int = SchemaField(
-        default=0,
-        description="Number of URLs to return from each webpage",
-        placeholder="1",
-        ge=0,
-    )
-    image_links: int = SchemaField(
-        default=0,
-        description="Number of images to return for each result",
-        placeholder="1",
-        ge=0,
-    )
-
-
-class ContextEnabled(BaseModel):
-    discriminator: Literal["enabled"] = "enabled"
-
-
-class ContextDisabled(BaseModel):
-    discriminator: Literal["disabled"] = "disabled"
-
-
-class ContextAdvanced(BaseModel):
-    discriminator: Literal["advanced"] = "advanced"
-    max_characters: Optional[int] = SchemaField(
-        default=None,
-        description="Maximum character limit for context string",
-        placeholder="10000",
+        default="",
+        description="Query string for summarization",
+        placeholder="Enter query",
     )
 
 
 class ContentSettings(BaseModel):
-    text: Optional[Union[bool, TextEnabled, TextDisabled, TextAdvanced]] = SchemaField(
-        default=None,
-        description="Text content retrieval. Boolean for simple enable/disable or object for advanced settings",
+    text: TextSettings = SchemaField(
+        default=TextSettings(),
     )
-    highlights: Optional[HighlightSettings] = SchemaField(
-        default=None,
-        description="Text snippets most relevant from each page",
+    highlights: HighlightSettings = SchemaField(
+        default=HighlightSettings(),
     )
-    summary: Optional[SummarySettings] = SchemaField(
-        default=None,
-        description="LLM-generated summary of the webpage",
-    )
-    livecrawl: Optional[LivecrawlTypes] = SchemaField(
-        default=None,
-        description="Livecrawling options: never, fallback, always, preferred",
-        advanced=True,
-    )
-    livecrawl_timeout: Optional[int] = SchemaField(
-        default=None,
-        description="Timeout for livecrawling in milliseconds",
-        placeholder="10000",
-        advanced=True,
-    )
-    subpages: Optional[int] = SchemaField(
-        default=None,
-        description="Number of subpages to crawl",
-        placeholder="0",
-        ge=0,
-        advanced=True,
-    )
-    subpage_target: Optional[Union[str, list[str]]] = SchemaField(
-        default=None,
-        description="Keyword(s) to find specific subpages of search results",
-        advanced=True,
-    )
-    extras: Optional[ExtrasSettings] = SchemaField(
-        default=None,
-        description="Extra parameters for additional content",
-        advanced=True,
-    )
-    context: Optional[Union[bool, ContextEnabled, ContextDisabled, ContextAdvanced]] = (
-        SchemaField(
-            default=None,
-            description="Format search results into a context string for LLMs",
-            advanced=True,
-        )
+    summary: SummarySettings = SchemaField(
+        default=SummarySettings(),
     )
 
 
@@ -226,225 +127,3 @@ class WebsetEnrichmentConfig(BaseModel):
         default=None,
         description="Options for the enrichment",
     )
-
-
-# Shared result models
-class ExaSearchExtras(BaseModel):
-    links: list[str] = SchemaField(
-        default_factory=list, description="Array of links from the search result"
-    )
-    imageLinks: list[str] = SchemaField(
-        default_factory=list, description="Array of image links from the search result"
-    )
-
-
-class ExaSearchResults(BaseModel):
-    title: str | None = None
-    url: str | None = None
-    publishedDate: str | None = None
-    author: str | None = None
-    id: str
-    image: MediaFileType | None = None
-    favicon: MediaFileType | None = None
-    text: str | None = None
-    highlights: list[str] = SchemaField(default_factory=list)
-    highlightScores: list[float] = SchemaField(default_factory=list)
-    summary: str | None = None
-    subpages: list[dict] = SchemaField(default_factory=list)
-    extras: ExaSearchExtras | None = None
-
-    @classmethod
-    def from_sdk(cls, sdk_result) -> "ExaSearchResults":
-        """Convert SDK Result (dataclass) to our Pydantic model."""
-        return cls(
-            id=getattr(sdk_result, "id", ""),
-            url=getattr(sdk_result, "url", None),
-            title=getattr(sdk_result, "title", None),
-            author=getattr(sdk_result, "author", None),
-            publishedDate=getattr(sdk_result, "published_date", None),
-            text=getattr(sdk_result, "text", None),
-            highlights=getattr(sdk_result, "highlights", None) or [],
-            highlightScores=getattr(sdk_result, "highlight_scores", None) or [],
-            summary=getattr(sdk_result, "summary", None),
-            subpages=getattr(sdk_result, "subpages", None) or [],
-            image=getattr(sdk_result, "image", None),
-            favicon=getattr(sdk_result, "favicon", None),
-            extras=getattr(sdk_result, "extras", None),
-        )
-
-
-# Cost tracking models
-class CostBreakdown(BaseModel):
-    keywordSearch: float = SchemaField(default=0.0)
-    neuralSearch: float = SchemaField(default=0.0)
-    contentText: float = SchemaField(default=0.0)
-    contentHighlight: float = SchemaField(default=0.0)
-    contentSummary: float = SchemaField(default=0.0)
-
-
-class CostBreakdownItem(BaseModel):
-    search: float = SchemaField(default=0.0)
-    contents: float = SchemaField(default=0.0)
-    breakdown: CostBreakdown = SchemaField(default_factory=CostBreakdown)
-
-
-class PerRequestPrices(BaseModel):
-    neuralSearch_1_25_results: float = SchemaField(default=0.005)
-    neuralSearch_26_100_results: float = SchemaField(default=0.025)
-    neuralSearch_100_plus_results: float = SchemaField(default=1.0)
-    keywordSearch_1_100_results: float = SchemaField(default=0.0025)
-    keywordSearch_100_plus_results: float = SchemaField(default=3.0)
-
-
-class PerPagePrices(BaseModel):
-    contentText: float = SchemaField(default=0.001)
-    contentHighlight: float = SchemaField(default=0.001)
-    contentSummary: float = SchemaField(default=0.001)
-
-
-class CostDollars(BaseModel):
-    total: float = SchemaField(description="Total dollar cost for your request")
-    breakDown: list[CostBreakdownItem] = SchemaField(
-        default_factory=list, description="Breakdown of costs by operation type"
-    )
-    perRequestPrices: PerRequestPrices = SchemaField(
-        default_factory=PerRequestPrices,
-        description="Standard price per request for different operations",
-    )
-    perPagePrices: PerPagePrices = SchemaField(
-        default_factory=PerPagePrices,
-        description="Standard price per page for different content operations",
-    )
-
-
-# Helper functions for payload processing
-def process_text_field(
-    text: Union[bool, TextEnabled, TextDisabled, TextAdvanced, None]
-) -> Optional[Union[bool, Dict[str, Any]]]:
-    """Process text field for API payload."""
-    if text is None:
-        return None
-
-    # Handle backward compatibility with boolean
-    if isinstance(text, bool):
-        return text
-    elif isinstance(text, TextDisabled):
-        return False
-    elif isinstance(text, TextEnabled):
-        return True
-    elif isinstance(text, TextAdvanced):
-        text_dict = {}
-        if text.max_characters:
-            text_dict["maxCharacters"] = text.max_characters
-        if text.include_html_tags:
-            text_dict["includeHtmlTags"] = text.include_html_tags
-        return text_dict if text_dict else True
-    return None
-
-
-def process_contents_settings(contents: Optional[ContentSettings]) -> Dict[str, Any]:
-    """Process ContentSettings into API payload format."""
-    if not contents:
-        return {}
-
-    content_settings = {}
-
-    # Handle text field (can be boolean or object)
-    text_value = process_text_field(contents.text)
-    if text_value is not None:
-        content_settings["text"] = text_value
-
-    # Handle highlights
-    if contents.highlights:
-        highlights_dict: Dict[str, Any] = {
-            "numSentences": contents.highlights.num_sentences,
-            "highlightsPerUrl": contents.highlights.highlights_per_url,
-        }
-        if contents.highlights.query:
-            highlights_dict["query"] = contents.highlights.query
-        content_settings["highlights"] = highlights_dict
-
-    if contents.summary:
-        summary_dict = {}
-        if contents.summary.query:
-            summary_dict["query"] = contents.summary.query
-        if contents.summary.schema:
-            summary_dict["schema"] = contents.summary.schema
-        content_settings["summary"] = summary_dict
-
-    if contents.livecrawl:
-        content_settings["livecrawl"] = contents.livecrawl.value
-
-    if contents.livecrawl_timeout is not None:
-        content_settings["livecrawlTimeout"] = contents.livecrawl_timeout
-
-    if contents.subpages is not None:
-        content_settings["subpages"] = contents.subpages
-
-    if contents.subpage_target:
-        content_settings["subpageTarget"] = contents.subpage_target
-
-    if contents.extras:
-        extras_dict = {}
-        if contents.extras.links:
-            extras_dict["links"] = contents.extras.links
-        if contents.extras.image_links:
-            extras_dict["imageLinks"] = contents.extras.image_links
-        content_settings["extras"] = extras_dict
-
-    context_value = process_context_field(contents.context)
-    if context_value is not None:
-        content_settings["context"] = context_value
-
-    return content_settings
-
-
-def process_context_field(
-    context: Union[bool, dict, ContextEnabled, ContextDisabled, ContextAdvanced, None]
-) -> Optional[Union[bool, Dict[str, int]]]:
-    """Process context field for API payload."""
-    if context is None:
-        return None
-
-    # Handle backward compatibility with boolean
-    if isinstance(context, bool):
-        return context if context else None
-    elif isinstance(context, dict) and "maxCharacters" in context:
-        return {"maxCharacters": context["maxCharacters"]}
-    elif isinstance(context, ContextDisabled):
-        return None  # Don't send context field at all when disabled
-    elif isinstance(context, ContextEnabled):
-        return True
-    elif isinstance(context, ContextAdvanced):
-        if context.max_characters:
-            return {"maxCharacters": context.max_characters}
-        return True
-    return None
-
-
-def format_date_fields(
-    input_data: Any, date_field_mapping: Dict[str, str]
-) -> Dict[str, str]:
-    """Format datetime fields for API payload."""
-    formatted_dates = {}
-    for input_field, api_field in date_field_mapping.items():
-        value = getattr(input_data, input_field, None)
-        if value:
-            formatted_dates[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
-    return formatted_dates
-
-
-def add_optional_fields(
-    input_data: Any,
-    field_mapping: Dict[str, str],
-    payload: Dict[str, Any],
-    process_enums: bool = False,
-) -> None:
-    """Add optional fields to payload if they have values."""
-    for input_field, api_field in field_mapping.items():
-        value = getattr(input_data, input_field, None)
-        if value:  # Only add non-empty values
-            if process_enums and hasattr(value, "value"):
-                payload[api_field] = value.value
-            else:
-                payload[api_field] = value

autogpt_platform/backend/backend/blocks/exa/model.py

@@ -0,0 +1,247 @@
+from datetime import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+
+# Enum definitions based on available options
+class WebsetStatus(str, Enum):
+    IDLE = "idle"
+    PENDING = "pending"
+    RUNNING = "running"
+    PAUSED = "paused"
+
+
+class WebsetSearchStatus(str, Enum):
+    CREATED = "created"
+    # Add more if known, based on example it's "created"
+
+
+class ImportStatus(str, Enum):
+    PENDING = "pending"
+    # Add more if known
+
+
+class ImportFormat(str, Enum):
+    CSV = "csv"
+    # Add more if known
+
+
+class EnrichmentStatus(str, Enum):
+    PENDING = "pending"
+    # Add more if known
+
+
+class EnrichmentFormat(str, Enum):
+    TEXT = "text"
+    # Add more if known
+
+
+class MonitorStatus(str, Enum):
+    ENABLED = "enabled"
+    # Add more if known
+
+
+class MonitorBehaviorType(str, Enum):
+    SEARCH = "search"
+    # Add more if known
+
+
+class MonitorRunStatus(str, Enum):
+    CREATED = "created"
+    # Add more if known
+
+
+class CanceledReason(str, Enum):
+    WEBSET_DELETED = "webset_deleted"
+    # Add more if known
+
+
+class FailedReason(str, Enum):
+    INVALID_FORMAT = "invalid_format"
+    # Add more if known
+
+
+class Confidence(str, Enum):
+    HIGH = "high"
+    # Add more if known
+
+
+# Nested models
+
+
+class Entity(BaseModel):
+    type: str
+
+
+class Criterion(BaseModel):
+    description: str
+    successRate: Optional[int] = None
+
+
+class ExcludeItem(BaseModel):
+    source: str = Field(default="import")
+    id: str
+
+
+class Relationship(BaseModel):
+    definition: str
+    limit: Optional[float] = None
+
+
+class ScopeItem(BaseModel):
+    source: str = Field(default="import")
+    id: str
+    relationship: Optional[Relationship] = None
+
+
+class Progress(BaseModel):
+    found: int
+    analyzed: int
+    completion: int
+    timeLeft: int
+
+
+class Bounds(BaseModel):
+    min: int
+    max: int
+
+
+class Expected(BaseModel):
+    total: int
+    confidence: str = Field(default="high")  # Use str or Confidence enum
+    bounds: Bounds
+
+
+class Recall(BaseModel):
+    expected: Expected
+    reasoning: str
+
+
+class WebsetSearch(BaseModel):
+    id: str
+    object: str = Field(default="webset_search")
+    status: str = Field(default="created")  # Or use WebsetSearchStatus
+    websetId: str
+    query: str
+    entity: Entity
+    criteria: List[Criterion]
+    count: int
+    behavior: str = Field(default="override")
+    exclude: List[ExcludeItem]
+    scope: List[ScopeItem]
+    progress: Progress
+    recall: Recall
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    canceledAt: Optional[datetime] = None
+    canceledReason: Optional[str] = Field(default=None)  # Or use CanceledReason
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class ImportEntity(BaseModel):
+    type: str
+
+
+class Import(BaseModel):
+    id: str
+    object: str = Field(default="import")
+    status: str = Field(default="pending")  # Or use ImportStatus
+    format: str = Field(default="csv")  # Or use ImportFormat
+    entity: ImportEntity
+    title: str
+    count: int
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    failedReason: Optional[str] = Field(default=None)  # Or use FailedReason
+    failedAt: Optional[datetime] = None
+    failedMessage: Optional[str] = None
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Option(BaseModel):
+    label: str
+
+
+class WebsetEnrichment(BaseModel):
+    id: str
+    object: str = Field(default="webset_enrichment")
+    status: str = Field(default="pending")  # Or use EnrichmentStatus
+    websetId: str
+    title: str
+    description: str
+    format: str = Field(default="text")  # Or use EnrichmentFormat
+    options: List[Option]
+    instructions: str
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Cadence(BaseModel):
+    cron: str
+    timezone: str = Field(default="Etc/UTC")
+
+
+class BehaviorConfig(BaseModel):
+    query: Optional[str] = None
+    criteria: Optional[List[Criterion]] = None
+    entity: Optional[Entity] = None
+    count: Optional[int] = None
+    behavior: Optional[str] = Field(default=None)
+
+
+class Behavior(BaseModel):
+    type: str = Field(default="search")  # Or use MonitorBehaviorType
+    config: BehaviorConfig
+
+
+class MonitorRun(BaseModel):
+    id: str
+    object: str = Field(default="monitor_run")
+    status: str = Field(default="created")  # Or use MonitorRunStatus
+    monitorId: str
+    type: str = Field(default="search")
+    completedAt: Optional[datetime] = None
+    failedAt: Optional[datetime] = None
+    failedReason: Optional[str] = None
+    canceledAt: Optional[datetime] = None
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Monitor(BaseModel):
+    id: str
+    object: str = Field(default="monitor")
+    status: str = Field(default="enabled")  # Or use MonitorStatus
+    websetId: str
+    cadence: Cadence
+    behavior: Behavior
+    lastRun: Optional[MonitorRun] = None
+    nextRunAt: Optional[datetime] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Webset(BaseModel):
+    id: str
+    object: str = Field(default="webset")
+    status: WebsetStatus
+    externalId: Optional[str] = None
+    title: Optional[str] = None
+    searches: List[WebsetSearch]
+    imports: List[Import]
+    enrichments: List[WebsetEnrichment]
+    monitors: List[Monitor]
+    streams: List[Any]
+    createdAt: datetime
+    updatedAt: datetime
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class ListWebsets(BaseModel):
+    data: List[Webset]
+    hasMore: bool
+    nextCursor: Optional[str] = None

autogpt_platform/backend/backend/blocks/exa/research.py

@@ -1,518 +0,0 @@
-"""
-Exa Research Task Blocks
-
-Provides asynchronous research capabilities that explore the web, gather sources,
-synthesize findings, and return structured results with citations.
-"""
-
-import asyncio
-import time
-from enum import Enum
-from typing import Any, Dict, List, Optional
-
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-class ResearchModel(str, Enum):
-    """Available research models."""
-
-    FAST = "exa-research-fast"
-    STANDARD = "exa-research"
-    PRO = "exa-research-pro"
-
-
-class ResearchStatus(str, Enum):
-    """Research task status."""
-
-    PENDING = "pending"
-    RUNNING = "running"
-    COMPLETED = "completed"
-    CANCELED = "canceled"
-    FAILED = "failed"
-
-
-class ResearchCostModel(BaseModel):
-    """Cost breakdown for a research request."""
-
-    total: float
-    num_searches: int
-    num_pages: int
-    reasoning_tokens: int
-
-    @classmethod
-    def from_api(cls, data: dict) -> "ResearchCostModel":
-        """Convert API response, rounding fractional counts to integers."""
-        return cls(
-            total=data.get("total", 0.0),
-            num_searches=int(round(data.get("numSearches", 0))),
-            num_pages=int(round(data.get("numPages", 0))),
-            reasoning_tokens=int(round(data.get("reasoningTokens", 0))),
-        )
-
-
-class ResearchOutputModel(BaseModel):
-    """Research output with content and optional structured data."""
-
-    content: str
-    parsed: Optional[Dict[str, Any]] = None
-
-
-class ResearchTaskModel(BaseModel):
-    """Stable output model for research tasks."""
-
-    research_id: str
-    created_at: int
-    model: str
-    instructions: str
-    status: str
-    output_schema: Optional[Dict[str, Any]] = None
-    output: Optional[ResearchOutputModel] = None
-    cost_dollars: Optional[ResearchCostModel] = None
-    finished_at: Optional[int] = None
-    error: Optional[str] = None
-
-    @classmethod
-    def from_api(cls, data: dict) -> "ResearchTaskModel":
-        """Convert API response to our stable model."""
-        output_data = data.get("output")
-        output = None
-        if output_data:
-            output = ResearchOutputModel(
-                content=output_data.get("content", ""),
-                parsed=output_data.get("parsed"),
-            )
-
-        cost_data = data.get("costDollars")
-        cost = None
-        if cost_data:
-            cost = ResearchCostModel.from_api(cost_data)
-
-        return cls(
-            research_id=data.get("researchId", ""),
-            created_at=data.get("createdAt", 0),
-            model=data.get("model", "exa-research"),
-            instructions=data.get("instructions", ""),
-            status=data.get("status", "pending"),
-            output_schema=data.get("outputSchema"),
-            output=output,
-            cost_dollars=cost,
-            finished_at=data.get("finishedAt"),
-            error=data.get("error"),
-        )
-
-
-class ExaCreateResearchBlock(Block):
-    """Create an asynchronous research task that explores the web and synthesizes findings."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        instructions: str = SchemaField(
-            description="Research instructions - clearly define what information to find, how to conduct research, and desired output format.",
-            placeholder="Research the top 5 AI coding assistants, their features, pricing, and user reviews",
-        )
-        model: ResearchModel = SchemaField(
-            default=ResearchModel.STANDARD,
-            description="Research model: 'fast' for quick results, 'standard' for balanced quality, 'pro' for thorough analysis",
-        )
-        output_schema: Optional[dict] = SchemaField(
-            default=None,
-            description="JSON Schema to enforce structured output. When provided, results are validated and returned as parsed JSON.",
-            advanced=True,
-        )
-        wait_for_completion: bool = SchemaField(
-            default=True,
-            description="Wait for research to complete before returning. Ensures you get results immediately.",
-        )
-        polling_timeout: int = SchemaField(
-            default=600,
-            description="Maximum time to wait for completion in seconds (only if wait_for_completion is True)",
-            advanced=True,
-            ge=1,
-            le=3600,
-        )
-
-    class Output(BlockSchemaOutput):
-        research_id: str = SchemaField(
-            description="Unique identifier for tracking this research request"
-        )
-        status: str = SchemaField(description="Final status of the research")
-        model: str = SchemaField(description="The research model used")
-        instructions: str = SchemaField(
-            description="The research instructions provided"
-        )
-        created_at: int = SchemaField(
-            description="When the research was created (Unix timestamp in ms)"
-        )
-        output_content: Optional[str] = SchemaField(
-            description="Research output as text (only if wait_for_completion was True and completed)"
-        )
-        output_parsed: Optional[dict] = SchemaField(
-            description="Structured JSON output (only if wait_for_completion and outputSchema were provided)"
-        )
-        cost_total: Optional[float] = SchemaField(
-            description="Total cost in USD (only if wait_for_completion was True and completed)"
-        )
-        elapsed_time: Optional[float] = SchemaField(
-            description="Time taken to complete in seconds (only if wait_for_completion was True)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="a1f2e3d4-c5b6-4a78-9012-3456789abcde",
-            description="Create research task with optional waiting - explores web and synthesizes findings with citations",
-            categories={BlockCategory.SEARCH, BlockCategory.AI},
-            input_schema=ExaCreateResearchBlock.Input,
-            output_schema=ExaCreateResearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/research/v1"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        payload: Dict[str, Any] = {
-            "model": input_data.model.value,
-            "instructions": input_data.instructions,
-        }
-
-        if input_data.output_schema:
-            payload["outputSchema"] = input_data.output_schema
-
-        response = await Requests().post(url, headers=headers, json=payload)
-        data = response.json()
-
-        research_id = data.get("researchId", "")
-
-        if input_data.wait_for_completion:
-            start_time = time.time()
-            get_url = f"https://api.exa.ai/research/v1/{research_id}"
-            get_headers = {"x-api-key": credentials.api_key.get_secret_value()}
-            check_interval = 10
-
-            while time.time() - start_time < input_data.polling_timeout:
-                poll_response = await Requests().get(url=get_url, headers=get_headers)
-                poll_data = poll_response.json()
-
-                status = poll_data.get("status", "")
-
-                if status in ["completed", "failed", "canceled"]:
-                    elapsed = time.time() - start_time
-                    research = ResearchTaskModel.from_api(poll_data)
-
-                    yield "research_id", research.research_id
-                    yield "status", research.status
-                    yield "model", research.model
-                    yield "instructions", research.instructions
-                    yield "created_at", research.created_at
-                    yield "elapsed_time", elapsed
-
-                    if research.output:
-                        yield "output_content", research.output.content
-                        yield "output_parsed", research.output.parsed
-
-                    if research.cost_dollars:
-                        yield "cost_total", research.cost_dollars.total
-                    return
-
-                await asyncio.sleep(check_interval)
-
-            raise ValueError(
-                f"Research did not complete within {input_data.polling_timeout} seconds"
-            )
-        else:
-            yield "research_id", research_id
-            yield "status", data.get("status", "pending")
-            yield "model", data.get("model", input_data.model.value)
-            yield "instructions", data.get("instructions", input_data.instructions)
-            yield "created_at", data.get("createdAt", 0)
-
-
-class ExaGetResearchBlock(Block):
-    """Get the status and results of a research task."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        research_id: str = SchemaField(
-            description="The ID of the research task to retrieve",
-            placeholder="01jszdfs0052sg4jc552sg4jc5",
-        )
-        include_events: bool = SchemaField(
-            default=False,
-            description="Include detailed event log of research operations",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        research_id: str = SchemaField(description="The research task identifier")
-        status: str = SchemaField(
-            description="Current status: pending, running, completed, canceled, or failed"
-        )
-        instructions: str = SchemaField(
-            description="The original research instructions"
-        )
-        model: str = SchemaField(description="The research model used")
-        created_at: int = SchemaField(
-            description="When research was created (Unix timestamp in ms)"
-        )
-        finished_at: Optional[int] = SchemaField(
-            description="When research finished (Unix timestamp in ms, if completed/canceled/failed)"
-        )
-        output_content: Optional[str] = SchemaField(
-            description="Research output as text (if completed)"
-        )
-        output_parsed: Optional[dict] = SchemaField(
-            description="Structured JSON output matching outputSchema (if provided and completed)"
-        )
-        cost_total: Optional[float] = SchemaField(
-            description="Total cost in USD (if completed)"
-        )
-        cost_searches: Optional[int] = SchemaField(
-            description="Number of searches performed (if completed)"
-        )
-        cost_pages: Optional[int] = SchemaField(
-            description="Number of pages crawled (if completed)"
-        )
-        cost_reasoning_tokens: Optional[int] = SchemaField(
-            description="AI tokens used for reasoning (if completed)"
-        )
-        error_message: Optional[str] = SchemaField(
-            description="Error message if research failed"
-        )
-        events: Optional[List[dict]] = SchemaField(
-            description="Detailed event log (if include_events was True)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="b2e3f4a5-6789-4bcd-9012-3456789abcde",
-            description="Get status and results of a research task",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetResearchBlock.Input,
-            output_schema=ExaGetResearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/research/v1/{input_data.research_id}"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        params = {}
-        if input_data.include_events:
-            params["events"] = "true"
-
-        response = await Requests().get(url, headers=headers, params=params)
-        data = response.json()
-
-        research = ResearchTaskModel.from_api(data)
-
-        yield "research_id", research.research_id
-        yield "status", research.status
-        yield "instructions", research.instructions
-        yield "model", research.model
-        yield "created_at", research.created_at
-        yield "finished_at", research.finished_at
-
-        if research.output:
-            yield "output_content", research.output.content
-            yield "output_parsed", research.output.parsed
-
-        if research.cost_dollars:
-            yield "cost_total", research.cost_dollars.total
-            yield "cost_searches", research.cost_dollars.num_searches
-            yield "cost_pages", research.cost_dollars.num_pages
-            yield "cost_reasoning_tokens", research.cost_dollars.reasoning_tokens
-
-        yield "error_message", research.error
-
-        if input_data.include_events:
-            yield "events", data.get("events", [])
-
-
-class ExaWaitForResearchBlock(Block):
-    """Wait for a research task to complete with progress tracking."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        research_id: str = SchemaField(
-            description="The ID of the research task to wait for",
-            placeholder="01jszdfs0052sg4jc552sg4jc5",
-        )
-        timeout: int = SchemaField(
-            default=600,
-            description="Maximum time to wait in seconds",
-            ge=1,
-            le=3600,
-        )
-        check_interval: int = SchemaField(
-            default=10,
-            description="Seconds between status checks",
-            advanced=True,
-            ge=1,
-            le=60,
-        )
-
-    class Output(BlockSchemaOutput):
-        research_id: str = SchemaField(description="The research task identifier")
-        final_status: str = SchemaField(description="Final status when polling stopped")
-        output_content: Optional[str] = SchemaField(
-            description="Research output as text (if completed)"
-        )
-        output_parsed: Optional[dict] = SchemaField(
-            description="Structured JSON output (if outputSchema was provided and completed)"
-        )
-        cost_total: Optional[float] = SchemaField(description="Total cost in USD")
-        elapsed_time: float = SchemaField(description="Total time waited in seconds")
-        timed_out: bool = SchemaField(
-            description="Whether polling timed out before completion"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="c3d4e5f6-7890-4abc-9012-3456789abcde",
-            description="Wait for a research task to complete with configurable timeout",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaWaitForResearchBlock.Input,
-            output_schema=ExaWaitForResearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        start_time = time.time()
-        url = f"https://api.exa.ai/research/v1/{input_data.research_id}"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        while time.time() - start_time < input_data.timeout:
-            response = await Requests().get(url, headers=headers)
-            data = response.json()
-
-            status = data.get("status", "")
-
-            if status in ["completed", "failed", "canceled"]:
-                elapsed = time.time() - start_time
-                research = ResearchTaskModel.from_api(data)
-
-                yield "research_id", research.research_id
-                yield "final_status", research.status
-                yield "elapsed_time", elapsed
-                yield "timed_out", False
-
-                if research.output:
-                    yield "output_content", research.output.content
-                    yield "output_parsed", research.output.parsed
-
-                if research.cost_dollars:
-                    yield "cost_total", research.cost_dollars.total
-
-                return
-
-            await asyncio.sleep(input_data.check_interval)
-
-        elapsed = time.time() - start_time
-        response = await Requests().get(url, headers=headers)
-        data = response.json()
-
-        yield "research_id", input_data.research_id
-        yield "final_status", data.get("status", "unknown")
-        yield "elapsed_time", elapsed
-        yield "timed_out", True
-
-
-class ExaListResearchBlock(Block):
-    """List all research tasks with pagination support."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor for pagination through results",
-            advanced=True,
-        )
-        limit: int = SchemaField(
-            default=10,
-            description="Number of research tasks to return (1-50)",
-            ge=1,
-            le=50,
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        research_tasks: List[ResearchTaskModel] = SchemaField(
-            description="List of research tasks ordered by creation time (newest first)"
-        )
-        research_task: ResearchTaskModel = SchemaField(
-            description="Individual research task (yielded for each task)"
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more tasks to paginate through"
-        )
-        next_cursor: Optional[str] = SchemaField(
-            description="Cursor for the next page of results"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d4e5f6a7-8901-4bcd-9012-3456789abcde",
-            description="List all research tasks with pagination support",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaListResearchBlock.Input,
-            output_schema=ExaListResearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/research/v1"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        params: Dict[str, Any] = {
-            "limit": input_data.limit,
-        }
-        if input_data.cursor:
-            params["cursor"] = input_data.cursor
-
-        response = await Requests().get(url, headers=headers, params=params)
-        data = response.json()
-
-        tasks = [ResearchTaskModel.from_api(task) for task in data.get("data", [])]
-
-        yield "research_tasks", tasks
-
-        for task in tasks:
-            yield "research_task", task
-
-        yield "has_more", data.get("hasMore", False)
-        yield "next_cursor", data.get("nextCursor")

autogpt_platform/backend/backend/blocks/exa/search.py

@@ -1,8 +1,4 @@
 from datetime import datetime
-from enum import Enum
-from typing import Optional
-
-from exa_py import AsyncExa
 
 from backend.sdk import (
     APIKeyCredentials,
@@ -12,35 +8,12 @@ from backend.sdk import (
     BlockSchemaInput,
     BlockSchemaOutput,
     CredentialsMetaInput,
+    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import (
-    ContentSettings,
-    CostDollars,
-    ExaSearchResults,
-    process_contents_settings,
-)
-
-
-class ExaSearchTypes(Enum):
-    KEYWORD = "keyword"
-    NEURAL = "neural"
-    FAST = "fast"
-    AUTO = "auto"
-
-
-class ExaSearchCategories(Enum):
-    COMPANY = "company"
-    RESEARCH_PAPER = "research paper"
-    NEWS = "news"
-    PDF = "pdf"
-    GITHUB = "github"
-    TWEET = "tweet"
-    PERSONAL_SITE = "personal site"
-    LINKEDIN_PROFILE = "linkedin profile"
-    FINANCIAL_REPORT = "financial report"
+from .helpers import ContentSettings
 
 
 class ExaSearchBlock(Block):
@@ -49,18 +22,12 @@ class ExaSearchBlock(Block):
             description="The Exa integration requires an API Key."
         )
         query: str = SchemaField(description="The search query")
-        type: ExaSearchTypes = SchemaField(
-            description="Type of search", default=ExaSearchTypes.AUTO, advanced=True
+        use_auto_prompt: bool = SchemaField(
+            description="Whether to use autoprompt", default=True, advanced=True
         )
-        category: ExaSearchCategories | None = SchemaField(
-            description="Category to search within: company, research paper, news, pdf, github, tweet, personal site, linkedin profile, financial report",
-            default=None,
-            advanced=True,
-        )
-        user_location: str | None = SchemaField(
-            description="The two-letter ISO country code of the user (e.g., 'US')",
-            default=None,
-            advanced=True,
+        type: str = SchemaField(description="Type of search", default="", advanced=True)
+        category: str = SchemaField(
+            description="Category to search within", default="", advanced=True
         )
         number_of_results: int = SchemaField(
             description="Number of results to return", default=10, advanced=True
@@ -73,17 +40,17 @@ class ExaSearchBlock(Block):
             default_factory=list,
             advanced=True,
         )
-        start_crawl_date: datetime | None = SchemaField(
-            description="Start date for crawled content", advanced=True, default=None
+        start_crawl_date: datetime = SchemaField(
+            description="Start date for crawled content"
         )
-        end_crawl_date: datetime | None = SchemaField(
-            description="End date for crawled content", advanced=True, default=None
+        end_crawl_date: datetime = SchemaField(
+            description="End date for crawled content"
         )
-        start_published_date: datetime | None = SchemaField(
-            description="Start date for published content", advanced=True, default=None
+        start_published_date: datetime = SchemaField(
+            description="Start date for published content"
         )
-        end_published_date: datetime | None = SchemaField(
-            description="End date for published content", advanced=True, default=None
+        end_published_date: datetime = SchemaField(
+            description="End date for published content"
         )
         include_text: list[str] = SchemaField(
             description="Text patterns to include", default_factory=list, advanced=True
@@ -96,30 +63,14 @@ class ExaSearchBlock(Block):
             default=ContentSettings(),
             advanced=True,
         )
-        moderation: bool = SchemaField(
-            description="Enable content moderation to filter unsafe content from search results",
-            default=False,
-            advanced=True,
-        )
 
     class Output(BlockSchemaOutput):
-        results: list[ExaSearchResults] = SchemaField(
-            description="List of search results"
+        results: list = SchemaField(
+            description="List of search results", default_factory=list
         )
-        result: ExaSearchResults = SchemaField(description="Single search result")
-        context: str = SchemaField(
-            description="A formatted string of the search results ready for LLMs."
+        error: str = SchemaField(
+            description="Error message if the request failed",
         )
-        search_type: str = SchemaField(
-            description="For auto searches, indicates which search type was selected."
-        )
-        resolved_search_type: str = SchemaField(
-            description="The search type that was actually used for this request (neural or keyword)"
-        )
-        cost_dollars: Optional[CostDollars] = SchemaField(
-            description="Cost breakdown for the request"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -133,76 +84,51 @@ class ExaSearchBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        sdk_kwargs = {
-            "query": input_data.query,
-            "num_results": input_data.number_of_results,
+        url = "https://api.exa.ai/search"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
         }
 
-        if input_data.type:
-            sdk_kwargs["type"] = input_data.type.value
+        payload = {
+            "query": input_data.query,
+            "useAutoprompt": input_data.use_auto_prompt,
+            "numResults": input_data.number_of_results,
+            "contents": input_data.contents.model_dump(),
+        }
 
-        if input_data.category:
-            sdk_kwargs["category"] = input_data.category.value
+        date_field_mapping = {
+            "start_crawl_date": "startCrawlDate",
+            "end_crawl_date": "endCrawlDate",
+            "start_published_date": "startPublishedDate",
+            "end_published_date": "endPublishedDate",
+        }
 
-        if input_data.user_location:
-            sdk_kwargs["user_location"] = input_data.user_location
+        # Add dates if they exist
+        for input_field, api_field in date_field_mapping.items():
+            value = getattr(input_data, input_field, None)
+            if value:
+                payload[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
 
-        # Handle domains
-        if input_data.include_domains:
-            sdk_kwargs["include_domains"] = input_data.include_domains
-        if input_data.exclude_domains:
-            sdk_kwargs["exclude_domains"] = input_data.exclude_domains
+        optional_field_mapping = {
+            "type": "type",
+            "category": "category",
+            "include_domains": "includeDomains",
+            "exclude_domains": "excludeDomains",
+            "include_text": "includeText",
+            "exclude_text": "excludeText",
+        }
 
-        # Handle dates
-        if input_data.start_crawl_date:
-            sdk_kwargs["start_crawl_date"] = input_data.start_crawl_date.isoformat()
-        if input_data.end_crawl_date:
-            sdk_kwargs["end_crawl_date"] = input_data.end_crawl_date.isoformat()
-        if input_data.start_published_date:
-            sdk_kwargs["start_published_date"] = (
-                input_data.start_published_date.isoformat()
-            )
-        if input_data.end_published_date:
-            sdk_kwargs["end_published_date"] = input_data.end_published_date.isoformat()
+        # Add other fields
+        for input_field, api_field in optional_field_mapping.items():
+            value = getattr(input_data, input_field)
+            if value:  # Only add non-empty values
+                payload[api_field] = value
 
-        # Handle text filters
-        if input_data.include_text:
-            sdk_kwargs["include_text"] = input_data.include_text
-        if input_data.exclude_text:
-            sdk_kwargs["exclude_text"] = input_data.exclude_text
-
-        if input_data.moderation:
-            sdk_kwargs["moderation"] = input_data.moderation
-
-        # heck if we need to use search_and_contents
-        content_settings = process_contents_settings(input_data.contents)
-
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        if content_settings:
-            sdk_kwargs["text"] = content_settings.get("text", False)
-            if "highlights" in content_settings:
-                sdk_kwargs["highlights"] = content_settings["highlights"]
-            if "summary" in content_settings:
-                sdk_kwargs["summary"] = content_settings["summary"]
-            response = await aexa.search_and_contents(**sdk_kwargs)
-        else:
-            response = await aexa.search(**sdk_kwargs)
-
-        converted_results = [
-            ExaSearchResults.from_sdk(sdk_result)
-            for sdk_result in response.results or []
-        ]
-
-        yield "results", converted_results
-        for result in converted_results:
-            yield "result", result
-
-        if response.context:
-            yield "context", response.context
-
-        if response.resolved_search_type:
-            yield "resolved_search_type", response.resolved_search_type
-
-        if response.cost_dollars:
-            yield "cost_dollars", response.cost_dollars
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()
+            # Extract just the results array from the response
+            yield "results", data.get("results", [])
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/similar.py

@@ -1,7 +1,5 @@
 from datetime import datetime
-from typing import Optional
-
-from exa_py import AsyncExa
+from typing import Any
 
 from backend.sdk import (
     APIKeyCredentials,
@@ -11,16 +9,12 @@ from backend.sdk import (
     BlockSchemaInput,
     BlockSchemaOutput,
     CredentialsMetaInput,
+    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import (
-    ContentSettings,
-    CostDollars,
-    ExaSearchResults,
-    process_contents_settings,
-)
+from .helpers import ContentSettings
 
 
 class ExaFindSimilarBlock(Block):
@@ -35,7 +29,7 @@ class ExaFindSimilarBlock(Block):
             description="Number of results to return", default=10, advanced=True
         )
         include_domains: list[str] = SchemaField(
-            description="List of domains to include in the search. If specified, results will only come from these domains.",
+            description="Domains to include in search",
             default_factory=list,
             advanced=True,
         )
@@ -44,17 +38,17 @@ class ExaFindSimilarBlock(Block):
             default_factory=list,
             advanced=True,
         )
-        start_crawl_date: Optional[datetime] = SchemaField(
-            description="Start date for crawled content", advanced=True, default=None
+        start_crawl_date: datetime = SchemaField(
+            description="Start date for crawled content"
         )
-        end_crawl_date: Optional[datetime] = SchemaField(
-            description="End date for crawled content", advanced=True, default=None
+        end_crawl_date: datetime = SchemaField(
+            description="End date for crawled content"
         )
-        start_published_date: Optional[datetime] = SchemaField(
-            description="Start date for published content", advanced=True, default=None
+        start_published_date: datetime = SchemaField(
+            description="Start date for published content"
         )
-        end_published_date: Optional[datetime] = SchemaField(
-            description="End date for published content", advanced=True, default=None
+        end_published_date: datetime = SchemaField(
+            description="End date for published content"
         )
         include_text: list[str] = SchemaField(
             description="Text patterns to include (max 1 string, up to 5 words)",
@@ -71,27 +65,15 @@ class ExaFindSimilarBlock(Block):
             default=ContentSettings(),
             advanced=True,
         )
-        moderation: bool = SchemaField(
-            description="Enable content moderation to filter unsafe content from search results",
-            default=False,
-            advanced=True,
-        )
 
     class Output(BlockSchemaOutput):
-        results: list[ExaSearchResults] = SchemaField(
-            description="List of similar documents with metadata and content"
+        results: list[Any] = SchemaField(
+            description="List of similar documents with title, URL, published date, author, and score",
+            default_factory=list,
         )
-        result: ExaSearchResults = SchemaField(
-            description="Single similar document result"
+        error: str = SchemaField(
+            description="Error message if the request failed", default=""
         )
-        context: str = SchemaField(
-            description="A formatted string of the results ready for LLMs."
-        )
-        request_id: str = SchemaField(description="Unique identifier for the request")
-        cost_dollars: Optional[CostDollars] = SchemaField(
-            description="Cost breakdown for the request"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -105,65 +87,47 @@ class ExaFindSimilarBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        sdk_kwargs = {
-            "url": input_data.url,
-            "num_results": input_data.number_of_results,
+        url = "https://api.exa.ai/findSimilar"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
         }
 
-        # Handle domains
-        if input_data.include_domains:
-            sdk_kwargs["include_domains"] = input_data.include_domains
-        if input_data.exclude_domains:
-            sdk_kwargs["exclude_domains"] = input_data.exclude_domains
+        payload = {
+            "url": input_data.url,
+            "numResults": input_data.number_of_results,
+            "contents": input_data.contents.model_dump(),
+        }
 
-        # Handle dates
-        if input_data.start_crawl_date:
-            sdk_kwargs["start_crawl_date"] = input_data.start_crawl_date.isoformat()
-        if input_data.end_crawl_date:
-            sdk_kwargs["end_crawl_date"] = input_data.end_crawl_date.isoformat()
-        if input_data.start_published_date:
-            sdk_kwargs["start_published_date"] = (
-                input_data.start_published_date.isoformat()
-            )
-        if input_data.end_published_date:
-            sdk_kwargs["end_published_date"] = input_data.end_published_date.isoformat()
+        optional_field_mapping = {
+            "include_domains": "includeDomains",
+            "exclude_domains": "excludeDomains",
+            "include_text": "includeText",
+            "exclude_text": "excludeText",
+        }
 
-        # Handle text filters
-        if input_data.include_text:
-            sdk_kwargs["include_text"] = input_data.include_text
-        if input_data.exclude_text:
-            sdk_kwargs["exclude_text"] = input_data.exclude_text
+        # Add optional fields if they have values
+        for input_field, api_field in optional_field_mapping.items():
+            value = getattr(input_data, input_field)
+            if value:  # Only add non-empty values
+                payload[api_field] = value
 
-        if input_data.moderation:
-            sdk_kwargs["moderation"] = input_data.moderation
+        date_field_mapping = {
+            "start_crawl_date": "startCrawlDate",
+            "end_crawl_date": "endCrawlDate",
+            "start_published_date": "startPublishedDate",
+            "end_published_date": "endPublishedDate",
+        }
 
-        # check if we need to use find_similar_and_contents
-        content_settings = process_contents_settings(input_data.contents)
+        # Add dates if they exist
+        for input_field, api_field in date_field_mapping.items():
+            value = getattr(input_data, input_field, None)
+            if value:
+                payload[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
 
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        if content_settings:
-            # Use find_similar_and_contents when contents are requested
-            sdk_kwargs["text"] = content_settings.get("text", False)
-            if "highlights" in content_settings:
-                sdk_kwargs["highlights"] = content_settings["highlights"]
-            if "summary" in content_settings:
-                sdk_kwargs["summary"] = content_settings["summary"]
-            response = await aexa.find_similar_and_contents(**sdk_kwargs)
-        else:
-            response = await aexa.find_similar(**sdk_kwargs)
-
-        converted_results = [
-            ExaSearchResults.from_sdk(sdk_result)
-            for sdk_result in response.results or []
-        ]
-
-        yield "results", converted_results
-        for result in converted_results:
-            yield "result", result
-
-        if response.context:
-            yield "context", response.context
-
-        if response.cost_dollars:
-            yield "cost_dollars", response.cost_dollars
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()
+            yield "results", data.get("results", [])
+        except Exception as e:
+            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/webhook_blocks.py

@@ -132,33 +132,45 @@ class ExaWebsetWebhookBlock(Block):
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         """Process incoming Exa webhook payload."""
-        payload = input_data.payload
+        try:
+            payload = input_data.payload
 
-        # Extract event details
-        event_type = payload.get("eventType", "unknown")
-        event_id = payload.get("eventId", "")
+            # Extract event details
+            event_type = payload.get("eventType", "unknown")
+            event_id = payload.get("eventId", "")
 
-        # Get webset ID from payload or input
-        webset_id = payload.get("websetId", input_data.webset_id)
+            # Get webset ID from payload or input
+            webset_id = payload.get("websetId", input_data.webset_id)
 
-        # Check if we should process this event based on filter
-        should_process = self._should_process_event(event_type, input_data.event_filter)
+            # Check if we should process this event based on filter
+            should_process = self._should_process_event(
+                event_type, input_data.event_filter
+            )
 
-        if not should_process:
-            # Skip events that don't match our filter
-            return
+            if not should_process:
+                # Skip events that don't match our filter
+                return
 
-        # Extract event data
-        event_data = payload.get("data", {})
-        timestamp = payload.get("occurredAt", payload.get("createdAt", ""))
-        metadata = payload.get("metadata", {})
+            # Extract event data
+            event_data = payload.get("data", {})
+            timestamp = payload.get("occurredAt", payload.get("createdAt", ""))
+            metadata = payload.get("metadata", {})
 
