Merge branch 'dev' into otto/secrt-1887-content-based-detection

Security fix: Add scan_content_safe() call before writing files to prevent
bypass of ClamAV scanning infrastructure. Malicious base64-encoded content
matching a supported magic number would previously be saved without scanning.

Test coverage:
- Add tests for 4-byte aligned offset loop (handles marker bleed-in)
- Add tests for PDF_BASE64_START/END marker expansion
- Add tests verifying virus scanner integration
- Mock virus scanner in existing process_binary_outputs tests

Addresses review feedback from Claude sub-agents and ntindle.

.dockerignore

@@ -5,13 +5,42 @@
 !docs/
 
 # Platform - Libs
-!autogpt_platform/autogpt_libs/
+!autogpt_platform/autogpt_libs/autogpt_libs/
+!autogpt_platform/autogpt_libs/pyproject.toml
+!autogpt_platform/autogpt_libs/poetry.lock
+!autogpt_platform/autogpt_libs/README.md
 
 # Platform - Backend
-!autogpt_platform/backend/
+!autogpt_platform/backend/backend/
+!autogpt_platform/backend/test/e2e_test_data.py
+!autogpt_platform/backend/migrations/
+!autogpt_platform/backend/schema.prisma
+!autogpt_platform/backend/pyproject.toml
+!autogpt_platform/backend/poetry.lock
+!autogpt_platform/backend/README.md
+!autogpt_platform/backend/.env
+!autogpt_platform/backend/gen_prisma_types_stub.py
+
+# Platform - Market
+!autogpt_platform/market/market/
+!autogpt_platform/market/scripts.py
+!autogpt_platform/market/schema.prisma
+!autogpt_platform/market/pyproject.toml
+!autogpt_platform/market/poetry.lock
+!autogpt_platform/market/README.md
 
 # Platform - Frontend
-!autogpt_platform/frontend/
+!autogpt_platform/frontend/src/
+!autogpt_platform/frontend/public/
+!autogpt_platform/frontend/scripts/
+!autogpt_platform/frontend/package.json
+!autogpt_platform/frontend/pnpm-lock.yaml
+!autogpt_platform/frontend/tsconfig.json
+!autogpt_platform/frontend/README.md
+## config
+!autogpt_platform/frontend/*.config.*
+!autogpt_platform/frontend/.env.*
+!autogpt_platform/frontend/.env
 
 # Classic - AutoGPT
 !classic/original_autogpt/autogpt/
@@ -35,38 +64,6 @@
 # Classic - Frontend
 !classic/frontend/build/web/
 
-# Explicitly re-ignore unwanted files from whitelisted directories
+# Explicitly re-ignore some folders
-# Note: These patterns MUST come after the whitelist rules to take effect
+.*
-
+**/__pycache__
-# Hidden files and directories (but keep frontend .env files needed for build)
-**/.*
-!autogpt_platform/frontend/.env
-!autogpt_platform/frontend/.env.default
-!autogpt_platform/frontend/.env.production
-
-# Python artifacts
-**/__pycache__/
-**/*.pyc
-**/*.pyo
-**/.venv/
-**/.ruff_cache/
-**/.pytest_cache/
-**/.coverage
-**/htmlcov/
-
-# Node artifacts
-**/node_modules/
-**/.next/
-**/storybook-static/
-**/playwright-report/
-**/test-results/
-
-# Build artifacts
-**/dist/
-**/build/
-!autogpt_platform/frontend/src/**/build/
-**/target/
-
-# Logs and temp files
-**/*.log
-**/*.tmp

.github/workflows/claude-ci-failure-auto-fix.yml

@@ -22,7 +22,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.workflow_run.head_branch }}
           fetch-depth: 0

.github/workflows/claude-dependabot.yml

@@ -30,7 +30,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 

.github/workflows/claude.yml

@@ -40,7 +40,7 @@ jobs:
       actions: read # Required for CI access
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 

.github/workflows/codeql.yml

@@ -58,7 +58,7 @@ jobs:
         # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v6
+      uses: actions/checkout@v4
 
     # Initializes the CodeQL tools for scanning.
     - name: Initialize CodeQL

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

@@ -27,7 +27,7 @@ jobs:
     # If you do not check out your code, Copilot will do this for you.
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true

.github/workflows/docs-block-sync.yml

@@ -23,7 +23,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 

.github/workflows/docs-claude-review.yml

@@ -23,7 +23,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 

.github/workflows/docs-enhance.yml

@@ -28,7 +28,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 1
 

.github/workflows/platform-autogpt-deploy-dev.yaml

@@ -25,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
@@ -52,7 +52,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

.github/workflows/platform-autogpt-deploy-prod.yml

@@ -17,7 +17,7 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.ref_name || 'master' }}
 
@@ -45,7 +45,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -68,7 +68,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
           submodules: true

.github/workflows/platform-dev-deploy-event-dispatcher.yml

@@ -82,7 +82,7 @@ jobs:
           
       - name: Dispatch Deploy Event
         if: steps.check_status.outputs.should_deploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -110,7 +110,7 @@ jobs:
 
       - name: Dispatch Undeploy Event (from comment)
         if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -168,7 +168,7 @@ jobs:
           github.event_name == 'pull_request' &&
           github.event.action == 'closed' &&
           steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
         with:
           token: ${{ secrets.DISPATCH_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure

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

@@ -26,11 +26,12 @@ jobs:
   setup:
     runs-on: ubuntu-latest
     outputs:
+      cache-key: ${{ steps.cache-key.outputs.key }}
       components-changed: ${{ steps.filter.outputs.components }}
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Check for component changes
         uses: dorny/paths-filter@v3
@@ -40,17 +41,28 @@ jobs:
             components:
               - 'autogpt_platform/frontend/src/components/**'
 
-      - name: Enable corepack
+      - name: Set up Node.js
-        run: corepack enable
-
-      - name: Set up Node
         uses: actions/setup-node@v6
         with:
           node-version: "22.18.0"
-          cache: "pnpm"
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
 
-      - name: Install dependencies to populate cache
+      - name: Enable corepack
+        run: corepack enable
+
+      - name: Generate cache key
+        id: cache-key
+        run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
+
+      - name: Cache dependencies
+        uses: actions/cache@v5
+        with:
+          path: ~/.pnpm-store
+          key: ${{ steps.cache-key.outputs.key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
+
+      - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
   lint:
@@ -59,17 +71,24 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
+      - name: Restore dependencies cache
-        uses: actions/setup-node@v6
+        uses: actions/cache@v5
         with:
-          node-version: "22.18.0"
+          path: ~/.pnpm-store
-          cache: "pnpm"
+          key: ${{ needs.setup.outputs.cache-key }}
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -88,19 +107,26 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
+      - name: Set up Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "22.18.0"
+
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
+      - name: Restore dependencies cache
-        uses: actions/setup-node@v6
+        uses: actions/cache@v5
         with:
-          node-version: "22.18.0"
+          path: ~/.pnpm-store
-          cache: "pnpm"
+          key: ${{ needs.setup.outputs.cache-key }}
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
@@ -115,20 +141,30 @@ jobs:
           exitOnceUploaded: true
 
   e2e_test:
-    name: end-to-end tests
     runs-on: big-boi
+    needs: setup
+    strategy:
+      fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
-      - name: Set up Platform - Copy default supabase .env
+      - name: Set up Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "22.18.0"
+
+      - name: Enable corepack
+        run: corepack enable
+
+      - name: Copy default supabase .env
         run: |
           cp ../.env.default ../.env
 
-      - name: Set up Platform - Copy backend .env and set OpenAI API key
+      - name: Copy backend .env and set OpenAI API key
         run: |
           cp ../backend/.env.default ../backend/.env
           echo "OPENAI_INTERNAL_API_KEY=${{ secrets.OPENAI_API_KEY }}" >> ../backend/.env
@@ -136,125 +172,77 @@ jobs:
           # Used by E2E test data script to generate embeddings for approved store agents
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
 
-      - name: Set up Platform - Set up Docker Buildx
+      - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
-        with:
-          driver: docker-container
-          driver-opts: network=host
 
-      - name: Set up Platform - Expose GHA cache to docker buildx CLI
+      - name: Cache Docker layers
-        uses: crazy-max/ghaction-github-runtime@v3
-
-      - name: Set up Platform - Build Docker images (with cache)
-        working-directory: autogpt_platform
-        run: |
-          pip install pyyaml
-
-          # Resolve extends and generate a flat compose file that bake can understand
-          docker compose -f docker-compose.yml config > docker-compose.resolved.yml
-
-          # Add cache configuration to the resolved compose file
-          python ../.github/workflows/scripts/docker-ci-fix-compose-build-cache.py \
-            --source docker-compose.resolved.yml \
-            --cache-from "type=gha" \
-            --cache-to "type=gha,mode=max" \
-            --backend-hash "${{ hashFiles('autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/poetry.lock', 'autogpt_platform/backend/backend') }}" \
-            --frontend-hash "${{ hashFiles('autogpt_platform/frontend/Dockerfile', 'autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/src') }}" \
-            --git-ref "${{ github.ref }}"
-
-          # Build with bake using the resolved compose file (now includes cache config)
-          docker buildx bake --allow=fs.read=.. -f docker-compose.resolved.yml --load
-        env:
-          NEXT_PUBLIC_PW_TEST: true
-
-      - name: Set up tests - Cache E2E test data
-        id: e2e-data-cache
         uses: actions/cache@v5
         with:
-          path: /tmp/e2e_test_data.sql
+          path: /tmp/.buildx-cache
-          key: e2e-test-data-${{ hashFiles('autogpt_platform/backend/test/e2e_test_data.py', 'autogpt_platform/backend/migrations/**', '.github/workflows/platform-frontend-ci.yml') }}
+          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
+          restore-keys: |
+            ${{ runner.os }}-buildx-frontend-test-
 
-      - name: Set up Platform - Start Supabase DB + Auth
+      - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.resolved.yml up -d db auth --no-build
+          NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
-          echo "Waiting for database to be ready..."
-          timeout 60 sh -c 'until docker compose -f ../docker-compose.resolved.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done'
-          echo "Waiting for auth service to be ready..."
-          timeout 60 sh -c 'until docker compose -f ../docker-compose.resolved.yml exec -T db psql -U postgres -d postgres -c "SELECT 1 FROM auth.users LIMIT 1" 2>/dev/null; do sleep 2; done' || echo "Auth schema check timeout, continuing..."
-
-      - name: Set up Platform - Run migrations
-        run: |
-          echo "Running migrations..."
-          docker compose -f ../docker-compose.resolved.yml run --rm migrate
-          echo "✅ Migrations completed"
         env:
-          NEXT_PUBLIC_PW_TEST: true
+          DOCKER_BUILDKIT: 1
+          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
+          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
 
-      - name: Set up tests - Load cached E2E test data
+      - name: Move cache
-        if: steps.e2e-data-cache.outputs.cache-hit == 'true'
         run: |
-          echo "✅ Found cached E2E test data, restoring..."
+          rm -rf /tmp/.buildx-cache
-          {
+          if [ -d "/tmp/.buildx-cache-new" ]; then
-            echo "SET session_replication_role = 'replica';"
+            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
-            cat /tmp/e2e_test_data.sql
+          fi
-            echo "SET session_replication_role = 'origin';"
-          } | docker compose -f ../docker-compose.resolved.yml exec -T db psql -U postgres -d postgres -b
-          # Refresh materialized views after restore
-          docker compose -f ../docker-compose.resolved.yml exec -T db \
-            psql -U postgres -d postgres -b -c "SET search_path TO platform; SELECT refresh_store_materialized_views();" || true
 
-          echo "✅ E2E test data restored from cache"
+      - name: Wait for services to be ready
-
-      - name: Set up Platform - Start (all other services)
         run: |
-          docker compose -f ../docker-compose.resolved.yml up -d --no-build
           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..."
-        env:
+          echo "Waiting for database to be ready..."
-          NEXT_PUBLIC_PW_TEST: true
+          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: Set up tests - Create E2E test data
+      - name: Create E2E test data
-        if: steps.e2e-data-cache.outputs.cache-hit != 'true'
         run: |
           echo "Creating E2E test data..."
-          docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.resolved.yml ps -q rest_server):/tmp/e2e_test_data.py
+          # First try to run the script from inside the container
-          docker compose -f ../docker-compose.resolved.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
+          if docker compose -f ../docker-compose.yml exec -T rest_server test -f /app/autogpt_platform/backend/test/e2e_test_data.py; then
-            echo "❌ E2E test data creation failed!"
+            echo "✅ Found e2e_test_data.py in container, running it..."
-            docker compose -f ../docker-compose.resolved.yml logs --tail=50 rest_server
+            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python backend/test/e2e_test_data.py" || {
-            exit 1
+              echo "❌ E2E test data creation failed!"
-          }
+              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
+              exit 1
+            }
+          else
+            echo "⚠️ e2e_test_data.py not found in container, copying and running..."
+            # Copy the script into the container and run it
+            docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.yml ps -q rest_server):/tmp/e2e_test_data.py || {
+              echo "❌ Failed to copy script to container"
+              exit 1
+            }
+            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
+              echo "❌ E2E test data creation failed!"
+              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
+              exit 1
+            }
+          fi
 
-          # Dump auth.users + platform schema for cache (two separate dumps)
+      - name: Restore dependencies cache
-          echo "Dumping database for cache..."
+        uses: actions/cache@v5
-          {
-            docker compose -f ../docker-compose.resolved.yml exec -T db \
-              pg_dump -U postgres --data-only --column-inserts \
-              --table='auth.users' postgres
-            docker compose -f ../docker-compose.resolved.yml exec -T db \
-              pg_dump -U postgres --data-only --column-inserts \
-              --schema=platform \
-              --exclude-table='platform._prisma_migrations' \
-              --exclude-table='platform.apscheduler_jobs' \
-              --exclude-table='platform.apscheduler_jobs_batched_notifications' \
-              postgres
-          } > /tmp/e2e_test_data.sql
-
-          echo "✅ Database dump created for caching ($(wc -l < /tmp/e2e_test_data.sql) lines)"
-
-      - name: Set up tests - Enable corepack
-        run: corepack enable
-
-      - name: Set up tests - Set up Node
-        uses: actions/setup-node@v6
         with:
-          node-version: "22.18.0"
+          path: ~/.pnpm-store
-          cache: "pnpm"
+          key: ${{ needs.setup.outputs.cache-key }}
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
-      - name: Set up tests - Install dependencies
+      - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      - name: Set up tests - Install browser 'chromium'
+      - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 
       - name: Run Playwright tests
@@ -281,7 +269,7 @@ jobs:
 
       - name: Print Final Docker Compose logs
         if: always()
-        run: docker compose -f ../docker-compose.resolved.yml logs
+        run: docker compose -f ../docker-compose.yml logs
 
   integration_test:
     runs-on: ubuntu-latest
@@ -289,19 +277,26 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
+      - name: Set up Node.js
+        uses: actions/setup-node@v6
+        with:
+          node-version: "22.18.0"
+
       - name: Enable corepack
         run: corepack enable
 
-      - name: Set up Node
+      - name: Restore dependencies cache
-        uses: actions/setup-node@v6
+        uses: actions/cache@v5
         with:
-          node-version: "22.18.0"
+          path: ~/.pnpm-store
-          cache: "pnpm"
+          key: ${{ needs.setup.outputs.cache-key }}
-          cache-dependency-path: autogpt_platform/frontend/pnpm-lock.yaml
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
 
       - name: Install dependencies
         run: pnpm install --frozen-lockfile

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

@@ -29,7 +29,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
 
       - name: Set up Node.js
         uses: actions/setup-node@v6
@@ -56,14 +56,14 @@ jobs:
         run: pnpm install --frozen-lockfile
 
   types:
-    runs-on: big-boi
+    runs-on: ubuntu-latest
     needs: setup
     strategy:
       fail-fast: false
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
         with:
           submodules: recursive
 
@@ -85,7 +85,7 @@ jobs:
 
       - name: Run docker compose
         run: |
-          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
 
       - name: Restore dependencies cache
         uses: actions/cache@v5

.github/workflows/repo-workflow-checker.yml

@@ -11,7 +11,7 @@ jobs:
     steps:
       # - name: Wait some time for all actions to start
       #   run: sleep 30
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
         # with:
           # fetch-depth: 0
       - name: Set up Python

.github/workflows/scripts/docker-ci-fix-compose-build-cache.py

@@ -1,195 +0,0 @@
-#!/usr/bin/env python3
-"""
-Add cache configuration to a resolved docker-compose file for all services
-that have a build key, and ensure image names match what docker compose expects.
-"""
-
-import argparse
-
-import yaml
-
-
-DEFAULT_BRANCH = "dev"
-CACHE_BUILDS_FOR_COMPONENTS = ["backend", "frontend"]
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Add cache config to a resolved compose file"
-    )
-    parser.add_argument(
-        "--source",
-        required=True,
-        help="Source compose file to read (should be output of `docker compose config`)",
-    )
-    parser.add_argument(
-        "--cache-from",
-        default="type=gha",
-        help="Cache source configuration",
-    )
-    parser.add_argument(
-        "--cache-to",
-        default="type=gha,mode=max",
-        help="Cache destination configuration",
-    )
-    for component in CACHE_BUILDS_FOR_COMPONENTS:
-        parser.add_argument(
-            f"--{component}-hash",
-            default="",
-            help=f"Hash for {component} cache scope (e.g., from hashFiles())",
-        )
-    parser.add_argument(
-        "--git-ref",
-        default="",
-        help="Git ref for branch-based cache scope (e.g., refs/heads/master)",
-    )
-    args = parser.parse_args()
-
-    # Normalize git ref to a safe scope name (e.g., refs/heads/master -> master)
-    git_ref_scope = ""
-    if args.git_ref:
-        git_ref_scope = args.git_ref.replace("refs/heads/", "").replace("/", "-")
-
-    with open(args.source, "r") as f:
-        compose = yaml.safe_load(f)
-
-    # Get project name from compose file or default
-    project_name = compose.get("name", "autogpt_platform")
-
-    def get_image_name(dockerfile: str, target: str) -> str:
-        """Generate image name based on Dockerfile folder and build target."""
-        dockerfile_parts = dockerfile.replace("\\", "/").split("/")
-        if len(dockerfile_parts) >= 2:
-            folder_name = dockerfile_parts[-2]  # e.g., "backend" or "frontend"
-        else:
-            folder_name = "app"
-        return f"{project_name}-{folder_name}:{target}"
-
-    def get_build_key(dockerfile: str, target: str) -> str:
-        """Generate a unique key for a Dockerfile+target combination."""
-        return f"{dockerfile}:{target}"
-
-    def get_component(dockerfile: str) -> str | None:
-        """Get component name (frontend/backend) from dockerfile path."""
-        for component in CACHE_BUILDS_FOR_COMPONENTS:
-            if component in dockerfile:
-                return component
-        return None
-
-    # First pass: collect all services with build configs and identify duplicates
-    # Track which (dockerfile, target) combinations we've seen
-    build_key_to_first_service: dict[str, str] = {}
-    services_to_build: list[str] = []
-    services_to_dedupe: list[str] = []
-
-    for service_name, service_config in compose.get("services", {}).items():
-        if "build" not in service_config:
-            continue
-
-        build_config = service_config["build"]
-        dockerfile = build_config.get("dockerfile", "Dockerfile")
-        target = build_config.get("target", "default")
-        build_key = get_build_key(dockerfile, target)
-
-        if build_key not in build_key_to_first_service:
-            # First service with this build config - it will do the actual build
-            build_key_to_first_service[build_key] = service_name
-            services_to_build.append(service_name)
-        else:
-            # Duplicate - will just use the image from the first service
-            services_to_dedupe.append(service_name)
-
-    # Second pass: configure builds and deduplicate
-    modified_services = []
-    for service_name, service_config in compose.get("services", {}).items():
-        if "build" not in service_config:
-            continue
-
-        build_config = service_config["build"]
-        dockerfile = build_config.get("dockerfile", "Dockerfile")
-        target = build_config.get("target", "latest")
-        image_name = get_image_name(dockerfile, target)
-
-        # Set image name for all services (needed for both builders and deduped)
-        service_config["image"] = image_name
-
-        if service_name in services_to_dedupe:
-            # Remove build config - this service will use the pre-built image
-            del service_config["build"]
-            continue
-
-        # This service will do the actual build - add cache config
-        cache_from_list = []
-        cache_to_list = []
-
-        component = get_component(dockerfile)
-        if not component:
-            # Skip services that don't clearly match frontend/backend
-            continue
-
-        # Get the hash for this component
-        component_hash = getattr(args, f"{component}_hash")
-
-        # Scope format: platform-{component}-{target}-{hash|ref}
-        # Example: platform-backend-server-abc123
-
-        if "type=gha" in args.cache_from:
-            # 1. Primary: exact hash match (most specific)
-            if component_hash:
-                hash_scope = f"platform-{component}-{target}-{component_hash}"
-                cache_from_list.append(f"{args.cache_from},scope={hash_scope}")
-
-            # 2. Fallback: branch-based cache
-            if git_ref_scope:
-                ref_scope = f"platform-{component}-{target}-{git_ref_scope}"
-                cache_from_list.append(f"{args.cache_from},scope={ref_scope}")
-
-            # 3. Fallback: dev branch cache (for PRs/feature branches)
-            if git_ref_scope and git_ref_scope != DEFAULT_BRANCH:
-                master_scope = f"platform-{component}-{target}-{DEFAULT_BRANCH}"
-                cache_from_list.append(f"{args.cache_from},scope={master_scope}")
-
-        if "type=gha" in args.cache_to:
-            # Write to both hash-based and branch-based scopes
-            if component_hash:
-                hash_scope = f"platform-{component}-{target}-{component_hash}"
-                cache_to_list.append(f"{args.cache_to},scope={hash_scope}")
-
-            if git_ref_scope:
-                ref_scope = f"platform-{component}-{target}-{git_ref_scope}"
-                cache_to_list.append(f"{args.cache_to},scope={ref_scope}")
-
-        # Ensure we have at least one cache source/target
-        if not cache_from_list:
-            cache_from_list.append(args.cache_from)
-        if not cache_to_list:
-            cache_to_list.append(args.cache_to)
-
-        build_config["cache_from"] = cache_from_list
-        build_config["cache_to"] = cache_to_list
-        modified_services.append(service_name)
-
-    # Write back to the same file
-    with open(args.source, "w") as f:
-        yaml.dump(compose, f, default_flow_style=False, sort_keys=False)
-
-    print(f"Added cache config to {len(modified_services)} services in {args.source}:")
-    for svc in modified_services:
-        svc_config = compose["services"][svc]
-        build_cfg = svc_config.get("build", {})
-        cache_from_list = build_cfg.get("cache_from", ["none"])
-        cache_to_list = build_cfg.get("cache_to", ["none"])
-        print(f"  - {svc}")
-        print(f"      image: {svc_config.get('image', 'N/A')}")
-        print(f"      cache_from: {cache_from_list}")
-        print(f"      cache_to: {cache_to_list}")
-    if services_to_dedupe:
-        print(
-            f"Deduplicated {len(services_to_dedupe)} services (will use pre-built images):"
-        )
-        for svc in services_to_dedupe:
-            print(f"  - {svc} -> {compose['services'][svc].get('image', 'N/A')}")
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/CLAUDE.md

@@ -45,11 +45,6 @@ AutoGPT Platform is a monorepo containing:
 - Backend/Frontend services use YAML anchors for consistent configuration
 - Supabase services (`db/docker/docker-compose.yml`) follow the same pattern
 
-### Branching Strategy
-
-- **`dev`** is the main development branch. All PRs should target `dev`.
-- **`master`** is the production branch. Only used for production releases.
-
 ### Creating Pull Requests
 
 - Create the PR against the `dev` branch of the repository.

autogpt_platform/autogpt_libs/poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
 
 [[package]]
 name = "annotated-doc"
@@ -67,7 +67,7 @@ description = "Backport of asyncio.Runner, a context manager that controls event
 optional = false
 python-versions = "<3.11,>=3.8"
 groups = ["dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
     {file = "backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5"},
     {file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
@@ -326,118 +326,100 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.13.4"
+version = "7.10.5"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+    {file = "coverage-7.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c6a5c3414bfc7451b879141ce772c546985163cf553f08e0f135f0699a911801"},
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+    {file = "coverage-7.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:bc8e4d99ce82f1710cc3c125adc30fd1487d3cf6c2cd4994d78d68a47b16989a"},
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 [package.dependencies]
@@ -448,61 +430,61 @@ toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 
 [[package]]
 name = "cryptography"
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 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = "!=3.9.0,!=3.9.1,>=3.8"
 groups = ["main"]
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 ]
 
 [package.dependencies]
@@ -516,7 +498,7 @@ nox = ["nox[uv] (>=2024.4.15)"]
 pep8test = ["check-sdist", "click (>=8.0.1)", "mypy (>=1.14)", "ruff (>=0.11.11)"]
 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi (>=2024)", "cryptography-vectors (==46.0.5)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
+test = ["certifi (>=2024)", "cryptography-vectors (==46.0.4)", "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]]
@@ -541,7 +523,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
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@@ -570,25 +552,24 @@ tests = ["coverage", "coveralls", "dill", "mock", "nose"]
 
