Update Node.js version from 21 to 22

.github/copilot-instructions.md

@@ -142,7 +142,7 @@ pnpm storybook                      # Start component development server
 ### Security & Middleware
 
 **Cache Protection**: Backend includes middleware preventing sensitive data caching in browsers/proxies
-**Authentication**: JWT-based with native authentication
+**Authentication**: JWT-based with Supabase integration
 **User ID Validation**: All data access requires user ID checks - verify this for any `data/*.py` changes
 
 ### Development Workflow
@@ -168,9 +168,9 @@ pnpm storybook                      # Start component development server
 
 - `frontend/src/app/layout.tsx` - Root application layout
 - `frontend/src/app/page.tsx` - Home page
-- `frontend/src/lib/auth/` - Authentication client
+- `frontend/src/lib/supabase/` - Authentication and database client
 
-**Protected Routes**: Update `frontend/middleware.ts` when adding protected routes
+**Protected Routes**: Update `frontend/lib/supabase/middleware.ts` when adding protected routes
 
 ### Agent Block System
 
@@ -194,7 +194,7 @@ Agents are built using a visual block-based system where each block performs a s
 
 1. **Backend**: `/backend/.env.default` → `/backend/.env` (user overrides)
 2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)
-3. **Platform**: `/.env.default` (shared) → `/.env` (user overrides)
+3. **Platform**: `/.env.default` (Supabase/shared) → `/.env` (user overrides)
 4. Docker Compose `environment:` sections override file-based config
 5. Shell environment variables have highest precedence
 

.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
@@ -144,7 +144,11 @@ jobs:
             "rabbitmq:management"
             "clamav/clamav-debian:latest"
             "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
           )
           
           # Check if any cached tar files exist (more reliable than cache-hit)

.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
@@ -160,7 +154,11 @@ jobs:
             "rabbitmq:management"
             "clamav/clamav-debian:latest"
             "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
           )
           
           # Check if any cached tar files exist (more reliable than cache-hit)

.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
@@ -142,7 +142,11 @@ jobs:
             "rabbitmq:management"
             "clamav/clamav-debian:latest"
             "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
           )
           
           # Check if any cached tar files exist (more reliable than cache-hit)
@@ -295,4 +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

@@ -2,13 +2,13 @@ name: AutoGPT Platform - Backend CI
 
 on:
   push:
-    branches: [master, dev, ci-test*, native-auth]
+    branches: [master, dev, ci-test*]
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
       - "autogpt_platform/autogpt_libs/**"
   pull_request:
-    branches: [master, dev, release-*, native-auth]
+    branches: [master, dev, release-*]
     paths:
       - ".github/workflows/platform-backend-ci.yml"
       - "autogpt_platform/backend/**"
@@ -36,19 +36,6 @@ jobs:
     runs-on: ubuntu-latest
 
     services:
-      postgres:
-        image: pgvector/pgvector:pg18
-        ports:
-          - 5432:5432
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
-          POSTGRES_DB: postgres
-        options: >-
-          --health-cmd "pg_isready -U postgres"
-          --health-interval 5s
-          --health-timeout 5s
-          --health-retries 10
       redis:
         image: redis:latest
         ports:
@@ -91,6 +78,11 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
 
+      - name: Setup Supabase
+        uses: supabase/setup-cli@v1
+        with:
+          version: 1.178.1
+
       - id: get_date
         name: Get date
         run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
@@ -144,6 +136,16 @@ jobs:
       - 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
+        # 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..."
@@ -176,8 +178,8 @@ jobs:
       - name: Run Database Migrations
         run: poetry run prisma migrate dev --name updates
         env:
-          DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
+          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
 
       - id: lint
         name: Run Linter
@@ -193,9 +195,11 @@ jobs:
         if: success() || (failure() && steps.lint.outcome == 'failure')
         env:
           LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
-          DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
+          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=" # DO NOT USE IN PRODUCTION!!

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

@@ -2,21 +2,16 @@ name: AutoGPT Platform - Frontend CI
 
 on:
   push:
-    branches: [master, dev, native-auth]
+    branches: [master, dev]
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "autogpt_platform/frontend/**"
   pull_request:
-    branches: [master, dev, native-auth]
     paths:
       - ".github/workflows/platform-frontend-ci.yml"
       - "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
@@ -148,7 +143,7 @@ jobs:
       - name: Enable corepack
         run: corepack enable
 
-      - name: Copy default platform .env
+      - name: Copy default supabase .env
         run: |
           cp ../.env.default ../.env
 

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

@@ -1,22 +1,17 @@
-name: AutoGPT Platform - Fullstack CI
+name: AutoGPT Platform - Frontend CI
 
 on:
   push:
-    branches: [master, dev, native-auth]
+    branches: [master, dev]
     paths:
       - ".github/workflows/platform-fullstack-ci.yml"
       - "autogpt_platform/**"
   pull_request:
-    branches: [master, dev, native-auth]
     paths:
       - ".github/workflows/platform-fullstack-ci.yml"
       - "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
@@ -59,11 +54,14 @@ jobs:
   types:
     runs-on: ubuntu-latest
     needs: setup
-    timeout-minutes: 10
+    strategy:
+      fail-fast: false
 
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
+        with:
+          submodules: recursive
 
       - name: Set up Node.js
         uses: actions/setup-node@v4
@@ -73,6 +71,18 @@ jobs:
       - name: Enable corepack
         run: corepack enable
 
+      - name: Copy default supabase .env
+        run: |
+          cp ../.env.default ../.env
+
+      - name: Copy backend .env
+        run: |
+          cp ../backend/.env.default ../backend/.env
+
+      - name: Run docker compose
+        run: |
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
+
       - name: Restore dependencies cache
         uses: actions/cache@v4
         with:
@@ -87,12 +97,36 @@ jobs:
       - name: Setup .env
         run: cp .env.default .env
 
+      - name: Wait for services to be ready
+        run: |
+          echo "Waiting for rest_server to be ready..."
+          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 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
+        run: pnpm generate:api:force
+
+      - name: Check for API schema changes
+        run: |
+          if ! git diff --exit-code src/app/api/openapi.json; then
+            echo "❌ API schema changes detected in src/app/api/openapi.json"
+            echo ""
+            echo "The openapi.json file has been modified after running 'pnpm generate:api-all'."
+            echo "This usually means changes have been made in the BE endpoints without updating the Frontend."
+            echo "The API schema is now out of sync with the Front-end queries."
+            echo ""
+            echo "To fix this:"
+            echo "1. Pull the backend 'docker compose pull && docker compose up -d --build --force-recreate'"
+            echo "2. Run 'pnpm generate:api' locally"
+            echo "3. Run 'pnpm types' locally"
+            echo "4. Fix any TypeScript errors that may have been introduced"
+            echo "5. Commit and push your changes"
+            echo ""
+            exit 1
+          else
+            echo "✅ No API schema changes detected"
+          fi
 
       - name: Run Typescript checks
         run: pnpm types
-
-    env:
-      CI: true
-      PLAIN_OUTPUT: True

.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

AGENTS.md

@@ -49,5 +49,5 @@ Use conventional commit messages for all commits (e.g. `feat(backend): add API`)
 - Keep out-of-scope changes under 20% of the PR.
 - Ensure PR descriptions are complete.
 - For changes touching `data/*.py`, validate user ID checks or explain why not needed.
-- If adding protected frontend routes, update `frontend/lib/auth/helpers.ts`.
+- If adding protected frontend routes, update `frontend/lib/supabase/middleware.ts`.
 - Use the linear ticket branch structure if given codex/open-1668-resume-dropped-runs

autogpt_platform/.env.default

@@ -5,6 +5,12 @@
 
 POSTGRES_PASSWORD=your-super-secret-and-long-postgres-password
 JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
+ANON_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
+SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
+DASHBOARD_USERNAME=supabase
+DASHBOARD_PASSWORD=this_password_is_insecure_and_should_be_updated
+SECRET_KEY_BASE=UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq
+VAULT_ENC_KEY=your-encryption-key-32-chars-min
 
 
 ############
@@ -18,31 +24,100 @@ POSTGRES_PORT=5432
 
 
 ############
-# Auth - Native authentication configuration
+# Supavisor -- Database pooler
+############
+POOLER_PROXY_PORT_TRANSACTION=6543
+POOLER_DEFAULT_POOL_SIZE=20
+POOLER_MAX_CLIENT_CONN=100
+POOLER_TENANT_ID=your-tenant-id
+
+
+############
+# API Proxy - Configuration for the Kong Reverse proxy.
 ############
 
+KONG_HTTP_PORT=8000
+KONG_HTTPS_PORT=8443
+
+
+############
+# API - Configuration for PostgREST.
+############
+
+PGRST_DB_SCHEMAS=public,storage,graphql_public
+
+
+############
+# Auth - Configuration for the GoTrue authentication server.
+############
+
+## General
 SITE_URL=http://localhost:3000
+ADDITIONAL_REDIRECT_URLS=
+JWT_EXPIRY=3600
+DISABLE_SIGNUP=false
+API_EXTERNAL_URL=http://localhost:8000
 
-# JWT token configuration
-ACCESS_TOKEN_EXPIRE_MINUTES=15
-REFRESH_TOKEN_EXPIRE_DAYS=7
-JWT_ISSUER=autogpt-platform
+## Mailer Config
+MAILER_URLPATHS_CONFIRMATION="/auth/v1/verify"
+MAILER_URLPATHS_INVITE="/auth/v1/verify"
+MAILER_URLPATHS_RECOVERY="/auth/v1/verify"
+MAILER_URLPATHS_EMAIL_CHANGE="/auth/v1/verify"
 
-# Google OAuth (optional)
-GOOGLE_CLIENT_ID=
-GOOGLE_CLIENT_SECRET=
+## Email auth
+ENABLE_EMAIL_SIGNUP=true
+ENABLE_EMAIL_AUTOCONFIRM=false
+SMTP_ADMIN_EMAIL=admin@example.com
+SMTP_HOST=supabase-mail
+SMTP_PORT=2500
+SMTP_USER=fake_mail_user
+SMTP_PASS=fake_mail_password
+SMTP_SENDER_NAME=fake_sender
+ENABLE_ANONYMOUS_USERS=false
+
+## Phone auth
+ENABLE_PHONE_SIGNUP=true
+ENABLE_PHONE_AUTOCONFIRM=true
 
 
 ############
-# Email configuration (optional)
+# Studio - Configuration for the Dashboard
 ############
 
-SMTP_HOST=
-SMTP_PORT=587
-SMTP_USER=
-SMTP_PASS=
-SMTP_FROM_EMAIL=noreply@example.com
+STUDIO_DEFAULT_ORGANIZATION=Default Organization
+STUDIO_DEFAULT_PROJECT=Default Project
 
+STUDIO_PORT=3000
+# replace if you intend to use Studio outside of localhost
+SUPABASE_PUBLIC_URL=http://localhost:8000
+
+# Enable webp support
+IMGPROXY_ENABLE_WEBP_DETECTION=true
+
+# Add your OpenAI API key to enable SQL Editor Assistant
+OPENAI_API_KEY=
+
+
+############
+# Functions - Configuration for Functions
+############
+# NOTE: VERIFY_JWT applies to all functions. Per-function VERIFY_JWT is not supported yet.
+FUNCTIONS_VERIFY_JWT=false
+
+
+############
+# Logs - Configuration for Logflare
+# Please refer to https://supabase.com/docs/reference/self-hosting-analytics/introduction
+############
+
+LOGFLARE_LOGGER_BACKEND_API_KEY=your-super-secret-and-long-logflare-key
+
+# Change vector.toml sinks to reflect this change
+LOGFLARE_API_KEY=your-super-secret-and-long-logflare-key
 
 # Docker socket location - this value will differ depending on your OS
 DOCKER_SOCKET_LOCATION=/var/run/docker.sock
+
+# Google Cloud Project details
+GOOGLE_PROJECT_ID=GOOGLE_PROJECT_ID
+GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER

autogpt_platform/Makefile

@@ -1,6 +1,6 @@
-.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 PostgreSQL + Redis + RabbitMQ + ClamAV
+# Run just Supabase + Redis + RabbitMQ
 start-core:
 	docker compose up -d deps
 
@@ -42,14 +42,11 @@ 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:"
-	@echo "  start-core - Start just the core services (PostgreSQL, Redis, RabbitMQ, ClamAV) in background"
+	@echo "  start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
 	@echo "  stop-core - Stop the core services"
 	@echo "  reset-db - Reset the database by deleting the volume"
 	@echo "  logs-core - Tail the logs for core services"
@@ -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/api_key/keysmith.py

@@ -57,9 +57,6 @@ class APIKeySmith:
 
     def hash_key(self, raw_key: str) -> tuple[str, str]:
         """Migrate a legacy hash to secure hash format."""
-        if not raw_key.startswith(self.PREFIX):
-            raise ValueError("Key without 'agpt_' prefix would fail validation")
-
         salt = self._generate_salt()
         hash = self._hash_key_with_salt(raw_key, salt)
         return hash, salt.hex()

autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py

@@ -16,37 +16,17 @@ ALGO_RECOMMENDATION = (
     "We highly recommend using an asymmetric algorithm such as ES256, "
     "because when leaked, a shared secret would allow anyone to "
     "forge valid tokens and impersonate users. "
-    "More info: https://pyjwt.readthedocs.io/en/stable/algorithms.html"
+    "More info: https://supabase.com/docs/guides/auth/signing-keys#choosing-the-right-signing-algorithm"  # noqa
 )
 
 
 class Settings:
     def __init__(self):
-        # JWT verification key (public key for asymmetric, shared secret for symmetric)
         self.JWT_VERIFY_KEY: str = os.getenv(
             "JWT_VERIFY_KEY", os.getenv("SUPABASE_JWT_SECRET", "")
         ).strip()
-
-        # JWT signing key (private key for asymmetric, shared secret for symmetric)
-        # Falls back to JWT_VERIFY_KEY for symmetric algorithms like HS256
-        self.JWT_SIGN_KEY: str = os.getenv("JWT_SIGN_KEY", self.JWT_VERIFY_KEY).strip()
-
         self.JWT_ALGORITHM: str = os.getenv("JWT_SIGN_ALGORITHM", "HS256").strip()
 
-        # Token expiration settings
-        self.ACCESS_TOKEN_EXPIRE_MINUTES: int = int(
-            os.getenv("ACCESS_TOKEN_EXPIRE_MINUTES", "15")
-        )
-        self.REFRESH_TOKEN_EXPIRE_DAYS: int = int(
-            os.getenv("REFRESH_TOKEN_EXPIRE_DAYS", "7")
-        )
-
-        # JWT issuer claim
-        self.JWT_ISSUER: str = os.getenv("JWT_ISSUER", "autogpt-platform").strip()
-
-        # JWT audience claim
-        self.JWT_AUDIENCE: str = os.getenv("JWT_AUDIENCE", "authenticated").strip()
-
         self.validate()
 
     def validate(self):

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -1,8 +1,4 @@
-import hashlib
 import logging
-import secrets
-import uuid
-from datetime import datetime, timedelta, timezone
 from typing import Any
 
 import jwt
@@ -20,57 +16,6 @@ bearer_jwt_auth = HTTPBearer(
 )
 