-        yield "event_type", event_type
-        yield "event_id", event_id
-        yield "webset_id", webset_id
-        yield "data", event_data
-        yield "timestamp", timestamp
-        yield "metadata", metadata
+            yield "event_type", event_type
+            yield "event_id", event_id
+            yield "webset_id", webset_id
+            yield "data", event_data
+            yield "timestamp", timestamp
+            yield "metadata", metadata
+
+        except Exception as e:
+            # Handle errors gracefully
+            yield "event_type", "error"
+            yield "event_id", ""
+            yield "webset_id", input_data.webset_id
+            yield "data", {"error": str(e)}
+            yield "timestamp", ""
+            yield "metadata", {}
 
     def _should_process_event(
         self, event_type: str, event_filter: WebsetEventFilter

autogpt_platform/backend/backend/blocks/exa/websets_enrichment.py

@@ -1,554 +0,0 @@
-"""
-Exa Websets Enrichment Management Blocks
-
-This module provides blocks for creating and managing enrichments on webset items,
-allowing extraction of additional structured data from existing items.
-"""
-
-from enum import Enum
-from typing import Any, Dict, List, Optional
-
-from exa_py import AsyncExa
-from exa_py.websets.types import WebsetEnrichment as SdkWebsetEnrichment
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-# Mirrored model for stability
-class WebsetEnrichmentModel(BaseModel):
-    """Stable output model mirroring SDK WebsetEnrichment."""
-
-    id: str
-    webset_id: str
-    status: str
-    title: Optional[str]
-    description: str
-    format: str
-    options: List[str]
-    instructions: Optional[str]
-    metadata: Dict[str, Any]
-    created_at: str
-    updated_at: str
-
-    @classmethod
-    def from_sdk(cls, enrichment: SdkWebsetEnrichment) -> "WebsetEnrichmentModel":
-        """Convert SDK WebsetEnrichment to our stable model."""
-        # Extract options
-        options_list = []
-        if enrichment.options:
-            for option in enrichment.options:
-                option_dict = option.model_dump(by_alias=True)
-                options_list.append(option_dict.get("label", ""))
-
-        return cls(
-            id=enrichment.id,
-            webset_id=enrichment.webset_id,
-            status=(
-                enrichment.status.value
-                if hasattr(enrichment.status, "value")
-                else str(enrichment.status)
-            ),
-            title=enrichment.title,
-            description=enrichment.description,
-            format=(
-                enrichment.format.value
-                if enrichment.format and hasattr(enrichment.format, "value")
-                else "text"
-            ),
-            options=options_list,
-            instructions=enrichment.instructions,
-            metadata=enrichment.metadata if enrichment.metadata else {},
-            created_at=(
-                enrichment.created_at.isoformat() if enrichment.created_at else ""
-            ),
-            updated_at=(
-                enrichment.updated_at.isoformat() if enrichment.updated_at else ""
-            ),
-        )
-
-
-class EnrichmentFormat(str, Enum):
-    """Format types for enrichment responses."""
-
-    TEXT = "text"  # Free text response
-    DATE = "date"  # Date/datetime format
-    NUMBER = "number"  # Numeric value
-    OPTIONS = "options"  # Multiple choice from provided options
-    EMAIL = "email"  # Email address format
-    PHONE = "phone"  # Phone number format
-
-
-class ExaCreateEnrichmentBlock(Block):
-    """Create a new enrichment to extract additional data from webset items."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        description: str = SchemaField(
-            description="What data to extract from each item",
-            placeholder="Extract the company's main product or service offering",
-        )
-        title: Optional[str] = SchemaField(
-            default=None,
-            description="Short title for this enrichment (auto-generated if not provided)",
-            placeholder="Main Product",
-        )
-        format: EnrichmentFormat = SchemaField(
-            default=EnrichmentFormat.TEXT,
-            description="Expected format of the extracted data",
-        )
-        options: list[str] = SchemaField(
-            default_factory=list,
-            description="Available options when format is 'options'",
-            placeholder='["B2B", "B2C", "Both", "Unknown"]',
-            advanced=True,
-        )
-        apply_to_existing: bool = SchemaField(
-            default=True,
-            description="Apply this enrichment to existing items in the webset",
-        )
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="Metadata to attach to the enrichment",
-            advanced=True,
-        )
-        wait_for_completion: bool = SchemaField(
-            default=False,
-            description="Wait for the enrichment to complete on existing items",
-        )
-        polling_timeout: int = SchemaField(
-            default=300,
-            description="Maximum time to wait for completion in seconds",
-            advanced=True,
-            ge=1,
-            le=600,
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(
-            description="The unique identifier for the created enrichment"
-        )
-        webset_id: str = SchemaField(
-            description="The webset this enrichment belongs to"
-        )
-        status: str = SchemaField(description="Current status of the enrichment")
-        title: str = SchemaField(description="Title of the enrichment")
-        description: str = SchemaField(
-            description="Description of what data is extracted"
-        )
-        format: str = SchemaField(description="Format of the extracted data")
-        instructions: str = SchemaField(
-            description="Generated instructions for the enrichment"
-        )
-        items_enriched: Optional[int] = SchemaField(
-            description="Number of items enriched (if wait_for_completion was True)"
-        )
-        completion_time: Optional[float] = SchemaField(
-            description="Time taken to complete in seconds (if wait_for_completion was True)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="71146ae8-0cb1-4a15-8cde-eae30de71cb6",
-            description="Create enrichments to extract additional structured data from webset items",
-            categories={BlockCategory.AI, BlockCategory.SEARCH},
-            input_schema=ExaCreateEnrichmentBlock.Input,
-            output_schema=ExaCreateEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        import time
-
-        # Build the payload
-        payload: dict[str, Any] = {
-            "description": input_data.description,
-            "format": input_data.format.value,
-        }
-
-        # Add title if provided
-        if input_data.title:
-            payload["title"] = input_data.title
-
-        # Add options for 'options' format
-        if input_data.format == EnrichmentFormat.OPTIONS and input_data.options:
-            payload["options"] = [{"label": opt} for opt in input_data.options]
-
-        # Add metadata if provided
-        if input_data.metadata:
-            payload["metadata"] = input_data.metadata
-
-        start_time = time.time()
-
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_enrichment = aexa.websets.enrichments.create(
-            webset_id=input_data.webset_id, params=payload
-        )
-
-        enrichment_id = sdk_enrichment.id
-        status = (
-            sdk_enrichment.status.value
-            if hasattr(sdk_enrichment.status, "value")
-            else str(sdk_enrichment.status)
-        )
-
-        # If wait_for_completion is True and apply_to_existing is True, poll for completion
-        if input_data.wait_for_completion and input_data.apply_to_existing:
-            import asyncio
-
-            poll_interval = 5
-            max_interval = 30
-            poll_start = time.time()
-            items_enriched = 0
-
-            while time.time() - poll_start < input_data.polling_timeout:
-                current_enrich = aexa.websets.enrichments.get(
-                    webset_id=input_data.webset_id, id=enrichment_id
-                )
-                current_status = (
-                    current_enrich.status.value
-                    if hasattr(current_enrich.status, "value")
-                    else str(current_enrich.status)
-                )
-
-                if current_status in ["completed", "failed", "cancelled"]:
-                    # Estimate items from webset searches
-                    webset = aexa.websets.get(id=input_data.webset_id)
-                    if webset.searches:
-                        for search in webset.searches:
-                            if search.progress:
-                                items_enriched += search.progress.found
-                    completion_time = time.time() - start_time
-
-                    yield "enrichment_id", enrichment_id
-                    yield "webset_id", input_data.webset_id
-                    yield "status", current_status
-                    yield "title", sdk_enrichment.title
-                    yield "description", input_data.description
-                    yield "format", input_data.format.value
-                    yield "instructions", sdk_enrichment.instructions
-                    yield "items_enriched", items_enriched
-                    yield "completion_time", completion_time
-                    return
-
-                await asyncio.sleep(poll_interval)
-                poll_interval = min(poll_interval * 1.5, max_interval)
-
-            # Timeout
-            completion_time = time.time() - start_time
-            yield "enrichment_id", enrichment_id
-            yield "webset_id", input_data.webset_id
-            yield "status", status
-            yield "title", sdk_enrichment.title
-            yield "description", input_data.description
-            yield "format", input_data.format.value
-            yield "instructions", sdk_enrichment.instructions
-            yield "items_enriched", 0
-            yield "completion_time", completion_time
-        else:
-            yield "enrichment_id", enrichment_id
-            yield "webset_id", input_data.webset_id
-            yield "status", status
-            yield "title", sdk_enrichment.title
-            yield "description", input_data.description
-            yield "format", input_data.format.value
-            yield "instructions", sdk_enrichment.instructions
-
-
-class ExaGetEnrichmentBlock(Block):
-    """Get the status and details of a webset enrichment."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        enrichment_id: str = SchemaField(
-            description="The ID of the enrichment to retrieve",
-            placeholder="enrichment-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(
-            description="The unique identifier for the enrichment"
-        )
-        status: str = SchemaField(description="Current status of the enrichment")
-        title: str = SchemaField(description="Title of the enrichment")
-        description: str = SchemaField(
-            description="Description of what data is extracted"
-        )
-        format: str = SchemaField(description="Format of the extracted data")
-        options: list[str] = SchemaField(
-            description="Available options (for 'options' format)"
-        )
-        instructions: str = SchemaField(
-            description="Generated instructions for the enrichment"
-        )
-        created_at: str = SchemaField(description="When the enrichment was created")
-        updated_at: str = SchemaField(
-            description="When the enrichment was last updated"
-        )
-        metadata: dict = SchemaField(description="Metadata attached to the enrichment")
-
-    def __init__(self):
-        super().__init__(
-            id="b8c9d0e1-f2a3-4567-89ab-cdef01234567",
-            description="Get the status and details of a webset enrichment",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetEnrichmentBlock.Input,
-            output_schema=ExaGetEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_enrichment = aexa.websets.enrichments.get(
-            webset_id=input_data.webset_id, id=input_data.enrichment_id
-        )
-
-        enrichment = WebsetEnrichmentModel.from_sdk(sdk_enrichment)
-
-        yield "enrichment_id", enrichment.id
-        yield "status", enrichment.status
-        yield "title", enrichment.title
-        yield "description", enrichment.description
-        yield "format", enrichment.format
-        yield "options", enrichment.options
-        yield "instructions", enrichment.instructions
-        yield "created_at", enrichment.created_at
-        yield "updated_at", enrichment.updated_at
-        yield "metadata", enrichment.metadata
-
-
-class ExaUpdateEnrichmentBlock(Block):
-    """Update an existing enrichment configuration."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        enrichment_id: str = SchemaField(
-            description="The ID of the enrichment to update",
-            placeholder="enrichment-id",
-        )
-        description: Optional[str] = SchemaField(
-            default=None,
-            description="New description for what data to extract",
-        )
-        format: Optional[EnrichmentFormat] = SchemaField(
-            default=None,
-            description="New format for the extracted data",
-        )
-        options: Optional[list[str]] = SchemaField(
-            default=None,
-            description="New options when format is 'options'",
-        )
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="New metadata to attach to the enrichment",
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(
-            description="The unique identifier for the enrichment"
-        )
-        status: str = SchemaField(description="Current status of the enrichment")
-        title: str = SchemaField(description="Title of the enrichment")
-        description: str = SchemaField(description="Updated description")
-        format: str = SchemaField(description="Updated format")
-        success: str = SchemaField(description="Whether the update was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="c8d5c5fb-9684-4a29-bd2a-5b38d71776c9",
-            description="Update an existing enrichment configuration",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaUpdateEnrichmentBlock.Input,
-            output_schema=ExaUpdateEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}/enrichments/{input_data.enrichment_id}"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        # Build the update payload
-        payload = {}
-
-        if input_data.description is not None:
-            payload["description"] = input_data.description
-
-        if input_data.format is not None:
-            payload["format"] = input_data.format.value
-
-        if input_data.options is not None:
-            payload["options"] = [{"label": opt} for opt in input_data.options]
-
-        if input_data.metadata is not None:
-            payload["metadata"] = input_data.metadata
-
-        try:
-            response = await Requests().patch(url, headers=headers, json=payload)
-            data = response.json()
-
-            yield "enrichment_id", data.get("id", "")
-            yield "status", data.get("status", "")
-            yield "title", data.get("title", "")
-            yield "description", data.get("description", "")
-            yield "format", data.get("format", "")
-            yield "success", "true"
-
-        except ValueError as e:
-            # Re-raise user input validation errors
-            raise ValueError(f"Failed to update enrichment: {e}") from e
-        # Let all other exceptions propagate naturally
-
-
-class ExaDeleteEnrichmentBlock(Block):
-    """Delete an enrichment from a webset."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        enrichment_id: str = SchemaField(
-            description="The ID of the enrichment to delete",
-            placeholder="enrichment-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(description="The ID of the deleted enrichment")
-        success: str = SchemaField(description="Whether the deletion was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="b250de56-2ca6-4237-a7b8-b5684892189f",
-            description="Delete an enrichment from a webset",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaDeleteEnrichmentBlock.Input,
-            output_schema=ExaDeleteEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        deleted_enrichment = aexa.websets.enrichments.delete(
-            webset_id=input_data.webset_id, id=input_data.enrichment_id
-        )
-
-        yield "enrichment_id", deleted_enrichment.id
-        yield "success", "true"
-
-
-class ExaCancelEnrichmentBlock(Block):
-    """Cancel a running enrichment operation."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        enrichment_id: str = SchemaField(
-            description="The ID of the enrichment to cancel",
-            placeholder="enrichment-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(
-            description="The ID of the canceled enrichment"
-        )
-        status: str = SchemaField(description="Status after cancellation")
-        items_enriched_before_cancel: int = SchemaField(
-            description="Approximate number of items enriched before cancellation"
-        )
-        success: str = SchemaField(
-            description="Whether the cancellation was successful"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="7e1f8f0f-b6ab-43b3-bd1d-0c534a649295",
-            description="Cancel a running enrichment operation",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCancelEnrichmentBlock.Input,
-            output_schema=ExaCancelEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        canceled_enrichment = aexa.websets.enrichments.cancel(
-            webset_id=input_data.webset_id, id=input_data.enrichment_id
-        )
-
-        # Try to estimate how many items were enriched before cancellation
-        items_enriched = 0
-        items_response = aexa.websets.items.list(
-            webset_id=input_data.webset_id, limit=100
-        )
-
-        for sdk_item in items_response.data:
-            # Check if this enrichment is present
-            for enrich_result in sdk_item.enrichments:
-                if enrich_result.enrichment_id == input_data.enrichment_id:
-                    items_enriched += 1
-                    break
-
-        status = (
-            canceled_enrichment.status.value
-            if hasattr(canceled_enrichment.status, "value")
-            else str(canceled_enrichment.status)
-        )
-
-        yield "enrichment_id", canceled_enrichment.id
-        yield "status", status
-        yield "items_enriched_before_cancel", items_enriched
-        yield "success", "true"

autogpt_platform/backend/backend/blocks/exa/websets_import_export.py

@@ -1,676 +0,0 @@
-"""
-Exa Websets Import/Export Management Blocks
-
-This module provides blocks for importing data into websets from CSV files
-and exporting webset data in various formats.
-"""
-
-import csv
-import json
-from enum import Enum
-from io import StringIO
-from typing import Optional, Union
-
-from exa_py import AsyncExa
-from exa_py.websets.types import CreateImportResponse
-from exa_py.websets.types import Import as SdkImport
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._config import exa
-from ._test import TEST_CREDENTIALS, TEST_CREDENTIALS_INPUT
-
-
-# Mirrored model for stability - don't use SDK types directly in block outputs
-class ImportModel(BaseModel):
-    """Stable output model mirroring SDK Import."""
-
-    id: str
-    status: str
-    title: str
-    format: str
-    entity_type: str
-    count: int
-    upload_url: Optional[str]  # Only in CreateImportResponse
-    upload_valid_until: Optional[str]  # Only in CreateImportResponse
-    failed_reason: str
-    failed_message: str
-    metadata: dict
-    created_at: str
-    updated_at: str
-
-    @classmethod
-    def from_sdk(
-        cls, import_obj: Union[SdkImport, CreateImportResponse]
-    ) -> "ImportModel":
-        """Convert SDK Import or CreateImportResponse to our stable model."""
-        # Extract entity type from union (may be None)
-        entity_type = "unknown"
-        if import_obj.entity:
-            entity_dict = import_obj.entity.model_dump(by_alias=True, exclude_none=True)
-            entity_type = entity_dict.get("type", "unknown")
-
-        # Handle status enum
-        status_str = (
-            import_obj.status.value
-            if hasattr(import_obj.status, "value")
-            else str(import_obj.status)
-        )
-
-        # Handle format enum
-        format_str = (
-            import_obj.format.value
-            if hasattr(import_obj.format, "value")
-            else str(import_obj.format)
-        )
-
-        # Handle failed_reason enum (may be None or enum)
-        failed_reason_str = ""
-        if import_obj.failed_reason:
-            failed_reason_str = (
-                import_obj.failed_reason.value
-                if hasattr(import_obj.failed_reason, "value")
-                else str(import_obj.failed_reason)
-            )
-
-        return cls(
-            id=import_obj.id,
-            status=status_str,
-            title=import_obj.title or "",
-            format=format_str,
-            entity_type=entity_type,
-            count=int(import_obj.count or 0),
-            upload_url=getattr(
-                import_obj, "upload_url", None
-            ),  # Only in CreateImportResponse
-            upload_valid_until=getattr(
-                import_obj, "upload_valid_until", None
-            ),  # Only in CreateImportResponse
-            failed_reason=failed_reason_str,
-            failed_message=import_obj.failed_message or "",
-            metadata=import_obj.metadata or {},
-            created_at=(
-                import_obj.created_at.isoformat() if import_obj.created_at else ""
-            ),
-            updated_at=(
-                import_obj.updated_at.isoformat() if import_obj.updated_at else ""
-            ),
-        )
-
-
-class ImportFormat(str, Enum):
-    """Supported import formats."""
-
-    CSV = "csv"
-    # JSON = "json"  # Future support
-
-
-class ImportEntityType(str, Enum):
-    """Entity types for imports."""
-
-    COMPANY = "company"
-    PERSON = "person"
-    ARTICLE = "article"
-    RESEARCH_PAPER = "research_paper"
-    CUSTOM = "custom"
-
-
-class ExportFormat(str, Enum):
-    """Supported export formats."""
-
-    JSON = "json"
-    CSV = "csv"
-    JSON_LINES = "jsonl"
-
-
-class ExaCreateImportBlock(Block):
-    """Create an import to load external data that can be used with websets."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        title: str = SchemaField(
-            description="Title for this import",
-            placeholder="Customer List Import",
-        )
-        csv_data: str = SchemaField(
-            description="CSV data to import (as a string)",
-            placeholder="name,url\nAcme Corp,https://acme.com\nExample Inc,https://example.com",
-        )
-        entity_type: ImportEntityType = SchemaField(
-            default=ImportEntityType.COMPANY,
-            description="Type of entities being imported",
-        )
-        entity_description: Optional[str] = SchemaField(
-            default=None,
-            description="Description for custom entity type",
-            advanced=True,
-        )
-        identifier_column: int = SchemaField(
-            default=0,
-            description="Column index containing the identifier (0-based)",
-            ge=0,
-        )
-        url_column: Optional[int] = SchemaField(
-            default=None,
-            description="Column index containing URLs (optional)",
-            ge=0,
-            advanced=True,
-        )
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="Metadata to attach to the import",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        import_id: str = SchemaField(
-            description="The unique identifier for the created import"
-        )
-        status: str = SchemaField(description="Current status of the import")
-        title: str = SchemaField(description="Title of the import")
-        count: int = SchemaField(description="Number of items in the import")
-        entity_type: str = SchemaField(description="Type of entities imported")
-        upload_url: Optional[str] = SchemaField(
-            description="Upload URL for CSV data (only if csv_data not provided in request)"
-        )
-        upload_valid_until: Optional[str] = SchemaField(
-            description="Expiration time for upload URL (only if upload_url is provided)"
-        )
-        created_at: str = SchemaField(description="When the import was created")
-
-    def __init__(self):
-        super().__init__(
-            id="020a35d8-8a53-4e60-8b60-1de5cbab1df3",
-            description="Import CSV data to use with websets for targeted searches",
-            categories={BlockCategory.DATA},
-            input_schema=ExaCreateImportBlock.Input,
-            output_schema=ExaCreateImportBlock.Output,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "title": "Test Import",
-                "csv_data": "name,url\nAcme,https://acme.com",
-                "entity_type": ImportEntityType.COMPANY,
-                "identifier_column": 0,
-            },
-            test_output=[
-                ("import_id", "import-123"),
-                ("status", "pending"),
-                ("title", "Test Import"),
-                ("count", 1),
-                ("entity_type", "company"),
-                ("upload_url", None),
-                ("upload_valid_until", None),
-                ("created_at", "2024-01-01T00:00:00"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock=self._create_test_mock(),
-        )
-
-    @staticmethod
-    def _create_test_mock():
-        """Create test mocks for the AsyncExa SDK."""
-        from datetime import datetime
-        from unittest.mock import MagicMock
-
-        # Create mock SDK import object
-        mock_import = MagicMock()
-        mock_import.id = "import-123"
-        mock_import.status = MagicMock(value="pending")
-        mock_import.title = "Test Import"
-        mock_import.format = MagicMock(value="csv")
-        mock_import.count = 1
-        mock_import.upload_url = None
-        mock_import.upload_valid_until = None
-        mock_import.failed_reason = None
-        mock_import.failed_message = ""
-        mock_import.metadata = {}
-        mock_import.created_at = datetime.fromisoformat("2024-01-01T00:00:00")
-        mock_import.updated_at = datetime.fromisoformat("2024-01-01T00:00:00")
-
-        # Mock entity
-        mock_entity = MagicMock()
-        mock_entity.model_dump = MagicMock(return_value={"type": "company"})
-        mock_import.entity = mock_entity
-
-        return {
-            "_get_client": lambda *args, **kwargs: MagicMock(
-                websets=MagicMock(
-                    imports=MagicMock(create=lambda *args, **kwargs: mock_import)
-                )
-            )
-        }
-
-    def _get_client(self, api_key: str) -> AsyncExa:
-        """Get Exa client (separated for testing)."""
-        return AsyncExa(api_key=api_key)
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        aexa = self._get_client(credentials.api_key.get_secret_value())
-
-        csv_reader = csv.reader(StringIO(input_data.csv_data))
-        rows = list(csv_reader)
-        count = len(rows) - 1 if len(rows) > 1 else 0
-
-        size = len(input_data.csv_data.encode("utf-8"))
-
-        payload = {
-            "title": input_data.title,
-            "format": ImportFormat.CSV.value,
-            "count": count,
-            "size": size,
-            "csv": {
-                "identifier": input_data.identifier_column,
-            },
-        }
-
-        # Add URL column if specified
-        if input_data.url_column is not None:
-            payload["csv"]["url"] = input_data.url_column
-
-        # Add entity configuration
-        entity = {"type": input_data.entity_type.value}
-        if (
-            input_data.entity_type == ImportEntityType.CUSTOM
-            and input_data.entity_description
-        ):
-            entity["description"] = input_data.entity_description
-        payload["entity"] = entity
-
-        # Add metadata if provided
-        if input_data.metadata:
-            payload["metadata"] = input_data.metadata
-
-        sdk_import = aexa.websets.imports.create(
-            params=payload, csv_data=input_data.csv_data
-        )
-
-        import_obj = ImportModel.from_sdk(sdk_import)
-
-        yield "import_id", import_obj.id
-        yield "status", import_obj.status
-        yield "title", import_obj.title
-        yield "count", import_obj.count
-        yield "entity_type", import_obj.entity_type
-        yield "upload_url", import_obj.upload_url
-        yield "upload_valid_until", import_obj.upload_valid_until
-        yield "created_at", import_obj.created_at
-
-
-class ExaGetImportBlock(Block):
-    """Get the status and details of an import."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        import_id: str = SchemaField(
-            description="The ID of the import to retrieve",
-            placeholder="import-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        import_id: str = SchemaField(description="The unique identifier for the import")
-        status: str = SchemaField(description="Current status of the import")
-        title: str = SchemaField(description="Title of the import")
-        format: str = SchemaField(description="Format of the imported data")
-        entity_type: str = SchemaField(description="Type of entities imported")
-        count: int = SchemaField(description="Number of items imported")
-        upload_url: Optional[str] = SchemaField(
-            description="Upload URL for CSV data (if import not yet uploaded)"
-        )
-        upload_valid_until: Optional[str] = SchemaField(
-            description="Expiration time for upload URL (if applicable)"
-        )
-        failed_reason: Optional[str] = SchemaField(
-            description="Reason for failure (if applicable)"
-        )
-        failed_message: Optional[str] = SchemaField(
-            description="Detailed failure message (if applicable)"
-        )
-        created_at: str = SchemaField(description="When the import was created")
-        updated_at: str = SchemaField(description="When the import was last updated")
-        metadata: dict = SchemaField(description="Metadata attached to the import")
-
-    def __init__(self):
-        super().__init__(
-            id="236663c8-a8dc-45f7-a050-2676bb0a3dd2",
-            description="Get the status and details of an import",
-            categories={BlockCategory.DATA},
-            input_schema=ExaGetImportBlock.Input,
-            output_schema=ExaGetImportBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_import = aexa.websets.imports.get(import_id=input_data.import_id)
-
-        import_obj = ImportModel.from_sdk(sdk_import)
-
-        # Yield all fields
-        yield "import_id", import_obj.id
-        yield "status", import_obj.status
-        yield "title", import_obj.title
-        yield "format", import_obj.format
-        yield "entity_type", import_obj.entity_type
-        yield "count", import_obj.count
-        yield "upload_url", import_obj.upload_url
-        yield "upload_valid_until", import_obj.upload_valid_until
-        yield "failed_reason", import_obj.failed_reason
-        yield "failed_message", import_obj.failed_message
-        yield "created_at", import_obj.created_at
-        yield "updated_at", import_obj.updated_at
-        yield "metadata", import_obj.metadata
-
-
-class ExaListImportsBlock(Block):
-    """List all imports with pagination."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        limit: int = SchemaField(
-            default=25,
-            description="Number of imports to return",
-            ge=1,
-            le=100,
-        )
-        cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor for pagination",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        imports: list[dict] = SchemaField(description="List of imports")
-        import_item: dict = SchemaField(
-            description="Individual import (yielded for each import)"
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more imports to paginate through"
-        )
-        next_cursor: Optional[str] = SchemaField(
-            description="Cursor for the next page of results"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="65323630-f7e9-4692-a624-184ba14c0686",
-            description="List all imports with pagination support",
-            categories={BlockCategory.DATA},
-            input_schema=ExaListImportsBlock.Input,
-            output_schema=ExaListImportsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        response = aexa.websets.imports.list(
-            cursor=input_data.cursor,
-            limit=input_data.limit,
-        )
-
-        # Convert SDK imports to our stable models
-        imports = [ImportModel.from_sdk(i) for i in response.data]
-
-        yield "imports", [i.model_dump() for i in imports]
-
-        for import_obj in imports:
-            yield "import_item", import_obj.model_dump()
-
-        yield "has_more", response.has_more
-        yield "next_cursor", response.next_cursor
-
-
-class ExaDeleteImportBlock(Block):
-    """Delete an import."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        import_id: str = SchemaField(
-            description="The ID of the import to delete",
-            placeholder="import-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        import_id: str = SchemaField(description="The ID of the deleted import")
-        success: str = SchemaField(description="Whether the deletion was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="81ae30ed-c7ba-4b5d-8483-b726846e570c",
-            description="Delete an import",
-            categories={BlockCategory.DATA},
-            input_schema=ExaDeleteImportBlock.Input,
-            output_schema=ExaDeleteImportBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        deleted_import = aexa.websets.imports.delete(import_id=input_data.import_id)
-
-        yield "import_id", deleted_import.id
-        yield "success", "true"
-
-
-class ExaExportWebsetBlock(Block):
-    """Export all data from a webset in various formats."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to export",
-            placeholder="webset-id-or-external-id",
-        )
-        format: ExportFormat = SchemaField(
-            default=ExportFormat.JSON,
-            description="Export format",
-        )
-        include_content: bool = SchemaField(
-            default=True,
-            description="Include full content in export",
-        )
-        include_enrichments: bool = SchemaField(
-            default=True,
-            description="Include enrichment data in export",
-        )
-        max_items: int = SchemaField(
-            default=100,
-            description="Maximum number of items to export",
-            ge=1,
-            le=100,
-        )
-
-    class Output(BlockSchemaOutput):
-        export_data: str = SchemaField(
-            description="Exported data in the requested format"
-        )
-        item_count: int = SchemaField(description="Number of items exported")
-        total_items: int = SchemaField(
-            description="Total number of items in the webset"
-        )
-        truncated: bool = SchemaField(
-            description="Whether the export was truncated due to max_items limit"
-        )
-        format: str = SchemaField(description="Format of the exported data")
-
-    def __init__(self):
-        super().__init__(
-            id="5da9d0fd-4b5b-4318-8302-8f71d0ccce9d",
-            description="Export webset data in JSON, CSV, or JSON Lines format",
-            categories={BlockCategory.DATA},
-            input_schema=ExaExportWebsetBlock.Input,
-            output_schema=ExaExportWebsetBlock.Output,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "webset_id": "test-webset",
-                "format": ExportFormat.JSON,
-                "include_content": True,
-                "include_enrichments": True,
-                "max_items": 10,
-            },
-            test_output=[
-                ("export_data", str),
-                ("item_count", 2),
-                ("total_items", 2),
-                ("truncated", False),
-                ("format", "json"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock=self._create_test_mock(),
-        )
-
-    @staticmethod
-    def _create_test_mock():
-        """Create test mocks for the AsyncExa SDK."""
-        from unittest.mock import MagicMock
-
-        # Create mock webset items
-        mock_item1 = MagicMock()
-        mock_item1.model_dump = MagicMock(
-            return_value={
-                "id": "item-1",
-                "url": "https://example.com",
-                "title": "Test Item 1",
-            }
-        )
-
-        mock_item2 = MagicMock()
-        mock_item2.model_dump = MagicMock(
-            return_value={
-                "id": "item-2",
-                "url": "https://example.org",
-                "title": "Test Item 2",
-            }
-        )
-
-        # Create mock iterator
-        mock_items = [mock_item1, mock_item2]
-
-        return {
-            "_get_client": lambda *args, **kwargs: MagicMock(
-                websets=MagicMock(
-                    items=MagicMock(list_all=lambda *args, **kwargs: iter(mock_items))
-                )
-            )
-        }
-
-    def _get_client(self, api_key: str) -> AsyncExa:
-        """Get Exa client (separated for testing)."""
-        return AsyncExa(api_key=api_key)
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = self._get_client(credentials.api_key.get_secret_value())
-
-        try:
-            all_items = []
-
-            # Use SDK's list_all iterator to fetch items
-            item_iterator = aexa.websets.items.list_all(
-                webset_id=input_data.webset_id, limit=input_data.max_items
-            )
-
-            for sdk_item in item_iterator:
-                if len(all_items) >= input_data.max_items:
-                    break
-
-                # Convert to dict for export
-                item_dict = sdk_item.model_dump(by_alias=True, exclude_none=True)
-                all_items.append(item_dict)
-
-            # Calculate total and truncated
-            total_items = len(all_items)  # SDK doesn't provide total count
-            truncated = len(all_items) >= input_data.max_items
-
-            # Process items based on include flags
-            if not input_data.include_content:
-                for item in all_items:
-                    item.pop("content", None)
-
-            if not input_data.include_enrichments:
-                for item in all_items:
-                    item.pop("enrichments", None)
-
-            # Format the export data
-            export_data = ""
-
-            if input_data.format == ExportFormat.JSON:
-                export_data = json.dumps(all_items, indent=2, default=str)
-
-            elif input_data.format == ExportFormat.JSON_LINES:
-                lines = [json.dumps(item, default=str) for item in all_items]
-                export_data = "\n".join(lines)
-
-            elif input_data.format == ExportFormat.CSV:
-                # Extract all unique keys for CSV headers
-                all_keys = set()
-                for item in all_items:
-                    all_keys.update(self._flatten_dict(item).keys())
-
-                # Create CSV
-                output = StringIO()
-                writer = csv.DictWriter(output, fieldnames=sorted(all_keys))
-                writer.writeheader()
-
-                for item in all_items:
-                    flat_item = self._flatten_dict(item)
-                    writer.writerow(flat_item)
-
-                export_data = output.getvalue()
-
-            yield "export_data", export_data
-            yield "item_count", len(all_items)
-            yield "total_items", total_items
-            yield "truncated", truncated
-            yield "format", input_data.format.value
-
-        except ValueError as e:
-            # Re-raise user input validation errors
-            raise ValueError(f"Failed to export webset: {e}") from e
-        # Let all other exceptions propagate naturally
-
-    def _flatten_dict(self, d: dict, parent_key: str = "", sep: str = "_") -> dict:
-        """Flatten nested dictionaries for CSV export."""
-        items = []
-        for k, v in d.items():
-            new_key = f"{parent_key}{sep}{k}" if parent_key else k
-            if isinstance(v, dict):
-                items.extend(self._flatten_dict(v, new_key, sep=sep).items())
-            elif isinstance(v, list):
-                # Convert lists to JSON strings for CSV
-                items.append((new_key, json.dumps(v, default=str)))
-            else:
-                items.append((new_key, v))
-        return dict(items)

autogpt_platform/backend/backend/blocks/exa/websets_items.py

@@ -1,591 +0,0 @@
-"""
-Exa Websets Item Management Blocks
-
-This module provides blocks for managing items within Exa websets, including
-retrieving, listing, deleting, and bulk operations on webset items.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from exa_py import AsyncExa
-from exa_py.websets.types import WebsetItem as SdkWebsetItem
-from exa_py.websets.types import (
-    WebsetItemArticleProperties,
-    WebsetItemCompanyProperties,
-    WebsetItemCustomProperties,
-    WebsetItemPersonProperties,
-    WebsetItemResearchPaperProperties,
-)
-from pydantic import AnyUrl, BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-# Mirrored model for enrichment results
-class EnrichmentResultModel(BaseModel):
-    """Stable output model mirroring SDK EnrichmentResult."""
-
-    enrichment_id: str
-    format: str
-    result: Optional[List[str]]
-    reasoning: Optional[str]
-    references: List[Dict[str, Any]]
-
-    @classmethod
-    def from_sdk(cls, sdk_enrich) -> "EnrichmentResultModel":
-        """Convert SDK EnrichmentResult to our model."""
-        format_str = (
-            sdk_enrich.format.value
-            if hasattr(sdk_enrich.format, "value")
-            else str(sdk_enrich.format)
-        )
-
-        # Convert references to dicts
-        references_list = []
-        if sdk_enrich.references:
-            for ref in sdk_enrich.references:
-                references_list.append(ref.model_dump(by_alias=True, exclude_none=True))
-
-        return cls(
-            enrichment_id=sdk_enrich.enrichment_id,
-            format=format_str,
-            result=sdk_enrich.result,
-            reasoning=sdk_enrich.reasoning,
-            references=references_list,
-        )
-
-
-# Mirrored model for stability - don't use SDK types directly in block outputs
-class WebsetItemModel(BaseModel):
-    """Stable output model mirroring SDK WebsetItem."""
-
-    id: str
-    url: Optional[AnyUrl]
-    title: str
-    content: str
-    entity_data: Dict[str, Any]
-    enrichments: Dict[str, EnrichmentResultModel]
-    created_at: str
-    updated_at: str
-
-    @classmethod
-    def from_sdk(cls, item: SdkWebsetItem) -> "WebsetItemModel":
-        """Convert SDK WebsetItem to our stable model."""
-        # Extract properties from the union type
-        properties_dict = {}
-        url_value = None
-        title = ""
-        content = ""
-
-        if hasattr(item, "properties") and item.properties:
-            properties_dict = item.properties.model_dump(
-                by_alias=True, exclude_none=True
-            )
-
-            # URL is always available on all property types
-            url_value = item.properties.url
-
-            # Extract title using isinstance checks on the union type
-            if isinstance(item.properties, WebsetItemPersonProperties):
-                title = item.properties.person.name
-                content = ""  # Person type has no content
-            elif isinstance(item.properties, WebsetItemCompanyProperties):
-                title = item.properties.company.name
-                content = item.properties.content or ""
-            elif isinstance(item.properties, WebsetItemArticleProperties):
-                title = item.properties.description
-                content = item.properties.content or ""
-            elif isinstance(item.properties, WebsetItemResearchPaperProperties):
-                title = item.properties.description
-                content = item.properties.content or ""
-            elif isinstance(item.properties, WebsetItemCustomProperties):
-                title = item.properties.description
-                content = item.properties.content or ""
-            else:
-                # Fallback
-                title = item.properties.description
-                content = getattr(item.properties, "content", "")
-
-        # Convert enrichments from list to dict keyed by enrichment_id using Pydantic models
-        enrichments_dict: Dict[str, EnrichmentResultModel] = {}
-        if hasattr(item, "enrichments") and item.enrichments:
-            for sdk_enrich in item.enrichments:
-                enrich_model = EnrichmentResultModel.from_sdk(sdk_enrich)
-                enrichments_dict[enrich_model.enrichment_id] = enrich_model
-
-        return cls(
-            id=item.id,
-            url=url_value,
-            title=title,
-            content=content or "",
-            entity_data=properties_dict,
-            enrichments=enrichments_dict,
-            created_at=item.created_at.isoformat() if item.created_at else "",
-            updated_at=item.updated_at.isoformat() if item.updated_at else "",
-        )
-
-
-class ExaGetWebsetItemBlock(Block):
-    """Get a specific item from a webset by its ID."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        item_id: str = SchemaField(
-            description="The ID of the specific item to retrieve",
-            placeholder="item-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        item_id: str = SchemaField(description="The unique identifier for the item")
-        url: str = SchemaField(description="The URL of the original source")
-        title: str = SchemaField(description="The title of the item")
-        content: str = SchemaField(description="The main content of the item")
-        entity_data: dict = SchemaField(description="Entity-specific structured data")
-        enrichments: dict = SchemaField(description="Enrichment data added to the item")
-        created_at: str = SchemaField(
-            description="When the item was added to the webset"
-        )
-        updated_at: str = SchemaField(description="When the item was last updated")
-
-    def __init__(self):
-        super().__init__(
-            id="c4a7d9e2-8f3b-4a6c-9d8e-a5b6c7d8e9f0",
-            description="Get a specific item from a webset by its ID",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetWebsetItemBlock.Input,
-            output_schema=ExaGetWebsetItemBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_item = aexa.websets.items.get(
-            webset_id=input_data.webset_id, id=input_data.item_id
-        )
-
-        item = WebsetItemModel.from_sdk(sdk_item)
-
-        yield "item_id", item.id
-        yield "url", item.url
-        yield "title", item.title
-        yield "content", item.content
-        yield "entity_data", item.entity_data
-        yield "enrichments", item.enrichments
-        yield "created_at", item.created_at
-        yield "updated_at", item.updated_at
-
-
-class ExaListWebsetItemsBlock(Block):
-    """List items in a webset with pagination and optional filtering."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        limit: int = SchemaField(
-            default=25,
-            description="Number of items to return (1-100)",
-            ge=1,
-            le=100,
-        )
-        cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor for pagination through results",
-            advanced=True,
-        )
-        wait_for_items: bool = SchemaField(
-            default=False,
-            description="Wait for items to be available if webset is still processing",
-            advanced=True,
-        )
-        wait_timeout: int = SchemaField(
-            default=60,
-            description="Maximum time to wait for items in seconds",
-            advanced=True,
-            ge=1,
-            le=300,
-        )
-
-    class Output(BlockSchemaOutput):
-        items: list[WebsetItemModel] = SchemaField(
-            description="List of webset items",
-        )
-        webset_id: str = SchemaField(
-            description="The ID of the webset",
-        )
-        item: WebsetItemModel = SchemaField(
-            description="Individual item (yielded for each item in the list)",
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more items to paginate through",
-        )
-        next_cursor: Optional[str] = SchemaField(
-            description="Cursor for the next page of results",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="7b5e8c9f-01a2-43c4-95e6-f7a8b9c0d1e2",
-            description="List items in a webset with pagination support",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaListWebsetItemsBlock.Input,
-            output_schema=ExaListWebsetItemsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        if input_data.wait_for_items:
-            import asyncio
-            import time
-
-            start_time = time.time()
-            interval = 2
-            response = None
-
-            while time.time() - start_time < input_data.wait_timeout:
-                response = aexa.websets.items.list(
-                    webset_id=input_data.webset_id,
-                    cursor=input_data.cursor,
-                    limit=input_data.limit,
-                )
-
-                if response.data:
-                    break
-
-                await asyncio.sleep(interval)
-                interval = min(interval * 1.2, 10)
-
-            if not response:
-                response = aexa.websets.items.list(
-                    webset_id=input_data.webset_id,
-                    cursor=input_data.cursor,
-                    limit=input_data.limit,
-                )
-        else:
-            response = aexa.websets.items.list(
-                webset_id=input_data.webset_id,
-                cursor=input_data.cursor,
-                limit=input_data.limit,
-            )
-
-        items = [WebsetItemModel.from_sdk(item) for item in response.data]
-
-        yield "items", items
-
-        for item in items:
-            yield "item", item
-
-        yield "has_more", response.has_more
-        yield "next_cursor", response.next_cursor
-        yield "webset_id", input_data.webset_id
-
-
-class ExaDeleteWebsetItemBlock(Block):
-    """Delete a specific item from a webset."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        item_id: str = SchemaField(
-            description="The ID of the item to delete",
-            placeholder="item-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        item_id: str = SchemaField(description="The ID of the deleted item")
-        success: str = SchemaField(description="Whether the deletion was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="12c57fbe-c270-4877-a2b6-d2d05529ba79",
-            description="Delete a specific item from a webset",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaDeleteWebsetItemBlock.Input,
-            output_schema=ExaDeleteWebsetItemBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        deleted_item = aexa.websets.items.delete(
-            webset_id=input_data.webset_id, id=input_data.item_id
-        )
-
-        yield "item_id", deleted_item.id
-        yield "success", "true"
-
-
-class ExaBulkWebsetItemsBlock(Block):
-    """Get all items from a webset in a single operation (with size limits)."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        max_items: int = SchemaField(
-            default=100,
-            description="Maximum number of items to retrieve (1-1000). Note: Large values may take longer.",
-            ge=1,
-            le=1000,
-        )
-        include_enrichments: bool = SchemaField(
-            default=True,
-            description="Include enrichment data for each item",
-        )
-        include_content: bool = SchemaField(
-            default=True,
-            description="Include full content for each item",
-        )
-
-    class Output(BlockSchemaOutput):
-        items: list[WebsetItemModel] = SchemaField(
-            description="All items from the webset"
-        )
-        item: WebsetItemModel = SchemaField(
-            description="Individual item (yielded for each item)"
-        )
-        total_retrieved: int = SchemaField(
-            description="Total number of items retrieved"
-        )
-        truncated: bool = SchemaField(
-            description="Whether results were truncated due to max_items limit"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="dbd619f5-476e-4395-af9a-a7a7c0fb8c4e",
-            description="Get all items from a webset in bulk (with configurable limits)",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaBulkWebsetItemsBlock.Input,
-            output_schema=ExaBulkWebsetItemsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        all_items: List[WebsetItemModel] = []
-        item_iterator = aexa.websets.items.list_all(
-            webset_id=input_data.webset_id, limit=input_data.max_items
-        )
-
-        for sdk_item in item_iterator:
-            if len(all_items) >= input_data.max_items:
-                break
-
-            item = WebsetItemModel.from_sdk(sdk_item)
-
-            if not input_data.include_enrichments:
-                item.enrichments = {}
-            if not input_data.include_content:
-                item.content = ""
-
-            all_items.append(item)
-
-        yield "items", all_items
-
-        for item in all_items:
-            yield "item", item
-
-        yield "total_retrieved", len(all_items)
-        yield "truncated", len(all_items) >= input_data.max_items
-
-
-class ExaWebsetItemsSummaryBlock(Block):
-    """Get a summary of items in a webset without retrieving all data."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        sample_size: int = SchemaField(
-            default=5,
-            description="Number of sample items to include",
-            ge=0,
-            le=10,
-        )
-
-    class Output(BlockSchemaOutput):
-        total_items: int = SchemaField(
-            description="Total number of items in the webset"
-        )
-        entity_type: str = SchemaField(description="Type of entities in the webset")
-        sample_items: list[WebsetItemModel] = SchemaField(
-            description="Sample of items from the webset"
-        )
-        enrichment_columns: list[str] = SchemaField(
-            description="List of enrichment columns available"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="db7813ad-10bd-4652-8623-5667d6fecdd5",
-            description="Get a summary of webset items without retrieving all data",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaWebsetItemsSummaryBlock.Input,
-            output_schema=ExaWebsetItemsSummaryBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        webset = aexa.websets.get(id=input_data.webset_id)
-
-        entity_type = "unknown"
-        if webset.searches:
-            first_search = webset.searches[0]
-            if first_search.entity:
-                # The entity is a union type, extract type field
-                entity_dict = first_search.entity.model_dump(by_alias=True)
-                entity_type = entity_dict.get("type", "unknown")
-
-        # Get enrichment columns
-        enrichment_columns = []
-        if webset.enrichments:
-            enrichment_columns = [
-                e.title if e.title else e.description for e in webset.enrichments
-            ]
-
-        # Get sample items if requested
-        sample_items: List[WebsetItemModel] = []
-        if input_data.sample_size > 0:
-            items_response = aexa.websets.items.list(
-                webset_id=input_data.webset_id, limit=input_data.sample_size
-            )
-            # Convert to our stable models
-            sample_items = [
-                WebsetItemModel.from_sdk(item) for item in items_response.data
-            ]
-
-        total_items = 0
-        if webset.searches:
-            for search in webset.searches:
-                if search.progress:
-                    total_items += search.progress.found
-
-        yield "total_items", total_items
-        yield "entity_type", entity_type
-        yield "sample_items", sample_items
-        yield "enrichment_columns", enrichment_columns
-
-
-class ExaGetNewItemsBlock(Block):
-    """Get items added to a webset since a specific cursor (incremental processing helper)."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        since_cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor from previous run - only items after this will be returned. Leave empty on first run.",
-            placeholder="cursor-from-previous-run",
-        )
-        max_items: int = SchemaField(
-            default=100,
-            description="Maximum number of new items to retrieve",
-            ge=1,
-            le=1000,
-        )
-
-    class Output(BlockSchemaOutput):
-        new_items: list[WebsetItemModel] = SchemaField(
-            description="Items added since the cursor"
-        )
-        item: WebsetItemModel = SchemaField(
-            description="Individual item (yielded for each new item)"
-        )
-        count: int = SchemaField(description="Number of new items found")
-        next_cursor: Optional[str] = SchemaField(
-            description="Save this cursor for the next run to get only newer items"
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more new items beyond max_items"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3ff9bdf5-9613-4d21-8a60-90eb8b69c414",
-            description="Get items added since a cursor - enables incremental processing without reprocessing",
-            categories={BlockCategory.SEARCH, BlockCategory.DATA},
-            input_schema=ExaGetNewItemsBlock.Input,
-            output_schema=ExaGetNewItemsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        # Get items starting from cursor
-        response = aexa.websets.items.list(
-            webset_id=input_data.webset_id,
-            cursor=input_data.since_cursor,
-            limit=input_data.max_items,
-        )
-
-        # Convert SDK items to our stable models
-        new_items = [WebsetItemModel.from_sdk(item) for item in response.data]
-
-        # Yield the full list
-        yield "new_items", new_items
-
-        # Yield individual items for processing
-        for item in new_items:
-            yield "item", item
-
-        # Yield metadata for next run
-        yield "count", len(new_items)
-        yield "next_cursor", response.next_cursor
-        yield "has_more", response.has_more