 [[package]]
 name = "fastapi"
-version = "0.128.7"
+version = "0.128.0"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "fastapi-0.128.0.tar.gz", hash = "sha256:1cc179e1cef10a6be60ffe429f79b829dce99d8de32d7acb7e6c8dfdf7f2645a"},
 ]
 
 [package.dependencies]
 annotated-doc = ">=0.0.2"
 pydantic = ">=2.7.0"
-starlette = ">=0.40.0,<1.0.0"
+starlette = ">=0.40.0,<0.51.0"
 typing-extensions = ">=4.8.0"
-typing-inspection = ">=0.4.2"
 
 [package.extras]
-all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.9.3)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=5.8.0)", "uvicorn[standard] (>=0.12.0)"]
+all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
 standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "jinja2 (>=3.1.5)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 standard-no-fastapi-cloud-cli = ["email-validator (>=2.0.0)", "fastapi-cli[standard-no-fastapi-cloud-cli] (>=0.0.8)", "httpx (>=0.23.0,<1.0.0)", "jinja2 (>=3.1.5)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 
@@ -1063,14 +1044,14 @@ urllib3 = ">=1.26.0,<3"
 
 [[package]]
 name = "launchdarkly-server-sdk"
-version = "9.15.0"
+version = "9.14.1"
 description = "LaunchDarkly SDK for Python"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "launchdarkly_server_sdk-9.14.1-py3-none-any.whl", hash = "sha256:a9e2bd9ecdef845cd631ae0d4334a1115e5b44257c42eb2349492be4bac7815c"},
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+    {file = "launchdarkly_server_sdk-9.14.1.tar.gz", hash = "sha256:1df44baf0a0efa74d8c1dad7a00592b98bce7d19edded7f770da8dbc49922213"},
 ]
 
 [package.dependencies]
@@ -1479,14 +1460,14 @@ testing = ["coverage", "pytest", "pytest-benchmark"]
 
 [[package]]
 name = "postgrest"
-version = "2.28.0"
+version = "2.27.2"
 description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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+    {file = "postgrest-2.27.2-py3-none-any.whl", hash = "sha256:1666fef3de05ca097a314433dd5ae2f2d71c613cb7b233d0f468c4ffe37277da"},
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+    {file = "postgrest-2.27.2.tar.gz", hash = "sha256:55407d530b5af3d64e883a71fec1f345d369958f723ce4a8ab0b7d169e313242"},
 ]
 
 [package.dependencies]
@@ -2181,23 +2162,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
 [[package]]
 name = "pytest-cov"
-version = "7.0.0"
+version = "6.2.1"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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+    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
-    {file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
+    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
 ]
 
 [package.dependencies]
-coverage = {version = ">=7.10.6", extras = ["toml"]}
+coverage = {version = ">=7.5", extras = ["toml"]}
 pluggy = ">=1.2"
-pytest = ">=7"
+pytest = ">=6.2.5"
 
 [package.extras]
-testing = ["process-tests", "pytest-xdist", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "pytest-mock"
@@ -2249,14 +2230,14 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "realtime"
-version = "2.28.0"
+version = "2.27.2"
 description = ""
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "realtime-2.28.0-py3-none-any.whl", hash = "sha256:db1bd59bab9b1fcc9f9d3b1a073bed35bf4994d720e6751f10031a58d57a3836"},
+    {file = "realtime-2.27.2-py3-none-any.whl", hash = "sha256:34a9cbb26a274e707e8fc9e3ee0a66de944beac0fe604dc336d1e985db2c830f"},
-    {file = "realtime-2.28.0.tar.gz", hash = "sha256:d18cedcebd6a8f22fcd509bc767f639761eb218b7b2b6f14fc4205b6259b50fc"},
+    {file = "realtime-2.27.2.tar.gz", hash = "sha256:b960a90294d2cea1b3f1275ecb89204304728e08fff1c393cc1b3150739556b3"},
 ]
 
 [package.dependencies]
@@ -2437,14 +2418,14 @@ full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart
 
 [[package]]
 name = "storage3"
-version = "2.28.0"
+version = "2.27.2"
 description = "Supabase Storage client for Python."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "storage3-2.28.0-py3-none-any.whl", hash = "sha256:ecb50efd2ac71dabbdf97e99ad346eafa630c4c627a8e5a138ceb5fbbadae716"},
+    {file = "storage3-2.27.2-py3-none-any.whl", hash = "sha256:e6f16e7a260729e7b1f46e9bf61746805a02e30f5e419ee1291007c432e3ec63"},
-    {file = "storage3-2.28.0.tar.gz", hash = "sha256:bc1d008aff67de7a0f2bd867baee7aadbcdb6f78f5a310b4f7a38e8c13c19865"},
+    {file = "storage3-2.27.2.tar.gz", hash = "sha256:cb4807b7f86b4bb1272ac6fdd2f3cfd8ba577297046fa5f88557425200275af5"},
 ]
 
 [package.dependencies]
@@ -2488,35 +2469,35 @@ python-dateutil = ">=2.6.0"
 
 [[package]]
 name = "supabase"
-version = "2.28.0"
+version = "2.27.2"
 description = "Supabase client for Python."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "supabase-2.28.0-py3-none-any.whl", hash = "sha256:42776971c7d0ccca16034df1ab96a31c50228eb1eb19da4249ad2f756fc20272"},
+    {file = "supabase-2.27.2-py3-none-any.whl", hash = "sha256:d4dce00b3a418ee578017ec577c0e5be47a9a636355009c76f20ed2faa15bc54"},
-    {file = "supabase-2.28.0.tar.gz", hash = "sha256:aea299aaab2a2eed3c57e0be7fc035c6807214194cce795a3575add20268ece1"},
+    {file = "supabase-2.27.2.tar.gz", hash = "sha256:2aed40e4f3454438822442a1e94a47be6694c2c70392e7ae99b51a226d4293f7"},
 ]
 
 [package.dependencies]
 httpx = ">=0.26,<0.29"
-postgrest = "2.28.0"
+postgrest = "2.27.2"
-realtime = "2.28.0"
+realtime = "2.27.2"
-storage3 = "2.28.0"
+storage3 = "2.27.2"
-supabase-auth = "2.28.0"
+supabase-auth = "2.27.2"
-supabase-functions = "2.28.0"
+supabase-functions = "2.27.2"
 yarl = ">=1.22.0"
 
 [[package]]
 name = "supabase-auth"
-version = "2.28.0"
+version = "2.27.2"
 description = "Python Client Library for Supabase Auth"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "supabase_auth-2.28.0-py3-none-any.whl", hash = "sha256:2ac85026cc285054c7fa6d41924f3a333e9ec298c013e5b5e1754039ba7caec9"},
+    {file = "supabase_auth-2.27.2-py3-none-any.whl", hash = "sha256:78ec25b11314d0a9527a7205f3b1c72560dccdc11b38392f80297ef98664ee91"},
-    {file = "supabase_auth-2.28.0.tar.gz", hash = "sha256:2bb8f18ff39934e44b28f10918db965659f3735cd6fbfcc022fe0b82dbf8233e"},
+    {file = "supabase_auth-2.27.2.tar.gz", hash = "sha256:0f5bcc79b3677cb42e9d321f3c559070cfa40d6a29a67672cc8382fb7dc2fe97"},
 ]
 
 [package.dependencies]
@@ -2526,14 +2507,14 @@ pyjwt = {version = ">=2.10.1", extras = ["crypto"]}
 
 [[package]]
 name = "supabase-functions"
-version = "2.28.0"
+version = "2.27.2"
 description = "Library for Supabase Functions"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "supabase_functions-2.28.0-py3-none-any.whl", hash = "sha256:30bf2d586f8df285faf0621bb5d5bb3ec3157234fc820553ca156f009475e4ae"},
+    {file = "supabase_functions-2.27.2-py3-none-any.whl", hash = "sha256:db480efc669d0bca07605b9b6f167312af43121adcc842a111f79bea416ef754"},
-    {file = "supabase_functions-2.28.0.tar.gz", hash = "sha256:db3dddfc37aca5858819eb461130968473bd8c75bd284581013958526dac718b"},
+    {file = "supabase_functions-2.27.2.tar.gz", hash = "sha256:d0c8266207a94371cb3fd35ad3c7f025b78a97cf026861e04ccd35ac1775f80b"},
 ]
 
 [package.dependencies]
@@ -2564,7 +2545,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version < \"3.11\""
+markers = "python_version == \"3.10\""
 files = [
     {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
     {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -2912,4 +2893,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "9619cae908ad38fa2c48016a58bcf4241f6f5793aa0e6cc140276e91c433cbbb"
+content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -11,14 +11,14 @@ python = ">=3.10,<4.0"
 colorama = "^0.4.6"
 cryptography = "^46.0"
 expiringdict = "^1.2.2"
-fastapi = "^0.128.7"
+fastapi = "^0.128.0"
 google-cloud-logging = "^3.13.0"
-launchdarkly-server-sdk = "^9.15.0"
+launchdarkly-server-sdk = "^9.14.1"
 pydantic = "^2.12.5"
 pydantic-settings = "^2.12.0"
 pyjwt = { version = "^2.11.0", extras = ["crypto"] }
 redis = "^6.2.0"
-supabase = "^2.28.0"
+supabase = "^2.27.2"
 uvicorn = "^0.40.0"
 
 [tool.poetry.group.dev.dependencies]
@@ -26,7 +26,7 @@ pyright = "^1.1.408"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.3.0"
 pytest-mock = "^3.15.1"
-pytest-cov = "^7.0.0"
+pytest-cov = "^6.2.1"
 ruff = "^0.15.0"
 
 [build-system]

autogpt_platform/backend/Dockerfile

@@ -1,5 +1,3 @@
-# ============================ DEPENDENCY BUILDER ============================ #
-
 FROM debian:13-slim AS builder
 
 # Set environment variables
@@ -53,9 +51,7 @@ COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/parti
 COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
 RUN poetry run prisma generate && poetry run gen-prisma-stub
 
-# ============================== BACKEND SERVER ============================== #
+FROM debian:13-slim AS server_dependencies
-
-FROM debian:13-slim AS server
 
 WORKDIR /app
 
@@ -66,21 +62,16 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use.
+# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
-# bubblewrap provides OS-level sandbox (whitelist-only FS + no network)
+RUN apt-get update && apt-get install -y \
-# for the bash_exec MCP tool.
-# Using --no-install-recommends saves ~650MB by skipping unnecessary deps like llvm, mesa, etc.
-RUN apt-get update && apt-get install -y --no-install-recommends \
     python3.13 \
     python3-pip \
     ffmpeg \
     imagemagick \
-    jq \
-    ripgrep \
-    tree \
-    bubblewrap \
     && rm -rf /var/lib/apt/lists/*
 
+# Copy only necessary files from builder
+COPY --from=builder /app /app
 COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
 COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
 # Copy Node.js installation for Prisma
@@ -90,54 +81,30 @@ COPY --from=builder /usr/bin/npm /usr/bin/npm
 COPY --from=builder /usr/bin/npx /usr/bin/npx
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
-WORKDIR /app/autogpt_platform/backend
-
-# Copy only the .venv from builder (not the entire /app directory)
-# The .venv includes the generated Prisma client
-COPY --from=builder /app/autogpt_platform/backend/.venv ./.venv
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
 
-# Copy dependency files + autogpt_libs (path dependency)
+RUN mkdir -p /app/autogpt_platform/autogpt_libs
-COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
+RUN mkdir -p /app/autogpt_platform/backend
-COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml ./
 
-# Copy backend code + docs (for Copilot docs search)
+COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
-COPY autogpt_platform/backend ./
+
+COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.toml /app/autogpt_platform/backend/
+
+WORKDIR /app/autogpt_platform/backend
+
+FROM server_dependencies AS migrate
+
+# Migration stage only needs schema and migrations - much lighter than full backend
+COPY autogpt_platform/backend/schema.prisma /app/autogpt_platform/backend/
+COPY autogpt_platform/backend/backend/data/partial_types.py /app/autogpt_platform/backend/backend/data/partial_types.py
+COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migrations
+
+FROM server_dependencies AS server
+
+COPY autogpt_platform/backend /app/autogpt_platform/backend
 COPY docs /app/docs
 RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000
 
 CMD ["poetry", "run", "rest"]
-
-# =============================== DB MIGRATOR =============================== #
-
-# Lightweight migrate stage - only needs Prisma CLI, not full Python environment
-FROM debian:13-slim AS migrate
-
-WORKDIR /app/autogpt_platform/backend
-
-ENV DEBIAN_FRONTEND=noninteractive
-
-# Install only what's needed for prisma migrate: Node.js and minimal Python for prisma-python
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.13 \
-    python3-pip \
-    ca-certificates \
-    && rm -rf /var/lib/apt/lists/*
-
-# Copy Node.js from builder (needed for Prisma CLI)
-COPY --from=builder /usr/bin/node /usr/bin/node
-COPY --from=builder /usr/lib/node_modules /usr/lib/node_modules
-COPY --from=builder /usr/bin/npm /usr/bin/npm
-
-# Copy Prisma binaries
-COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
-
-# Install prisma-client-py directly (much smaller than copying full venv)
-RUN pip3 install prisma>=0.15.0 --break-system-packages
-
-COPY autogpt_platform/backend/schema.prisma ./
-COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
-COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
-COPY autogpt_platform/backend/migrations ./migrations

autogpt_platform/backend/backend/api/external/v1/routes.py

@@ -10,7 +10,7 @@ from typing_extensions import TypedDict
 
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.model as store_model
-import backend.blocks
+import backend.data.block
 from backend.api.external.middleware import require_permission
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
@@ -67,7 +67,7 @@ async def get_user_info(
     dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
 async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
-    blocks = [block() for block in backend.blocks.get_blocks().values()]
+    blocks = [block() for block in backend.data.block.get_blocks().values()]
     return [b.to_dict() for b in blocks if not b.disabled]
 
 
@@ -83,7 +83,7 @@ async def execute_graph_block(
         require_permission(APIKeyPermission.EXECUTE_BLOCK)
     ),
 ) -> CompletedBlockOutput:
-    obj = backend.blocks.get_block(block_id)
+    obj = backend.data.block.get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
     if obj.disabled:

autogpt_platform/backend/backend/api/features/builder/db.py

@@ -10,15 +10,10 @@ import backend.api.features.library.db as library_db
 import backend.api.features.library.model as library_model
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
+import backend.data.block
 from backend.blocks import load_all_blocks
-from backend.blocks._base import (
-    AnyBlockSchema,
-    BlockCategory,
-    BlockInfo,
-    BlockSchema,
-    BlockType,
-)
 from backend.blocks.llm import LlmModel
+from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
 from backend.data.db import query_raw_with_schema
 from backend.integrations.providers import ProviderName
 from backend.util.cache import cached
@@ -27,7 +22,7 @@ from backend.util.models import Pagination
 from .model import (
     BlockCategoryResponse,
     BlockResponse,
-    BlockTypeFilter,
+    BlockType,
     CountResponse,
     FilterType,
     Provider,
@@ -93,7 +88,7 @@ def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse
 def get_blocks(
     *,
     category: str | None = None,
-    type: BlockTypeFilter | None = None,
+    type: BlockType | None = None,
     provider: ProviderName | None = None,
     page: int = 1,
     page_size: int = 50,
@@ -674,9 +669,9 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
     for block_type in load_all_blocks().values():
         block: AnyBlockSchema = block_type()
         if block.disabled or block.block_type in (
-            BlockType.INPUT,
+            backend.data.block.BlockType.INPUT,
-            BlockType.OUTPUT,
+            backend.data.block.BlockType.OUTPUT,
-            BlockType.AGENT,
+            backend.data.block.BlockType.AGENT,
         ):
             continue
         # Find the execution count for this block

autogpt_platform/backend/backend/api/features/builder/model.py

@@ -4,7 +4,7 @@ from pydantic import BaseModel
 
 import backend.api.features.library.model as library_model
 import backend.api.features.store.model as store_model
-from backend.blocks._base import BlockInfo
+from backend.data.block import BlockInfo
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 
@@ -15,7 +15,7 @@ FilterType = Literal[
     "my_agents",
 ]
 
-BlockTypeFilter = Literal["all", "input", "action", "output"]
+BlockType = Literal["all", "input", "action", "output"]
 
 
 class SearchEntry(BaseModel):

autogpt_platform/backend/backend/api/features/builder/routes.py

@@ -88,7 +88,7 @@ async def get_block_categories(
 )
 async def get_blocks(
     category: Annotated[str | None, fastapi.Query()] = None,
-    type: Annotated[builder_model.BlockTypeFilter | None, fastapi.Query()] = None,
+    type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
     provider: Annotated[ProviderName | None, fastapi.Query()] = None,
     page: Annotated[int, fastapi.Query()] = 1,
     page_size: Annotated[int, fastapi.Query()] = 50,

autogpt_platform/backend/backend/api/features/chat/config.py

@@ -27,11 +27,12 @@ class ChatConfig(BaseSettings):
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
     # Streaming Configuration
-    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_context_messages: int = Field(
-    max_retries: int = Field(
+        default=50, ge=1, le=200, description="Maximum context messages"
-        default=3,
-        description="Max retries for fallback path (SDK handles retries internally)",
     )
+
+    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_retries: int = Field(default=3, description="Maximum number of retries")
     max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
         default=30, description="Maximum number of agent schedules"
@@ -92,37 +93,6 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
-    # Claude Agent SDK Configuration
-    use_claude_agent_sdk: bool = Field(
-        default=True,
-        description="Use Claude Agent SDK for chat completions",
-    )
-    claude_agent_model: str | None = Field(
-        default=None,
-        description="Model for the Claude Agent SDK path. If None, derives from "
-        "the `model` field by stripping the OpenRouter provider prefix.",
-    )
-    claude_agent_max_buffer_size: int = Field(
-        default=10 * 1024 * 1024,  # 10MB (default SDK is 1MB)
-        description="Max buffer size in bytes for Claude Agent SDK JSON message parsing. "
-        "Increase if tool outputs exceed the limit.",
-    )
-    claude_agent_max_subtasks: int = Field(
-        default=10,
-        description="Max number of sub-agent Tasks the SDK can spawn per session.",
-    )
-    claude_agent_use_resume: bool = Field(
-        default=True,
-        description="Use --resume for multi-turn conversations instead of "
-        "history compression. Falls back to compression when unavailable.",
-    )
-
-    # Extended thinking configuration for Claude models
-    thinking_enabled: bool = Field(
-        default=True,
-        description="Enable adaptive thinking for Claude models via OpenRouter",
-    )
-
     @field_validator("api_key", mode="before")
     @classmethod
     def get_api_key(cls, v):
@@ -162,17 +132,6 @@ class ChatConfig(BaseSettings):
             v = os.getenv("CHAT_INTERNAL_API_KEY")
         return v
 
-    @field_validator("use_claude_agent_sdk", mode="before")
-    @classmethod
-    def get_use_claude_agent_sdk(cls, v):
-        """Get use_claude_agent_sdk from environment if not provided."""
-        # Check environment variable - default to True if not set
-        env_val = os.getenv("CHAT_USE_CLAUDE_AGENT_SDK", "").lower()
-        if env_val:
-            return env_val in ("true", "1", "yes", "on")
-        # Default to True (SDK enabled by default)
-        return True if v is None else v
-
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

autogpt_platform/backend/backend/api/features/chat/db.py

@@ -45,7 +45,10 @@ async def create_chat_session(
         successfulAgentRuns=SafeJson({}),
         successfulAgentSchedules=SafeJson({}),
     )
-    return await PrismaChatSession.prisma().create(data=data)
+    return await PrismaChatSession.prisma().create(
+        data=data,
+        include={"Messages": True},
+    )
 
 
 async def update_chat_session(

autogpt_platform/backend/backend/api/features/chat/model.py

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any, cast
+from typing import Any
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -104,26 +104,6 @@ class ChatSession(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
-    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
-        """Attach a tool_call to the current turn's assistant message.
-
-        Searches backwards for the most recent assistant message (stopping at
-        any user message boundary). If found, appends the tool_call to it.
-        Otherwise creates a new assistant message with the tool_call.
-        """
-        for msg in reversed(self.messages):
-            if msg.role == "user":
-                break
-            if msg.role == "assistant":
-                if not msg.tool_calls:
-                    msg.tool_calls = []
-                msg.tool_calls.append(tool_call)
-                return
-
-        self.messages.append(
-            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
-        )
-
     @staticmethod
     def new(user_id: str) -> "ChatSession":
         return ChatSession(
@@ -192,47 +172,6 @@ class ChatSession(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
-    @staticmethod
-    def _merge_consecutive_assistant_messages(
-        messages: list[ChatCompletionMessageParam],
-    ) -> list[ChatCompletionMessageParam]:
-        """Merge consecutive assistant messages into single messages.
-
-        Long-running tool flows can create split assistant messages: one with
-        text content and another with tool_calls. Anthropic's API requires
-        tool_result blocks to reference a tool_use in the immediately preceding
-        assistant message, so these splits cause 400 errors via OpenRouter.
-        """
-        if len(messages) < 2:
-            return messages
-
-        result: list[ChatCompletionMessageParam] = [messages[0]]
-        for msg in messages[1:]:
-            prev = result[-1]
-            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
-                result.append(msg)
-                continue
-
-            prev = cast(ChatCompletionAssistantMessageParam, prev)
-            curr = cast(ChatCompletionAssistantMessageParam, msg)
-
-            curr_content = curr.get("content") or ""
-            if curr_content:
-                prev_content = prev.get("content") or ""
-                prev["content"] = (
-                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
-                )
-
-            curr_tool_calls = curr.get("tool_calls")
-            if curr_tool_calls:
-                prev_tool_calls = prev.get("tool_calls")
-                prev["tool_calls"] = (
-                    list(prev_tool_calls) + list(curr_tool_calls)
-                    if prev_tool_calls
-                    else list(curr_tool_calls)
-                )
-        return result
-
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -319,7 +258,7 @@ class ChatSession(BaseModel):
                         name=message.name or "",
                     )
                 )
-        return self._merge_consecutive_assistant_messages(messages)
+        return messages
 
 
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:
@@ -334,8 +273,9 @@ async def _get_session_from_cache(session_id: str) -> ChatSession | None:
     try:
         session = ChatSession.model_validate_json(raw_session)
         logger.info(
-            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
+            f"Loading session {session_id} from cache: "
-            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
+            f"message_count={len(session.messages)}, "
+            f"roles={[m.role for m in session.messages]}"
         )
         return session
     except Exception as e:
@@ -377,9 +317,11 @@ async def _get_session_from_db(session_id: str) -> ChatSession | None:
         return None
 
     messages = prisma_session.Messages
-    logger.debug(
+    logger.info(
-        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
+        f"Loading session {session_id} from DB: "
-        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
+        f"has_messages={messages is not None}, "
+        f"message_count={len(messages) if messages else 0}, "
+        f"roles={[m.role for m in messages] if messages else []}"
     )
 
     return ChatSession.from_db(prisma_session, messages)
@@ -430,9 +372,10 @@ async def _save_session_to_db(
                     "function_call": msg.function_call,
                 }
             )
-        logger.debug(
+        logger.info(
-            f"[DB] Saving {len(new_messages)} messages to session {session.session_id}, "
+            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}"
+            f"roles={[m['role'] for m in messages_data]}, "
+            f"start_sequence={existing_message_count}"
         )
         await chat_db.add_chat_messages_batch(
             session_id=session.session_id,
@@ -472,7 +415,7 @@ async def get_chat_session(
         logger.warning(f"Unexpected cache error for session {session_id}: {e}")
 
     # Fall back to database
-    logger.debug(f"Session {session_id} not in cache, checking database")
+    logger.info(f"Session {session_id} not in cache, checking database")
     session = await _get_session_from_db(session_id)
 
     if session is None:
@@ -489,6 +432,7 @@ async def get_chat_session(
     # Cache the session from DB
     try:
         await _cache_session(session)
+        logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
 
@@ -553,40 +497,6 @@ async def upsert_chat_session(
         return session
 
 
-async def append_and_save_message(session_id: str, message: ChatMessage) -> ChatSession:
-    """Atomically append a message to a session and persist it.
-
-    Acquires the session lock, re-fetches the latest session state,
-    appends the message, and saves — preventing message loss when
-    concurrent requests modify the same session.
-    """
-    lock = await _get_session_lock(session_id)
-
-    async with lock:
-        session = await get_chat_session(session_id)
-        if session is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        session.messages.append(message)
-        existing_message_count = await chat_db.get_chat_session_message_count(
-            session_id
-        )
-
-        try:
-            await _save_session_to_db(session, existing_message_count)
-        except Exception as e:
-            raise DatabaseError(
-                f"Failed to persist message to session {session_id}"
-            ) from e
-
-        try:
-            await _cache_session(session)
-        except Exception as e:
-            logger.warning(f"Cache write failed for session {session_id}: {e}")
-
-        return session
-
-
 async def create_chat_session(user_id: str) -> ChatSession:
     """Create a new chat session and persist it.
 