 
-def create_access_token(
-    user_id: str,
-    email: str,
-    role: str = "authenticated",
-    email_verified: bool = False,
-) -> str:
-    """
-    Generate a new JWT access token.
-
-    :param user_id: The user's unique identifier
-    :param email: The user's email address
-    :param role: The user's role (default: "authenticated")
-    :param email_verified: Whether the user's email is verified
-    :return: Encoded JWT token
-    """
-    settings = get_settings()
-    now = datetime.now(timezone.utc)
-
-    payload = {
-        "sub": user_id,
-        "email": email,
-        "role": role,
-        "email_verified": email_verified,
-        "aud": settings.JWT_AUDIENCE,
-        "iss": settings.JWT_ISSUER,
-        "iat": now,
-        "exp": now + timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES),
-        "jti": str(uuid.uuid4()),  # Unique token ID
-    }
-
-    return jwt.encode(payload, settings.JWT_SIGN_KEY, algorithm=settings.JWT_ALGORITHM)
-
-
-def create_refresh_token() -> tuple[str, str]:
-    """
-    Generate a new refresh token.
-
-    Returns a tuple of (raw_token, hashed_token).
-    The raw token should be sent to the client.
-    The hashed token should be stored in the database.
-    """
-    raw_token = secrets.token_urlsafe(64)
-    hashed_token = hashlib.sha256(raw_token.encode()).hexdigest()
-    return raw_token, hashed_token
-
-
-def hash_token(token: str) -> str:
-    """Hash a token using SHA-256."""
-    return hashlib.sha256(token.encode()).hexdigest()
-
-
 async def get_jwt_payload(
     credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
@@ -107,19 +52,11 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
     """
     settings = get_settings()
     try:
-        # Build decode options
-        options = {
-            "verify_aud": True,
-            "verify_iss": bool(settings.JWT_ISSUER),
-        }
-
         payload = jwt.decode(
             token,
             settings.JWT_VERIFY_KEY,
             algorithms=[settings.JWT_ALGORITHM],
-            audience=settings.JWT_AUDIENCE,
-            issuer=settings.JWT_ISSUER if settings.JWT_ISSUER else None,
-            options=options,
+            audience="authenticated",
         )
         return payload
     except jwt.ExpiredSignatureError:

autogpt_platform/autogpt_libs/autogpt_libs/auth/models.py

@@ -11,7 +11,6 @@ class User:
     email: str
     phone_number: str
     role: str
-    email_verified: bool = False
 
     @classmethod
     def from_payload(cls, payload):
@@ -19,6 +18,5 @@ class User:
             user_id=payload["sub"],
             email=payload.get("email", ""),
             phone_number=payload.get("phone", ""),
-            role=payload.get("role", "authenticated"),
-            email_verified=payload.get("email_verified", False),
+            role=payload["role"],
         )

autogpt_platform/autogpt_libs/poetry.lock

@@ -48,21 +48,6 @@ files = [
     {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
 ]
 
-[[package]]
-name = "authlib"
-version = "1.6.6"
-description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "authlib-1.6.6-py2.py3-none-any.whl", hash = "sha256:7d9e9bc535c13974313a87f53e8430eb6ea3d1cf6ae4f6efcd793f2e949143fd"},
-    {file = "authlib-1.6.6.tar.gz", hash = "sha256:45770e8e056d0f283451d9996fbb59b70d45722b45d854d58f32878d0a40c38e"},
-]
-
-[package.dependencies]
-cryptography = "*"
-
 [[package]]
 name = "backports-asyncio-runner"
 version = "1.2.0"
@@ -76,71 +61,6 @@ files = [
     {file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
 ]
 
-[[package]]
-name = "bcrypt"
-version = "4.3.0"
-description = "Modern password hashing for your software and your servers"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-files = [
-    {file = "bcrypt-4.3.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f01e060f14b6b57bbb72fc5b4a83ac21c443c9a2ee708e04a10e9192f90a6281"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5eeac541cefd0bb887a371ef73c62c3cd78535e4887b310626036a7c0a817bb"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:59e1aa0e2cd871b08ca146ed08445038f42ff75968c7ae50d2fdd7860ade2180"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:0042b2e342e9ae3d2ed22727c1262f76cc4f345683b5c1715f0250cf4277294f"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74a8d21a09f5e025a9a23e7c0fd2c7fe8e7503e4d356c0a2c1486ba010619f09"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:0142b2cb84a009f8452c8c5a33ace5e3dfec4159e7735f5afe9a4d50a8ea722d"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_34_aarch64.whl", hash = "sha256:12fa6ce40cde3f0b899729dbd7d5e8811cb892d31b6f7d0334a1f37748b789fd"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_34_x86_64.whl", hash = "sha256:5bd3cca1f2aa5dbcf39e2aa13dd094ea181f48959e1071265de49cc2b82525af"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-musllinux_1_1_aarch64.whl", hash = "sha256:335a420cfd63fc5bc27308e929bee231c15c85cc4c496610ffb17923abf7f231"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-musllinux_1_1_x86_64.whl", hash = "sha256:0e30e5e67aed0187a1764911af023043b4542e70a7461ad20e837e94d23e1d6c"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-musllinux_1_2_aarch64.whl", hash = "sha256:3b8d62290ebefd49ee0b3ce7500f5dbdcf13b81402c05f6dafab9a1e1b27212f"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:2ef6630e0ec01376f59a006dc72918b1bf436c3b571b80fa1968d775fa02fe7d"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-win32.whl", hash = "sha256:7a4be4cbf241afee43f1c3969b9103a41b40bcb3a3f467ab19f891d9bc4642e4"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-win_amd64.whl", hash = "sha256:5c1949bf259a388863ced887c7861da1df681cb2388645766c89fdfd9004c669"},
-    {file = "bcrypt-4.3.0-cp38-abi3-macosx_10_12_universal2.whl", hash = "sha256:f81b0ed2639568bf14749112298f9e4e2b28853dab50a8b357e31798686a036d"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:864f8f19adbe13b7de11ba15d85d4a428c7e2f344bac110f667676a0ff84924b"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3e36506d001e93bffe59754397572f21bb5dc7c83f54454c990c74a468cd589e"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:842d08d75d9fe9fb94b18b071090220697f9f184d4547179b60734846461ed59"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:7c03296b85cb87db865d91da79bf63d5609284fc0cab9472fdd8367bbd830753"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:62f26585e8b219cdc909b6a0069efc5e4267e25d4a3770a364ac58024f62a761"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:beeefe437218a65322fbd0069eb437e7c98137e08f22c4660ac2dc795c31f8bb"},
-    {file = "bcrypt-4.3.0-cp38-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:97eea7408db3a5bcce4a55d13245ab3fa566e23b4c67cd227062bb49e26c585d"},
-    {file = "bcrypt-4.3.0-cp38-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:191354ebfe305e84f344c5964c7cd5f924a3bfc5d405c75ad07f232b6dffb49f"},
-    {file = "bcrypt-4.3.0-cp38-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:41261d64150858eeb5ff43c753c4b216991e0ae16614a308a15d909503617732"},
-    {file = "bcrypt-4.3.0-cp38-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:33752b1ba962ee793fa2b6321404bf20011fe45b9afd2a842139de3011898fef"},
-    {file = "bcrypt-4.3.0-cp38-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:50e6e80a4bfd23a25f5c05b90167c19030cf9f87930f7cb2eacb99f45d1c3304"},
-    {file = "bcrypt-4.3.0-cp38-abi3-win32.whl", hash = "sha256:67a561c4d9fb9465ec866177e7aebcad08fe23aaf6fbd692a6fab69088abfc51"},
-    {file = "bcrypt-4.3.0-cp38-abi3-win_amd64.whl", hash = "sha256:584027857bc2843772114717a7490a37f68da563b3620f78a849bcb54dc11e62"},
-    {file = "bcrypt-4.3.0-cp39-abi3-macosx_10_12_universal2.whl", hash = "sha256:0d3efb1157edebfd9128e4e46e2ac1a64e0c1fe46fb023158a407c7892b0f8c3"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:08bacc884fd302b611226c01014eca277d48f0a05187666bca23aac0dad6fe24"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f6746e6fec103fcd509b96bacdfdaa2fbde9a553245dbada284435173a6f1aef"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:afe327968aaf13fc143a56a3360cb27d4ad0345e34da12c7290f1b00b8fe9a8b"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:d9af79d322e735b1fc33404b5765108ae0ff232d4b54666d46730f8ac1a43676"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:f1e3ffa1365e8702dc48c8b360fef8d7afeca482809c5e45e653af82ccd088c1"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:3004df1b323d10021fda07a813fd33e0fd57bef0e9a480bb143877f6cba996fe"},
-    {file = "bcrypt-4.3.0-cp39-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:531457e5c839d8caea9b589a1bcfe3756b0547d7814e9ce3d437f17da75c32b0"},
-    {file = "bcrypt-4.3.0-cp39-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:17a854d9a7a476a89dcef6c8bd119ad23e0f82557afbd2c442777a16408e614f"},
-    {file = "bcrypt-4.3.0-cp39-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:6fb1fd3ab08c0cbc6826a2e0447610c6f09e983a281b919ed721ad32236b8b23"},
-    {file = "bcrypt-4.3.0-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:e965a9c1e9a393b8005031ff52583cedc15b7884fce7deb8b0346388837d6cfe"},
-    {file = "bcrypt-4.3.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:79e70b8342a33b52b55d93b3a59223a844962bef479f6a0ea318ebbcadf71505"},
-    {file = "bcrypt-4.3.0-cp39-abi3-win32.whl", hash = "sha256:b4d4e57f0a63fd0b358eb765063ff661328f69a04494427265950c71b992a39a"},
-    {file = "bcrypt-4.3.0-cp39-abi3-win_amd64.whl", hash = "sha256:e53e074b120f2877a35cc6c736b8eb161377caae8925c17688bd46ba56daaa5b"},
-    {file = "bcrypt-4.3.0-pp310-pypy310_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:c950d682f0952bafcceaf709761da0a32a942272fad381081b51096ffa46cea1"},
-    {file = "bcrypt-4.3.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:107d53b5c67e0bbc3f03ebf5b030e0403d24dda980f8e244795335ba7b4a027d"},
-    {file = "bcrypt-4.3.0-pp310-pypy310_pp73-manylinux_2_34_aarch64.whl", hash = "sha256:b693dbb82b3c27a1604a3dff5bfc5418a7e6a781bb795288141e5f80cf3a3492"},
-    {file = "bcrypt-4.3.0-pp310-pypy310_pp73-manylinux_2_34_x86_64.whl", hash = "sha256:b6354d3760fcd31994a14c89659dee887f1351a06e5dac3c1142307172a79f90"},
-    {file = "bcrypt-4.3.0-pp311-pypy311_pp73-manylinux_2_28_aarch64.whl", hash = "sha256:a839320bf27d474e52ef8cb16449bb2ce0ba03ca9f44daba6d93fa1d8828e48a"},
-    {file = "bcrypt-4.3.0-pp311-pypy311_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:bdc6a24e754a555d7316fa4774e64c6c3997d27ed2d1964d55920c7c227bc4ce"},
-    {file = "bcrypt-4.3.0-pp311-pypy311_pp73-manylinux_2_34_aarch64.whl", hash = "sha256:55a935b8e9a1d2def0626c4269db3fcd26728cbff1e84f0341465c31c4ee56d8"},
-    {file = "bcrypt-4.3.0-pp311-pypy311_pp73-manylinux_2_34_x86_64.whl", hash = "sha256:57967b7a28d855313a963aaea51bf6df89f833db4320da458e5b3c5ab6d4c938"},
-    {file = "bcrypt-4.3.0.tar.gz", hash = "sha256:3a3fd2204178b6d2adcf09cb4f6426ffef54762577a7c9b54c159008cb288c18"},
-]
-
-[package.extras]
-tests = ["pytest (>=3.2.1,!=3.3.0)"]
-typecheck = ["mypy"]
-
 [[package]]
 name = "cachetools"
 version = "5.5.2"
@@ -539,6 +459,21 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi (>=2024)", "cryptography-vectors (==45.0.6)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
 test-randomorder = ["pytest-randomly"]
 
+[[package]]
+name = "deprecation"
+version = "2.1.0"
+description = "A library to handle automated deprecations"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "deprecation-2.1.0-py2.py3-none-any.whl", hash = "sha256:a10811591210e1fb0e768a8c25517cabeabcba6f0bf96564f8ff45189f90b14a"},
+    {file = "deprecation-2.1.0.tar.gz", hash = "sha256:72b3bde64e5d778694b0cf68178aed03d15e15477116add3fb773e581f9518ff"},
+]
+
+[package.dependencies]
+packaging = "*"
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -760,6 +695,23 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0)"]
 
+[[package]]
+name = "gotrue"
+version = "2.12.3"
+description = "Python Client Library for Supabase Auth"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "gotrue-2.12.3-py3-none-any.whl", hash = "sha256:b1a3c6a5fe3f92e854a026c4c19de58706a96fd5fbdcc3d620b2802f6a46a26b"},
+    {file = "gotrue-2.12.3.tar.gz", hash = "sha256:f874cf9d0b2f0335bfbd0d6e29e3f7aff79998cd1c14d2ad814db8c06cee3852"},
+]
+
+[package.dependencies]
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+pydantic = ">=1.10,<3"
+pyjwt = ">=2.10.1,<3.0.0"
+
 [[package]]
 name = "grpc-google-iam-v1"
 version = "0.14.2"
@@ -870,6 +822,94 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "h2"
+version = "4.2.0"
+description = "Pure-Python HTTP/2 protocol implementation"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "h2-4.2.0-py3-none-any.whl", hash = "sha256:479a53ad425bb29af087f3458a61d30780bc818e4ebcf01f0b536ba916462ed0"},
+    {file = "h2-4.2.0.tar.gz", hash = "sha256:c8a52129695e88b1a0578d8d2cc6842bbd79128ac685463b887ee278126ad01f"},
+]
+
+[package.dependencies]
+hpack = ">=4.1,<5"
+hyperframe = ">=6.1,<7"
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+description = "Pure-Python HPACK header encoding"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496"},
+    {file = "hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
+]
+
+[package.dependencies]
+certifi = "*"
+h11 = ">=0.16"
+
+[package.extras]
+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<1.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "httpx-0.28.1-py3-none-any.whl", hash = "sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad"},
+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
+]
+
+[package.dependencies]
+anyio = "*"
+certifi = "*"
+h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
+httpcore = "==1.*"
+idna = "*"
+
+[package.extras]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+description = "Pure-Python HTTP/2 framing"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5"},
+    {file = "hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08"},
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -996,7 +1036,7 @@ version = "25.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
     {file = "packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484"},
     {file = "packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f"},
@@ -1018,6 +1058,24 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["coverage", "pytest", "pytest-benchmark"]
 
+[[package]]
+name = "postgrest"
+version = "1.1.1"
+description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "postgrest-1.1.1-py3-none-any.whl", hash = "sha256:98a6035ee1d14288484bfe36235942c5fb2d26af6d8120dfe3efbe007859251a"},
+    {file = "postgrest-1.1.1.tar.gz", hash = "sha256:f3bb3e8c4602775c75c844a31f565f5f3dd584df4d36d683f0b67d01a86be322"},
+]
+
+[package.dependencies]
+deprecation = ">=2.1.0,<3.0.0"
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+pydantic = ">=1.9,<3.0"
+strenum = {version = ">=0.4.9,<0.5.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "proto-plus"
 version = "1.26.1"
@@ -1404,6 +1462,21 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]
 
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
 [[package]]
 name = "python-dotenv"
 version = "1.1.1"
@@ -1419,6 +1492,22 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]
 
+[[package]]
+name = "realtime"
+version = "2.5.3"
+description = ""
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "realtime-2.5.3-py3-none-any.whl", hash = "sha256:eb0994636946eff04c4c7f044f980c8c633c7eb632994f549f61053a474ac970"},
+    {file = "realtime-2.5.3.tar.gz", hash = "sha256:0587594f3bc1c84bf007ff625075b86db6528843e03250dc84f4f2808be3d99a"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.14.0,<5.0.0"
+websockets = ">=11,<16"
+
 [[package]]
 name = "redis"
 version = "6.2.0"
@@ -1517,6 +1606,18 @@ files = [
     {file = "semver-3.0.4.tar.gz", hash = "sha256:afc7d8c584a5ed0a11033af086e8af226a9c0b206f313e0301f8dd7b6b589602"},
 ]
 
+[[package]]
+name = "six"
+version = "1.17.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
+    {file = "six-1.17.0-py2.py3-none-any.whl", hash = "sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274"},
+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -1548,6 +1649,76 @@ typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""
 [package.extras]
 full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
 