autogpt_platform/backend/backend/blocks/exa/websets_monitor.py

@@ -1,600 +0,0 @@
-"""
-Exa Websets Monitor Management Blocks
-
-This module provides blocks for creating and managing monitors that automatically
-keep websets updated with fresh data on a schedule.
-"""
-
-from enum import Enum
-from typing import Optional
-
-from exa_py import AsyncExa
-from exa_py.websets.types import Monitor as SdkMonitor
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._config import exa
-from ._test import TEST_CREDENTIALS, TEST_CREDENTIALS_INPUT
-
-
-# Mirrored model for stability - don't use SDK types directly in block outputs
-class MonitorModel(BaseModel):
-    """Stable output model mirroring SDK Monitor."""
-
-    id: str
-    status: str
-    webset_id: str
-    behavior_type: str
-    behavior_config: dict
-    cron_expression: str
-    timezone: str
-    next_run_at: str
-    last_run: dict
-    metadata: dict
-    created_at: str
-    updated_at: str
-
-    @classmethod
-    def from_sdk(cls, monitor: SdkMonitor) -> "MonitorModel":
-        """Convert SDK Monitor to our stable model."""
-        # Extract behavior information
-        behavior_dict = monitor.behavior.model_dump(by_alias=True, exclude_none=True)
-        behavior_type = behavior_dict.get("type", "unknown")
-        behavior_config = behavior_dict.get("config", {})
-
-        # Extract cadence information
-        cadence_dict = monitor.cadence.model_dump(by_alias=True, exclude_none=True)
-        cron_expr = cadence_dict.get("cron", "")
-        timezone = cadence_dict.get("timezone", "Etc/UTC")
-
-        # Extract last run information
-        last_run_dict = {}
-        if monitor.last_run:
-            last_run_dict = monitor.last_run.model_dump(
-                by_alias=True, exclude_none=True
-            )
-
-        # Handle status enum
-        status_str = (
-            monitor.status.value
-            if hasattr(monitor.status, "value")
-            else str(monitor.status)
-        )
-
-        return cls(
-            id=monitor.id,
-            status=status_str,
-            webset_id=monitor.webset_id,
-            behavior_type=behavior_type,
-            behavior_config=behavior_config,
-            cron_expression=cron_expr,
-            timezone=timezone,
-            next_run_at=monitor.next_run_at.isoformat() if monitor.next_run_at else "",
-            last_run=last_run_dict,
-            metadata=monitor.metadata or {},
-            created_at=monitor.created_at.isoformat() if monitor.created_at else "",
-            updated_at=monitor.updated_at.isoformat() if monitor.updated_at else "",
-        )
-
-
-class MonitorStatus(str, Enum):
-    """Status of a monitor."""
-
-    ENABLED = "enabled"
-    DISABLED = "disabled"
-    PAUSED = "paused"
-
-
-class MonitorBehaviorType(str, Enum):
-    """Type of behavior for a monitor."""
-
-    SEARCH = "search"  # Run new searches
-    REFRESH = "refresh"  # Refresh existing items
-
-
-class SearchBehavior(str, Enum):
-    """How search results interact with existing items."""
-
-    APPEND = "append"
-    OVERRIDE = "override"
-
-
-class ExaCreateMonitorBlock(Block):
-    """Create a monitor to automatically keep a webset updated on a schedule."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to monitor",
-            placeholder="webset-id-or-external-id",
-        )
-
-        # Schedule configuration
-        cron_expression: str = SchemaField(
-            description="Cron expression for scheduling (5 fields, max once per day)",
-            placeholder="0 9 * * 1",  # Every Monday at 9 AM
-        )
-        timezone: str = SchemaField(
-            default="Etc/UTC",
-            description="IANA timezone for the schedule",
-            placeholder="America/New_York",
-            advanced=True,
-        )
-
-        # Behavior configuration
-        behavior_type: MonitorBehaviorType = SchemaField(
-            default=MonitorBehaviorType.SEARCH,
-            description="Type of monitor behavior (search for new items or refresh existing)",
-        )
-
-        # Search configuration (for SEARCH behavior)
-        search_query: Optional[str] = SchemaField(
-            default=None,
-            description="Search query for finding new items (required for search behavior)",
-            placeholder="AI startups that raised funding in the last week",
-        )
-        search_count: int = SchemaField(
-            default=10,
-            description="Number of items to find in each search",
-            ge=1,
-            le=100,
-        )
-        search_criteria: list[str] = SchemaField(
-            default_factory=list,
-            description="Criteria that items must meet",
-            advanced=True,
-        )
-        search_behavior: SearchBehavior = SchemaField(
-            default=SearchBehavior.APPEND,
-            description="How new results interact with existing items",
-            advanced=True,
-        )
-        entity_type: Optional[str] = SchemaField(
-            default=None,
-            description="Type of entity to search for (company, person, etc.)",
-            advanced=True,
-        )
-
-        # Refresh configuration (for REFRESH behavior)
-        refresh_content: bool = SchemaField(
-            default=True,
-            description="Refresh content from source URLs (for refresh behavior)",
-            advanced=True,
-        )
-        refresh_enrichments: bool = SchemaField(
-            default=True,
-            description="Re-run enrichments on items (for refresh behavior)",
-            advanced=True,
-        )
-
-        # Metadata
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="Metadata to attach to the monitor",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        monitor_id: str = SchemaField(
-            description="The unique identifier for the created monitor"
-        )
-        webset_id: str = SchemaField(description="The webset this monitor belongs to")
-        status: str = SchemaField(description="Status of the monitor")
-        behavior_type: str = SchemaField(description="Type of monitor behavior")
-        next_run_at: Optional[str] = SchemaField(
-            description="When the monitor will next run"
-        )
-        cron_expression: str = SchemaField(description="The schedule cron expression")
-        timezone: str = SchemaField(description="The timezone for scheduling")
-        created_at: str = SchemaField(description="When the monitor was created")
-
-    def __init__(self):
-        super().__init__(
-            id="f8a9b0c1-d2e3-4567-890a-bcdef1234567",
-            description="Create automated monitors to keep websets updated with fresh data on a schedule",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCreateMonitorBlock.Input,
-            output_schema=ExaCreateMonitorBlock.Output,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "webset_id": "test-webset",
-                "cron_expression": "0 9 * * 1",
-                "behavior_type": MonitorBehaviorType.SEARCH,
-                "search_query": "AI startups",
-                "search_count": 10,
-            },
-            test_output=[
-                ("monitor_id", "monitor-123"),
-                ("webset_id", "test-webset"),
-                ("status", "enabled"),
-                ("behavior_type", "search"),
-                ("next_run_at", "2024-01-01T00:00:00"),
-                ("cron_expression", "0 9 * * 1"),
-                ("timezone", "Etc/UTC"),
-                ("created_at", "2024-01-01T00:00:00"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock=self._create_test_mock(),
-        )
-
-    @staticmethod
-    def _create_test_mock():
-        """Create test mocks for the AsyncExa SDK."""
-        from datetime import datetime
-        from unittest.mock import MagicMock
-
-        # Create mock SDK monitor object
-        mock_monitor = MagicMock()
-        mock_monitor.id = "monitor-123"
-        mock_monitor.status = MagicMock(value="enabled")
-        mock_monitor.webset_id = "test-webset"
-        mock_monitor.next_run_at = datetime.fromisoformat("2024-01-01T00:00:00")
-        mock_monitor.created_at = datetime.fromisoformat("2024-01-01T00:00:00")
-        mock_monitor.updated_at = datetime.fromisoformat("2024-01-01T00:00:00")
-        mock_monitor.metadata = {}
-        mock_monitor.last_run = None
-
-        # Mock behavior
-        mock_behavior = MagicMock()
-        mock_behavior.model_dump = MagicMock(
-            return_value={"type": "search", "config": {}}
-        )
-        mock_monitor.behavior = mock_behavior
-
-        # Mock cadence
-        mock_cadence = MagicMock()
-        mock_cadence.model_dump = MagicMock(
-            return_value={"cron": "0 9 * * 1", "timezone": "Etc/UTC"}
-        )
-        mock_monitor.cadence = mock_cadence
-
-        return {
-            "_get_client": lambda *args, **kwargs: MagicMock(
-                websets=MagicMock(
-                    monitors=MagicMock(create=lambda *args, **kwargs: mock_monitor)
-                )
-            )
-        }
-
-    def _get_client(self, api_key: str) -> AsyncExa:
-        """Get Exa client (separated for testing)."""
-        return AsyncExa(api_key=api_key)
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        aexa = self._get_client(credentials.api_key.get_secret_value())
-
-        # Build the payload
-        payload = {
-            "websetId": input_data.webset_id,
-            "cadence": {
-                "cron": input_data.cron_expression,
-                "timezone": input_data.timezone,
-            },
-        }
-
-        # Build behavior configuration based on type
-        if input_data.behavior_type == MonitorBehaviorType.SEARCH:
-            behavior_config = {
-                "query": input_data.search_query or "",
-                "count": input_data.search_count,
-                "behavior": input_data.search_behavior.value,
-            }
-
-            if input_data.search_criteria:
-                behavior_config["criteria"] = [
-                    {"description": c} for c in input_data.search_criteria
-                ]
-
-            if input_data.entity_type:
-                behavior_config["entity"] = {"type": input_data.entity_type}
-
-            payload["behavior"] = {
-                "type": "search",
-                "config": behavior_config,
-            }
-        else:
-            # REFRESH behavior
-            payload["behavior"] = {
-                "type": "refresh",
-                "config": {
-                    "content": input_data.refresh_content,
-                    "enrichments": input_data.refresh_enrichments,
-                },
-            }
-
-        # Add metadata if provided
-        if input_data.metadata:
-            payload["metadata"] = input_data.metadata
-
-        sdk_monitor = aexa.websets.monitors.create(params=payload)
-
-        monitor = MonitorModel.from_sdk(sdk_monitor)
-
-        # Yield all fields
-        yield "monitor_id", monitor.id
-        yield "webset_id", monitor.webset_id
-        yield "status", monitor.status
-        yield "behavior_type", monitor.behavior_type
-        yield "next_run_at", monitor.next_run_at
-        yield "cron_expression", monitor.cron_expression
-        yield "timezone", monitor.timezone
-        yield "created_at", monitor.created_at
-
-
-class ExaGetMonitorBlock(Block):
-    """Get the details and status of a monitor."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        monitor_id: str = SchemaField(
-            description="The ID of the monitor to retrieve",
-            placeholder="monitor-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        monitor_id: str = SchemaField(
-            description="The unique identifier for the monitor"
-        )
-        webset_id: str = SchemaField(description="The webset this monitor belongs to")
-        status: str = SchemaField(description="Current status of the monitor")
-        behavior_type: str = SchemaField(description="Type of monitor behavior")
-        behavior_config: dict = SchemaField(
-            description="Configuration for the monitor behavior"
-        )
-        cron_expression: str = SchemaField(description="The schedule cron expression")
-        timezone: str = SchemaField(description="The timezone for scheduling")
-        next_run_at: Optional[str] = SchemaField(
-            description="When the monitor will next run"
-        )
-        last_run: Optional[dict] = SchemaField(
-            description="Information about the last run"
-        )
-        created_at: str = SchemaField(description="When the monitor was created")
-        updated_at: str = SchemaField(description="When the monitor was last updated")
-        metadata: dict = SchemaField(description="Metadata attached to the monitor")
-
-    def __init__(self):
-        super().__init__(
-            id="5c852a2d-d505-4a56-b711-7def8dd14e72",
-            description="Get the details and status of a webset monitor",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetMonitorBlock.Input,
-            output_schema=ExaGetMonitorBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_monitor = aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
-
-        monitor = MonitorModel.from_sdk(sdk_monitor)
-
-        # Yield all fields
-        yield "monitor_id", monitor.id
-        yield "webset_id", monitor.webset_id
-        yield "status", monitor.status
-        yield "behavior_type", monitor.behavior_type
-        yield "behavior_config", monitor.behavior_config
-        yield "cron_expression", monitor.cron_expression
-        yield "timezone", monitor.timezone
-        yield "next_run_at", monitor.next_run_at
-        yield "last_run", monitor.last_run
-        yield "created_at", monitor.created_at
-        yield "updated_at", monitor.updated_at
-        yield "metadata", monitor.metadata
-
-
-class ExaUpdateMonitorBlock(Block):
-    """Update a monitor's configuration."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        monitor_id: str = SchemaField(
-            description="The ID of the monitor to update",
-            placeholder="monitor-id",
-        )
-        status: Optional[MonitorStatus] = SchemaField(
-            default=None,
-            description="New status for the monitor",
-        )
-        cron_expression: Optional[str] = SchemaField(
-            default=None,
-            description="New cron expression for scheduling",
-        )
-        timezone: Optional[str] = SchemaField(
-            default=None,
-            description="New timezone for the schedule",
-            advanced=True,
-        )
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="New metadata for the monitor",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        monitor_id: str = SchemaField(
-            description="The unique identifier for the monitor"
-        )
-        status: str = SchemaField(description="Updated status of the monitor")
-        next_run_at: Optional[str] = SchemaField(
-            description="When the monitor will next run"
-        )
-        updated_at: str = SchemaField(description="When the monitor was updated")
-        success: str = SchemaField(description="Whether the update was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="245102c3-6af3-4515-a308-c2210b7939d2",
-            description="Update a monitor's status, schedule, or metadata",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaUpdateMonitorBlock.Input,
-            output_schema=ExaUpdateMonitorBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        # Build update payload
-        payload = {}
-
-        if input_data.status is not None:
-            payload["status"] = input_data.status.value
-
-        if input_data.cron_expression is not None or input_data.timezone is not None:
-            cadence = {}
-            if input_data.cron_expression:
-                cadence["cron"] = input_data.cron_expression
-            if input_data.timezone:
-                cadence["timezone"] = input_data.timezone
-            payload["cadence"] = cadence
-
-        if input_data.metadata is not None:
-            payload["metadata"] = input_data.metadata
-
-        sdk_monitor = aexa.websets.monitors.update(
-            monitor_id=input_data.monitor_id, params=payload
-        )
-
-        # Convert to our stable model
-        monitor = MonitorModel.from_sdk(sdk_monitor)
-
-        # Yield fields
-        yield "monitor_id", monitor.id
-        yield "status", monitor.status
-        yield "next_run_at", monitor.next_run_at
-        yield "updated_at", monitor.updated_at
-        yield "success", "true"
-
-
-class ExaDeleteMonitorBlock(Block):
-    """Delete a monitor from a webset."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        monitor_id: str = SchemaField(
-            description="The ID of the monitor to delete",
-            placeholder="monitor-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        monitor_id: str = SchemaField(description="The ID of the deleted monitor")
-        success: str = SchemaField(description="Whether the deletion was successful")
-
-    def __init__(self):
-        super().__init__(
-            id="f16f9b10-0c4d-4db8-997d-7b96b6026094",
-            description="Delete a monitor from a webset",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaDeleteMonitorBlock.Input,
-            output_schema=ExaDeleteMonitorBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        deleted_monitor = aexa.websets.monitors.delete(monitor_id=input_data.monitor_id)
-
-        yield "monitor_id", deleted_monitor.id
-        yield "success", "true"
-
-
-class ExaListMonitorsBlock(Block):
-    """List all monitors with pagination."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: Optional[str] = SchemaField(
-            default=None,
-            description="Filter monitors by webset ID",
-            placeholder="webset-id",
-        )
-        limit: int = SchemaField(
-            default=25,
-            description="Number of monitors to return",
-            ge=1,
-            le=100,
-        )
-        cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor for pagination",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        monitors: list[dict] = SchemaField(description="List of monitors")
-        monitor: dict = SchemaField(
-            description="Individual monitor (yielded for each monitor)"
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more monitors to paginate through"
-        )
-        next_cursor: Optional[str] = SchemaField(
-            description="Cursor for the next page of results"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="f06e2b38-5397-4e8f-aa85-491149dd98df",
-            description="List all monitors with optional webset filtering",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaListMonitorsBlock.Input,
-            output_schema=ExaListMonitorsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        response = aexa.websets.monitors.list(
-            cursor=input_data.cursor,
-            limit=input_data.limit,
-            webset_id=input_data.webset_id,
-        )
-
-        # Convert SDK monitors to our stable models
-        monitors = [MonitorModel.from_sdk(m) for m in response.data]
-
-        # Yield the full list
-        yield "monitors", [m.model_dump() for m in monitors]
-
-        # Yield individual monitors for graph chaining
-        for monitor in monitors:
-            yield "monitor", monitor.model_dump()
-
-        # Yield pagination metadata
-        yield "has_more", response.has_more
-        yield "next_cursor", response.next_cursor

autogpt_platform/backend/backend/blocks/exa/websets_polling.py

@@ -1,600 +0,0 @@
-"""
-Exa Websets Polling Blocks
-
-This module provides dedicated polling blocks for waiting on webset operations
-to complete, with progress tracking and timeout management.
-"""
-
-import asyncio
-import time
-from enum import Enum
-from typing import Any, Dict
-
-from exa_py import AsyncExa
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._config import exa
-
-# Import WebsetItemModel for use in enrichment samples
-# This is safe as websets_items doesn't import from websets_polling
-from .websets_items import WebsetItemModel
-
-
-# Model for sample enrichment data
-class SampleEnrichmentModel(BaseModel):
-    """Sample enrichment result for display."""
-
-    item_id: str
-    item_title: str
-    enrichment_data: Dict[str, Any]
-
-
-class WebsetTargetStatus(str, Enum):
-    IDLE = "idle"
-    COMPLETED = "completed"
-    RUNNING = "running"
-    PAUSED = "paused"
-    ANY_COMPLETE = "any_complete"  # Either idle or completed
-
-
-class ExaWaitForWebsetBlock(Block):
-    """Wait for a webset to reach a specific status with progress tracking."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to monitor",
-            placeholder="webset-id-or-external-id",
-        )
-        target_status: WebsetTargetStatus = SchemaField(
-            default=WebsetTargetStatus.IDLE,
-            description="Status to wait for (idle=all operations complete, completed=search done, running=actively processing)",
-        )
-        timeout: int = SchemaField(
-            default=300,
-            description="Maximum time to wait in seconds",
-            ge=1,
-            le=1800,  # 30 minutes max
-        )
-        check_interval: int = SchemaField(
-            default=5,
-            description="Initial interval between status checks in seconds",
-            advanced=True,
-            ge=1,
-            le=60,
-        )
-        max_interval: int = SchemaField(
-            default=30,
-            description="Maximum interval between checks (for exponential backoff)",
-            advanced=True,
-            ge=5,
-            le=120,
-        )
-        include_progress: bool = SchemaField(
-            default=True,
-            description="Include detailed progress information in output",
-        )
-
-    class Output(BlockSchemaOutput):
-        webset_id: str = SchemaField(description="The webset ID that was monitored")
-        final_status: str = SchemaField(description="The final status of the webset")
-        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
-        item_count: int = SchemaField(description="Number of items found")
-        search_progress: dict = SchemaField(
-            description="Detailed search progress information"
-        )
-        enrichment_progress: dict = SchemaField(
-            description="Detailed enrichment progress information"
-        )
-        timed_out: bool = SchemaField(description="Whether the operation timed out")
-
-    def __init__(self):
-        super().__init__(
-            id="619d71e8-b72a-434d-8bd4-23376dd0342c",
-            description="Wait for a webset to reach a specific status with progress tracking",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaWaitForWebsetBlock.Input,
-            output_schema=ExaWaitForWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        start_time = time.time()
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        try:
-            if input_data.target_status in [
-                WebsetTargetStatus.IDLE,
-                WebsetTargetStatus.ANY_COMPLETE,
-            ]:
-                final_webset = aexa.websets.wait_until_idle(
-                    id=input_data.webset_id,
-                    timeout=input_data.timeout,
-                    poll_interval=input_data.check_interval,
-                )
-
-                elapsed = time.time() - start_time
-
-                status_str = (
-                    final_webset.status.value
-                    if hasattr(final_webset.status, "value")
-                    else str(final_webset.status)
-                )
-
-                item_count = 0
-                if final_webset.searches:
-                    for search in final_webset.searches:
-                        if search.progress:
-                            item_count += search.progress.found
-
-                # Extract progress if requested
-                search_progress = {}
-                enrichment_progress = {}
-                if input_data.include_progress:
-                    webset_dict = final_webset.model_dump(
-                        by_alias=True, exclude_none=True
-                    )
-                    search_progress = self._extract_search_progress(webset_dict)
-                    enrichment_progress = self._extract_enrichment_progress(webset_dict)
-
-                yield "webset_id", input_data.webset_id
-                yield "final_status", status_str
-                yield "elapsed_time", elapsed
-                yield "item_count", item_count
-                if input_data.include_progress:
-                    yield "search_progress", search_progress
-                    yield "enrichment_progress", enrichment_progress
-                yield "timed_out", False
-            else:
-                # For other status targets, manually poll
-                interval = input_data.check_interval
-                while time.time() - start_time < input_data.timeout:
-                    # Get current webset status
-                    webset = aexa.websets.get(id=input_data.webset_id)
-                    current_status = (
-                        webset.status.value
-                        if hasattr(webset.status, "value")
-                        else str(webset.status)
-                    )
-
-                    # Check if target status reached
-                    if current_status == input_data.target_status.value:
-                        elapsed = time.time() - start_time
-
-                        # Estimate item count from search progress
-                        item_count = 0
-                        if webset.searches:
-                            for search in webset.searches:
-                                if search.progress:
-                                    item_count += search.progress.found
-
-                        search_progress = {}
-                        enrichment_progress = {}
-                        if input_data.include_progress:
-                            webset_dict = webset.model_dump(
-                                by_alias=True, exclude_none=True
-                            )
-                            search_progress = self._extract_search_progress(webset_dict)
-                            enrichment_progress = self._extract_enrichment_progress(
-                                webset_dict
-                            )
-
-                        yield "webset_id", input_data.webset_id
-                        yield "final_status", current_status
-                        yield "elapsed_time", elapsed
-                        yield "item_count", item_count
-                        if input_data.include_progress:
-                            yield "search_progress", search_progress
-                            yield "enrichment_progress", enrichment_progress
-                        yield "timed_out", False
-                        return
-
-                    # Wait before next check with exponential backoff
-                    await asyncio.sleep(interval)
-                    interval = min(interval * 1.5, input_data.max_interval)
-
-                # Timeout reached
-                elapsed = time.time() - start_time
-                webset = aexa.websets.get(id=input_data.webset_id)
-                final_status = (
-                    webset.status.value
-                    if hasattr(webset.status, "value")
-                    else str(webset.status)
-                )
-
-                item_count = 0
-                if webset.searches:
-                    for search in webset.searches:
-                        if search.progress:
-                            item_count += search.progress.found
-
-                search_progress = {}
-                enrichment_progress = {}
-                if input_data.include_progress:
-                    webset_dict = webset.model_dump(by_alias=True, exclude_none=True)
-                    search_progress = self._extract_search_progress(webset_dict)
-                    enrichment_progress = self._extract_enrichment_progress(webset_dict)
-
-                yield "webset_id", input_data.webset_id
-                yield "final_status", final_status
-                yield "elapsed_time", elapsed
-                yield "item_count", item_count
-                if input_data.include_progress:
-                    yield "search_progress", search_progress
-                    yield "enrichment_progress", enrichment_progress
-                yield "timed_out", True
-
-        except asyncio.TimeoutError:
-            raise ValueError(
-                f"Polling timed out after {input_data.timeout} seconds"
-            ) from None
-
-    def _extract_search_progress(self, webset_data: dict) -> dict:
-        """Extract search progress information from webset data."""
-        progress = {}
-        searches = webset_data.get("searches", [])
-
-        for idx, search in enumerate(searches):
-            search_id = search.get("id", f"search_{idx}")
-            search_progress = search.get("progress", {})
-
-            progress[search_id] = {
-                "status": search.get("status", "unknown"),
-                "found": search_progress.get("found", 0),
-                "analyzed": search_progress.get("analyzed", 0),
-                "completion": search_progress.get("completion", 0),
-                "time_left": search_progress.get("timeLeft", 0),
-            }
-
-        return progress
-
-    def _extract_enrichment_progress(self, webset_data: dict) -> dict:
-        """Extract enrichment progress information from webset data."""
-        progress = {}
-        enrichments = webset_data.get("enrichments", [])
-
-        for idx, enrichment in enumerate(enrichments):
-            enrich_id = enrichment.get("id", f"enrichment_{idx}")
-
-            progress[enrich_id] = {
-                "status": enrichment.get("status", "unknown"),
-                "title": enrichment.get("title", ""),
-                "description": enrichment.get("description", ""),
-            }
-
-        return progress
-
-
-class ExaWaitForSearchBlock(Block):
-    """Wait for a specific webset search to complete with progress tracking."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        search_id: str = SchemaField(
-            description="The ID of the search to monitor",
-            placeholder="search-id",
-        )
-        timeout: int = SchemaField(
-            default=300,
-            description="Maximum time to wait in seconds",
-            ge=1,
-            le=1800,
-        )
-        check_interval: int = SchemaField(
-            default=5,
-            description="Initial interval between status checks in seconds",
-            advanced=True,
-            ge=1,
-            le=60,
-        )
-
-    class Output(BlockSchemaOutput):
-        search_id: str = SchemaField(description="The search ID that was monitored")
-        final_status: str = SchemaField(description="The final status of the search")
-        items_found: int = SchemaField(
-            description="Number of items found by the search"
-        )
-        items_analyzed: int = SchemaField(description="Number of items analyzed")
-        completion_percentage: int = SchemaField(
-            description="Completion percentage (0-100)"
-        )
-        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
-        recall_info: dict = SchemaField(
-            description="Information about expected results and confidence"
-        )
-        timed_out: bool = SchemaField(description="Whether the operation timed out")
-
-    def __init__(self):
-        super().__init__(
-            id="14da21ae-40a1-41bc-a111-c8e5c9ef012b",
-            description="Wait for a specific webset search to complete with progress tracking",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaWaitForSearchBlock.Input,
-            output_schema=ExaWaitForSearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        start_time = time.time()
-        interval = input_data.check_interval
-        max_interval = 30
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        try:
-            while time.time() - start_time < input_data.timeout:
-                # Get current search status using SDK
-                search = aexa.websets.searches.get(
-                    webset_id=input_data.webset_id, id=input_data.search_id
-                )
-
-                # Extract status
-                status = (
-                    search.status.value
-                    if hasattr(search.status, "value")
-                    else str(search.status)
-                )
-
-                # Check if search is complete
-                if status in ["completed", "failed", "canceled"]:
-                    elapsed = time.time() - start_time
-
-                    # Extract progress information
-                    progress_dict = {}
-                    if search.progress:
-                        progress_dict = search.progress.model_dump(
-                            by_alias=True, exclude_none=True
-                        )
-
-                    # Extract recall information
-                    recall_info = {}
-                    if search.recall:
-                        recall_dict = search.recall.model_dump(
-                            by_alias=True, exclude_none=True
-                        )
-                        expected = recall_dict.get("expected", {})
-                        recall_info = {
-                            "expected_total": expected.get("total", 0),
-                            "confidence": expected.get("confidence", ""),
-                            "min_expected": expected.get("bounds", {}).get("min", 0),
-                            "max_expected": expected.get("bounds", {}).get("max", 0),
-                            "reasoning": recall_dict.get("reasoning", ""),
-                        }
-
-                    yield "search_id", input_data.search_id
-                    yield "final_status", status
-                    yield "items_found", progress_dict.get("found", 0)
-                    yield "items_analyzed", progress_dict.get("analyzed", 0)
-                    yield "completion_percentage", progress_dict.get("completion", 0)
-                    yield "elapsed_time", elapsed
-                    yield "recall_info", recall_info
-                    yield "timed_out", False
-
-                    return
-
-                # Wait before next check with exponential backoff
-                await asyncio.sleep(interval)
-                interval = min(interval * 1.5, max_interval)
-
-            # Timeout reached
-            elapsed = time.time() - start_time
-
-            # Get last known status
-            search = aexa.websets.searches.get(
-                webset_id=input_data.webset_id, id=input_data.search_id
-            )
-            final_status = (
-                search.status.value
-                if hasattr(search.status, "value")
-                else str(search.status)
-            )
-
-            progress_dict = {}
-            if search.progress:
-                progress_dict = search.progress.model_dump(
-                    by_alias=True, exclude_none=True
-                )
-
-            yield "search_id", input_data.search_id
-            yield "final_status", final_status
-            yield "items_found", progress_dict.get("found", 0)
-            yield "items_analyzed", progress_dict.get("analyzed", 0)
-            yield "completion_percentage", progress_dict.get("completion", 0)
-            yield "elapsed_time", elapsed
-            yield "timed_out", True
-
-        except asyncio.TimeoutError:
-            raise ValueError(
-                f"Search polling timed out after {input_data.timeout} seconds"
-            ) from None
-
-
-class ExaWaitForEnrichmentBlock(Block):
-    """Wait for a webset enrichment to complete with progress tracking."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        enrichment_id: str = SchemaField(
-            description="The ID of the enrichment to monitor",
-            placeholder="enrichment-id",
-        )
-        timeout: int = SchemaField(
-            default=300,
-            description="Maximum time to wait in seconds",
-            ge=1,
-            le=1800,
-        )
-        check_interval: int = SchemaField(
-            default=5,
-            description="Initial interval between status checks in seconds",
-            advanced=True,
-            ge=1,
-            le=60,
-        )
-        sample_results: bool = SchemaField(
-            default=True,
-            description="Include sample enrichment results in output",
-        )
-
-    class Output(BlockSchemaOutput):
-        enrichment_id: str = SchemaField(
-            description="The enrichment ID that was monitored"
-        )
-        final_status: str = SchemaField(
-            description="The final status of the enrichment"
-        )
-        items_enriched: int = SchemaField(
-            description="Number of items successfully enriched"
-        )
-        enrichment_title: str = SchemaField(
-            description="Title/description of the enrichment"
-        )
-        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
-        sample_data: list[SampleEnrichmentModel] = SchemaField(
-            description="Sample of enriched data (if requested)"
-        )
-        timed_out: bool = SchemaField(description="Whether the operation timed out")
-
-    def __init__(self):
-        super().__init__(
-            id="a11865c3-ac80-4721-8a40-ac4e3b71a558",
-            description="Wait for a webset enrichment to complete with progress tracking",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaWaitForEnrichmentBlock.Input,
-            output_schema=ExaWaitForEnrichmentBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        start_time = time.time()
-        interval = input_data.check_interval
-        max_interval = 30
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        try:
-            while time.time() - start_time < input_data.timeout:
-                # Get current enrichment status using SDK
-                enrichment = aexa.websets.enrichments.get(
-                    webset_id=input_data.webset_id, id=input_data.enrichment_id
-                )
-
-                # Extract status
-                status = (
-                    enrichment.status.value
-                    if hasattr(enrichment.status, "value")
-                    else str(enrichment.status)
-                )
-
-                # Check if enrichment is complete
-                if status in ["completed", "failed", "canceled"]:
-                    elapsed = time.time() - start_time
-
-                    # Get sample enriched items if requested
-                    sample_data = []
-                    items_enriched = 0
-
-                    if input_data.sample_results and status == "completed":
-                        sample_data, items_enriched = (
-                            await self._get_sample_enrichments(
-                                input_data.webset_id, input_data.enrichment_id, aexa
-                            )
-                        )
-
-                    yield "enrichment_id", input_data.enrichment_id
-                    yield "final_status", status
-                    yield "items_enriched", items_enriched
-                    yield "enrichment_title", enrichment.title or enrichment.description or ""
-                    yield "elapsed_time", elapsed
-                    if input_data.sample_results:
-                        yield "sample_data", sample_data
-                    yield "timed_out", False
-
-                    return
-
-                # Wait before next check with exponential backoff
-                await asyncio.sleep(interval)
-                interval = min(interval * 1.5, max_interval)
-
-            # Timeout reached
-            elapsed = time.time() - start_time
-
-            # Get last known status
-            enrichment = aexa.websets.enrichments.get(
-                webset_id=input_data.webset_id, id=input_data.enrichment_id
-            )
-            final_status = (
-                enrichment.status.value
-                if hasattr(enrichment.status, "value")
-                else str(enrichment.status)
-            )
-            title = enrichment.title or enrichment.description or ""
-
-            yield "enrichment_id", input_data.enrichment_id
-            yield "final_status", final_status
-            yield "items_enriched", 0
-            yield "enrichment_title", title
-            yield "elapsed_time", elapsed
-            yield "timed_out", True
-
-        except asyncio.TimeoutError:
-            raise ValueError(
-                f"Enrichment polling timed out after {input_data.timeout} seconds"
-            ) from None
-
-    async def _get_sample_enrichments(
-        self, webset_id: str, enrichment_id: str, aexa: AsyncExa
-    ) -> tuple[list[SampleEnrichmentModel], int]:
-        """Get sample enriched data and count."""
-        # Get a few items to see enrichment results using SDK
-        response = aexa.websets.items.list(webset_id=webset_id, limit=5)
-
-        sample_data: list[SampleEnrichmentModel] = []
-        enriched_count = 0
-
-        for sdk_item in response.data:
-            # Convert to our WebsetItemModel first
-            item = WebsetItemModel.from_sdk(sdk_item)
-
-            # Check if this item has the enrichment we're looking for
-            if enrichment_id in item.enrichments:
-                enriched_count += 1
-                enrich_model = item.enrichments[enrichment_id]
-
-                # Create sample using our typed model
-                sample = SampleEnrichmentModel(
-                    item_id=item.id,
-                    item_title=item.title,
-                    enrichment_data=enrich_model.model_dump(exclude_none=True),
-                )
-                sample_data.append(sample)
-
-        return sample_data, enriched_count

autogpt_platform/backend/backend/blocks/exa/websets_search.py

@@ -1,650 +0,0 @@
-"""
-Exa Websets Search Management Blocks
-
-This module provides blocks for creating and managing searches within websets,
-including adding new searches, checking status, and canceling operations.
-"""
-
-from enum import Enum
-from typing import Any, Dict, List, Optional
-
-from exa_py import AsyncExa
-from exa_py.websets.types import WebsetSearch as SdkWebsetSearch
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-# Mirrored model for stability
-class WebsetSearchModel(BaseModel):
-    """Stable output model mirroring SDK WebsetSearch."""
-
-    id: str
-    webset_id: str
-    status: str
-    query: str
-    entity_type: str
-    criteria: List[Dict[str, Any]]
-    count: int
-    behavior: str
-    progress: Dict[str, Any]
-    recall: Optional[Dict[str, Any]]
-    created_at: str
-    updated_at: str
-    canceled_at: Optional[str]
-    canceled_reason: Optional[str]
-    metadata: Dict[str, Any]
-
-    @classmethod
-    def from_sdk(cls, search: SdkWebsetSearch) -> "WebsetSearchModel":
-        """Convert SDK WebsetSearch to our stable model."""
-        # Extract entity type
-        entity_type = "auto"
-        if search.entity:
-            entity_dict = search.entity.model_dump(by_alias=True)
-            entity_type = entity_dict.get("type", "auto")
-
-        # Convert criteria
-        criteria = [c.model_dump(by_alias=True) for c in search.criteria]
-
-        # Convert progress
-        progress_dict = {}
-        if search.progress:
-            progress_dict = search.progress.model_dump(by_alias=True)
-
-        # Convert recall
-        recall_dict = None
-        if search.recall:
-            recall_dict = search.recall.model_dump(by_alias=True)
-
-        return cls(
-            id=search.id,
-            webset_id=search.webset_id,
-            status=(
-                search.status.value
-                if hasattr(search.status, "value")
-                else str(search.status)
-            ),
-            query=search.query,
-            entity_type=entity_type,
-            criteria=criteria,
-            count=search.count,
-            behavior=search.behavior.value if search.behavior else "override",
-            progress=progress_dict,
-            recall=recall_dict,
-            created_at=search.created_at.isoformat() if search.created_at else "",
-            updated_at=search.updated_at.isoformat() if search.updated_at else "",
-            canceled_at=search.canceled_at.isoformat() if search.canceled_at else None,
-            canceled_reason=(
-                search.canceled_reason.value if search.canceled_reason else None
-            ),
-            metadata=search.metadata if search.metadata else {},
-        )
-
-
-class SearchBehavior(str, Enum):
-    """Behavior for how new search results interact with existing items."""
-
-    OVERRIDE = "override"  # Replace existing items
-    APPEND = "append"  # Add to existing items
-    MERGE = "merge"  # Merge with existing items
-
-
-class SearchEntityType(str, Enum):
-    COMPANY = "company"
-    PERSON = "person"
-    ARTICLE = "article"
-    RESEARCH_PAPER = "research_paper"
-    CUSTOM = "custom"
-    AUTO = "auto"
-
-
-class ExaCreateWebsetSearchBlock(Block):
-    """Add a new search to an existing webset."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        query: str = SchemaField(
-            description="Search query describing what to find",
-            placeholder="Engineering managers at Fortune 500 companies",
-        )
-        count: int = SchemaField(
-            default=10,
-            description="Number of items to find",
-            ge=1,
-            le=1000,
-        )
-
-        # Entity configuration
-        entity_type: SearchEntityType = SchemaField(
-            default=SearchEntityType.AUTO,
-            description="Type of entity to search for",
-        )
-        entity_description: Optional[str] = SchemaField(
-            default=None,
-            description="Description for custom entity type",
-            advanced=True,
-        )
-
-        # Criteria for verification
-        criteria: list[str] = SchemaField(
-            default_factory=list,
-            description="List of criteria that items must meet. If not provided, auto-detected from query.",
-            advanced=True,
-        )
-
-        # Advanced search options
-        behavior: SearchBehavior = SchemaField(
-            default=SearchBehavior.APPEND,
-            description="How new results interact with existing items",
-            advanced=True,
-        )
-        recall: bool = SchemaField(
-            default=True,
-            description="Enable recall estimation for expected results",
-            advanced=True,
-        )
-
-        # Exclude sources
-        exclude_source_ids: list[str] = SchemaField(
-            default_factory=list,
-            description="IDs of imports/websets to exclude from results",
-            advanced=True,
-        )
-        exclude_source_types: list[str] = SchemaField(
-            default_factory=list,
-            description="Types of sources to exclude ('import' or 'webset')",
-            advanced=True,
-        )
-
-        # Scope sources
-        scope_source_ids: list[str] = SchemaField(
-            default_factory=list,
-            description="IDs of imports/websets to limit search scope to",
-            advanced=True,
-        )
-        scope_source_types: list[str] = SchemaField(
-            default_factory=list,
-            description="Types of scope sources ('import' or 'webset')",
-            advanced=True,
-        )
-        scope_relationships: list[str] = SchemaField(
-            default_factory=list,
-            description="Relationship definitions for hop searches",
-            advanced=True,
-        )
-        scope_relationship_limits: list[int] = SchemaField(
-            default_factory=list,
-            description="Limits on related entities to find",
-            advanced=True,
-        )
-
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="Metadata to attach to the search",
-            advanced=True,
-        )
-
-        # Polling options
-        wait_for_completion: bool = SchemaField(
-            default=False,
-            description="Wait for the search to complete before returning",
-        )
-        polling_timeout: int = SchemaField(
-            default=300,
-            description="Maximum time to wait for completion in seconds",
-            advanced=True,
-            ge=1,
-            le=600,
-        )
-
-    class Output(BlockSchemaOutput):
-        search_id: str = SchemaField(
-            description="The unique identifier for the created search"
-        )
-        webset_id: str = SchemaField(description="The webset this search belongs to")
-        status: str = SchemaField(description="Current status of the search")
-        query: str = SchemaField(description="The search query")
-        expected_results: dict = SchemaField(
-            description="Recall estimation of expected results"
-        )
-        items_found: Optional[int] = SchemaField(
-            description="Number of items found (if wait_for_completion was True)"
-        )
-        completion_time: Optional[float] = SchemaField(
-            description="Time taken to complete in seconds (if wait_for_completion was True)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="342ff776-2e2c-4cdb-b392-4eeb34b21d5f",
-            description="Add a new search to an existing webset to find more items",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCreateWebsetSearchBlock.Input,
-            output_schema=ExaCreateWebsetSearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        import time
-
-        # Build the payload
-        payload = {
-            "query": input_data.query,
-            "count": input_data.count,
-            "behavior": input_data.behavior.value,
-            "recall": input_data.recall,
-        }
-
-        # Add entity configuration
-        if input_data.entity_type != SearchEntityType.AUTO:
-            entity = {"type": input_data.entity_type.value}
-            if (
-                input_data.entity_type == SearchEntityType.CUSTOM
-                and input_data.entity_description
-            ):
-                entity["description"] = input_data.entity_description
-            payload["entity"] = entity
-
-        # Add criteria if provided
-        if input_data.criteria:
-            payload["criteria"] = [{"description": c} for c in input_data.criteria]
-
-        # Add exclude sources
-        if input_data.exclude_source_ids:
-            exclude_list = []
-            for idx, src_id in enumerate(input_data.exclude_source_ids):
-                src_type = "import"
-                if input_data.exclude_source_types and idx < len(
-                    input_data.exclude_source_types
-                ):
-                    src_type = input_data.exclude_source_types[idx]
-                exclude_list.append({"source": src_type, "id": src_id})
-            payload["exclude"] = exclude_list
-
-        # Add scope sources
-        if input_data.scope_source_ids:
-            scope_list: list[dict[str, Any]] = []
-            for idx, src_id in enumerate(input_data.scope_source_ids):
-                scope_item: dict[str, Any] = {"source": "import", "id": src_id}
-
-                if input_data.scope_source_types and idx < len(
-                    input_data.scope_source_types
-                ):
-                    scope_item["source"] = input_data.scope_source_types[idx]
-
-                # Add relationship if provided
-                if input_data.scope_relationships and idx < len(
-                    input_data.scope_relationships
-                ):
-                    relationship: dict[str, Any] = {
-                        "definition": input_data.scope_relationships[idx]
-                    }
-                    if input_data.scope_relationship_limits and idx < len(
-                        input_data.scope_relationship_limits
-                    ):
-                        relationship["limit"] = input_data.scope_relationship_limits[
-                            idx
-                        ]
-                    scope_item["relationship"] = relationship
-
-                scope_list.append(scope_item)
-            payload["scope"] = scope_list
-
-        # Add metadata if provided
-        if input_data.metadata:
-            payload["metadata"] = input_data.metadata
-
-        start_time = time.time()
-
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_search = aexa.websets.searches.create(
-            webset_id=input_data.webset_id, params=payload
-        )
-
-        search_id = sdk_search.id
-        status = (
-            sdk_search.status.value
-            if hasattr(sdk_search.status, "value")
-            else str(sdk_search.status)
-        )
-
-        # Extract expected results from recall
-        expected_results = {}
-        if sdk_search.recall:
-            recall_dict = sdk_search.recall.model_dump(by_alias=True)
-            expected = recall_dict.get("expected", {})
-            expected_results = {
-                "total": expected.get("total", 0),
-                "confidence": expected.get("confidence", ""),
-                "min": expected.get("bounds", {}).get("min", 0),
-                "max": expected.get("bounds", {}).get("max", 0),
-                "reasoning": recall_dict.get("reasoning", ""),
-            }
-
-        # If wait_for_completion is True, poll for completion
-        if input_data.wait_for_completion:
-            import asyncio
-
-            poll_interval = 5
-            max_interval = 30
-            poll_start = time.time()
-
-            while time.time() - poll_start < input_data.polling_timeout:
-                current_search = aexa.websets.searches.get(
-                    webset_id=input_data.webset_id, id=search_id
-                )
-                current_status = (
-                    current_search.status.value
-                    if hasattr(current_search.status, "value")
-                    else str(current_search.status)
-                )
-
-                if current_status in ["completed", "failed", "cancelled"]:
-                    items_found = 0
-                    if current_search.progress:
-                        items_found = current_search.progress.found
-                    completion_time = time.time() - start_time
-
-                    yield "search_id", search_id
-                    yield "webset_id", input_data.webset_id
-                    yield "status", current_status
-                    yield "query", input_data.query
-                    yield "expected_results", expected_results
-                    yield "items_found", items_found
-                    yield "completion_time", completion_time
-                    return
-
-                await asyncio.sleep(poll_interval)
-                poll_interval = min(poll_interval * 1.5, max_interval)
-
-            # Timeout - yield what we have
-            yield "search_id", search_id
-            yield "webset_id", input_data.webset_id
-            yield "status", status
-            yield "query", input_data.query
-            yield "expected_results", expected_results
-            yield "items_found", 0
-            yield "completion_time", time.time() - start_time
-        else:
-            yield "search_id", search_id
-            yield "webset_id", input_data.webset_id
-            yield "status", status
-            yield "query", input_data.query
-            yield "expected_results", expected_results
-
-
-class ExaGetWebsetSearchBlock(Block):
-    """Get the status and details of a webset search."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        search_id: str = SchemaField(
-            description="The ID of the search to retrieve",
-            placeholder="search-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        search_id: str = SchemaField(description="The unique identifier for the search")
-        status: str = SchemaField(description="Current status of the search")
-        query: str = SchemaField(description="The search query")
-        entity_type: str = SchemaField(description="Type of entity being searched")
-        criteria: list[dict] = SchemaField(description="Criteria used for verification")
-        progress: dict = SchemaField(description="Search progress information")
-        recall: dict = SchemaField(description="Recall estimation information")
-        created_at: str = SchemaField(description="When the search was created")
-        updated_at: str = SchemaField(description="When the search was last updated")
-        canceled_at: Optional[str] = SchemaField(
-            description="When the search was canceled (if applicable)"
-        )
-        canceled_reason: Optional[str] = SchemaField(
-            description="Reason for cancellation (if applicable)"
-        )
-        metadata: dict = SchemaField(description="Metadata attached to the search")
-
-    def __init__(self):
-        super().__init__(
-            id="4fa3e627-a0ff-485f-8732-52148051646c",
-            description="Get the status and details of a webset search",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetWebsetSearchBlock.Input,
-            output_schema=ExaGetWebsetSearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        sdk_search = aexa.websets.searches.get(
-            webset_id=input_data.webset_id, id=input_data.search_id
-        )
-
-        search = WebsetSearchModel.from_sdk(sdk_search)
-
-        # Extract progress information
-        progress_info = {
-            "found": search.progress.get("found", 0),
-            "analyzed": search.progress.get("analyzed", 0),
-            "completion": search.progress.get("completion", 0),
-            "time_left": search.progress.get("timeLeft", 0),
-        }
-
-        # Extract recall information
-        recall_data = {}
-        if search.recall:
-            expected = search.recall.get("expected", {})
-            recall_data = {
-                "expected_total": expected.get("total", 0),
-                "confidence": expected.get("confidence", ""),
-                "min_expected": expected.get("bounds", {}).get("min", 0),
-                "max_expected": expected.get("bounds", {}).get("max", 0),
-                "reasoning": search.recall.get("reasoning", ""),
-            }
-
-        yield "search_id", search.id
-        yield "status", search.status
-        yield "query", search.query
-        yield "entity_type", search.entity_type
-        yield "criteria", search.criteria
-        yield "progress", progress_info
-        yield "recall", recall_data
-        yield "created_at", search.created_at
-        yield "updated_at", search.updated_at
-        yield "canceled_at", search.canceled_at
-        yield "canceled_reason", search.canceled_reason
-        yield "metadata", search.metadata
-
-
-class ExaCancelWebsetSearchBlock(Block):
-    """Cancel a running webset search."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        search_id: str = SchemaField(
-            description="The ID of the search to cancel",
-            placeholder="search-id",
-        )
-
-    class Output(BlockSchemaOutput):
-        search_id: str = SchemaField(description="The ID of the canceled search")
-        status: str = SchemaField(description="Status after cancellation")
-        items_found_before_cancel: int = SchemaField(
-            description="Number of items found before cancellation"
-        )
-        success: str = SchemaField(
-            description="Whether the cancellation was successful"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="74ef9f1e-ae89-4c7f-9d7d-d217214815b4",
-            description="Cancel a running webset search",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCancelWebsetSearchBlock.Input,
-            output_schema=ExaCancelWebsetSearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        canceled_search = aexa.websets.searches.cancel(
-            webset_id=input_data.webset_id, id=input_data.search_id
-        )
-
-        # Extract items found before cancellation
-        items_found = 0
-        if canceled_search.progress:
-            items_found = canceled_search.progress.found
-
-        status = (
-            canceled_search.status.value
-            if hasattr(canceled_search.status, "value")
-            else str(canceled_search.status)
-        )
-
-        yield "search_id", canceled_search.id
-        yield "status", status
-        yield "items_found_before_cancel", items_found
-        yield "success", "true"
-
-
-class ExaFindOrCreateSearchBlock(Block):
-    """Find existing search by query or create new one (prevents duplicate searches)."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset",
-            placeholder="webset-id-or-external-id",
-        )
-        query: str = SchemaField(
-            description="Search query to find or create",
-            placeholder="AI companies in San Francisco",
-        )
-        count: int = SchemaField(
-            default=10,
-            description="Number of items to find (only used if creating new search)",
-            ge=1,
-            le=1000,
-        )
-        entity_type: SearchEntityType = SchemaField(
-            default=SearchEntityType.AUTO,
-            description="Entity type (only used if creating)",
-            advanced=True,
-        )
-        behavior: SearchBehavior = SchemaField(
-            default=SearchBehavior.OVERRIDE,
-            description="Search behavior (only used if creating)",
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        search_id: str = SchemaField(description="The search ID (existing or new)")
-        webset_id: str = SchemaField(description="The webset ID")
-        status: str = SchemaField(description="Current search status")
-        query: str = SchemaField(description="The search query")
-        was_created: bool = SchemaField(
-            description="True if search was newly created, False if already existed"
-        )
-        items_found: int = SchemaField(
-            description="Number of items found (0 if still running)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="cbdb05ac-cb73-4b03-a493-6d34e9a011da",
-            description="Find existing search by query or create new - prevents duplicate searches in workflows",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaFindOrCreateSearchBlock.Input,
-            output_schema=ExaFindOrCreateSearchBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Use AsyncExa SDK
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-
-        # Get webset to check existing searches
-        webset = aexa.websets.get(id=input_data.webset_id)
-
-        # Look for existing search with same query
-        existing_search = None
-        if webset.searches:
-            for search in webset.searches:
-                if search.query.strip().lower() == input_data.query.strip().lower():
-                    existing_search = search
-                    break
-
-        if existing_search:
-            # Found existing search
-            search = WebsetSearchModel.from_sdk(existing_search)
-
-            yield "search_id", search.id
-            yield "webset_id", input_data.webset_id
-            yield "status", search.status
-            yield "query", search.query
-            yield "was_created", False
-            yield "items_found", search.progress.get("found", 0)
-        else:
-            # Create new search
-            payload: Dict[str, Any] = {
-                "query": input_data.query,
-                "count": input_data.count,
-                "behavior": input_data.behavior.value,
-            }
-
-            # Add entity if not auto
-            if input_data.entity_type != SearchEntityType.AUTO:
-                payload["entity"] = {"type": input_data.entity_type.value}
-
-            sdk_search = aexa.websets.searches.create(
-                webset_id=input_data.webset_id, params=payload
-            )
-
-            search = WebsetSearchModel.from_sdk(sdk_search)
-
-            yield "search_id", search.id
-            yield "webset_id", input_data.webset_id
-            yield "status", search.status
-            yield "query", search.query
-            yield "was_created", True
-            yield "items_found", 0  # Newly created, no items yet

autogpt_platform/backend/backend/blocks/github/example_payloads/discussion.created.json