@@ -693,19 +603,13 @@ async def update_session_title(session_id: str, title: str) -> bool:
             logger.warning(f"Session {session_id} not found for title update")
             return False
 
-        # Update title in cache if it exists (instead of invalidating).
+        # Invalidate cache so next fetch gets updated title
-        # This prevents race conditions where cache invalidation causes
-        # the frontend to see stale DB data while streaming is still in progress.
         try:
-            cached = await _get_session_from_cache(session_id)
+            redis_key = _get_session_cache_key(session_id)
-            if cached:
+            async_redis = await get_redis_async()
-                cached.title = title
+            await async_redis.delete(redis_key)
-                await _cache_session(cached)
         except Exception as e:
-            # Not critical - title will be correct on next full cache refresh
+            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-            logger.warning(
-                f"Failed to update title in cache for session {session_id}: {e}"
-            )
 
         return True
     except Exception as e:

autogpt_platform/backend/backend/api/features/chat/model_test.py

@@ -1,16 +1,4 @@
-from typing import cast
-
 import pytest
-from openai.types.chat import (
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionMessageParam,
-    ChatCompletionToolMessageParam,
-    ChatCompletionUserMessageParam,
-)
-from openai.types.chat.chat_completion_message_tool_call_param import (
-    ChatCompletionMessageToolCallParam,
-    Function,
-)
 
 from .model import (
     ChatMessage,
@@ -129,205 +117,3 @@ async def test_chatsession_db_storage(setup_test_user, test_user_id):
                 loaded.tool_calls is not None
             ), f"Tool calls missing for {orig.role} message"
             assert len(orig.tool_calls) == len(loaded.tool_calls)
-
-
-# --------------------------------------------------------------------------- #
-#  _merge_consecutive_assistant_messages                                       #
-# --------------------------------------------------------------------------- #
-
-_tc = ChatCompletionMessageToolCallParam(
-    id="tc1", type="function", function=Function(name="do_stuff", arguments="{}")
-)
-_tc2 = ChatCompletionMessageToolCallParam(
-    id="tc2", type="function", function=Function(name="other", arguments="{}")
-)
-
-
-def test_merge_noop_when_no_consecutive_assistants():
-    """Messages without consecutive assistants are returned unchanged."""
-    msgs = [
-        ChatCompletionUserMessageParam(role="user", content="hi"),
-        ChatCompletionAssistantMessageParam(role="assistant", content="hello"),
-        ChatCompletionUserMessageParam(role="user", content="bye"),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
-    assert len(merged) == 3
-    assert [m["role"] for m in merged] == ["user", "assistant", "user"]
-
-
-def test_merge_splits_text_and_tool_calls():
-    """The exact bug scenario: text-only assistant followed by tool_calls-only assistant."""
-    msgs = [
-        ChatCompletionUserMessageParam(role="user", content="build agent"),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="Let me build that"
-        ),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc]
-        ),
-        ChatCompletionToolMessageParam(role="tool", content="ok", tool_call_id="tc1"),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
-
-    assert len(merged) == 3
-    assert merged[0]["role"] == "user"
-    assert merged[2]["role"] == "tool"
-    a = cast(ChatCompletionAssistantMessageParam, merged[1])
-    assert a["role"] == "assistant"
-    assert a.get("content") == "Let me build that"
-    assert a.get("tool_calls") == [_tc]
-
-
-def test_merge_combines_tool_calls_from_both():
-    """Both consecutive assistants have tool_calls — they get merged."""
-    msgs: list[ChatCompletionAssistantMessageParam] = [
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="text", tool_calls=[_tc]
-        ),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc2]
-        ),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
-
-    assert len(merged) == 1
-    a = cast(ChatCompletionAssistantMessageParam, merged[0])
-    assert a.get("tool_calls") == [_tc, _tc2]
-    assert a.get("content") == "text"
-
-
-def test_merge_three_consecutive_assistants():
-    """Three consecutive assistants collapse into one."""
-    msgs: list[ChatCompletionAssistantMessageParam] = [
-        ChatCompletionAssistantMessageParam(role="assistant", content="a"),
-        ChatCompletionAssistantMessageParam(role="assistant", content="b"),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc]
-        ),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
-
-    assert len(merged) == 1
-    a = cast(ChatCompletionAssistantMessageParam, merged[0])
-    assert a.get("content") == "a\nb"
-    assert a.get("tool_calls") == [_tc]
-
-
-def test_merge_empty_and_single_message():
-    """Edge cases: empty list and single message."""
-    assert ChatSession._merge_consecutive_assistant_messages([]) == []
-
-    single: list[ChatCompletionMessageParam] = [
-        ChatCompletionUserMessageParam(role="user", content="hi")
-    ]
-    assert ChatSession._merge_consecutive_assistant_messages(single) == single
-
-
-# --------------------------------------------------------------------------- #
-#  add_tool_call_to_current_turn                                               #
-# --------------------------------------------------------------------------- #
-
-_raw_tc = {
-    "id": "tc1",
-    "type": "function",
-    "function": {"name": "f", "arguments": "{}"},
-}
-_raw_tc2 = {
-    "id": "tc2",
-    "type": "function",
-    "function": {"name": "g", "arguments": "{}"},
-}
-
-
-def test_add_tool_call_appends_to_existing_assistant():
-    """When the last assistant is from the current turn, tool_call is added to it."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-        ChatMessage(role="assistant", content="working on it"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 2  # no new message created
-    assert session.messages[1].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_creates_assistant_when_none_exists():
-    """When there's no current-turn assistant, a new one is created."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 2
-    assert session.messages[1].role == "assistant"
-    assert session.messages[1].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_does_not_cross_user_boundary():
-    """A user message acts as a boundary — previous assistant is not modified."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="assistant", content="old turn"),
-        ChatMessage(role="user", content="new message"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 3  # new assistant was created
-    assert session.messages[0].tool_calls is None  # old assistant untouched
-    assert session.messages[2].role == "assistant"
-    assert session.messages[2].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_multiple_times():
-    """Multiple long-running tool calls accumulate on the same assistant."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-        ChatMessage(role="assistant", content="doing stuff"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-    # Simulate a pending tool result in between (like _yield_tool_call does)
-    session.messages.append(
-        ChatMessage(role="tool", content="pending", tool_call_id="tc1")
-    )
-    session.add_tool_call_to_current_turn(_raw_tc2)
-
-    assert len(session.messages) == 3  # user, assistant, tool — no extra assistant
-    assert session.messages[1].tool_calls == [_raw_tc, _raw_tc2]
-
-
-def test_to_openai_messages_merges_split_assistants():
-    """End-to-end: session with split assistants produces valid OpenAI messages."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="build agent"),
-        ChatMessage(role="assistant", content="Let me build that"),
-        ChatMessage(
-            role="assistant",
-            content="",
-            tool_calls=[
-                {
-                    "id": "tc1",
-                    "type": "function",
-                    "function": {"name": "create_agent", "arguments": "{}"},
-                }
-            ],
-        ),
-        ChatMessage(role="tool", content="done", tool_call_id="tc1"),
-        ChatMessage(role="assistant", content="Saved!"),
-        ChatMessage(role="user", content="show me an example run"),
-    ]
-    openai_msgs = session.to_openai_messages()
-
-    # The two consecutive assistants at index 1,2 should be merged
-    roles = [m["role"] for m in openai_msgs]
-    assert roles == ["user", "assistant", "tool", "assistant", "user"]
-
-    # The merged assistant should have both content and tool_calls
-    merged = cast(ChatCompletionAssistantMessageParam, openai_msgs[1])
-    assert merged.get("content") == "Let me build that"
-    tc_list = merged.get("tool_calls")
-    assert tc_list is not None and len(list(tc_list)) == 1
-    assert list(tc_list)[0]["id"] == "tc1"

autogpt_platform/backend/backend/api/features/chat/response_model.py

@@ -10,8 +10,6 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from backend.util.json import dumps as json_dumps
-
 
 class ResponseType(str, Enum):
     """Types of streaming responses following AI SDK protocol."""
@@ -20,10 +18,6 @@ class ResponseType(str, Enum):
     START = "start"
     FINISH = "finish"
 
-    # Step lifecycle (one LLM API call within a message)
-    START_STEP = "start-step"
-    FINISH_STEP = "finish-step"
-
     # Text streaming
     TEXT_START = "text-start"
     TEXT_DELTA = "text-delta"
@@ -63,16 +57,6 @@ class StreamStart(StreamBaseResponse):
         description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-protocol fields like taskId."""
-        import json
-
-        data: dict[str, Any] = {
-            "type": self.type.value,
-            "messageId": self.messageId,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 class StreamFinish(StreamBaseResponse):
     """End of message/stream."""
@@ -80,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
     type: ResponseType = ResponseType.FINISH
 
 
-class StreamStartStep(StreamBaseResponse):
-    """Start of a step (one LLM API call within a message).
-
-    The AI SDK uses this to add a step-start boundary to message.parts,
-    enabling visual separation between multiple LLM calls in a single message.
-    """
-
-    type: ResponseType = ResponseType.START_STEP
-
-
-class StreamFinishStep(StreamBaseResponse):
-    """End of a step (one LLM API call within a message).
-
-    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
-    so the next LLM call in a tool-call continuation starts with clean state.
-    """
-
-    type: ResponseType = ResponseType.FINISH_STEP
-
-
 # ========== Text Streaming ==========
 
 
@@ -153,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
     type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
     toolCallId: str = Field(..., description="Tool call ID this responds to")
     output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
     toolName: str | None = Field(
         default=None, description="Name of the tool that was executed"
     )
@@ -161,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
         default=True, description="Whether the tool execution succeeded"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, excluding non-spec fields."""
-        import json
-
-        data = {
-            "type": self.type.value,
-            "toolCallId": self.toolCallId,
-            "output": self.output,
-        }
-        return f"data: {json.dumps(data)}\n\n"
-
 
 # ========== Other ==========
 
@@ -195,18 +148,6 @@ class StreamError(StreamBaseResponse):
         default=None, description="Additional error details"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, only emitting fields required by AI SDK protocol.
-
-        The AI SDK uses z.strictObject({type, errorText}) which rejects
-        any extra fields like `code` or `details`.
-        """
-        data = {
-            "type": self.type.value,
-            "errorText": self.errorText,
-        }
-        return f"data: {json_dumps(data)}\n\n"
-
 
 class StreamHeartbeat(StreamBaseResponse):
     """Heartbeat to keep SSE connection alive during long-running operations.

autogpt_platform/backend/backend/api/features/chat/routes.py

@@ -1,57 +1,23 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
-import asyncio
 import logging
 import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
 from backend.util.exceptions import NotFoundError
-from backend.util.feature_flag import Flag, is_feature_enabled
 
 from . import service as chat_service
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
-from .model import (
+from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-    ChatMessage,
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart
-    ChatSession,
-    append_and_save_message,
-    create_chat_session,
-    get_chat_session,
-    get_user_sessions,
-)
-from .response_model import StreamError, StreamFinish, StreamHeartbeat, StreamStart
-from .sdk import service as sdk_service
-from .tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockDetailsResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
-from .tracking import track_user_message
 
 config = ChatConfig()
 
@@ -243,10 +209,6 @@ async def get_session(
     active_task, last_message_id = await stream_registry.get_active_task_for_session(
         session_id, user_id
     )
-    logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
-        f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
-    )
     if active_task:
         # Filter out the in-progress assistant message from the session response.
         # The client will receive the complete assistant response through the SSE
@@ -304,54 +266,12 @@ async def stream_chat_post(
 
     """
     import asyncio
-    import time
 
-    stream_start_time = time.perf_counter()
-    log_meta = {"component": "ChatStream", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
-        f"user={user_id}, message_len={len(request.message)}",
-        extra={"json_fields": log_meta},
-    )
     session = await _validate_and_get_session(session_id, user_id)
-    logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
-            }
-        },
-    )
-
-    # Atomically append user message to session BEFORE creating task to avoid
-    # race condition where GET_SESSION sees task as "running" but message isn't
-    # saved yet.  append_and_save_message re-fetches inside a lock to prevent
-    # message loss from concurrent requests.
-    if request.message:
-        message = ChatMessage(
-            role="user" if request.is_user_message else "assistant",
-            content=request.message,
-        )
-        if request.is_user_message:
-            track_user_message(
-                user_id=user_id,
-                session_id=session_id,
-                message_length=len(request.message),
-            )
-        logger.info(f"[STREAM] Saving user message to session {session_id}")
-        session = await append_and_save_message(session_id, message)
-        logger.info(f"[STREAM] User message saved for session {session_id}")
 
     # Create a task in the stream registry for reconnection support
     task_id = str(uuid_module.uuid4())
     operation_id = str(uuid_module.uuid4())
-    log_meta["task_id"] = task_id
-
-    task_create_start = time.perf_counter()
     await stream_registry.create_task(
         task_id=task_id,
         session_id=session_id,
@@ -360,151 +280,40 @@ async def stream_chat_post(
         tool_name="chat",
         operation_id=operation_id,
     )
-    logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
-            }
-        },
-    )
 
     # Background task that runs the AI generation independently of SSE connection
     async def run_ai_generation():
-        import time as time_module
-
-        gen_start_time = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
-            extra={"json_fields": log_meta},
-        )
-        first_chunk_time, ttfc = None, None
-        chunk_count = 0
         try:
             # Emit a start event with task_id for reconnection
             start_chunk = StreamStart(messageId=task_id, taskId=task_id)
             await stream_registry.publish_chunk(task_id, start_chunk)
-            logger.info(
-                f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time) * 1000:.1f}ms",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": (time_module.perf_counter() - gen_start_time)
-                        * 1000,
-                    }
-                },
-            )
 
-            # Choose service based on LaunchDarkly flag (falls back to config default)
+            async for chunk in chat_service.stream_chat_completion(
-            use_sdk = await is_feature_enabled(
-                Flag.COPILOT_SDK,
-                user_id or "anonymous",
-                default=config.use_claude_agent_sdk,
-            )
-            stream_fn = (
-                sdk_service.stream_chat_completion_sdk
-                if use_sdk
-                else chat_service.stream_chat_completion
-            )
-            logger.info(
-                f"[TIMING] Calling {'sdk' if use_sdk else 'standard'} stream_chat_completion",
-                extra={"json_fields": log_meta},
-            )
-            # Pass message=None since we already added it to the session above
-            async for chunk in stream_fn(
                 session_id,
-                None,  # Message already in session
+                request.message,
                 is_user_message=request.is_user_message,
                 user_id=user_id,
-                session=session,  # Pass session with message already added
+                session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
             ):
-                # Skip duplicate StreamStart — we already published one above
-                if isinstance(chunk, StreamStart):
-                    continue
-                chunk_count += 1
-                if first_chunk_time is None:
-                    first_chunk_time = time_module.perf_counter()
-                    ttfc = first_chunk_time - gen_start_time
-                    logger.info(
-                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
-                        extra={
-                            "json_fields": {
-                                **log_meta,
-                                "chunk_type": type(chunk).__name__,
-                                "time_to_first_chunk_ms": ttfc * 1000,
-                            }
-                        },
-                    )
                 # Write to Redis (subscribers will receive via XREAD)
                 await stream_registry.publish_chunk(task_id, chunk)
 
-            gen_end_time = time_module.perf_counter()
+            # Mark task as completed
-            total_time = (gen_end_time - gen_start_time) * 1000
-            logger.info(
-                f"[TIMING] run_ai_generation FINISHED in {total_time / 1000:.1f}s; "
-                f"task={task_id}, session={session_id}, "
-                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "time_to_first_chunk_ms": (
-                            ttfc * 1000 if ttfc is not None else None
-                        ),
-                        "n_chunks": chunk_count,
-                    }
-                },
-            )
             await stream_registry.mark_task_completed(task_id, "completed")
         except Exception as e:
-            elapsed = time_module.perf_counter() - gen_start_time
             logger.error(
-                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
+                f"Error in background AI generation for session {session_id}: {e}"
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": elapsed * 1000,
-                        "error": str(e),
-                    }
-                },
             )
-            # Publish a StreamError so the frontend can display an error message
-            try:
-                await stream_registry.publish_chunk(
-                    task_id,
-                    StreamError(
-                        errorText="An error occurred. Please try again.",
-                        code="stream_error",
-                    ),
-                )
-            except Exception:
-                pass  # Best-effort; mark_task_completed will publish StreamFinish
             await stream_registry.mark_task_completed(task_id, "failed")
 
     # Start the AI generation in a background task
     bg_task = asyncio.create_task(run_ai_generation())
     await stream_registry.set_task_asyncio_task(task_id, bg_task)
-    setup_time = (time.perf_counter() - stream_start_time) * 1000
-    logger.info(
-        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
 
     # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        import time as time_module
-
-        event_gen_start = time_module.perf_counter()
-        logger.info(
-            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
-            f"user={user_id}",
-            extra={"json_fields": log_meta},
-        )
         subscriber_queue = None
-        first_chunk_yielded = False
-        chunks_yielded = 0
         try:
             # Subscribe to the task stream (this replays existing messages + live updates)
             subscriber_queue = await stream_registry.subscribe_to_task(
@@ -519,78 +328,24 @@ async def stream_chat_post(
                 return
 
             # Read from the subscriber queue and yield to SSE
-            logger.info(
-                "[TIMING] Starting to read from subscriber_queue",
-                extra={"json_fields": log_meta},
-            )
             while True:
                 try:
                     chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
-                    chunks_yielded += 1
-
-                    if not first_chunk_yielded:
-                        first_chunk_yielded = True
-                        elapsed = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
-                            f"type={type(chunk).__name__}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunk_type": type(chunk).__name__,
-                                    "elapsed_ms": elapsed * 1000,
-                                }
-                            },
-                        )
-
                     yield chunk.to_sse()
 
                     # Check for finish signal
                     if isinstance(chunk, StreamFinish):
-                        total_time = time_module.perf_counter() - event_gen_start
-                        logger.info(
-                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
-                            f"n_chunks={chunks_yielded}",
-                            extra={
-                                "json_fields": {
-                                    **log_meta,
-                                    "chunks_yielded": chunks_yielded,
-                                    "total_time_ms": total_time * 1000,
-                                }
-                            },
-                        )
                         break
                 except asyncio.TimeoutError:
+                    # Send heartbeat to keep connection alive
                     yield StreamHeartbeat().to_sse()
 
         except GeneratorExit:
-            logger.info(
-                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "chunks_yielded": chunks_yielded,
-                        "reason": "client_disconnect",
-                    }
-                },
-            )
             pass  # Client disconnected - background task continues
         except Exception as e:
-            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
-            logger.error(
-                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
-                extra={
-                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
-                },
-            )
-            # Surface error to frontend so it doesn't appear stuck
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="stream_error",
-            ).to_sse()
-            yield StreamFinish().to_sse()
         finally:
-            # Unsubscribe when client disconnects or stream ends
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
                 try:
                     await stream_registry.unsubscribe_from_task(
@@ -602,18 +357,6 @@ async def stream_chat_post(
                         exc_info=True,
                     )
             # AI SDK protocol termination - always yield even if unsubscribe fails
-            total_time = time_module.perf_counter() - event_gen_start
-            logger.info(
-                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
-                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time * 1000,
-                        "chunks_yielded": chunks_yielded,
-                    }
-                },
-            )
             yield "data: [DONE]\n\n"
 
     return StreamingResponse(
@@ -631,90 +374,63 @@ async def stream_chat_post(
 @router.get(
     "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
     session_id: str,
+    message: Annotated[str, Query(min_length=1, max_length=10000)],
     user_id: str | None = Depends(auth.get_user_id),
+    is_user_message: bool = Query(default=True),
 ):
     """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).
 
-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
-    Checks for an active (in-progress) task on the session and either replays
+      - Text fragments as they are generated
-    the full SSE stream or returns 204 No Content if nothing is running.
+      - Tool call UI elements (if invoked)
+      - Tool execution results
 
     Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
+        message: The user's new message to process.
         user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
     Returns:
-        StreamingResponse (SSE) when an active stream exists,
+        StreamingResponse: SSE-formatted response chunks.
-        or 204 No Content when there is nothing to resume.
+
     """
-    import asyncio
+    session = await _validate_and_get_session(session_id, user_id)
-
-    active_task, _last_id = await stream_registry.get_active_task_for_session(
-        session_id, user_id
-    )
-
-    if not active_task:
-        return Response(status_code=204)
-
-    subscriber_queue = await stream_registry.subscribe_to_task(
-        task_id=active_task.task_id,
-        user_id=user_id,
-        last_message_id="0-0",  # Full replay so useChat rebuilds the message
-    )
-
-    if subscriber_queue is None:
-        return Response(status_code=204)
 
     async def event_generator() -> AsyncGenerator[str, None]:
         chunk_count = 0
         first_chunk_type: str | None = None
-        try:
+        async for chunk in chat_service.stream_chat_completion(
-            while True:
+            session_id,
-                try:
+            message,
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
+            is_user_message=is_user_message,
-                    if chunk_count < 3:
+            user_id=user_id,
-                        logger.info(
+            session=session,  # Pass pre-fetched session to avoid double-fetch
-                            "Resume stream chunk",
+        ):
-                            extra={
+            if chunk_count < 3:
-                                "session_id": session_id,
+                logger.info(
-                                "chunk_type": str(chunk.type),
+                    "Chat stream chunk",
-                            },
+                    extra={
-                        )
+                        "session_id": session_id,
-                    if not first_chunk_type:
+                        "chunk_type": str(chunk.type),
-                        first_chunk_type = str(chunk.type)
+                    },
-                    chunk_count += 1
-                    yield chunk.to_sse()
-
-                    if isinstance(chunk, StreamFinish):
-                        break
-                except asyncio.TimeoutError:
-                    yield StreamHeartbeat().to_sse()
-        except GeneratorExit:
-            pass
-        except Exception as e:
-            logger.error(f"Error in resume stream for session {session_id}: {e}")
-        finally:
-            try:
-                await stream_registry.unsubscribe_from_task(
-                    active_task.task_id, subscriber_queue
                 )
-            except Exception as unsub_err:
+            if not first_chunk_type:
-                logger.error(
+                first_chunk_type = str(chunk.type)
-                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
+            chunk_count += 1
-                    exc_info=True,
+            yield chunk.to_sse()
-                )
+        logger.info(
-            logger.info(
+            "Chat stream completed",
-                "Resume stream completed",
+            extra={
-                extra={
+                "session_id": session_id,
-                    "session_id": session_id,
+                "chunk_count": chunk_count,
-                    "n_chunks": chunk_count,
+                "first_chunk_type": first_chunk_type,
-                    "first_chunk_type": first_chunk_type,
+            },
-                },
+        )
-            )
+        # AI SDK protocol termination
-            yield "data: [DONE]\n\n"
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -722,8 +438,8 @@ async def resume_session_stream(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
-            "x-vercel-ai-ui-message-stream": "v1",
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -834,6 +550,8 @@ async def stream_task(
         )
 
     async def event_generator() -> AsyncGenerator[str, None]:
+        import asyncio
+
         heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
         try:
             while True:
@@ -1033,43 +751,3 @@ async def health_check() -> dict:
         "service": "chat",
         "version": "0.1.0",
     }
-
-
-# ========== Schema Export (for OpenAPI / Orval codegen) ==========
-
-ToolResponseUnion = (
-    AgentsFoundResponse
-    | NoResultsResponse
-    | AgentDetailsResponse
-    | SetupRequirementsResponse
-    | ExecutionStartedResponse
-    | NeedLoginResponse
-    | ErrorResponse
-    | InputValidationErrorResponse
-    | AgentOutputResponse
-    | UnderstandingUpdatedResponse
-    | AgentPreviewResponse
-    | AgentSavedResponse
-    | ClarificationNeededResponse
-    | BlockListResponse
-    | BlockDetailsResponse
-    | BlockOutputResponse
-    | DocSearchResultsResponse
-    | DocPageResponse
-    | OperationStartedResponse
-    | OperationPendingResponse
-    | OperationInProgressResponse
-)
-
-
-@router.get(
-    "/schema/tool-responses",
-    response_model=ToolResponseUnion,
-    include_in_schema=True,
-    summary="[Dummy] Tool response type export for codegen",
-    description="This endpoint is not meant to be called. It exists solely to "
-    "expose tool response models in the OpenAPI schema for frontend codegen.",
-)
-async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
-    """Never called at runtime. Exists only so Orval generates TS types."""
-    raise HTTPException(status_code=501, detail="Schema-only endpoint")

autogpt_platform/backend/backend/api/features/chat/sdk/__init__.py

@@ -1,14 +0,0 @@
-"""Claude Agent SDK integration for CoPilot.
-
-This module provides the integration layer between the Claude Agent SDK
-and the existing CoPilot tool system, enabling drop-in replacement of
-the current LLM orchestration with the battle-tested Claude Agent SDK.
-"""
-
-from .service import stream_chat_completion_sdk
-from .tool_adapter import create_copilot_mcp_server
-
-__all__ = [
-    "stream_chat_completion_sdk",
-    "create_copilot_mcp_server",
-]