+[[package]]
+name = "storage3"
+version = "0.12.0"
+description = "Supabase Storage client for Python."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "storage3-0.12.0-py3-none-any.whl", hash = "sha256:1c4585693ca42243ded1512b58e54c697111e91a20916cd14783eebc37e7c87d"},
+    {file = "storage3-0.12.0.tar.gz", hash = "sha256:94243f20922d57738bf42e96b9f5582b4d166e8bf209eccf20b146909f3f71b0"},
+]
+
+[package.dependencies]
+deprecation = ">=2.1.0,<3.0.0"
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+python-dateutil = ">=2.8.2,<3.0.0"
+
+[[package]]
+name = "strenum"
+version = "0.4.15"
+description = "An Enum that inherits from str."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "StrEnum-0.4.15-py3-none-any.whl", hash = "sha256:a30cda4af7cc6b5bf52c8055bc4bf4b2b6b14a93b574626da33df53cf7740659"},
+    {file = "StrEnum-0.4.15.tar.gz", hash = "sha256:878fb5ab705442070e4dd1929bb5e2249511c0bcf2b0eeacf3bcd80875c82eff"},
+]
+
+[package.extras]
+docs = ["myst-parser[linkify]", "sphinx", "sphinx-rtd-theme"]
+release = ["twine"]
+test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"]
+
+[[package]]
+name = "supabase"
+version = "2.16.0"
+description = "Supabase client for Python."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "supabase-2.16.0-py3-none-any.whl", hash = "sha256:99065caab3d90a56650bf39fbd0e49740995da3738ab28706c61bd7f2401db55"},
+    {file = "supabase-2.16.0.tar.gz", hash = "sha256:98f3810158012d4ec0e3083f2e5515f5e10b32bd71e7d458662140e963c1d164"},
+]
+
+[package.dependencies]
+gotrue = ">=2.11.0,<3.0.0"
+httpx = ">=0.26,<0.29"
+postgrest = ">0.19,<1.2"
+realtime = ">=2.4.0,<2.6.0"
+storage3 = ">=0.10,<0.13"
+supafunc = ">=0.9,<0.11"
+
+[[package]]
+name = "supafunc"
+version = "0.10.1"
+description = "Library for Supabase Functions"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
+    {file = "supafunc-0.10.1-py3-none-any.whl", hash = "sha256:26df9bd25ff2ef56cb5bfb8962de98f43331f7f8ff69572bac3ed9c3a9672040"},
+    {file = "supafunc-0.10.1.tar.gz", hash = "sha256:a5b33c8baecb6b5297d25da29a2503e2ec67ee6986f3d44c137e651b8a59a17d"},
+]
+
+[package.dependencies]
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+strenum = ">=0.4.15,<0.5.0"
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -1656,6 +1827,85 @@ typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
 [package.extras]
 standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]
 
+[[package]]
+name = "websockets"
+version = "15.0.1"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d63efaa0cd96cf0c5fe4d581521d9fa87744540d4bc999ae6e08595a1014b45b"},
+    {file = "websockets-15.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac60e3b188ec7574cb761b08d50fcedf9d77f1530352db4eef1707fe9dee7205"},
+    {file = "websockets-15.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5756779642579d902eed757b21b0164cd6fe338506a8083eb58af5c372e39d9a"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0fdfe3e2a29e4db3659dbd5bbf04560cea53dd9610273917799f1cde46aa725e"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4c2529b320eb9e35af0fa3016c187dffb84a3ecc572bcee7c3ce302bfeba52bf"},
+    {file = "websockets-15.0.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac1e5c9054fe23226fb11e05a6e630837f074174c4c2f0fe442996112a6de4fb"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:5df592cd503496351d6dc14f7cdad49f268d8e618f80dce0cd5a36b93c3fc08d"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:0a34631031a8f05657e8e90903e656959234f3a04552259458aac0b0f9ae6fd9"},
+    {file = "websockets-15.0.1-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:3d00075aa65772e7ce9e990cab3ff1de702aa09be3940d1dc88d5abf1ab8a09c"},
+    {file = "websockets-15.0.1-cp310-cp310-win32.whl", hash = "sha256:1234d4ef35db82f5446dca8e35a7da7964d02c127b095e172e54397fb6a6c256"},
+    {file = "websockets-15.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:39c1fec2c11dc8d89bba6b2bf1556af381611a173ac2b511cf7231622058af41"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:823c248b690b2fd9303ba00c4f66cd5e2d8c3ba4aa968b2779be9532a4dad431"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678999709e68425ae2593acf2e3ebcbcf2e69885a5ee78f9eb80e6e371f1bf57"},
+    {file = "websockets-15.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:d50fd1ee42388dcfb2b3676132c78116490976f1300da28eb629272d5d93e905"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d99e5546bf73dbad5bf3547174cd6cb8ba7273062a23808ffea025ecb1cf8562"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:66dd88c918e3287efc22409d426c8f729688d89a0c587c88971a0faa2c2f3792"},
+    {file = "websockets-15.0.1-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8dd8327c795b3e3f219760fa603dcae1dcc148172290a8ab15158cf85a953413"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:8fdc51055e6ff4adeb88d58a11042ec9a5eae317a0a53d12c062c8a8865909e8"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:693f0192126df6c2327cce3baa7c06f2a117575e32ab2308f7f8216c29d9e2e3"},
+    {file = "websockets-15.0.1-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:54479983bd5fb469c38f2f5c7e3a24f9a4e70594cd68cd1fa6b9340dadaff7cf"},
+    {file = "websockets-15.0.1-cp311-cp311-win32.whl", hash = "sha256:16b6c1b3e57799b9d38427dda63edcbe4926352c47cf88588c0be4ace18dac85"},
+    {file = "websockets-15.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:27ccee0071a0e75d22cb35849b1db43f2ecd3e161041ac1ee9d2352ddf72f065"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:3e90baa811a5d73f3ca0bcbf32064d663ed81318ab225ee4f427ad4e26e5aff3"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:592f1a9fe869c778694f0aa806ba0374e97648ab57936f092fd9d87f8bc03665"},
+    {file = "websockets-15.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:0701bc3cfcb9164d04a14b149fd74be7347a530ad3bbf15ab2c678a2cd3dd9a2"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8b56bdcdb4505c8078cb6c7157d9811a85790f2f2b3632c7d1462ab5783d215"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0af68c55afbd5f07986df82831c7bff04846928ea8d1fd7f30052638788bc9b5"},
+    {file = "websockets-15.0.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:64dee438fed052b52e4f98f76c5790513235efaa1ef7f3f2192c392cd7c91b65"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:d5f6b181bb38171a8ad1d6aa58a67a6aa9d4b38d0f8c5f496b9e42561dfc62fe"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:5d54b09eba2bada6011aea5375542a157637b91029687eb4fdb2dab11059c1b4"},
+    {file = "websockets-15.0.1-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:3be571a8b5afed347da347bfcf27ba12b069d9d7f42cb8c7028b5e98bbb12597"},
+    {file = "websockets-15.0.1-cp312-cp312-win32.whl", hash = "sha256:c338ffa0520bdb12fbc527265235639fb76e7bc7faafbb93f6ba80d9c06578a9"},
+    {file = "websockets-15.0.1-cp312-cp312-win_amd64.whl", hash = "sha256:fcd5cf9e305d7b8338754470cf69cf81f420459dbae8a3b40cee57417f4614a7"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675"},
+    {file = "websockets-15.0.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f"},
+    {file = "websockets-15.0.1-cp313-cp313-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d"},
+    {file = "websockets-15.0.1-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4"},
+    {file = "websockets-15.0.1-cp313-cp313-win32.whl", hash = "sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa"},
+    {file = "websockets-15.0.1-cp313-cp313-win_amd64.whl", hash = "sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:5f4c04ead5aed67c8a1a20491d54cdfba5884507a48dd798ecaf13c74c4489f5"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:abdc0c6c8c648b4805c5eacd131910d2a7f6455dfd3becab248ef108e89ab16a"},
+    {file = "websockets-15.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a625e06551975f4b7ea7102bc43895b90742746797e2e14b70ed61c43a90f09b"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d591f8de75824cbb7acad4e05d2d710484f15f29d4a915092675ad3456f11770"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47819cea040f31d670cc8d324bb6435c6f133b8c7a19ec3d61634e62f8d8f9eb"},
+    {file = "websockets-15.0.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ac017dd64572e5c3bd01939121e4d16cf30e5d7e110a119399cf3133b63ad054"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:4a9fac8e469d04ce6c25bb2610dc535235bd4aa14996b4e6dbebf5e007eba5ee"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:363c6f671b761efcb30608d24925a382497c12c506b51661883c3e22337265ed"},
+    {file = "websockets-15.0.1-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:2034693ad3097d5355bfdacfffcbd3ef5694f9718ab7f29c29689a9eae841880"},
+    {file = "websockets-15.0.1-cp39-cp39-win32.whl", hash = "sha256:3b1ac0d3e594bf121308112697cf4b32be538fb1444468fb0a6ae4feebc83411"},
+    {file = "websockets-15.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:b7643a03db5c95c799b89b31c036d5f27eeb4d259c798e878d6937d71832b1e4"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-macosx_10_15_x86_64.whl", hash = "sha256:0c9e74d766f2818bb95f84c25be4dea09841ac0f734d1966f415e4edfc4ef1c3"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1009ee0c7739c08a0cd59de430d6de452a55e42d6b522de7aa15e6f67db0b8e1"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76d1f20b1c7a2fa82367e04982e708723ba0e7b8d43aa643d3dcd404d74f1475"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f29d80eb9a9263b8d109135351caf568cc3f80b9928bccde535c235de55c22d9"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b359ed09954d7c18bbc1680f380c7301f92c60bf924171629c5db97febb12f04"},
+    {file = "websockets-15.0.1-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:cad21560da69f4ce7658ca2cb83138fb4cf695a2ba3e475e0559e05991aa8122"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-macosx_10_15_x86_64.whl", hash = "sha256:7f493881579c90fc262d9cdbaa05a6b54b3811c2f300766748db79f098db9940"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:47b099e1f4fbc95b701b6e85768e1fcdaf1630f3cbe4765fa216596f12310e2e"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67f2b6de947f8c757db2db9c71527933ad0019737ec374a8a6be9a956786aaf9"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d08eb4c2b7d6c41da6ca0600c077e93f5adcfd979cd777d747e9ee624556da4b"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b826973a4a2ae47ba357e4e82fa44a463b8f168e1ca775ac64521442b19e87f"},
+    {file = "websockets-15.0.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:21c1fa28a6a7e3cbdc171c694398b6df4744613ce9b36b1a498e816787e28123"},
+    {file = "websockets-15.0.1-py3-none-any.whl", hash = "sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f"},
+    {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.23.0"
@@ -1679,4 +1929,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "de209c97aa0feb29d669a20e4422d51bdf3a0872ec37e85ce9b88ce726fcee7a"
+content-hash = "0c40b63c3c921846cf05ccfb4e685d4959854b29c2c302245f9832e20aac6954"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -18,8 +18,7 @@ pydantic = "^2.11.7"
 pydantic-settings = "^2.10.1"
 pyjwt = { version = "^2.10.1", extras = ["crypto"] }
 redis = "^6.2.0"
-bcrypt = "^4.1.0"
-authlib = "^1.3.0"
+supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]

autogpt_platform/backend/.env.default

@@ -27,15 +27,10 @@ REDIS_PORT=6379
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
 RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
 
-# JWT Authentication
-# Generate a secure random key: python -c "import secrets; print(secrets.token_urlsafe(32))"
-JWT_SIGN_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
+# Supabase Authentication
+SUPABASE_URL=http://localhost:8000
+SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
 JWT_VERIFY_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
-JWT_SIGN_ALGORITHM=HS256
-ACCESS_TOKEN_EXPIRE_MINUTES=15
-REFRESH_TOKEN_EXPIRE_DAYS=7
-JWT_ISSUER=autogpt-platform
-JWT_AUDIENCE=authenticated
 
 ## ===== REQUIRED SECURITY KEYS ===== ##
 # Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()

autogpt_platform/backend/.gitignore

@@ -18,6 +18,3 @@ load-tests/results/
 load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
-
-# Migration backups (contain user data)
-migration_backups/

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": [
-    {
-      "id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "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": {
-        "position": {
-          "x": 2329.937006807125,
-          "y": 80.49068076698347
-        }
-      },
-      "input_links": [
-        {
-          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Theme",
-        "value": "Cooking"
-      },
-      "metadata": {
-        "position": {
-          "x": -1219.5966324967521,
-          "y": 80.50339731789956
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_THEME",
-          "is_static": true
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 1132.373897280427,
-          "y": 88.44610377514573
-        }
-      },
-      "input_links": [
-        {
-          "id": "54588c74-e090-4e49-89e4-844b9952a585",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 590.7543882245375,
-          "y": 85.69546832466654
-        }
-      },
-      "input_links": [
-        {
-          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 60.48904654237981,
-          "y": 86.06183359510214
-        }
-      },
-      "input_links": [
-        {
-          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
-      "input_default": {
-        "prompt": "A cat sprawled dramatically across an important-looking document during a work-from-home meeting, making direct eye contact with the camera while knocking over a coffee mug in slow motion. Text Overlay: \"Chaos is a career path. Be the obstacle everyone has to work around.\"",
-        "upscale": "No Upscale"
-      },
-      "metadata": {
-        "position": {
-          "x": 1668.3572666956795,
-          "y": 89.69665262457966
-        }
-      },
-      "input_links": [
-        {
-          "id": "509b7587-1940-4a06-808d-edde9a74f400",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o",
-        "prompt": "<example_output>\nA photo of a sloth lounging on a desk, with its head resting on a keyboard. The keyboard is on top of a laptop with a blank spreadsheet open. A to-do list is placed beside the laptop, with the top item written as \"Do literally anything\". There is a text overlay that says \"If you can't outwork them, outnap them.\".\n</example_output>\n\nCreate a relatable satirical, snarky, user-deprecating motivational style image based on the theme: \"{{THEME}}\".\n\nOutput only the image description and caption, without any additional commentary or formatting.",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": -561.1139207164056,
-          "y": 78.60434452403524
-        }
-      },
-      "input_links": [
-        {
-          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_THEME",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "54588c74-e090-4e49-89e4-844b9952a585",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "509b7587-1940-4a06-808d-edde9a74f400",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        },
-        {
-          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "66646786-3006-4417-a6b7-0158f2603d1d",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
-      "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
-      "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "20845dda-91de-4508-8077-0504b1a5ae03",
-      "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
-      "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
-      "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_THEME",
-      "is_static": true
-    },
-    {
-      "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
-      "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
-      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "54588c74-e090-4e49-89e4-844b9952a585",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    },
-    {
-      "id": "509b7587-1940-4a06-808d-edde9a74f400",
-      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
-      "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T19:58:34.390Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Theme": {
-        "advanced": false,
-        "secret": false,
-        "title": "Theme",
-        "default": "Cooking"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Generated Image": {
-        "advanced": false,
-        "secret": false,
-        "title": "Generated Image",
-        "description": "The resulting generated image ready for you to review and post."
-      }
-    },
-    "required": [
-      "Generated Image"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "ideogram_api_key_credentials": {
-        "credentials_provider": [
-          "ideogram"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "ideogram",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.IDEOGRAM: 'ideogram'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "gpt-4o"
-        ]
-      }
-    },
-    "required": [
-      "ideogram_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "UnspirationalPosterMakerCredentialsInputSchema",
-    "type": "object"
-  }
-}

autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json

@@ -1,447 +0,0 @@
-{
-  "id": "622849a7-5848-4838-894d-01f8f07e3fad",
-  "version": 18,
-  "is_active": true,
-  "name": "AI Function",
-  "description": "## AI-Powered Function Magic: Never code again!\nProvide a description of a python function and your inputs and AI will provide the results.",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "return",
-        "title": null,
-        "value": null,
-        "format": "",
-        "secret": false,
-        "advanced": false,
-        "description": "The value returned by the function"
-      },
-      "metadata": {
-        "position": {
-          "x": 1598.8622921127233,
-          "y": 291.59140862204725
-        }
-      },
-      "input_links": [
-        {
-          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "o3-mini",
-        "retry": 3,
-        "prompt": "{{ARGS}}",
-        "sys_prompt": "You are now the following python function:\n\n```\n# {{DESCRIPTION}}\n{{FUNCTION}}\n```\n\nThe user will provide your input arguments.\nOnly respond with your `return` value.\nDo not include any commentary or additional text in your response. \nDo not include ``` backticks or any other decorators.",
-        "ollama_host": "localhost:11434",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 995,
-          "y": 290.50000000000006
-        }
-      },
-      "input_links": [
-        {
-          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_FUNCTION",
-          "is_static": true
-        },
-        {
-          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_ARGS",
-          "is_static": true
-        },
-        {
-          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_DESCRIPTION",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
-      "input_default": {
-        "name": "Function Definition",
-        "title": null,
-        "value": "def fake_people(n: int) -> list[dict]:",
-        "secret": false,
-        "advanced": false,
-        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": -672.6908629664215,
-          "y": 302.42044359789116
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_FUNCTION",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "844530de-2354-46d8-b748-67306b7bbca1",
-      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
-      "input_default": {
-        "name": "Arguments",
-        "title": null,
-        "value": "20",
-        "secret": false,
-        "advanced": false,
-        "description": "The function's inputs\n\ne.g \"20\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": -158.1623599617334,
-          "y": 295.410856928333
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_ARGS",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-      "block_id": "90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
-      "input_default": {
-        "name": "Description",
-        "title": null,
-        "value": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.",
-        "secret": false,
-        "advanced": false,
-        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
-        "placeholder_values": []
-      },
-      "metadata": {
-        "position": {
-          "x": 374.4548658057796,
-          "y": 290.3779121974126
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_DESCRIPTION",
-          "is_static": true
-        }
-      ],
-      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
-      "graph_version": 18,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
-      "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
-      "source_name": "response",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
-      "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_DESCRIPTION",
-      "is_static": true
-    },
-    {
-      "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
-      "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_ARGS",
-      "is_static": true
-    },
-    {
-      "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
-      "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
-      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_FUNCTION",
-      "is_static": true
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2025-04-19T17:10:48.857Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Function Definition": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "short-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Function Definition",
-        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
-        "default": "def fake_people(n: int) -> list[dict]:"
-      },
-      "Arguments": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "short-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Arguments",
-        "description": "The function's inputs\n\ne.g \"20\"",
-        "default": "20"
-      },
-      "Description": {
-        "advanced": false,
-        "anyOf": [
-          {
-            "format": "long-text",
-            "type": "string"
-          },
-          {
-            "type": "null"
-          }
-        ],
-        "secret": false,
-        "title": "Description",
-        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
-        "default": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age."
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "return": {
-        "advanced": false,
-        "secret": false,
-        "title": "return",
-        "description": "The value returned by the function"
-      }
-    },
-    "required": [
-      "return"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "o3-mini"
-        ]
-      }
-    },
-    "required": [
-      "openai_api_key_credentials"
-    ],
-    "title": "AIFunctionCredentialsInputSchema",
-    "type": "object"
-  }
-}