@@ -1,108 +0,0 @@
-{
-  "action": "created",
-  "discussion": {
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "category": {
-      "id": 12345678,
-      "node_id": "DIC_kwDOJKSTjM4CXXXX",
-      "repository_id": 614765452,
-      "emoji": ":pray:",
-      "name": "Q&A",
-      "description": "Ask the community for help",
-      "created_at": "2023-03-16T09:21:07Z",
-      "updated_at": "2023-03-16T09:21:07Z",
-      "slug": "q-a",
-      "is_answerable": true
-    },
-    "answer_html_url": null,
-    "answer_chosen_at": null,
-    "answer_chosen_by": null,
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/discussions/9999",
-    "id": 5000000001,
-    "node_id": "D_kwDOJKSTjM4AYYYY",
-    "number": 9999,
-    "title": "How do I configure custom blocks?",
-    "user": {
-      "login": "curious-user",
-      "id": 22222222,
-      "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-      "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-      "url": "https://api.github.com/users/curious-user",
-      "html_url": "https://github.com/curious-user",
-      "type": "User",
-      "site_admin": false
-    },
-    "state": "open",
-    "state_reason": null,
-    "locked": false,
-    "comments": 0,
-    "created_at": "2024-12-01T17:00:00Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Question\n\nI'm trying to create a custom block for my specific use case. I've read the documentation but I'm not sure how to:\n\n1. Define the input/output schema\n2. Handle authentication\n3. Test my block locally\n\nCan someone point me to examples or provide guidance?\n\n## Environment\n\n- AutoGPT Platform version: latest\n- Python: 3.11",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/discussions/9999/timeline"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T17:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "has_discussions": true,
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "curious-user",
-    "id": 22222222,
-    "node_id": "MDQ6VXNlcjIyMjIyMjIy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/22222222?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/curious-user",
-    "html_url": "https://github.com/curious-user",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/issues.opened.json

@@ -1,112 +0,0 @@
-{
-  "action": "opened",
-  "issue": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345",
-    "repository_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "labels_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/labels{/name}",
-    "comments_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/comments",
-    "events_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/events",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/issues/12345",
-    "id": 2000000001,
-    "node_id": "I_kwDOJKSTjM5wXXXX",
-    "number": 12345,
-    "title": "Bug: Application crashes when processing large files",
-    "user": {
-      "login": "bug-reporter",
-      "id": 11111111,
-      "node_id": "MDQ6VXNlcjExMTExMTEx",
-      "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-      "url": "https://api.github.com/users/bug-reporter",
-      "html_url": "https://github.com/bug-reporter",
-      "type": "User",
-      "site_admin": false
-    },
-    "labels": [
-      {
-        "id": 5272676214,
-        "node_id": "LA_kwDOJKSTjM8AAAABOkandg",
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/labels/bug",
-        "name": "bug",
-        "color": "d73a4a",
-        "default": true,
-        "description": "Something isn't working"
-      }
-    ],
-    "state": "open",
-    "locked": false,
-    "assignee": null,
-    "assignees": [],
-    "milestone": null,
-    "comments": 0,
-    "created_at": "2024-12-01T16:00:00Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "closed_at": null,
-    "author_association": "NONE",
-    "active_lock_reason": null,
-    "body": "## Description\n\nWhen I try to process a file larger than 100MB, the application crashes with an out of memory error.\n\n## Steps to Reproduce\n\n1. Open the application\n2. Select a file larger than 100MB\n3. Click 'Process'\n4. Application crashes\n\n## Expected Behavior\n\nThe application should handle large files gracefully.\n\n## Environment\n\n- OS: Ubuntu 22.04\n- Python: 3.11\n- AutoGPT Version: 1.0.0",
-    "reactions": {
-      "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/reactions",
-      "total_count": 0,
-      "+1": 0,
-      "-1": 0,
-      "laugh": 0,
-      "hooray": 0,
-      "confused": 0,
-      "heart": 0,
-      "rocket": 0,
-      "eyes": 0
-    },
-    "timeline_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/issues/12345/timeline",
-    "state_reason": null
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T16:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "open_issues_count": 190,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "bug-reporter",
-    "id": 11111111,
-    "node_id": "MDQ6VXNlcjExMTExMTEx",
-    "avatar_url": "https://avatars.githubusercontent.com/u/11111111?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/bug-reporter",
-    "html_url": "https://github.com/bug-reporter",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/release.published.json

@@ -1,97 +0,0 @@
-{
-  "action": "published",
-  "release": {
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789",
-    "assets_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets",
-    "upload_url": "https://uploads.github.com/repos/Significant-Gravitas/AutoGPT/releases/123456789/assets{?name,label}",
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/tag/v1.0.0",
-    "id": 123456789,
-    "author": {
-      "login": "ntindle",
-      "id": 12345678,
-      "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-      "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-      "gravatar_id": "",
-      "url": "https://api.github.com/users/ntindle",
-      "html_url": "https://github.com/ntindle",
-      "type": "User",
-      "site_admin": false
-    },
-    "node_id": "RE_kwDOJKSTjM4HWwAA",
-    "tag_name": "v1.0.0",
-    "target_commitish": "master",
-    "name": "AutoGPT Platform v1.0.0",
-    "draft": false,
-    "prerelease": false,
-    "created_at": "2024-12-01T10:00:00Z",
-    "published_at": "2024-12-01T12:00:00Z",
-    "assets": [
-      {
-        "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/releases/assets/987654321",
-        "id": 987654321,
-        "node_id": "RA_kwDOJKSTjM4HWwBB",
-        "name": "autogpt-v1.0.0.zip",
-        "label": "Release Package",
-        "content_type": "application/zip",
-        "state": "uploaded",
-        "size": 52428800,
-        "download_count": 0,
-        "created_at": "2024-12-01T11:30:00Z",
-        "updated_at": "2024-12-01T11:35:00Z",
-        "browser_download_url": "https://github.com/Significant-Gravitas/AutoGPT/releases/download/v1.0.0/autogpt-v1.0.0.zip"
-      }
-    ],
-    "tarball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/tarball/v1.0.0",
-    "zipball_url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT/zipball/v1.0.0",
-    "body": "## What's New\n\n- Feature 1: Amazing new capability\n- Feature 2: Performance improvements\n- Bug fixes and stability improvements\n\n## Breaking Changes\n\nNone\n\n## Contributors\n\nThanks to all our contributors!"
-  },
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T12:00:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170000,
-    "watchers_count": 170000,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "ntindle",
-    "id": 12345678,
-    "node_id": "MDQ6VXNlcjEyMzQ1Njc4",
-    "avatar_url": "https://avatars.githubusercontent.com/u/12345678?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/ntindle",
-    "html_url": "https://github.com/ntindle",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/example_payloads/star.created.json

@@ -1,53 +0,0 @@
-{
-  "action": "created",
-  "starred_at": "2024-12-01T15:30:00Z",
-  "repository": {
-    "id": 614765452,
-    "node_id": "R_kgDOJKSTjA",
-    "name": "AutoGPT",
-    "full_name": "Significant-Gravitas/AutoGPT",
-    "private": false,
-    "owner": {
-      "login": "Significant-Gravitas",
-      "id": 130738209,
-      "node_id": "O_kgDOB8roIQ",
-      "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-      "url": "https://api.github.com/users/Significant-Gravitas",
-      "html_url": "https://github.com/Significant-Gravitas",
-      "type": "Organization",
-      "site_admin": false
-    },
-    "html_url": "https://github.com/Significant-Gravitas/AutoGPT",
-    "description": "AutoGPT is the vision of accessible AI for everyone, to use and to build on.",
-    "fork": false,
-    "url": "https://api.github.com/repos/Significant-Gravitas/AutoGPT",
-    "created_at": "2023-03-16T09:21:07Z",
-    "updated_at": "2024-12-01T15:30:00Z",
-    "pushed_at": "2024-12-01T12:00:00Z",
-    "stargazers_count": 170001,
-    "watchers_count": 170001,
-    "language": "Python",
-    "forks_count": 45000,
-    "visibility": "public",
-    "default_branch": "master"
-  },
-  "organization": {
-    "login": "Significant-Gravitas",
-    "id": 130738209,
-    "node_id": "O_kgDOB8roIQ",
-    "url": "https://api.github.com/orgs/Significant-Gravitas",
-    "avatar_url": "https://avatars.githubusercontent.com/u/130738209?v=4",
-    "description": ""
-  },
-  "sender": {
-    "login": "awesome-contributor",
-    "id": 98765432,
-    "node_id": "MDQ6VXNlcjk4NzY1NDMy",
-    "avatar_url": "https://avatars.githubusercontent.com/u/98765432?v=4",
-    "gravatar_id": "",
-    "url": "https://api.github.com/users/awesome-contributor",
-    "html_url": "https://github.com/awesome-contributor",
-    "type": "User",
-    "site_admin": false
-  }
-}

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -159,391 +159,3 @@ class GithubPullRequestTriggerBlock(GitHubTriggerBase, Block):
 
 
 # --8<-- [end:GithubTriggerExample]
-
-
-class GithubStarTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub star events - useful for milestone celebrations."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "star.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#star
-            """
-
-            created: bool = False
-            deleted: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The star events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The star event that triggered the webhook ('created' or 'deleted')"
-        )
-        starred_at: str = SchemaField(
-            description="ISO timestamp when the repo was starred (empty if deleted)"
-        )
-        stargazers_count: int = SchemaField(
-            description="Current number of stars on the repository"
-        )
-        repository_name: str = SchemaField(
-            description="Full name of the repository (owner/repo)"
-        )
-        repository_url: str = SchemaField(description="URL to the repository")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="551e0a35-100b-49b7-89b8-3031322239b6",
-            description="This block triggers on GitHub star events. "
-            "Useful for celebrating milestones (e.g., 1k, 10k stars) or tracking engagement.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubStarTriggerBlock.Input,
-            output_schema=GithubStarTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="star.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("starred_at", example_payload.get("starred_at", "")),
-                ("stargazers_count", example_payload["repository"]["stargazers_count"]),
-                ("repository_name", example_payload["repository"]["full_name"]),
-                ("repository_url", example_payload["repository"]["html_url"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        yield "event", input_data.payload["action"]
-        yield "starred_at", input_data.payload.get("starred_at", "")
-        yield "stargazers_count", input_data.payload["repository"]["stargazers_count"]
-        yield "repository_name", input_data.payload["repository"]["full_name"]
-        yield "repository_url", input_data.payload["repository"]["html_url"]
-
-
-class GithubReleaseTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub release events - ideal for announcing new versions."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "release.published.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#release
-            """
-
-            published: bool = False
-            unpublished: bool = False
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            prereleased: bool = False
-            released: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The release events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The release event that triggered the webhook (e.g., 'published')"
-        )
-        release: dict = SchemaField(description="The full release object")
-        release_url: str = SchemaField(description="URL to the release page")
-        tag_name: str = SchemaField(description="The release tag name (e.g., 'v1.0.0')")
-        release_name: str = SchemaField(description="Human-readable release name")
-        body: str = SchemaField(description="Release notes/description")
-        prerelease: bool = SchemaField(description="Whether this is a prerelease")
-        draft: bool = SchemaField(description="Whether this is a draft release")
-        assets: list = SchemaField(description="List of release assets/files")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="2052dd1b-74e1-46ac-9c87-c7a0e057b60b",
-            description="This block triggers on GitHub release events. "
-            "Perfect for automating announcements to Discord, Twitter, or other platforms.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubReleaseTriggerBlock.Input,
-            output_schema=GithubReleaseTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="release.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"published": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("release", example_payload["release"]),
-                ("release_url", example_payload["release"]["html_url"]),
-                ("tag_name", example_payload["release"]["tag_name"]),
-                ("release_name", example_payload["release"]["name"]),
-                ("body", example_payload["release"]["body"]),
-                ("prerelease", example_payload["release"]["prerelease"]),
-                ("draft", example_payload["release"]["draft"]),
-                ("assets", example_payload["release"]["assets"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        release = input_data.payload["release"]
-        yield "event", input_data.payload["action"]
-        yield "release", release
-        yield "release_url", release["html_url"]
-        yield "tag_name", release["tag_name"]
-        yield "release_name", release.get("name", "")
-        yield "body", release.get("body", "")
-        yield "prerelease", release["prerelease"]
-        yield "draft", release["draft"]
-        yield "assets", release["assets"]
-
-
-class GithubIssuesTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub issues events - great for triage and notifications."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "issues.opened.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#issues
-            """
-
-            opened: bool = False
-            edited: bool = False
-            deleted: bool = False
-            closed: bool = False
-            reopened: bool = False
-            assigned: bool = False
-            unassigned: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            transferred: bool = False
-            milestoned: bool = False
-            demilestoned: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The issue events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The issue event that triggered the webhook (e.g., 'opened')"
-        )
-        number: int = SchemaField(description="The issue number")
-        issue: dict = SchemaField(description="The full issue object")
-        issue_url: str = SchemaField(description="URL to the issue")
-        issue_title: str = SchemaField(description="The issue title")
-        issue_body: str = SchemaField(description="The issue body/description")
-        labels: list = SchemaField(description="List of labels on the issue")
-        assignees: list = SchemaField(description="List of assignees")
-        state: str = SchemaField(description="Issue state ('open' or 'closed')")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="b2605464-e486-4bf4-aad3-d8a213c8a48a",
-            description="This block triggers on GitHub issues events. "
-            "Useful for automated triage, notifications, and welcoming first-time contributors.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubIssuesTriggerBlock.Input,
-            output_schema=GithubIssuesTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="issues.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"opened": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["issue"]["number"]),
-                ("issue", example_payload["issue"]),
-                ("issue_url", example_payload["issue"]["html_url"]),
-                ("issue_title", example_payload["issue"]["title"]),
-                ("issue_body", example_payload["issue"]["body"]),
-                ("labels", example_payload["issue"]["labels"]),
-                ("assignees", example_payload["issue"]["assignees"]),
-                ("state", example_payload["issue"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        issue = input_data.payload["issue"]
-        yield "event", input_data.payload["action"]
-        yield "number", issue["number"]
-        yield "issue", issue
-        yield "issue_url", issue["html_url"]
-        yield "issue_title", issue["title"]
-        yield "issue_body", issue.get("body") or ""
-        yield "labels", issue["labels"]
-        yield "assignees", issue["assignees"]
-        yield "state", issue["state"]
-
-
-class GithubDiscussionTriggerBlock(GitHubTriggerBase, Block):
-    """Trigger block for GitHub discussion events - perfect for community Q&A sync."""
-
-    EXAMPLE_PAYLOAD_FILE = (
-        Path(__file__).parent / "example_payloads" / "discussion.created.json"
-    )
-
-    class Input(GitHubTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """
-            https://docs.github.com/en/webhooks/webhook-events-and-payloads#discussion
-            """
-
-            created: bool = False
-            edited: bool = False
-            deleted: bool = False
-            answered: bool = False
-            unanswered: bool = False
-            labeled: bool = False
-            unlabeled: bool = False
-            locked: bool = False
-            unlocked: bool = False
-            category_changed: bool = False
-            transferred: bool = False
-            pinned: bool = False
-            unpinned: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Events", description="The discussion events to subscribe to"
-        )
-
-    class Output(GitHubTriggerBase.Output):
-        event: str = SchemaField(
-            description="The discussion event that triggered the webhook"
-        )
-        number: int = SchemaField(description="The discussion number")
-        discussion: dict = SchemaField(description="The full discussion object")
-        discussion_url: str = SchemaField(description="URL to the discussion")
-        title: str = SchemaField(description="The discussion title")
-        body: str = SchemaField(description="The discussion body")
-        category: dict = SchemaField(description="The discussion category object")
-        category_name: str = SchemaField(description="Name of the category")
-        state: str = SchemaField(description="Discussion state")
-
-    def __init__(self):
-        from backend.integrations.webhooks.github import GithubWebhookType
-
-        example_payload = json.loads(
-            self.EXAMPLE_PAYLOAD_FILE.read_text(encoding="utf-8")
-        )
-
-        super().__init__(
-            id="87f847b3-d81a-424e-8e89-acadb5c9d52b",
-            description="This block triggers on GitHub Discussions events. "
-            "Great for syncing Q&A to Discord or auto-responding to common questions. "
-            "Note: Discussions must be enabled on the repository.",
-            categories={BlockCategory.DEVELOPER_TOOLS, BlockCategory.INPUT},
-            input_schema=GithubDiscussionTriggerBlock.Input,
-            output_schema=GithubDiscussionTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.GITHUB,
-                webhook_type=GithubWebhookType.REPO,
-                resource_format="{repo}",
-                event_filter_input="events",
-                event_format="discussion.{event}",
-            ),
-            test_input={
-                "repo": "Significant-Gravitas/AutoGPT",
-                "events": {"created": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": example_payload,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", example_payload),
-                ("triggered_by_user", example_payload["sender"]),
-                ("event", example_payload["action"]),
-                ("number", example_payload["discussion"]["number"]),
-                ("discussion", example_payload["discussion"]),
-                ("discussion_url", example_payload["discussion"]["html_url"]),
-                ("title", example_payload["discussion"]["title"]),
-                ("body", example_payload["discussion"]["body"]),
-                ("category", example_payload["discussion"]["category"]),
-                ("category_name", example_payload["discussion"]["category"]["name"]),
-                ("state", example_payload["discussion"]["state"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:  # type: ignore
-        async for name, value in super().run(input_data, **kwargs):
-            yield name, value
-        discussion = input_data.payload["discussion"]
-        yield "event", input_data.payload["action"]
-        yield "number", discussion["number"]
-        yield "discussion", discussion
-        yield "discussion_url", discussion["html_url"]
-        yield "title", discussion["title"]
-        yield "body", discussion.get("body") or ""
-        yield "category", discussion["category"]
-        yield "category_name", discussion["category"]["name"]
-        yield "state", discussion["state"]

autogpt_platform/backend/backend/blocks/google/_drive.py

@@ -1,155 +0,0 @@
-from typing import Any, Literal, Optional
-
-from pydantic import BaseModel, ConfigDict, Field
-
-from backend.data.model import SchemaField
-
-AttachmentView = Literal[
-    "DOCS",
-    "DOCUMENTS",
-    "SPREADSHEETS",
-    "PRESENTATIONS",
-    "DOCS_IMAGES",
-    "FOLDERS",
-]
-ATTACHMENT_VIEWS: tuple[AttachmentView, ...] = (
-    "DOCS",
-    "DOCUMENTS",
-    "SPREADSHEETS",
-    "PRESENTATIONS",
-    "DOCS_IMAGES",
-    "FOLDERS",
-)
-
-
-class _GoogleDriveFileBase(BaseModel):
-    """Internal base class for Google Drive file representation."""
-
-    model_config = ConfigDict(populate_by_name=True)
-
-    id: str = Field(description="Google Drive file/folder ID")
-    name: Optional[str] = Field(None, description="File/folder name")
-    mime_type: Optional[str] = Field(
-        None,
-        alias="mimeType",
-        description="MIME type (e.g., application/vnd.google-apps.document)",
-    )
-    url: Optional[str] = Field(None, description="URL to open the file")
-    icon_url: Optional[str] = Field(None, alias="iconUrl", description="Icon URL")
-    is_folder: Optional[bool] = Field(
-        None, alias="isFolder", description="Whether this is a folder"
-    )
-
-
-class GoogleDriveFile(_GoogleDriveFileBase):
-    """
-    Represents a Google Drive file/folder with optional credentials for chaining.
-
-    Used for both inputs and outputs in Google Drive blocks. The `_credentials_id`
-    field enables chaining between blocks - when one block outputs a file, the
-    next block can use the same credentials to access it.
-
-    When used with GoogleDriveFileField(), the frontend renders a combined
-    auth + file picker UI that automatically populates `_credentials_id`.
-    """
-
-    # Hidden field for credential ID - populated by frontend, preserved in outputs
-    credentials_id: Optional[str] = Field(
-        None,
-        alias="_credentials_id",
-        description="Internal: credential ID for authentication",
-    )
-
-
-def GoogleDriveFileField(
-    *,
-    title: str,
-    description: str | None = None,
-    credentials_kwarg: str = "credentials",
-    credentials_scopes: list[str] | None = None,
-    allowed_views: list[AttachmentView] | None = None,
-    allowed_mime_types: list[str] | None = None,
-    placeholder: str | None = None,
-    **kwargs: Any,
-) -> Any:
-    """
-    Creates a Google Drive file input field with auto-generated credentials.
-
-    This field type produces a single UI element that handles both:
-    1. Google OAuth authentication
-    2. File selection via Google Drive Picker
-
-    The system automatically generates a credentials field, and the credentials
-    are passed to the run() method using the specified kwarg name.
-
-    Args:
-        title: Field title shown in UI
-        description: Field description/help text
-        credentials_kwarg: Name of the kwarg that will receive GoogleCredentials
-                          in the run() method (default: "credentials")
-        credentials_scopes: OAuth scopes required (default: drive.file)
-        allowed_views: List of view types to show in picker (default: ["DOCS"])
-        allowed_mime_types: Filter by MIME types
-        placeholder: Placeholder text for the button
-        **kwargs: Additional SchemaField arguments
-
-    Returns:
-        Field definition that produces GoogleDriveFile
-
-    Example:
-        >>> class MyBlock(Block):
-        ...     class Input(BlockSchemaInput):
-        ...         spreadsheet: GoogleDriveFile = GoogleDriveFileField(
-        ...             title="Select Spreadsheet",
-        ...             credentials_kwarg="creds",
-        ...             allowed_views=["SPREADSHEETS"],
-        ...         )
-        ...
-        ...     async def run(
-        ...         self, input_data: Input, *, creds: GoogleCredentials, **kwargs
-        ...     ):
-        ...         # creds is automatically populated
-        ...         file = input_data.spreadsheet
-    """
-
-    # Determine scopes - drive.file is sufficient for picker-selected files
-    scopes = credentials_scopes or ["https://www.googleapis.com/auth/drive.file"]
-
-    # Build picker configuration with auto_credentials embedded
-    picker_config = {
-        "multiselect": False,
-        "allow_folder_selection": False,
-        "allowed_views": list(allowed_views) if allowed_views else ["DOCS"],
-        "scopes": scopes,
-        # Auto-credentials config tells frontend to include _credentials_id in output
-        "auto_credentials": {
-            "provider": "google",
-            "type": "oauth2",
-            "scopes": scopes,
-            "kwarg_name": credentials_kwarg,
-        },
-    }
-
-    if allowed_mime_types:
-        picker_config["allowed_mime_types"] = list(allowed_mime_types)
-
-    return SchemaField(
-        default=None,
-        title=title,
-        description=description,
-        placeholder=placeholder or "Select from Google Drive",
-        # Use google-drive-picker format so frontend renders existing component
-        format="google-drive-picker",
-        advanced=False,
-        json_schema_extra={
-            "google_drive_picker_config": picker_config,
-            # Also keep auto_credentials at top level for backend detection
-            "auto_credentials": {
-                "provider": "google",
-                "type": "oauth2",
-                "scopes": scopes,
-                "kwarg_name": credentials_kwarg,
-            },
-            **kwargs,
-        },
-    )

autogpt_platform/backend/backend/blocks/http.py

@@ -184,13 +184,7 @@ class SendWebRequestBlock(Block):
             )
 
         # ─── Execute request ─────────────────────────────────────────
-        # Use raise_for_status=False so HTTP errors (4xx, 5xx) are returned
-        # as response objects instead of raising exceptions, allowing proper
-        # handling via client_error and server_error outputs
-        response = await Requests(
-            raise_for_status=False,
-            retry_max_attempts=1,  # allow callers to handle HTTP errors immediately
-        ).request(
+        response = await Requests().request(
             input_data.method.value,
             input_data.url,
             headers=input_data.headers,

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -1,166 +0,0 @@
-import logging
-from typing import Any
-
-from prisma.enums import ReviewStatus
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    BlockType,
-)
-from backend.data.execution import ExecutionContext, ExecutionStatus
-from backend.data.human_review import ReviewResult
-from backend.data.model import SchemaField
-from backend.executor.manager import async_update_node_execution_status
-from backend.util.clients import get_database_manager_async_client
-
-logger = logging.getLogger(__name__)
-
-
-class HumanInTheLoopBlock(Block):
-    """
-    This block pauses execution and waits for human approval or modification of the data.
-
-    When executed, it creates a pending review entry and sets the node execution status
-    to REVIEW. The execution will remain paused until a human user either:
-    - Approves the data (with or without modifications)
-    - Rejects the data
-
-    This is useful for workflows that require human validation or intervention before
-    proceeding to the next steps.
-    """
-
-    class Input(BlockSchemaInput):
-        data: Any = SchemaField(description="The data to be reviewed by a human user")
-        name: str = SchemaField(
-            description="A descriptive name for what this data represents",
-        )
-        editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data",
-            default=True,
-            advanced=True,
-        )
-
-    class Output(BlockSchemaOutput):
-        approved_data: Any = SchemaField(
-            description="The data when approved (may be modified by reviewer)"
-        )
-        rejected_data: Any = SchemaField(
-            description="The data when rejected (may be modified by reviewer)"
-        )
-        review_message: str = SchemaField(
-            description="Any message provided by the reviewer", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution and wait for human approval or modification of data",
-            categories={BlockCategory.BASIC},
-            input_schema=HumanInTheLoopBlock.Input,
-            output_schema=HumanInTheLoopBlock.Output,
-            block_type=BlockType.HUMAN_IN_THE_LOOP,
-            test_input={
-                "data": {"name": "John Doe", "age": 30},
-                "name": "User profile data",
-                "editable": True,
-            },
-            test_output=[
-                ("approved_data", {"name": "John Doe", "age": 30}),
-            ],
-            test_mock={
-                "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
-                    data={"name": "John Doe", "age": 30},
-                    status=ReviewStatus.APPROVED,
-                    message="",
-                    processed=False,
-                    node_exec_id="test-node-exec-id",
-                ),
-                "update_node_execution_status": lambda *_args, **_kwargs: None,
-                "update_review_processed_status": lambda *_args, **_kwargs: None,
-            },
-        )
-
-    async def get_or_create_human_review(self, **kwargs):
-        return await get_database_manager_async_client().get_or_create_human_review(
-            **kwargs
-        )
-
-    async def update_node_execution_status(self, **kwargs):
-        return await async_update_node_execution_status(
-            db_client=get_database_manager_async_client(), **kwargs
-        )
-
-    async def update_review_processed_status(self, node_exec_id: str, processed: bool):
-        return await get_database_manager_async_client().update_review_processed_status(
-            node_exec_id, processed
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
-        if not execution_context.safe_mode:
-            logger.info(
-                f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
-            )
-            yield "approved_data", input_data.data
-            yield "review_message", "Auto-approved (safe mode disabled)"
-            return
-
-        try:
-            result = await self.get_or_create_human_review(
-                user_id=user_id,
-                node_exec_id=node_exec_id,
-                graph_exec_id=graph_exec_id,
-                graph_id=graph_id,
-                graph_version=graph_version,
-                input_data=input_data.data,
-                message=input_data.name,
-                editable=input_data.editable,
-            )
-        except Exception as e:
-            logger.error(f"Error in HITL block for node {node_exec_id}: {str(e)}")
-            raise
-
-        if result is None:
-            logger.info(
-                f"HITL block pausing execution for node {node_exec_id} - awaiting human review"
-            )
-            try:
-                await self.update_node_execution_status(
-                    exec_id=node_exec_id,
-                    status=ExecutionStatus.REVIEW,
-                )
-                return
-            except Exception as e:
-                logger.error(
-                    f"Failed to update node status for HITL block {node_exec_id}: {str(e)}"
-                )
-                raise
-
-        if not result.processed:
-            await self.update_review_processed_status(
-                node_exec_id=node_exec_id, processed=True
-            )
-
-            if result.status == ReviewStatus.APPROVED:
-                yield "approved_data", result.data
-                if result.message:
-                    yield "review_message", result.message
-
-            elif result.status == ReviewStatus.REJECTED:
-                yield "rejected_data", result.data
-                if result.message:
-                    yield "review_message", result.message

autogpt_platform/backend/backend/blocks/io.py

@@ -2,8 +2,6 @@ import copy
 from datetime import date, time
 from typing import Any, Optional
 
-# Import for Google Drive file input block
-from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -648,119 +646,6 @@ class AgentTableInputBlock(AgentInputBlock):
         yield "result", input_data.value if input_data.value is not None else []
 
 
-class AgentGoogleDriveFileInputBlock(AgentInputBlock):
-    """
-    This block allows users to select a file from Google Drive.
-
-    It provides a Google Drive file picker UI that handles both authentication
-    and file selection. The selected file information (ID, name, URL, etc.)
-    is output for use by other blocks like Google Sheets Read.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[GoogleDriveFile] = SchemaField(
-            description="The selected Google Drive file.",
-            default=None,
-            advanced=False,
-            title="Selected File",
-        )
-        allowed_views: list[AttachmentView] = SchemaField(
-            description="Which views to show in the file picker (DOCS, SPREADSHEETS, PRESENTATIONS, etc.).",
-            default_factory=lambda: ["DOCS", "SPREADSHEETS", "PRESENTATIONS"],
-            advanced=False,
-            title="Allowed Views",
-        )
-        allow_folder_selection: bool = SchemaField(
-            description="Whether to allow selecting folders.",
-            default=False,
-            advanced=True,
-            title="Allow Folder Selection",
-        )
-
-        def generate_schema(self):
-            """Generate schema for the value field with Google Drive picker format."""
-            schema = super().generate_schema()
-
-            # Default scopes for drive.file access
-            scopes = ["https://www.googleapis.com/auth/drive.file"]
-
-            # Build picker configuration
-            picker_config = {
-                "multiselect": False,  # Single file selection only for now
-                "allow_folder_selection": self.allow_folder_selection,
-                "allowed_views": (
-                    list(self.allowed_views) if self.allowed_views else ["DOCS"]
-                ),
-                "scopes": scopes,
-                # Auto-credentials config tells frontend to include _credentials_id in output
-                "auto_credentials": {
-                    "provider": "google",
-                    "type": "oauth2",
-                    "scopes": scopes,
-                    "kwarg_name": "credentials",
-                },
-            }
-
-            # Set format and config for frontend to render Google Drive picker
-            schema["format"] = "google-drive-picker"
-            schema["google_drive_picker_config"] = picker_config
-            # Also keep auto_credentials at top level for backend detection
-            schema["auto_credentials"] = {
-                "provider": "google",
-                "type": "oauth2",
-                "scopes": scopes,
-                "kwarg_name": "credentials",
-            }
-
-            if self.value is not None:
-                schema["default"] = self.value.model_dump()
-
-            return schema
-
-    class Output(AgentInputBlock.Output):
-        result: GoogleDriveFile = SchemaField(
-            description="The selected Google Drive file with ID, name, URL, and other metadata."
-        )
-
-    def __init__(self):
-        test_file = GoogleDriveFile.model_validate(
-            {
-                "id": "test-file-id",
-                "name": "Test Spreadsheet",
-                "mimeType": "application/vnd.google-apps.spreadsheet",
-                "url": "https://docs.google.com/spreadsheets/d/test-file-id",
-            }
-        )
-        super().__init__(
-            id="d3b32f15-6fd7-40e3-be52-e083f51b19a2",
-            description="Block for selecting a file from Google Drive.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentGoogleDriveFileInputBlock.Input,
-            output_schema=AgentGoogleDriveFileInputBlock.Output,
-            test_input=[
-                {
-                    "name": "spreadsheet_input",
-                    "description": "Select a spreadsheet from Google Drive",
-                    "allowed_views": ["SPREADSHEETS"],
-                    "value": {
-                        "id": "test-file-id",
-                        "name": "Test Spreadsheet",
-                        "mimeType": "application/vnd.google-apps.spreadsheet",
-                        "url": "https://docs.google.com/spreadsheets/d/test-file-id",
-                    },
-                }
-            ],
-            test_output=[("result", test_file)],
-        )
-
-    async def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        """
-        Yields the selected Google Drive file.
-        """
-        if input_data.value is not None:
-            yield "result", input_data.value
-
-
 IO_BLOCK_IDs = [
     AgentInputBlock().id,
     AgentOutputBlock().id,
@@ -773,5 +658,4 @@ IO_BLOCK_IDs = [
     AgentDropdownInputBlock().id,
     AgentToggleInputBlock().id,
     AgentTableInputBlock().id,
-    AgentGoogleDriveFileInputBlock().id,
 ]

autogpt_platform/backend/backend/blocks/linear/_api.py

@@ -265,68 +265,3 @@ class LinearClient:
             return [Issue(**issue) for issue in issues["searchIssues"]["nodes"]]
         except LinearAPIException as e:
             raise e
-
-    async def try_get_issues(
-        self, project: str, status: str, is_assigned: bool, include_comments: bool
-    ) -> list[Issue]:
-        try:
-            query = """    
-                    query IssuesByProjectStatusAndAssignee(
-                      $projectName: String!
-                      $statusName: String!
-                      $isAssigned: Boolean!
-                      $includeComments: Boolean! = false
-                    ) {
-                      issues(
-                        filter: {
-                          project: { name: { eq: $projectName } }
-                          state: { name: { eq: $statusName } }
-                          assignee: { null: $isAssigned }
-                        }
-                      ) {
-                        nodes {
-                          id
-                          title
-                          identifier
-                          description
-                          createdAt
-                          priority
-                          assignee {
-                            id
-                            name
-                          }
-                          project {
-                            id
-                            name
-                          }
-                          state {
-                            id
-                            name
-                          }
-                          comments @include(if: $includeComments) {
-                            nodes {
-                              id
-                              body
-                              createdAt
-                              user {
-                                id
-                                name
-                              }
-                            }
-                          }
-                        }
-                      }
-                    }
-            """
-
-            variables: dict[str, Any] = {
-                "projectName": project,
-                "statusName": status,
-                "isAssigned": not is_assigned,
-                "includeComments": include_comments,
-            }
-
-            issues = await self.query(query, variables)
-            return [Issue(**issue) for issue in issues["issues"]["nodes"]]
-        except LinearAPIException as e:
-            raise e

autogpt_platform/backend/backend/blocks/linear/issues.py

@@ -203,106 +203,3 @@ class LinearSearchIssuesBlock(Block):
             yield "error", str(e)
         except Exception as e:
             yield "error", f"Unexpected error: {str(e)}"
-
-
-class LinearGetProjectIssuesBlock(Block):
-    """Block for getting issues from a Linear project filtered by status and assignee"""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = linear.credentials_field(
-            description="Linear credentials with read permissions",
-            required_scopes={LinearScope.READ},
-        )
-        project: str = SchemaField(description="Name of the project to get issues from")
-        status: str = SchemaField(
-            description="Status/state name to filter issues by (e.g., 'In Progress', 'Done')"
-        )
-        is_assigned: bool = SchemaField(
-            description="Filter by assignee status - True to get assigned issues, False to get unassigned issues",
-            default=False,
-        )
-        include_comments: bool = SchemaField(
-            description="Whether to include comments in the response",
-            default=False,
-        )
-
-    class Output(BlockSchemaOutput):
-        issues: list[Issue] = SchemaField(
-            description="List of issues matching the criteria"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="c7d3f1e8-45a9-4b2c-9f81-3e6a8d7c5b1a",
-            description="Gets issues from a Linear project filtered by status and assignee",
-            input_schema=self.Input,
-            output_schema=self.Output,
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.ISSUE_TRACKING},
-            test_input={
-                "project": "Test Project",
-                "status": "In Progress",
-                "is_assigned": False,
-                "include_comments": False,
-                "credentials": TEST_CREDENTIALS_INPUT_OAUTH,
-            },
-            test_credentials=TEST_CREDENTIALS_OAUTH,
-            test_output=[
-                (
-                    "issues",
-                    [
-                        Issue(
-                            id="abc123",
-                            identifier="TST-123",
-                            title="Test issue",
-                            description="Test description",
-                            priority=1,
-                        )
-                    ],
-                ),
-            ],
-            test_mock={
-                "get_project_issues": lambda *args, **kwargs: [
-                    Issue(
-                        id="abc123",
-                        identifier="TST-123",
-                        title="Test issue",
-                        description="Test description",
-                        priority=1,
-                    )
-                ]
-            },
-        )
-
-    @staticmethod
-    async def get_project_issues(
-        credentials: OAuth2Credentials | APIKeyCredentials,
-        project: str,
-        status: str,
-        is_assigned: bool,
-        include_comments: bool,
-    ) -> list[Issue]:
-        client = LinearClient(credentials=credentials)
-        response: list[Issue] = await client.try_get_issues(
-            project=project,
-            status=status,
-            is_assigned=is_assigned,
-            include_comments=include_comments,
-        )
-        return response
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials | APIKeyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        """Execute getting project issues"""
-        issues = await self.get_project_issues(
-            credentials=credentials,
-            project=input_data.project,
-            status=input_data.status,
-            is_assigned=input_data.is_assigned,
-            include_comments=input_data.include_comments,
-        )
-        yield "issues", issues

autogpt_platform/backend/backend/blocks/linear/models.py

@@ -1,16 +1,9 @@
 from backend.sdk import BaseModel
 
 
-class User(BaseModel):
-    id: str
-    name: str
-
-
 class Comment(BaseModel):
     id: str
     body: str
-    createdAt: str | None = None
-    user: User | None = None
 
 
 class CreateCommentInput(BaseModel):
@@ -27,26 +20,22 @@ class CreateCommentResponseWrapper(BaseModel):
     commentCreate: CreateCommentResponse
 
 
-class Project(BaseModel):
-    id: str
-    name: str
-    description: str | None = None
-    priority: int | None = None
-    progress: float | None = None
-    content: str | None = None
-
-
 class Issue(BaseModel):
     id: str
     identifier: str
     title: str
     description: str | None
     priority: int
-    project: Project | None = None
-    createdAt: str | None = None
-    comments: list[Comment] | None = None
-    assignee: User | None = None
 
 
 class CreateIssueResponse(BaseModel):
     issue: Issue
+
+
+class Project(BaseModel):
+    id: str
+    name: str
+    description: str
+    priority: int
+    progress: float
+    content: str | None

autogpt_platform/backend/backend/blocks/llm.py

@@ -1,5 +1,6 @@
 # This file contains a lot of prompt block strings that would trigger "line too long"
 # flake8: noqa: E501
+import ast
 import logging
 import re
 import secrets
@@ -93,7 +94,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     O1_MINI = "o1-mini"
     # GPT-5 models
     GPT5 = "gpt-5-2025-08-07"
-    GPT5_1 = "gpt-5.1-2025-11-13"
     GPT5_MINI = "gpt-5-mini-2025-08-07"
     GPT5_NANO = "gpt-5-nano-2025-08-07"
     GPT5_CHAT = "gpt-5-chat-latest"
@@ -107,7 +107,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
     CLAUDE_4_OPUS = "claude-opus-4-20250514"
     CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
-    CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
     CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
     CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
     CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
@@ -119,8 +118,13 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     AIML_API_META_LLAMA_3_1_70B = "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo"
     AIML_API_LLAMA_3_2_3B = "meta-llama/Llama-3.2-3B-Instruct-Turbo"
     # Groq models
+    GEMMA2_9B = "gemma2-9b-it"
     LLAMA3_3_70B = "llama-3.3-70b-versatile"
     LLAMA3_1_8B = "llama-3.1-8b-instant"
+    LLAMA3_70B = "llama3-70b-8192"
+    LLAMA3_8B = "llama3-8b-8192"
+    # Groq preview models
+    DEEPSEEK_LLAMA_70B = "deepseek-r1-distill-llama-70b"
     # Ollama models
     OLLAMA_LLAMA3_3 = "llama3.3"
     OLLAMA_LLAMA3_2 = "llama3.2"
@@ -130,8 +134,8 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     # OpenRouter models
     OPENAI_GPT_OSS_120B = "openai/gpt-oss-120b"
     OPENAI_GPT_OSS_20B = "openai/gpt-oss-20b"
+    GEMINI_FLASH_1_5 = "google/gemini-flash-1.5"
     GEMINI_2_5_PRO = "google/gemini-2.5-pro-preview-03-25"
-    GEMINI_3_PRO_PREVIEW = "google/gemini-3-pro-preview"
     GEMINI_2_5_FLASH = "google/gemini-2.5-flash"
     GEMINI_2_0_FLASH = "google/gemini-2.0-flash-001"
     GEMINI_2_5_FLASH_LITE_PREVIEW = "google/gemini-2.5-flash-lite-preview-06-17"
@@ -154,9 +158,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     META_LLAMA_4_SCOUT = "meta-llama/llama-4-scout"
     META_LLAMA_4_MAVERICK = "meta-llama/llama-4-maverick"
     GROK_4 = "x-ai/grok-4"
-    GROK_4_FAST = "x-ai/grok-4-fast"
-    GROK_4_1_FAST = "x-ai/grok-4.1-fast"
-    GROK_CODE_FAST_1 = "x-ai/grok-code-fast-1"
     KIMI_K2 = "moonshotai/kimi-k2"
     QWEN3_235B_A22B_THINKING = "qwen/qwen3-235b-a22b-thinking-2507"
     QWEN3_CODER = "qwen/qwen3-coder"
@@ -195,7 +196,6 @@ MODEL_METADATA = {
     LlmModel.O1_MINI: ModelMetadata("openai", 128000, 65536),  # o1-mini-2024-09-12
     # GPT-5 models
     LlmModel.GPT5: ModelMetadata("openai", 400000, 128000),
-    LlmModel.GPT5_1: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_MINI: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_NANO: ModelMetadata("openai", 400000, 128000),
     LlmModel.GPT5_CHAT: ModelMetadata("openai", 400000, 16384),
@@ -219,9 +219,6 @@ MODEL_METADATA = {
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
         "anthropic", 200000, 64000
     ),  # claude-4-sonnet-20250514
-    LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
-        "anthropic", 200000, 64000
-    ),  # claude-opus-4-5-20251101
     LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
         "anthropic", 200000, 64000
     ),  # claude-sonnet-4-5-20250929
@@ -241,8 +238,12 @@ MODEL_METADATA = {
     LlmModel.AIML_API_META_LLAMA_3_1_70B: ModelMetadata("aiml_api", 131000, 2000),
     LlmModel.AIML_API_LLAMA_3_2_3B: ModelMetadata("aiml_api", 128000, None),
     # https://console.groq.com/docs/models
+    LlmModel.GEMMA2_9B: ModelMetadata("groq", 8192, None),
     LlmModel.LLAMA3_3_70B: ModelMetadata("groq", 128000, 32768),
     LlmModel.LLAMA3_1_8B: ModelMetadata("groq", 128000, 8192),
+    LlmModel.LLAMA3_70B: ModelMetadata("groq", 8192, None),
+    LlmModel.LLAMA3_8B: ModelMetadata("groq", 8192, None),
+    LlmModel.DEEPSEEK_LLAMA_70B: ModelMetadata("groq", 128000, None),
     # https://ollama.com/library
     LlmModel.OLLAMA_LLAMA3_3: ModelMetadata("ollama", 8192, None),
     LlmModel.OLLAMA_LLAMA3_2: ModelMetadata("ollama", 8192, None),
@@ -250,8 +251,8 @@ MODEL_METADATA = {
     LlmModel.OLLAMA_LLAMA3_405B: ModelMetadata("ollama", 8192, None),
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
+    LlmModel.GEMINI_FLASH_1_5: ModelMetadata("open_router", 1000000, 8192),
     LlmModel.GEMINI_2_5_PRO: ModelMetadata("open_router", 1050000, 8192),
-    LlmModel.GEMINI_3_PRO_PREVIEW: ModelMetadata("open_router", 1048576, 65535),
     LlmModel.GEMINI_2_5_FLASH: ModelMetadata("open_router", 1048576, 65535),
     LlmModel.GEMINI_2_0_FLASH: ModelMetadata("open_router", 1048576, 8192),
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: ModelMetadata(
@@ -263,12 +264,12 @@ MODEL_METADATA = {
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: ModelMetadata("open_router", 128000, 4096),
     LlmModel.DEEPSEEK_CHAT: ModelMetadata("open_router", 64000, 2048),
     LlmModel.DEEPSEEK_R1_0528: ModelMetadata("open_router", 163840, 163840),
-    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 8000),
+    LlmModel.PERPLEXITY_SONAR: ModelMetadata("open_router", 127000, 127000),
     LlmModel.PERPLEXITY_SONAR_PRO: ModelMetadata("open_router", 200000, 8000),
     LlmModel.PERPLEXITY_SONAR_DEEP_RESEARCH: ModelMetadata(
         "open_router",
         128000,
-        16000,
+        128000,
     ),
     LlmModel.NOUSRESEARCH_HERMES_3_LLAMA_3_1_405B: ModelMetadata(
         "open_router", 131000, 4096
@@ -286,9 +287,6 @@ MODEL_METADATA = {
     LlmModel.META_LLAMA_4_SCOUT: ModelMetadata("open_router", 131072, 131072),
     LlmModel.META_LLAMA_4_MAVERICK: ModelMetadata("open_router", 1048576, 1000000),
     LlmModel.GROK_4: ModelMetadata("open_router", 256000, 256000),
-    LlmModel.GROK_4_FAST: ModelMetadata("open_router", 2000000, 30000),
-    LlmModel.GROK_4_1_FAST: ModelMetadata("open_router", 2000000, 30000),
-    LlmModel.GROK_CODE_FAST_1: ModelMetadata("open_router", 256000, 10000),
     LlmModel.KIMI_K2: ModelMetadata("open_router", 131000, 131000),
     LlmModel.QWEN3_235B_A22B_THINKING: ModelMetadata("open_router", 262144, 262144),
     LlmModel.QWEN3_CODER: ModelMetadata("open_router", 262144, 262144),
@@ -811,7 +809,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             default="",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[dict] | None = SchemaField(
+        conversation_history: list[dict] = SchemaField(
             default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
@@ -918,7 +916,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         logger.debug(f"Calling LLM with input data: {input_data}")
-        prompt = [json.to_dict(p) for p in input_data.conversation_history or [] if p]
+        prompt = [json.to_dict(p) for p in input_data.conversation_history]
 
         values = input_data.prompt_values
         if values:
@@ -1646,17 +1644,6 @@ class AIListGeneratorBlock(AIBlockBase):
             ge=1,
             le=5,
         )
-        force_json_output: bool = SchemaField(
-            title="Restrict LLM to pure JSON output",
-            default=False,
-            description=(
-                "Whether to force the LLM to produce a JSON-only response. "
-                "This can increase the block's reliability, "
-                "but may also reduce the quality of the response "
-                "because it prohibits the LLM from reasoning "
-                "before providing its JSON response."
-            ),
-        )
         max_tokens: int | None = SchemaField(
             advanced=True,
             default=None,
@@ -1669,7 +1656,7 @@ class AIListGeneratorBlock(AIBlockBase):
         )
 
     class Output(BlockSchemaOutput):
-        generated_list: list[str] = SchemaField(description="The generated list.")
+        generated_list: List[str] = SchemaField(description="The generated list.")
         list_item: str = SchemaField(
             description="Each individual item in the list.",
         )
@@ -1678,7 +1665,7 @@ class AIListGeneratorBlock(AIBlockBase):
     def __init__(self):
         super().__init__(
             id="9c0b0450-d199-458b-a731-072189dd6593",
-            description="Generate a list of values based on the given prompt using a Large Language Model (LLM).",
+            description="Generate a Python list based on the given prompt using a Large Language Model (LLM).",
             categories={BlockCategory.AI, BlockCategory.TEXT},
             input_schema=AIListGeneratorBlock.Input,
             output_schema=AIListGeneratorBlock.Output,
@@ -1695,7 +1682,6 @@ class AIListGeneratorBlock(AIBlockBase):
                 "model": LlmModel.GPT4O,
                 "credentials": TEST_CREDENTIALS_INPUT,
                 "max_retries": 3,
-                "force_json_output": False,
             },
             test_credentials=TEST_CREDENTIALS,
             test_output=[
@@ -1712,13 +1698,7 @@ class AIListGeneratorBlock(AIBlockBase):
             ],
             test_mock={
                 "llm_call": lambda input_data, credentials: {
-                    "list": [
-                        "Zylora Prime",
-                        "Kharon-9",
-                        "Vortexia",
-                        "Oceara",
-                        "Draknos",
-                    ]
+                    "response": "['Zylora Prime', 'Kharon-9', 'Vortexia', 'Oceara', 'Draknos']"
                 },
             },
         )
@@ -1727,7 +1707,7 @@ class AIListGeneratorBlock(AIBlockBase):
         self,
         input_data: AIStructuredResponseGeneratorBlock.Input,
         credentials: APIKeyCredentials,
-    ) -> dict[str, Any]:
+    ) -> dict[str, str]:
         llm_block = AIStructuredResponseGeneratorBlock()
         response = await llm_block.run_once(
             input_data, "response", credentials=credentials
@@ -1735,23 +1715,71 @@ class AIListGeneratorBlock(AIBlockBase):
         self.merge_llm_stats(llm_block)
         return response
 
+    @staticmethod
+    def string_to_list(string):
+        """
+        Converts a string representation of a list into an actual Python list object.
+        """
+        logger.debug(f"Converting string to list. Input string: {string}")
+        try:
+            # Use ast.literal_eval to safely evaluate the string
+            python_list = ast.literal_eval(string)
+            if isinstance(python_list, list):
+                logger.debug(f"Successfully converted string to list: {python_list}")
+                return python_list
+            else:
+                logger.error(f"The provided string '{string}' is not a valid list")
+                raise ValueError(f"The provided string '{string}' is not a valid list.")
+        except (SyntaxError, ValueError) as e:
+            logger.error(f"Failed to convert string to list: {e}")
+            raise ValueError("Invalid list format. Could not convert to list.")
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         logger.debug(f"Starting AIListGeneratorBlock.run with input data: {input_data}")
 
-        # Create a proper expected format for the structured response generator
-        expected_format = {
-            "list": "A JSON array containing the generated string values"
-        }
-        if input_data.force_json_output:
-            # Add reasoning field for better performance
-            expected_format = {
-                "reasoning": "... (optional)",
-                **expected_format,
-            }
+        # Check for API key
+        api_key_check = credentials.api_key.get_secret_value()
+        if not api_key_check:
+            raise ValueError("No LLM API key provided.")
 