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py

@@ -1,203 +0,0 @@
-"""Response adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-This module provides the adapter layer that converts streaming messages from
-the Claude Agent SDK into the Vercel AI SDK UI Stream Protocol format that
-the frontend expects.
-"""
-
-import json
-import logging
-import uuid
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    Message,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-)
-from backend.api.features.chat.sdk.tool_adapter import (
-    MCP_TOOL_PREFIX,
-    pop_pending_tool_output,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class SDKResponseAdapter:
-    """Adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-    This class maintains state during a streaming session to properly track
-    text blocks, tool calls, and message lifecycle.
-    """
-
-    def __init__(self, message_id: str | None = None):
-        self.message_id = message_id or str(uuid.uuid4())
-        self.text_block_id = str(uuid.uuid4())
-        self.has_started_text = False
-        self.has_ended_text = False
-        self.current_tool_calls: dict[str, dict[str, str]] = {}
-        self.task_id: str | None = None
-        self.step_open = False
-
-    def set_task_id(self, task_id: str) -> None:
-        """Set the task ID for reconnection support."""
-        self.task_id = task_id
-
-    def convert_message(self, sdk_message: Message) -> list[StreamBaseResponse]:
-        """Convert a single SDK message to Vercel AI SDK format."""
-        responses: list[StreamBaseResponse] = []
-
-        if isinstance(sdk_message, SystemMessage):
-            if sdk_message.subtype == "init":
-                responses.append(
-                    StreamStart(messageId=self.message_id, taskId=self.task_id)
-                )
-                # Open the first step (matches non-SDK: StreamStart then StreamStartStep)
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-        elif isinstance(sdk_message, AssistantMessage):
-            # After tool results, the SDK sends a new AssistantMessage for the
-            # next LLM turn. Open a new step if the previous one was closed.
-            if not self.step_open:
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-            for block in sdk_message.content:
-                if isinstance(block, TextBlock):
-                    if block.text:
-                        self._ensure_text_started(responses)
-                        responses.append(
-                            StreamTextDelta(id=self.text_block_id, delta=block.text)
-                        )
-
-                elif isinstance(block, ToolUseBlock):
-                    self._end_text_if_open(responses)
-
-                    # Strip MCP prefix so frontend sees "find_block"
-                    # instead of "mcp__copilot__find_block".
-                    tool_name = block.name.removeprefix(MCP_TOOL_PREFIX)
-
-                    responses.append(
-                        StreamToolInputStart(toolCallId=block.id, toolName=tool_name)
-                    )
-                    responses.append(
-                        StreamToolInputAvailable(
-                            toolCallId=block.id,
-                            toolName=tool_name,
-                            input=block.input,
-                        )
-                    )
-                    self.current_tool_calls[block.id] = {"name": tool_name}
-
-        elif isinstance(sdk_message, UserMessage):
-            # UserMessage carries tool results back from tool execution.
-            content = sdk_message.content
-            blocks = content if isinstance(content, list) else []
-            for block in blocks:
-                if isinstance(block, ToolResultBlock) and block.tool_use_id:
-                    tool_info = self.current_tool_calls.get(block.tool_use_id, {})
-                    tool_name = tool_info.get("name", "unknown")
-
-                    # Prefer the stashed full output over the SDK's
-                    # (potentially truncated) ToolResultBlock content.
-                    # The SDK truncates large results, writing them to disk,
-                    # which breaks frontend widget parsing.
-                    output = pop_pending_tool_output(tool_name) or (
-                        _extract_tool_output(block.content)
-                    )
-
-                    responses.append(
-                        StreamToolOutputAvailable(
-                            toolCallId=block.tool_use_id,
-                            toolName=tool_name,
-                            output=output,
-                            success=not (block.is_error or False),
-                        )
-                    )
-
-            # Close the current step after tool results — the next
-            # AssistantMessage will open a new step for the continuation.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-        elif isinstance(sdk_message, ResultMessage):
-            self._end_text_if_open(responses)
-            # Close the step before finishing.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-            if sdk_message.subtype == "success":
-                responses.append(StreamFinish())
-            elif sdk_message.subtype in ("error", "error_during_execution"):
-                error_msg = getattr(sdk_message, "result", None) or "Unknown error"
-                responses.append(
-                    StreamError(errorText=str(error_msg), code="sdk_error")
-                )
-                responses.append(StreamFinish())
-            else:
-                logger.warning(
-                    f"Unexpected ResultMessage subtype: {sdk_message.subtype}"
-                )
-                responses.append(StreamFinish())
-
-        else:
-            logger.debug(f"Unhandled SDK message type: {type(sdk_message).__name__}")
-
-        return responses
-
-    def _ensure_text_started(self, responses: list[StreamBaseResponse]) -> None:
-        """Start (or restart) a text block if needed."""
-        if not self.has_started_text or self.has_ended_text:
-            if self.has_ended_text:
-                self.text_block_id = str(uuid.uuid4())
-                self.has_ended_text = False
-            responses.append(StreamTextStart(id=self.text_block_id))
-            self.has_started_text = True
-
-    def _end_text_if_open(self, responses: list[StreamBaseResponse]) -> None:
-        """End the current text block if one is open."""
-        if self.has_started_text and not self.has_ended_text:
-            responses.append(StreamTextEnd(id=self.text_block_id))
-            self.has_ended_text = True
-
-
-def _extract_tool_output(content: str | list[dict[str, str]] | None) -> str:
-    """Extract a string output from a ToolResultBlock's content field."""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts = [item.get("text", "") for item in content if item.get("type") == "text"]
-        if parts:
-            return "".join(parts)
-        try:
-            return json.dumps(content)
-        except (TypeError, ValueError):
-            return str(content)
-    if content is None:
-        return ""
-    try:
-        return json.dumps(content)
-    except (TypeError, ValueError):
-        return str(content)

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py

@@ -1,366 +0,0 @@
-"""Unit tests for the SDK response adapter."""
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-)
-
-from .response_adapter import SDKResponseAdapter
-from .tool_adapter import MCP_TOOL_PREFIX
-
-
-def _adapter() -> SDKResponseAdapter:
-    a = SDKResponseAdapter(message_id="msg-1")
-    a.set_task_id("task-1")
-    return a
-
-
-# -- SystemMessage -----------------------------------------------------------
-
-
-def test_system_init_emits_start_and_step():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="init", data={}))
-    assert len(results) == 2
-    assert isinstance(results[0], StreamStart)
-    assert results[0].messageId == "msg-1"
-    assert results[0].taskId == "task-1"
-    assert isinstance(results[1], StreamStartStep)
-
-
-def test_system_non_init_emits_nothing():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="other", data={}))
-    assert results == []
-
-
-# -- AssistantMessage with TextBlock -----------------------------------------
-
-
-def test_text_block_emits_step_start_and_delta():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="hello")], model="test")
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "hello"
-
-
-def test_empty_text_block_emits_only_step():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="")], model="test")
-    results = adapter.convert_message(msg)
-    # Empty text skipped, but step still opens
-    assert len(results) == 1
-    assert isinstance(results[0], StreamStartStep)
-
-
-def test_multiple_text_deltas_reuse_block_id():
-    adapter = _adapter()
-    msg1 = AssistantMessage(content=[TextBlock(text="a")], model="test")
-    msg2 = AssistantMessage(content=[TextBlock(text="b")], model="test")
-    r1 = adapter.convert_message(msg1)
-    r2 = adapter.convert_message(msg2)
-    # First gets step+start+delta, second only delta (block & step already started)
-    assert len(r1) == 3
-    assert isinstance(r1[0], StreamStartStep)
-    assert isinstance(r1[1], StreamTextStart)
-    assert len(r2) == 1
-    assert isinstance(r2[0], StreamTextDelta)
-    assert r1[1].id == r2[0].id  # same block ID
-
-
-# -- AssistantMessage with ToolUseBlock --------------------------------------
-
-
-def test_tool_use_emits_input_start_and_available():
-    """Tool names arrive with MCP prefix and should be stripped for the frontend."""
-    adapter = _adapter()
-    msg = AssistantMessage(
-        content=[
-            ToolUseBlock(
-                id="tool-1",
-                name=f"{MCP_TOOL_PREFIX}find_agent",
-                input={"q": "x"},
-            )
-        ],
-        model="test",
-    )
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamToolInputStart)
-    assert results[1].toolCallId == "tool-1"
-    assert results[1].toolName == "find_agent"  # prefix stripped
-    assert isinstance(results[2], StreamToolInputAvailable)
-    assert results[2].toolName == "find_agent"  # prefix stripped
-    assert results[2].input == {"q": "x"}
-
-
-def test_text_then_tool_ends_text_block():
-    adapter = _adapter()
-    text_msg = AssistantMessage(content=[TextBlock(text="thinking...")], model="test")
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-        model="test",
-    )
-    adapter.convert_message(text_msg)  # opens step + text
-    results = adapter.convert_message(tool_msg)
-    # Step already open, so: TextEnd, ToolInputStart, ToolInputAvailable
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamToolInputStart)
-
-
-# -- UserMessage with ToolResultBlock ----------------------------------------
-
-
-def test_tool_result_emits_output_and_finish_step():
-    adapter = _adapter()
-    # First register the tool call (opens step) — SDK sends prefixed name
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}find_agent", input={})],
-        model="test",
-    )
-    adapter.convert_message(tool_msg)
-
-    # Now send tool result
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="found 3 agents")]
-    )
-    results = adapter.convert_message(result_msg)
-    assert len(results) == 2
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].toolCallId == "t1"
-    assert results[0].toolName == "find_agent"  # prefix stripped
-    assert results[0].output == "found 3 agents"
-    assert results[0].success is True
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_error():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[
-                ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}run_agent", input={})
-            ],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="timeout", is_error=True)]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].success is False
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_list_content():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[
-            ToolResultBlock(
-                tool_use_id="t1",
-                content=[
-                    {"type": "text", "text": "line1"},
-                    {"type": "text", "text": "line2"},
-                ],
-            )
-        ]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].output == "line1line2"
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_string_user_message_ignored():
-    """A plain string UserMessage (not tool results) produces no output."""
-    adapter = _adapter()
-    results = adapter.convert_message(UserMessage(content="hello"))
-    assert results == []
-
-
-# -- ResultMessage -----------------------------------------------------------
-
-
-def test_result_success_emits_finish_step_and_finish():
-    adapter = _adapter()
-    # Start some text first (opens step)
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="done")], model="test")
-    )
-    msg = ResultMessage(
-        subtype="success",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=False,
-        num_turns=1,
-        session_id="s1",
-    )
-    results = adapter.convert_message(msg)
-    # TextEnd + FinishStep + StreamFinish
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamFinishStep)
-    assert isinstance(results[2], StreamFinish)
-
-
-def test_result_error_emits_error_and_finish():
-    adapter = _adapter()
-    msg = ResultMessage(
-        subtype="error",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=True,
-        num_turns=0,
-        session_id="s1",
-        result="API rate limited",
-    )
-    results = adapter.convert_message(msg)
-    # No step was open, so no FinishStep — just Error + Finish
-    assert len(results) == 2
-    assert isinstance(results[0], StreamError)
-    assert "API rate limited" in results[0].errorText
-    assert isinstance(results[1], StreamFinish)
-
-
-# -- Text after tools (new block ID) ----------------------------------------
-
-
-def test_text_after_tool_gets_new_block_id():
-    adapter = _adapter()
-    # Text -> Tool -> ToolResult -> Text should get a new text block ID and step
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="before")], model="test")
-    )
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    # Send tool result (closes step)
-    adapter.convert_message(
-        UserMessage(content=[ToolResultBlock(tool_use_id="t1", content="ok")])
-    )
-    results = adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="after")], model="test")
-    )
-    # Should get StreamStartStep (new step) + StreamTextStart (new block) + StreamTextDelta
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "after"
-
-
-# -- Full conversation flow --------------------------------------------------
-
-
-def test_full_conversation_flow():
-    """Simulate a complete conversation: init -> text -> tool -> result -> text -> finish."""
-    adapter = _adapter()
-    all_responses: list[StreamBaseResponse] = []
-
-    # 1. Init
-    all_responses.extend(
-        adapter.convert_message(SystemMessage(subtype="init", data={}))
-    )
-    # 2. Assistant text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="Let me search")], model="test")
-        )
-    )
-    # 3. Tool use
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(
-                content=[
-                    ToolUseBlock(
-                        id="t1",
-                        name=f"{MCP_TOOL_PREFIX}find_agent",
-                        input={"query": "email"},
-                    )
-                ],
-                model="test",
-            )
-        )
-    )
-    # 4. Tool result
-    all_responses.extend(
-        adapter.convert_message(
-            UserMessage(
-                content=[ToolResultBlock(tool_use_id="t1", content="Found 2 agents")]
-            )
-        )
-    )
-    # 5. More text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="I found 2")], model="test")
-        )
-    )
-    # 6. Result
-    all_responses.extend(
-        adapter.convert_message(
-            ResultMessage(
-                subtype="success",
-                duration_ms=500,
-                duration_api_ms=400,
-                is_error=False,
-                num_turns=2,
-                session_id="s1",
-            )
-        )
-    )
-
-    types = [type(r).__name__ for r in all_responses]
-    assert types == [
-        "StreamStart",
-        "StreamStartStep",  # step 1: text + tool call
-        "StreamTextStart",
-        "StreamTextDelta",  # "Let me search"
-        "StreamTextEnd",  # closed before tool
-        "StreamToolInputStart",
-        "StreamToolInputAvailable",
-        "StreamToolOutputAvailable",  # tool result
-        "StreamFinishStep",  # step 1 closed after tool result
-        "StreamStartStep",  # step 2: continuation text
-        "StreamTextStart",  # new block after tool
-        "StreamTextDelta",  # "I found 2"
-        "StreamTextEnd",  # closed by result
-        "StreamFinishStep",  # step 2 closed
-        "StreamFinish",
-    ]

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py

@@ -1,335 +0,0 @@
-"""Security hooks for Claude Agent SDK integration.
-
-This module provides security hooks that validate tool calls before execution,
-ensuring multi-user isolation and preventing unauthorized operations.
-"""
-
-import json
-import logging
-import os
-import re
-from collections.abc import Callable
-from typing import Any, cast
-
-from backend.api.features.chat.sdk.tool_adapter import MCP_TOOL_PREFIX
-
-logger = logging.getLogger(__name__)
-
-# Tools that are blocked entirely (CLI/system access).
-# "Bash" (capital) is the SDK built-in — it's NOT in allowed_tools but blocked
-# here as defence-in-depth.  The agent uses mcp__copilot__bash_exec instead,
-# which has kernel-level network isolation (unshare --net).
-BLOCKED_TOOLS = {
-    "Bash",
-    "bash",
-    "shell",
-    "exec",
-    "terminal",
-    "command",
-}
-
-# Tools allowed only when their path argument stays within the SDK workspace.
-# The SDK uses these to handle oversized tool results (writes to tool-results/
-# files, then reads them back) and for workspace file operations.
-WORKSPACE_SCOPED_TOOLS = {"Read", "Write", "Edit", "Glob", "Grep"}
-
-# Dangerous patterns in tool inputs
-DANGEROUS_PATTERNS = [
-    r"sudo",
-    r"rm\s+-rf",
-    r"dd\s+if=",
-    r"/etc/passwd",
-    r"/etc/shadow",
-    r"chmod\s+777",
-    r"curl\s+.*\|.*sh",
-    r"wget\s+.*\|.*sh",
-    r"eval\s*\(",
-    r"exec\s*\(",
-    r"__import__",
-    r"os\.system",
-    r"subprocess",
-]
-
-
-def _deny(reason: str) -> dict[str, Any]:
-    """Return a hook denial response."""
-    return {
-        "hookSpecificOutput": {
-            "hookEventName": "PreToolUse",
-            "permissionDecision": "deny",
-            "permissionDecisionReason": reason,
-        }
-    }
-
-
-def _validate_workspace_path(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate that a workspace-scoped tool only accesses allowed paths.
-
-    Allowed directories:
-    - The SDK working directory (``/tmp/copilot-<session>/``)
-    - The SDK tool-results directory (``~/.claude/projects/…/tool-results/``)
-    """
-    path = tool_input.get("file_path") or tool_input.get("path") or ""
-    if not path:
-        # Glob/Grep without a path default to cwd which is already sandboxed
-        return {}
-
-    # Resolve relative paths against sdk_cwd (the SDK sets cwd so the LLM
-    # naturally uses relative paths like "test.txt" instead of absolute ones).
-    # Tilde paths (~/) are home-dir references, not relative — expand first.
-    if path.startswith("~"):
-        resolved = os.path.realpath(os.path.expanduser(path))
-    elif not os.path.isabs(path) and sdk_cwd:
-        resolved = os.path.realpath(os.path.join(sdk_cwd, path))
-    else:
-        resolved = os.path.realpath(path)
-
-    # Allow access within the SDK working directory
-    if sdk_cwd:
-        norm_cwd = os.path.realpath(sdk_cwd)
-        if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-            return {}
-
-    # Allow access to ~/.claude/projects/*/tool-results/ (big tool results)
-    claude_dir = os.path.realpath(os.path.expanduser("~/.claude/projects"))
-    tool_results_seg = os.sep + "tool-results" + os.sep
-    if resolved.startswith(claude_dir + os.sep) and tool_results_seg in resolved:
-        return {}
-
-    logger.warning(
-        f"Blocked {tool_name} outside workspace: {path} (resolved={resolved})"
-    )
-    workspace_hint = f" Allowed workspace: {sdk_cwd}" if sdk_cwd else ""
-    return _deny(
-        f"[SECURITY] Tool '{tool_name}' can only access files within the workspace "
-        f"directory.{workspace_hint} "
-        "This is enforced by the platform and cannot be bypassed."
-    )
-
-
-def _validate_tool_access(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Validate that a tool call is allowed.
-
-    Returns:
-        Empty dict to allow, or dict with hookSpecificOutput to deny
-    """
-    # Block forbidden tools
-    if tool_name in BLOCKED_TOOLS:
-        logger.warning(f"Blocked tool access attempt: {tool_name}")
-        return _deny(
-            f"[SECURITY] Tool '{tool_name}' is blocked for security. "
-            "This is enforced by the platform and cannot be bypassed. "
-            "Use the CoPilot-specific MCP tools instead."
-        )
-
-    # Workspace-scoped tools: allowed only within the SDK workspace directory
-    if tool_name in WORKSPACE_SCOPED_TOOLS:
-        return _validate_workspace_path(tool_name, tool_input, sdk_cwd)
-
-    # Check for dangerous patterns in tool input
-    # Use json.dumps for predictable format (str() produces Python repr)
-    input_str = json.dumps(tool_input) if tool_input else ""
-
-    for pattern in DANGEROUS_PATTERNS:
-        if re.search(pattern, input_str, re.IGNORECASE):
-            logger.warning(
-                f"Blocked dangerous pattern in tool input: {pattern} in {tool_name}"
-            )
-            return _deny(
-                "[SECURITY] Input contains a blocked pattern. "
-                "This is enforced by the platform and cannot be bypassed."
-            )
-
-    return {}
-
-
-def _validate_user_isolation(
-    tool_name: str, tool_input: dict[str, Any], user_id: str | None
-) -> dict[str, Any]:
-    """Validate that tool calls respect user isolation."""
-    # For workspace file tools, ensure path doesn't escape
-    if "workspace" in tool_name.lower():
-        path = tool_input.get("path", "") or tool_input.get("file_path", "")
-        if path:
-            # Check for path traversal
-            if ".." in path or path.startswith("/"):
-                logger.warning(
-                    f"Blocked path traversal attempt: {path} by user {user_id}"
-                )
-                return {
-                    "hookSpecificOutput": {
-                        "hookEventName": "PreToolUse",
-                        "permissionDecision": "deny",
-                        "permissionDecisionReason": "Path traversal not allowed",
-                    }
-                }
-
-    return {}
-
-
-def create_security_hooks(
-    user_id: str | None,
-    sdk_cwd: str | None = None,
-    max_subtasks: int = 3,
-    on_stop: Callable[[str, str], None] | None = None,
-) -> dict[str, Any]:
-    """Create the security hooks configuration for Claude Agent SDK.
-
-    Includes security validation and observability hooks:
-    - PreToolUse: Security validation before tool execution
-    - PostToolUse: Log successful tool executions
-    - PostToolUseFailure: Log and handle failed tool executions
-    - PreCompact: Log context compaction events (SDK handles compaction automatically)
-    - Stop: Capture transcript path for stateless resume (when *on_stop* is provided)
-
-    Args:
-        user_id: Current user ID for isolation validation
-        sdk_cwd: SDK working directory for workspace-scoped tool validation
-        max_subtasks: Maximum Task (sub-agent) spawns allowed per session
-        on_stop: Callback ``(transcript_path, sdk_session_id)`` invoked when
-            the SDK finishes processing — used to read the JSONL transcript
-            before the CLI process exits.
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        # Per-session counter for Task sub-agent spawns
-        task_spawn_count = 0
-
-        async def pre_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Combined pre-tool-use validation hook."""
-            nonlocal task_spawn_count
-            _ = context  # unused but required by signature
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            tool_input = cast(dict[str, Any], input_data.get("tool_input", {}))
-
-            # Rate-limit Task (sub-agent) spawns per session
-            if tool_name == "Task":
-                task_spawn_count += 1
-                if task_spawn_count > max_subtasks:
-                    logger.warning(
-                        f"[SDK] Task limit reached ({max_subtasks}), user={user_id}"
-                    )
-                    return cast(
-                        SyncHookJSONOutput,
-                        _deny(
-                            f"Maximum {max_subtasks} sub-tasks per session. "
-                            "Please continue in the main conversation."
-                        ),
-                    )
-
-            # Strip MCP prefix for consistent validation
-            is_copilot_tool = tool_name.startswith(MCP_TOOL_PREFIX)
-            clean_name = tool_name.removeprefix(MCP_TOOL_PREFIX)
-
-            # Only block non-CoPilot tools; our MCP-registered tools
-            # (including Read for oversized results) are already sandboxed.
-            if not is_copilot_tool:
-                result = _validate_tool_access(clean_name, tool_input, sdk_cwd)
-                if result:
-                    return cast(SyncHookJSONOutput, result)
-
-            # Validate user isolation
-            result = _validate_user_isolation(clean_name, tool_input, user_id)
-            if result:
-                return cast(SyncHookJSONOutput, result)
-
-            logger.debug(f"[SDK] Tool start: {tool_name}, user={user_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log successful tool executions for observability."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            logger.debug(f"[SDK] Tool success: {tool_name}, tool_use_id={tool_use_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_failure_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log failed tool executions for debugging."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            error = input_data.get("error", "Unknown error")
-            logger.warning(
-                f"[SDK] Tool failed: {tool_name}, error={error}, "
-                f"user={user_id}, tool_use_id={tool_use_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        async def pre_compact_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log when SDK triggers context compaction.
-
-            The SDK automatically compacts conversation history when it grows too large.
-            This hook provides visibility into when compaction happens.
-            """
-            _ = context, tool_use_id
-            trigger = input_data.get("trigger", "auto")
-            logger.info(
-                f"[SDK] Context compaction triggered: {trigger}, user={user_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        # --- Stop hook: capture transcript path for stateless resume ---
-        async def stop_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Capture transcript path when SDK finishes processing.
-
-            The Stop hook fires while the CLI process is still alive, giving us
-            a reliable window to read the JSONL transcript before SIGTERM.
-            """
-            _ = context, tool_use_id
-            transcript_path = cast(str, input_data.get("transcript_path", ""))
-            sdk_session_id = cast(str, input_data.get("session_id", ""))
-
-            if transcript_path and on_stop:
-                logger.info(
-                    f"[SDK] Stop hook: transcript_path={transcript_path}, "
-                    f"sdk_session_id={sdk_session_id[:12]}..."
-                )
-                on_stop(transcript_path, sdk_session_id)
-
-            return cast(SyncHookJSONOutput, {})
-
-        hooks: dict[str, Any] = {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[pre_tool_use_hook])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[post_tool_use_hook])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[post_tool_failure_hook])
-            ],
-            "PreCompact": [HookMatcher(matcher="*", hooks=[pre_compact_hook])],
-        }
-
-        if on_stop is not None:
-            hooks["Stop"] = [HookMatcher(matcher=None, hooks=[stop_hook])]
-
-        return hooks
-    except ImportError:
-        # Fallback for when SDK isn't available - return empty hooks
-        logger.warning("claude-agent-sdk not available, security hooks disabled")
-        return {}