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

@@ -1,403 +0,0 @@
-{
-  "id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-  "version": 12,
-  "is_active": true,
-  "name": "Flux AI Image Generator",
-  "description": "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!",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "7482c59d-725f-4686-82b9-0dfdc4e92316",
-      "block_id": "cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
-      "input_default": {
-        "text": "Press the \"Advanced\" toggle and input your replicate API key.\n\nYou can get one here:\nhttps://replicate.com/account/api-tokens\n"
-      },
-      "metadata": {
-        "position": {
-          "x": 872.8268131538296,
-          "y": 614.9436919065381
-        }
-      },
-      "input_links": [],
-      "output_links": [],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Generated Image"
-      },
-      "metadata": {
-        "position": {
-          "x": 1453.6844137728922,
-          "y": 963.2466395125115
-        }
-      },
-      "input_links": [
-        {
-          "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Image Subject",
-        "value": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks.",
-        "description": "The subject of the image"
-      },
-      "metadata": {
-        "position": {
-          "x": -314.43009631839783,
-          "y": 962.935949165938
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
-          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "block_id": "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-      "input_default": {
-        "prompt": "dog",
-        "output_format": "png",
-        "replicate_model_name": "Flux Pro 1.1"
-      },
-      "metadata": {
-        "position": {
-          "x": 873.0119949791526,
-          "y": 966.1604399052493
-        }
-      },
-      "input_links": [
-        {
-          "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
-          "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o-mini",
-        "prompt": "Generate an incredibly detailed, photorealistic image prompt about {{TOPIC}}, describing the camera it's taken with and prompting the diffusion model to use all the best quality techniques.\n\nOutput only the prompt with no additional commentary.",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 277.3057034159709,
-          "y": 962.8382498113764
-        }
-      },
-      "input_links": [
-        {
-          "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
-          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
-          "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
-      "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-      "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_TOPIC",
-      "is_static": true
-    },
-    {
-      "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-      "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
-      "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T18:46:11.492Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Image Subject": {
-        "advanced": false,
-        "secret": false,
-        "title": "Image Subject",
-        "description": "The subject of the image",
-        "default": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks."
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Generated Image": {
-        "advanced": false,
-        "secret": false,
-        "title": "Generated Image"
-      }
-    },
-    "required": [
-      "Generated Image"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "replicate_api_key_credentials": {
-        "credentials_provider": [
-          "replicate"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "replicate",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.REPLICATE: 'replicate'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "gpt-4o-mini"
-        ]
-      }
-    },
-    "required": [
-      "replicate_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "FluxAIImageGeneratorCredentialsInputSchema",
-    "type": "object"
-  }
-}

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

@@ -1,505 +0,0 @@
-{
-  "id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-  "version": 12,
-  "is_active": true,
-  "name": "AI Webpage Copy Improver",
-  "description": "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.",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Improved Webpage Copy"
-      },
-      "metadata": {
-        "position": {
-          "x": 1039.5884372540172,
-          "y": -0.8359099621230968
-        }
-      },
-      "input_links": [
-        {
-          "id": "d4334477-3616-454f-a430-614ca27f5b36",
-          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Original Page Analysis",
-        "description": "Analysis of the webpage as it currently stands."
-      },
-      "metadata": {
-        "position": {
-          "x": 1037.7724103954706,
-          "y": -606.5934325506903
-        }
-      },
-      "input_links": [
-        {
-          "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
-          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Homepage URL",
-        "value": "https://agpt.co",
-        "description": "Enter the URL of the homepage you want to improve"
-      },
-      "metadata": {
-        "position": {
-          "x": -1195.1455674454749,
-          "y": 0
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "cbb12335-fefd-4560-9fff-98675130fbad",
-          "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
-          "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "source_name": "result",
-          "sink_name": "url",
-          "is_static": true
-        }
-      ],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-      "block_id": "436c3984-57fd-4b85-8e9a-459b356883bd",
-      "input_default": {
-        "raw_content": false
-      },
-      "metadata": {
-        "position": {
-          "x": -631.7330786555249,
-          "y": 1.9638396496230826
-        }
-      },
-      "input_links": [
-        {
-          "id": "cbb12335-fefd-4560-9fff-98675130fbad",
-          "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
-          "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "source_name": "result",
-          "sink_name": "url",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
-          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "source_name": "content",
-          "sink_name": "prompt_values_#_CONTENT",
-          "is_static": false
-        },
-        {
-          "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
-          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "source_name": "content",
-          "sink_name": "prompt_values_#_CONTENT",
-          "is_static": false
-        }
-      ],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o",
-        "prompt": "Current Webpage Content:\n```\n{{CONTENT}}\n```\n\nBased on the following analysis of the webpage content:\n\n```\n{{ANALYSIS}}\n```\n\nRewrite and improve the content to address the identified issues. Focus on:\n1. Enhancing clarity and readability\n2. Optimizing for SEO (suggest and incorporate relevant keywords)\n3. Improving calls-to-action for better conversion rates\n4. Refining the structure and organization\n5. Maintaining brand consistency while improving the overall tone\n\nProvide the improved content in HTML format inside a code-block with \"```\" backticks, preserving the original structure where appropriate. Also, include a brief summary of the changes made and their potential impact.",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 488.37278423303917,
-          "y": 0
-        }
-      },
-      "input_links": [
-        {
-          "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
-          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "source_name": "content",
-          "sink_name": "prompt_values_#_CONTENT",
-          "is_static": false
-        },
-        {
-          "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
-          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "source_name": "response",
-          "sink_name": "prompt_values_#_ANALYSIS",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "d4334477-3616-454f-a430-614ca27f5b36",
-          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "08612ce2-625b-4c17-accd-3acace7b6477",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o",
-        "prompt": "Analyze the following webpage content and provide a detailed report on its current state, including strengths and weaknesses in terms of clarity, SEO optimization, and potential for conversion:\n\n{{CONTENT}}\n\nInclude observations on:\n1. Overall readability and clarity\n2. Use of keywords and SEO-friendly language\n3. Effectiveness of calls-to-action\n4. Structure and organization of content\n5. Tone and brand consistency",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": -72.66206703605442,
-          "y": -0.58403945075381
-        }
-      },
-      "input_links": [
-        {
-          "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
-          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-          "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "source_name": "content",
-          "sink_name": "prompt_values_#_CONTENT",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
-          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
-          "source_name": "response",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
-          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-          "source_name": "response",
-          "sink_name": "prompt_values_#_ANALYSIS",
-          "is_static": false
-        }
-      ],
-      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
-      "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-      "source_name": "content",
-      "sink_name": "prompt_values_#_CONTENT",
-      "is_static": false
-    },
-    {
-      "id": "d4334477-3616-454f-a430-614ca27f5b36",
-      "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-      "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
-      "source_name": "response",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
-      "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-      "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-      "source_name": "content",
-      "sink_name": "prompt_values_#_CONTENT",
-      "is_static": false
-    },
-    {
-      "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
-      "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-      "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
-      "source_name": "response",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
-      "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
-      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
-      "source_name": "response",
-      "sink_name": "prompt_values_#_ANALYSIS",
-      "is_static": false
-    },
-    {
-      "id": "cbb12335-fefd-4560-9fff-98675130fbad",
-      "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
-      "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
-      "source_name": "result",
-      "sink_name": "url",
-      "is_static": true
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T19:47:22.036Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Homepage URL": {
-        "advanced": false,
-        "secret": false,
-        "title": "Homepage URL",
-        "description": "Enter the URL of the homepage you want to improve",
-        "default": "https://agpt.co"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Improved Webpage Copy": {
-        "advanced": false,
-        "secret": false,
-        "title": "Improved Webpage Copy"
-      },
-      "Original Page Analysis": {
-        "advanced": false,
-        "secret": false,
-        "title": "Original Page Analysis",
-        "description": "Analysis of the webpage as it currently stands."
-      }
-    },
-    "required": [
-      "Improved Webpage Copy",
-      "Original Page Analysis"
-    ]
-  },
-  "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": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
-          "openai"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "openai",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], 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": [
-          "gpt-4o"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "AIWebpageCopyImproverCredentialsInputSchema",
-    "type": "object"
-  }
-}

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

@@ -1,615 +0,0 @@
-{
-  "id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-  "version": 29,
-  "is_active": true,
-  "name": "Email Address Finder",
-  "description": "Input information of a business and find their email address",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Address",
-        "value": "USA"
-      },
-      "metadata": {
-        "position": {
-          "x": 1047.9357219838776,
-          "y": 1067.9123910370954
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
-      "input_default": {
-        "group": 1,
-        "pattern": "<email>(.*?)<\\/email>"
-      },
-      "metadata": {
-        "position": {
-          "x": 3381.2821481740634,
-          "y": 246.091098184158
-        }
-      },
-      "input_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
-        }
-      ],
-      "output_links": [
-        {
-          "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": "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Email"
-      },
-      "metadata": {
-        "position": {
-          "x": 4525.4246310882,
-          "y": 246.36913665010354
-        }
-      },
-      "input_links": [
-        {
-          "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": "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
-        }
-      ],
-      "output_links": [],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
-      "input_default": {},
-      "metadata": {
-        "position": {
-          "x": 2182.7499999999995,
-          "y": 242.00001144409185
-        }
-      },
-      "input_links": [
-        {
-          "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
-        }
-      ],
-      "output_links": [
-        {
-          "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Business Name",
-        "value": "Tim Cook"
-      },
-      "metadata": {
-        "position": {
-          "x": 1049.9704155272595,
-          "y": 244.49931152418344
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "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": "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Email Address of {{NAME}}, {{ADDRESS}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 1625.25,
-          "y": 243.25001144409185
-        }
-      },
-      "input_links": [
-        {
-          "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": "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
-        }
-      ],
-      "output_links": [
-        {
-          "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Failed to find email. \nResult:\n{{RESULT}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 3949.7493830805934,
-          "y": 705.209819698647
-        }
-      },
-      "input_links": [
-        {
-          "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
-        }
-      ],
-      "output_links": [
-        {
-          "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
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "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 +83,8 @@ 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,
+            is_sub_graph=True,  # AgentExecutorBlock executions are always sub-graphs
         )
 
         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/ai_shortform_video_block.py

@@ -20,7 +20,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.exceptions import BlockExecutionError
 from backend.util.request import Requests
 
 TEST_CREDENTIALS = APIKeyCredentials(
@@ -247,11 +246,7 @@ class AIShortformVideoCreatorBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(
@@ -427,11 +422,7 @@ class AIAdMakerVideoCreatorBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(
@@ -608,11 +599,7 @@ class AIScreenshotToVideoAdBlock(Block):
             await asyncio.sleep(10)
 
         logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")
 
     def __init__(self):
         super().__init__(

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/branching.py

@@ -106,10 +106,7 @@ class ConditionBlock(Block):
             ComparisonOperator.LESS_THAN_OR_EQUAL: lambda a, b: a <= b,
         }
 
-        try:
-            result = comparison_funcs[operator](value1, value2)
-        except Exception as e:
-            raise ValueError(f"Comparison failed: {e}") from e
+        result = comparison_funcs[operator](value1, value2)
 
         yield "result", result
 

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/helpers.py

@@ -319,7 +319,7 @@ class CostDollars(BaseModel):
 
 # Helper functions for payload processing
 def process_text_field(
-    text: Union[bool, TextEnabled, TextDisabled, TextAdvanced, None],
+    text: Union[bool, TextEnabled, TextDisabled, TextAdvanced, None]
 ) -> Optional[Union[bool, Dict[str, Any]]]:
     """Process text field for API payload."""
     if text is None:
@@ -400,7 +400,7 @@ def process_contents_settings(contents: Optional[ContentSettings]) -> Dict[str,
 
 
 def process_context_field(
-    context: Union[bool, dict, ContextEnabled, ContextDisabled, ContextAdvanced, None],
+    context: Union[bool, dict, ContextEnabled, ContextDisabled, ContextAdvanced, None]
 ) -> Optional[Union[bool, Dict[str, int]]]:
     """Process context field for API payload."""
     if context is None:

autogpt_platform/backend/backend/blocks/firecrawl/extract.py

@@ -15,7 +15,6 @@ from backend.sdk import (
     SchemaField,
     cost,
 )
-from backend.util.exceptions import BlockExecutionError
 
 from ._config import firecrawl
 
@@ -60,18 +59,11 @@ class FirecrawlExtractBlock(Block):
     ) -> BlockOutput:
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
-        try:
-            extract_result = app.extract(
-                urls=input_data.urls,
-                prompt=input_data.prompt,
-                schema=input_data.output_schema,
-                enable_web_search=input_data.enable_web_search,
-            )
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Extract failed: {e}",
-                block_name=self.name,
-                block_id=self.id,
-            ) from e
+        extract_result = app.extract(
+            urls=input_data.urls,
+            prompt=input_data.prompt,
+            schema=input_data.output_schema,
+            enable_web_search=input_data.enable_web_search,
+        )
 
         yield "data", extract_result.data

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -19,7 +19,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.exceptions import ModerationError
 from backend.util.file import MediaFileType, store_media_file
 
 TEST_CREDENTIALS = APIKeyCredentials(
@@ -154,8 +153,6 @@ class AIImageEditorBlock(Block):
             ),
             aspect_ratio=input_data.aspect_ratio.value,
             seed=input_data.seed,
-            user_id=user_id,
-            graph_exec_id=graph_exec_id,
         )
         yield "output_image", result
 
@@ -167,8 +164,6 @@ class AIImageEditorBlock(Block):
         input_image_b64: Optional[str],
         aspect_ratio: str,
         seed: Optional[int],
-        user_id: str,
-        graph_exec_id: str,
     ) -> MediaFileType:
         client = ReplicateClient(api_token=api_key.get_secret_value())
         input_params = {
@@ -178,21 +173,11 @@ class AIImageEditorBlock(Block):
             **({"seed": seed} if seed is not None else {}),
         }
 