-        # Build the prompt
+        # Prepare the system prompt
+        sys_prompt = """You are a Python list generator. Your task is to generate a Python list based on the user's prompt. 
+            |Respond ONLY with a valid python list. 
+            |The list can contain strings, numbers, or nested lists as appropriate. 
+            |Do not include any explanations or additional text.
+
+            |Valid Example string formats:
+
+            |Example 1:
+            |```
+            |['1', '2', '3', '4']
+            |```
+
+            |Example 2:
+            |```
+            |[['1', '2'], ['3', '4'], ['5', '6']]
+            |```
+
+            |Example 3:
+            |```
+            |['1', ['2', '3'], ['4', ['5', '6']]]
+            |```
+
+            |Example 4:
+            |```
+            |['a', 'b', 'c']
+            |```
+
+            |Example 5:
+            |```
+            |['1', '2.5', 'string', 'True', ['False', 'None']]
+            |```
+
+            |Do not include any explanations or additional text, just respond with the list in the format specified above.
+            """
+        # If a focus is provided, add it to the prompt
         if input_data.focus:
             prompt = f"Generate a list with the following focus:\n<focus>\n\n{input_data.focus}</focus>"
         else:
@@ -1759,7 +1787,7 @@ class AIListGeneratorBlock(AIBlockBase):
             if input_data.source_data:
                 prompt = "Extract the main focus of the source data to a list.\ni.e if the source data is a news website, the focus would be the news stories rather than the social links in the footer."
             else:
-                # No focus or source data provided, generate a random list
+                # No focus or source data provided, generat a random list
                 prompt = "Generate a random list."
 
         # If the source data is provided, add it to the prompt
@@ -1769,56 +1797,63 @@ class AIListGeneratorBlock(AIBlockBase):
         else:
             prompt += "\n\nInvent the data to generate the list from."
 
-        # Use the structured response generator to handle all the complexity
-        response_obj = await self.llm_call(
-            AIStructuredResponseGeneratorBlock.Input(
-                sys_prompt=self.SYSTEM_PROMPT,
-                prompt=prompt,
-                credentials=input_data.credentials,
-                model=input_data.model,
-                expected_format=expected_format,
-                force_json_output=input_data.force_json_output,
-                retry=input_data.max_retries,
-                max_tokens=input_data.max_tokens,
-                ollama_host=input_data.ollama_host,
-            ),
-            credentials=credentials,
-        )
-        logger.debug(f"Response object: {response_obj}")
+        for attempt in range(input_data.max_retries):
+            try:
+                logger.debug("Calling LLM")
+                llm_response = await self.llm_call(
+                    AIStructuredResponseGeneratorBlock.Input(
+                        sys_prompt=sys_prompt,
+                        prompt=prompt,
+                        credentials=input_data.credentials,
+                        model=input_data.model,
+                        expected_format={},  # Do not use structured response
+                        ollama_host=input_data.ollama_host,
+                    ),
+                    credentials=credentials,
+                )
 
-        # Extract the list from the response object
-        if isinstance(response_obj, dict) and "list" in response_obj:
-            parsed_list = response_obj["list"]
-        else:
-            # Fallback - treat the whole response as the list
-            parsed_list = response_obj
+                logger.debug(f"LLM response: {llm_response}")
 
-        # Validate that we got a list
-        if not isinstance(parsed_list, list):
-            raise ValueError(
-                f"Expected a list, but got {type(parsed_list).__name__}: {parsed_list}"
-            )
+                # Extract Response string
+                response_string = llm_response["response"]
+                logger.debug(f"Response string: {response_string}")
 
-        logger.debug(f"Parsed list: {parsed_list}")
+                # Convert the string to a Python list
+                logger.debug("Converting string to Python list")
+                parsed_list = self.string_to_list(response_string)
+                logger.debug(f"Parsed list: {parsed_list}")
 
-        # Yield the results
-        yield "generated_list", parsed_list
-        yield "prompt", self.prompt
+                # If we reach here, we have a valid Python list
+                logger.debug("Successfully generated a valid Python list")
+                yield "generated_list", parsed_list
+                yield "prompt", self.prompt
 
-        # Yield each item in the list
-        for item in parsed_list:
-            yield "list_item", item
+                # Yield each item in the list
+                for item in parsed_list:
+                    yield "list_item", item
+                return
 