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks_test.py

@@ -1,165 +0,0 @@
-"""Unit tests for SDK security hooks."""
-
-import os
-
-from .security_hooks import _validate_tool_access, _validate_user_isolation
-
-SDK_CWD = "/tmp/copilot-abc123"
-
-
-def _is_denied(result: dict) -> bool:
-    hook = result.get("hookSpecificOutput", {})
-    return hook.get("permissionDecision") == "deny"
-
-
-# -- Blocked tools -----------------------------------------------------------
-
-
-def test_blocked_tools_denied():
-    for tool in ("bash", "shell", "exec", "terminal", "command"):
-        result = _validate_tool_access(tool, {})
-        assert _is_denied(result), f"{tool} should be blocked"
-
-
-def test_unknown_tool_allowed():
-    result = _validate_tool_access("SomeCustomTool", {})
-    assert result == {}
-
-
-# -- Workspace-scoped tools --------------------------------------------------
-
-
-def test_read_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Read", {"file_path": f"{SDK_CWD}/file.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_write_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Write", {"file_path": f"{SDK_CWD}/output.json"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_edit_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Edit", {"file_path": f"{SDK_CWD}/src/main.py"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_glob_within_workspace_allowed():
-    result = _validate_tool_access("Glob", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_grep_within_workspace_allowed():
-    result = _validate_tool_access("Grep", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Read", {"file_path": "/etc/passwd"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_write_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Write", {"file_path": "/home/user/secrets.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_traversal_attack_denied():
-    result = _validate_tool_access(
-        "Read",
-        {"file_path": f"{SDK_CWD}/../../etc/passwd"},
-        sdk_cwd=SDK_CWD,
-    )
-    assert _is_denied(result)
-
-
-def test_no_path_allowed():
-    """Glob/Grep without a path argument defaults to cwd — should pass."""
-    result = _validate_tool_access("Glob", {}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_no_cwd_denies_absolute():
-    """If no sdk_cwd is set, absolute paths are denied."""
-    result = _validate_tool_access("Read", {"file_path": "/tmp/anything"})
-    assert _is_denied(result)
-
-
-# -- Tool-results directory --------------------------------------------------
-
-
-def test_read_tool_results_allowed():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/tool-results/12345.txt"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_claude_projects_without_tool_results_denied():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/settings.json"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Built-in Bash is blocked (use bash_exec MCP tool instead) ---------------
-
-
-def test_bash_builtin_always_blocked():
-    """SDK built-in Bash is blocked — bash_exec MCP tool with bubblewrap is used instead."""
-    result = _validate_tool_access("Bash", {"command": "echo hello"}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Dangerous patterns ------------------------------------------------------
-
-
-def test_dangerous_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"cmd": "sudo rm -rf /"})
-    assert _is_denied(result)
-
-
-def test_subprocess_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"code": "subprocess.run(...)"})
-    assert _is_denied(result)
-
-
-# -- User isolation ----------------------------------------------------------
-
-
-def test_workspace_path_traversal_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "../../../etc/shadow"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_absolute_path_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "/etc/passwd"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_normal_path_allowed():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "src/main.py"}, user_id="user-1"
-    )
-    assert result == {}
-
-
-def test_non_workspace_tool_passes_isolation():
-    result = _validate_user_isolation(
-        "find_agent", {"query": "email"}, user_id="user-1"
-    )
-    assert result == {}

autogpt_platform/backend/backend/api/features/chat/sdk/service.py

@@ -1,751 +0,0 @@
-"""Claude Agent SDK service layer for CoPilot chat completions."""
-
-import asyncio
-import json
-import logging
-import os
-import uuid
-from collections.abc import AsyncGenerator
-from dataclasses import dataclass
-from typing import Any
-
-from backend.util.exceptions import NotFoundError
-
-from .. import stream_registry
-from ..config import ChatConfig
-from ..model import (
-    ChatMessage,
-    ChatSession,
-    get_chat_session,
-    update_session_title,
-    upsert_chat_session,
-)
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamStart,
-    StreamTextDelta,
-    StreamToolInputAvailable,
-    StreamToolOutputAvailable,
-)
-from ..service import (
-    _build_system_prompt,
-    _execute_long_running_tool_with_streaming,
-    _generate_session_title,
-)
-from ..tools.models import OperationPendingResponse, OperationStartedResponse
-from ..tools.sandbox import WORKSPACE_PREFIX, make_session_path
-from ..tracking import track_user_message
-from .response_adapter import SDKResponseAdapter
-from .security_hooks import create_security_hooks
-from .tool_adapter import (
-    COPILOT_TOOL_NAMES,
-    LongRunningCallback,
-    create_copilot_mcp_server,
-    set_execution_context,
-)
-from .transcript import (
-    download_transcript,
-    read_transcript_file,
-    upload_transcript,
-    validate_transcript,
-    write_transcript_to_tempfile,
-)
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Set to hold background tasks to prevent garbage collection
-_background_tasks: set[asyncio.Task[Any]] = set()
-
-
-@dataclass
-class CapturedTranscript:
-    """Info captured by the SDK Stop hook for stateless --resume."""
-
-    path: str = ""
-    sdk_session_id: str = ""
-
-    @property
-    def available(self) -> bool:
-        return bool(self.path)
-
-
-_SDK_CWD_PREFIX = WORKSPACE_PREFIX
-
-# Appended to the system prompt to inform the agent about available tools.
-# The SDK built-in Bash is NOT available — use mcp__copilot__bash_exec instead,
-# which has kernel-level network isolation (unshare --net).
-_SDK_TOOL_SUPPLEMENT = """
-
-## Tool notes
-
-- The SDK built-in Bash tool is NOT available.  Use the `bash_exec` MCP tool
-  for shell commands — it runs in a network-isolated sandbox.
-- **Shared workspace**: The SDK Read/Write tools and `bash_exec` share the
-  same working directory. Files created by one are readable by the other.
-  These files are **ephemeral** — they exist only for the current session.
-- **Persistent storage**: Use `write_workspace_file` / `read_workspace_file`
-  for files that should persist across sessions (stored in cloud storage).
-- Long-running tools (create_agent, edit_agent, etc.) are handled
-  asynchronously.  You will receive an immediate response; the actual result
-  is delivered to the user via a background stream.
-"""
-
-
-def _build_long_running_callback(user_id: str | None) -> LongRunningCallback:
-    """Build a callback that delegates long-running tools to the non-SDK infrastructure.
-
-    Long-running tools (create_agent, edit_agent, etc.) are delegated to the
-    existing background infrastructure: stream_registry (Redis Streams),
-    database persistence, and SSE reconnection.  This means results survive
-    page refreshes / pod restarts, and the frontend shows the proper loading
-    widget with progress updates.
-
-    The returned callback matches the ``LongRunningCallback`` signature:
-    ``(tool_name, args, session) -> MCP response dict``.
-    """
-
-    async def _callback(
-        tool_name: str, args: dict[str, Any], session: ChatSession
-    ) -> dict[str, Any]:
-        operation_id = str(uuid.uuid4())
-        task_id = str(uuid.uuid4())
-        tool_call_id = f"sdk-{uuid.uuid4().hex[:12]}"
-        session_id = session.session_id
-
-        # --- Build user-friendly messages (matches non-SDK service) ---
-        if tool_name == "create_agent":
-            desc = args.get("description", "")
-            desc_preview = (desc[:100] + "...") if len(desc) > 100 else desc
-            pending_msg = (
-                f"Creating your agent: {desc_preview}"
-                if desc_preview
-                else "Creating agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent creation started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        elif tool_name == "edit_agent":
-            changes = args.get("changes", "")
-            changes_preview = (changes[:100] + "...") if len(changes) > 100 else changes
-            pending_msg = (
-                f"Editing agent: {changes_preview}"
-                if changes_preview
-                else "Editing agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent edit started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        else:
-            pending_msg = f"Running {tool_name}... This may take a few minutes."
-            started_msg = (
-                f"{tool_name} started. You can close this tab - "
-                "check back in a few minutes."
-            )
-
-        # --- Register task in Redis for SSE reconnection ---
-        await stream_registry.create_task(
-            task_id=task_id,
-            session_id=session_id,
-            user_id=user_id,
-            tool_call_id=tool_call_id,
-            tool_name=tool_name,
-            operation_id=operation_id,
-        )
-
-        # --- Save OperationPendingResponse to chat history ---
-        pending_message = ChatMessage(
-            role="tool",
-            content=OperationPendingResponse(
-                message=pending_msg,
-                operation_id=operation_id,
-                tool_name=tool_name,
-            ).model_dump_json(),
-            tool_call_id=tool_call_id,
-        )
-        session.messages.append(pending_message)
-        await upsert_chat_session(session)
-
-        # --- Spawn background task (reuses non-SDK infrastructure) ---
-        bg_task = asyncio.create_task(
-            _execute_long_running_tool_with_streaming(
-                tool_name=tool_name,
-                parameters=args,
-                tool_call_id=tool_call_id,
-                operation_id=operation_id,
-                task_id=task_id,
-                session_id=session_id,
-                user_id=user_id,
-            )
-        )
-        _background_tasks.add(bg_task)
-        bg_task.add_done_callback(_background_tasks.discard)
-        await stream_registry.set_task_asyncio_task(task_id, bg_task)
-
-        logger.info(
-            f"[SDK] Long-running tool {tool_name} delegated to background "
-            f"(operation_id={operation_id}, task_id={task_id})"
-        )
-
-        # --- Return OperationStartedResponse as MCP tool result ---
-        # This flows through SDK → response adapter → frontend, triggering
-        # the loading widget with SSE reconnection support.
-        started_json = OperationStartedResponse(
-            message=started_msg,
-            operation_id=operation_id,
-            tool_name=tool_name,
-            task_id=task_id,
-        ).model_dump_json()
-
-        return {
-            "content": [{"type": "text", "text": started_json}],
-            "isError": False,
-        }
-
-    return _callback
-
-
-def _resolve_sdk_model() -> str | None:
-    """Resolve the model name for the Claude Agent SDK CLI.
-
-    Uses ``config.claude_agent_model`` if set, otherwise derives from
-    ``config.model`` by stripping the OpenRouter provider prefix (e.g.,
-    ``"anthropic/claude-opus-4.6"`` → ``"claude-opus-4.6"``).
-    """
-    if config.claude_agent_model:
-        return config.claude_agent_model
-    model = config.model
-    if "/" in model:
-        return model.split("/", 1)[1]
-    return model
-
-
-def _build_sdk_env() -> dict[str, str]:
-    """Build env vars for the SDK CLI process.
-
-    Routes API calls through OpenRouter (or a custom base_url) using
-    the same ``config.api_key`` / ``config.base_url`` as the non-SDK path.
-    This gives per-call token and cost tracking on the OpenRouter dashboard.
-
-    Only overrides ``ANTHROPIC_API_KEY`` when a valid proxy URL and auth
-    token are both present — otherwise returns an empty dict so the SDK
-    falls back to its default credentials.
-    """
-    env: dict[str, str] = {}
-    if config.api_key and config.base_url:
-        # Strip /v1 suffix — SDK expects the base URL without a version path
-        base = config.base_url.rstrip("/")
-        if base.endswith("/v1"):
-            base = base[:-3]
-        if not base or not base.startswith("http"):
-            # Invalid base_url — don't override SDK defaults
-            return env
-        env["ANTHROPIC_BASE_URL"] = base
-        env["ANTHROPIC_AUTH_TOKEN"] = config.api_key
-        # Must be explicitly empty so the CLI uses AUTH_TOKEN instead
-        env["ANTHROPIC_API_KEY"] = ""
-    return env
-
-
-def _make_sdk_cwd(session_id: str) -> str:
-    """Create a safe, session-specific working directory path.
-
-    Delegates to :func:`~backend.api.features.chat.tools.sandbox.make_session_path`
-    (single source of truth for path sanitization) and adds a defence-in-depth
-    assertion.
-    """
-    cwd = make_session_path(session_id)
-    # Defence-in-depth: normpath + startswith is a CodeQL-recognised sanitizer
-    cwd = os.path.normpath(cwd)
-    if not cwd.startswith(_SDK_CWD_PREFIX):
-        raise ValueError(f"SDK cwd escaped prefix: {cwd}")
-    return cwd
-
-
-def _cleanup_sdk_tool_results(cwd: str) -> None:
-    """Remove SDK tool-result files for a specific session working directory.
-
-    The SDK creates tool-result files under ~/.claude/projects/<encoded-cwd>/tool-results/.
-    We clean only the specific cwd's results to avoid race conditions between
-    concurrent sessions.
-
-    Security: cwd MUST be created by _make_sdk_cwd() which sanitizes session_id.
-    """
-    import shutil
-
-    # Validate cwd is under the expected prefix
-    normalized = os.path.normpath(cwd)
-    if not normalized.startswith(_SDK_CWD_PREFIX):
-        logger.warning(f"[SDK] Rejecting cleanup for path outside workspace: {cwd}")
-        return
-
-    # SDK encodes the cwd path by replacing '/' with '-'
-    encoded_cwd = normalized.replace("/", "-")
-
-    # Construct the project directory path (known-safe home expansion)
-    claude_projects = os.path.expanduser("~/.claude/projects")
-    project_dir = os.path.join(claude_projects, encoded_cwd)
-
-    # Security check 3: Validate project_dir is under ~/.claude/projects
-    project_dir = os.path.normpath(project_dir)
-    if not project_dir.startswith(claude_projects):
-        logger.warning(
-            f"[SDK] Rejecting cleanup for escaped project path: {project_dir}"
-        )
-        return
-
-    results_dir = os.path.join(project_dir, "tool-results")
-    if os.path.isdir(results_dir):
-        for filename in os.listdir(results_dir):
-            file_path = os.path.join(results_dir, filename)
-            try:
-                if os.path.isfile(file_path):
-                    os.remove(file_path)
-            except OSError:
-                pass
-
-    # Also clean up the temp cwd directory itself
-    try:
-        shutil.rmtree(normalized, ignore_errors=True)
-    except OSError:
-        pass
-
-
-async def _compress_conversation_history(
-    session: ChatSession,
-) -> list[ChatMessage]:
-    """Compress prior conversation messages if they exceed the token threshold.
-
-    Uses the shared compress_context() from prompt.py which supports:
-    - LLM summarization of old messages (keeps recent ones intact)
-    - Progressive content truncation as fallback
-    - Middle-out deletion as last resort
-
-    Returns the compressed prior messages (everything except the current message).
-    """
-    prior = session.messages[:-1]
-    if len(prior) < 2:
-        return prior
-
-    from backend.util.prompt import compress_context
-
-    # Convert ChatMessages to dicts for compress_context
-    messages_dict = []
-    for msg in prior:
-        msg_dict: dict[str, Any] = {"role": msg.role}
-        if msg.content:
-            msg_dict["content"] = msg.content
-        if msg.tool_calls:
-            msg_dict["tool_calls"] = msg.tool_calls
-        if msg.tool_call_id:
-            msg_dict["tool_call_id"] = msg.tool_call_id
-        messages_dict.append(msg_dict)
-
-    try:
-        import openai
-
-        async with openai.AsyncOpenAI(
-            api_key=config.api_key, base_url=config.base_url, timeout=30.0
-        ) as client:
-            result = await compress_context(
-                messages=messages_dict,
-                model=config.model,
-                client=client,
-            )
-    except Exception as e:
-        logger.warning(f"[SDK] Context compression with LLM failed: {e}")
-        # Fall back to truncation-only (no LLM summarization)
-        result = await compress_context(
-            messages=messages_dict,
-            model=config.model,
-            client=None,
-        )
-
-    if result.was_compacted:
-        logger.info(
-            f"[SDK] Context compacted: {result.original_token_count} -> "
-            f"{result.token_count} tokens "
-            f"({result.messages_summarized} summarized, "
-            f"{result.messages_dropped} dropped)"
-        )
-        # Convert compressed dicts back to ChatMessages
-        return [
-            ChatMessage(
-                role=m["role"],
-                content=m.get("content"),
-                tool_calls=m.get("tool_calls"),
-                tool_call_id=m.get("tool_call_id"),
-            )
-            for m in result.messages
-        ]
-
-    return prior
-
-
-def _format_conversation_context(messages: list[ChatMessage]) -> str | None:
-    """Format conversation messages into a context prefix for the user message.
-
-    Returns a string like:
-        <conversation_history>
-        User: hello
-        You responded: Hi! How can I help?
-        </conversation_history>
-
-    Returns None if there are no messages to format.
-    """
-    if not messages:
-        return None
-
-    lines: list[str] = []
-    for msg in messages:
-        if not msg.content:
-            continue
-        if msg.role == "user":
-            lines.append(f"User: {msg.content}")
-        elif msg.role == "assistant":
-            lines.append(f"You responded: {msg.content}")
-        # Skip tool messages — they're internal details
-
-    if not lines:
-        return None
-
-    return "<conversation_history>\n" + "\n".join(lines) + "\n</conversation_history>"
-
-
-async def stream_chat_completion_sdk(
-    session_id: str,
-    message: str | None = None,
-    tool_call_response: str | None = None,  # noqa: ARG001
-    is_user_message: bool = True,
-    user_id: str | None = None,
-    retry_count: int = 0,  # noqa: ARG001
-    session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # noqa: ARG001
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream chat completion using Claude Agent SDK.
-
-    Drop-in replacement for stream_chat_completion with improved reliability.
-    """
-
-    if session is None:
-        session = await get_chat_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(
-            f"Session {session_id} not found. Please create a new session first."
-        )
-
-    if message:
-        session.messages.append(
-            ChatMessage(
-                role="user" if is_user_message else "assistant", content=message
-            )
-        )
-        if is_user_message:
-            track_user_message(
-                user_id=user_id, session_id=session_id, message_length=len(message)
-            )
-
-    session = await upsert_chat_session(session)
-
-    # Generate title for new sessions (first user message)
-    if is_user_message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            first_message = user_messages[0].content or message or ""
-            if first_message:
-                task = asyncio.create_task(
-                    _update_title_async(session_id, first_message, user_id)
-                )
-                _background_tasks.add(task)
-                task.add_done_callback(_background_tasks.discard)
-
-    # Build system prompt (reuses non-SDK path with Langfuse support)
-    has_history = len(session.messages) > 1
-    system_prompt, _ = await _build_system_prompt(
-        user_id, has_conversation_history=has_history
-    )
-    system_prompt += _SDK_TOOL_SUPPLEMENT
-    message_id = str(uuid.uuid4())
-    task_id = str(uuid.uuid4())
-
-    yield StreamStart(messageId=message_id, taskId=task_id)
-
-    stream_completed = False
-    # Initialise sdk_cwd before the try so the finally can reference it
-    # even if _make_sdk_cwd raises (in that case it stays as "").
-    sdk_cwd = ""
-    use_resume = False
-
-    try:
-        # Use a session-specific temp dir to avoid cleanup race conditions
-        # between concurrent sessions.
-        sdk_cwd = _make_sdk_cwd(session_id)
-        os.makedirs(sdk_cwd, exist_ok=True)
-
-        set_execution_context(
-            user_id,
-            session,
-            long_running_callback=_build_long_running_callback(user_id),
-        )
-        try:
-            from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
-
-            # Fail fast when no API credentials are available at all
-            sdk_env = _build_sdk_env()
-            if not sdk_env and not os.environ.get("ANTHROPIC_API_KEY"):
-                raise RuntimeError(
-                    "No API key configured. Set OPEN_ROUTER_API_KEY "
-                    "(or CHAT_API_KEY) for OpenRouter routing, "
-                    "or ANTHROPIC_API_KEY for direct Anthropic access."
-                )
-
-            mcp_server = create_copilot_mcp_server()
-
-            sdk_model = _resolve_sdk_model()
-
-            # --- Transcript capture via Stop hook ---
-            captured_transcript = CapturedTranscript()
-
-            def _on_stop(transcript_path: str, sdk_session_id: str) -> None:
-                captured_transcript.path = transcript_path
-                captured_transcript.sdk_session_id = sdk_session_id
-
-            security_hooks = create_security_hooks(
-                user_id,
-                sdk_cwd=sdk_cwd,
-                max_subtasks=config.claude_agent_max_subtasks,
-                on_stop=_on_stop if config.claude_agent_use_resume else None,
-            )
-
-            # --- Resume strategy: download transcript from bucket ---
-            resume_file: str | None = None
-            use_resume = False
-
-            if config.claude_agent_use_resume and user_id and len(session.messages) > 1:
-                transcript_content = await download_transcript(user_id, session_id)
-                if transcript_content and validate_transcript(transcript_content):
-                    resume_file = write_transcript_to_tempfile(
-                        transcript_content, session_id, sdk_cwd
-                    )
-                    if resume_file:
-                        use_resume = True
-                        logger.info(
-                            f"[SDK] Using --resume with transcript "
-                            f"({len(transcript_content)} bytes)"
-                        )
-
-            sdk_options_kwargs: dict[str, Any] = {
-                "system_prompt": system_prompt,
-                "mcp_servers": {"copilot": mcp_server},
-                "allowed_tools": COPILOT_TOOL_NAMES,
-                "disallowed_tools": ["Bash"],
-                "hooks": security_hooks,
-                "cwd": sdk_cwd,
-                "max_buffer_size": config.claude_agent_max_buffer_size,
-            }
-            if sdk_env:
-                sdk_options_kwargs["model"] = sdk_model
-                sdk_options_kwargs["env"] = sdk_env
-            if use_resume and resume_file:
-                sdk_options_kwargs["resume"] = resume_file
-
-            options = ClaudeAgentOptions(**sdk_options_kwargs)  # type: ignore[arg-type]
-
-            adapter = SDKResponseAdapter(message_id=message_id)
-            adapter.set_task_id(task_id)
-
-            async with ClaudeSDKClient(options=options) as client:
-                current_message = message or ""
-                if not current_message and session.messages:
-                    last_user = [m for m in session.messages if m.role == "user"]
-                    if last_user:
-                        current_message = last_user[-1].content or ""
-
-                if not current_message.strip():
-                    yield StreamError(
-                        errorText="Message cannot be empty.",
-                        code="empty_prompt",
-                    )
-                    yield StreamFinish()
-                    return
-
-                # Build query: with --resume the CLI already has full
-                # context, so we only send the new message.  Without
-                # resume, compress history into a context prefix.
-                query_message = current_message
-                if not use_resume and len(session.messages) > 1:
-                    logger.warning(
-                        f"[SDK] Using compression fallback for session "
-                        f"{session_id} ({len(session.messages)} messages) — "
-                        f"no transcript available for --resume"
-                    )
-                    compressed = await _compress_conversation_history(session)
-                    history_context = _format_conversation_context(compressed)
-                    if history_context:
-                        query_message = (
-                            f"{history_context}\n\n"
-                            f"Now, the user says:\n{current_message}"
-                        )
-
-                logger.info(
-                    f"[SDK] Sending query ({len(session.messages)} msgs in session)"
-                )
-                logger.debug(f"[SDK] Query preview: {current_message[:80]!r}")
-                await client.query(query_message, session_id=session_id)
-
-                assistant_response = ChatMessage(role="assistant", content="")
-                accumulated_tool_calls: list[dict[str, Any]] = []
-                has_appended_assistant = False
-                has_tool_results = False
-
-                async for sdk_msg in client.receive_messages():
-                    logger.debug(
-                        f"[SDK] Received: {type(sdk_msg).__name__} "
-                        f"{getattr(sdk_msg, 'subtype', '')}"
-                    )
-                    for response in adapter.convert_message(sdk_msg):
-                        if isinstance(response, StreamStart):
-                            continue
-
-                        yield response
-
-                        if isinstance(response, StreamTextDelta):
-                            delta = response.delta or ""
-                            # After tool results, start a new assistant
-                            # message for the post-tool text.
-                            if has_tool_results and has_appended_assistant:
-                                assistant_response = ChatMessage(
-                                    role="assistant", content=delta
-                                )
-                                accumulated_tool_calls = []
-                                has_appended_assistant = False
-                                has_tool_results = False
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-                            else:
-                                assistant_response.content = (
-                                    assistant_response.content or ""
-                                ) + delta
-                                if not has_appended_assistant:
-                                    session.messages.append(assistant_response)
-                                    has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolInputAvailable):
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": response.toolCallId,
-                                    "type": "function",
-                                    "function": {
-                                        "name": response.toolName,
-                                        "arguments": json.dumps(response.input or {}),
-                                    },
-                                }
-                            )
-                            assistant_response.tool_calls = accumulated_tool_calls
-                            if not has_appended_assistant:
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolOutputAvailable):
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=(
-                                        response.output
-                                        if isinstance(response.output, str)
-                                        else str(response.output)
-                                    ),
-                                    tool_call_id=response.toolCallId,
-                                )
-                            )
-                            has_tool_results = True
-
-                        elif isinstance(response, StreamFinish):
-                            stream_completed = True
-
-                    if stream_completed:
-                        break
-
-                if (
-                    assistant_response.content or assistant_response.tool_calls
-                ) and not has_appended_assistant:
-                    session.messages.append(assistant_response)
-
-                # --- Capture transcript while CLI is still alive ---
-                # Must happen INSIDE async with: close() sends SIGTERM
-                # which kills the CLI before it can flush the JSONL.
-                if (
-                    config.claude_agent_use_resume
-                    and user_id
-                    and captured_transcript.available
-                ):
-                    # Give CLI time to flush JSONL writes before we read
-                    await asyncio.sleep(0.5)
-                    raw_transcript = read_transcript_file(captured_transcript.path)
-                    if raw_transcript:
-                        task = asyncio.create_task(
-                            _upload_transcript_bg(user_id, session_id, raw_transcript)
-                        )
-                        _background_tasks.add(task)
-                        task.add_done_callback(_background_tasks.discard)
-                    else:
-                        logger.debug("[SDK] Stop hook fired but transcript not usable")
-
-        except ImportError:
-            raise RuntimeError(
-                "claude-agent-sdk is not installed. "
-                "Disable SDK mode (CHAT_USE_CLAUDE_AGENT_SDK=false) "
-                "to use the OpenAI-compatible fallback."
-            )
-
-        await upsert_chat_session(session)
-        logger.debug(
-            f"[SDK] Session {session_id} saved with {len(session.messages)} messages"
-        )
-        if not stream_completed:
-            yield StreamFinish()
-
-    except Exception as e:
-        logger.error(f"[SDK] Error: {e}", exc_info=True)
-        try:
-            await upsert_chat_session(session)
-        except Exception as save_err:
-            logger.error(f"[SDK] Failed to save session on error: {save_err}")
-        yield StreamError(
-            errorText="An error occurred. Please try again.",
-            code="sdk_error",
-        )
-        yield StreamFinish()
-    finally:
-        if sdk_cwd:
-            _cleanup_sdk_tool_results(sdk_cwd)
-
-
-async def _upload_transcript_bg(
-    user_id: str, session_id: str, raw_content: str
-) -> None:
-    """Background task to strip progress entries and upload transcript."""
-    try:
-        await upload_transcript(user_id, session_id, raw_content)
-    except Exception as e:
-        logger.error(f"[SDK] Failed to upload transcript for {session_id}: {e}")
-
-
-async def _update_title_async(
-    session_id: str, message: str, user_id: str | None = None
-) -> None:
-    """Background task to update session title."""
-    try:
-        title = await _generate_session_title(
-            message, user_id=user_id, session_id=session_id
-        )
-        if title:
-            await update_session_title(session_id, title)
-            logger.debug(f"[SDK] Generated title for {session_id}: {title}")
-    except Exception as e:
-        logger.warning(f"[SDK] Failed to update session title: {e}")

autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py

@@ -1,325 +0,0 @@
-"""Tool adapter for wrapping existing CoPilot tools as Claude Agent SDK MCP tools.
-
-This module provides the adapter layer that converts existing BaseTool implementations
-into in-process MCP tools that can be used with the Claude Agent SDK.
-
-Long-running tools (``is_long_running=True``) are delegated to the non-SDK
-background infrastructure (stream_registry, Redis persistence, SSE reconnection)
-via a callback provided by the service layer.  This avoids wasteful SDK polling
-and makes results survive page refreshes.
-"""
-
-import json
-import logging
-import os
-import uuid
-from collections.abc import Awaitable, Callable
-from contextvars import ContextVar
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools import TOOL_REGISTRY
-from backend.api.features.chat.tools.base import BaseTool
-
-logger = logging.getLogger(__name__)
-
-# Allowed base directory for the Read tool (SDK saves oversized tool results here).
-# Restricted to ~/.claude/projects/ and further validated to require "tool-results"
-# in the path — prevents reading settings, credentials, or other sensitive files.
-_SDK_PROJECTS_DIR = os.path.expanduser("~/.claude/projects/")
-
-# MCP server naming - the SDK prefixes tool names as "mcp__{server_name}__{tool}"
-MCP_SERVER_NAME = "copilot"
-MCP_TOOL_PREFIX = f"mcp__{MCP_SERVER_NAME}__"
-
-# Context variables to pass user/session info to tool execution
-_current_user_id: ContextVar[str | None] = ContextVar("current_user_id", default=None)
-_current_session: ContextVar[ChatSession | None] = ContextVar(
-    "current_session", default=None
-)
-# Stash for MCP tool outputs before the SDK potentially truncates them.
-# Keyed by tool_name → full output string. Consumed (popped) by the
-# response adapter when it builds StreamToolOutputAvailable.
-_pending_tool_outputs: ContextVar[dict[str, str]] = ContextVar(
-    "pending_tool_outputs", default=None  # type: ignore[arg-type]
-)
-
-# Callback type for delegating long-running tools to the non-SDK infrastructure.
-# Args: (tool_name, arguments, session) → MCP-formatted response dict.
-LongRunningCallback = Callable[
-    [str, dict[str, Any], ChatSession], Awaitable[dict[str, Any]]
-]
-
-# ContextVar so the service layer can inject the callback per-request.
-_long_running_callback: ContextVar[LongRunningCallback | None] = ContextVar(
-    "long_running_callback", default=None
-)
-
-
-def set_execution_context(
-    user_id: str | None,
-    session: ChatSession,
-    long_running_callback: LongRunningCallback | None = None,
-) -> None:
-    """Set the execution context for tool calls.
-
-    This must be called before streaming begins to ensure tools have access
-    to user_id and session information.
-
-    Args:
-        user_id: Current user's ID.
-        session: Current chat session.
-        long_running_callback: Optional callback to delegate long-running tools
-            to the non-SDK background infrastructure (stream_registry + Redis).
-    """
-    _current_user_id.set(user_id)
-    _current_session.set(session)
-    _pending_tool_outputs.set({})
-    _long_running_callback.set(long_running_callback)
-
-
-def get_execution_context() -> tuple[str | None, ChatSession | None]:
-    """Get the current execution context."""
-    return (
-        _current_user_id.get(),
-        _current_session.get(),
-    )
-
-
-def pop_pending_tool_output(tool_name: str) -> str | None:
-    """Pop and return the stashed full output for *tool_name*.
-
-    The SDK CLI may truncate large tool results (writing them to disk and
-    replacing the content with a file reference). This stash keeps the
-    original MCP output so the response adapter can forward it to the
-    frontend for proper widget rendering.
-
-    Returns ``None`` if nothing was stashed for *tool_name*.
-    """
-    pending = _pending_tool_outputs.get(None)
-    if pending is None:
-        return None
-    return pending.pop(tool_name, None)
-
-
-async def _execute_tool_sync(
-    base_tool: BaseTool,
-    user_id: str | None,
-    session: ChatSession,
-    args: dict[str, Any],
-) -> dict[str, Any]:
-    """Execute a tool synchronously and return MCP-formatted response."""
-    effective_id = f"sdk-{uuid.uuid4().hex[:12]}"
-    result = await base_tool.execute(
-        user_id=user_id,
-        session=session,
-        tool_call_id=effective_id,
-        **args,
-    )
-
-    text = (
-        result.output if isinstance(result.output, str) else json.dumps(result.output)
-    )
-
-    # Stash the full output before the SDK potentially truncates it.
-    pending = _pending_tool_outputs.get(None)
-    if pending is not None:
-        pending[base_tool.name] = text
-
-    return {
-        "content": [{"type": "text", "text": text}],
-        "isError": not result.success,
-    }
-
-
-def _mcp_error(message: str) -> dict[str, Any]:
-    return {
-        "content": [
-            {"type": "text", "text": json.dumps({"error": message, "type": "error"})}
-        ],
-        "isError": True,
-    }
-
-
-def create_tool_handler(base_tool: BaseTool):
-    """Create an async handler function for a BaseTool.
-
-    This wraps the existing BaseTool._execute method to be compatible
-    with the Claude Agent SDK MCP tool format.
-
-    Long-running tools (``is_long_running=True``) are delegated to the
-    non-SDK background infrastructure via a callback set in the execution
-    context.  The callback persists the operation in Redis (stream_registry)
-    so results survive page refreshes and pod restarts.
-    """
-
-    async def tool_handler(args: dict[str, Any]) -> dict[str, Any]:
-        """Execute the wrapped tool and return MCP-formatted response."""
-        user_id, session = get_execution_context()
-
-        if session is None:
-            return _mcp_error("No session context available")
-
-        # --- Long-running: delegate to non-SDK background infrastructure ---
-        if base_tool.is_long_running:
-            callback = _long_running_callback.get(None)
-            if callback:
-                try:
-                    return await callback(base_tool.name, args, session)
-                except Exception as e:
-                    logger.error(
-                        f"Long-running callback failed for {base_tool.name}: {e}",
-                        exc_info=True,
-                    )
-                    return _mcp_error(f"Failed to start {base_tool.name}: {e}")
-            # No callback — fall through to synchronous execution
-            logger.warning(
-                f"[SDK] No long-running callback for {base_tool.name}, "
-                f"executing synchronously (may block)"
-            )
-
-        # --- Normal (fast) tool: execute synchronously ---
-        try:
-            return await _execute_tool_sync(base_tool, user_id, session, args)
-        except Exception as e:
-            logger.error(f"Error executing tool {base_tool.name}: {e}", exc_info=True)
-            return _mcp_error(f"Failed to execute {base_tool.name}: {e}")
-
-    return tool_handler
-
-
-def _build_input_schema(base_tool: BaseTool) -> dict[str, Any]:
-    """Build a JSON Schema input schema for a tool."""
-    return {
-        "type": "object",
-        "properties": base_tool.parameters.get("properties", {}),
-        "required": base_tool.parameters.get("required", []),
-    }
-
-
-async def _read_file_handler(args: dict[str, Any]) -> dict[str, Any]:
-    """Read a file with optional offset/limit. Restricted to SDK working directory.
-
-    After reading, the file is deleted to prevent accumulation in long-running pods.
-    """
-    file_path = args.get("file_path", "")
-    offset = args.get("offset", 0)
-    limit = args.get("limit", 2000)
-
-    # Security: only allow reads under ~/.claude/projects/**/tool-results/
-    real_path = os.path.realpath(file_path)
-    if not real_path.startswith(_SDK_PROJECTS_DIR) or "tool-results" not in real_path:
-        return {
-            "content": [{"type": "text", "text": f"Access denied: {file_path}"}],
-            "isError": True,
-        }
-
-    try:
-        with open(real_path) as f:
-            lines = f.readlines()
-        selected = lines[offset : offset + limit]
-        content = "".join(selected)
-        # Clean up to prevent accumulation in long-running pods
-        try:
-            os.remove(real_path)
-        except OSError:
-            pass
-        return {"content": [{"type": "text", "text": content}], "isError": False}
-    except FileNotFoundError:
-        return {
-            "content": [{"type": "text", "text": f"File not found: {file_path}"}],
-            "isError": True,
-        }
-    except Exception as e:
-        return {
-            "content": [{"type": "text", "text": f"Error reading file: {e}"}],
-            "isError": True,
-        }
-
-
-_READ_TOOL_NAME = "Read"
-_READ_TOOL_DESCRIPTION = (
-    "Read a file from the local filesystem. "
-    "Use offset and limit to read specific line ranges for large files."
-)
-_READ_TOOL_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "file_path": {
-            "type": "string",
-            "description": "The absolute path to the file to read",
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Line number to start reading from (0-indexed). Default: 0",
-        },
-        "limit": {
-            "type": "integer",
-            "description": "Number of lines to read. Default: 2000",
-        },
-    },
-    "required": ["file_path"],
-}
-
-
-# Create the MCP server configuration
-def create_copilot_mcp_server():
-    """Create an in-process MCP server configuration for CoPilot tools.
-
-    This can be passed to ClaudeAgentOptions.mcp_servers.
-
-    Note: The actual SDK MCP server creation depends on the claude-agent-sdk
-    package being available. This function returns the configuration that
-    can be used with the SDK.
-    """
-    try:
-        from claude_agent_sdk import create_sdk_mcp_server, tool
-
-        # Create decorated tool functions
-        sdk_tools = []
-
-        for tool_name, base_tool in TOOL_REGISTRY.items():
-            handler = create_tool_handler(base_tool)
-            decorated = tool(
-                tool_name,
-                base_tool.description,
-                _build_input_schema(base_tool),
-            )(handler)
-            sdk_tools.append(decorated)
-
-        # Add the Read tool so the SDK can read back oversized tool results
-        read_tool = tool(
-            _READ_TOOL_NAME,
-            _READ_TOOL_DESCRIPTION,
-            _READ_TOOL_SCHEMA,
-        )(_read_file_handler)
-        sdk_tools.append(read_tool)
-
-        server = create_sdk_mcp_server(
-            name=MCP_SERVER_NAME,
-            version="1.0.0",
-            tools=sdk_tools,
-        )
-
-        return server
-
-    except ImportError:
-        # Let ImportError propagate so service.py handles the fallback
-        raise
-
-
-# SDK built-in tools allowed within the workspace directory.
-# Security hooks validate that file paths stay within sdk_cwd.
-# Bash is NOT included — use the sandboxed MCP bash_exec tool instead,
-# which provides kernel-level network isolation via unshare --net.
-# Task allows spawning sub-agents (rate-limited by security hooks).
-_SDK_BUILTIN_TOOLS = ["Read", "Write", "Edit", "Glob", "Grep", "Task"]
-
-# List of tool names for allowed_tools configuration
-# Include MCP tools, the MCP Read tool for oversized results,
-# and SDK built-in file tools for workspace operations.
-COPILOT_TOOL_NAMES = [
-    *[f"{MCP_TOOL_PREFIX}{name}" for name in TOOL_REGISTRY.keys()],
-    f"{MCP_TOOL_PREFIX}{_READ_TOOL_NAME}",
-    *_SDK_BUILTIN_TOOLS,
-]

autogpt_platform/backend/backend/api/features/chat/sdk/transcript.py

@@ -1,355 +0,0 @@
-"""JSONL transcript management for stateless multi-turn resume.
-
-The Claude Code CLI persists conversations as JSONL files (one JSON object per
-line).  When the SDK's ``Stop`` hook fires we read this file, strip bloat
-(progress entries, metadata), and upload the result to bucket storage.  On the
-next turn we download the transcript, write it to a temp file, and pass
-``--resume`` so the CLI can reconstruct the full conversation.
-
-Storage is handled via ``WorkspaceStorageBackend`` (GCS in prod, local
-filesystem for self-hosted) — no DB column needed.
-"""
-
-import json
-import logging
-import os
-import re
-
-logger = logging.getLogger(__name__)
-
-# UUIDs are hex + hyphens; strip everything else to prevent path injection.
-_SAFE_ID_RE = re.compile(r"[^0-9a-fA-F-]")
-
-# Entry types that can be safely removed from the transcript without breaking
-# the parentUuid conversation tree that ``--resume`` relies on.
-# - progress: UI progress ticks, no message content (avg 97KB for agent_progress)
-# - file-history-snapshot: undo tracking metadata
-# - queue-operation: internal queue bookkeeping
-# - summary: session summaries
-# - pr-link: PR link metadata
-STRIPPABLE_TYPES = frozenset(
-    {"progress", "file-history-snapshot", "queue-operation", "summary", "pr-link"}
-)
-
-# Workspace storage constants — deterministic path from session_id.
-TRANSCRIPT_STORAGE_PREFIX = "chat-transcripts"
-
-
-# ---------------------------------------------------------------------------
-# Progress stripping
-# ---------------------------------------------------------------------------
-
-
-def strip_progress_entries(content: str) -> str:
-    """Remove progress/metadata entries from a JSONL transcript.
-
-    Removes entries whose ``type`` is in ``STRIPPABLE_TYPES`` and reparents
-    any remaining child entries so the ``parentUuid`` chain stays intact.
-    Typically reduces transcript size by ~30%.
-    """
-    lines = content.strip().split("\n")
-
-    entries: list[dict] = []
-    for line in lines:
-        try:
-            entries.append(json.loads(line))
-        except json.JSONDecodeError:
-            # Keep unparseable lines as-is (safety)
-            entries.append({"_raw": line})
-
-    stripped_uuids: set[str] = set()
-    uuid_to_parent: dict[str, str] = {}
-    kept: list[dict] = []
-
-    for entry in entries:
-        if "_raw" in entry:
-            kept.append(entry)
-            continue
-        uid = entry.get("uuid", "")
-        parent = entry.get("parentUuid", "")
-        entry_type = entry.get("type", "")
-
-        if uid:
-            uuid_to_parent[uid] = parent
-
-        if entry_type in STRIPPABLE_TYPES:
-            if uid:
-                stripped_uuids.add(uid)
-        else:
-            kept.append(entry)
-
-    # Reparent: walk up chain through stripped entries to find surviving ancestor
-    for entry in kept:
-        if "_raw" in entry:
-            continue
-        parent = entry.get("parentUuid", "")
-        original_parent = parent
-        while parent in stripped_uuids:
-            parent = uuid_to_parent.get(parent, "")
-        if parent != original_parent:
-            entry["parentUuid"] = parent
-
-    result_lines: list[str] = []
-    for entry in kept:
-        if "_raw" in entry:
-            result_lines.append(entry["_raw"])
-        else:
-            result_lines.append(json.dumps(entry, separators=(",", ":")))
-
-    return "\n".join(result_lines) + "\n"
-
-
-# ---------------------------------------------------------------------------
-# Local file I/O (read from CLI's JSONL, write temp file for --resume)
-# ---------------------------------------------------------------------------
-
-
-def read_transcript_file(transcript_path: str) -> str | None:
-    """Read a JSONL transcript file from disk.
-
-    Returns the raw JSONL content, or ``None`` if the file is missing, empty,
-    or only contains metadata (≤2 lines with no conversation messages).
-    """
-    if not transcript_path or not os.path.isfile(transcript_path):
-        logger.debug(f"[Transcript] File not found: {transcript_path}")
-        return None
-
-    try:
-        with open(transcript_path) as f:
-            content = f.read()
-
-        if not content.strip():
-            logger.debug(f"[Transcript] Empty file: {transcript_path}")
-            return None
-
-        lines = content.strip().split("\n")
-        if len(lines) < 2:
-            # Metadata-only files have 1 line (single queue-operation or snapshot).
-            logger.debug(
-                f"[Transcript] Too few lines ({len(lines)}): {transcript_path}"
-            )
-            return None
-
-        # Quick structural validation — parse first and last lines.
-        json.loads(lines[0])
-        json.loads(lines[-1])
-
-        logger.info(
-            f"[Transcript] Read {len(lines)} lines, "
-            f"{len(content)} bytes from {transcript_path}"
-        )
-        return content
-
-    except (json.JSONDecodeError, OSError) as e:
-        logger.warning(f"[Transcript] Failed to read {transcript_path}: {e}")
-        return None
-
-
-def _sanitize_id(raw_id: str, max_len: int = 36) -> str:
-    """Sanitize an ID for safe use in file paths.
-
-    Session/user IDs are expected to be UUIDs (hex + hyphens).  Strip
-    everything else and truncate to *max_len* so the result cannot introduce
-    path separators or other special characters.
-    """
-    cleaned = _SAFE_ID_RE.sub("", raw_id or "")[:max_len]
-    return cleaned or "unknown"
-
-
-_SAFE_CWD_PREFIX = os.path.realpath("/tmp/copilot-")
-
-
-def write_transcript_to_tempfile(
-    transcript_content: str,
-    session_id: str,
-    cwd: str,
-) -> str | None:
-    """Write JSONL transcript to a temp file inside *cwd* for ``--resume``.
-
-    The file lives in the session working directory so it is cleaned up
-    automatically when the session ends.
-
-    Returns the absolute path to the file, or ``None`` on failure.
-    """
-    # Validate cwd is under the expected sandbox prefix (CodeQL sanitizer).
-    real_cwd = os.path.realpath(cwd)
-    if not real_cwd.startswith(_SAFE_CWD_PREFIX):
-        logger.warning(f"[Transcript] cwd outside sandbox: {cwd}")
-        return None
-
-    try:
-        os.makedirs(real_cwd, exist_ok=True)
-        safe_id = _sanitize_id(session_id, max_len=8)
-        jsonl_path = os.path.realpath(
-            os.path.join(real_cwd, f"transcript-{safe_id}.jsonl")
-        )
-        if not jsonl_path.startswith(real_cwd):
-            logger.warning(f"[Transcript] Path escaped cwd: {jsonl_path}")
-            return None
-
-        with open(jsonl_path, "w") as f:
-            f.write(transcript_content)
-
-        logger.info(f"[Transcript] Wrote resume file: {jsonl_path}")
-        return jsonl_path
-
-    except OSError as e:
-        logger.warning(f"[Transcript] Failed to write resume file: {e}")
-        return None
-
-
-def validate_transcript(content: str | None) -> bool:
-    """Check that a transcript has actual conversation messages.
-
-    A valid transcript for resume needs at least one user message and one
-    assistant message (not just queue-operation / file-history-snapshot
-    metadata).
-    """
-    if not content or not content.strip():
-        return False
-
-    lines = content.strip().split("\n")
-    if len(lines) < 2:
-        return False
-
-    has_user = False
-    has_assistant = False
-
-    for line in lines:
-        try:
-            entry = json.loads(line)
-            msg_type = entry.get("type")
-            if msg_type == "user":
-                has_user = True
-            elif msg_type == "assistant":
-                has_assistant = True
-        except json.JSONDecodeError:
-            return False
-
-    return has_user and has_assistant
-
-
-# ---------------------------------------------------------------------------
-# Bucket storage (GCS / local via WorkspaceStorageBackend)
-# ---------------------------------------------------------------------------
-
-
-def _storage_path_parts(user_id: str, session_id: str) -> tuple[str, str, str]:
-    """Return (workspace_id, file_id, filename) for a session's transcript.
-
-    Path structure: ``chat-transcripts/{user_id}/{session_id}.jsonl``
-    IDs are sanitized to hex+hyphen to prevent path traversal.
-    """
-    return (
-        TRANSCRIPT_STORAGE_PREFIX,
-        _sanitize_id(user_id),
-        f"{_sanitize_id(session_id)}.jsonl",
-    )
-
-
-def _build_storage_path(user_id: str, session_id: str, backend: object) -> str:
-    """Build the full storage path string that ``retrieve()`` expects.
-
-    ``store()`` returns a path like ``gcs://bucket/workspaces/...`` or
-    ``local://workspace_id/file_id/filename``.  Since we use deterministic
-    arguments we can reconstruct the same path for download/delete without
-    having stored the return value.
-    """
-    from backend.util.workspace_storage import GCSWorkspaceStorage
-
-    wid, fid, fname = _storage_path_parts(user_id, session_id)
-
-    if isinstance(backend, GCSWorkspaceStorage):
-        blob = f"workspaces/{wid}/{fid}/{fname}"
-        return f"gcs://{backend.bucket_name}/{blob}"
-    else:
-        # LocalWorkspaceStorage returns local://{relative_path}
-        return f"local://{wid}/{fid}/{fname}"
-
-
-async def upload_transcript(user_id: str, session_id: str, content: str) -> None:
-    """Strip progress entries and upload transcript to bucket storage.
-
-    Safety: only overwrites when the new (stripped) transcript is larger than
-    what is already stored.  Since JSONL is append-only, the latest transcript
-    is always the longest.  This prevents a slow/stale background task from
-    clobbering a newer upload from a concurrent turn.
-    """
-    from backend.util.workspace_storage import get_workspace_storage
-
-    stripped = strip_progress_entries(content)
-    if not validate_transcript(stripped):
-        logger.warning(
-            f"[Transcript] Skipping upload — stripped content is not a valid "
-            f"transcript for session {session_id}"
-        )
-        return
-
-    storage = await get_workspace_storage()
-    wid, fid, fname = _storage_path_parts(user_id, session_id)
-    encoded = stripped.encode("utf-8")
-    new_size = len(encoded)
-
-    # Check existing transcript size to avoid overwriting newer with older
-    path = _build_storage_path(user_id, session_id, storage)
-    try:
-        existing = await storage.retrieve(path)
-        if len(existing) >= new_size:
-            logger.info(
-                f"[Transcript] Skipping upload — existing transcript "
-                f"({len(existing)}B) >= new ({new_size}B) for session "
-                f"{session_id}"
-            )
-            return
-    except (FileNotFoundError, Exception):
-        pass  # No existing transcript or retrieval error — proceed with upload
-
-    await storage.store(
-        workspace_id=wid,
-        file_id=fid,
-        filename=fname,
-        content=encoded,
-    )
-    logger.info(
-        f"[Transcript] Uploaded {new_size} bytes "
-        f"(stripped from {len(content)}) for session {session_id}"
-    )
-
-
-async def download_transcript(user_id: str, session_id: str) -> str | None:
-    """Download transcript from bucket storage.
-
-    Returns the JSONL content string, or ``None`` if not found.
-    """
-    from backend.util.workspace_storage import get_workspace_storage
-
-    storage = await get_workspace_storage()
-    path = _build_storage_path(user_id, session_id, storage)
-
-    try:
-        data = await storage.retrieve(path)
-        content = data.decode("utf-8")
-        logger.info(
-            f"[Transcript] Downloaded {len(content)} bytes for session {session_id}"
-        )
-        return content
-    except FileNotFoundError:
-        logger.debug(f"[Transcript] No transcript in storage for {session_id}")
-        return None
-    except Exception as e:
-        logger.warning(f"[Transcript] Failed to download transcript: {e}")
-        return None
-
-
-async def delete_transcript(user_id: str, session_id: str) -> None:
-    """Delete transcript from bucket storage (e.g. after resume failure)."""
-    from backend.util.workspace_storage import get_workspace_storage
-
-    storage = await get_workspace_storage()
-    path = _build_storage_path(user_id, session_id, storage)
-
-    try:
-        await storage.delete(path)
-        logger.info(f"[Transcript] Deleted transcript for session {session_id}")
-    except Exception as e:
-        logger.warning(f"[Transcript] Failed to delete transcript: {e}")

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -52,10 +52,8 @@ from .response_model import (
     StreamBaseResponse,
     StreamError,
     StreamFinish,
-    StreamFinishStep,
     StreamHeartbeat,
     StreamStart,
-    StreamStartStep,
     StreamTextDelta,
     StreamTextEnd,
     StreamTextStart,
@@ -245,16 +243,12 @@ async def _get_system_prompt_template(context: str) -> str:
     return DEFAULT_SYSTEM_PROMPT.format(users_information=context)
 
 
-async def _build_system_prompt(
+async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
-    user_id: str | None, has_conversation_history: bool = False
-) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
     Args:
-        user_id: The user ID for fetching business understanding.
+        user_id: The user ID for fetching business understanding
-        has_conversation_history: Whether there's existing conversation history.
+                     If "default" and this is the user's first session, will use "onboarding" instead.
-            If True, we don't tell the model to greet/introduce (since they're
-            already in a conversation).
 