-        try:
-            output: FileOutput | list[FileOutput] = await client.async_run(  # type: ignore
-                model_name,
-                input=input_params,
-                wait=False,
-            )
-        except Exception as e:
-            if "flagged as sensitive" in str(e).lower():
-                raise ModerationError(
-                    message="Content was flagged as sensitive by the model provider",
-                    user_id=user_id,
-                    graph_exec_id=graph_exec_id,
-                    moderation_type="model_provider",
-                )
-            raise ValueError(f"Model execution failed: {e}") from e
+        output: FileOutput | list[FileOutput] = await client.async_run(  # type: ignore
+            model_name,
+            input=input_params,
+            wait=False,
+        )
 
         if isinstance(output, list) and output:
             output = output[0]

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/ideogram.py

@@ -2,6 +2,7 @@ from enum import Enum
 from typing import Any, Dict, Literal, Optional
 
 from pydantic import SecretStr
+from requests.exceptions import RequestException
 
 from backend.data.block import (
     Block,
@@ -331,8 +332,8 @@ class IdeogramModelBlock(Block):
         try:
             response = await Requests().post(url, headers=headers, json=data)
             return response.json()["data"][0]["url"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch image with V3 endpoint: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to fetch image with V3 endpoint: {str(e)}")
 
     async def _run_model_legacy(
         self,
@@ -384,8 +385,8 @@ class IdeogramModelBlock(Block):
         try:
             response = await Requests().post(url, headers=headers, json=data)
             return response.json()["data"][0]["url"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch image with legacy endpoint: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to fetch image with legacy endpoint: {str(e)}")
 
     async def upscale_image(self, api_key: SecretStr, image_url: str):
         url = "https://api.ideogram.ai/upscale"
@@ -412,5 +413,5 @@ class IdeogramModelBlock(Block):
 
             return (response.json())["data"][0]["url"]
 
-        except Exception as e:
-            raise ValueError(f"Failed to upscale image: {e}") from e
+        except RequestException as e:
+            raise Exception(f"Failed to upscale image: {str(e)}")

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/jina/search.py

@@ -16,7 +16,6 @@ from backend.data.block import (
     BlockSchemaOutput,
 )
 from backend.data.model import SchemaField
-from backend.util.exceptions import BlockExecutionError
 
 
 class SearchTheWebBlock(Block, GetRequest):
@@ -57,17 +56,7 @@ class SearchTheWebBlock(Block, GetRequest):
 
         # Prepend the Jina Search URL to the encoded query
         jina_search_url = f"https://s.jina.ai/{encoded_query}"
-
-        try:
-            results = await self.get_request(
-                jina_search_url, headers=headers, json=False
-            )
-        except Exception as e:
-            raise BlockExecutionError(
-                message=f"Search failed: {e}",
-                block_name=self.name,
-                block_id=self.id,
-            ) from e
+        results = await self.get_request(jina_search_url, headers=headers, json=False)
 
         # Output the search results
         yield "results", results

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

@@ -93,7 +93,6 @@ 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 +106,6 @@ 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"
@@ -131,7 +129,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     OPENAI_GPT_OSS_120B = "openai/gpt-oss-120b"
     OPENAI_GPT_OSS_20B = "openai/gpt-oss-20b"
     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 +151,6 @@ 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 +189,6 @@ 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 +212,6 @@ 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
@@ -251,7 +241,6 @@ MODEL_METADATA = {
     LlmModel.OLLAMA_DOLPHIN: ModelMetadata("ollama", 32768, None),
     # https://openrouter.ai/models
     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(
@@ -286,9 +275,6 @@ 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),

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/replicate/replicate_block.py

@@ -18,7 +18,6 @@ from backend.data.block import (
     BlockSchemaOutput,
 )
 from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
-from backend.util.exceptions import BlockExecutionError, BlockInputError
 
 logger = logging.getLogger(__name__)
 
@@ -112,27 +111,9 @@ class ReplicateModelBlock(Block):
             yield "status", "succeeded"
             yield "model_name", input_data.model_name
         except Exception as e:
-            error_msg = str(e)
-            logger.error(f"Error running Replicate model: {error_msg}")
-
-            # Input validation errors (422, 400) → BlockInputError
-            if (
-                "422" in error_msg
-                or "Input validation failed" in error_msg
-                or "400" in error_msg
-            ):
-                raise BlockInputError(
-                    message=f"Invalid model inputs: {error_msg}",
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from e
-            # Everything else → BlockExecutionError
-            else:
-                raise BlockExecutionError(
-                    message=f"Replicate model error: {error_msg}",
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from e
+            error_msg = f"Unexpected error running Replicate model: {str(e)}"
+            logger.error(error_msg)
+            raise RuntimeError(error_msg)
 
     async def run_model(self, model_ref: str, model_inputs: dict, api_key: SecretStr):
         """

autogpt_platform/backend/backend/blocks/search.py

@@ -45,16 +45,10 @@ class GetWikipediaSummaryBlock(Block, GetRequest):
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         topic = input_data.topic
         url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{topic}"
-
-        # Note: User-Agent is now automatically set by the request library
-        # to comply with Wikimedia's robot policy (https://w.wiki/4wJS)
-        try:
-            response = await self.get_request(url, json=True)
-            if "extract" not in response:
-                raise ValueError(f"Unable to parse Wikipedia response: {response}")
-            yield "summary", response["extract"]
-        except Exception as e:
-            raise ValueError(f"Failed to fetch Wikipedia summary: {e}") from e
+        response = await self.get_request(url, json=True)
+        if "extract" not in response:
+            raise RuntimeError(f"Unable to parse Wikipedia response: {response}")
+        yield "summary", response["extract"]
 
 
 TEST_CREDENTIALS = APIKeyCredentials(

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 (
@@ -23,41 +20,16 @@ from backend.data.dynamic_fields import (
     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 +105,6 @@ 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.
@@ -276,17 +204,6 @@ 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]:
@@ -589,7 +506,6 @@ 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,
@@ -677,291 +593,6 @@ 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,
@@ -972,12 +603,8 @@ 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)
         yield "tool_functions", json.dumps(tool_functions)
 
@@ -1021,52 +648,24 @@ 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

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

@@ -1,24 +1,14 @@
-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)
 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)
@@ -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_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)
 
 
@@ -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
 
@@ -218,6 +216,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_tool_node_signatures method to avoid database calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call",
@@ -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
 
@@ -322,6 +311,8 @@ 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,
@@ -338,17 +329,8 @@ 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 +342,6 @@ 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
 
@@ -389,6 +368,8 @@ 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,
@@ -404,17 +385,8 @@ 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 +398,6 @@ 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
 
@@ -449,6 +418,8 @@ 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,
@@ -464,19 +435,10 @@ 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,
@@ -485,9 +447,6 @@ 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
 
@@ -513,6 +472,8 @@ 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,
@@ -528,19 +489,10 @@ 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,
@@ -549,9 +501,6 @@ 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
 
@@ -564,6 +513,8 @@ async def test_smart_decision_maker_parameter_validation():
 @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
 
@@ -633,6 +584,7 @@ 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
@@ -651,19 +603,10 @@ 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,
@@ -672,9 +615,6 @@ 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
 
@@ -710,6 +650,8 @@ 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,
@@ -724,18 +666,9 @@ 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 +677,6 @@ 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
 
@@ -766,6 +696,8 @@ 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,
@@ -780,18 +712,9 @@ 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 +723,8 @@ 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
 
@@ -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(
+        # Mock the function signature creation
+        with patch.object(
             block, "_create_tool_node_signatures", 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",
@@ -374,16 +337,10 @@ 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,
@@ -392,9 +349,6 @@ 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
 
@@ -557,108 +511,45 @@ async def test_validation_errors_dont_pollute_conversation():
                 }
             ]
 
-            # 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/cli/__init__.py

@@ -1 +0,0 @@
-"""CLI utilities for backend development & administration"""

autogpt_platform/backend/backend/cli/generate_openapi_json.py

@@ -1,57 +0,0 @@
-#!/usr/bin/env python3
-"""
-Script to generate OpenAPI JSON specification for the FastAPI app.
-
-This script imports the FastAPI app from backend.server.rest_api and outputs
-the OpenAPI specification as JSON to stdout or a specified file.
-
-Usage:
-  `poetry run python generate_openapi_json.py`
-  `poetry run python generate_openapi_json.py --output openapi.json`
-  `poetry run python generate_openapi_json.py --indent 4 --output openapi.json`
-"""
-
-import json
-import os
-from pathlib import Path
-
-import click
-
-
-@click.command()
-@click.option(
-    "--output",
-    type=click.Path(dir_okay=False, path_type=Path),
-    help="Output file path (default: stdout)",
-)
-@click.option(
-    "--pretty",
-    type=click.BOOL,
-    default=False,
-    help="Pretty-print JSON output (indented 2 spaces)",
-)
-def main(output: Path, pretty: bool):
-    """Generate and output the OpenAPI JSON specification."""
-    openapi_schema = get_openapi_schema()
-
-    json_output = json.dumps(openapi_schema, indent=2 if pretty else None)
-
-    if output:
-        output.write_text(json_output)
-        click.echo(f"✅ OpenAPI specification written to {output}\n\nPreview:")
-        click.echo(f"\n{json_output[:500]} ...")
-    else:
-        print(json_output)
-
-
-def get_openapi_schema():
-    """Get the OpenAPI schema from the FastAPI app"""
-    from backend.server.rest_api import app
-
-    return app.openapi()
-
-
-if __name__ == "__main__":
-    os.environ["LOG_LEVEL"] = "ERROR"  # disable stdout log output
-
-    main()

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/api_key.py

@@ -1,24 +1,22 @@
 import logging
 import uuid
 from datetime import datetime, timezone
-from typing import Literal, Optional, cast
+from typing import Optional
 
 from autogpt_libs.api_key.keysmith import APIKeySmith
 from prisma.enums import APIKeyPermission, APIKeyStatus
 from prisma.models import APIKey as PrismaAPIKey
-from prisma.types import APIKeyCreateInput, APIKeyWhereUniqueInput
-from pydantic import Field
+from prisma.types import APIKeyWhereUniqueInput
+from pydantic import BaseModel, Field
 
 from backend.data.includes import MAX_USER_API_KEYS_FETCH
 from backend.util.exceptions import NotAuthorizedError, NotFoundError
 
-from .base import APIAuthorizationInfo
-
 logger = logging.getLogger(__name__)
 keysmith = APIKeySmith()
 
 
-class APIKeyInfo(APIAuthorizationInfo):
+class APIKeyInfo(BaseModel):
     id: str
     name: str
     head: str = Field(
@@ -28,9 +26,12 @@ class APIKeyInfo(APIAuthorizationInfo):
         description=f"The last {APIKeySmith.TAIL_LENGTH} characters of the key"
     )
     status: APIKeyStatus
+    permissions: list[APIKeyPermission]
+    created_at: datetime
+    last_used_at: Optional[datetime] = None
+    revoked_at: Optional[datetime] = None
     description: Optional[str] = None
-
-    type: Literal["api_key"] = "api_key"  # type: ignore
+    user_id: str
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
@@ -40,7 +41,7 @@ class APIKeyInfo(APIAuthorizationInfo):
             head=api_key.head,
             tail=api_key.tail,
             status=APIKeyStatus(api_key.status),
-            scopes=[APIKeyPermission(p) for p in api_key.permissions],
+            permissions=[APIKeyPermission(p) for p in api_key.permissions],
             created_at=api_key.createdAt,
             last_used_at=api_key.lastUsedAt,
             revoked_at=api_key.revokedAt,
@@ -82,20 +83,17 @@ async def create_api_key(
     generated_key = keysmith.generate_key()
 
     saved_key_obj = await PrismaAPIKey.prisma().create(
-        data=cast(
-            APIKeyCreateInput,
-            {
-                "id": str(uuid.uuid4()),
-                "name": name,
-                "head": generated_key.head,
-                "tail": generated_key.tail,
-                "hash": generated_key.hash,
-                "salt": generated_key.salt,
-                "permissions": [p for p in permissions],
-                "description": description,
-                "userId": user_id,
-            },
-        )
+        data={
+            "id": str(uuid.uuid4()),
+            "name": name,
+            "head": generated_key.head,
+            "tail": generated_key.tail,
+            "hash": generated_key.hash,
+            "salt": generated_key.salt,
+            "permissions": [p for p in permissions],
+            "description": description,
+            "userId": user_id,
+        }
     )
 
     return APIKeyInfo.from_db(saved_key_obj), generated_key.key
@@ -213,7 +211,7 @@ async def suspend_api_key(key_id: str, user_id: str) -> APIKeyInfo:
 
 
 def has_permission(api_key: APIKeyInfo, required_permission: APIKeyPermission) -> bool:
-    return required_permission in api_key.scopes
+    return required_permission in api_key.permissions
 
 
 async def get_api_key_by_id(key_id: str, user_id: str) -> Optional[APIKeyInfo]:

autogpt_platform/backend/backend/data/auth/base.py

@@ -1,15 +0,0 @@
-from datetime import datetime
-from typing import Literal, Optional
-
-from prisma.enums import APIKeyPermission
-from pydantic import BaseModel
-
-
-class APIAuthorizationInfo(BaseModel):
-    user_id: str
-    scopes: list[APIKeyPermission]
-    type: Literal["oauth", "api_key"]
-    created_at: datetime
-    expires_at: Optional[datetime] = None
-    last_used_at: Optional[datetime] = None
-    revoked_at: Optional[datetime] = None