-    SYSTEM_PROMPT = trim_prompt(
-        """
-        |You are a JSON array generator. Your task is to generate a JSON array of string values based on the user's prompt.
-        |
-        |The 'list' field should contain a JSON array with the generated string values.
-        |The array can contain ONLY strings.
-        |
-        |Valid JSON array formats include:
-        |• ["string1", "string2", "string3"]
-        |
-        |Ensure you provide a proper JSON array with only string values in the 'list' field.
-        """
-    )
+            except Exception as e:
+                logger.error(f"Error in attempt {attempt + 1}: {str(e)}")
+                if attempt == input_data.max_retries - 1:
+                    logger.error(
+                        f"Failed to generate a valid Python list after {input_data.max_retries} attempts"
+                    )
+                    raise RuntimeError(
+                        f"Failed to generate a valid Python list after {input_data.max_retries} attempts. Last error: {str(e)}"
+                    )
+                else:
+                    # Add a retry prompt
+                    logger.debug("Preparing retry prompt")
+                    prompt = f"""
+                    The previous attempt failed due to `{e}`
+                    Generate a valid Python list based on the original prompt.
+                    Remember to respond ONLY with a valid Python list as per the format specified earlier.
+                    Original prompt: 
+                    ```{prompt}```
+                    
+                    Respond only with the list in the format specified with no commentary or apologies.
+                    """
+                    logger.debug(f"Retry prompt: {prompt}")
+
+        logger.debug("AIListGeneratorBlock.run completed")

autogpt_platform/backend/backend/blocks/reddit.py

@@ -1,4 +1,3 @@
-import logging
 from datetime import datetime, timezone
 from typing import Iterator, Literal
 
@@ -65,7 +64,6 @@ class RedditComment(BaseModel):
 
 
 settings = Settings()
-logger = logging.getLogger(__name__)
 
 
 def get_praw(creds: RedditCredentials) -> praw.Reddit:
@@ -79,7 +77,7 @@ def get_praw(creds: RedditCredentials) -> praw.Reddit:
     me = client.user.me()
     if not me:
         raise ValueError("Invalid Reddit credentials.")
-    logger.info(f"Logged in as Reddit user: {me.name}")
+    print(f"Logged in as Reddit user: {me.name}")
     return client
 
 

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -1,11 +1,8 @@
 import logging
 import re
 from collections import Counter
-from concurrent.futures import Future
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel
-
 import backend.blocks.llm as llm
 from backend.blocks.agent import AgentExecutorBlock
 from backend.data.block import (
@@ -21,43 +18,17 @@ from backend.data.dynamic_fields import (
     extract_base_field_name,
     get_dynamic_field_description,
     is_dynamic_field,
-    is_tool_pin,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
-from backend.util.prompt import MAIN_OBJECTIVE_PREFIX
 
 if TYPE_CHECKING:
     from backend.data.graph import Link, Node
-    from backend.executor.manager import ExecutionProcessor
 
 logger = logging.getLogger(__name__)
 
 
-class ToolInfo(BaseModel):
-    """Processed tool call information."""
-
-    tool_call: Any  # The original tool call object from LLM response
-    tool_name: str  # The function name
-    tool_def: dict[str, Any]  # The tool definition from tool_functions
-    input_data: dict[str, Any]  # Processed input data ready for tool execution
-    field_mapping: dict[str, str]  # Field name mapping for the tool
-
-
-class ExecutionParams(BaseModel):
-    """Tool execution parameters."""
-
-    user_id: str
-    graph_id: str
-    node_id: str
-    graph_version: int
-    graph_exec_id: str
-    node_exec_id: str
-    execution_context: "ExecutionContext"
-
-
 def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
     """
     Return a list of tool_call_ids if the entry is a tool request.
@@ -133,50 +104,6 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
-def _combine_tool_responses(tool_outputs: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """
-    Combine multiple Anthropic tool responses into a single user message.
-    For non-Anthropic formats, returns the original list unchanged.
-    """
-    if len(tool_outputs) <= 1:
-        return tool_outputs
-
-    # Anthropic responses have role="user", type="message", and content is a list with tool_result items
-    anthropic_responses = [
-        output
-        for output in tool_outputs
-        if (
-            output.get("role") == "user"
-            and output.get("type") == "message"
-            and isinstance(output.get("content"), list)
-            and any(
-                item.get("type") == "tool_result"
-                for item in output.get("content", [])
-                if isinstance(item, dict)
-            )
-        )
-    ]
-
-    if len(anthropic_responses) > 1:
-        combined_content = [
-            item for response in anthropic_responses for item in response["content"]
-        ]
-
-        combined_response = {
-            "role": "user",
-            "type": "message",
-            "content": combined_content,
-        }
-
-        non_anthropic_responses = [
-            output for output in tool_outputs if output not in anthropic_responses
-        ]
-
-        return [combined_response] + non_anthropic_responses
-
-    return tool_outputs
-
-
 def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
     """
     Safely convert raw_response to dictionary format for conversation history.
@@ -193,16 +120,13 @@ def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
         return json.to_dict(raw_response)
 
 
-def get_pending_tool_calls(conversation_history: list[Any] | None) -> dict[str, int]:
+def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
     """
     All the tool calls entry in the conversation history requires a response.
     This function returns the pending tool calls that has not generated an output yet.
 
     Return: dict[str, int] - A dictionary of pending tool call IDs with their count.
     """
-    if not conversation_history:
-        return {}
-
     pending_calls = Counter()
     for history in conversation_history:
         for call_id in _get_tool_requests(history):
@@ -248,7 +172,7 @@ class SmartDecisionMakerBlock(Block):
             "Function parameters that has no default value and not optional typed has to be provided. ",
             description="The system prompt to provide additional context to the model.",
         )
-        conversation_history: list[dict] | None = SchemaField(
+        conversation_history: list[dict] = SchemaField(
             default_factory=list,
             description="The conversation history to provide context for the prompt.",
         )
@@ -276,17 +200,6 @@ class SmartDecisionMakerBlock(Block):
             default="localhost:11434",
             description="Ollama host for local  models",
         )
-        agent_mode_max_iterations: int = SchemaField(
-            title="Agent Mode Max Iterations",
-            description="Maximum iterations for agent mode. 0 = traditional mode (single LLM call, yield tool calls for external execution), -1 = infinite agent mode (loop until finished), 1+ = agent mode with max iterations limit.",
-            advanced=True,
-            default=0,
-        )
-        conversation_compaction: bool = SchemaField(
-            default=True,
-            title="Context window auto-compaction",
-            description="Automatically compact the context window once it hits the limit",
-        )
 
         @classmethod
         def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
@@ -454,9 +367,8 @@ class SmartDecisionMakerBlock(Block):
             "required": sorted(required_fields),
         }
 
-        # Store field mapping and node info for later use in output processing
+        # Store field mapping for later use in output processing
         tool_function["_field_mapping"] = field_mapping
-        tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
 
@@ -519,13 +431,10 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        # Store node info for later use in output processing
-        tool_function["_sink_node_id"] = sink_node.id
-
         return {"type": "function", "function": tool_function}
 
     @staticmethod
-    async def _create_tool_node_signatures(
+    async def _create_function_signature(
         node_id: str,
     ) -> list[dict[str, Any]]:
         """
@@ -541,7 +450,7 @@ class SmartDecisionMakerBlock(Block):
         tools = [
             (link, node)
             for link, node in await db_client.get_connected_output_nodes(node_id)
-            if is_tool_pin(link.source_name) and link.source_id == node_id
+            if link.source_name.startswith("tools_^_") and link.source_id == node_id
         ]
         if not tools:
             raise ValueError("There is no next node to execute.")
@@ -589,7 +498,6 @@ class SmartDecisionMakerBlock(Block):
         Returns the response if successful, raises ValueError if validation fails.
         """
         resp = await llm.llm_call(
-            compress_prompt_to_fit=input_data.conversation_compaction,
             credentials=credentials,
             llm_model=input_data.model,
             prompt=current_prompt,
@@ -630,14 +538,8 @@ class SmartDecisionMakerBlock(Block):
                 ),
                 None,
             )
-            if tool_def is None:
-                if len(tool_functions) == 1:
-                    tool_def = tool_functions[0]
-                else:
-                    validation_errors_list.append(
-                        f"Tool call for '{tool_name}' does not match any known "
-                        "tool definition."
-                    )
+            if tool_def is None and len(tool_functions) == 1:
+                tool_def = tool_functions[0]
 
             # Get parameters schema from tool definition
             if (
@@ -677,291 +579,6 @@ class SmartDecisionMakerBlock(Block):
 
         return resp
 
-    def _process_tool_calls(
-        self, response, tool_functions: list[dict[str, Any]]
-    ) -> list[ToolInfo]:
-        """Process tool calls and extract tool definitions, arguments, and input data.
-
-        Returns a list of tool info dicts with:
-        - tool_call: The original tool call object
-        - tool_name: The function name
-        - tool_def: The tool definition from tool_functions
-        - input_data: Processed input data dict (includes None values)
-        - field_mapping: Field name mapping for the tool
-        """
-        if not response.tool_calls:
-            return []
-
-        processed_tools = []
-        for tool_call in response.tool_calls:
-            tool_name = tool_call.function.name
-            tool_args = json.loads(tool_call.function.arguments)
-
-            tool_def = next(
-                (
-                    tool
-                    for tool in tool_functions
-                    if tool["function"]["name"] == tool_name
-                ),
-                None,
-            )
-            if not tool_def:
-                if len(tool_functions) == 1:
-                    tool_def = tool_functions[0]
-                else:
-                    continue
-
-            # Build input data for the tool
-            input_data = {}
-            field_mapping = tool_def["function"].get("_field_mapping", {})
-            if "function" in tool_def and "parameters" in tool_def["function"]:
-                expected_args = tool_def["function"]["parameters"].get("properties", {})
-                for clean_arg_name in expected_args:
-                    original_field_name = field_mapping.get(
-                        clean_arg_name, clean_arg_name
-                    )
-                    arg_value = tool_args.get(clean_arg_name)
-                    # Include all expected parameters, even if None (for backward compatibility with tests)
-                    input_data[original_field_name] = arg_value
-
-            processed_tools.append(
-                ToolInfo(
-                    tool_call=tool_call,
-                    tool_name=tool_name,
-                    tool_def=tool_def,
-                    input_data=input_data,
-                    field_mapping=field_mapping,
-                )
-            )
-
-        return processed_tools
-
-    def _update_conversation(
-        self, prompt: list[dict], response, tool_outputs: list | None = None
-    ):
-        """Update conversation history with response and tool outputs."""
-        # Don't add separate reasoning message with tool calls (breaks Anthropic's tool_use->tool_result pairing)
-        assistant_message = _convert_raw_response_to_dict(response.raw_response)
-        has_tool_calls = isinstance(assistant_message.get("content"), list) and any(
-            item.get("type") == "tool_use"
-            for item in assistant_message.get("content", [])
-        )
-
-        if response.reasoning and not has_tool_calls:
-            prompt.append(
-                {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
-            )
-
-        prompt.append(assistant_message)
-
-        if tool_outputs:
-            prompt.extend(tool_outputs)
-
-    async def _execute_single_tool_with_manager(
-        self,
-        tool_info: ToolInfo,
-        execution_params: ExecutionParams,
-        execution_processor: "ExecutionProcessor",
-    ) -> dict:
-        """Execute a single tool using the execution manager for proper integration."""
-        # Lazy imports to avoid circular dependencies
-        from backend.data.execution import NodeExecutionEntry
-
-        tool_call = tool_info.tool_call
-        tool_def = tool_info.tool_def
-        raw_input_data = tool_info.input_data
-
-        # Get sink node and field mapping
-        sink_node_id = tool_def["function"]["_sink_node_id"]
-
-        # Use proper database operations for tool execution
-        db_client = get_database_manager_async_client()
-
-        # Get target node
-        target_node = await db_client.get_node(sink_node_id)
-        if not target_node:
-            raise ValueError(f"Target node {sink_node_id} not found")
-
-        # Create proper node execution using upsert_execution_input
-        node_exec_result = None
-        final_input_data = None
-
-        # Add all inputs to the execution
-        if not raw_input_data:
-            raise ValueError(f"Tool call has no input data: {tool_call}")
-
-        for input_name, input_value in raw_input_data.items():
-            node_exec_result, final_input_data = await db_client.upsert_execution_input(
-                node_id=sink_node_id,
-                graph_exec_id=execution_params.graph_exec_id,
-                input_name=input_name,
-                input_data=input_value,
-            )
-
-        assert node_exec_result is not None, "node_exec_result should not be None"
-
-        # Create NodeExecutionEntry for execution manager
-        node_exec_entry = NodeExecutionEntry(
-            user_id=execution_params.user_id,
-            graph_exec_id=execution_params.graph_exec_id,
-            graph_id=execution_params.graph_id,
-            graph_version=execution_params.graph_version,
-            node_exec_id=node_exec_result.node_exec_id,
-            node_id=sink_node_id,
-            block_id=target_node.block_id,
-            inputs=final_input_data or {},
-            execution_context=execution_params.execution_context,
-        )
-
-        # Use the execution manager to execute the tool node
-        try:
-            # Get NodeExecutionProgress from the execution manager's running nodes
-            node_exec_progress = execution_processor.running_node_execution[
-                sink_node_id
-            ]
-
-            # Use the execution manager's own graph stats
-            graph_stats_pair = (
-                execution_processor.execution_stats,
-                execution_processor.execution_stats_lock,
-            )
-
-            # Create a completed future for the task tracking system
-            node_exec_future = Future()
-            node_exec_progress.add_task(
-                node_exec_id=node_exec_result.node_exec_id,
-                task=node_exec_future,
-            )
-
-            # Execute the node directly since we're in the SmartDecisionMaker context
-            node_exec_future.set_result(
-                await execution_processor.on_node_execution(
-                    node_exec=node_exec_entry,
-                    node_exec_progress=node_exec_progress,
-                    nodes_input_masks=None,
-                    graph_stats_pair=graph_stats_pair,
-                )
-            )
-
-            # Get outputs from database after execution completes using database manager client
-            node_outputs = await db_client.get_execution_outputs_by_node_exec_id(
-                node_exec_result.node_exec_id
-            )
-
-            # Create tool response
-            tool_response_content = (
-                json.dumps(node_outputs)
-                if node_outputs
-                else "Tool executed successfully"
-            )
-            return _create_tool_response(tool_call.id, tool_response_content)
-
-        except Exception as e:
-            logger.error(f"Tool execution with manager failed: {e}")
-            # Return error response
-            return _create_tool_response(
-                tool_call.id, f"Tool execution failed: {str(e)}"
-            )
-
-    async def _execute_tools_agent_mode(
-        self,
-        input_data,
-        credentials,
-        tool_functions: list[dict[str, Any]],
-        prompt: list[dict],
-        graph_exec_id: str,
-        node_id: str,
-        node_exec_id: str,
-        user_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
-    ):
-        """Execute tools in agent mode with a loop until finished."""
-        max_iterations = input_data.agent_mode_max_iterations
-        iteration = 0
-
-        # Execution parameters for tool execution
-        execution_params = ExecutionParams(
-            user_id=user_id,
-            graph_id=graph_id,
-            node_id=node_id,
-            graph_version=graph_version,
-            graph_exec_id=graph_exec_id,
-            node_exec_id=node_exec_id,
-            execution_context=execution_context,
-        )
-
-        current_prompt = list(prompt)
-
-        while max_iterations < 0 or iteration < max_iterations:
-            iteration += 1
-            logger.debug(f"Agent mode iteration {iteration}")
-
-            # Prepare prompt for this iteration
-            iteration_prompt = list(current_prompt)
-
-            # On the last iteration, add a special system message to encourage completion
-            if max_iterations > 0 and iteration == max_iterations:
-                last_iteration_message = {
-                    "role": "system",
-                    "content": f"{MAIN_OBJECTIVE_PREFIX}This is your last iteration ({iteration}/{max_iterations}). "
-                    "Try to complete the task with the information you have. If you cannot fully complete it, "
-                    "provide a summary of what you've accomplished and what remains to be done. "
-                    "Prefer finishing with a clear response rather than making additional tool calls.",
-                }
-                iteration_prompt.append(last_iteration_message)
-
-            # Get LLM response
-            try:
-                response = await self._attempt_llm_call_with_validation(
-                    credentials, input_data, iteration_prompt, tool_functions
-                )
-            except Exception as e:
-                yield "error", f"LLM call failed in agent mode iteration {iteration}: {str(e)}"
-                return
-
-            # Process tool calls
-            processed_tools = self._process_tool_calls(response, tool_functions)
-
-            # If no tool calls, we're done
-            if not processed_tools:
-                yield "finished", response.response
-                self._update_conversation(current_prompt, response)
-                yield "conversations", current_prompt
-                return
-
-            # Execute tools and collect responses
-            tool_outputs = []
-            for tool_info in processed_tools:
-                try:
-                    tool_response = await self._execute_single_tool_with_manager(
-                        tool_info, execution_params, execution_processor
-                    )
-                    tool_outputs.append(tool_response)
-                except Exception as e:
-                    logger.error(f"Tool execution failed: {e}")
-                    # Create error response for the tool
-                    error_response = _create_tool_response(
-                        tool_info.tool_call.id, f"Error: {str(e)}"
-                    )
-                    tool_outputs.append(error_response)
-
-            tool_outputs = _combine_tool_responses(tool_outputs)
-
-            self._update_conversation(current_prompt, response, tool_outputs)
-
-            # Yield intermediate conversation state
-            yield "conversations", current_prompt
-
-        # If we reach max iterations, yield the current state
-        if max_iterations < 0:
-            yield "finished", f"Agent mode completed after {iteration} iterations"
-        else:
-            yield "finished", f"Agent mode completed after {max_iterations} iterations (limit reached)"
-        yield "conversations", current_prompt
-
     async def run(
         self,
         input_data: Input,
@@ -972,19 +589,15 @@ class SmartDecisionMakerBlock(Block):
         graph_exec_id: str,
         node_exec_id: str,
         user_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
         **kwargs,
     ) -> BlockOutput:
-
-        tool_functions = await self._create_tool_node_signatures(node_id)
+        tool_functions = await self._create_function_signature(node_id)
         yield "tool_functions", json.dumps(tool_functions)
 
-        conversation_history = input_data.conversation_history or []
-        prompt = [json.to_dict(p) for p in conversation_history if p]
+        input_data.conversation_history = input_data.conversation_history or []
+        prompt = [json.to_dict(p) for p in input_data.conversation_history if p]
 
-        pending_tool_calls = get_pending_tool_calls(conversation_history)
+        pending_tool_calls = get_pending_tool_calls(input_data.conversation_history)
         if pending_tool_calls and input_data.last_tool_output is None:
             raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
 
@@ -1021,52 +634,24 @@ class SmartDecisionMakerBlock(Block):
             input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
             input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
 
+        prefix = "[Main Objective Prompt]: "
+
         if input_data.sys_prompt and not any(
-            p["role"] == "system" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {
-                    "role": "system",
-                    "content": MAIN_OBJECTIVE_PREFIX + input_data.sys_prompt,
-                }
-            )
+            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
 
         if input_data.prompt and not any(
-            p["role"] == "user" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {"role": "user", "content": MAIN_OBJECTIVE_PREFIX + input_data.prompt}
-            )
+            prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        # Execute tools based on the selected mode
-        if input_data.agent_mode_max_iterations != 0:
-            # In agent mode, execute tools directly in a loop until finished
-            async for result in self._execute_tools_agent_mode(
-                input_data=input_data,
-                credentials=credentials,
-                tool_functions=tool_functions,
-                prompt=prompt,
-                graph_exec_id=graph_exec_id,
-                node_id=node_id,
-                node_exec_id=node_exec_id,
-                user_id=user_id,
-                graph_id=graph_id,
-                graph_version=graph_version,
-                execution_context=execution_context,
-                execution_processor=execution_processor,
-            ):
-                yield result
-            return
-
-        # One-off mode: single LLM call and yield tool calls for external execution
         current_prompt = list(prompt)
         max_attempts = max(1, int(input_data.retry))
         response = None
 
         last_error = None
-        for _ in range(max_attempts):
+        for attempt in range(max_attempts):
             try:
                 response = await self._attempt_llm_call_with_validation(
                     credentials, input_data, current_prompt, tool_functions
@@ -1076,9 +661,9 @@ class SmartDecisionMakerBlock(Block):
             except ValueError as e:
                 last_error = e
                 error_feedback = (
-                    "Your tool call had errors. Please fix the following issues and try again:\n"
+                    "Your tool call had parameter errors. Please fix the following issues and try again:\n"
                     + f"- {str(e)}\n"
-                    + "\nPlease make sure to use the exact tool and parameter names as specified in the function schema."
+                    + "\nPlease make sure to use the exact parameter names as specified in the function schema."
                 )
                 current_prompt = list(current_prompt) + [
                     {"role": "user", "content": error_feedback}
@@ -1105,23 +690,21 @@ class SmartDecisionMakerBlock(Block):
                 ),
                 None,
             )
-            if not tool_def:
-                # NOTE: This matches the logic in _attempt_llm_call_with_validation and
-                # relies on its validation for the assumption that this is valid to use.
-                if len(tool_functions) == 1:
-                    tool_def = tool_functions[0]
-                else:
-                    # This should not happen due to prior validation
-                    continue
-
-            if "function" in tool_def and "parameters" in tool_def["function"]:
+            if (
+                tool_def
+                and "function" in tool_def
+                and "parameters" in tool_def["function"]
+            ):
                 expected_args = tool_def["function"]["parameters"].get("properties", {})
             else:
                 expected_args = {arg: {} for arg in tool_args.keys()}
 
-            # Get the sink node ID and field mapping from tool definition
-            field_mapping = tool_def["function"].get("_field_mapping", {})
-            sink_node_id = tool_def["function"]["_sink_node_id"]
+            # Get field mapping from tool definition
+            field_mapping = (
+                tool_def.get("function", {}).get("_field_mapping", {})
+                if tool_def
+                else {}
+            )
 
             for clean_arg_name in expected_args:
                 # arg_name is now always the cleaned field name (for Anthropic API compliance)
@@ -1129,8 +712,9 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_arg_name)
 
+                sanitized_tool_name = self.cleanup(tool_name)
                 sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
+                emit_key = f"tools_^_{sanitized_tool_name}_~_{sanitized_arg_name}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

autogpt_platform/backend/backend/blocks/test/test_block.py

@@ -1,27 +1,17 @@
-from typing import Any, Type
+from typing import Type
 
 import pytest
 
-from backend.data.block import Block, BlockSchemaInput, get_blocks
-from backend.data.model import SchemaField
+from backend.data.block import Block, get_blocks
 from backend.util.test import execute_block_test
 
-SKIP_BLOCK_TESTS = {
-    "HumanInTheLoopBlock",
-}
 
-
-@pytest.mark.parametrize("block", get_blocks().values(), ids=lambda b: b().name)
+@pytest.mark.parametrize("block", get_blocks().values(), ids=lambda b: b.name)
 async def test_available_blocks(block: Type[Block]):
-    block_instance = block()
-    if block_instance.__class__.__name__ in SKIP_BLOCK_TESTS:
-        pytest.skip(
-            f"Skipping {block_instance.__class__.__name__} - requires external service"
-        )
-    await execute_block_test(block_instance)
+    await execute_block_test(block())
 
 
-@pytest.mark.parametrize("block", get_blocks().values(), ids=lambda b: b().name)
+@pytest.mark.parametrize("block", get_blocks().values(), ids=lambda b: b.name)
 async def test_block_ids_valid(block: Type[Block]):
     # add the tests here to check they are uuid4
     import uuid
@@ -133,148 +123,3 @@ async def test_block_ids_valid(block: Type[Block]):
         ), f"Block {block.name} ID is UUID version {parsed_uuid.version}, expected version 4"
     except ValueError:
         pytest.fail(f"Block {block.name} has invalid UUID format: {block_instance.id}")
-
-
-class TestAutoCredentialsFieldsValidation:
-    """Tests for auto_credentials field validation in BlockSchema."""
-
-    def test_duplicate_auto_credentials_kwarg_name_raises_error(self):
-        """Test that duplicate kwarg_name in auto_credentials raises ValueError."""
-
-        class DuplicateKwargSchema(BlockSchemaInput):
-            """Schema with duplicate auto_credentials kwarg_name."""
-
-            # Both fields explicitly use the same kwarg_name "credentials"
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "credentials",
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "credentials",  # Duplicate kwarg_name!
-                    }
-                },
-            )
-
-        with pytest.raises(ValueError) as exc_info:
-            DuplicateKwargSchema.get_auto_credentials_fields()
-
-        error_message = str(exc_info.value)
-        assert "Duplicate auto_credentials kwarg_name 'credentials'" in error_message
-        assert "file1" in error_message
-        assert "file2" in error_message
-
-    def test_unique_auto_credentials_kwarg_names_succeed(self):
-        """Test that unique kwarg_name values work correctly."""
-
-        class UniqueKwargSchema(BlockSchemaInput):
-            """Schema with unique auto_credentials kwarg_name values."""
-
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "file1_credentials",
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        "kwarg_name": "file2_credentials",  # Different kwarg_name
-                    }
-                },
-            )
-
-        # Should not raise
-        result = UniqueKwargSchema.get_auto_credentials_fields()
-
-        assert "file1_credentials" in result
-        assert "file2_credentials" in result
-        assert result["file1_credentials"]["field_name"] == "file1"
-        assert result["file2_credentials"]["field_name"] == "file2"
-
-    def test_default_kwarg_name_is_credentials(self):
-        """Test that missing kwarg_name defaults to 'credentials'."""
-
-        class DefaultKwargSchema(BlockSchemaInput):
-            """Schema with auto_credentials missing kwarg_name."""
-
-            file: dict[str, Any] | None = SchemaField(
-                description="File input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name specified - should default to "credentials"
-                    }
-                },
-            )
-
-        result = DefaultKwargSchema.get_auto_credentials_fields()
-
-        assert "credentials" in result
-        assert result["credentials"]["field_name"] == "file"
-
-    def test_duplicate_default_kwarg_name_raises_error(self):
-        """Test that two fields with default kwarg_name raises ValueError."""
-
-        class DefaultDuplicateSchema(BlockSchemaInput):
-            """Schema where both fields omit kwarg_name, defaulting to 'credentials'."""
-
-            file1: dict[str, Any] | None = SchemaField(
-                description="First file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name - defaults to "credentials"
-                    }
-                },
-            )
-            file2: dict[str, Any] | None = SchemaField(
-                description="Second file input",
-                default=None,
-                json_schema_extra={
-                    "auto_credentials": {
-                        "provider": "google",
-                        "type": "oauth2",
-                        "scopes": ["https://www.googleapis.com/auth/drive.file"],
-                        # No kwarg_name - also defaults to "credentials"
-                    }
-                },
-            )
-
-        with pytest.raises(ValueError) as exc_info:
-            DefaultDuplicateSchema.get_auto_credentials_fields()
-
-        assert "Duplicate auto_credentials kwarg_name 'credentials'" in str(
-            exc_info.value
-        )

autogpt_platform/backend/backend/blocks/test/test_llm.py

@@ -365,22 +365,37 @@ class TestLLMStatsTracking:
         assert outputs["response"] == "AI response to conversation"
 
     @pytest.mark.asyncio
-    async def test_ai_list_generator_basic_functionality(self):
-        """Test that AIListGeneratorBlock correctly works with structured responses."""
+    async def test_ai_list_generator_with_retries(self):
+        """Test that AIListGeneratorBlock correctly tracks stats with retries."""
         import backend.blocks.llm as llm
 
         block = llm.AIListGeneratorBlock()
 
-        # Mock the llm_call to return a structured response
+        # Counter to track calls
+        call_count = 0
+
         async def mock_llm_call(input_data, credentials):
-            # Update stats to simulate LLM call
-            block.execution_stats = NodeExecutionStats(
-                input_token_count=50,
-                output_token_count=30,
-                llm_call_count=1,
-            )
-            # Return a structured response with the expected format
-            return {"list": ["item1", "item2", "item3"]}
+            nonlocal call_count
+            call_count += 1
+
+            # Update stats
+            if hasattr(block, "execution_stats") and block.execution_stats:
+                block.execution_stats.input_token_count += 40
+                block.execution_stats.output_token_count += 20
+                block.execution_stats.llm_call_count += 1
+            else:
+                block.execution_stats = NodeExecutionStats(
+                    input_token_count=40,
+                    output_token_count=20,
+                    llm_call_count=1,
+                )
+
+            if call_count == 1:
+                # First call returns invalid format
+                return {"response": "not a valid list"}
+            else:
+                # Second call returns valid list
+                return {"response": "['item1', 'item2', 'item3']"}
 
         block.llm_call = mock_llm_call  # type: ignore
 
@@ -398,20 +413,14 @@ class TestLLMStatsTracking:
         ):
             outputs[output_name] = output_data
 
-        # Check stats
-        assert block.execution_stats.input_token_count == 50
-        assert block.execution_stats.output_token_count == 30
-        assert block.execution_stats.llm_call_count == 1
+        # Check stats - should have 2 calls
+        assert call_count == 2
+        assert block.execution_stats.input_token_count == 80  # 40 * 2
+        assert block.execution_stats.output_token_count == 40  # 20 * 2
+        assert block.execution_stats.llm_call_count == 2
 
         # Check output
         assert outputs["generated_list"] == ["item1", "item2", "item3"]
-        # Check that individual items were yielded
-        # Note: outputs dict will only contain the last value for each key
-        # So we need to check that the list_item output exists
-        assert "list_item" in outputs
-        # The list_item output should be the last item in the list
-        assert outputs["list_item"] == "item3"
-        assert "prompt" in outputs
 
     @pytest.mark.asyncio
     async def test_merge_llm_stats(self):

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1,11 +1,7 @@
 import logging
-import threading
-from collections import defaultdict
-from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.data.execution import ExecutionContext
 from backend.data.model import ProviderName, User
 from backend.server.model import CreateGraph
 from backend.server.rest_api import AgentServer
@@ -21,10 +17,10 @@ async def create_graph(s: SpinTestServer, g, u: User):
 
 
 async def create_credentials(s: SpinTestServer, u: User):
-    import backend.blocks.llm as llm_module
+    import backend.blocks.llm as llm
 
     provider = ProviderName.OPENAI
-    credentials = llm_module.TEST_CREDENTIALS
+    credentials = llm.TEST_CREDENTIALS
     return await s.agent_server.test_create_credentials(u.id, provider, credentials)
 
 
@@ -169,7 +165,7 @@ async def test_smart_decision_maker_function_signature(server: SpinTestServer):
     )
     test_graph = await create_graph(server, test_graph, test_user)
 
-    tool_functions = await SmartDecisionMakerBlock._create_tool_node_signatures(
+    tool_functions = await SmartDecisionMakerBlock._create_function_signature(
         test_graph.nodes[0].id
     )
     assert tool_functions is not None, "Tool functions should not be None"
@@ -200,6 +196,8 @@ async def test_smart_decision_maker_function_signature(server: SpinTestServer):
 @pytest.mark.asyncio
 async def test_smart_decision_maker_tracks_llm_stats():
     """Test that SmartDecisionMakerBlock correctly tracks LLM usage stats."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -217,7 +215,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
         "content": "I need to think about this.",
     }
 
-    # Mock the _create_tool_node_signatures method to avoid database calls
+    # Mock the _create_function_signature method to avoid database calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call",
@@ -225,7 +224,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
         return_value=mock_response,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=[],
     ):
@@ -235,19 +234,10 @@ async def test_smart_decision_maker_tracks_llm_stats():
             prompt="Should I continue with this task?",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Execute the block
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -256,9 +246,6 @@ async def test_smart_decision_maker_tracks_llm_stats():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -276,6 +263,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_parameter_validation():
     """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -304,7 +293,6 @@ async def test_smart_decision_maker_parameter_validation():
                     },
                     "required": ["query", "max_keyword_difficulty"],
                 },
-                "_sink_node_id": "test-sink-node-id",
             },
         }
     ]
@@ -322,13 +310,15 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_with_typo.reasoning = None
     mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_with_typo,
     ) as mock_llm_call, patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=mock_tool_functions,
     ):
@@ -338,17 +328,8 @@ async def test_smart_decision_maker_parameter_validation():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,  # Set retry to 2 for testing
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError after retries due to typo'd parameter name
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -360,9 +341,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -389,13 +367,15 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_missing_required.reasoning = None
     mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_missing_required,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=mock_tool_functions,
     ):
@@ -404,17 +384,8 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError due to missing required parameter
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -426,9 +397,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -449,13 +417,15 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_valid.reasoning = None
     mock_response_valid.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_valid,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=mock_tool_functions,
     ):
@@ -464,19 +434,10 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed - optional parameter missing is OK
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -485,20 +446,17 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         # Verify tool outputs were generated correctly
-        assert "tools_^_test-sink-node-id_~_query" in outputs
-        assert outputs["tools_^_test-sink-node-id_~_query"] == "test"
-        assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
-        assert outputs["tools_^_test-sink-node-id_~_max_keyword_difficulty"] == 50
+        assert "tools_^_search_keywords_~_query" in outputs
+        assert outputs["tools_^_search_keywords_~_query"] == "test"
+        assert "tools_^_search_keywords_~_max_keyword_difficulty" in outputs
+        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
         # Optional parameter should be None when not provided
-        assert "tools_^_test-sink-node-id_~_optional_param" in outputs
-        assert outputs["tools_^_test-sink-node-id_~_optional_param"] is None
+        assert "tools_^_search_keywords_~_optional_param" in outputs
+        assert outputs["tools_^_search_keywords_~_optional_param"] is None
 
     # Test case 4: Valid tool call with ALL parameters (should succeed)
     mock_tool_call_all_params = MagicMock()
@@ -513,13 +471,15 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_all_params.reasoning = None
     mock_response_all_params.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_all_params,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=mock_tool_functions,
     ):
@@ -528,19 +488,10 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed with all parameters
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -549,21 +500,20 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         # Verify all tool outputs were generated correctly
-        assert outputs["tools_^_test-sink-node-id_~_query"] == "test"
-        assert outputs["tools_^_test-sink-node-id_~_max_keyword_difficulty"] == 50
-        assert outputs["tools_^_test-sink-node-id_~_optional_param"] == "custom_value"
+        assert outputs["tools_^_search_keywords_~_query"] == "test"
+        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
+        assert outputs["tools_^_search_keywords_~_optional_param"] == "custom_value"
 
 
 @pytest.mark.asyncio
 async def test_smart_decision_maker_raw_response_conversion():
     """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -580,7 +530,6 @@ async def test_smart_decision_maker_raw_response_conversion():
                     "properties": {"param": {"type": "string"}},
                     "required": ["param"],
                 },
-                "_sink_node_id": "test-sink-node-id",
             },
         }
     ]
@@ -633,12 +582,13 @@ async def test_smart_decision_maker_raw_response_conversion():
     )
 
     # Mock llm_call to return different responses on different calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call", new_callable=AsyncMock
     ) as mock_llm_call, patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=mock_tool_functions,
     ):
@@ -651,19 +601,10 @@ async def test_smart_decision_maker_raw_response_conversion():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed after retry, demonstrating our helper function works
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -672,15 +613,12 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         # Verify the tool output was generated successfully
-        assert "tools_^_test-sink-node-id_~_param" in outputs
-        assert outputs["tools_^_test-sink-node-id_~_param"] == "test_value"
+        assert "tools_^_test_tool_~_param" in outputs
+        assert outputs["tools_^_test_tool_~_param"] == "test_value"
 
         # Verify conversation history was properly maintained
         assert "conversations" in outputs
@@ -710,13 +648,15 @@ async def test_smart_decision_maker_raw_response_conversion():
         "I'll help you with that."  # Ollama returns string
     )
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_ollama,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=[],  # No tools for this test
     ):
@@ -724,18 +664,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Simple prompt",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -744,9 +675,6 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -766,13 +694,15 @@ async def test_smart_decision_maker_raw_response_conversion():
         "content": "Test response",
     }  # Dict format
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
         return_value=mock_response_dict,
     ), patch.object(
         SmartDecisionMakerBlock,
-        "_create_tool_node_signatures",
+        "_create_function_signature",
         new_callable=AsyncMock,
         return_value=[],
     ):
@@ -780,18 +710,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Another test",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -800,260 +721,8 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         assert "finished" in outputs
         assert outputs["finished"] == "Test response"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_agent_mode():
-    """Test that agent mode executes tools directly and loops until finished."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call that requires multiple iterations
-    mock_tool_call_1 = MagicMock()
-    mock_tool_call_1.id = "call_1"
-    mock_tool_call_1.function.name = "search_keywords"
-    mock_tool_call_1.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response_1 = MagicMock()
-    mock_response_1.response = None
-    mock_response_1.tool_calls = [mock_tool_call_1]
-    mock_response_1.prompt_tokens = 50
-    mock_response_1.completion_tokens = 25
-    mock_response_1.reasoning = "Using search tool"
-    mock_response_1.raw_response = {
-        "role": "assistant",
-        "content": None,
-        "tool_calls": [{"id": "call_1", "type": "function"}],
-    }
-
-    # Final response with no tool calls (finished)
-    mock_response_2 = MagicMock()
-    mock_response_2.response = "Task completed successfully"
-    mock_response_2.tool_calls = []
-    mock_response_2.prompt_tokens = 30
-    mock_response_2.completion_tokens = 15
-    mock_response_2.reasoning = None
-    mock_response_2.raw_response = {
-        "role": "assistant",
-        "content": "Task completed successfully",
-    }
-
-    # Mock the LLM call to return different responses on each iteration
-    llm_call_mock = AsyncMock()
-    llm_call_mock.side_effect = [mock_response_1, mock_response_2]
-
-    # Mock tool node signatures
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    # Mock database and execution components
-    mock_db_client = AsyncMock()
-    mock_node = MagicMock()
-    mock_node.block_id = "test-block-id"
-    mock_db_client.get_node.return_value = mock_node
-
-    # Mock upsert_execution_input to return proper NodeExecutionResult and input data
-    mock_node_exec_result = MagicMock()
-    mock_node_exec_result.node_exec_id = "test-tool-exec-id"
-    mock_input_data = {"query": "test", "max_keyword_difficulty": 50}
-    mock_db_client.upsert_execution_input.return_value = (
-        mock_node_exec_result,
-        mock_input_data,
-    )
-
-    # No longer need mock_execute_node since we use execution_processor.on_node_execution
-
-    with patch("backend.blocks.llm.llm_call", llm_call_mock), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ), patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ), patch(
-        "backend.executor.manager.async_update_node_execution_status",
-        new_callable=AsyncMock,
-    ), patch(
-        "backend.integrations.creds_manager.IntegrationCredentialsManager"
-    ):
-
-        # Create a mock execution context
-
-        mock_execution_context = ExecutionContext(
-            safe_mode=False,
-        )
-
-        # Create a mock execution processor for agent mode tests
-
-        mock_execution_processor = AsyncMock()
-        # Configure the execution processor mock with required attributes
-        mock_execution_processor.running_node_execution = defaultdict(MagicMock)
-        mock_execution_processor.execution_stats = MagicMock()
-        mock_execution_processor.execution_stats_lock = threading.Lock()
-
-        # Mock the on_node_execution method to return successful stats
-        mock_node_stats = MagicMock()
-        mock_node_stats.error = None  # No error
-        mock_execution_processor.on_node_execution = AsyncMock(
-            return_value=mock_node_stats
-        )
-
-        # Mock the get_execution_outputs_by_node_exec_id method
-        mock_db_client.get_execution_outputs_by_node_exec_id.return_value = {
-            "result": {"status": "success", "data": "search completed"}
-        }
-
-        # Test agent mode with max_iterations = 3
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Complete this task using tools",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=3,  # Enable agent mode with 3 max iterations
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify agent mode behavior
-        assert "tool_functions" in outputs  # tool_functions is yielded in both modes
-        assert "finished" in outputs
-        assert outputs["finished"] == "Task completed successfully"
-        assert "conversations" in outputs
-
-        # Verify the conversation includes tool responses
-        conversations = outputs["conversations"]
-        assert len(conversations) > 2  # Should have multiple conversation entries
-
-        # Verify LLM was called twice (once for tool call, once for finish)
-        assert llm_call_mock.call_count == 2
-
-        # Verify tool was executed via execution processor
-        assert mock_execution_processor.on_node_execution.call_count == 1
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_traditional_mode_default():
-    """Test that default behavior (agent_mode_max_iterations=0) works as traditional mode."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call
-    mock_tool_call = MagicMock()
-    mock_tool_call.function.name = "search_keywords"
-    mock_tool_call.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response = MagicMock()
-    mock_response.response = None
-    mock_response.tool_calls = [mock_tool_call]
-    mock_response.prompt_tokens = 50
-    mock_response.completion_tokens = 25
-    mock_response.reasoning = None
-    mock_response.raw_response = {"role": "assistant", "content": None}
-
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response,
-    ), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ):
-
-        # Test default behavior (traditional mode)
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Test prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,  # Traditional mode
-        )
-
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify traditional mode behavior
-        assert (
-            "tool_functions" in outputs
-        )  # Should yield tool_functions in traditional mode
-        assert (
-            "tools_^_test-sink-node-id_~_query" in outputs
-        )  # Should yield individual tool parameters
-        assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
-        assert "conversations" in outputs

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -1,7 +1,7 @@
 """Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
 
 import json
-from unittest.mock import AsyncMock, MagicMock, Mock, patch
+from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
@@ -192,7 +192,7 @@ async def test_create_block_function_signature_with_object_fields():
 
 
 @pytest.mark.asyncio
-async def test_create_tool_node_signatures():
+async def test_create_function_signature():
     """Test that the mapping between sanitized and original field names is built correctly."""
     block = SmartDecisionMakerBlock()
 
@@ -241,7 +241,7 @@ async def test_create_tool_node_signatures():
         ]
 
         # Call the method that builds signatures
-        tool_functions = await block._create_tool_node_signatures("test_node_id")
+        tool_functions = await block._create_function_signature("test_node_id")
 
         # Verify we got 2 tool functions (one for dict, one for list)
         assert len(tool_functions) == 2
@@ -308,47 +308,10 @@ async def test_output_yielding_with_dynamic_fields():
     ) as mock_llm:
         mock_llm.return_value = mock_response
 
-        # Mock the database manager to avoid HTTP calls during tool execution
-        with patch(
-            "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-        ) as mock_db_manager, patch.object(
-            block, "_create_tool_node_signatures", new_callable=AsyncMock
+        # Mock the function signature creation
+        with patch.object(
+            block, "_create_function_signature", new_callable=AsyncMock
         ) as mock_sig:
-            # Set up the mock database manager
-            mock_db_client = AsyncMock()
-            mock_db_manager.return_value = mock_db_client
-
-            # Mock the node retrieval
-            mock_target_node = Mock()
-            mock_target_node.id = "test-sink-node-id"
-            mock_target_node.block_id = "CreateDictionaryBlock"
-            mock_target_node.block = Mock()
-            mock_target_node.block.name = "Create Dictionary"
-            mock_db_client.get_node.return_value = mock_target_node
-
-            # Mock the execution result creation
-            mock_node_exec_result = Mock()
-            mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-            mock_final_input_data = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.upsert_execution_input.return_value = (
-                mock_node_exec_result,
-                mock_final_input_data,
-            )
-
-            # Mock the output retrieval
-            mock_outputs = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                mock_outputs
-            )
-
             mock_sig.return_value = [
                 {
                     "type": "function",
@@ -362,7 +325,6 @@ async def test_output_yielding_with_dynamic_fields():
                                 "values___email": {"type": "string"},
                             },
                         },
-                        "_sink_node_id": "test-sink-node-id",
                     },
                 }
             ]
@@ -374,16 +336,10 @@ async def test_output_yielding_with_dynamic_fields():
                 prompt="Create a user dictionary",
                 credentials=llm.TEST_CREDENTIALS_INPUT,
                 model=llm.LlmModel.GPT4O,
-                agent_mode_max_iterations=0,  # Use traditional mode to test output yielding
             )
 
             # Run the block
             outputs = {}
-            from backend.data.execution import ExecutionContext
-
-            mock_execution_context = ExecutionContext(safe_mode=False)
-            mock_execution_processor = MagicMock()
-
             async for output_name, output_value in block.run(
                 input_data,
                 credentials=llm.TEST_CREDENTIALS,
@@ -392,22 +348,19 @@ async def test_output_yielding_with_dynamic_fields():
                 graph_exec_id="test_exec",
                 node_exec_id="test_node_exec",
                 user_id="test_user",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_value
 
-            # Verify the outputs use sink node ID in output keys
-            assert "tools_^_test-sink-node-id_~_values___name" in outputs
-            assert outputs["tools_^_test-sink-node-id_~_values___name"] == "Alice"
+            # Verify the outputs use sanitized field names (matching frontend normalizeToolName)
+            assert "tools_^_createdictionaryblock_~_values___name" in outputs
+            assert outputs["tools_^_createdictionaryblock_~_values___name"] == "Alice"
 
-            assert "tools_^_test-sink-node-id_~_values___age" in outputs
-            assert outputs["tools_^_test-sink-node-id_~_values___age"] == 30
+            assert "tools_^_createdictionaryblock_~_values___age" in outputs
+            assert outputs["tools_^_createdictionaryblock_~_values___age"] == 30
 
-            assert "tools_^_test-sink-node-id_~_values___email" in outputs
+            assert "tools_^_createdictionaryblock_~_values___email" in outputs
             assert (
-                outputs["tools_^_test-sink-node-id_~_values___email"]
+                outputs["tools_^_createdictionaryblock_~_values___email"]
                 == "alice@example.com"
             )
 
@@ -535,7 +488,7 @@ async def test_validation_errors_dont_pollute_conversation():
 
         # Mock the function signature creation
         with patch.object(
-            block, "_create_tool_node_signatures", new_callable=AsyncMock
+            block, "_create_function_signature", new_callable=AsyncMock
         ) as mock_sig:
             mock_sig.return_value = [
                 {
@@ -552,113 +505,49 @@ async def test_validation_errors_dont_pollute_conversation():
                             },
                             "required": ["correct_param"],
                         },
-                        "_sink_node_id": "test-sink-node-id",
                     },
                 }
             ]
 
-            # Mock the database manager to avoid HTTP calls during tool execution
-            with patch(
-                "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-            ) as mock_db_manager:
-                # Set up the mock database manager for agent mode
-                mock_db_client = AsyncMock()
-                mock_db_manager.return_value = mock_db_client
+            # Create input data
+            from backend.blocks import llm
 
-                # Mock the node retrieval
-                mock_target_node = Mock()
-                mock_target_node.id = "test-sink-node-id"
-                mock_target_node.block_id = "TestBlock"
-                mock_target_node.block = Mock()
-                mock_target_node.block.name = "Test Block"
-                mock_db_client.get_node.return_value = mock_target_node
+            input_data = block.input_schema(
+                prompt="Test prompt",
+                credentials=llm.TEST_CREDENTIALS_INPUT,
+                model=llm.LlmModel.GPT4O,
+                retry=3,  # Allow retries
+            )
 
-                # Mock the execution result creation
-                mock_node_exec_result = Mock()
-                mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-                mock_final_input_data = {"correct_param": "value"}
-                mock_db_client.upsert_execution_input.return_value = (
-                    mock_node_exec_result,
-                    mock_final_input_data,
-                )
+            # Run the block
+            outputs = {}
+            async for output_name, output_value in block.run(
+                input_data,
+                credentials=llm.TEST_CREDENTIALS,
+                graph_id="test_graph",
+                node_id="test_node",
+                graph_exec_id="test_exec",
+                node_exec_id="test_node_exec",
+                user_id="test_user",
+            ):
+                outputs[output_name] = output_value
 
-                # Mock the output retrieval
-                mock_outputs = {"correct_param": "value"}
-                mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                    mock_outputs
-                )
+            # Verify we had 2 LLM calls (initial + retry)
+            assert call_count == 2
 
-                # Create input data
-                from backend.blocks import llm
+            # Check the final conversation output
+            final_conversation = outputs.get("conversations", [])
 
-                input_data = block.input_schema(
-                    prompt="Test prompt",
-                    credentials=llm.TEST_CREDENTIALS_INPUT,
-                    model=llm.LlmModel.GPT4O,
-                    retry=3,  # Allow retries
-                    agent_mode_max_iterations=1,
-                )
+            # The final conversation should NOT contain the validation error message
+            error_messages = [
+                msg
+                for msg in final_conversation
+                if msg.get("role") == "user"
+                and "parameter errors" in msg.get("content", "")
+            ]
+            assert (
+                len(error_messages) == 0
+            ), "Validation error leaked into final conversation"
 
-                # Run the block
-                outputs = {}
-                from backend.data.execution import ExecutionContext
-
-                mock_execution_context = ExecutionContext(safe_mode=False)
-
-                # Create a proper mock execution processor for agent mode
-                from collections import defaultdict
-
-                mock_execution_processor = AsyncMock()
-                mock_execution_processor.execution_stats = MagicMock()
-                mock_execution_processor.execution_stats_lock = MagicMock()
-
-                # Create a mock NodeExecutionProgress for the sink node
-                mock_node_exec_progress = MagicMock()
-                mock_node_exec_progress.add_task = MagicMock()
-                mock_node_exec_progress.pop_output = MagicMock(
-                    return_value=None
-                )  # No outputs to process
-
-                # Set up running_node_execution as a defaultdict that returns our mock for any key
-                mock_execution_processor.running_node_execution = defaultdict(
-                    lambda: mock_node_exec_progress
-                )
-
-                # Mock the on_node_execution method that gets called during tool execution
-                mock_node_stats = MagicMock()
-                mock_node_stats.error = None
-                mock_execution_processor.on_node_execution.return_value = (
-                    mock_node_stats
-                )
-
-                async for output_name, output_value in block.run(
-                    input_data,
-                    credentials=llm.TEST_CREDENTIALS,
-                    graph_id="test_graph",
-                    node_id="test_node",
-                    graph_exec_id="test_exec",
-                    node_exec_id="test_node_exec",
-                    user_id="test_user",
-                    graph_version=1,
-                    execution_context=mock_execution_context,
-                    execution_processor=mock_execution_processor,
-                ):
-                    outputs[output_name] = output_value
-
-                # Verify we had at least 1 LLM call
-                assert call_count >= 1
-
-                # Check the final conversation output
-                final_conversation = outputs.get("conversations", [])
-
-                # The final conversation should NOT contain validation error messages
-                # Even if retries don't happen in agent mode, we should not leak errors
-                error_messages = [
-                    msg
-                    for msg in final_conversation
-                    if msg.get("role") == "user"
-                    and "parameter errors" in msg.get("content", "")
-                ]
-                assert (
-                    len(error_messages) == 0
-                ), "Validation error leaked into final conversation"
+            # The final conversation should only have the successful response
+            assert final_conversation[-1]["content"] == "valid"

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -14,7 +14,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
+from backend.data.execution import UserContext
 from backend.data.model import SchemaField
 
 # Shared timezone literal type for all time/date blocks
@@ -188,9 +188,10 @@ class GetCurrentTimeBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
+        self, input_data: Input, *, user_context: UserContext, **kwargs
     ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+        # Extract timezone from user_context (always present)
+        effective_timezone = user_context.timezone
 
         # Get the appropriate timezone
         tz = _get_timezone(input_data.format_type, effective_timezone)
@@ -297,10 +298,10 @@ class GetCurrentDateBlock(Block):
             ],
         )
 
-    async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
-    ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # Extract timezone from user_context (required keyword argument)
+        user_context: UserContext = kwargs["user_context"]
+        effective_timezone = user_context.timezone
 
         try:
             offset = int(input_data.offset)
@@ -403,10 +404,10 @@ class GetCurrentDateAndTimeBlock(Block):
             ],
         )
 
-    async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
-    ) -> BlockOutput:
-        effective_timezone = execution_context.user_timezone
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        # Extract timezone from user_context (required keyword argument)
+        user_context: UserContext = kwargs["user_context"]
+        effective_timezone = user_context.timezone
 
         # Get the appropriate timezone
         tz = _get_timezone(input_data.format_type, effective_timezone)

autogpt_platform/backend/backend/blocks/twitter/_builders.py

@@ -1,4 +1,4 @@
-from datetime import datetime, timedelta, timezone
+from datetime import datetime
 from typing import Any, Dict
 
 from backend.blocks.twitter._mappers import (
@@ -237,12 +237,6 @@ class TweetDurationBuilder:
 
     def add_start_time(self, start_time: datetime | None):
         if start_time:
-            # Twitter API requires start_time to be at least 10 seconds before now
-            max_start_time = datetime.now(timezone.utc) - timedelta(seconds=10)
-            if start_time.tzinfo is None:
-                start_time = start_time.replace(tzinfo=timezone.utc)
-            if start_time > max_start_time:
-                start_time = max_start_time
             self.params["start_time"] = start_time
         return self
 

autogpt_platform/backend/backend/blocks/twitter/_serializer.py

@@ -51,10 +51,8 @@ class ResponseDataSerializer(BaseSerializer):
         return serialized_item
 
     @classmethod
-    def serialize_list(cls, data: List[Dict[str, Any]] | None) -> List[Dict[str, Any]]:
+    def serialize_list(cls, data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
         """Serializes a list of dictionary items"""
-        if not data:
-            return []
         return [cls.serialize_dict(item) for item in data]
 
 

autogpt_platform/backend/backend/blocks/twitter/_types.py

@@ -408,7 +408,7 @@ class ListExpansionInputs(BlockSchemaInput):
 
 class TweetTimeWindowInputs(BlockSchemaInput):
     start_time: datetime | None = SchemaField(
-        description="Start time in YYYY-MM-DDTHH:mm:ssZ format. If set to a time less than 10 seconds ago, it will be automatically adjusted to 10 seconds ago (Twitter API requirement).",
+        description="Start time in YYYY-MM-DDTHH:mm:ssZ format",
         placeholder="Enter start time",
         default=None,
         advanced=False,

autogpt_platform/backend/backend/blocks/youtube.py

@@ -1,13 +1,9 @@
-import logging
-from typing import Literal
 from urllib.parse import parse_qs, urlparse
 
-from pydantic import SecretStr
 from youtube_transcript_api._api import YouTubeTranscriptApi
 from youtube_transcript_api._errors import NoTranscriptFound
 from youtube_transcript_api._transcripts import FetchedTranscript
 from youtube_transcript_api.formatters import TextFormatter
-from youtube_transcript_api.proxies import WebshareProxyConfig
 
 from backend.data.block import (
     Block,
@@ -16,42 +12,7 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.model import (
-    CredentialsField,
-    CredentialsMetaInput,
-    SchemaField,