     Returns:
         Tuple of (compiled prompt string, business understanding object)
@@ -270,8 +264,6 @@ async def _build_system_prompt(
 
     if understanding:
         context = format_understanding_for_prompt(understanding)
-    elif has_conversation_history:
-        context = "No prior understanding saved yet. Continue the existing conversation naturally."
     else:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
@@ -359,10 +351,6 @@ async def stream_chat_completion(
     retry_count: int = 0,
     session: ChatSession | None = None,
     context: dict[str, str] | None = None,  # {url: str, content: str}
-    _continuation_message_id: (
-        str | None
-    ) = None,  # Internal: reuse message ID for tool call continuations
-    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
     """Main entry point for streaming chat completions with database handling.
 
@@ -380,47 +368,24 @@ async def stream_chat_completion(
 
     Raises:
         NotFoundError: If session_id is invalid
+        ValueError: If max_context_messages is exceeded
 
     """
-    completion_start = time.monotonic()
-
-    # Build log metadata for structured logging
-    log_meta = {"component": "ChatService", "session_id": session_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
     logger.info(
-        f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
-        f"message_len={len(message) if message else 0}, is_user={is_user_message}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "message_len": len(message) if message else 0,
-                "is_user_message": is_user_message,
-            }
-        },
     )
 
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
-        fetch_start = time.monotonic()
         session = await get_chat_session(session_id, user_id)
-        fetch_time = (time.monotonic() - fetch_start) * 1000
         logger.info(
-            f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
-            f"n_messages={len(session.messages) if session else 0}",
+            f"message_count={len(session.messages) if session else 0}"
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "duration_ms": fetch_time,
-                    "n_messages": len(session.messages) if session else 0,
-                }
-            },
         )
     else:
         logger.info(
-            f"[TIMING] Using provided session, messages={len(session.messages)}",
+            f"Using provided session object: {session.session_id}, "
-            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
+            f"message_count={len(session.messages)}"
         )
 
     if not session:
@@ -441,32 +406,23 @@ async def stream_chat_completion(
 
         # Track user message in PostHog
         if is_user_message:
-            posthog_start = time.monotonic()
             track_user_message(
                 user_id=user_id,
                 session_id=session_id,
                 message_length=len(message),
             )
-            posthog_time = (time.monotonic() - posthog_start) * 1000
-            logger.info(
-                f"[TIMING] track_user_message took {posthog_time:.1f}ms",
-                extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
-            )
 
-    upsert_start = time.monotonic()
-    session = await upsert_chat_session(session)
-    upsert_time = (time.monotonic() - upsert_start) * 1000
     logger.info(
-        f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
-        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
+        f"message_count={len(session.messages)}"
     )
+    session = await upsert_chat_session(session)
     assert session, "Session not found"
 
     # Generate title for new sessions on first user message (non-blocking)
     # Check: is_user_message, no title yet, and this is the first user message
-    user_messages = [m for m in session.messages if m.role == "user"]
+    if is_user_message and message and not session.title:
-    first_user_msg = message or (user_messages[0].content if user_messages else None)
+        user_messages = [m for m in session.messages if m.role == "user"]
-    if is_user_message and first_user_msg and not session.title:
         if len(user_messages) == 1:
             # First user message - generate title in background
             import asyncio
@@ -474,7 +430,7 @@ async def stream_chat_completion(
             # Capture only the values we need (not the session object) to avoid
             # stale data issues when the main flow modifies the session
             captured_session_id = session_id
-            captured_message = first_user_msg
+            captured_message = message
             captured_user_id = user_id
 
             async def _update_title():
@@ -498,13 +454,7 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    prompt_start = time.monotonic()
     system_prompt, understanding = await _build_system_prompt(user_id)
-    prompt_time = (time.monotonic() - prompt_start) * 1000
-    logger.info(
-        f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
-    )
 
     # Initialize variables for streaming
     assistant_response = ChatMessage(
@@ -529,27 +479,13 @@ async def stream_chat_completion(
     # Generate unique IDs for AI SDK protocol
     import uuid as uuid_module
 
-    is_continuation = _continuation_message_id is not None
+    message_id = str(uuid_module.uuid4())
-    message_id = _continuation_message_id or str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
-    # Only yield message start for the initial call, not for continuations.
+    # Yield message start
-    setup_time = (time.monotonic() - completion_start) * 1000
+    yield StreamStart(messageId=message_id)
-    logger.info(
-        f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
-    )
-    if not is_continuation:
-        yield StreamStart(messageId=message_id, taskId=_task_id)
-
-    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
-    yield StreamStartStep()
 
     try:
-        logger.info(
-            "[TIMING] Calling _stream_chat_chunks",
-            extra={"json_fields": log_meta},
-        )
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
@@ -649,10 +585,6 @@ async def stream_chat_completion(
                     )
                 yield chunk
             elif isinstance(chunk, StreamFinish):
-                if has_done_tool_call:
-                    # Tool calls happened — close the step but don't send message-level finish.
-                    # The continuation will open a new step, and finish will come at the end.
-                    yield StreamFinishStep()
                 if not has_done_tool_call:
                     # Emit text-end before finish if we received text but haven't closed it
                     if has_received_text and not text_streaming_ended:
@@ -684,8 +616,6 @@ async def stream_chat_completion(
                             has_saved_assistant_message = True
 
                     has_yielded_end = True
-                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
-                    yield StreamFinishStep()
                     yield chunk
             elif isinstance(chunk, StreamError):
                 has_yielded_error = True
@@ -735,10 +665,6 @@ async def stream_chat_completion(
             logger.info(
                 f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
             )
-            # Close the current step before retrying so the recursive call's
-            # StreamStartStep doesn't produce unbalanced step events.
-            if not has_yielded_end:
-                yield StreamFinishStep()
             should_retry = True
         else:
             # Non-retryable error or max retries exceeded
@@ -774,7 +700,6 @@ async def stream_chat_completion(
                 error_response = StreamError(errorText=error_message)
                 yield error_response
             if not has_yielded_end:
-                yield StreamFinishStep()
                 yield StreamFinish()
             return
 
@@ -789,8 +714,6 @@ async def stream_chat_completion(
             retry_count=retry_count + 1,
             session=session,
             context=context,
-            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
-            _task_id=_task_id,
         ):
             yield chunk
         return  # Exit after retry to avoid double-saving in finally block
@@ -806,13 +729,9 @@ async def stream_chat_completion(
         # Build the messages list in the correct order
         messages_to_save: list[ChatMessage] = []
 
-        # Add assistant message with tool_calls if any.
+        # Add assistant message with tool_calls if any
-        # Use extend (not assign) to preserve tool_calls already added by
-        # _yield_tool_call for long-running tools.
         if accumulated_tool_calls:
-            if not assistant_response.tool_calls:
+            assistant_response.tool_calls = accumulated_tool_calls
-                assistant_response.tool_calls = []
-            assistant_response.tool_calls.extend(accumulated_tool_calls)
             logger.info(
                 f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
             )
@@ -864,8 +783,6 @@ async def stream_chat_completion(
             session=session,  # Pass session object to avoid Redis refetch
             context=context,
             tool_call_response=str(tool_response_messages),
-            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
-            _task_id=_task_id,
         ):
             yield chunk
 
@@ -976,21 +893,9 @@ async def _stream_chat_chunks(
         SSE formatted JSON response objects
 
     """
-    import time as time_module
-
-    stream_chunks_start = time_module.perf_counter()
     model = config.model
 
-    # Build log metadata for structured logging
+    logger.info("Starting pure chat stream")
-    log_meta = {"component": "ChatService", "session_id": session.session_id}
-    if session.user_id:
-        log_meta["user_id"] = session.user_id
-
-    logger.info(
-        f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
-        f"user={session.user_id}, n_messages={len(session.messages)}",
-        extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
-    )
 
     messages = session.to_openai_messages()
     if system_prompt:
@@ -1001,18 +906,12 @@ async def _stream_chat_chunks(
         messages = [system_message] + messages
 
     # Apply context window management
-    context_start = time_module.perf_counter()
     context_result = await _manage_context_window(
         messages=messages,
         model=model,
         api_key=config.api_key,
         base_url=config.base_url,
     )
-    context_time = (time_module.perf_counter() - context_start) * 1000
-    logger.info(
-        f"[TIMING] _manage_context_window took {context_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": context_time}},
-    )
 
     if context_result.error:
         if "System prompt dropped" in context_result.error:
@@ -1047,19 +946,9 @@ async def _stream_chat_chunks(
 
         while retry_count <= MAX_RETRIES:
             try:
-                elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
-                retry_info = (
-                    f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
-                )
                 logger.info(
-                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
+                    f"Creating OpenAI chat completion stream..."
-                    extra={
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
-                        "json_fields": {
-                            **log_meta,
-                            "elapsed_ms": elapsed,
-                            "retry_count": retry_count,
-                        }
-                    },
                 )
 
                 # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1076,11 +965,6 @@ async def _stream_chat_chunks(
                         :128
                     ]  # OpenRouter limit
 
-                # Enable adaptive thinking for Anthropic models via OpenRouter
-                if config.thinking_enabled and "anthropic" in model.lower():
-                    extra_body["reasoning"] = {"enabled": True}
-
-                api_call_start = time_module.perf_counter()
                 stream = await client.chat.completions.create(
                     model=model,
                     messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1090,11 +974,6 @@ async def _stream_chat_chunks(
                     stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
                     extra_body=extra_body,
                 )
-                api_init_time = (time_module.perf_counter() - api_call_start) * 1000
-                logger.info(
-                    f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
-                    extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
-                )
 
                 # Variables to accumulate tool calls
                 tool_calls: list[dict[str, Any]] = []
@@ -1105,13 +984,10 @@ async def _stream_chat_chunks(
 
                 # Track if we've started the text block
                 text_started = False
-                first_content_chunk = True
-                chunk_count = 0
 
                 # Process the stream
                 chunk: ChatCompletionChunk
                 async for chunk in stream:
-                    chunk_count += 1
                     if chunk.usage:
                         yield StreamUsage(
                             promptTokens=chunk.usage.prompt_tokens,
@@ -1134,23 +1010,6 @@ async def _stream_chat_chunks(
                             if not text_started and text_block_id:
                                 yield StreamTextStart(id=text_block_id)
                                 text_started = True
-                            # Log timing for first content chunk
-                            if first_content_chunk:
-                                first_content_chunk = False
-                                ttfc = (
-                                    time_module.perf_counter() - api_call_start
-                                ) * 1000
-                                logger.info(
-                                    f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
-                                    f"(since API call), n_chunks={chunk_count}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "time_to_first_chunk_ms": ttfc,
-                                            "n_chunks": chunk_count,
-                                        }
-                                    },
-                                )
                             # Stream the text delta
                             text_response = StreamTextDelta(
                                 id=text_block_id or "",
@@ -1207,21 +1066,7 @@ async def _stream_chat_chunks(
                                         toolName=tool_calls[idx]["function"]["name"],
                                     )
                                     emitted_start_for_idx.add(idx)
-                stream_duration = time_module.perf_counter() - api_call_start
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")
-                logger.info(
-                    f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
-                    f"duration={stream_duration:.2f}s, "
-                    f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "stream_duration_ms": stream_duration * 1000,
-                            "finish_reason": finish_reason,
-                            "n_chunks": chunk_count,
-                            "n_tool_calls": len(tool_calls),
-                        }
-                    },
-                )
 
                 # Yield all accumulated tool calls after the stream is complete
                 # This ensures all tool call arguments have been fully received
@@ -1241,12 +1086,6 @@ async def _stream_chat_chunks(
                         # Re-raise to trigger retry logic in the parent function
                         raise
 
-                total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
-                logger.info(
-                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time / 1000:.1f}s; "
-                    f"session={session.session_id}, user={session.user_id}",
-                    extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-                )
                 yield StreamFinish()
                 return
             except Exception as e:
@@ -1414,9 +1253,13 @@ async def _yield_tool_call(
                 operation_id=operation_id,
             )
 
-            # Attach the tool_call to the current turn's assistant message
+            # Save assistant message with tool_call FIRST (required by LLM)
-            # (or create one if this is a tool-only response with no text).
+            assistant_message = ChatMessage(
-            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
+                role="assistant",
+                content="",
+                tool_calls=[tool_calls[yield_idx]],
+            )
+            session.messages.append(assistant_message)
 
             # Then save pending tool result
             pending_message = ChatMessage(
@@ -1722,7 +1565,6 @@ async def _execute_long_running_tool_with_streaming(
             task_id,
             StreamError(errorText=str(e)),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())
 
         await _update_pending_operation(
@@ -1839,10 +1681,6 @@ async def _generate_llm_continuation(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
         retry_count = 0
         last_error: Exception | None = None
         response = None
@@ -1973,10 +1811,6 @@ async def _generate_llm_continuation_with_streaming(
         if session_id:
             extra_body["session_id"] = session_id[:128]
 
-        # Enable adaptive thinking for Anthropic models via OpenRouter
-        if config.thinking_enabled and "anthropic" in config.model.lower():
-            extra_body["reasoning"] = {"enabled": True}
-
         # Make streaming LLM call (no tools - just text response)
         from typing import cast
 
@@ -1988,7 +1822,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish start event
         await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
-        await stream_registry.publish_chunk(task_id, StreamStartStep())
         await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
 
         # Stream the response
@@ -2012,7 +1845,6 @@ async def _generate_llm_continuation_with_streaming(
 
         # Publish end events
         await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
 
         if assistant_content:
             # Reload session from DB to avoid race condition with user messages
@@ -2054,5 +1886,4 @@ async def _generate_llm_continuation_with_streaming(
             task_id,
             StreamError(errorText=f"Failed to generate response: {e}"),
         )
-        await stream_registry.publish_chunk(task_id, StreamFinishStep())
         await stream_registry.publish_chunk(task_id, StreamFinish())

autogpt_platform/backend/backend/api/features/chat/service_test.py

@@ -1,4 +1,3 @@
-import asyncio
 import logging
 from os import getenv
 
@@ -12,8 +11,6 @@ from .response_model import (
     StreamTextDelta,
     StreamToolOutputAvailable,
 )
-from .sdk import service as sdk_service
-from .sdk.transcript import download_transcript
 
 logger = logging.getLogger(__name__)
 
@@ -83,96 +80,3 @@ async def test_stream_chat_completion_with_tool_calls(setup_test_user, test_user
     session = await get_chat_session(session.session_id)
     assert session, "Session not found"
     assert session.usage, "Usage is empty"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_sdk_resume_multi_turn(setup_test_user, test_user_id):
-    """Test that the SDK --resume path captures and uses transcripts across turns.
-
-    Turn 1: Send a message containing a unique keyword.
-    Turn 2: Ask the model to recall that keyword — proving the transcript was
-    persisted and restored via --resume.
-    """
-    api_key: str | None = getenv("OPEN_ROUTER_API_KEY")
-    if not api_key:
-        return pytest.skip("OPEN_ROUTER_API_KEY is not set, skipping test")
-
-    from .config import ChatConfig
-
-    cfg = ChatConfig()
-    if not cfg.claude_agent_use_resume:
-        return pytest.skip("CLAUDE_AGENT_USE_RESUME is not enabled, skipping test")
-
-    session = await create_chat_session(test_user_id)
-    session = await upsert_chat_session(session)
-
-    # --- Turn 1: send a message with a unique keyword ---
-    keyword = "ZEPHYR42"
-    turn1_msg = (
-        f"Please remember this special keyword: {keyword}. "
-        "Just confirm you've noted it, keep your response brief."
-    )
-    turn1_text = ""
-    turn1_errors: list[str] = []
-    turn1_ended = False
-
-    async for chunk in sdk_service.stream_chat_completion_sdk(
-        session.session_id,
-        turn1_msg,
-        user_id=test_user_id,
-    ):
-        if isinstance(chunk, StreamTextDelta):
-            turn1_text += chunk.delta
-        elif isinstance(chunk, StreamError):
-            turn1_errors.append(chunk.errorText)
-        elif isinstance(chunk, StreamFinish):
-            turn1_ended = True
-
-    assert turn1_ended, "Turn 1 did not finish"
-    assert not turn1_errors, f"Turn 1 errors: {turn1_errors}"
-    assert turn1_text, "Turn 1 produced no text"
-
-    # Wait for background upload task to complete (retry up to 5s)
-    transcript = None
-    for _ in range(10):
-        await asyncio.sleep(0.5)
-        transcript = await download_transcript(test_user_id, session.session_id)
-        if transcript:
-            break
-    assert transcript, (
-        "Transcript was not uploaded to bucket after turn 1 — "
-        "Stop hook may not have fired or transcript was too small"
-    )
-    logger.info(f"Turn 1 transcript uploaded: {len(transcript)} bytes")
-
-    # Reload session for turn 2
-    session = await get_chat_session(session.session_id, test_user_id)
-    assert session, "Session not found after turn 1"
-
-    # --- Turn 2: ask model to recall the keyword ---
-    turn2_msg = "What was the special keyword I asked you to remember?"
-    turn2_text = ""
-    turn2_errors: list[str] = []
-    turn2_ended = False
-
-    async for chunk in sdk_service.stream_chat_completion_sdk(
-        session.session_id,
-        turn2_msg,
-        user_id=test_user_id,
-        session=session,
-    ):
-        if isinstance(chunk, StreamTextDelta):
-            turn2_text += chunk.delta
-        elif isinstance(chunk, StreamError):
-            turn2_errors.append(chunk.errorText)
-        elif isinstance(chunk, StreamFinish):
-            turn2_ended = True
-
-    assert turn2_ended, "Turn 2 did not finish"
-    assert not turn2_errors, f"Turn 2 errors: {turn2_errors}"
-    assert turn2_text, "Turn 2 produced no text"
-    assert keyword in turn2_text, (
-        f"Model did not recall keyword '{keyword}' in turn 2. "
-        f"Response: {turn2_text[:200]}"
-    )
-    logger.info(f"Turn 2 recalled keyword successfully: {turn2_text[:100]}")

autogpt_platform/backend/backend/api/features/chat/stream_registry.py

@@ -104,24 +104,6 @@ async def create_task(
     Returns:
         The created ActiveTask instance (metadata only)
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata for structured logging
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "session_id": session_id,
-    }
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
-        extra={"json_fields": log_meta},
-    )
-
     task = ActiveTask(
         task_id=task_id,
         session_id=session_id,
@@ -132,18 +114,10 @@ async def create_task(
     )
 
     # Store metadata in Redis
-    redis_start = time.perf_counter()
     redis = await get_redis_async()
-    redis_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] get_redis_async took {redis_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
-    )
-
     meta_key = _get_task_meta_key(task_id)
     op_key = _get_operation_mapping_key(operation_id)
 
-    hset_start = time.perf_counter()
     await redis.hset(  # type: ignore[misc]
         meta_key,
         mapping={
@@ -157,22 +131,12 @@ async def create_task(
             "created_at": task.created_at.isoformat(),
         },
     )
-    hset_time = (time.perf_counter() - hset_start) * 1000
-    logger.info(
-        f"[TIMING] redis.hset took {hset_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
-    )
-
     await redis.expire(meta_key, config.stream_ttl)
 
     # Create operation_id -> task_id mapping for webhook lookups
     await redis.set(op_key, task_id, ex=config.stream_ttl)
 
-    total_time = (time.perf_counter() - start_time) * 1000
+    logger.debug(f"Created task {task_id} for session {session_id}")
-    logger.info(
-        f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
-        extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
-    )
 
     return task
 
@@ -192,60 +156,26 @@ async def publish_chunk(
     Returns:
         The Redis Stream message ID
     """
-    import time
-
-    start_time = time.perf_counter()
-    chunk_type = type(chunk).__name__
     chunk_json = chunk.model_dump_json()
     message_id = "0-0"
 
-    # Build log metadata
-    log_meta = {
-        "component": "StreamRegistry",
-        "task_id": task_id,
-        "chunk_type": chunk_type,
-    }
-
     try:
         redis = await get_redis_async()
         stream_key = _get_task_stream_key(task_id)
 
         # Write to Redis Stream for persistence and real-time delivery
-        xadd_start = time.perf_counter()
         raw_id = await redis.xadd(
             stream_key,
             {"data": chunk_json},
             maxlen=config.stream_max_length,
         )
-        xadd_time = (time.perf_counter() - xadd_start) * 1000
         message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
 
         # Set TTL on stream to match task metadata TTL
         await redis.expire(stream_key, config.stream_ttl)
-
-        total_time = (time.perf_counter() - start_time) * 1000
-        # Only log timing for significant chunks or slow operations
-        if (
-            chunk_type
-            in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
-            or total_time > 50
-        ):
-            logger.info(
-                f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "total_time_ms": total_time,
-                        "xadd_time_ms": xadd_time,
-                        "message_id": message_id,
-                    }
-                },
-            )
     except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
         logger.error(
-            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
+            f"Failed to publish chunk for task {task_id}: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
             exc_info=True,
         )
 
@@ -270,61 +200,24 @@ async def subscribe_to_task(
         An asyncio Queue that will receive stream chunks, or None if task not found
         or user doesn't have access
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Build log metadata
-    log_meta = {"component": "StreamRegistry", "task_id": task_id}
-    if user_id:
-        log_meta["user_id"] = user_id
-
-    logger.info(
-        f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
-        extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
-    )
-
-    redis_start = time.perf_counter()
     redis = await get_redis_async()
     meta_key = _get_task_meta_key(task_id)
     meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
-    hgetall_time = (time.perf_counter() - redis_start) * 1000
-    logger.info(
-        f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
-        extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
-    )
 
     if not meta:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.debug(f"Task {task_id} not found in Redis")
-        logger.info(
-            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "reason": "task_not_found",
-                }
-            },
-        )
         return None
 
     # Note: Redis client uses decode_responses=True, so keys are strings
     task_status = meta.get("status", "")
     task_user_id = meta.get("user_id", "") or None
-    log_meta["session_id"] = meta.get("session_id", "")
 
     # Validate ownership - if task has an owner, requester must match
     if task_user_id:
         if user_id != task_user_id:
             logger.warning(
-                f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
+                f"User {user_id} denied access to task {task_id} "
-                extra={
+                f"owned by {task_user_id}"
-                    "json_fields": {
-                        **log_meta,
-                        "task_owner": task_user_id,
-                        "reason": "access_denied",
-                    }
-                },
             )
             return None
 
@@ -332,19 +225,7 @@ async def subscribe_to_task(
     stream_key = _get_task_stream_key(task_id)
 
     # Step 1: Replay messages from Redis Stream
-    xread_start = time.perf_counter()
     messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
-    xread_time = (time.perf_counter() - xread_start) * 1000
-    logger.info(
-        f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "duration_ms": xread_time,
-                "task_status": task_status,
-            }
-        },
-    )
 
     replayed_count = 0
     replay_last_id = last_message_id
@@ -363,48 +244,19 @@ async def subscribe_to_task(
                     except Exception as e:
                         logger.warning(f"Failed to replay message: {e}")
 
-    logger.info(
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
-        f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "n_messages_replayed": replayed_count,
-                "replay_last_id": replay_last_id,
-            }
-        },
-    )
 
     # Step 2: If task is still running, start stream listener for live updates
     if task_status == "running":
-        logger.info(
-            "[TIMING] Task still running, starting _stream_listener",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
         listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
+            _stream_listener(task_id, subscriber_queue, replay_last_id)
         )
         # Track listener task for cleanup on unsubscribe
         _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
     else:
         # Task is completed/failed - add finish marker
-        logger.info(
-            f"[TIMING] Task already {task_status}, adding StreamFinish",
-            extra={"json_fields": {**log_meta, "task_status": task_status}},
-        )
         await subscriber_queue.put(StreamFinish())
 
-    total_time = (time.perf_counter() - start_time) * 1000
-    logger.info(
-        f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
-        f"n_messages_replayed={replayed_count}",
-        extra={
-            "json_fields": {
-                **log_meta,
-                "total_time_ms": total_time,
-                "n_messages_replayed": replayed_count,
-            }
-        },
-    )
     return subscriber_queue
 
 
@@ -412,7 +264,6 @@ async def _stream_listener(
     task_id: str,
     subscriber_queue: asyncio.Queue[StreamBaseResponse],
     last_replayed_id: str,
-    log_meta: dict | None = None,
 ) -> None:
     """Listen to Redis Stream for new messages using blocking XREAD.
 