autogpt_platform/backend/backend/data/auth/oauth.py

@@ -1,886 +0,0 @@
-"""
-OAuth 2.0 Provider Data Layer
-
-Handles management of OAuth applications, authorization codes,
-access tokens, and refresh tokens.
-
-Hashing strategy:
-- Access tokens & Refresh tokens: SHA256 (deterministic, allows direct lookup by hash)
-- Client secrets: Scrypt with salt (lookup by client_id, then verify with salt)
-"""
-
-import hashlib
-import logging
-import secrets
-import uuid
-from datetime import datetime, timedelta, timezone
-from typing import Literal, Optional, cast
-
-from autogpt_libs.api_key.keysmith import APIKeySmith
-from prisma.enums import APIKeyPermission as APIPermission
-from prisma.models import OAuthAccessToken as PrismaOAuthAccessToken
-from prisma.models import OAuthApplication as PrismaOAuthApplication
-from prisma.models import OAuthAuthorizationCode as PrismaOAuthAuthorizationCode
-from prisma.models import OAuthRefreshToken as PrismaOAuthRefreshToken
-from prisma.types import (
-    OAuthAccessTokenCreateInput,
-    OAuthApplicationUpdateInput,
-    OAuthAuthorizationCodeCreateInput,
-    OAuthRefreshTokenCreateInput,
-)
-from pydantic import BaseModel, Field, SecretStr
-
-from .base import APIAuthorizationInfo
-
-logger = logging.getLogger(__name__)
-keysmith = APIKeySmith()  # Only used for client secret hashing (Scrypt)
-
-
-def _generate_token() -> str:
-    """Generate a cryptographically secure random token."""
-    return secrets.token_urlsafe(32)
-
-
-def _hash_token(token: str) -> str:
-    """Hash a token using SHA256 (deterministic, for direct lookup)."""
-    return hashlib.sha256(token.encode()).hexdigest()
-
-
-# Token TTLs
-AUTHORIZATION_CODE_TTL = timedelta(minutes=10)
-ACCESS_TOKEN_TTL = timedelta(hours=1)
-REFRESH_TOKEN_TTL = timedelta(days=30)
-
-ACCESS_TOKEN_PREFIX = "agpt_xt_"
-REFRESH_TOKEN_PREFIX = "agpt_rt_"
-
-
-# ============================================================================
-# Exception Classes
-# ============================================================================
-
-
-class OAuthError(Exception):
-    """Base OAuth error"""
-
-    pass
-
-
-class InvalidClientError(OAuthError):
-    """Invalid client_id or client_secret"""
-
-    pass
-
-
-class InvalidGrantError(OAuthError):
-    """Invalid or expired authorization code/refresh token"""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Invalid grant: {reason}")
-
-
-class InvalidTokenError(OAuthError):
-    """Invalid, expired, or revoked token"""
-
-    def __init__(self, reason: str):
-        self.reason = reason
-        super().__init__(f"Invalid token: {reason}")
-
-
-# ============================================================================
-# Data Models
-# ============================================================================
-
-
-class OAuthApplicationInfo(BaseModel):
-    """OAuth application information (without client secret hash)"""
-
-    id: str
-    name: str
-    description: Optional[str] = None
-    logo_url: Optional[str] = None
-    client_id: str
-    redirect_uris: list[str]
-    grant_types: list[str]
-    scopes: list[APIPermission]
-    owner_id: str
-    is_active: bool
-    created_at: datetime
-    updated_at: datetime
-
-    @staticmethod
-    def from_db(app: PrismaOAuthApplication):
-        return OAuthApplicationInfo(
-            id=app.id,
-            name=app.name,
-            description=app.description,
-            logo_url=app.logoUrl,
-            client_id=app.clientId,
-            redirect_uris=app.redirectUris,
-            grant_types=app.grantTypes,
-            scopes=[APIPermission(s) for s in app.scopes],
-            owner_id=app.ownerId,
-            is_active=app.isActive,
-            created_at=app.createdAt,
-            updated_at=app.updatedAt,
-        )
-
-
-class OAuthApplicationInfoWithSecret(OAuthApplicationInfo):
-    """OAuth application with client secret hash (for validation)"""
-
-    client_secret_hash: str
-    client_secret_salt: str
-
-    @staticmethod
-    def from_db(app: PrismaOAuthApplication):
-        return OAuthApplicationInfoWithSecret(
-            **OAuthApplicationInfo.from_db(app).model_dump(),
-            client_secret_hash=app.clientSecret,
-            client_secret_salt=app.clientSecretSalt,
-        )
-
-    def verify_secret(self, plaintext_secret: str) -> bool:
-        """Verify a plaintext client secret against the stored hash"""
-        # Use keysmith.verify_key() with stored salt
-        return keysmith.verify_key(
-            plaintext_secret, self.client_secret_hash, self.client_secret_salt
-        )
-
-
-class OAuthAuthorizationCodeInfo(BaseModel):
-    """Authorization code information"""
-
-    id: str
-    code: str
-    created_at: datetime
-    expires_at: datetime
-    application_id: str
-    user_id: str
-    scopes: list[APIPermission]
-    redirect_uri: str
-    code_challenge: Optional[str] = None
-    code_challenge_method: Optional[str] = None
-    used_at: Optional[datetime] = None
-
-    @property
-    def is_used(self) -> bool:
-        return self.used_at is not None
-
-    @staticmethod
-    def from_db(code: PrismaOAuthAuthorizationCode):
-        return OAuthAuthorizationCodeInfo(
-            id=code.id,
-            code=code.code,
-            created_at=code.createdAt,
-            expires_at=code.expiresAt,
-            application_id=code.applicationId,
-            user_id=code.userId,
-            scopes=[APIPermission(s) for s in code.scopes],
-            redirect_uri=code.redirectUri,
-            code_challenge=code.codeChallenge,
-            code_challenge_method=code.codeChallengeMethod,
-            used_at=code.usedAt,
-        )
-
-
-class OAuthAccessTokenInfo(APIAuthorizationInfo):
-    """Access token information"""
-
-    id: str
-    expires_at: datetime  # type: ignore
-    application_id: str
-
-    type: Literal["oauth"] = "oauth"  # type: ignore
-
-    @staticmethod
-    def from_db(token: PrismaOAuthAccessToken):
-        return OAuthAccessTokenInfo(
-            id=token.id,
-            user_id=token.userId,
-            scopes=[APIPermission(s) for s in token.scopes],
-            created_at=token.createdAt,
-            expires_at=token.expiresAt,
-            last_used_at=None,
-            revoked_at=token.revokedAt,
-            application_id=token.applicationId,
-        )
-
-
-class OAuthAccessToken(OAuthAccessTokenInfo):
-    """Access token with plaintext token included (sensitive)"""
-
-    token: SecretStr = Field(description="Plaintext token (sensitive)")
-
-    @staticmethod
-    def from_db(token: PrismaOAuthAccessToken, plaintext_token: str):  # type: ignore
-        return OAuthAccessToken(
-            **OAuthAccessTokenInfo.from_db(token).model_dump(),
-            token=SecretStr(plaintext_token),
-        )
-
-
-class OAuthRefreshTokenInfo(BaseModel):
-    """Refresh token information"""
-
-    id: str
-    user_id: str
-    scopes: list[APIPermission]
-    created_at: datetime
-    expires_at: datetime
-    application_id: str
-    revoked_at: Optional[datetime] = None
-
-    @property
-    def is_revoked(self) -> bool:
-        return self.revoked_at is not None
-
-    @staticmethod
-    def from_db(token: PrismaOAuthRefreshToken):
-        return OAuthRefreshTokenInfo(
-            id=token.id,
-            user_id=token.userId,
-            scopes=[APIPermission(s) for s in token.scopes],
-            created_at=token.createdAt,
-            expires_at=token.expiresAt,
-            application_id=token.applicationId,
-            revoked_at=token.revokedAt,
-        )
-
-
-class OAuthRefreshToken(OAuthRefreshTokenInfo):
-    """Refresh token with plaintext token included (sensitive)"""
-
-    token: SecretStr = Field(description="Plaintext token (sensitive)")
-
-    @staticmethod
-    def from_db(token: PrismaOAuthRefreshToken, plaintext_token: str):  # type: ignore
-        return OAuthRefreshToken(
-            **OAuthRefreshTokenInfo.from_db(token).model_dump(),
-            token=SecretStr(plaintext_token),
-        )
-
-
-class TokenIntrospectionResult(BaseModel):
-    """Result of token introspection (RFC 7662)"""
-
-    active: bool
-    scopes: Optional[list[str]] = None
-    client_id: Optional[str] = None
-    user_id: Optional[str] = None
-    exp: Optional[int] = None  # Unix timestamp
-    token_type: Optional[Literal["access_token", "refresh_token"]] = None
-
-
-# ============================================================================
-# OAuth Application Management
-# ============================================================================
-
-
-async def get_oauth_application(client_id: str) -> Optional[OAuthApplicationInfo]:
-    """Get OAuth application by client ID (without secret)"""
-    app = await PrismaOAuthApplication.prisma().find_unique(
-        where={"clientId": client_id}
-    )
-    if not app:
-        return None
-    return OAuthApplicationInfo.from_db(app)
-
-
-async def get_oauth_application_with_secret(
-    client_id: str,
-) -> Optional[OAuthApplicationInfoWithSecret]:
-    """Get OAuth application by client ID (with secret hash for validation)"""
-    app = await PrismaOAuthApplication.prisma().find_unique(
-        where={"clientId": client_id}
-    )
-    if not app:
-        return None
-    return OAuthApplicationInfoWithSecret.from_db(app)
-
-
-async def validate_client_credentials(
-    client_id: str, client_secret: str
-) -> OAuthApplicationInfo:
-    """
-    Validate client credentials and return application info.
-
-    Raises:
-        InvalidClientError: If client_id or client_secret is invalid, or app is inactive
-    """
-    app = await get_oauth_application_with_secret(client_id)
-    if not app:
-        raise InvalidClientError("Invalid client_id")
-
-    if not app.is_active:
-        raise InvalidClientError("Application is not active")
-
-    # Verify client secret
-    if not app.verify_secret(client_secret):
-        raise InvalidClientError("Invalid client_secret")
-
-    # Return without secret hash
-    return OAuthApplicationInfo(**app.model_dump(exclude={"client_secret_hash"}))
-
-
-def validate_redirect_uri(app: OAuthApplicationInfo, redirect_uri: str) -> bool:
-    """Validate that redirect URI is registered for the application"""
-    return redirect_uri in app.redirect_uris
-
-
-def validate_scopes(
-    app: OAuthApplicationInfo, requested_scopes: list[APIPermission]
-) -> bool:
-    """Validate that all requested scopes are allowed for the application"""
-    return all(scope in app.scopes for scope in requested_scopes)
-
-
-# ============================================================================
-# Authorization Code Flow
-# ============================================================================
-
-
-def _generate_authorization_code() -> str:
-    """Generate a cryptographically secure authorization code"""
-    # 32 bytes = 256 bits of entropy
-    return secrets.token_urlsafe(32)
-
-
-async def create_authorization_code(
-    application_id: str,
-    user_id: str,
-    scopes: list[APIPermission],
-    redirect_uri: str,
-    code_challenge: Optional[str] = None,
-    code_challenge_method: Optional[Literal["S256", "plain"]] = None,
-) -> OAuthAuthorizationCodeInfo:
-    """
-    Create a new authorization code.
-    Expires in 10 minutes and can only be used once.
-    """
-    code = _generate_authorization_code()
-    now = datetime.now(timezone.utc)
-    expires_at = now + AUTHORIZATION_CODE_TTL
-
-    saved_code = await PrismaOAuthAuthorizationCode.prisma().create(
-        data=cast(
-            OAuthAuthorizationCodeCreateInput,
-            {
-                "id": str(uuid.uuid4()),
-                "code": code,
-                "expiresAt": expires_at,
-                "applicationId": application_id,
-                "userId": user_id,
-                "scopes": [s for s in scopes],
-                "redirectUri": redirect_uri,
-                "codeChallenge": code_challenge,
-                "codeChallengeMethod": code_challenge_method,
-            },
-        )
-    )
-
-    return OAuthAuthorizationCodeInfo.from_db(saved_code)
-
-
-async def consume_authorization_code(
-    code: str,
-    application_id: str,
-    redirect_uri: str,
-    code_verifier: Optional[str] = None,
-) -> tuple[str, list[APIPermission]]:
-    """
-    Consume an authorization code and return (user_id, scopes).
-
-    This marks the code as used and validates:
-    - Code exists and matches application
-    - Code is not expired
-    - Code has not been used
-    - Redirect URI matches
-    - PKCE code verifier matches (if code challenge was provided)
-
-    Raises:
-        InvalidGrantError: If code is invalid, expired, used, or PKCE fails
-    """
-    auth_code = await PrismaOAuthAuthorizationCode.prisma().find_unique(
-        where={"code": code}
-    )
-
-    if not auth_code:
-        raise InvalidGrantError("authorization code not found")
-
-    # Validate application
-    if auth_code.applicationId != application_id:
-        raise InvalidGrantError(
-            "authorization code does not belong to this application"
-        )
-
-    # Check if already used
-    if auth_code.usedAt is not None:
-        raise InvalidGrantError(
-            f"authorization code already used at {auth_code.usedAt}"
-        )
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if auth_code.expiresAt < now:
-        raise InvalidGrantError("authorization code expired")
-
-    # Validate redirect URI
-    if auth_code.redirectUri != redirect_uri:
-        raise InvalidGrantError("redirect_uri mismatch")
-
-    # Validate PKCE if code challenge was provided
-    if auth_code.codeChallenge:
-        if not code_verifier:
-            raise InvalidGrantError("code_verifier required but not provided")
-
-        if not _verify_pkce(
-            code_verifier, auth_code.codeChallenge, auth_code.codeChallengeMethod
-        ):
-            raise InvalidGrantError("PKCE verification failed")
-
-    # Mark code as used
-    await PrismaOAuthAuthorizationCode.prisma().update(
-        where={"code": code},
-        data={"usedAt": now},
-    )
-
-    return auth_code.userId, [APIPermission(s) for s in auth_code.scopes]
-
-
-def _verify_pkce(
-    code_verifier: str, code_challenge: str, code_challenge_method: Optional[str]
-) -> bool:
-    """
-    Verify PKCE code verifier against code challenge.
-
-    Supports:
-    - S256: SHA256(code_verifier) == code_challenge
-    - plain: code_verifier == code_challenge
-    """
-    if code_challenge_method == "S256":
-        # Hash the verifier with SHA256 and base64url encode
-        hashed = hashlib.sha256(code_verifier.encode("ascii")).digest()
-        computed_challenge = (
-            secrets.token_urlsafe(len(hashed)).encode("ascii").decode("ascii")
-        )
-        # For proper base64url encoding
-        import base64
-
-        computed_challenge = (
-            base64.urlsafe_b64encode(hashed).decode("ascii").rstrip("=")
-        )
-        return secrets.compare_digest(computed_challenge, code_challenge)
-    elif code_challenge_method == "plain" or code_challenge_method is None:
-        # Plain comparison
-        return secrets.compare_digest(code_verifier, code_challenge)
-    else:
-        logger.warning(f"Unsupported code challenge method: {code_challenge_method}")
-        return False
-
-
-# ============================================================================
-# Access Token Management
-# ============================================================================
-
-
-async def create_access_token(
-    application_id: str, user_id: str, scopes: list[APIPermission]
-) -> OAuthAccessToken:
-    """
-    Create a new access token.
-    Returns OAuthAccessToken (with plaintext token).
-    """
-    plaintext_token = ACCESS_TOKEN_PREFIX + _generate_token()
-    token_hash = _hash_token(plaintext_token)
-    now = datetime.now(timezone.utc)
-    expires_at = now + ACCESS_TOKEN_TTL
-
-    saved_token = await PrismaOAuthAccessToken.prisma().create(
-        data=cast(
-            OAuthAccessTokenCreateInput,
-            {
-                "id": str(uuid.uuid4()),
-                "token": token_hash,  # SHA256 hash for direct lookup
-                "expiresAt": expires_at,
-                "applicationId": application_id,
-                "userId": user_id,
-                "scopes": [s for s in scopes],
-            },
-        )
-    )
-
-    return OAuthAccessToken.from_db(saved_token, plaintext_token=plaintext_token)
-
-
-async def validate_access_token(
-    token: str,
-) -> tuple[OAuthAccessTokenInfo, OAuthApplicationInfo]:
-    """
-    Validate an access token and return token info.
-
-    Raises:
-        InvalidTokenError: If token is invalid, expired, or revoked
-        InvalidClientError: If the client application is not marked as active
-    """
-    token_hash = _hash_token(token)
-
-    # Direct lookup by hash
-    access_token = await PrismaOAuthAccessToken.prisma().find_unique(
-        where={"token": token_hash}, include={"Application": True}
-    )
-
-    if not access_token:
-        raise InvalidTokenError("access token not found")
-
-    if not access_token.Application:  # should be impossible
-        raise InvalidClientError("Client application not found")
-
-    if not access_token.Application.isActive:
-        raise InvalidClientError("Client application is disabled")
-
-    if access_token.revokedAt is not None:
-        raise InvalidTokenError("access token has been revoked")
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if access_token.expiresAt < now:
-        raise InvalidTokenError("access token expired")
-
-    return (
-        OAuthAccessTokenInfo.from_db(access_token),
-        OAuthApplicationInfo.from_db(access_token.Application),
-    )
-
-
-async def revoke_access_token(
-    token: str, application_id: str
-) -> OAuthAccessTokenInfo | None:
-    """
-    Revoke an access token.
-
-    Args:
-        token: The plaintext access token to revoke
-        application_id: The application ID making the revocation request.
-            Only tokens belonging to this application will be revoked.
-
-    Returns:
-        OAuthAccessTokenInfo if token was found and revoked, None otherwise.
-
-    Note:
-        Always performs exactly 2 DB queries regardless of outcome to prevent
-        timing side-channel attacks that could reveal token existence.
-    """
-    try:
-        token_hash = _hash_token(token)
-
-        # Use update_many to filter by both token and applicationId
-        updated_count = await PrismaOAuthAccessToken.prisma().update_many(
-            where={
-                "token": token_hash,
-                "applicationId": application_id,
-                "revokedAt": None,
-            },
-            data={"revokedAt": datetime.now(timezone.utc)},
-        )
-
-        # Always perform second query to ensure constant time
-        result = await PrismaOAuthAccessToken.prisma().find_unique(
-            where={"token": token_hash}
-        )
-
-        # Only return result if we actually revoked something
-        if updated_count == 0:
-            return None
-
-        return OAuthAccessTokenInfo.from_db(result) if result else None
-    except Exception as e:
-        logger.exception(f"Error revoking access token: {e}")
-        return None
-
-
-# ============================================================================
-# Refresh Token Management
-# ============================================================================
-
-
-async def create_refresh_token(
-    application_id: str, user_id: str, scopes: list[APIPermission]
-) -> OAuthRefreshToken:
-    """
-    Create a new refresh token.
-    Returns OAuthRefreshToken (with plaintext token).
-    """
-    plaintext_token = REFRESH_TOKEN_PREFIX + _generate_token()
-    token_hash = _hash_token(plaintext_token)
-    now = datetime.now(timezone.utc)
-    expires_at = now + REFRESH_TOKEN_TTL
-
-    saved_token = await PrismaOAuthRefreshToken.prisma().create(
-        data=cast(
-            OAuthRefreshTokenCreateInput,
-            {
-                "id": str(uuid.uuid4()),
-                "token": token_hash,  # SHA256 hash for direct lookup
-                "expiresAt": expires_at,
-                "applicationId": application_id,
-                "userId": user_id,
-                "scopes": [s for s in scopes],
-            },
-        )
-    )
-
-    return OAuthRefreshToken.from_db(saved_token, plaintext_token=plaintext_token)
-
-
-async def refresh_tokens(
-    refresh_token: str, application_id: str
-) -> tuple[OAuthAccessToken, OAuthRefreshToken]:
-    """
-    Use a refresh token to create new access and refresh tokens.
-    Returns (new_access_token, new_refresh_token) both with plaintext tokens included.
-
-    Raises:
-        InvalidGrantError: If refresh token is invalid, expired, or revoked
-    """
-    token_hash = _hash_token(refresh_token)
-
-    # Direct lookup by hash
-    rt = await PrismaOAuthRefreshToken.prisma().find_unique(where={"token": token_hash})
-
-    if not rt:
-        raise InvalidGrantError("refresh token not found")
-
-    # NOTE: no need to check Application.isActive, this is checked by the token endpoint
-
-    if rt.revokedAt is not None:
-        raise InvalidGrantError("refresh token has been revoked")
-
-    # Validate application
-    if rt.applicationId != application_id:
-        raise InvalidGrantError("refresh token does not belong to this application")
-
-    # Check expiration
-    now = datetime.now(timezone.utc)
-    if rt.expiresAt < now:
-        raise InvalidGrantError("refresh token expired")
-
-    # Revoke old refresh token
-    await PrismaOAuthRefreshToken.prisma().update(
-        where={"token": token_hash},
-        data={"revokedAt": now},
-    )
-
-    # Create new access and refresh tokens with same scopes
-    scopes = [APIPermission(s) for s in rt.scopes]
-    new_access_token = await create_access_token(
-        rt.applicationId,
-        rt.userId,
-        scopes,
-    )
-    new_refresh_token = await create_refresh_token(
-        rt.applicationId,
-        rt.userId,
-        scopes,
-    )
-
-    return new_access_token, new_refresh_token
-
-
-async def revoke_refresh_token(
-    token: str, application_id: str
-) -> OAuthRefreshTokenInfo | None:
-    """
-    Revoke a refresh token.
-
-    Args:
-        token: The plaintext refresh token to revoke
-        application_id: The application ID making the revocation request.
-            Only tokens belonging to this application will be revoked.
-
-    Returns:
-        OAuthRefreshTokenInfo if token was found and revoked, None otherwise.
-
-    Note:
-        Always performs exactly 2 DB queries regardless of outcome to prevent
-        timing side-channel attacks that could reveal token existence.
-    """
-    try:
-        token_hash = _hash_token(token)
-
-        # Use update_many to filter by both token and applicationId
-        updated_count = await PrismaOAuthRefreshToken.prisma().update_many(
-            where={
-                "token": token_hash,
-                "applicationId": application_id,
-                "revokedAt": None,
-            },
-            data={"revokedAt": datetime.now(timezone.utc)},
-        )
-
-        # Always perform second query to ensure constant time
-        result = await PrismaOAuthRefreshToken.prisma().find_unique(
-            where={"token": token_hash}
-        )
-
-        # Only return result if we actually revoked something
-        if updated_count == 0:
-            return None
-
-        return OAuthRefreshTokenInfo.from_db(result) if result else None
-    except Exception as e:
-        logger.exception(f"Error revoking refresh token: {e}")
-        return None
-
-
-# ============================================================================
-# Token Introspection
-# ============================================================================
-
-
-async def introspect_token(
-    token: str,
-    token_type_hint: Optional[Literal["access_token", "refresh_token"]] = None,
-) -> TokenIntrospectionResult:
-    """
-    Introspect a token and return its metadata (RFC 7662).
-
-    Returns TokenIntrospectionResult with active=True and metadata if valid,
-    or active=False if the token is invalid/expired/revoked.
-    """
-    # Try as access token first (or if hint says "access_token")
-    if token_type_hint != "refresh_token":
-        try:
-            token_info, app = await validate_access_token(token)
-            return TokenIntrospectionResult(
-                active=True,
-                scopes=list(s.value for s in token_info.scopes),
-                client_id=app.client_id if app else None,
-                user_id=token_info.user_id,
-                exp=int(token_info.expires_at.timestamp()),
-                token_type="access_token",
-            )
-        except InvalidTokenError:
-            pass  # Try as refresh token
-
-    # Try as refresh token
-    token_hash = _hash_token(token)
-    refresh_token = await PrismaOAuthRefreshToken.prisma().find_unique(
-        where={"token": token_hash}
-    )
-
-    if refresh_token and refresh_token.revokedAt is None:
-        # Check if valid (not expired)
-        now = datetime.now(timezone.utc)
-        if refresh_token.expiresAt > now:
-            app = await get_oauth_application_by_id(refresh_token.applicationId)
-            return TokenIntrospectionResult(
-                active=True,
-                scopes=list(s for s in refresh_token.scopes),
-                client_id=app.client_id if app else None,
-                user_id=refresh_token.userId,
-                exp=int(refresh_token.expiresAt.timestamp()),
-                token_type="refresh_token",
-            )
-
-    # Token not found or inactive
-    return TokenIntrospectionResult(active=False)
-
-
-async def get_oauth_application_by_id(app_id: str) -> Optional[OAuthApplicationInfo]:
-    """Get OAuth application by ID"""
-    app = await PrismaOAuthApplication.prisma().find_unique(where={"id": app_id})
-    if not app:
-        return None
-    return OAuthApplicationInfo.from_db(app)
-
-
-async def list_user_oauth_applications(user_id: str) -> list[OAuthApplicationInfo]:
-    """Get all OAuth applications owned by a user"""
-    apps = await PrismaOAuthApplication.prisma().find_many(
-        where={"ownerId": user_id},
-        order={"createdAt": "desc"},
-    )
-    return [OAuthApplicationInfo.from_db(app) for app in apps]
-
-
-async def update_oauth_application(
-    app_id: str,
-    *,
-    owner_id: str,
-    is_active: Optional[bool] = None,
-    logo_url: Optional[str] = None,
-) -> Optional[OAuthApplicationInfo]:
-    """
-    Update OAuth application active status.
-    Only the owner can update their app's status.
-
-    Returns the updated app info, or None if app not found or not owned by user.
-    """
-    # First verify ownership
-    app = await PrismaOAuthApplication.prisma().find_first(
-        where={"id": app_id, "ownerId": owner_id}
-    )
-    if not app:
-        return None
-
-    patch: OAuthApplicationUpdateInput = {}
-    if is_active is not None:
-        patch["isActive"] = is_active
-    if logo_url:
-        patch["logoUrl"] = logo_url
-    if not patch:
-        return OAuthApplicationInfo.from_db(app)  # return unchanged
-
-    updated_app = await PrismaOAuthApplication.prisma().update(
-        where={"id": app_id},
-        data=patch,
-    )
-    return OAuthApplicationInfo.from_db(updated_app) if updated_app else None
-
-
-# ============================================================================
-# Token Cleanup
-# ============================================================================
-
-
-async def cleanup_expired_oauth_tokens() -> dict[str, int]:
-    """
-    Delete expired OAuth tokens from the database.
-
-    This removes:
-    - Expired authorization codes (10 min TTL)
-    - Expired access tokens (1 hour TTL)
-    - Expired refresh tokens (30 day TTL)
-
-    Returns a dict with counts of deleted tokens by type.
-    """
-    now = datetime.now(timezone.utc)
-
-    # Delete expired authorization codes
-    codes_result = await PrismaOAuthAuthorizationCode.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    # Delete expired access tokens
-    access_result = await PrismaOAuthAccessToken.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    # Delete expired refresh tokens
-    refresh_result = await PrismaOAuthRefreshToken.prisma().delete_many(
-        where={"expiresAt": {"lt": now}}
-    )
-
-    deleted = {
-        "authorization_codes": codes_result,
-        "access_tokens": access_result,
-        "refresh_tokens": refresh_result,
-    }
-
-    total = sum(deleted.values())
-    if total > 0:
-        logger.info(f"Cleaned up {total} expired OAuth tokens: {deleted}")
-
-    return deleted