-    UserPasswordCredentials,
-)
-from backend.integrations.providers import ProviderName
-
-logger = logging.getLogger(__name__)
-
-TEST_CREDENTIALS = UserPasswordCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="webshare_proxy",
-    username=SecretStr("mock-webshare-username"),
-    password=SecretStr("mock-webshare-password"),
-    title="Mock Webshare Proxy credentials",
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-WebshareProxyCredentials = UserPasswordCredentials
-WebshareProxyCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.WEBSHARE_PROXY],
-    Literal["user_password"],
-]
-
-
-def WebshareProxyCredentialsField() -> WebshareProxyCredentialsInput:
-    return CredentialsField(
-        description="Webshare proxy credentials for fetching YouTube transcripts",
-    )
+from backend.data.model import SchemaField
 
 
 class TranscribeYoutubeVideoBlock(Block):
@@ -61,7 +22,6 @@ class TranscribeYoutubeVideoBlock(Block):
             description="The URL of the YouTube video to transcribe",
             placeholder="https://www.youtube.com/watch?v=dQw4w9WgXcQ",
         )
-        credentials: WebshareProxyCredentialsInput = WebshareProxyCredentialsField()
 
     class Output(BlockSchemaOutput):
         video_id: str = SchemaField(description="The extracted YouTube video ID")
@@ -75,12 +35,9 @@ class TranscribeYoutubeVideoBlock(Block):
             id="f3a8f7e1-4b1d-4e5f-9f2a-7c3d5a2e6b4c",
             input_schema=TranscribeYoutubeVideoBlock.Input,
             output_schema=TranscribeYoutubeVideoBlock.Output,
-            description="Transcribes a YouTube video using a proxy.",
+            description="Transcribes a YouTube video.",
             categories={BlockCategory.SOCIAL},
-            test_input={
-                "youtube_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
+            test_input={"youtube_url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"},
             test_output=[
                 ("video_id", "dQw4w9WgXcQ"),
                 (
@@ -88,9 +45,8 @@ class TranscribeYoutubeVideoBlock(Block):
                     "Never gonna give you up\nNever gonna let you down",
                 ),
             ],
-            test_credentials=TEST_CREDENTIALS,
             test_mock={
-                "get_transcript": lambda video_id, credentials: [
+                "get_transcript": lambda video_id: [
                     {"text": "Never gonna give you up"},
                     {"text": "Never gonna let you down"},
                 ],
@@ -113,27 +69,16 @@ class TranscribeYoutubeVideoBlock(Block):
                 return parsed_url.path.split("/")[2]
         raise ValueError(f"Invalid YouTube URL: {url}")
 
-    def get_transcript(
-        self, video_id: str, credentials: WebshareProxyCredentials
-    ) -> FetchedTranscript:
+    @staticmethod
+    def get_transcript(video_id: str) -> FetchedTranscript:
         """
         Get transcript for a video, preferring English but falling back to any available language.
 
         :param video_id: The YouTube video ID
-        :param credentials: The Webshare proxy credentials
         :return: The fetched transcript
         :raises: Any exception except NoTranscriptFound for requested languages
         """
-        logger.warning(
-            "Using Webshare proxy for YouTube transcript fetch (video_id=%s)",
-            video_id,
-        )
-        proxy_config = WebshareProxyConfig(
-            proxy_username=credentials.username.get_secret_value(),
-            proxy_password=credentials.password.get_secret_value(),
-        )
-
-        api = YouTubeTranscriptApi(proxy_config=proxy_config)
+        api = YouTubeTranscriptApi()
         try:
             # Try to get English transcript first (default behavior)
             return api.fetch(video_id=video_id)
@@ -156,17 +101,11 @@ class TranscribeYoutubeVideoBlock(Block):
         transcript_text = formatter.format_transcript(transcript)
         return transcript_text
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: WebshareProxyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         video_id = self.extract_video_id(input_data.youtube_url)
         yield "video_id", video_id
 
-        transcript = self.get_transcript(video_id, credentials)
+        transcript = self.get_transcript(video_id)
         transcript_text = self.format_transcript(transcript=transcript)
 
         yield "transcript", transcript_text

autogpt_platform/backend/backend/check_db.py

@@ -5,8 +5,6 @@ from datetime import datetime
 from faker import Faker
 from prisma import Prisma
 
-from backend.data.db import query_raw_with_schema
-
 faker = Faker()
 
 
@@ -17,9 +15,9 @@ async def check_cron_job(db):
 
     try:
         # Check if pg_cron extension exists
-        extension_check = await query_raw_with_schema("CREATE EXTENSION pg_cron;")
+        extension_check = await db.query_raw("CREATE EXTENSION pg_cron;")
         print(extension_check)
-        extension_check = await query_raw_with_schema(
+        extension_check = await db.query_raw(
             "SELECT COUNT(*) as count FROM pg_extension WHERE extname = 'pg_cron'"
         )
         if extension_check[0]["count"] == 0:
@@ -27,7 +25,7 @@ async def check_cron_job(db):
             return False
 
         # Check if the refresh job exists
-        job_check = await query_raw_with_schema(
+        job_check = await db.query_raw(
             """
             SELECT jobname, schedule, command 
             FROM cron.job 
@@ -57,33 +55,33 @@ async def get_materialized_view_counts(db):
     print("-" * 40)
 
     # Get counts from mv_agent_run_counts
-    agent_runs = await query_raw_with_schema(
+    agent_runs = await db.query_raw(
         """
         SELECT COUNT(*) as total_agents, 
                SUM(run_count) as total_runs,
                MAX(run_count) as max_runs,
                MIN(run_count) as min_runs
-        FROM {schema_prefix}mv_agent_run_counts
+        FROM mv_agent_run_counts
     """
     )
 
     # Get counts from mv_review_stats
-    review_stats = await query_raw_with_schema(
+    review_stats = await db.query_raw(
         """
         SELECT COUNT(*) as total_listings,
                SUM(review_count) as total_reviews,
                AVG(avg_rating) as overall_avg_rating
-        FROM {schema_prefix}mv_review_stats
+        FROM mv_review_stats
     """
     )
 
     # Get sample data from StoreAgent view
-    store_agents = await query_raw_with_schema(
+    store_agents = await db.query_raw(
         """
         SELECT COUNT(*) as total_store_agents,
                AVG(runs) as avg_runs,
                AVG(rating) as avg_rating
-        FROM {schema_prefix}"StoreAgent"
+        FROM "StoreAgent"
     """
     )
 

autogpt_platform/backend/backend/check_store_data.py

@@ -5,8 +5,6 @@ import asyncio
 
 from prisma import Prisma
 
-from backend.data.db import query_raw_with_schema
-
 
 async def check_store_data(db):
     """Check what store data exists in the database."""
@@ -91,11 +89,11 @@ async def check_store_data(db):
         sa.creator_username,
         sa.categories,
         sa.updated_at
-    FROM {schema_prefix}"StoreAgent" sa
+    FROM "StoreAgent" sa
     LIMIT 10;
     """
 
-    store_agents = await query_raw_with_schema(query)
+    store_agents = await db.query_raw(query)
     print(f"Total store agents in view: {len(store_agents)}")
 
     if store_agents:
@@ -113,22 +111,22 @@ async def check_store_data(db):
     # Check for any APPROVED store listing versions
     query = """
     SELECT COUNT(*) as count
-    FROM {schema_prefix}"StoreListingVersion"
+    FROM "StoreListingVersion"
     WHERE "submissionStatus" = 'APPROVED'
     """
 
-    result = await query_raw_with_schema(query)
+    result = await db.query_raw(query)
     approved_count = result[0]["count"] if result else 0
     print(f"Approved store listing versions: {approved_count}")
 
     # Check for store listings with hasApprovedVersion = true
     query = """
     SELECT COUNT(*) as count
-    FROM {schema_prefix}"StoreListing"
+    FROM "StoreListing"
     WHERE "hasApprovedVersion" = true AND "isDeleted" = false
     """
 
-    result = await query_raw_with_schema(query)
+    result = await db.query_raw(query)
     has_approved_count = result[0]["count"] if result else 0
     print(f"Store listings with approved versions: {has_approved_count}")
 
@@ -136,10 +134,10 @@ async def check_store_data(db):
     query = """
     SELECT COUNT(DISTINCT "agentGraphId") as unique_agents,
            COUNT(*) as total_executions
-    FROM {schema_prefix}"AgentGraphExecution"
+    FROM "AgentGraphExecution"
     """
 
-    result = await query_raw_with_schema(query)
+    result = await db.query_raw(query)
     if result:
         print("\nAgent Graph Executions:")
         print(f"  Unique agents with executions: {result[0]['unique_agents']}")

autogpt_platform/backend/backend/data/analytics.py

@@ -1,45 +1,12 @@
 import logging
-from datetime import datetime, timedelta, timezone
-from typing import Optional
 
 import prisma.types
-from pydantic import BaseModel
 
-from backend.data.db import query_raw_with_schema
 from backend.util.json import SafeJson
 
 logger = logging.getLogger(__name__)
 
 
-class AccuracyAlertData(BaseModel):
-    """Alert data when accuracy drops significantly."""
-
-    graph_id: str
-    user_id: Optional[str]
-    drop_percent: float
-    three_day_avg: float
-    seven_day_avg: float
-    detected_at: datetime
-
-
-class AccuracyLatestData(BaseModel):
-    """Latest execution accuracy data point."""
-
-    date: datetime
-    daily_score: Optional[float]
-    three_day_avg: Optional[float]
-    seven_day_avg: Optional[float]
-    fourteen_day_avg: Optional[float]
-
-
-class AccuracyTrendsResponse(BaseModel):
-    """Response model for accuracy trends and alerts."""
-
-    latest_data: AccuracyLatestData
-    alert: Optional[AccuracyAlertData]
-    historical_data: Optional[list[AccuracyLatestData]] = None
-
-
 async def log_raw_analytics(
     user_id: str,
     type: str,
@@ -76,217 +43,3 @@ async def log_raw_metric(
     )
 
     return result
-
-
-async def get_accuracy_trends_and_alerts(
-    graph_id: str,
-    days_back: int = 30,
-    user_id: Optional[str] = None,
-    drop_threshold: float = 10.0,
-    include_historical: bool = False,
-) -> AccuracyTrendsResponse:
-    """Get accuracy trends and detect alerts for a specific graph."""
-    query_template = """
-    WITH daily_scores AS (
-        SELECT 
-            DATE(e."createdAt") as execution_date,
-            AVG(CASE 
-                WHEN e.stats IS NOT NULL 
-                AND e.stats::json->>'correctness_score' IS NOT NULL
-                AND e.stats::json->>'correctness_score' != 'null'
-                THEN (e.stats::json->>'correctness_score')::float * 100
-                ELSE NULL 
-            END) as daily_score
-        FROM {schema_prefix}"AgentGraphExecution" e
-        WHERE e."agentGraphId" = $1::text
-            AND e."isDeleted" = false
-            AND e."createdAt" >= $2::timestamp
-            AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-            {user_filter}
-        GROUP BY DATE(e."createdAt")
-        HAVING COUNT(*) >= 3  -- Need at least 3 executions per day
-    ),
-    trends AS (
-        SELECT 
-            execution_date,
-            daily_score,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 2 PRECEDING AND CURRENT ROW
-            ) as three_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
-            ) as seven_day_avg,
-            AVG(daily_score) OVER (
-                ORDER BY execution_date 
-                ROWS BETWEEN 13 PRECEDING AND CURRENT ROW
-            ) as fourteen_day_avg
-        FROM daily_scores
-    )
-    SELECT *,
-        CASE 
-            WHEN three_day_avg IS NOT NULL AND seven_day_avg IS NOT NULL AND seven_day_avg > 0
-            THEN ((seven_day_avg - three_day_avg) / seven_day_avg * 100)
-            ELSE NULL
-        END as drop_percent
-    FROM trends
-    ORDER BY execution_date DESC
-    {limit_clause}
-    """
-
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    params = [graph_id, start_date]
-    user_filter = ""
-    if user_id:
-        user_filter = 'AND e."userId" = $3::text'
-        params.append(user_id)
-
-    # Determine limit clause
-    limit_clause = "" if include_historical else "LIMIT 1"
-
-    final_query = query_template.format(
-        schema_prefix="{schema_prefix}",
-        user_filter=user_filter,
-        limit_clause=limit_clause,
-    )
-
-    result = await query_raw_with_schema(final_query, *params)
-
-    if not result:
-        return AccuracyTrendsResponse(
-            latest_data=AccuracyLatestData(
-                date=datetime.now(timezone.utc),
-                daily_score=None,
-                three_day_avg=None,
-                seven_day_avg=None,
-                fourteen_day_avg=None,
-            ),
-            alert=None,
-        )
-
-    latest = result[0]
-
-    alert = None
-    if (
-        latest["drop_percent"] is not None
-        and latest["drop_percent"] >= drop_threshold
-        and latest["three_day_avg"] is not None
-        and latest["seven_day_avg"] is not None
-    ):
-        alert = AccuracyAlertData(
-            graph_id=graph_id,
-            user_id=user_id,
-            drop_percent=float(latest["drop_percent"]),
-            three_day_avg=float(latest["three_day_avg"]),
-            seven_day_avg=float(latest["seven_day_avg"]),
-            detected_at=datetime.now(timezone.utc),
-        )
-
-    # Prepare historical data if requested
-    historical_data = None
-    if include_historical:
-        historical_data = []
-        for row in result:
-            historical_data.append(
-                AccuracyLatestData(
-                    date=row["execution_date"],
-                    daily_score=(
-                        float(row["daily_score"])
-                        if row["daily_score"] is not None
-                        else None
-                    ),
-                    three_day_avg=(
-                        float(row["three_day_avg"])
-                        if row["three_day_avg"] is not None
-                        else None
-                    ),
-                    seven_day_avg=(
-                        float(row["seven_day_avg"])
-                        if row["seven_day_avg"] is not None
-                        else None
-                    ),
-                    fourteen_day_avg=(
-                        float(row["fourteen_day_avg"])
-                        if row["fourteen_day_avg"] is not None
-                        else None
-                    ),
-                )
-            )
-
-    return AccuracyTrendsResponse(
-        latest_data=AccuracyLatestData(
-            date=latest["execution_date"],
-            daily_score=(
-                float(latest["daily_score"])
-                if latest["daily_score"] is not None
-                else None
-            ),
-            three_day_avg=(
-                float(latest["three_day_avg"])
-                if latest["three_day_avg"] is not None
-                else None
-            ),
-            seven_day_avg=(
-                float(latest["seven_day_avg"])
-                if latest["seven_day_avg"] is not None
-                else None
-            ),
-            fourteen_day_avg=(
-                float(latest["fourteen_day_avg"])
-                if latest["fourteen_day_avg"] is not None
-                else None
-            ),
-        ),
-        alert=alert,
-        historical_data=historical_data,
-    )
-
-
-class MarketplaceGraphData(BaseModel):
-    """Data structure for marketplace graph monitoring."""
-
-    graph_id: str
-    user_id: Optional[str]
-    execution_count: int
-
-
-async def get_marketplace_graphs_for_monitoring(
-    days_back: int = 30,
-    min_executions: int = 10,
-) -> list[MarketplaceGraphData]:
-    """Get published marketplace graphs with recent executions for monitoring."""
-    query_template = """
-    WITH marketplace_graphs AS (
-        SELECT DISTINCT 
-            slv."agentGraphId" as graph_id,
-            slv."agentGraphVersion" as graph_version
-        FROM {schema_prefix}"StoreListing" sl
-        JOIN {schema_prefix}"StoreListingVersion" slv ON sl."activeVersionId" = slv."id"
-        WHERE sl."hasApprovedVersion" = true
-            AND sl."isDeleted" = false
-    )
-    SELECT DISTINCT 
-        mg.graph_id,
-        NULL as user_id,  -- Marketplace graphs don't have a specific user_id for monitoring
-        COUNT(*) as execution_count
-    FROM marketplace_graphs mg
-    JOIN {schema_prefix}"AgentGraphExecution" e ON e."agentGraphId" = mg.graph_id
-    WHERE e."createdAt" >= $1::timestamp
-        AND e."isDeleted" = false
-        AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-    GROUP BY mg.graph_id
-    HAVING COUNT(*) >= $2
-    ORDER BY execution_count DESC
-    """
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    result = await query_raw_with_schema(query_template, start_date, min_executions)
-
-    return [
-        MarketplaceGraphData(
-            graph_id=row["graph_id"],
-            user_id=row["user_id"],
-            execution_count=int(row["execution_count"]),
-        )
-        for row in result
-    ]

autogpt_platform/backend/backend/data/block.py

@@ -29,13 +29,6 @@ from backend.data.model import NodeExecutionStats
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.cache import cached
-from backend.util.exceptions import (
-    BlockError,
-    BlockExecutionError,
-    BlockInputError,
-    BlockOutputError,
-    BlockUnknownError,
-)
 from backend.util.settings import Config
 
 from .model import (
@@ -71,7 +64,6 @@ class BlockType(Enum):
     AGENT = "Agent"
     AI = "AI"
     AYRSHARE = "Ayrshare"
-    HUMAN_IN_THE_LOOP = "Human In The Loop"
 
 
 class BlockCategory(Enum):
@@ -266,61 +258,14 @@ class BlockSchema(BaseModel):
             )
         }
 
-    @classmethod
-    def get_auto_credentials_fields(cls) -> dict[str, dict[str, Any]]:
-        """
-        Get fields that have auto_credentials metadata (e.g., GoogleDriveFileInput).
-
-        Returns a dict mapping kwarg_name -> {field_name, auto_credentials_config}
-
-        Raises:
-            ValueError: If multiple fields have the same kwarg_name, as this would
-                cause silent overwriting and only the last field would be processed.
-        """
-        result: dict[str, dict[str, Any]] = {}
-        schema = cls.jsonschema()
-        properties = schema.get("properties", {})
-
-        for field_name, field_schema in properties.items():
-            auto_creds = field_schema.get("auto_credentials")
-            if auto_creds:
-                kwarg_name = auto_creds.get("kwarg_name", "credentials")
-                if kwarg_name in result:
-                    raise ValueError(
-                        f"Duplicate auto_credentials kwarg_name '{kwarg_name}' "
-                        f"in fields '{result[kwarg_name]['field_name']}' and "
-                        f"'{field_name}' on {cls.__qualname__}"
-                    )
-                result[kwarg_name] = {
-                    "field_name": field_name,
-                    "config": auto_creds,
-                }
-        return result
-
     @classmethod
     def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
-        result = {}
-
-        # Regular credentials fields
-        for field_name in cls.get_credentials_fields().keys():
-            result[field_name] = CredentialsFieldInfo.model_validate(
+        return {
+            field_name: CredentialsFieldInfo.model_validate(
                 cls.get_field_schema(field_name), by_alias=True
             )
-
-        # Auto-generated credentials fields (from GoogleDriveFileInput etc.)
-        for kwarg_name, info in cls.get_auto_credentials_fields().items():
-            config = info["config"]
-            # Build a schema-like dict that CredentialsFieldInfo can parse
-            auto_schema = {
-                "credentials_provider": [config.get("provider", "google")],
-                "credentials_types": [config.get("type", "oauth2")],
-                "credentials_scopes": config.get("scopes"),
-            }
-            result[kwarg_name] = CredentialsFieldInfo.model_validate(
-                auto_schema, by_alias=True
-            )
-
-        return result
+            for field_name in cls.get_credentials_fields().keys()
+        }
 
     @classmethod
     def get_input_defaults(cls, data: BlockInput) -> BlockInput:
@@ -597,29 +542,9 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         )
 
     async def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        try:
-            async for output_name, output_data in self._execute(input_data, **kwargs):
-                yield output_name, output_data
-        except Exception as ex:
-            if isinstance(ex, BlockError):
-                raise ex
-            else:
-                raise (
-                    BlockExecutionError
-                    if isinstance(ex, ValueError)
-                    else BlockUnknownError
-                )(
-                    message=str(ex),
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from ex
-
-    async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
         if error := self.input_schema.validate_data(input_data):
-            raise BlockInputError(
-                message=f"Unable to execute block with invalid input data: {error}",
-                block_name=self.name,
-                block_id=self.id,
+            raise ValueError(
+                f"Unable to execute block with invalid input data: {error}"
             )
 
         async for output_name, output_data in self.run(
@@ -627,17 +552,11 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             **kwargs,
         ):
             if output_name == "error":
-                raise BlockExecutionError(
-                    message=output_data, block_name=self.name, block_id=self.id
-                )
+                raise RuntimeError(output_data)
             if self.block_type == BlockType.STANDARD and (
                 error := self.output_schema.validate_field(output_name, output_data)
             ):
-                raise BlockOutputError(
-                    message=f"Block produced an invalid output data: {error}",
-                    block_name=self.name,
-                    block_id=self.id,
-                )
+                raise ValueError(f"Block produced an invalid output data: {error}")
             yield output_name, output_data
 
     def is_triggered_by_event_type(
@@ -848,12 +767,3 @@ def get_io_block_ids() -> Sequence[str]:
         for id, B in get_blocks().items()
         if B().block_type in (BlockType.INPUT, BlockType.OUTPUT)
     ]
-
-
-@cached(ttl_seconds=3600)
-def get_human_in_the_loop_block_ids() -> Sequence[str]:
-    return [
-        id
-        for id, B in get_blocks().items()
-        if B().block_type == BlockType.HUMAN_IN_THE_LOOP
-    ]

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -1,7 +1,5 @@
 from typing import Type
 
-from backend.blocks.ai_image_customizer import AIImageCustomizerBlock, GeminiImageModel
-from backend.blocks.ai_image_generator_block import AIImageGeneratorBlock, ImageGenModel
 from backend.blocks.ai_music_generator import AIMusicGeneratorBlock
 from backend.blocks.ai_shortform_video_block import (
     AIAdMakerVideoCreatorBlock,
@@ -11,7 +9,6 @@ from backend.blocks.ai_shortform_video_block import (
 from backend.blocks.apollo.organization import SearchOrganizationsBlock
 from backend.blocks.apollo.people import SearchPeopleBlock
 from backend.blocks.apollo.person import GetPersonDetailBlock
-from backend.blocks.codex import CodeGenerationBlock, CodexModel
 from backend.blocks.enrichlayer.linkedin import (
     GetLinkedinProfileBlock,
     GetLinkedinProfilePictureBlock,
@@ -64,10 +61,9 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.O1_MINI: 4,
     # GPT-5 models
     LlmModel.GPT5: 2,
-    LlmModel.GPT5_1: 5,
     LlmModel.GPT5_MINI: 1,
     LlmModel.GPT5_NANO: 1,
-    LlmModel.GPT5_CHAT: 5,
+    LlmModel.GPT5_CHAT: 2,
     LlmModel.GPT41: 2,
     LlmModel.GPT41_MINI: 1,
     LlmModel.GPT4O_MINI: 1,
@@ -78,7 +74,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_OPUS: 21,
     LlmModel.CLAUDE_4_SONNET: 5,
     LlmModel.CLAUDE_4_5_HAIKU: 4,
-    LlmModel.CLAUDE_4_5_OPUS: 14,
     LlmModel.CLAUDE_4_5_SONNET: 9,
     LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_HAIKU: 1,
@@ -87,17 +82,21 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.AIML_API_LLAMA3_3_70B: 1,
     LlmModel.AIML_API_META_LLAMA_3_1_70B: 1,
     LlmModel.AIML_API_LLAMA_3_2_3B: 1,
+    LlmModel.LLAMA3_8B: 1,
+    LlmModel.LLAMA3_70B: 1,
+    LlmModel.GEMMA2_9B: 1,
     LlmModel.LLAMA3_3_70B: 1,  # $0.59 / $0.79
     LlmModel.LLAMA3_1_8B: 1,
     LlmModel.OLLAMA_LLAMA3_3: 1,
     LlmModel.OLLAMA_LLAMA3_2: 1,
     LlmModel.OLLAMA_LLAMA3_8B: 1,
     LlmModel.OLLAMA_LLAMA3_405B: 1,
+    LlmModel.DEEPSEEK_LLAMA_70B: 1,  # ? / ?
     LlmModel.OLLAMA_DOLPHIN: 1,
     LlmModel.OPENAI_GPT_OSS_120B: 1,
     LlmModel.OPENAI_GPT_OSS_20B: 1,
+    LlmModel.GEMINI_FLASH_1_5: 1,
     LlmModel.GEMINI_2_5_PRO: 4,
-    LlmModel.GEMINI_3_PRO_PREVIEW: 5,
     LlmModel.MISTRAL_NEMO: 1,
     LlmModel.COHERE_COMMAND_R_08_2024: 1,
     LlmModel.COHERE_COMMAND_R_PLUS_08_2024: 3,
@@ -119,9 +118,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.LLAMA_API_LLAMA3_3_8B: 1,
     LlmModel.LLAMA_API_LLAMA3_3_70B: 1,
     LlmModel.GROK_4: 9,
-    LlmModel.GROK_4_FAST: 1,
-    LlmModel.GROK_4_1_FAST: 1,
-    LlmModel.GROK_CODE_FAST_1: 1,
     LlmModel.KIMI_K2: 1,
     LlmModel.QWEN3_235B_A22B_THINKING: 1,
     LlmModel.QWEN3_CODER: 9,
@@ -267,20 +263,6 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
     AIStructuredResponseGeneratorBlock: LLM_COST,
     AITextSummarizerBlock: LLM_COST,
     AIListGeneratorBlock: LLM_COST,
-    CodeGenerationBlock: [
-        BlockCost(
-            cost_type=BlockCostType.RUN,
-            cost_filter={
-                "model": CodexModel.GPT5_1_CODEX,
-                "credentials": {
-                    "id": openai_credentials.id,
-                    "provider": openai_credentials.provider,
-                    "type": openai_credentials.type,
-                },
-            },
-            cost_amount=5,
-        )
-    ],
     CreateTalkingAvatarVideoBlock: [
         BlockCost(
             cost_amount=15,
@@ -558,85 +540,4 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         )
     ],
-    AIImageGeneratorBlock: [
-        BlockCost(
-            cost_amount=5,  # SD3.5 Medium: ~$0.035 per image
-            cost_filter={
-                "model": ImageGenModel.SD3_5,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-        BlockCost(
-            cost_amount=6,  # Flux 1.1 Pro: ~$0.04 per image
-            cost_filter={
-                "model": ImageGenModel.FLUX,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-        BlockCost(
-            cost_amount=10,  # Flux 1.1 Pro Ultra: ~$0.08 per image
-            cost_filter={
-                "model": ImageGenModel.FLUX_ULTRA,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-        BlockCost(
-            cost_amount=7,  # Recraft v3: ~$0.05 per image
-            cost_filter={
-                "model": ImageGenModel.RECRAFT,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-        BlockCost(
-            cost_amount=14,  # Nano Banana Pro: $0.14 per image at 2K
-            cost_filter={
-                "model": ImageGenModel.NANO_BANANA_PRO,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-    ],
-    AIImageCustomizerBlock: [
-        BlockCost(
-            cost_amount=10,  # Nano Banana (original)
-            cost_filter={
-                "model": GeminiImageModel.NANO_BANANA,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-        BlockCost(
-            cost_amount=14,  # Nano Banana Pro: $0.14 per image at 2K
-            cost_filter={
-                "model": GeminiImageModel.NANO_BANANA_PRO,
-                "credentials": {
-                    "id": replicate_credentials.id,
-                    "provider": replicate_credentials.provider,
-                    "type": replicate_credentials.type,
-                },
-            },
-        ),
-    ],
 }

autogpt_platform/backend/backend/data/credit_test.py

@@ -7,7 +7,7 @@ from prisma.models import CreditTransaction, UserBalance
 from backend.blocks.llm import AITextGeneratorBlock
 from backend.data.block import get_block
 from backend.data.credit import BetaUserCredit, UsageTransactionMetadata
-from backend.data.execution import ExecutionContext, NodeExecutionEntry
+from backend.data.execution import NodeExecutionEntry, UserContext
 from backend.data.user import DEFAULT_USER_ID
 from backend.executor.utils import block_usage_cost
 from backend.integrations.credentials_store import openai_credentials
@@ -73,7 +73,6 @@ async def test_block_credit_usage(server: SpinTestServer):
         NodeExecutionEntry(
             user_id=DEFAULT_USER_ID,
             graph_id="test_graph",
-            graph_version=1,
             node_id="test_node",
             graph_exec_id="test_graph_exec",
             node_exec_id="test_node_exec",
@@ -86,7 +85,7 @@ async def test_block_credit_usage(server: SpinTestServer):
                     "type": openai_credentials.type,
                 },
             },
-            execution_context=ExecutionContext(user_timezone="UTC"),
+            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_1 > 0
@@ -95,13 +94,12 @@ async def test_block_credit_usage(server: SpinTestServer):
         NodeExecutionEntry(
             user_id=DEFAULT_USER_ID,
             graph_id="test_graph",
-            graph_version=1,
             node_id="test_node",
             graph_exec_id="test_graph_exec",
             node_exec_id="test_node_exec",
             block_id=AITextGeneratorBlock().id,
             inputs={"model": "gpt-4-turbo", "api_key": "owned_api_key"},
-            execution_context=ExecutionContext(user_timezone="UTC"),
+            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_2 == 0

autogpt_platform/backend/backend/data/dynamic_fields.py

@@ -92,18 +92,6 @@ def get_dynamic_field_description(field_name: str) -> str:
     return f"Value for {field_name}"
 
 
-def is_tool_pin(name: str) -> bool:
-    """Check if a pin name represents a tool connection."""
-    return name.startswith("tools_^_") or name == "tools"
-
-
-def sanitize_pin_name(name: str) -> str:
-    sanitized_name = extract_base_field_name(name)
-    if is_tool_pin(sanitized_name):
-        return "tools"
-    return sanitized_name
-
-
 # --------------------------------------------------------------------------- #
 #  Dynamic field parsing and merging utilities
 # --------------------------------------------------------------------------- #
@@ -149,64 +137,30 @@ def _tokenise(path: str) -> list[tuple[str, str]] | None:
     return tokens
 
 
-def parse_execution_output(
-    output_item: tuple[str, Any],
-    link_output_selector: str,
-    sink_node_id: str | None = None,
-    sink_pin_name: str | None = None,
-) -> Any:
+def parse_execution_output(output: tuple[str, Any], name: str) -> Any:
     """
-    Retrieve a nested value out of `output` using the flattened `link_output_selector`.
+    Retrieve a nested value out of `output` using the flattened *name*.
 
-    On any failure (wrong name, wrong type, out-of-range, bad path) returns **None**.
-
-    ### Special Case: Tool pins
-    For regular output pins, the `output_item`'s name will simply be the field name, and
-    `link_output_selector` (= the `source_name` of the link) may provide a "selector"
-    used to extract part of the output value and route it through the link
-    to the next node.
-
-    However, for tool pins, it is the other way around: the `output_item`'s name
-    provides the routing information (`tools_^_{sink_node_id}_~_{field_name}`),
-    and the `link_output_selector` is simply `"tools"`
-    (or `"tools_^_{tool_name}_~_{field_name}"` for backward compatibility).
+    On any failure (wrong name, wrong type, out-of-range, bad path)
+    returns **None**.
 
     Args:
-        output_item: Tuple of (base_name, data) representing a block output entry.
-        link_output_selector: The flattened field name to extract from the output data.
-        sink_node_id: Sink node ID, used for tool use routing.
-        sink_pin_name: Sink pin name, used for tool use routing.
+        output: Tuple of (base_name, data) representing a block output entry
+        name: The flattened field name to extract from the output data
 
     Returns:
-        The value at the specified path, or `None` if not found/invalid.
+        The value at the specified path, or None if not found/invalid
     """
-    output_pin_name, data = output_item
-
-    # Special handling for tool pins
-    if is_tool_pin(link_output_selector) and (  # "tools" or "tools_^_…"
-        output_pin_name.startswith("tools_^_") and "_~_" in output_pin_name
-    ):
-        if not (sink_node_id and sink_pin_name):
-            raise ValueError(
-                "sink_node_id and sink_pin_name must be provided for tool pin routing"
-            )
-
-        # Extract routing information from emit key: tools_^_{node_id}_~_{field}
-        selector = output_pin_name[8:]  # Remove "tools_^_" prefix
-        target_node_id, target_input_pin = selector.split("_~_", 1)
-        if target_node_id == sink_node_id and target_input_pin == sink_pin_name:
-            return data
-        else:
-            return None
+    base_name, data = output
 
     # Exact match → whole object
-    if link_output_selector == output_pin_name:
+    if name == base_name:
         return data
 
     # Must start with the expected name
-    if not link_output_selector.startswith(output_pin_name):
+    if not name.startswith(base_name):
         return None
-    path = link_output_selector[len(output_pin_name) :]
+    path = name[len(base_name) :]
     if not path:
         return None  # nothing left to parse
 

autogpt_platform/backend/backend/data/execution.py

@@ -5,7 +5,6 @@ from enum import Enum
 from multiprocessing import Manager
 from queue import Empty
 from typing import (
-    TYPE_CHECKING,
     Annotated,
     Any,
     AsyncGenerator,
@@ -35,7 +34,6 @@ from prisma.types import (
     AgentNodeExecutionKeyValueDataCreateInput,
     AgentNodeExecutionUpdateInput,
     AgentNodeExecutionWhereInput,
-    AgentNodeExecutionWhereUniqueInput,
 )
 from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
 from pydantic.fields import Field
@@ -66,27 +64,12 @@ from .includes import (
 )
 from .model import CredentialsMetaInput, GraphExecutionStats, NodeExecutionStats
 
-if TYPE_CHECKING:
-    pass
-
 T = TypeVar("T")
 
 logger = logging.getLogger(__name__)
 config = Config()
 
 
-class ExecutionContext(BaseModel):
-    """
-    Unified context that carries execution-level data throughout the entire execution flow.
-    This includes information needed by blocks, sub-graphs, and execution management.
-    """
-
-    safe_mode: bool = True
-    user_timezone: str = "UTC"
-    root_execution_id: Optional[str] = None
-    parent_execution_id: Optional[str] = None
-
-
 # -------------------------- Models -------------------------- #
 
 
@@ -113,14 +96,11 @@ NodesInputMasks = Mapping[str, NodeInputMask]
 VALID_STATUS_TRANSITIONS = {
     ExecutionStatus.QUEUED: [
         ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.TERMINATED,  # For resuming halted execution
-        ExecutionStatus.REVIEW,  # For resuming after review
     ],
     ExecutionStatus.RUNNING: [
         ExecutionStatus.INCOMPLETE,
         ExecutionStatus.QUEUED,
         ExecutionStatus.TERMINATED,  # For resuming halted execution
-        ExecutionStatus.REVIEW,  # For resuming after review
     ],
     ExecutionStatus.COMPLETED: [
         ExecutionStatus.RUNNING,
@@ -129,16 +109,11 @@ VALID_STATUS_TRANSITIONS = {
         ExecutionStatus.INCOMPLETE,
         ExecutionStatus.QUEUED,
         ExecutionStatus.RUNNING,
-        ExecutionStatus.REVIEW,
     ],
     ExecutionStatus.TERMINATED: [
         ExecutionStatus.INCOMPLETE,
         ExecutionStatus.QUEUED,
         ExecutionStatus.RUNNING,
-        ExecutionStatus.REVIEW,
-    ],
-    ExecutionStatus.REVIEW: [
-        ExecutionStatus.RUNNING,
     ],
 }
 
@@ -200,10 +175,6 @@ class GraphExecutionMeta(BaseDbModel):
             default=None,
             description="AI-generated summary of what the agent did",
         )
-        correctness_score: float | None = Field(
-            default=None,
-            description="AI-generated score (0.0-1.0) indicating how well the execution achieved its intended purpose",
-        )
 
         def to_db(self) -> GraphExecutionStats:
             return GraphExecutionStats(
@@ -216,13 +187,6 @@ class GraphExecutionMeta(BaseDbModel):
                 node_error_count=self.node_error_count,
                 error=self.error,
                 activity_status=self.activity_status,
-                correctness_score=self.correctness_score,
-            )
-
-        def without_activity_features(self) -> "GraphExecutionMeta.Stats":
-            """Return a copy of stats with activity features (activity_status, correctness_score) set to None."""
-            return self.model_copy(
-                update={"activity_status": None, "correctness_score": None}
             )
 
     stats: Stats | None
@@ -280,7 +244,6 @@ class GraphExecutionMeta(BaseDbModel):
                         else stats.error
                     ),
                     activity_status=stats.activity_status,
-                    correctness_score=stats.correctness_score,
                 )
                 if stats
                 else None
@@ -381,8 +344,9 @@ class GraphExecutionWithNodes(GraphExecution):
 
     def to_graph_execution_entry(
         self,
-        execution_context: ExecutionContext,
+        user_context: "UserContext",
         compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
+        parent_graph_exec_id: Optional[str] = None,
     ):
         return GraphExecutionEntry(
             user_id=self.user_id,
@@ -390,7 +354,8 @@ class GraphExecutionWithNodes(GraphExecution):
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
             nodes_input_masks=compiled_nodes_input_masks,
-            execution_context=execution_context,
+            user_context=user_context,
+            parent_graph_exec_id=parent_graph_exec_id,
         )
 
 
@@ -463,18 +428,17 @@ class NodeExecutionResult(BaseModel):
         )
 
     def to_node_execution_entry(
-        self, execution_context: ExecutionContext
+        self, user_context: "UserContext"
     ) -> "NodeExecutionEntry":
         return NodeExecutionEntry(
             user_id=self.user_id,
             graph_exec_id=self.graph_exec_id,
             graph_id=self.graph_id,
-            graph_version=self.graph_version,
             node_exec_id=self.node_exec_id,
             node_id=self.node_id,
             block_id=self.block_id,
             inputs=self.input_data,
-            execution_context=execution_context,
+            user_context=user_context,
         )
 
 
@@ -484,7 +448,6 @@ class NodeExecutionResult(BaseModel):
 async def get_graph_executions(
     graph_exec_id: Optional[str] = None,
     graph_id: Optional[str] = None,
-    graph_version: Optional[int] = None,
     user_id: Optional[str] = None,
     statuses: Optional[list[ExecutionStatus]] = None,
     created_time_gte: Optional[datetime] = None,
@@ -501,8 +464,6 @@ async def get_graph_executions(
         where_filter["userId"] = user_id
     if graph_id:
         where_filter["agentGraphId"] = graph_id
-    if graph_version is not None:
-        where_filter["agentGraphVersion"] = graph_version
     if created_time_gte or created_time_lte:
         where_filter["createdAt"] = {
             "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),
@@ -752,7 +713,7 @@ async def upsert_execution_input(
     input_name: str,
     input_data: JsonValue,
     node_exec_id: str | None = None,
-) -> tuple[NodeExecutionResult, BlockInput]:
+) -> tuple[str, BlockInput]:
     """
     Insert AgentNodeExecutionInputOutput record for as one of AgentNodeExecution.Input.
     If there is no AgentNodeExecution that has no `input_name` as input, create new one.
@@ -785,7 +746,7 @@ async def upsert_execution_input(
     existing_execution = await AgentNodeExecution.prisma().find_first(
         where=existing_exec_query_filter,
         order={"addedTime": "asc"},
-        include={"Input": True, "GraphExecution": True},
+        include={"Input": True},
     )
     json_input_data = SafeJson(input_data)
 
@@ -797,7 +758,7 @@ async def upsert_execution_input(
                 referencedByInputExecId=existing_execution.id,
             )
         )
-        return NodeExecutionResult.from_db(existing_execution), {
+        return existing_execution.id, {
             **{
                 input_data.name: type_utils.convert(input_data.data, JsonValue)
                 for input_data in existing_execution.Input or []
@@ -812,10 +773,9 @@ async def upsert_execution_input(
                 agentGraphExecutionId=graph_exec_id,
                 executionStatus=ExecutionStatus.INCOMPLETE,
                 Input={"create": {"name": input_name, "data": json_input_data}},
-            ),
-            include={"GraphExecution": True},
+            )
         )
-        return NodeExecutionResult.from_db(result), {input_name: input_data}
+        return result.id, {input_name: input_data}
 
     else:
         raise ValueError(
@@ -840,30 +800,6 @@ async def upsert_execution_output(
     await AgentNodeExecutionInputOutput.prisma().create(data=data)
 
 
-async def get_execution_outputs_by_node_exec_id(
-    node_exec_id: str,
-) -> dict[str, Any]:
-    """
-    Get all execution outputs for a specific node execution ID.
-
-    Args:
-        node_exec_id: The node execution ID to get outputs for
-
-    Returns:
-        Dictionary mapping output names to their data values
-    """
-    outputs = await AgentNodeExecutionInputOutput.prisma().find_many(
-        where={"referencedByOutputExecId": node_exec_id}
-    )
-
-    result = {}
-    for output in outputs:
-        if output.data is not None:
-            result[output.name] = type_utils.convert(output.data, JsonValue)
-
-    return result
-
-
 async def update_graph_execution_start_time(
     graph_exec_id: str,
 ) -> GraphExecution | None:
@@ -935,25 +871,9 @@ async def update_node_execution_status_batch(
     node_exec_ids: list[str],
     status: ExecutionStatus,
     stats: dict[str, Any] | None = None,
-) -> int:
-    # Validate status transitions - allowed_from should never be empty for valid statuses
-    allowed_from = VALID_STATUS_TRANSITIONS.get(status, [])
-    if not allowed_from:
-        raise ValueError(
-            f"Invalid status transition: {status} has no valid source statuses"
-        )
-
-    # For batch updates, we filter to only update nodes with valid current statuses
-    where_clause = cast(
-        AgentNodeExecutionWhereInput,
-        {
-            "id": {"in": node_exec_ids},
-            "executionStatus": {"in": [s.value for s in allowed_from]},
-        },
-    )
-
-    return await AgentNodeExecution.prisma().update_many(
-        where=where_clause,
+):
+    await AgentNodeExecution.prisma().update_many(
+        where={"id": {"in": node_exec_ids}},
         data=_get_update_status_data(status, None, stats),
     )
 
@@ -967,32 +887,15 @@ async def update_node_execution_status(
     if status == ExecutionStatus.QUEUED and execution_data is None:
         raise ValueError("Execution data must be provided when queuing an execution.")
 
-    # Validate status transitions - allowed_from should never be empty for valid statuses
-    allowed_from = VALID_STATUS_TRANSITIONS.get(status, [])
-    if not allowed_from:
-        raise ValueError(
-            f"Invalid status transition: {status} has no valid source statuses"
-        )
-
-    if res := await AgentNodeExecution.prisma().update(
-        where=cast(
-            AgentNodeExecutionWhereUniqueInput,
-            {
-                "id": node_exec_id,
-                "executionStatus": {"in": [s.value for s in allowed_from]},
-            },
-        ),
+    res = await AgentNodeExecution.prisma().update(
+        where={"id": node_exec_id},
         data=_get_update_status_data(status, execution_data, stats),
         include=EXECUTION_RESULT_INCLUDE,
-    ):
-        return NodeExecutionResult.from_db(res)
+    )
+    if not res:
+        raise ValueError(f"Execution {node_exec_id} not found.")
 
-    if res := await AgentNodeExecution.prisma().find_unique(
-        where={"id": node_exec_id}, include=EXECUTION_RESULT_INCLUDE
-    ):
-        return NodeExecutionResult.from_db(res)
-
-    raise ValueError(f"Execution {node_exec_id} not found.")
+    return NodeExecutionResult.from_db(res)
 
 
 def _get_update_status_data(
@@ -1046,17 +949,17 @@ async def get_node_execution(node_exec_id: str) -> NodeExecutionResult | None:
     return NodeExecutionResult.from_db(execution)
 
 
-def _build_node_execution_where_clause(
+async def get_node_executions(
     graph_exec_id: str | None = None,
     node_id: str | None = None,
     block_ids: list[str] | None = None,
     statuses: list[ExecutionStatus] | None = None,
+    limit: int | None = None,
     created_time_gte: datetime | None = None,
     created_time_lte: datetime | None = None,
-) -> AgentNodeExecutionWhereInput:
-    """
-    Build where clause for node execution queries.
-    """
+    include_exec_data: bool = True,
+) -> list[NodeExecutionResult]:
+    """⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints."""
     where_clause: AgentNodeExecutionWhereInput = {}
     if graph_exec_id:
         where_clause["agentGraphExecutionId"] = graph_exec_id
@@ -1073,29 +976,6 @@ def _build_node_execution_where_clause(
             "lte": created_time_lte or datetime.max.replace(tzinfo=timezone.utc),
         }
 
-    return where_clause
-
-
-async def get_node_executions(
-    graph_exec_id: str | None = None,
-    node_id: str | None = None,
-    block_ids: list[str] | None = None,
-    statuses: list[ExecutionStatus] | None = None,
-    limit: int | None = None,
-    created_time_gte: datetime | None = None,
-    created_time_lte: datetime | None = None,
-    include_exec_data: bool = True,
-) -> list[NodeExecutionResult]:
-    """⚠️ No `user_id` check: DO NOT USE without check in user-facing endpoints."""
-    where_clause = _build_node_execution_where_clause(
-        graph_exec_id=graph_exec_id,
-        node_id=node_id,
-        block_ids=block_ids,
-        statuses=statuses,
-        created_time_gte=created_time_gte,
-        created_time_lte=created_time_lte,
-    )
-
     executions = await AgentNodeExecution.prisma().find_many(
         where=where_clause,
         include=(
@@ -1137,29 +1017,31 @@ async def get_latest_node_execution(
 # ----------------- Execution Infrastructure ----------------- #
 
 
-class GraphExecutionEntry(BaseModel):
-    model_config = {"extra": "ignore"}
+class UserContext(BaseModel):
+    """Generic user context for graph execution containing user-specific settings."""
 
+    timezone: str
+
+
+class GraphExecutionEntry(BaseModel):
     user_id: str
     graph_exec_id: str
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[NodesInputMasks] = None
-    execution_context: ExecutionContext = Field(default_factory=ExecutionContext)
+    user_context: UserContext
+    parent_graph_exec_id: Optional[str] = None
 
 
 class NodeExecutionEntry(BaseModel):
-    model_config = {"extra": "ignore"}
-
     user_id: str
     graph_exec_id: str
     graph_id: str
-    graph_version: int
     node_exec_id: str
     node_id: str
     block_id: str
     inputs: BlockInput
-    execution_context: ExecutionContext = Field(default_factory=ExecutionContext)
+    user_context: UserContext
 
 
 class ExecutionQueue(Generic[T]):
@@ -1493,35 +1375,3 @@ async def get_graph_execution_by_share_token(
         created_at=execution.createdAt,
         outputs=outputs,
     )
-
-
-async def get_frequently_executed_graphs(
-    days_back: int = 30,
-    min_executions: int = 10,
-) -> list[dict]:
-    """Get graphs that have been frequently executed for monitoring."""
-    query_template = """
-    SELECT DISTINCT 
-        e."agentGraphId" as graph_id,
-        e."userId" as user_id,
-        COUNT(*) as execution_count
-    FROM {schema_prefix}"AgentGraphExecution" e
-    WHERE e."createdAt" >= $1::timestamp
-        AND e."isDeleted" = false
-        AND e."executionStatus" IN ('COMPLETED', 'FAILED', 'TERMINATED')
-    GROUP BY e."agentGraphId", e."userId"
-    HAVING COUNT(*) >= $2
-    ORDER BY execution_count DESC
-    """
-
-    start_date = datetime.now(timezone.utc) - timedelta(days=days_back)
-    result = await query_raw_with_schema(query_template, start_date, min_executions)
-
-    return [
-        {
-            "graph_id": row["graph_id"],
-            "user_id": row["user_id"],
-            "execution_count": int(row["execution_count"]),
-        }
-        for row in result
-    ]

autogpt_platform/backend/backend/data/graph.py

@@ -1,4 +1,3 @@
-import asyncio
 import logging
 import uuid
 from collections import defaultdict
@@ -18,6 +17,7 @@ from prisma.types import (
     AgentGraphWhereInput,
     AgentNodeCreateInput,
     AgentNodeLinkCreateInput,
+    LibraryAgentWhereInput,
     StoreListingVersionWhereInput,
 )
 from pydantic import BaseModel, Field, create_model
@@ -27,7 +27,7 @@ from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.llm import LlmModel
 from backend.data.db import prisma as db
-from backend.data.dynamic_fields import is_tool_pin, sanitize_pin_name
+from backend.data.dynamic_fields import extract_base_field_name
 from backend.data.includes import MAX_GRAPH_VERSIONS_FETCH
 from backend.data.model import (
     CredentialsField,
@@ -37,7 +37,7 @@ from backend.data.model import (
 )
 from backend.integrations.providers import ProviderName
 from backend.util import type as type_utils
-from backend.util.exceptions import GraphNotAccessibleError, GraphNotInLibraryError
+from backend.util.exceptions import GraphNotInLibraryError
 from backend.util.json import SafeJson
 from backend.util.models import Pagination
 
@@ -61,10 +61,6 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-class GraphSettings(BaseModel):
-    human_in_the_loop_safe_mode: bool | None = None
-
-
 class Link(BaseDbModel):
     source_id: str
     sink_id: str
@@ -229,15 +225,6 @@ class BaseGraph(BaseDbModel):
     def has_external_trigger(self) -> bool:
         return self.webhook_input_node is not None
 
-    @computed_field
-    @property
-    def has_human_in_the_loop(self) -> bool:
-        return any(
-            node.block_id
-            for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-        )
-
     @property
     def webhook_input_node(self) -> Node | None:
         return next(
@@ -592,9 +579,9 @@ class GraphModel(Graph):
                 nodes_input_masks.get(node.id, {}) if nodes_input_masks else {}
             )
             provided_inputs = set(
-                [sanitize_pin_name(name) for name in node.input_default]
+                [_sanitize_pin_name(name) for name in node.input_default]
                 + [
-                    sanitize_pin_name(link.sink_name)
+                    _sanitize_pin_name(link.sink_name)
                     for link in input_links.get(node.id, [])
                 ]
                 + ([name for name in node_input_mask] if node_input_mask else [])
@@ -710,7 +697,7 @@ class GraphModel(Graph):
                         f"{prefix}, {node.block_id} is invalid block id, available blocks: {blocks}"
                     )
 
-                sanitized_name = sanitize_pin_name(name)
+                sanitized_name = _sanitize_pin_name(name)
                 vals = node.input_default
                 if i == 0:
                     fields = (
@@ -724,7 +711,7 @@ class GraphModel(Graph):
                         if block.block_type not in [BlockType.AGENT]
                         else vals.get("input_schema", {}).get("properties", {}).keys()
                     )
-                if sanitized_name not in fields and not is_tool_pin(name):
+                if sanitized_name not in fields and not _is_tool_pin(name):
                     fields_msg = f"Allowed fields: {fields}"
                     raise ValueError(f"{prefix}, `{name}` invalid, {fields_msg}")
 
@@ -764,6 +751,17 @@ class GraphModel(Graph):
         )
 
 
+def _is_tool_pin(name: str) -> bool:
+    return name.startswith("tools_^_")
+
+
+def _sanitize_pin_name(name: str) -> str:
+    sanitized_name = extract_base_field_name(name)
+    if _is_tool_pin(sanitized_name):
+        return "tools"
+    return sanitized_name
+
+
 class GraphMeta(Graph):
     user_id: str
 
@@ -898,12 +896,10 @@ async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph
 
 async def get_graph(
     graph_id: str,
-    version: int | None,
-    user_id: str | None,
-    *,
+    version: int | None = None,
+    user_id: str | None = None,
     for_export: bool = False,
     include_subgraphs: bool = False,
-    skip_access_check: bool = False,
 ) -> GraphModel | None:
     """
     Retrieves a graph from the DB.
@@ -911,44 +907,36 @@ async def get_graph(
 
     Returns `None` if the record is not found.
     """
-    graph = None
+    where_clause: AgentGraphWhereInput = {
+        "id": graph_id,
+    }
 
-    # Only search graph directly on owned graph (or access check is skipped)
-    if skip_access_check or user_id is not None:
-        graph_where_clause: AgentGraphWhereInput = {
-            "id": graph_id,
-        }
-        if version is not None:
-            graph_where_clause["version"] = version
-        if not skip_access_check and user_id is not None:
-            graph_where_clause["userId"] = user_id
-
-        graph = await AgentGraph.prisma().find_first(
-            where=graph_where_clause,
-            include=AGENT_GRAPH_INCLUDE,
-            order={"version": "desc"},
-        )
-
-    # Use store listed graph to find not owned graph
-    if graph is None:
-        store_where_clause: StoreListingVersionWhereInput = {
-            "agentGraphId": graph_id,
-            "submissionStatus": SubmissionStatus.APPROVED,
-            "isDeleted": False,
-        }
-        if version is not None:
-            store_where_clause["agentGraphVersion"] = version
-
-        if store_listing := await StoreListingVersion.prisma().find_first(
-            where=store_where_clause,
-            order={"agentGraphVersion": "desc"},
-            include={"AgentGraph": {"include": AGENT_GRAPH_INCLUDE}},
-        ):
-            graph = store_listing.AgentGraph
+    if version is not None:
+        where_clause["version"] = version
 
+    graph = await AgentGraph.prisma().find_first(
+        where=where_clause,
+        include=AGENT_GRAPH_INCLUDE,
+        order={"version": "desc"},
+    )
     if graph is None:
         return None
 
+    if graph.userId != user_id:
+        store_listing_filter: StoreListingVersionWhereInput = {
+            "agentGraphId": graph_id,
+            "isDeleted": False,
+            "submissionStatus": SubmissionStatus.APPROVED,
+        }
+        if version is not None:
+            store_listing_filter["agentGraphVersion"] = version
+
+        # For access, the graph must be owned by the user or listed in the store
+        if not await StoreListingVersion.prisma().find_first(
+            where=store_listing_filter, order={"agentGraphVersion": "desc"}
+        ):
+            return None
+
     if include_subgraphs or for_export:
         sub_graphs = await get_sub_graphs(graph)
         return GraphModel.from_db(
@@ -990,8 +978,13 @@ async def get_graph_as_admin(
     # For access, the graph must be owned by the user or listed in the store
     if graph is None or (
         graph.userId != user_id
-        and not await is_graph_published_in_marketplace(
-            graph_id, version or graph.version
+        and not (
+            await StoreListingVersion.prisma().find_first(
+                where={
+                    "agentGraphId": graph_id,
+                    "agentGraphVersion": version or graph.version,
+                }
+            )
         )
     ):
         return None
@@ -1118,30 +1111,8 @@ async def delete_graph(graph_id: str, user_id: str) -> int:
     return entries_count
 
 
-async def get_graph_settings(user_id: str, graph_id: str) -> GraphSettings:
-    lib = await LibraryAgent.prisma().find_first(
-        where={
-            "userId": user_id,
-            "agentGraphId": graph_id,
-            "isDeleted": False,
-            "isArchived": False,
-        },
-        order={"agentGraphVersion": "desc"},
-    )
-    if not lib or not lib.settings:
-        return GraphSettings()
-
-    try:
-        return GraphSettings.model_validate(lib.settings)
-    except Exception:
-        logger.warning(
-            f"Malformed settings for LibraryAgent user={user_id} graph={graph_id}"
-        )
-        return GraphSettings()
-
-
 async def validate_graph_execution_permissions(
-    user_id: str, graph_id: str, graph_version: int, is_sub_graph: bool = False
+    graph_id: str, user_id: str, graph_version: Optional[int] = None
 ) -> None:
     """
     Validate that a user has permission to execute a specific graph.
@@ -1149,88 +1120,47 @@ async def validate_graph_execution_permissions(
     This function performs comprehensive authorization checks and raises specific
     exceptions for different types of failures to enable appropriate error handling.
 
-    ## Logic
-    A user can execute a graph if any of these is true:
-    1. They own the graph and some version of it is still listed in their library
-    2. The graph is published in the marketplace and listed in their library
-    3. The graph is published in the marketplace and is being executed as a sub-agent
-
     Args:
         graph_id: The ID of the graph to check
         user_id: The ID of the user
-        graph_version: The version of the graph to check
-        is_sub_graph: Whether this is being executed as a sub-graph.
-            If `True`, the graph isn't required to be in the user's Library.
+        graph_version: Optional specific version to check. If None (recommended),
+                      performs version-agnostic check allowing execution of any
+                      version as long as the graph is in the user's library.
+                      This is important for sub-graphs that may reference older
+                      versions no longer in the library.
 
     Raises:
-        GraphNotAccessibleError: If the graph is not accessible to the user.
-        GraphNotInLibraryError: If the graph is not in the user's library (deleted/archived).
+        GraphNotInLibraryError: If the graph is not in the user's library (deleted/archived)
         NotAuthorizedError: If the user lacks execution permissions for other reasons
     """
-    graph, library_agent = await asyncio.gather(
-        AgentGraph.prisma().find_unique(
-            where={"graphVersionId": {"id": graph_id, "version": graph_version}}
-        ),
-        LibraryAgent.prisma().find_first(
-            where={
-                "userId": user_id,
-                "agentGraphId": graph_id,
-                "isDeleted": False,
-                "isArchived": False,
-            }
-        ),
-    )
 
-    # Step 1: Check if user owns this graph
-    user_owns_graph = graph and graph.userId == user_id
+    # Step 1: Check library membership (raises specific GraphNotInLibraryError)
+    where_clause: LibraryAgentWhereInput = {
+        "userId": user_id,
+        "agentGraphId": graph_id,
+        "isDeleted": False,
+        "isArchived": False,
+    }
 
-    # Step 2: Check if agent is in the library *and not deleted*
-    user_has_in_library = library_agent is not None
+    if graph_version is not None:
+        where_clause["agentGraphVersion"] = graph_version
 
-    # Step 3: Apply permission logic
-    if not (
-        user_owns_graph
-        or await is_graph_published_in_marketplace(graph_id, graph_version)
-    ):
-        raise GraphNotAccessibleError(
-            f"You do not have access to graph #{graph_id} v{graph_version}: "
-            "it is not owned by you and not available in the Marketplace"
+    count = await LibraryAgent.prisma().count(where=where_clause)
+    if count == 0:
+        raise GraphNotInLibraryError(
+            f"Graph #{graph_id} is not accessible in your library"
         )
-    elif not (user_has_in_library or is_sub_graph):
-        raise GraphNotInLibraryError(f"Graph #{graph_id} is not in your library")
 
-    # Step 6: Check execution-specific permissions (raises generic NotAuthorizedError)
-    # Additional authorization checks beyond the above:
+    # Step 2: Check execution-specific permissions (raises generic NotAuthorizedError)
+    # Additional authorization checks beyond library membership:
     # 1. Check if user has execution credits (future)
     # 2. Check if graph is suspended/disabled (future)
     # 3. Check rate limiting rules (future)
     # 4. Check organization-level permissions (future)
 
-    # For now, the above check logic is sufficient for execution permission.
-    # Future enhancements can add more granular permission checks here.
-    # When adding new checks, raise NotAuthorizedError for non-library issues.
-
-
-async def is_graph_published_in_marketplace(graph_id: str, graph_version: int) -> bool:
-    """
-    Check if a graph is published in the marketplace.
-
-    Params:
-        graph_id: The ID of the graph to check
-        graph_version: The version of the graph to check
-
-    Returns:
-        True if the graph is published and approved in the marketplace, False otherwise
-    """
-    marketplace_listing = await StoreListingVersion.prisma().find_first(
-        where={
-            "agentGraphId": graph_id,
-            "agentGraphVersion": graph_version,
-            "submissionStatus": SubmissionStatus.APPROVED,
-            "isDeleted": False,
-        }
-    )
-    return marketplace_listing is not None
+    # For now, library membership is sufficient for execution permission
+    # Future enhancements can add more granular permission checks here
+    # When adding new checks, raise NotAuthorizedError for non-library issues
 
 
 async def create_graph(graph: Graph, user_id: str) -> GraphModel:
@@ -1247,7 +1177,7 @@ async def fork_graph(graph_id: str, graph_version: int, user_id: str) -> GraphMo
     """
     Forks a graph by copying it and all its nodes and links to a new graph.
     """
-    graph = await get_graph(graph_id, graph_version, user_id=user_id, for_export=True)
+    graph = await get_graph(graph_id, graph_version, user_id, True)
     if not graph:
         raise ValueError(f"Graph {graph_id} v{graph_version} not found")
 

autogpt_platform/backend/backend/data/human_review.py

@@ -1,258 +0,0 @@
-"""
-Data layer for Human In The Loop (HITL) review operations.
-Handles all database operations for pending human reviews.
-"""
-
-import asyncio
-import logging
-from datetime import datetime, timezone
-from typing import Optional
-
-from prisma.enums import ReviewStatus
-from prisma.models import PendingHumanReview
-from prisma.types import PendingHumanReviewUpdateInput
-from pydantic import BaseModel
-
-from backend.server.v2.executions.review.model import (
-    PendingHumanReviewModel,
-    SafeJsonData,
-)
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-
-class ReviewResult(BaseModel):
-    """Result of a review operation."""
-
-    data: Optional[SafeJsonData] = None
-    status: ReviewStatus
-    message: str = ""
-    processed: bool
-    node_exec_id: str
-
-
-async def get_or_create_human_review(
-    user_id: str,
-    node_exec_id: str,
-    graph_exec_id: str,
-    graph_id: str,
-    graph_version: int,
-    input_data: SafeJsonData,
-    message: str,
-    editable: bool,
-) -> Optional[ReviewResult]:
-    """
-    Get existing review or create a new pending review entry.
-
-    Uses upsert with empty update to get existing or create new review in a single operation.
-
-    Args:
-        user_id: ID of the user who owns this review
-        node_exec_id: ID of the node execution
-        graph_exec_id: ID of the graph execution
-        graph_id: ID of the graph template
-        graph_version: Version of the graph template
-        input_data: The data to be reviewed
-        message: Instructions for the reviewer
-        editable: Whether the data can be edited
-
-    Returns:
-        ReviewResult if the review is complete, None if waiting for human input
-    """
-    try:
-        logger.debug(f"Getting or creating review for node {node_exec_id}")
-
-        # Upsert - get existing or create new review
-        review = await PendingHumanReview.prisma().upsert(
-            where={"nodeExecId": node_exec_id},
-            data={
-                "create": {
-                    "userId": user_id,
-                    "nodeExecId": node_exec_id,
-                    "graphExecId": graph_exec_id,
-                    "graphId": graph_id,
-                    "graphVersion": graph_version,
-                    "payload": SafeJson(input_data),
-                    "instructions": message,
-                    "editable": editable,
-                    "status": ReviewStatus.WAITING,
-                },
-                "update": {},  # Do nothing on update - keep existing review as is
-            },
-        )
-
-        logger.info(
-            f"Review {'created' if review.createdAt == review.updatedAt else 'retrieved'} for node {node_exec_id} with status {review.status}"
-        )
-    except Exception as e:
-        logger.error(
-            f"Database error in get_or_create_human_review for node {node_exec_id}: {str(e)}"
-        )
-        raise
-
-    # Early return if already processed
-    if review.processed:
-        return None
-
-    # If pending, return None to continue waiting, otherwise return the review result
-    if review.status == ReviewStatus.WAITING:
-        return None
-    else:
-        return ReviewResult(
-            data=review.payload,
-            status=review.status,
-            message=review.reviewMessage or "",
-            processed=review.processed,
-            node_exec_id=review.nodeExecId,
-        )
-
-
-async def has_pending_reviews_for_graph_exec(graph_exec_id: str) -> bool:
-    """
-    Check if a graph execution has any pending reviews.
-
-    Args:
-        graph_exec_id: The graph execution ID to check
-
-    Returns:
-        True if there are reviews waiting for human input, False otherwise
-    """
-    # Check if there are any reviews waiting for human input
-    count = await PendingHumanReview.prisma().count(
-        where={"graphExecId": graph_exec_id, "status": ReviewStatus.WAITING}
-    )
-    return count > 0
-
-
-async def get_pending_reviews_for_user(
-    user_id: str, page: int = 1, page_size: int = 25
-) -> list["PendingHumanReviewModel"]:
-    """
-    Get all pending reviews for a user with pagination.
-
-    Args:
-        user_id: User ID to get reviews for
-        page: Page number (1-indexed)
-        page_size: Number of reviews per page
-
-    Returns:
-        List of pending review models
-    """
-    # Calculate offset for pagination
-    offset = (page - 1) * page_size
-
-    reviews = await PendingHumanReview.prisma().find_many(
-        where={"userId": user_id, "status": ReviewStatus.WAITING},
-        order={"createdAt": "desc"},
-        skip=offset,
-        take=page_size,
-    )
-
-    return [PendingHumanReviewModel.from_db(review) for review in reviews]
-
-
-async def get_pending_reviews_for_execution(
-    graph_exec_id: str, user_id: str
-) -> list["PendingHumanReviewModel"]:
-    """
-    Get all pending reviews for a specific graph execution.
-
-    Args:
-        graph_exec_id: Graph execution ID
-        user_id: User ID for security validation
-
-    Returns:
-        List of pending review models
-    """
-    reviews = await PendingHumanReview.prisma().find_many(
-        where={
-            "userId": user_id,
-            "graphExecId": graph_exec_id,
-            "status": ReviewStatus.WAITING,
-        },
-        order={"createdAt": "asc"},
-    )
-
-    return [PendingHumanReviewModel.from_db(review) for review in reviews]
-
-
-async def process_all_reviews_for_execution(
-    user_id: str,
-    review_decisions: dict[str, tuple[ReviewStatus, SafeJsonData | None, str | None]],
-) -> dict[str, PendingHumanReviewModel]:
-    """Process all pending reviews for an execution with approve/reject decisions.
-
-    Args:
-        user_id: User ID for ownership validation
-        review_decisions: Map of node_exec_id -> (status, reviewed_data, message)
-
-    Returns:
-        Dict of node_exec_id -> updated review model
-    """
-    if not review_decisions:
-        return {}
-
-    node_exec_ids = list(review_decisions.keys())
-
-    # Get all reviews for validation
-    reviews = await PendingHumanReview.prisma().find_many(
-        where={
-            "nodeExecId": {"in": node_exec_ids},
-            "userId": user_id,
-            "status": ReviewStatus.WAITING,
-        },
-    )
-
-    # Validate all reviews can be processed
-    if len(reviews) != len(node_exec_ids):
-        missing_ids = set(node_exec_ids) - {review.nodeExecId for review in reviews}
-        raise ValueError(
-            f"Reviews not found, access denied, or not in WAITING status: {', '.join(missing_ids)}"
-        )
-
-    # Create parallel update tasks
-    update_tasks = []
-
-    for review in reviews:
-        new_status, reviewed_data, message = review_decisions[review.nodeExecId]
-        has_data_changes = reviewed_data is not None and reviewed_data != review.payload
-
-        # Check edit permissions for actual data modifications
-        if has_data_changes and not review.editable:
-            raise ValueError(f"Review {review.nodeExecId} is not editable")
-
-        update_data: PendingHumanReviewUpdateInput = {
-            "status": new_status,
-            "reviewMessage": message,
-            "wasEdited": has_data_changes,
-            "reviewedAt": datetime.now(timezone.utc),
-        }
-
-        if has_data_changes:
-            update_data["payload"] = SafeJson(reviewed_data)
-
-        task = PendingHumanReview.prisma().update(
-            where={"nodeExecId": review.nodeExecId},
-            data=update_data,
-        )
-        update_tasks.append(task)
-
-    # Execute all updates in parallel and get updated reviews
-    updated_reviews = await asyncio.gather(*update_tasks)
-
-    # Note: Execution resumption is now handled at the API layer after ALL reviews
-    # for an execution are processed (both approved and rejected)
-
-    # Return as dict for easy access
-    return {
-        review.nodeExecId: PendingHumanReviewModel.from_db(review)
-        for review in updated_reviews
-    }
-
-
-async def update_review_processed_status(node_exec_id: str, processed: bool) -> None:
-    """Update the processed status of a review."""
-    await PendingHumanReview.prisma().update(
-        where={"nodeExecId": node_exec_id}, data={"processed": processed}
-    )

autogpt_platform/backend/backend/data/human_review_test.py

@@ -1,342 +0,0 @@
-import datetime
-from unittest.mock import AsyncMock, Mock
-
-import pytest
-import pytest_mock
-from prisma.enums import ReviewStatus
-
-from backend.data.human_review import (
-    get_or_create_human_review,
-    get_pending_reviews_for_execution,
-    get_pending_reviews_for_user,
-    has_pending_reviews_for_graph_exec,
-    process_all_reviews_for_execution,
-)
-
-
-@pytest.fixture
-def sample_db_review():
-    """Create a sample database review object"""
-    mock_review = Mock()
-    mock_review.nodeExecId = "test_node_123"
-    mock_review.userId = "test-user-123"
-    mock_review.graphExecId = "test_graph_exec_456"
-    mock_review.graphId = "test_graph_789"
-    mock_review.graphVersion = 1
-    mock_review.payload = {"data": "test payload"}
-    mock_review.instructions = "Please review"
-    mock_review.editable = True
-    mock_review.status = ReviewStatus.WAITING
-    mock_review.reviewMessage = None
-    mock_review.wasEdited = False
-    mock_review.processed = False
-    mock_review.createdAt = datetime.datetime.now(datetime.timezone.utc)
-    mock_review.updatedAt = None
-    mock_review.reviewedAt = None
-    return mock_review
-
-
-@pytest.mark.asyncio
-async def test_get_or_create_human_review_new(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test creating a new human review"""
-    # Mock the upsert to return a new review (created_at == updated_at)
-    sample_db_review.status = ReviewStatus.WAITING
-    sample_db_review.processed = False
-
-    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
-
-    result = await get_or_create_human_review(
-        user_id="test-user-123",
-        node_exec_id="test_node_123",
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        input_data={"data": "test payload"},
-        message="Please review",
-        editable=True,
-    )
-
-    # Should return None for pending reviews (waiting for human input)
-    assert result is None
-
-
-@pytest.mark.asyncio
-async def test_get_or_create_human_review_approved(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test retrieving an already approved review"""
-    # Set up review as already approved
-    sample_db_review.status = ReviewStatus.APPROVED
-    sample_db_review.processed = False
-    sample_db_review.reviewMessage = "Looks good"
-
-    mock_upsert = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_upsert.return_value.upsert = AsyncMock(return_value=sample_db_review)
-
-    result = await get_or_create_human_review(
-        user_id="test-user-123",
-        node_exec_id="test_node_123",
-        graph_exec_id="test_graph_exec_456",
-        graph_id="test_graph_789",
-        graph_version=1,
-        input_data={"data": "test payload"},
-        message="Please review",
-        editable=True,
-    )
-
-    # Should return the approved result
-    assert result is not None
-    assert result.status == ReviewStatus.APPROVED
-    assert result.data == {"data": "test payload"}
-    assert result.message == "Looks good"
-
-
-@pytest.mark.asyncio
-async def test_has_pending_reviews_for_graph_exec_true(
-    mocker: pytest_mock.MockFixture,
-):
-    """Test when there are pending reviews"""
-    mock_count = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_count.return_value.count = AsyncMock(return_value=2)
-
-    result = await has_pending_reviews_for_graph_exec("test_graph_exec")
-
-    assert result is True
-
-
-@pytest.mark.asyncio
-async def test_has_pending_reviews_for_graph_exec_false(
-    mocker: pytest_mock.MockFixture,
-):
-    """Test when there are no pending reviews"""
-    mock_count = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_count.return_value.count = AsyncMock(return_value=0)
-
-    result = await has_pending_reviews_for_graph_exec("test_graph_exec")
-
-    assert result is False
-
-
-@pytest.mark.asyncio
-async def test_get_pending_reviews_for_user(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test getting pending reviews for a user with pagination"""
-    mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_find_many.return_value.find_many = AsyncMock(return_value=[sample_db_review])
-
-    result = await get_pending_reviews_for_user("test_user", page=2, page_size=10)
-
-    assert len(result) == 1
-    assert result[0].node_exec_id == "test_node_123"
-
-    # Verify pagination parameters
-    call_args = mock_find_many.return_value.find_many.call_args
-    assert call_args.kwargs["skip"] == 10  # (page-1) * page_size = (2-1) * 10
-    assert call_args.kwargs["take"] == 10
-
-
-@pytest.mark.asyncio
-async def test_get_pending_reviews_for_execution(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test getting pending reviews for specific execution"""
-    mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_find_many.return_value.find_many = AsyncMock(return_value=[sample_db_review])
-
-    result = await get_pending_reviews_for_execution(
-        "test_graph_exec_456", "test-user-123"
-    )
-
-    assert len(result) == 1
-    assert result[0].graph_exec_id == "test_graph_exec_456"
-
-    # Verify it filters by execution and user
-    call_args = mock_find_many.return_value.find_many.call_args
-    where_clause = call_args.kwargs["where"]
-    assert where_clause["userId"] == "test-user-123"
-    assert where_clause["graphExecId"] == "test_graph_exec_456"
-    assert where_clause["status"] == ReviewStatus.WAITING
-
-
-@pytest.mark.asyncio
-async def test_process_all_reviews_for_execution_success(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test successful processing of reviews for an execution"""
-    # Mock finding reviews
-    mock_prisma = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_prisma.return_value.find_many = AsyncMock(return_value=[sample_db_review])
-
-    # Mock updating reviews
-    updated_review = Mock()
-    updated_review.nodeExecId = "test_node_123"
-    updated_review.userId = "test-user-123"
-    updated_review.graphExecId = "test_graph_exec_456"
-    updated_review.graphId = "test_graph_789"
-    updated_review.graphVersion = 1
-    updated_review.payload = {"data": "modified"}
-    updated_review.instructions = "Please review"
-    updated_review.editable = True
-    updated_review.status = ReviewStatus.APPROVED
-    updated_review.reviewMessage = "Approved"
-    updated_review.wasEdited = True
-    updated_review.processed = False
-    updated_review.createdAt = datetime.datetime.now(datetime.timezone.utc)
-    updated_review.updatedAt = datetime.datetime.now(datetime.timezone.utc)
-    updated_review.reviewedAt = datetime.datetime.now(datetime.timezone.utc)
-    mock_prisma.return_value.update = AsyncMock(return_value=updated_review)
-
-    # Mock gather to simulate parallel updates
-    mocker.patch(
-        "backend.data.human_review.asyncio.gather",
-        new=AsyncMock(return_value=[updated_review]),
-    )
-
-    result = await process_all_reviews_for_execution(
-        user_id="test-user-123",
-        review_decisions={
-            "test_node_123": (ReviewStatus.APPROVED, {"data": "modified"}, "Approved")
-        },
-    )
-
-    assert len(result) == 1
-    assert "test_node_123" in result
-    assert result["test_node_123"].status == ReviewStatus.APPROVED
-
-
-@pytest.mark.asyncio
-async def test_process_all_reviews_for_execution_validation_errors(
-    mocker: pytest_mock.MockFixture,
-):
-    """Test validation errors in process_all_reviews_for_execution"""
-    # Mock finding fewer reviews than requested (some not found)
-    mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_find_many.return_value.find_many = AsyncMock(
-        return_value=[]
-    )  # No reviews found
-
-    with pytest.raises(ValueError, match="Reviews not found"):
-        await process_all_reviews_for_execution(
-            user_id="test-user-123",
-            review_decisions={
-                "nonexistent_node": (ReviewStatus.APPROVED, {"data": "test"}, "message")
-            },
-        )
-
-
-@pytest.mark.asyncio
-async def test_process_all_reviews_edit_permission_error(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test editing non-editable review"""
-    # Set review as non-editable
-    sample_db_review.editable = False
-
-    # Mock finding reviews
-    mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_find_many.return_value.find_many = AsyncMock(return_value=[sample_db_review])
-
-    with pytest.raises(ValueError, match="not editable"):
-        await process_all_reviews_for_execution(
-            user_id="test-user-123",
-            review_decisions={
-                "test_node_123": (
-                    ReviewStatus.APPROVED,
-                    {"data": "modified"},
-                    "message",
-                )
-            },
-        )
-
-
-@pytest.mark.asyncio
-async def test_process_all_reviews_mixed_approval_rejection(
-    mocker: pytest_mock.MockFixture,
-    sample_db_review,
-):
-    """Test processing mixed approval and rejection decisions"""
-    # Create second review for rejection
-    second_review = Mock()
-    second_review.nodeExecId = "test_node_456"
-    second_review.userId = "test-user-123"
-    second_review.graphExecId = "test_graph_exec_456"
-    second_review.graphId = "test_graph_789"
-    second_review.graphVersion = 1
-    second_review.payload = {"data": "original"}
-    second_review.instructions = "Second review"
-    second_review.editable = True
-    second_review.status = ReviewStatus.WAITING
-    second_review.reviewMessage = None
-    second_review.wasEdited = False
-    second_review.processed = False
-    second_review.createdAt = datetime.datetime.now(datetime.timezone.utc)
-    second_review.updatedAt = None
-    second_review.reviewedAt = None
-
-    # Mock finding reviews
-    mock_find_many = mocker.patch("backend.data.human_review.PendingHumanReview.prisma")
-    mock_find_many.return_value.find_many = AsyncMock(
-        return_value=[sample_db_review, second_review]
-    )
-
-    # Mock updating reviews
-    approved_review = Mock()
-    approved_review.nodeExecId = "test_node_123"
-    approved_review.userId = "test-user-123"
-    approved_review.graphExecId = "test_graph_exec_456"
-    approved_review.graphId = "test_graph_789"
-    approved_review.graphVersion = 1
-    approved_review.payload = {"data": "modified"}
-    approved_review.instructions = "Please review"
-    approved_review.editable = True
-    approved_review.status = ReviewStatus.APPROVED
-    approved_review.reviewMessage = "Approved"
-    approved_review.wasEdited = True
-    approved_review.processed = False
-    approved_review.createdAt = datetime.datetime.now(datetime.timezone.utc)
-    approved_review.updatedAt = datetime.datetime.now(datetime.timezone.utc)
-    approved_review.reviewedAt = datetime.datetime.now(datetime.timezone.utc)
-
-    rejected_review = Mock()
-    rejected_review.nodeExecId = "test_node_456"
-    rejected_review.userId = "test-user-123"
-    rejected_review.graphExecId = "test_graph_exec_456"
-    rejected_review.graphId = "test_graph_789"
-    rejected_review.graphVersion = 1
-    rejected_review.payload = {"data": "original"}
-    rejected_review.instructions = "Please review"
-    rejected_review.editable = True
-    rejected_review.status = ReviewStatus.REJECTED
-    rejected_review.reviewMessage = "Rejected"
-    rejected_review.wasEdited = False
-    rejected_review.processed = False
-    rejected_review.createdAt = datetime.datetime.now(datetime.timezone.utc)
-    rejected_review.updatedAt = datetime.datetime.now(datetime.timezone.utc)
-    rejected_review.reviewedAt = datetime.datetime.now(datetime.timezone.utc)
-
-    mocker.patch(
-        "backend.data.human_review.asyncio.gather",
-        new=AsyncMock(return_value=[approved_review, rejected_review]),
-    )
-
-    result = await process_all_reviews_for_execution(
-        user_id="test-user-123",
-        review_decisions={
-            "test_node_123": (ReviewStatus.APPROVED, {"data": "modified"}, "Approved"),
-            "test_node_456": (ReviewStatus.REJECTED, None, "Rejected"),
-        },
-    )
-
-    assert len(result) == 2
-    assert "test_node_123" in result
-    assert "test_node_456" in result

autogpt_platform/backend/backend/data/integrations.py

@@ -1,5 +1,5 @@
 import logging
-from typing import TYPE_CHECKING, AsyncGenerator, Literal, Optional, overload
+from typing import AsyncGenerator, Literal, Optional, overload
 
 from prisma.models import AgentNode, AgentPreset, IntegrationWebhook
 from prisma.types import (
@@ -19,12 +19,10 @@ from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks import get_webhook_manager
 from backend.integrations.webhooks.utils import webhook_ingress_url
+from backend.server.v2.library.model import LibraryAgentPreset
 from backend.util.exceptions import NotFoundError
 from backend.util.json import SafeJson
 
-if TYPE_CHECKING:
-    from backend.server.v2.library.model import LibraryAgentPreset
-
 from .db import BaseDbModel
 from .graph import NodeModel
 
@@ -66,7 +64,7 @@ class Webhook(BaseDbModel):
 
 class WebhookWithRelations(Webhook):
     triggered_nodes: list[NodeModel]
-    triggered_presets: list["LibraryAgentPreset"]
+    triggered_presets: list[LibraryAgentPreset]
 
     @staticmethod
     def from_db(webhook: IntegrationWebhook):
@@ -75,12 +73,6 @@ class WebhookWithRelations(Webhook):
                 "AgentNodes and AgentPresets must be included in "
                 "IntegrationWebhook query with relations"
             )
-        # LibraryAgentPreset import is moved to TYPE_CHECKING to avoid circular import:
-        # integrations.py → library/model.py → integrations.py (for Webhook)
-        # Runtime import is used in WebhookWithRelations.from_db() method instead
-        # Import at runtime to avoid circular dependency
-        from backend.server.v2.library.model import LibraryAgentPreset
-
         return WebhookWithRelations(
             **Webhook.from_db(webhook).model_dump(),
             triggered_nodes=[NodeModel.from_db(node) for node in webhook.AgentNodes],

autogpt_platform/backend/backend/data/model.py

@@ -22,7 +22,7 @@ from typing import (
 from urllib.parse import urlparse
 from uuid import uuid4
 
-from prisma.enums import CreditTransactionType, OnboardingStep
+from prisma.enums import CreditTransactionType
 from pydantic import (
     BaseModel,
     ConfigDict,
@@ -46,7 +46,6 @@ from backend.util.settings import Secrets
 
 # Type alias for any provider name (including custom ones)
 AnyProviderName = str  # Will be validated as ProviderName at runtime
-USER_TIMEZONE_NOT_SET = "not-set"
 
 
 class User(BaseModel):
@@ -99,7 +98,7 @@ class User(BaseModel):
 
     # User timezone for scheduling and time display
     timezone: str = Field(
-        default=USER_TIMEZONE_NOT_SET,
+        default="not-set",
         description="User timezone (IANA timezone identifier or 'not-set')",
     )
 
@@ -156,7 +155,7 @@ class User(BaseModel):
             notify_on_daily_summary=prisma_user.notifyOnDailySummary or True,
             notify_on_weekly_summary=prisma_user.notifyOnWeeklySummary or True,
             notify_on_monthly_summary=prisma_user.notifyOnMonthlySummary or True,
-            timezone=prisma_user.timezone or USER_TIMEZONE_NOT_SET,
+            timezone=prisma_user.timezone or "not-set",
         )
 
 
@@ -434,18 +433,6 @@ class OAuthState(BaseModel):
     code_verifier: Optional[str] = None
     """Unix timestamp (seconds) indicating when this OAuth state expires"""
     scopes: list[str]
-    # Fields for external API OAuth flows
-    callback_url: Optional[str] = None
-    """External app's callback URL for OAuth redirect"""
-    state_metadata: dict[str, Any] = Field(default_factory=dict)
-    """Metadata to echo back to external app on completion"""
-    initiated_by_api_key_id: Optional[str] = None
-    """ID of the API key that initiated this OAuth flow"""
-
-    @property
-    def is_external(self) -> bool:
-        """Whether this OAuth flow was initiated via external API."""
-        return self.callback_url is not None
 
 
 class UserMetadata(BaseModel):
@@ -846,10 +833,6 @@ class GraphExecutionStats(BaseModel):
     activity_status: Optional[str] = Field(
         default=None, description="AI-generated summary of what the agent did"
     )
-    correctness_score: Optional[float] = Field(
-        default=None,
-        description="AI-generated score (0.0-1.0) indicating how well the execution achieved its intended purpose",
-    )
 
 
 class UserExecutionSummaryStats(BaseModel):
@@ -868,20 +851,3 @@ class UserExecutionSummaryStats(BaseModel):
     total_execution_time: float = Field(default=0)
     average_execution_time: float = Field(default=0)
     cost_breakdown: dict[str, float] = Field(default_factory=dict)
-
-
-class UserOnboarding(BaseModel):
-    userId: str
-    completedSteps: list[OnboardingStep]
-    walletShown: bool
-    notified: list[OnboardingStep]
-    rewardedFor: list[OnboardingStep]
-    usageReason: Optional[str]
-    integrations: list[str]
-    otherIntegrations: Optional[str]
-    selectedStoreListingVersionId: Optional[str]
-    agentInput: Optional[dict[str, Any]]
-    onboardingAgentExecutionId: Optional[str]
-    agentRuns: int
-    lastRunAt: Optional[datetime]
-    consecutiveRunDays: int

autogpt_platform/backend/backend/data/notification_bus.py

@@ -1,38 +0,0 @@
-from __future__ import annotations
-
-from typing import AsyncGenerator
-
-from pydantic import BaseModel, field_serializer
-
-from backend.data.event_bus import AsyncRedisEventBus
-from backend.server.model import NotificationPayload
-from backend.util.settings import Settings
-
-
-class NotificationEvent(BaseModel):
-    """Generic notification event destined for websocket delivery."""
-
-    user_id: str
-    payload: NotificationPayload
-
-    @field_serializer("payload")
-    def serialize_payload(self, payload: NotificationPayload):
-        """Ensure extra fields survive Redis serialization."""
-        return payload.model_dump()
-
-
-class AsyncRedisNotificationEventBus(AsyncRedisEventBus[NotificationEvent]):
-    Model = NotificationEvent  # type: ignore
-
-    @property
-    def event_bus_name(self) -> str:
-        return Settings().config.notification_event_bus_name
-
-    async def publish(self, event: NotificationEvent) -> None:
-        await self.publish_event(event, event.user_id)
-
-    async def listen(
-        self, user_id: str = "*"
-    ) -> AsyncGenerator[NotificationEvent, None]:
-        async for event in self.listen_events(user_id):
-            yield event

autogpt_platform/backend/backend/data/onboarding.py

@@ -1,7 +1,6 @@
 import re
-from datetime import datetime, timedelta, timezone
-from typing import Any, Literal, Optional
-from zoneinfo import ZoneInfo
+from datetime import datetime
+from typing import Any, Optional
 
 import prisma
 import pydantic
@@ -9,18 +8,12 @@ from prisma.enums import OnboardingStep
 from prisma.models import UserOnboarding
 from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
 
-from backend.data import execution as execution_db
+from backend.data.block import get_blocks
 from backend.data.credit import get_user_credit_model
-from backend.data.notification_bus import (
-    AsyncRedisNotificationEventBus,
-    NotificationEvent,
-)
-from backend.data.user import get_user_by_id
-from backend.server.model import OnboardingNotificationPayload
+from backend.data.model import CredentialsMetaInput
 from backend.server.v2.store.model import StoreAgentDetails
 from backend.util.cache import cached
 from backend.util.json import SafeJson
-from backend.util.timezone_utils import get_user_timezone_or_utc
 
 # Mapping from user reason id to categories to search for when choosing agent to show
 REASON_MAPPING: dict[str, list[str]] = {
@@ -33,20 +26,9 @@ REASON_MAPPING: dict[str, list[str]] = {
 POINTS_AGENT_COUNT = 50  # Number of agents to calculate points for
 MIN_AGENT_COUNT = 2  # Minimum number of marketplace agents to enable onboarding
 
-FrontendOnboardingStep = Literal[
-    OnboardingStep.WELCOME,
-    OnboardingStep.USAGE_REASON,
-    OnboardingStep.INTEGRATIONS,
-    OnboardingStep.AGENT_CHOICE,
-    OnboardingStep.AGENT_NEW_RUN,
-    OnboardingStep.AGENT_INPUT,
-    OnboardingStep.CONGRATS,
-    OnboardingStep.MARKETPLACE_VISIT,
-    OnboardingStep.BUILDER_OPEN,
-]
-
 
 class UserOnboardingUpdate(pydantic.BaseModel):
+    completedSteps: Optional[list[OnboardingStep]] = None
     walletShown: Optional[bool] = None
     notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
@@ -55,6 +37,9 @@ class UserOnboardingUpdate(pydantic.BaseModel):
     selectedStoreListingVersionId: Optional[str] = None
     agentInput: Optional[dict[str, Any]] = None
     onboardingAgentExecutionId: Optional[str] = None
+    agentRuns: Optional[int] = None
+    lastRunAt: Optional[datetime] = None
+    consecutiveRunDays: Optional[int] = None
 
 
 async def get_user_onboarding(user_id: str):
@@ -93,7 +78,25 @@ async def reset_user_onboarding(user_id: str):
 async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
     update: UserOnboardingUpdateInput = {}
     onboarding = await get_user_onboarding(user_id)
-    if data.walletShown:
+    if data.completedSteps is not None:
+        update["completedSteps"] = list(set(data.completedSteps + onboarding.completedSteps))
+        for step in (
+            OnboardingStep.AGENT_NEW_RUN,
+            OnboardingStep.MARKETPLACE_VISIT,
+            OnboardingStep.MARKETPLACE_ADD_AGENT,
+            OnboardingStep.MARKETPLACE_RUN_AGENT,
+            OnboardingStep.BUILDER_SAVE_AGENT,
+            OnboardingStep.RE_RUN_AGENT,
+            OnboardingStep.SCHEDULE_AGENT,
+            OnboardingStep.RUN_AGENTS,
+            OnboardingStep.RUN_3_DAYS,
+            OnboardingStep.TRIGGER_WEBHOOK,
+            OnboardingStep.RUN_14_DAYS,
+            OnboardingStep.RUN_AGENTS_100,
+        ):
+            if step in data.completedSteps:
+                await reward_user(user_id, step, onboarding)
+    if data.walletShown == True:
         update["walletShown"] = data.walletShown
     if data.notified is not None:
         update["notified"] = list(set(data.notified + onboarding.notified))
@@ -109,6 +112,12 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["agentInput"] = SafeJson(data.agentInput)
     if data.onboardingAgentExecutionId is not None:
         update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
+    if data.agentRuns is not None and data.agentRuns > onboarding.agentRuns:
+        update["agentRuns"] = data.agentRuns
+    if data.lastRunAt is not None:
+        update["lastRunAt"] = data.lastRunAt
+    if data.consecutiveRunDays is not None:
+        update["consecutiveRunDays"] = data.consecutiveRunDays
 
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
@@ -119,7 +128,7 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
     )
 
 
-async def _reward_user(user_id: str, onboarding: UserOnboarding, step: OnboardingStep):
+async def reward_user(user_id: str, step: OnboardingStep, onboarding: UserOnboarding):
     reward = 0
     match step:
         # Reward user when they clicked New Run during onboarding
@@ -157,66 +166,35 @@ async def _reward_user(user_id: str, onboarding: UserOnboarding, step: Onboardin
     if step in onboarding.rewardedFor:
         return
 
+    onboarding.rewardedFor.append(step)
     user_credit_model = await get_user_credit_model(user_id)
     await user_credit_model.onboarding_reward(user_id, reward, step)
     await UserOnboarding.prisma().update(
         where={"userId": user_id},
         data={
-            "rewardedFor": list(set(onboarding.rewardedFor + [step])),
+            "completedSteps": list(set(onboarding.completedSteps + [step])),
+            "rewardedFor": onboarding.rewardedFor,
         },
     )
 
 
-async def complete_onboarding_step(user_id: str, step: OnboardingStep):
+async def complete_webhook_trigger_step(user_id: str):
     """
-    Completes the specified onboarding step for the user if not already completed.
+    Completes the TRIGGER_WEBHOOK onboarding step for the user if not already completed.
     """
+
     onboarding = await get_user_onboarding(user_id)
-    if step not in onboarding.completedSteps:
-        await UserOnboarding.prisma().update(
-            where={"userId": user_id},
-            data={
-                "completedSteps": list(set(onboarding.completedSteps + [step])),
-            },
+    if OnboardingStep.TRIGGER_WEBHOOK not in onboarding.completedSteps:
+        await update_user_onboarding(
+            user_id,
+            UserOnboardingUpdate(
+                completedSteps=onboarding.completedSteps
+                + [OnboardingStep.TRIGGER_WEBHOOK]
+            ),
         )
-        await _reward_user(user_id, onboarding, step)
-        await _send_onboarding_notification(user_id, step)
 
 
-async def _send_onboarding_notification(
-    user_id: str, step: OnboardingStep | None, event: str = "step_completed"
-):
-    """
-    Sends an onboarding notification to the user.
-    """
-    payload = OnboardingNotificationPayload(
-        type="onboarding",
-        event=event,
-        step=step,
-    )
-    await AsyncRedisNotificationEventBus().publish(
-        NotificationEvent(user_id=user_id, payload=payload)
-    )
-
-
-async def complete_re_run_agent(user_id: str, graph_id: str) -> None:
-    """
-    Complete RE_RUN_AGENT step when a user runs a graph they've run before.
-    Keeps overhead low by only counting executions if the step is still pending.
-    """
-    onboarding = await get_user_onboarding(user_id)
-    if OnboardingStep.RE_RUN_AGENT in onboarding.completedSteps:
-        return
-
-    # Includes current execution, so count > 1 means there was at least one prior run.
-    previous_exec_count = await execution_db.get_graph_executions_count(
-        user_id=user_id, graph_id=graph_id
-    )
-    if previous_exec_count > 1:
-        await complete_onboarding_step(user_id, OnboardingStep.RE_RUN_AGENT)
-
-
-def _clean_and_split(text: str) -> list[str]:
+def clean_and_split(text: str) -> list[str]:
     """
     Removes all special characters from a string, truncates it to 100 characters,
     and splits it by whitespace and commas.
@@ -239,7 +217,7 @@ def _clean_and_split(text: str) -> list[str]:
     return words
 
 
-def _calculate_points(
+def calculate_points(
     agent, categories: list[str], custom: list[str], integrations: list[str]
 ) -> int:
     """
@@ -283,85 +261,18 @@ def _calculate_points(
     return int(points)
 
 
-def _normalize_datetime(value: datetime | None) -> datetime | None:
-    if value is None:
-        return None
-    if value.tzinfo is None:
-        return value.replace(tzinfo=timezone.utc)
-    return value.astimezone(timezone.utc)
+def get_credentials_blocks() -> dict[str, str]:
+    # Returns a dictionary of block id to credentials field name
+    creds: dict[str, str] = {}
+    blocks = get_blocks()
+    for id, block in blocks.items():
+        for field_name, field_info in block().input_schema.model_fields.items():
+            if field_info.annotation == CredentialsMetaInput:
+                creds[id] = field_name
+    return creds
 
 
-def _calculate_consecutive_run_days(
-    last_run_at: datetime | None, current_consecutive_days: int, user_timezone: str
-) -> tuple[datetime, int]:
-    tz = ZoneInfo(user_timezone)
-    local_now = datetime.now(tz)
-    normalized_last_run = _normalize_datetime(last_run_at)
-
-    if normalized_last_run is None:
-        return local_now.astimezone(timezone.utc), 1
-
-    last_run_local = normalized_last_run.astimezone(tz)
-    last_run_date = last_run_local.date()
-    today = local_now.date()
-
-    if last_run_date == today:
-        return local_now.astimezone(timezone.utc), current_consecutive_days
-
-    if last_run_date == today - timedelta(days=1):
-        return local_now.astimezone(timezone.utc), current_consecutive_days + 1
-
-    return local_now.astimezone(timezone.utc), 1
-
-
-def _get_run_milestone_steps(
-    new_run_count: int, consecutive_days: int
-) -> list[OnboardingStep]:
-    milestones: list[OnboardingStep] = []
-    if new_run_count >= 10:
-        milestones.append(OnboardingStep.RUN_AGENTS)
-    if new_run_count >= 100:
-        milestones.append(OnboardingStep.RUN_AGENTS_100)
-    if consecutive_days >= 3:
-        milestones.append(OnboardingStep.RUN_3_DAYS)
-    if consecutive_days >= 14:
-        milestones.append(OnboardingStep.RUN_14_DAYS)
-    return milestones
-
-
-async def _get_user_timezone(user_id: str) -> str:
-    user = await get_user_by_id(user_id)
-    return get_user_timezone_or_utc(user.timezone if user else None)
-
-
-async def increment_runs(user_id: str):
-    """
-    Increment a user's run counters and trigger any onboarding milestones.
-    """
-    user_timezone = await _get_user_timezone(user_id)
-    onboarding = await get_user_onboarding(user_id)
-    new_run_count = onboarding.agentRuns + 1
-    last_run_at, consecutive_run_days = _calculate_consecutive_run_days(
-        onboarding.lastRunAt, onboarding.consecutiveRunDays, user_timezone
-    )
-
-    await UserOnboarding.prisma().update(
-        where={"userId": user_id},
-        data={
-            "agentRuns": {"increment": 1},
-            "lastRunAt": last_run_at,
-            "consecutiveRunDays": consecutive_run_days,
-        },
-    )
-
-    milestones = _get_run_milestone_steps(new_run_count, consecutive_run_days)
-    new_steps = [step for step in milestones if step not in onboarding.completedSteps]
-
-    for step in new_steps:
-        await complete_onboarding_step(user_id, step)
-    # Send progress notification if no steps were completed, so client refetches onboarding state
-    if not new_steps:
-        await _send_onboarding_notification(user_id, None, event="increment_runs")
+CREDENTIALS_FIELDS: dict[str, str] = get_credentials_blocks()
 
 
 async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
@@ -370,7 +281,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
 
     where_clause: dict[str, Any] = {}
 
-    custom = _clean_and_split((user_onboarding.usageReason or "").lower())
+    custom = clean_and_split((user_onboarding.usageReason or "").lower())
 
     if categories:
         where_clause["OR"] = [
@@ -418,7 +329,7 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
     # Calculate points for the first X agents and choose the top 2
     agent_points = []
     for agent in storeAgents[:POINTS_AGENT_COUNT]:
-        points = _calculate_points(
+        points = calculate_points(
             agent, categories, custom, user_onboarding.integrations
         )
         agent_points.append((agent, points))
@@ -432,7 +343,6 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
             slug=agent.slug,
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
-            agent_output_demo=agent.agent_output_demo or "",
             agent_image=agent.agent_image,
             creator=agent.creator_username,
             creator_avatar=agent.creator_avatar,

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -13,11 +13,12 @@ except ImportError:
 
 from pydantic import SecretStr
 
-from backend.blocks.llm import AIStructuredResponseGeneratorBlock, LlmModel
+from backend.blocks.llm import LlmModel, llm_call
 from backend.data.block import get_block
 from backend.data.execution import ExecutionStatus, NodeExecutionResult
 from backend.data.model import APIKeyCredentials, GraphExecutionStats
 from backend.util.feature_flag import Flag, is_feature_enabled
+from backend.util.retry import func_retry
 from backend.util.settings import Settings
 from backend.util.truncate import truncate
 
@@ -27,101 +28,6 @@ if TYPE_CHECKING:
 logger = logging.getLogger(__name__)
 
 
-# Default system prompt template for activity status generation
-DEFAULT_SYSTEM_PROMPT = """You are an AI assistant analyzing what an agent execution accomplished and whether it worked correctly. 
-You need to provide both a user-friendly summary AND a correctness assessment.
-
-FOR THE ACTIVITY STATUS:
-- Write from the user's perspective about what they accomplished, NOT about technical execution details
-- Focus on the ACTUAL TASK the user wanted done, not the internal workflow steps
-- Avoid technical terms like 'workflow', 'execution', 'components', 'nodes', 'processing', etc.
-- Keep it to 3 sentences maximum. Be conversational and human-friendly
-
-FOR THE CORRECTNESS SCORE:
-- Provide a score from 0.0 to 1.0 indicating how well the execution achieved its intended purpose
-- Use this scoring guide:
-  0.0-0.2: Failure - The result clearly did not meet the task requirements
-  0.2-0.4: Poor - Major issues; only small parts of the goal were achieved
-  0.4-0.6: Partial Success - Some objectives met, but with noticeable gaps or inaccuracies
-  0.6-0.8: Mostly Successful - Largely achieved the intended outcome, with minor flaws
-  0.8-1.0: Success - Fully met or exceeded the task requirements
-- Base the score on actual outputs produced, not just technical completion
-
-UNDERSTAND THE INTENDED PURPOSE:
-- FIRST: Read the graph description carefully to understand what the user wanted to accomplish
-- The graph name and description tell you the main goal/intention of this automation
-- Use this intended purpose as your PRIMARY criteria for success/failure evaluation
-- Ask yourself: 'Did this execution actually accomplish what the graph was designed to do?'
-
-CRITICAL OUTPUT ANALYSIS:
-- Check if blocks that should produce user-facing results actually produced outputs
-- Blocks with names containing 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' are usually meant to produce final results
-- If these critical blocks have NO outputs (empty recent_outputs), the task likely FAILED even if status shows 'completed'
-- Sub-agents (AgentExecutorBlock) that produce no outputs usually indicate failed sub-tasks
-- Most importantly: Does the execution result match what the graph description promised to deliver?
-
-SUCCESS EVALUATION BASED ON INTENTION:
-- If the graph is meant to 'create blog posts' → check if blog content was actually created
-- If the graph is meant to 'send emails' → check if emails were actually sent
-- If the graph is meant to 'analyze data' → check if analysis results were produced
-- If the graph is meant to 'generate reports' → check if reports were generated
-- Technical completion ≠ goal achievement. Focus on whether the USER'S INTENDED OUTCOME was delivered
-
-IMPORTANT: Be HONEST about what actually happened:
-- If the input was invalid/nonsensical, say so directly
-- If the task failed, explain what went wrong in simple terms
-- If errors occurred, focus on what the user needs to know
-- Only claim success if the INTENDED PURPOSE was genuinely accomplished AND produced expected outputs
-- Don't sugar-coat failures or present them as helpful feedback
-- ESPECIALLY: If the graph's main purpose wasn't achieved, this is a failure regardless of 'completed' status
-
-Understanding Errors:
-- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)
-- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished
-- Missing outputs from critical blocks: Even if no errors, this means the task failed to produce expected results
-- Focus on whether the graph's intended purpose was fulfilled, not whether technical steps completed"""
-
-# Default user prompt template for activity status generation
-DEFAULT_USER_PROMPT = """A user ran '{{GRAPH_NAME}}' to accomplish something. Based on this execution data, 
-provide both an activity summary and correctness assessment:
-
-{{EXECUTION_DATA}}
-
-ANALYSIS CHECKLIST:
-1. READ graph_info.description FIRST - this tells you what the user intended to accomplish
-2. Check overall_status.graph_error - if present, the entire execution failed
-3. Look for nodes with 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' in their block_name
-4. Check if these critical blocks have empty recent_outputs arrays - this indicates failure
-5. Look for AgentExecutorBlock (sub-agents) with no outputs - this suggests sub-task failures
-6. Count how many nodes produced outputs vs total nodes - low ratio suggests problems
-7. MOST IMPORTANT: Does the execution outcome match what graph_info.description promised?
-
-INTENTION-BASED EVALUATION:
-- If description mentions 'blog writing' → did it create blog content?
-- If description mentions 'email automation' → were emails actually sent?
-- If description mentions 'data analysis' → were analysis results produced?
-- If description mentions 'content generation' → was content actually generated?
-- If description mentions 'social media posting' → were posts actually made?
-- Match the outputs to the stated intention, not just technical completion
-
-PROVIDE:
-activity_status: 1-3 sentences about what the user accomplished, such as:
-- 'I analyzed your resume and provided detailed feedback for the IT industry.'
-- 'I couldn't complete the task because critical steps failed to produce any results.'
-- 'I failed to generate the content you requested due to missing API access.'
-- 'I extracted key information from your documents and organized it into a summary.'
-- 'The task failed because the blog post creation step didn't produce any output.'
-
-correctness_score: A float score from 0.0 to 1.0 based on how well the intended purpose was achieved:
-- 0.0-0.2: Failure (didn't meet requirements)
-- 0.2-0.4: Poor (major issues, minimal achievement)
-- 0.4-0.6: Partial Success (some objectives met with gaps)
-- 0.6-0.8: Mostly Successful (largely achieved with minor flaws)
-- 0.8-1.0: Success (fully met or exceeded requirements)
-
-BE CRITICAL: If the graph's intended purpose (from description) wasn't achieved, use a low score (0.0-0.4) even if status is 'completed'."""
-
-
 class ErrorInfo(TypedDict):
     """Type definition for error information."""
 
@@ -164,13 +70,6 @@ class NodeRelation(TypedDict):
     sink_block_name: NotRequired[str]  # Optional, only set if block exists
 
 
-class ActivityStatusResponse(TypedDict):
-    """Type definition for structured activity status response."""
-
-    activity_status: str
-    correctness_score: float
-
-
 def _truncate_uuid(uuid_str: str) -> str:
     """Truncate UUID to first segment to reduce payload size."""
     if not uuid_str:
@@ -186,14 +85,9 @@ async def generate_activity_status_for_execution(
     db_client: "DatabaseManagerAsyncClient",
     user_id: str,
     execution_status: ExecutionStatus | None = None,
-    model_name: str = "gpt-4o-mini",
-    skip_feature_flag: bool = False,
-    system_prompt: str = DEFAULT_SYSTEM_PROMPT,
-    user_prompt: str = DEFAULT_USER_PROMPT,
-    skip_existing: bool = True,
-) -> ActivityStatusResponse | None:
+) -> str | None:
     """
-    Generate an AI-based activity status summary and correctness assessment for a graph execution.
+    Generate an AI-based activity status summary for a graph execution.
 
     This function handles all the data collection and AI generation logic,
     keeping the manager integration simple.
@@ -206,37 +100,15 @@ async def generate_activity_status_for_execution(
         db_client: Database client for fetching data
         user_id: User ID for LaunchDarkly feature flag evaluation
         execution_status: The overall execution status (COMPLETED, FAILED, TERMINATED)
-        model_name: AI model to use for generation (default: gpt-4o-mini)
-        skip_feature_flag: Whether to skip LaunchDarkly feature flag check
-        system_prompt: Custom system prompt template (default: DEFAULT_SYSTEM_PROMPT)
-        user_prompt: Custom user prompt template with placeholders (default: DEFAULT_USER_PROMPT)
-        skip_existing: Whether to skip if activity_status and correctness_score already exist
 
     Returns:
-        AI-generated activity status response with activity_status and correctness_status,
-        or None if feature is disabled or skipped
+        AI-generated activity status string, or None if feature is disabled
     """
     # Check LaunchDarkly feature flag for AI activity status generation with full context support
-    if not skip_feature_flag and not await is_feature_enabled(
-        Flag.AI_ACTIVITY_STATUS, user_id
-    ):
+    if not await is_feature_enabled(Flag.AI_ACTIVITY_STATUS, user_id):
         logger.debug("AI activity status generation is disabled via LaunchDarkly")
         return None
 
-    # Check if we should skip existing data (for admin regeneration option)
-    if (
-        skip_existing
-        and execution_stats.activity_status
-        and execution_stats.correctness_score is not None
-    ):
-        logger.debug(
-            f"Skipping activity status generation for {graph_exec_id}: already exists"
-        )
-        return {
-            "activity_status": execution_stats.activity_status,
-            "correctness_score": execution_stats.correctness_score,
-        }
-
     # Check if we have OpenAI API key
     try:
         settings = Settings()
@@ -253,12 +125,7 @@ async def generate_activity_status_for_execution(
 
         # Get graph metadata and full graph structure for name, description, and links
         graph_metadata = await db_client.get_graph_metadata(graph_id, graph_version)
-        graph = await db_client.get_graph(
-            graph_id=graph_id,
-            version=graph_version,
-            user_id=user_id,
-            skip_access_check=True,
-        )
+        graph = await db_client.get_graph(graph_id, graph_version)
 
         graph_name = graph_metadata.name if graph_metadata else f"Graph {graph_id}"
         graph_description = graph_metadata.description if graph_metadata else ""
@@ -274,23 +141,76 @@ async def generate_activity_status_for_execution(
             execution_status,
         )
 
-        # Prepare execution data as JSON for template substitution
-        execution_data_json = json.dumps(execution_data, indent=2)
-
-        # Perform template substitution for user prompt
-        user_prompt_content = user_prompt.replace("{{GRAPH_NAME}}", graph_name).replace(
-            "{{EXECUTION_DATA}}", execution_data_json
-        )
-
-        # Prepare prompt for AI with structured output requirements
+        # Prepare prompt for AI
         prompt = [
             {
                 "role": "system",
-                "content": system_prompt,
+                "content": (
+                    "You are an AI assistant summarizing what you just did for a user in simple, friendly language. "
+                    "Write from the user's perspective about what they accomplished, NOT about technical execution details. "
+                    "Focus on the ACTUAL TASK the user wanted done, not the internal workflow steps. "
+                    "Avoid technical terms like 'workflow', 'execution', 'components', 'nodes', 'processing', etc. "
+                    "Keep it to 3 sentences maximum. Be conversational and human-friendly.\n\n"
+                    "UNDERSTAND THE INTENDED PURPOSE:\n"
+                    "- FIRST: Read the graph description carefully to understand what the user wanted to accomplish\n"
+                    "- The graph name and description tell you the main goal/intention of this automation\n"
+                    "- Use this intended purpose as your PRIMARY criteria for success/failure evaluation\n"
+                    "- Ask yourself: 'Did this execution actually accomplish what the graph was designed to do?'\n\n"
+                    "CRITICAL OUTPUT ANALYSIS:\n"
+                    "- Check if blocks that should produce user-facing results actually produced outputs\n"
+                    "- Blocks with names containing 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' are usually meant to produce final results\n"
+                    "- If these critical blocks have NO outputs (empty recent_outputs), the task likely FAILED even if status shows 'completed'\n"
+                    "- Sub-agents (AgentExecutorBlock) that produce no outputs usually indicate failed sub-tasks\n"
+                    "- Most importantly: Does the execution result match what the graph description promised to deliver?\n\n"
+                    "SUCCESS EVALUATION BASED ON INTENTION:\n"
+                    "- If the graph is meant to 'create blog posts' → check if blog content was actually created\n"
+                    "- If the graph is meant to 'send emails' → check if emails were actually sent\n"
+                    "- If the graph is meant to 'analyze data' → check if analysis results were produced\n"
+                    "- If the graph is meant to 'generate reports' → check if reports were generated\n"
+                    "- Technical completion ≠ goal achievement. Focus on whether the USER'S INTENDED OUTCOME was delivered\n\n"
+                    "IMPORTANT: Be HONEST about what actually happened:\n"
+                    "- If the input was invalid/nonsensical, say so directly\n"
+                    "- If the task failed, explain what went wrong in simple terms\n"
+                    "- If errors occurred, focus on what the user needs to know\n"
+                    "- Only claim success if the INTENDED PURPOSE was genuinely accomplished AND produced expected outputs\n"
+                    "- Don't sugar-coat failures or present them as helpful feedback\n"
+                    "- ESPECIALLY: If the graph's main purpose wasn't achieved, this is a failure regardless of 'completed' status\n\n"
+                    "Understanding Errors:\n"
+                    "- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)\n"
+                    "- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished\n"
+                    "- Missing outputs from critical blocks: Even if no errors, this means the task failed to produce expected results\n"
+                    "- Focus on whether the graph's intended purpose was fulfilled, not whether technical steps completed"
+                ),
             },
             {
                 "role": "user",
-                "content": user_prompt_content,
+                "content": (
+                    f"A user ran '{graph_name}' to accomplish something. Based on this execution data, "
+                    f"write what they achieved in simple, user-friendly terms:\n\n"
+                    f"{json.dumps(execution_data, indent=2)}\n\n"
+                    "ANALYSIS CHECKLIST:\n"
+                    "1. READ graph_info.description FIRST - this tells you what the user intended to accomplish\n"
+                    "2. Check overall_status.graph_error - if present, the entire execution failed\n"
+                    "3. Look for nodes with 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' in their block_name\n"
+                    "4. Check if these critical blocks have empty recent_outputs arrays - this indicates failure\n"
+                    "5. Look for AgentExecutorBlock (sub-agents) with no outputs - this suggests sub-task failures\n"
+                    "6. Count how many nodes produced outputs vs total nodes - low ratio suggests problems\n"
+                    "7. MOST IMPORTANT: Does the execution outcome match what graph_info.description promised?\n\n"
+                    "INTENTION-BASED EVALUATION:\n"
+                    "- If description mentions 'blog writing' → did it create blog content?\n"
+                    "- If description mentions 'email automation' → were emails actually sent?\n"
+                    "- If description mentions 'data analysis' → were analysis results produced?\n"
+                    "- If description mentions 'content generation' → was content actually generated?\n"
+                    "- If description mentions 'social media posting' → were posts actually made?\n"
+                    "- Match the outputs to the stated intention, not just technical completion\n\n"
+                    "Write 1-3 sentences about what the user accomplished, such as:\n"
+                    "- 'I analyzed your resume and provided detailed feedback for the IT industry.'\n"
+                    "- 'I couldn't complete the task because critical steps failed to produce any results.'\n"
+                    "- 'I failed to generate the content you requested due to missing API access.'\n"
+                    "- 'I extracted key information from your documents and organized it into a summary.'\n"
+                    "- 'The task failed because the blog post creation step didn't produce any output.'\n\n"
+                    "BE CRITICAL: If the graph's intended purpose (from description) wasn't achieved, report this as a failure even if status is 'completed'."
+                ),
             },
         ]
 
@@ -307,61 +227,17 @@ async def generate_activity_status_for_execution(
             title="System OpenAI",
         )
 
-        # Define expected response format
-        expected_format = {
-            "activity_status": "A user-friendly 1-3 sentence summary of what was accomplished",
-            "correctness_score": "Float score from 0.0 to 1.0 indicating how well the execution achieved its intended purpose",
-        }
-
-        # Use existing AIStructuredResponseGeneratorBlock for structured LLM call
-        structured_block = AIStructuredResponseGeneratorBlock()
-
-        # Convert credentials to the format expected by AIStructuredResponseGeneratorBlock
-        credentials_input = {
-            "provider": credentials.provider,
-            "id": credentials.id,
-            "type": credentials.type,
-            "title": credentials.title,
-        }
-
-        structured_input = AIStructuredResponseGeneratorBlock.Input(
-            prompt=prompt[1]["content"],  # User prompt content
-            sys_prompt=prompt[0]["content"],  # System prompt content
-            expected_format=expected_format,
-            model=LlmModel(model_name),
-            credentials=credentials_input,  # type: ignore
-            max_tokens=150,
-            retry=3,
-        )
-
-        # Execute the structured LLM call
-        async for output_name, output_data in structured_block.run(
-            structured_input, credentials=credentials
-        ):
-            if output_name == "response":
-                response = output_data
-                break
-        else:
-            raise RuntimeError("Failed to get response from structured LLM call")
-
-        # Create typed response with validation
-        correctness_score = float(response["correctness_score"])
-        # Clamp score to valid range
-        correctness_score = max(0.0, min(1.0, correctness_score))
-
-        activity_response: ActivityStatusResponse = {
-            "activity_status": response["activity_status"],
-            "correctness_score": correctness_score,
-        }
+        # Make LLM call using current event loop
+        activity_status = await _call_llm_direct(credentials, prompt)
 
         logger.debug(
-            f"Generated activity status for {graph_exec_id}: {activity_response}"
+            f"Generated activity status for {graph_exec_id}: {activity_status}"
         )
 
-        return activity_response
+        return activity_status
 
     except Exception as e:
-        logger.exception(
+        logger.error(
             f"Failed to generate activity status for execution {graph_exec_id}: {str(e)}"
         )
         return None
@@ -572,3 +448,23 @@ def _build_execution_summary(
             ),
         },
     }
+
+
+@func_retry
+async def _call_llm_direct(
+    credentials: APIKeyCredentials, prompt: list[dict[str, str]]
+) -> str:
+    """Make direct LLM call."""
+
+    response = await llm_call(
+        credentials=credentials,
+        llm_model=LlmModel.GPT4O_MINI,
+        prompt=prompt,
+        max_tokens=150,
+        compress_prompt_to_fit=True,
+    )
+
+    if response and response.response:
+        return response.response.strip()
+    else:
+        return "Unable to generate activity summary"

autogpt_platform/backend/backend/executor/activity_status_generator_test.py

@@ -7,11 +7,12 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.blocks.llm import LlmModel, LLMResponse
+from backend.blocks.llm import LLMResponse
 from backend.data.execution import ExecutionStatus, NodeExecutionResult
 from backend.data.model import GraphExecutionStats
 from backend.executor.activity_status_generator import (
     _build_execution_summary,
+    _call_llm_direct,
     generate_activity_status_for_execution,
 )
 
@@ -372,24 +373,25 @@ class TestLLMCall:
     """Tests for LLM calling functionality."""
 
     @pytest.mark.asyncio
-    async def test_structured_llm_call_success(self):
-        """Test successful structured LLM call."""
+    async def test_call_llm_direct_success(self):
+        """Test successful LLM call."""
         from pydantic import SecretStr
 
-        from backend.blocks.llm import AIStructuredResponseGeneratorBlock
         from backend.data.model import APIKeyCredentials
 
-        with patch("backend.blocks.llm.llm_call") as mock_llm_call, patch(
-            "backend.blocks.llm.secrets.token_hex", return_value="test123"
-        ):
-            mock_llm_call.return_value = LLMResponse(
-                raw_response={},
-                prompt=[],
-                response='<json_output id="test123">{"activity_status": "Test completed successfully", "correctness_score": 0.9}</json_output>',
-                tool_calls=None,
-                prompt_tokens=50,
-                completion_tokens=20,
-            )
+        mock_response = LLMResponse(
+            raw_response={},
+            prompt=[],
+            response="Agent successfully processed user input and generated response.",
+            tool_calls=None,
+            prompt_tokens=50,
+            completion_tokens=20,
+        )
+
+        with patch(
+            "backend.executor.activity_status_generator.llm_call"
+        ) as mock_llm_call:
+            mock_llm_call.return_value = mock_response
 
             credentials = APIKeyCredentials(
                 id="test",
@@ -399,61 +401,26 @@ class TestLLMCall:
             )
 
             prompt = [{"role": "user", "content": "Test prompt"}]
-            expected_format = {
-                "activity_status": "User-friendly summary",
-                "correctness_score": "Float score from 0.0 to 1.0",
-            }
 
-            # Create structured block and input
-            structured_block = AIStructuredResponseGeneratorBlock()
-            credentials_input = {
-                "provider": credentials.provider,
-                "id": credentials.id,
-                "type": credentials.type,
-                "title": credentials.title,
-            }
+            result = await _call_llm_direct(credentials, prompt)
 
-            structured_input = AIStructuredResponseGeneratorBlock.Input(
-                prompt=prompt[0]["content"],
-                expected_format=expected_format,
-                model=LlmModel.GPT4O_MINI,
-                credentials=credentials_input,  # type: ignore
+            assert (
+                result
+                == "Agent successfully processed user input and generated response."
             )
-
-            # Execute the structured LLM call
-            result = None
-            async for output_name, output_data in structured_block.run(
-                structured_input, credentials=credentials
-            ):
-                if output_name == "response":
-                    result = output_data
-                    break
-
-            assert result is not None
-            assert result["activity_status"] == "Test completed successfully"
-            assert result["correctness_score"] == 0.9
-            mock_llm_call.assert_called()
+            mock_llm_call.assert_called_once()
 
     @pytest.mark.asyncio
-    async def test_structured_llm_call_validation_error(self):
-        """Test structured LLM call with validation error."""
+    async def test_call_llm_direct_no_response(self):
+        """Test LLM call with no response."""
         from pydantic import SecretStr
 
-        from backend.blocks.llm import AIStructuredResponseGeneratorBlock
         from backend.data.model import APIKeyCredentials
 
-        with patch("backend.blocks.llm.llm_call") as mock_llm_call, patch(
-            "backend.blocks.llm.secrets.token_hex", return_value="test123"
-        ):
-            # Return invalid JSON that will fail validation (missing required field)
-            mock_llm_call.return_value = LLMResponse(
-                raw_response={},
-                prompt=[],
-                response='<json_output id="test123">{"activity_status": "Test completed successfully"}</json_output>',
-                tool_calls=None,
-                prompt_tokens=50,
-                completion_tokens=20,
-            )
+        with patch(
+            "backend.executor.activity_status_generator.llm_call"
+        ) as mock_llm_call:
+            mock_llm_call.return_value = None
 
             credentials = APIKeyCredentials(
                 id="test",
@@ -463,36 +430,10 @@ class TestLLMCall:
             )
 
             prompt = [{"role": "user", "content": "Test prompt"}]
-            expected_format = {
-                "activity_status": "User-friendly summary",
-                "correctness_score": "Float score from 0.0 to 1.0",
-            }
 
-            # Create structured block and input
-            structured_block = AIStructuredResponseGeneratorBlock()
-            credentials_input = {
-                "provider": credentials.provider,
-                "id": credentials.id,
-                "type": credentials.type,
-                "title": credentials.title,
-            }
+            result = await _call_llm_direct(credentials, prompt)
 
-            structured_input = AIStructuredResponseGeneratorBlock.Input(
-                prompt=prompt[0]["content"],
-                expected_format=expected_format,
-                model=LlmModel.GPT4O_MINI,
-                credentials=credentials_input,  # type: ignore
-                retry=1,  # Use fewer retries for faster test
-            )
-
-            with pytest.raises(
-                Exception
-            ):  # AIStructuredResponseGeneratorBlock may raise different exceptions
-                async for output_name, output_data in structured_block.run(
-                    structured_input, credentials=credentials
-                ):
-                    if output_name == "response":
-                        break
+            assert result == "Unable to generate activity summary"
 
 
 class TestGenerateActivityStatusForExecution:
@@ -520,25 +461,17 @@ class TestGenerateActivityStatusForExecution:
         ) as mock_get_block, patch(
             "backend.executor.activity_status_generator.Settings"
         ) as mock_settings, patch(
-            "backend.executor.activity_status_generator.AIStructuredResponseGeneratorBlock"
-        ) as mock_structured_block, patch(
+            "backend.executor.activity_status_generator._call_llm_direct"
+        ) as mock_llm, patch(
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
             mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
-
-            # Mock the structured block to return our expected response
-            mock_instance = mock_structured_block.return_value
-
-            async def mock_run(*args, **kwargs):
-                yield "response", {
-                    "activity_status": "I analyzed your data and provided the requested insights.",
-                    "correctness_score": 0.85,
-                }
-
-            mock_instance.run = mock_run
+            mock_llm.return_value = (
+                "I analyzed your data and provided the requested insights."
+            )
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -549,16 +482,11 @@ class TestGenerateActivityStatusForExecution:
                 user_id="test_user",
             )
 
-            assert result is not None
-            assert (
-                result["activity_status"]
-                == "I analyzed your data and provided the requested insights."
-            )
-            assert result["correctness_score"] == 0.85
+            assert result == "I analyzed your data and provided the requested insights."
             mock_db_client.get_node_executions.assert_called_once()
             mock_db_client.get_graph_metadata.assert_called_once()
             mock_db_client.get_graph.assert_called_once()
-            mock_structured_block.assert_called_once()
+            mock_llm.assert_called_once()
 
     @pytest.mark.asyncio
     async def test_generate_status_feature_disabled(self, mock_execution_stats):
@@ -646,25 +574,15 @@ class TestGenerateActivityStatusForExecution:
         ) as mock_get_block, patch(
             "backend.executor.activity_status_generator.Settings"
         ) as mock_settings, patch(
-            "backend.executor.activity_status_generator.AIStructuredResponseGeneratorBlock"
-        ) as mock_structured_block, patch(
+            "backend.executor.activity_status_generator._call_llm_direct"
+        ) as mock_llm, patch(
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
             mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
-
-            # Mock the structured block to return our expected response
-            mock_instance = mock_structured_block.return_value
-
-            async def mock_run(*args, **kwargs):
-                yield "response", {
-                    "activity_status": "Agent completed execution.",
-                    "correctness_score": 0.8,
-                }
-
-            mock_instance.run = mock_run
+            mock_llm.return_value = "Agent completed execution."
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -675,11 +593,10 @@ class TestGenerateActivityStatusForExecution:
                 user_id="test_user",
             )
 
-            assert result is not None
-            assert result["activity_status"] == "Agent completed execution."
-            assert result["correctness_score"] == 0.8
-            # The structured block should have been instantiated
-            assert mock_structured_block.called
+            assert result == "Agent completed execution."
+            # Should use fallback graph name in prompt
+            call_args = mock_llm.call_args[0][1]  # prompt argument
+            assert "Graph test_graph" in call_args[1]["content"]
 
 
 class TestIntegration:
@@ -709,8 +626,8 @@ class TestIntegration:
         ) as mock_get_block, patch(
             "backend.executor.activity_status_generator.Settings"
         ) as mock_settings, patch(
-            "backend.executor.activity_status_generator.AIStructuredResponseGeneratorBlock"
-        ) as mock_structured_block, patch(
+            "backend.executor.activity_status_generator.llm_call"
+        ) as mock_llm_call, patch(
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
@@ -718,16 +635,15 @@ class TestIntegration:
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
             mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
 
-            # Mock the structured block to return our expected response
-            mock_instance = mock_structured_block.return_value
-
-            async def mock_run(*args, **kwargs):
-                yield "response", {
-                    "activity_status": expected_activity,
-                    "correctness_score": 0.3,  # Low score since there was a failure
-                }
-
-            mock_instance.run = mock_run
+            mock_response = LLMResponse(
+                raw_response={},
+                prompt=[],
+                response=expected_activity,
+                tool_calls=None,
+                prompt_tokens=100,
+                completion_tokens=30,
+            )
+            mock_llm_call.return_value = mock_response
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -738,14 +654,24 @@ class TestIntegration:
                 user_id="test_user",
             )
 
-            assert result is not None
-            assert result["activity_status"] == expected_activity
-            assert result["correctness_score"] == 0.3
+            assert result == expected_activity
 
-            # Verify the structured block was called
-            assert mock_structured_block.called
-            # The structured block should have been instantiated
-            mock_structured_block.assert_called_once()
+            # Verify the correct data was passed to LLM
+            llm_call_args = mock_llm_call.call_args
+            prompt = llm_call_args[1]["prompt"]
+
+            # Check system prompt
+            assert prompt[0]["role"] == "system"
+            assert "user's perspective" in prompt[0]["content"]
+
+            # Check user prompt contains expected data
+            user_content = prompt[1]["content"]
+            assert "Test Integration Agent" in user_content
+            assert "user-friendly terms" in user_content.lower()
+
+            # Verify that execution data is present in the prompt
+            assert "{" in user_content  # Should contain JSON data
+            assert "overall_status" in user_content
 
     @pytest.mark.asyncio
     async def test_manager_integration_with_disabled_feature(