autogpt_platform/backend/backend/data/block.py

@@ -71,7 +71,6 @@ class BlockType(Enum):
     AGENT = "Agent"
     AI = "AI"
     AYRSHARE = "Ayrshare"
-    HUMAN_IN_THE_LOOP = "Human In The Loop"
 
 
 class BlockCategory(Enum):
@@ -266,61 +265,14 @@ 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:
@@ -601,18 +553,14 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             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
-                )(
+            if not isinstance(ex, BlockError):
+                raise BlockUnknownError(
                     message=str(ex),
                     block_name=self.name,
                     block_id=self.id,
                 ) from ex
+            else:
+                raise ex
 
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
         if error := self.input_schema.validate_data(input_data):
@@ -848,12 +796,3 @@ 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,
@@ -97,7 +92,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.OPENAI_GPT_OSS_120B: 1,
     LlmModel.OPENAI_GPT_OSS_20B: 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 +113,6 @@ 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 +258,6 @@ 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 +535,4 @@ 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_ceiling_test.py

@@ -5,14 +5,12 @@ This test was added to cover a previously untested code path that could lead to
 incorrect balance capping behavior.
 """
 
-from typing import cast
 from uuid import uuid4
 
 import pytest
 from prisma.enums import CreditTransactionType
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditTransaction, User, UserBalance
-from prisma.types import UserBalanceUpsertInput, UserCreateInput
 
 from backend.data.credit import UserCredit
 from backend.util.json import SafeJson
@@ -23,14 +21,11 @@ async def create_test_user(user_id: str) -> None:
     """Create a test user for ceiling tests."""
     try:
         await User.prisma().create(
-            data=cast(
-                UserCreateInput,
-                {
-                    "id": user_id,
-                    "email": f"test-{user_id}@example.com",
-                    "name": f"Test User {user_id[:8]}",
-                },
-            )
+            data={
+                "id": user_id,
+                "email": f"test-{user_id}@example.com",
+                "name": f"Test User {user_id[:8]}",
+            }
         )
     except UniqueViolationError:
         # User already exists, continue
@@ -38,10 +33,7 @@ async def create_test_user(user_id: str) -> None:
 
     await UserBalance.prisma().upsert(
         where={"userId": user_id},
-        data=cast(
-            UserBalanceUpsertInput,
-            {"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
-        ),
+        data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
     )
 
 

autogpt_platform/backend/backend/data/credit_concurrency_test.py

@@ -7,7 +7,6 @@ without race conditions, deadlocks, or inconsistent state.
 
 import asyncio
 import random
-from typing import cast
 from uuid import uuid4
 
 import prisma.enums
@@ -15,7 +14,6 @@ import pytest
 from prisma.enums import CreditTransactionType
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditTransaction, User, UserBalance
-from prisma.types import UserBalanceUpsertInput, UserCreateInput
 
 from backend.data.credit import POSTGRES_INT_MAX, UsageTransactionMetadata, UserCredit
 from backend.util.exceptions import InsufficientBalanceError
@@ -30,14 +28,11 @@ async def create_test_user(user_id: str) -> None:
     """Create a test user with initial balance."""
     try:
         await User.prisma().create(
-            data=cast(
-                UserCreateInput,
-                {
-                    "id": user_id,
-                    "email": f"test-{user_id}@example.com",
-                    "name": f"Test User {user_id[:8]}",
-                },
-            )
+            data={
+                "id": user_id,
+                "email": f"test-{user_id}@example.com",
+                "name": f"Test User {user_id[:8]}",
+            }
         )
     except UniqueViolationError:
         # User already exists, continue
@@ -46,10 +41,7 @@ async def create_test_user(user_id: str) -> None:
     # Ensure UserBalance record exists
     await UserBalance.prisma().upsert(
         where={"userId": user_id},
-        data=cast(
-            UserBalanceUpsertInput,
-            {"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
-        ),
+        data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
     )
 
 
@@ -350,13 +342,10 @@ async def test_integer_overflow_protection(server: SpinTestServer):
         # First, set balance near max
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": max_int - 100},
-                    "update": {"balance": max_int - 100},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": max_int - 100},
+                "update": {"balance": max_int - 100},
+            },
         )
 
         # Try to add more than possible - should clamp to POSTGRES_INT_MAX

autogpt_platform/backend/backend/data/credit_integration_test.py

@@ -5,12 +5,9 @@ These tests run actual database operations to ensure SQL queries work correctly,
 which would have caught the CreditTransactionType enum casting bug.
 """
 
-from typing import cast
-
 import pytest
 from prisma.enums import CreditTransactionType
 from prisma.models import CreditTransaction, User, UserBalance
-from prisma.types import UserCreateInput
 
 from backend.data.credit import (
     AutoTopUpConfig,
@@ -32,15 +29,12 @@ async def cleanup_test_user():
     # Create the user first
     try:
         await User.prisma().create(
-            data=cast(
-                UserCreateInput,
-                {
-                    "id": user_id,
-                    "email": f"test-{user_id}@example.com",
-                    "topUpConfig": SafeJson({}),
-                    "timezone": "UTC",
-                },
-            )
+            data={
+                "id": user_id,
+                "email": f"test-{user_id}@example.com",
+                "topUpConfig": SafeJson({}),
+                "timezone": "UTC",
+            }
         )
     except Exception:
         # User might already exist, that's fine

autogpt_platform/backend/backend/data/credit_refund_test.py

@@ -6,19 +6,12 @@ are atomic and maintain data consistency.
 """
 
 from datetime import datetime, timezone
-from typing import cast
 from unittest.mock import MagicMock, patch
 
 import pytest
 import stripe
 from prisma.enums import CreditTransactionType
 from prisma.models import CreditRefundRequest, CreditTransaction, User, UserBalance
-from prisma.types import (
-    CreditRefundRequestCreateInput,
-    CreditTransactionCreateInput,
-    UserBalanceCreateInput,
-    UserCreateInput,
-)
 
 from backend.data.credit import UserCredit
 from backend.util.json import SafeJson
@@ -42,41 +35,32 @@ async def setup_test_user_with_topup():
 
     # Create user
     await User.prisma().create(
-        data=cast(
-            UserCreateInput,
-            {
-                "id": REFUND_TEST_USER_ID,
-                "email": f"{REFUND_TEST_USER_ID}@example.com",
-                "name": "Refund Test User",
-            },
-        )
+        data={
+            "id": REFUND_TEST_USER_ID,
+            "email": f"{REFUND_TEST_USER_ID}@example.com",
+            "name": "Refund Test User",
+        }
     )
 
     # Create user balance
     await UserBalance.prisma().create(
-        data=cast(
-            UserBalanceCreateInput,
-            {
-                "userId": REFUND_TEST_USER_ID,
-                "balance": 1000,  # $10
-            },
-        )
+        data={
+            "userId": REFUND_TEST_USER_ID,
+            "balance": 1000,  # $10
+        }
     )
 
     # Create a top-up transaction that can be refunded
     topup_tx = await CreditTransaction.prisma().create(
-        data=cast(
-            CreditTransactionCreateInput,
-            {
-                "userId": REFUND_TEST_USER_ID,
-                "amount": 1000,
-                "type": CreditTransactionType.TOP_UP,
-                "transactionKey": "pi_test_12345",
-                "runningBalance": 1000,
-                "isActive": True,
-                "metadata": SafeJson({"stripe_payment_intent": "pi_test_12345"}),
-            },
-        )
+        data={
+            "userId": REFUND_TEST_USER_ID,
+            "amount": 1000,
+            "type": CreditTransactionType.TOP_UP,
+            "transactionKey": "pi_test_12345",
+            "runningBalance": 1000,
+            "isActive": True,
+            "metadata": SafeJson({"stripe_payment_intent": "pi_test_12345"}),
+        }
     )
 
     return topup_tx
@@ -109,15 +93,12 @@ async def test_deduct_credits_atomic(server: SpinTestServer):
 
         # Create refund request record (simulating webhook flow)
         await CreditRefundRequest.prisma().create(
-            data=cast(
-                CreditRefundRequestCreateInput,
-                {
-                    "userId": REFUND_TEST_USER_ID,
-                    "amount": 500,
-                    "transactionKey": topup_tx.transactionKey,  # Should match the original transaction
-                    "reason": "Test refund",
-                },
-            )
+            data={
+                "userId": REFUND_TEST_USER_ID,
+                "amount": 500,
+                "transactionKey": topup_tx.transactionKey,  # Should match the original transaction
+                "reason": "Test refund",
+            }
         )
 
         # Call deduct_credits
@@ -305,15 +286,12 @@ async def test_concurrent_refunds(server: SpinTestServer):
         refund_requests = []
         for i in range(5):
             req = await CreditRefundRequest.prisma().create(
-                data=cast(
-                    CreditRefundRequestCreateInput,
-                    {
-                        "userId": REFUND_TEST_USER_ID,
-                        "amount": 100,  # $1 each
-                        "transactionKey": topup_tx.transactionKey,
-                        "reason": f"Test refund {i}",
-                    },
-                )
+                data={
+                    "userId": REFUND_TEST_USER_ID,
+                    "amount": 100,  # $1 each
+                    "transactionKey": topup_tx.transactionKey,
+                    "reason": f"Test refund {i}",
+                }
             )
             refund_requests.append(req)
 

autogpt_platform/backend/backend/data/credit_test.py

@@ -1,15 +1,13 @@
 from datetime import datetime, timedelta, timezone
-from typing import cast
 
 import pytest
 from prisma.enums import CreditTransactionType
 from prisma.models import CreditTransaction, UserBalance
-from prisma.types import CreditTransactionCreateInput, UserBalanceUpsertInput
 
 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
@@ -25,13 +23,10 @@ async def disable_test_user_transactions():
     old_date = datetime.now(timezone.utc) - timedelta(days=35)  # More than a month ago
     await UserBalance.prisma().upsert(
         where={"userId": DEFAULT_USER_ID},
-        data=cast(
-            UserBalanceUpsertInput,
-            {
-                "create": {"userId": DEFAULT_USER_ID, "balance": 0},
-                "update": {"balance": 0, "updatedAt": old_date},
-            },
-        ),
+        data={
+            "create": {"userId": DEFAULT_USER_ID, "balance": 0},
+            "update": {"balance": 0, "updatedAt": old_date},
+        },
     )
 
 
@@ -78,7 +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",
@@ -91,7 +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
@@ -100,13 +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
@@ -145,29 +138,23 @@ async def test_block_credit_reset(server: SpinTestServer):
 
         # Manually create a transaction with month 1 timestamp to establish history
         await CreditTransaction.prisma().create(
-            data=cast(
-                CreditTransactionCreateInput,
-                {
-                    "userId": DEFAULT_USER_ID,
-                    "amount": 100,
-                    "type": CreditTransactionType.TOP_UP,
-                    "runningBalance": 1100,
-                    "isActive": True,
-                    "createdAt": month1,  # Set specific timestamp
-                },
-            )
+            data={
+                "userId": DEFAULT_USER_ID,
+                "amount": 100,
+                "type": CreditTransactionType.TOP_UP,
+                "runningBalance": 1100,
+                "isActive": True,
+                "createdAt": month1,  # Set specific timestamp
+            }
         )
 
         # Update user balance to match
         await UserBalance.prisma().upsert(
             where={"userId": DEFAULT_USER_ID},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": DEFAULT_USER_ID, "balance": 1100},
-                    "update": {"balance": 1100},
-                },
-            ),
+            data={
+                "create": {"userId": DEFAULT_USER_ID, "balance": 1100},
+                "update": {"balance": 1100},
+            },
         )
 
         # Now test month 2 behavior
@@ -186,17 +173,14 @@ async def test_block_credit_reset(server: SpinTestServer):
 
         # Create a month 2 transaction to update the last transaction time
         await CreditTransaction.prisma().create(
-            data=cast(
-                CreditTransactionCreateInput,
-                {
-                    "userId": DEFAULT_USER_ID,
-                    "amount": -700,  # Spent 700 to get to 400
-                    "type": CreditTransactionType.USAGE,
-                    "runningBalance": 400,
-                    "isActive": True,
-                    "createdAt": month2,
-                },
-            )
+            data={
+                "userId": DEFAULT_USER_ID,
+                "amount": -700,  # Spent 700 to get to 400
+                "type": CreditTransactionType.USAGE,
+                "runningBalance": 400,
+                "isActive": True,
+                "createdAt": month2,
+            }
         )
 
         # Move to month 3

autogpt_platform/backend/backend/data/credit_underflow_test.py

@@ -6,14 +6,12 @@ doesn't underflow below POSTGRES_INT_MIN, which could cause integer wraparound i
 """
 
 import asyncio
-from typing import cast
 from uuid import uuid4
 
 import pytest
 from prisma.enums import CreditTransactionType
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditTransaction, User, UserBalance
-from prisma.types import UserBalanceUpsertInput, UserCreateInput
 
 from backend.data.credit import POSTGRES_INT_MIN, UserCredit
 from backend.util.test import SpinTestServer
@@ -23,14 +21,11 @@ async def create_test_user(user_id: str) -> None:
     """Create a test user for underflow tests."""
     try:
         await User.prisma().create(
-            data=cast(
-                UserCreateInput,
-                {
-                    "id": user_id,
-                    "email": f"test-{user_id}@example.com",
-                    "name": f"Test User {user_id[:8]}",
-                },
-            )
+            data={
+                "id": user_id,
+                "email": f"test-{user_id}@example.com",
+                "name": f"Test User {user_id[:8]}",
+            }
         )
     except UniqueViolationError:
         # User already exists, continue
@@ -38,10 +33,7 @@ async def create_test_user(user_id: str) -> None:
 
     await UserBalance.prisma().upsert(
         where={"userId": user_id},
-        data=cast(
-            UserBalanceUpsertInput,
-            {"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
-        ),
+        data={"create": {"userId": user_id, "balance": 0}, "update": {"balance": 0}},
     )
 
 
@@ -78,13 +70,10 @@ async def test_debug_underflow_step_by_step(server: SpinTestServer):
 
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": initial_balance_target},
-                    "update": {"balance": initial_balance_target},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": initial_balance_target},
+                "update": {"balance": initial_balance_target},
+            },
         )
 
         current_balance = await credit_system.get_credits(user_id)
@@ -121,13 +110,10 @@ async def test_debug_underflow_step_by_step(server: SpinTestServer):
         # Set balance to exactly POSTGRES_INT_MIN
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": POSTGRES_INT_MIN},
-                    "update": {"balance": POSTGRES_INT_MIN},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": POSTGRES_INT_MIN},
+                "update": {"balance": POSTGRES_INT_MIN},
+            },
         )
 
         edge_balance = await credit_system.get_credits(user_id)
@@ -166,13 +152,10 @@ async def test_underflow_protection_large_refunds(server: SpinTestServer):
         test_balance = POSTGRES_INT_MIN + 1000
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": test_balance},
-                    "update": {"balance": test_balance},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": test_balance},
+                "update": {"balance": test_balance},
+            },
         )
 
         current_balance = await credit_system.get_credits(user_id)
@@ -234,13 +217,10 @@ async def test_multiple_large_refunds_cumulative_underflow(server: SpinTestServe
         initial_balance = POSTGRES_INT_MIN + 500  # Close to minimum but with some room
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": initial_balance},
-                    "update": {"balance": initial_balance},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": initial_balance},
+                "update": {"balance": initial_balance},
+            },
         )
 
         # Apply multiple refunds that would cumulatively underflow
@@ -315,13 +295,10 @@ async def test_concurrent_large_refunds_no_underflow(server: SpinTestServer):
         initial_balance = POSTGRES_INT_MIN + 1000  # Close to minimum
         await UserBalance.prisma().upsert(
             where={"userId": user_id},
-            data=cast(
-                UserBalanceUpsertInput,
-                {
-                    "create": {"userId": user_id, "balance": initial_balance},
-                    "update": {"balance": initial_balance},
-                },
-            ),
+            data={
+                "create": {"userId": user_id, "balance": initial_balance},
+                "update": {"balance": initial_balance},
+            },
         )
 
         async def large_refund(amount: int, label: str):

autogpt_platform/backend/backend/data/credit_user_balance_migration_test.py

@@ -9,13 +9,11 @@ This test ensures that:
 
 import asyncio
 from datetime import datetime
-from typing import cast
 
 import pytest
 from prisma.enums import CreditTransactionType
 from prisma.errors import UniqueViolationError
 from prisma.models import CreditTransaction, User, UserBalance
-from prisma.types import UserBalanceCreateInput, UserCreateInput
 
 from backend.data.credit import UsageTransactionMetadata, UserCredit
 from backend.util.json import SafeJson
@@ -26,14 +24,11 @@ async def create_test_user(user_id: str) -> None:
     """Create a test user for migration tests."""
     try:
         await User.prisma().create(
-            data=cast(
-                UserCreateInput,
-                {
-                    "id": user_id,
-                    "email": f"test-{user_id}@example.com",
-                    "name": f"Test User {user_id[:8]}",
-                },
-            )
+            data={
+                "id": user_id,
+                "email": f"test-{user_id}@example.com",
+                "name": f"Test User {user_id[:8]}",
+            }
         )
     except UniqueViolationError:
         # User already exists, continue
@@ -126,9 +121,7 @@ async def test_detect_stale_user_balance_queries(server: SpinTestServer):
     try:
         # Create UserBalance with specific value
         await UserBalance.prisma().create(
-            data=cast(
-                UserBalanceCreateInput, {"userId": user_id, "balance": 5000}
-            )  # $50
+            data={"userId": user_id, "balance": 5000}  # $50
         )
 
         # Verify that get_credits returns UserBalance value (5000), not any stale User.balance value
@@ -167,9 +160,7 @@ async def test_concurrent_operations_use_userbalance_only(server: SpinTestServer
 
     try:
         # Set initial balance in UserBalance
-        await UserBalance.prisma().create(
-            data=cast(UserBalanceCreateInput, {"userId": user_id, "balance": 1000})
-        )
+        await UserBalance.prisma().create(data={"userId": user_id, "balance": 1000})
 
         # Run concurrent operations to ensure they all use UserBalance atomic operations
         async def concurrent_spend(amount: int, label: str):

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,
@@ -28,7 +27,6 @@ from prisma.models import (
     AgentNodeExecutionKeyValueData,
 )
 from prisma.types import (
-    AgentGraphExecutionCreateInput,
     AgentGraphExecutionUpdateManyMutationInput,
     AgentGraphExecutionWhereInput,
     AgentNodeExecutionCreateInput,
@@ -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,
     ],
 }
 
@@ -381,8 +356,9 @@ 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 +366,8 @@ 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 +440,17 @@ 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,
         )
 
 
@@ -709,40 +685,37 @@ async def create_graph_execution(
         The id of the AgentGraphExecution and the list of ExecutionResult for each node.
     """
     result = await AgentGraphExecution.prisma().create(
-        data=cast(
-            AgentGraphExecutionCreateInput,
-            {
-                "agentGraphId": graph_id,
-                "agentGraphVersion": graph_version,
-                "executionStatus": ExecutionStatus.INCOMPLETE,
-                "inputs": SafeJson(inputs),
-                "credentialInputs": (
-                    SafeJson(credential_inputs) if credential_inputs else Json({})
-                ),
-                "nodesInputMasks": (
-                    SafeJson(nodes_input_masks) if nodes_input_masks else Json({})
-                ),
-                "NodeExecutions": {
-                    "create": [
-                        AgentNodeExecutionCreateInput(
-                            agentNodeId=node_id,
-                            executionStatus=ExecutionStatus.QUEUED,
-                            queuedTime=datetime.now(tz=timezone.utc),
-                            Input={
-                                "create": [
-                                    {"name": name, "data": SafeJson(data)}
-                                    for name, data in node_input.items()
-                                ]
-                            },
-                        )
-                        for node_id, node_input in starting_nodes_input
-                    ]
-                },
-                "userId": user_id,
-                "agentPresetId": preset_id,
-                "parentGraphExecutionId": parent_graph_exec_id,
+        data={
+            "agentGraphId": graph_id,
+            "agentGraphVersion": graph_version,
+            "executionStatus": ExecutionStatus.INCOMPLETE,
+            "inputs": SafeJson(inputs),
+            "credentialInputs": (
+                SafeJson(credential_inputs) if credential_inputs else Json({})
+            ),
+            "nodesInputMasks": (
+                SafeJson(nodes_input_masks) if nodes_input_masks else Json({})
+            ),
+            "NodeExecutions": {
+                "create": [
+                    AgentNodeExecutionCreateInput(
+                        agentNodeId=node_id,
+                        executionStatus=ExecutionStatus.QUEUED,
+                        queuedTime=datetime.now(tz=timezone.utc),
+                        Input={
+                            "create": [
+                                {"name": name, "data": SafeJson(data)}
+                                for name, data in node_input.items()
+                            ]
+                        },
+                    )
+                    for node_id, node_input in starting_nodes_input
+                ]
             },
-        ),
+            "userId": user_id,
+            "agentPresetId": preset_id,
+            "parentGraphExecutionId": parent_graph_exec_id,
+        },
         include=GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     )
 
@@ -755,7 +728,7 @@ 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.
@@ -788,7 +761,7 @@ 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)
 
@@ -800,7 +773,7 @@ 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 []
@@ -815,10 +788,9 @@ 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(
@@ -834,42 +806,15 @@ async def upsert_execution_output(
     """
     Insert AgentNodeExecutionInputOutput record for as one of AgentNodeExecution.Output.
     """
-    data: AgentNodeExecutionInputOutputCreateInput = cast(
-        AgentNodeExecutionInputOutputCreateInput,
-        {
-            "name": output_name,
-            "referencedByOutputExecId": node_exec_id,
-        },
-    )
+    data: AgentNodeExecutionInputOutputCreateInput = {
+        "name": output_name,
+        "referencedByOutputExecId": node_exec_id,
+    }
     if output_data is not None:
         data["data"] = SafeJson(output_data)
     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:
@@ -941,25 +886,9 @@ 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),
     )
 
@@ -973,37 +902,15 @@ 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"
-        )
-
-    # First verify the current status allows this transition
-    current_exec = await AgentNodeExecution.prisma().find_unique(
-        where={"id": node_exec_id}, include=EXECUTION_RESULT_INCLUDE
-    )
-
-    if not current_exec:
-        raise ValueError(f"Execution {node_exec_id} not found.")
-
-    # Check if current status allows the requested transition
-    if current_exec.executionStatus not in allowed_from:
-        # Status transition not allowed, return current state without updating
-        return NodeExecutionResult.from_db(current_exec)
-
-    # Status transition is valid, perform the update
-    updated_exec = await AgentNodeExecution.prisma().update(
+    res = await AgentNodeExecution.prisma().update(
         where={"id": node_exec_id},
         data=_get_update_status_data(status, execution_data, stats),
         include=EXECUTION_RESULT_INCLUDE,
     )
+    if not res:
+        raise ValueError(f"Execution {node_exec_id} not found.")
 
-    if not updated_exec:
-        raise ValueError(f"Failed to update execution {node_exec_id}.")
-
-    return NodeExecutionResult.from_db(updated_exec)
+    return NodeExecutionResult.from_db(res)
 
 
 def _get_update_status_data(
@@ -1057,17 +964,17 @@ 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
@@ -1084,29 +991,6 @@ 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=(
@@ -1148,29 +1032,31 @@ 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]):
@@ -1504,35 +1390,3 @@ 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

@@ -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(
@@ -1118,28 +1105,6 @@ 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
 ) -> None:

autogpt_platform/backend/backend/data/human_review.py

@@ -1,261 +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, cast
-
-from prisma.enums import ReviewStatus
-from prisma.models import PendingHumanReview
-from prisma.types import PendingHumanReviewUpdateInput, PendingHumanReviewUpsertInput
-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=cast(
-                PendingHumanReviewUpsertInput,
-                {
-                    "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