@@ -423,27 +274,10 @@ async def _stream_listener(
         task_id: Task ID to listen for
         subscriber_queue: Queue to deliver messages to
         last_replayed_id: Last message ID from replay (continue from here)
-        log_meta: Structured logging metadata
     """
-    import time
-
-    start_time = time.perf_counter()
-
-    # Use provided log_meta or build minimal one
-    if log_meta is None:
-        log_meta = {"component": "StreamRegistry", "task_id": task_id}
-
-    logger.info(
-        f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
-        extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
-    )
-
     queue_id = id(subscriber_queue)
     # Track the last successfully delivered message ID for recovery hints
     last_delivered_id = last_replayed_id
-    messages_delivered = 0
-    first_message_time = None
-    xread_count = 0
 
     try:
         redis = await get_redis_async()
@@ -453,39 +287,9 @@ async def _stream_listener(
         while True:
             # Block for up to 30 seconds waiting for new messages
             # This allows periodic checking if task is still running
-            xread_start = time.perf_counter()
-            xread_count += 1
             messages = await redis.xread(
                 {stream_key: current_id}, block=30000, count=100
             )
-            xread_time = (time.perf_counter() - xread_start) * 1000
-
-            if messages:
-                msg_count = sum(len(msgs) for _, msgs in messages)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "n_messages": msg_count,
-                            "duration_ms": xread_time,
-                        }
-                    },
-                )
-            elif xread_time > 1000:
-                # Only log timeouts (30s blocking)
-                logger.info(
-                    f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "xread_count": xread_count,
-                            "duration_ms": xread_time,
-                            "reason": "timeout",
-                        }
-                    },
-                )
 
             if not messages:
                 # Timeout - check if task is still running
@@ -522,30 +326,10 @@ async def _stream_listener(
                                 )
                                 # Update last delivered ID on successful delivery
                                 last_delivered_id = current_id
-                                messages_delivered += 1
-                                if first_message_time is None:
-                                    first_message_time = time.perf_counter()
-                                    elapsed = (first_message_time - start_time) * 1000
-                                    logger.info(
-                                        f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
-                                        extra={
-                                            "json_fields": {
-                                                **log_meta,
-                                                "elapsed_ms": elapsed,
-                                                "chunk_type": type(chunk).__name__,
-                                            }
-                                        },
-                                    )
                             except asyncio.TimeoutError:
                                 logger.warning(
-                                    f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
+                                    f"Subscriber queue full for task {task_id}, "
-                                    extra={
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
-                                        "json_fields": {
-                                            **log_meta,
-                                            "timeout_s": QUEUE_PUT_TIMEOUT,
-                                            "reason": "queue_full",
-                                        }
-                                    },
                                 )
                                 # Send overflow error with recovery info
                                 try:
@@ -567,44 +351,15 @@ async def _stream_listener(
 
                             # Stop listening on finish
                             if isinstance(chunk, StreamFinish):
-                                total_time = (time.perf_counter() - start_time) * 1000
-                                logger.info(
-                                    f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "total_time_ms": total_time,
-                                            "messages_delivered": messages_delivered,
-                                        }
-                                    },
-                                )
                                 return
                     except Exception as e:
-                        logger.warning(
+                        logger.warning(f"Error processing stream message: {e}")
-                            f"Error processing stream message: {e}",
-                            extra={"json_fields": {**log_meta, "error": str(e)}},
-                        )
 
     except asyncio.CancelledError:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.debug(f"Stream listener cancelled for task {task_id}")
-        logger.info(
-            f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "messages_delivered": messages_delivered,
-                    "reason": "cancelled",
-                }
-            },
-        )
         raise  # Re-raise to propagate cancellation
     except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.error(f"Stream listener error for task {task_id}: {e}")
-        logger.error(
-            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
-        )
         # On error, send finish to unblock subscriber
         try:
             await asyncio.wait_for(
@@ -613,24 +368,10 @@ async def _stream_listener(
             )
         except (asyncio.TimeoutError, asyncio.QueueFull):
             logger.warning(
-                "Could not deliver finish event after error",
+                f"Could not deliver finish event for task {task_id} after error"
-                extra={"json_fields": log_meta},
             )
     finally:
         # Clean up listener task mapping on exit
-        total_time = (time.perf_counter() - start_time) * 1000
-        logger.info(
-            f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
-            f"delivered={messages_delivered}, xread_count={xread_count}",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "total_time_ms": total_time,
-                    "messages_delivered": messages_delivered,
-                    "xread_count": xread_count,
-                }
-            },
-        )
         _listener_tasks.pop(queue_id, None)
 
 
@@ -814,28 +555,6 @@ async def get_active_task_for_session(
                 if task_user_id and user_id != task_user_id:
                     continue
 
-                # Auto-expire stale tasks that exceeded stream_timeout
-                created_at_str = meta.get("created_at", "")
-                if created_at_str:
-                    try:
-                        created_at = datetime.fromisoformat(created_at_str)
-                        age_seconds = (
-                            datetime.now(timezone.utc) - created_at
-                        ).total_seconds()
-                        if age_seconds > config.stream_timeout:
-                            logger.warning(
-                                f"[TASK_LOOKUP] Auto-expiring stale task {task_id[:8]}... "
-                                f"(age={age_seconds:.0f}s > timeout={config.stream_timeout}s)"
-                            )
-                            await mark_task_completed(task_id, "failed")
-                            continue
-                    except (ValueError, TypeError):
-                        pass
-
-                logger.info(
-                    f"[TASK_LOOKUP] Found running task {task_id[:8]}... for session {session_id[:8]}..."
-                )
-
                 # Get the last message ID from Redis Stream
                 stream_key = _get_task_stream_key(task_id)
                 last_id = "0-0"
@@ -879,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
         ResponseType,
         StreamError,
         StreamFinish,
-        StreamFinishStep,
         StreamHeartbeat,
         StreamStart,
-        StreamStartStep,
         StreamTextDelta,
         StreamTextEnd,
         StreamTextStart,
@@ -896,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
     type_to_class: dict[str, type[StreamBaseResponse]] = {
         ResponseType.START.value: StreamStart,
         ResponseType.FINISH.value: StreamFinish,
-        ResponseType.START_STEP.value: StreamStartStep,
-        ResponseType.FINISH_STEP.value: StreamFinishStep,
         ResponseType.TEXT_START.value: StreamTextStart,
         ResponseType.TEXT_DELTA.value: StreamTextDelta,
         ResponseType.TEXT_END.value: StreamTextEnd,

autogpt_platform/backend/backend/api/features/chat/tools/__init__.py

@@ -9,8 +9,6 @@ from backend.api.features.chat.tracking import track_tool_called
 from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
-from .bash_exec import BashExecTool
-from .check_operation_status import CheckOperationStatusTool
 from .create_agent import CreateAgentTool
 from .customize_agent import CustomizeAgentTool
 from .edit_agent import EditAgentTool
@@ -21,7 +19,6 @@ from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
 from .run_block import RunBlockTool
 from .search_docs import SearchDocsTool
-from .web_fetch import WebFetchTool
 from .workspace_files import (
     DeleteWorkspaceFileTool,
     ListWorkspaceFilesTool,
@@ -46,14 +43,9 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "run_agent": RunAgentTool(),
     "run_block": RunBlockTool(),
     "view_agent_output": AgentOutputTool(),
-    "check_operation_status": CheckOperationStatusTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
-    # Web fetch for safe URL retrieval
+    # Workspace tools for CoPilot file operations
-    "web_fetch": WebFetchTool(),
-    # Sandboxed code execution (bubblewrap)
-    "bash_exec": BashExecTool(),
-    # Persistent workspace tools (cloud storage, survives across sessions)
     "list_workspace_files": ListWorkspaceFilesTool(),
     "read_workspace_file": ReadWorkspaceFileTool(),
     "write_workspace_file": WriteWorkspaceFileTool(),

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/dummy.py

@@ -1,154 +0,0 @@
-"""Dummy Agent Generator for testing.
-
-Returns mock responses matching the format expected from the external service.
-Enable via AGENTGENERATOR_USE_DUMMY=true in settings.
-
-WARNING: This is for testing only. Do not use in production.
-"""
-
-import asyncio
-import logging
-import uuid
-from typing import Any
-
-logger = logging.getLogger(__name__)
-
-# Dummy decomposition result (instructions type)
-DUMMY_DECOMPOSITION_RESULT: dict[str, Any] = {
-    "type": "instructions",
-    "steps": [
-        {
-            "description": "Get input from user",
-            "action": "input",
-            "block_name": "AgentInputBlock",
-        },
-        {
-            "description": "Process the input",
-            "action": "process",
-            "block_name": "TextFormatterBlock",
-        },
-        {
-            "description": "Return output to user",
-            "action": "output",
-            "block_name": "AgentOutputBlock",
-        },
-    ],
-}
-
-# Block IDs from backend/blocks/io.py
-AGENT_INPUT_BLOCK_ID = "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b"
-AGENT_OUTPUT_BLOCK_ID = "363ae599-353e-4804-937e-b2ee3cef3da4"
-
-
-def _generate_dummy_agent_json() -> dict[str, Any]:
-    """Generate a minimal valid agent JSON for testing."""
-    input_node_id = str(uuid.uuid4())
-    output_node_id = str(uuid.uuid4())
-
-    return {
-        "id": str(uuid.uuid4()),
-        "version": 1,
-        "is_active": True,
-        "name": "Dummy Test Agent",
-        "description": "A dummy agent generated for testing purposes",
-        "nodes": [
-            {
-                "id": input_node_id,
-                "block_id": AGENT_INPUT_BLOCK_ID,
-                "input_default": {
-                    "name": "input",
-                    "title": "Input",
-                    "description": "Enter your input",
-                    "placeholder_values": [],
-                },
-                "metadata": {"position": {"x": 0, "y": 0}},
-            },
-            {
-                "id": output_node_id,
-                "block_id": AGENT_OUTPUT_BLOCK_ID,
-                "input_default": {
-                    "name": "output",
-                    "title": "Output",
-                    "description": "Agent output",
-                    "format": "{output}",
-                },
-                "metadata": {"position": {"x": 400, "y": 0}},
-            },
-        ],
-        "links": [
-            {
-                "id": str(uuid.uuid4()),
-                "source_id": input_node_id,
-                "sink_id": output_node_id,
-                "source_name": "result",
-                "sink_name": "value",
-                "is_static": False,
-            },
-        ],
-    }
-
-
-async def decompose_goal_dummy(
-    description: str,
-    context: str = "",
-    library_agents: list[dict[str, Any]] | None = None,
-) -> dict[str, Any]:
-    """Return dummy decomposition result."""
-    logger.info("Using dummy agent generator for decompose_goal")
-    return DUMMY_DECOMPOSITION_RESULT.copy()
-
-
-async def generate_agent_dummy(
-    instructions: dict[str, Any],
-    library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
-) -> dict[str, Any]:
-    """Return dummy agent JSON after a simulated delay."""
-    logger.info("Using dummy agent generator for generate_agent (30s delay)")
-    await asyncio.sleep(30)
-    return _generate_dummy_agent_json()
-
-
-async def generate_agent_patch_dummy(
-    update_request: str,
-    current_agent: dict[str, Any],
-    library_agents: list[dict[str, Any]] | None = None,
-    operation_id: str | None = None,
-    task_id: str | None = None,
-) -> dict[str, Any]:
-    """Return dummy patched agent (returns the current agent with updated description)."""
-    logger.info("Using dummy agent generator for generate_agent_patch")
-    patched = current_agent.copy()
-    patched["description"] = (
-        f"{current_agent.get('description', '')} (updated: {update_request})"
-    )
-    return patched
-
-
-async def customize_template_dummy(
-    template_agent: dict[str, Any],
-    modification_request: str,
-    context: str = "",
-) -> dict[str, Any]:
-    """Return dummy customized template (returns template with updated description)."""
-    logger.info("Using dummy agent generator for customize_template")
-    customized = template_agent.copy()
-    customized["description"] = (
-        f"{template_agent.get('description', '')} (customized: {modification_request})"
-    )
-    return customized
-
-
-async def get_blocks_dummy() -> list[dict[str, Any]]:
-    """Return dummy blocks list."""
-    logger.info("Using dummy agent generator for get_blocks")
-    return [
-        {"id": AGENT_INPUT_BLOCK_ID, "name": "AgentInputBlock"},
-        {"id": AGENT_OUTPUT_BLOCK_ID, "name": "AgentOutputBlock"},
-    ]
-
-
-async def health_check_dummy() -> bool:
-    """Always returns healthy for dummy service."""
-    return True

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py

@@ -12,19 +12,8 @@ import httpx
 
 from backend.util.settings import Settings
 
-from .dummy import (
-    customize_template_dummy,
-    decompose_goal_dummy,
-    generate_agent_dummy,
-    generate_agent_patch_dummy,
-    get_blocks_dummy,
-    health_check_dummy,
-)
-
 logger = logging.getLogger(__name__)
 
-_dummy_mode_warned = False
-
 
 def _create_error_response(
     error_message: str,
@@ -101,26 +90,10 @@ def _get_settings() -> Settings:
     return _settings
 
 
-def _is_dummy_mode() -> bool:
-    """Check if dummy mode is enabled for testing."""
-    global _dummy_mode_warned
-    settings = _get_settings()
-    is_dummy = bool(settings.config.agentgenerator_use_dummy)
-    if is_dummy and not _dummy_mode_warned:
-        logger.warning(
-            "Agent Generator running in DUMMY MODE - returning mock responses. "
-            "Do not use in production!"
-        )
-        _dummy_mode_warned = True
-    return is_dummy
-
-
 def is_external_service_configured() -> bool:
-    """Check if external Agent Generator service is configured (or dummy mode)."""
+    """Check if external Agent Generator service is configured."""
     settings = _get_settings()
-    return bool(settings.config.agentgenerator_host) or bool(
+    return bool(settings.config.agentgenerator_host)
-        settings.config.agentgenerator_use_dummy
-    )
 
 
 def _get_base_url() -> str:
@@ -164,9 +137,6 @@ async def decompose_goal_external(
         - {"type": "error", "error": "...", "error_type": "..."} on error
         Or None on unexpected error
     """
-    if _is_dummy_mode():
-        return await decompose_goal_dummy(description, context, library_agents)
-
     client = _get_client()
 
     if context:
@@ -256,11 +226,6 @@ async def generate_agent_external(
     Returns:
         Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
     """
-    if _is_dummy_mode():
-        return await generate_agent_dummy(
-            instructions, library_agents, operation_id, task_id
-        )
-
     client = _get_client()
 
     # Build request payload
@@ -332,11 +297,6 @@ async def generate_agent_patch_external(
     Returns:
         Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
     """
-    if _is_dummy_mode():
-        return await generate_agent_patch_dummy(
-            update_request, current_agent, library_agents, operation_id, task_id
-        )
-
     client = _get_client()
 
     # Build request payload
@@ -423,11 +383,6 @@ async def customize_template_external(
     Returns:
         Customized agent JSON, clarifying questions dict, or error dict on error
     """
-    if _is_dummy_mode():
-        return await customize_template_dummy(
-            template_agent, modification_request, context
-        )
-
     client = _get_client()
 
     request = modification_request
@@ -490,9 +445,6 @@ async def get_blocks_external() -> list[dict[str, Any]] | None:
     Returns:
         List of block info dicts or None on error
     """
-    if _is_dummy_mode():
-        return await get_blocks_dummy()
-
     client = _get_client()
 
     try:
@@ -526,9 +478,6 @@ async def health_check() -> bool:
     if not is_external_service_configured():
         return False
 
-    if _is_dummy_mode():
-        return await health_check_dummy()
-
     client = _get_client()
 
     try:

autogpt_platform/backend/backend/api/features/chat/tools/bash_exec.py

@@ -1,131 +0,0 @@
-"""Bash execution tool — run shell commands in a bubblewrap sandbox.
-
-Full Bash scripting is allowed (loops, conditionals, pipes, functions, etc.).
-Safety comes from OS-level isolation (bubblewrap): only system dirs visible
-read-only, writable workspace only, clean env, no network.
-
-Requires bubblewrap (``bwrap``) — the tool is disabled when bwrap is not
-available (e.g. macOS development).
-"""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool
-from backend.api.features.chat.tools.models import (
-    BashExecResponse,
-    ErrorResponse,
-    ToolResponseBase,
-)
-from backend.api.features.chat.tools.sandbox import (
-    get_workspace_dir,
-    has_full_sandbox,
-    run_sandboxed,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class BashExecTool(BaseTool):
-    """Execute Bash commands in a bubblewrap sandbox."""
-
-    @property
-    def name(self) -> str:
-        return "bash_exec"
-
-    @property
-    def description(self) -> str:
-        if not has_full_sandbox():
-            return (
-                "Bash execution is DISABLED — bubblewrap sandbox is not "
-                "available on this platform. Do not call this tool."
-            )
-        return (
-            "Execute a Bash command or script in a bubblewrap sandbox. "
-            "Full Bash scripting is supported (loops, conditionals, pipes, "
-            "functions, etc.). "
-            "The sandbox shares the same working directory as the SDK Read/Write "
-            "tools — files created by either are accessible to both. "
-            "SECURITY: Only system directories (/usr, /bin, /lib, /etc) are "
-            "visible read-only, the per-session workspace is the only writable "
-            "path, environment variables are wiped (no secrets), all network "
-            "access is blocked at the kernel level, and resource limits are "
-            "enforced (max 64 processes, 512MB memory, 50MB file size). "
-            "Application code, configs, and other directories are NOT accessible. "
-            "To fetch web content, use the web_fetch tool instead. "
-            "Execution is killed after the timeout (default 30s, max 120s). "
-            "Returns stdout and stderr. "
-            "Useful for file manipulation, data processing with Unix tools "
-            "(grep, awk, sed, jq, etc.), and running shell scripts."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "command": {
-                    "type": "string",
-                    "description": "Bash command or script to execute.",
-                },
-                "timeout": {
-                    "type": "integer",
-                    "description": (
-                        "Max execution time in seconds (default 30, max 120)."
-                    ),
-                    "default": 30,
-                },
-            },
-            "required": ["command"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs: Any,
-    ) -> ToolResponseBase:
-        session_id = session.session_id if session else None
-
-        if not has_full_sandbox():
-            return ErrorResponse(
-                message="bash_exec requires bubblewrap sandbox (Linux only).",
-                error="sandbox_unavailable",
-                session_id=session_id,
-            )
-
-        command: str = (kwargs.get("command") or "").strip()
-        timeout: int = kwargs.get("timeout", 30)
-
-        if not command:
-            return ErrorResponse(
-                message="No command provided.",
-                error="empty_command",
-                session_id=session_id,
-            )
-
-        workspace = get_workspace_dir(session_id or "default")
-
-        stdout, stderr, exit_code, timed_out = await run_sandboxed(
-            command=["bash", "-c", command],
-            cwd=workspace,
-            timeout=timeout,
-        )
-
-        return BashExecResponse(
-            message=(
-                "Execution timed out"
-                if timed_out
-                else f"Command executed (exit {exit_code})"
-            ),
-            stdout=stdout,
-            stderr=stderr,
-            exit_code=exit_code,
-            timed_out=timed_out,
-            session_id=session_id,
-        )

autogpt_platform/backend/backend/api/features/chat/tools/binary_output_processor.py

@@ -0,0 +1,251 @@
+"""
+Detect and save embedded binary data in block outputs.
+
+Scans stdout_logs and other string outputs for embedded base64 patterns,
+saves detected binary content to workspace, and replaces the base64 with
+workspace:// references. This reduces LLM output token usage by ~97% for
+file generation tasks.
+
+Primary use case: ExecuteCodeBlock prints base64 to stdout, which appears
+in stdout_logs. Without this processor, the LLM would re-type the entire
+base64 string when saving files.
+"""
+
+import base64
+import binascii
+import hashlib
+import logging
+import re
+import uuid
+from typing import Any, Optional
+
+from backend.util.file import sanitize_filename
+from backend.util.virus_scanner import scan_content_safe
+from backend.util.workspace import WorkspaceManager
+
+logger = logging.getLogger(__name__)
+
+# Minimum decoded size to process (filters out small base64 strings)
+MIN_DECODED_SIZE = 1024  # 1KB
+
+# Pattern to find base64 chunks in text (at least 100 chars to be worth checking)
+# Matches continuous base64 characters (with optional whitespace for line wrapping),
+# optionally ending with = padding
+EMBEDDED_BASE64_PATTERN = re.compile(r"[A-Za-z0-9+/\s]{100,}={0,2}")
+
+# Magic numbers for binary file detection
+MAGIC_SIGNATURES = [
+    (b"\x89PNG\r\n\x1a\n", "png"),
+    (b"\xff\xd8\xff", "jpg"),
+    (b"%PDF-", "pdf"),
+    (b"GIF87a", "gif"),
+    (b"GIF89a", "gif"),
+    (b"RIFF", "webp"),  # Also check content[8:12] == b'WEBP'
+]
+
+
+async def process_binary_outputs(
+    outputs: dict[str, list[Any]],
+    workspace_manager: WorkspaceManager,
+    block_name: str,
+) -> dict[str, list[Any]]:
+    """
+    Scan all string values in outputs for embedded base64 binary content.
+    Save detected binaries to workspace and replace with references.
+
+    Args:
+        outputs: Block execution outputs (dict of output_name -> list of values)
+        workspace_manager: WorkspaceManager instance with session scoping
+        block_name: Name of the block (used in generated filenames)
+
+    Returns:
+        Processed outputs with embedded base64 replaced by workspace references
+    """
+    cache: dict[str, str] = {}  # content_hash -> workspace_ref
+
+    processed: dict[str, list[Any]] = {}
+    for name, items in outputs.items():
+        processed_items = []
+        for item in items:
+            processed_items.append(
+                await _process_value(item, workspace_manager, block_name, cache)
+            )
+        processed[name] = processed_items
+    return processed
+
+
+async def _process_value(
+    value: Any,
+    wm: WorkspaceManager,
+    block: str,
+    cache: dict[str, str],
+) -> Any:
+    """Recursively process a value, detecting embedded base64 in strings."""
+    if isinstance(value, dict):
+        result = {}
+        for k, v in value.items():
+            result[k] = await _process_value(v, wm, block, cache)
+        return result
+    if isinstance(value, list):
+        return [await _process_value(v, wm, block, cache) for v in value]
+    if isinstance(value, str) and len(value) > MIN_DECODED_SIZE:
+        return await _extract_and_replace_base64(value, wm, block, cache)
+    return value
+
+
+async def _extract_and_replace_base64(
+    text: str,
+    wm: WorkspaceManager,
+    block: str,
+    cache: dict[str, str],
+) -> str:
+    """
+    Find embedded base64 in text, save binaries, replace with references.
+
+    Scans for base64 patterns, validates each as binary via magic numbers,
+    saves valid binaries to workspace, and replaces the base64 portion
+    (plus any surrounding markers) with the workspace reference.
+    """
+    result = text
+    offset = 0
+
+    for match in EMBEDDED_BASE64_PATTERN.finditer(text):
+        b64_str = match.group(0)
+
+        # Try to decode and validate
+        detection = _decode_and_validate(b64_str)
+        if detection is None:
+            continue
+
+        content, ext = detection
+
+        # Save to workspace
+        ref = await _save_binary(content, ext, wm, block, cache)
+        if ref is None:
+            continue
+
+        # Calculate replacement bounds (include surrounding markers if present)
+        start, end = match.start(), match.end()
+        start, end = _expand_to_markers(text, start, end)
+
+        # Apply replacement with offset adjustment
+        adj_start = start + offset
+        adj_end = end + offset
+        result = result[:adj_start] + ref + result[adj_end:]
+        offset += len(ref) - (end - start)
+
+    return result
+
+
+def _decode_and_validate(b64_str: str) -> Optional[tuple[bytes, str]]:
+    """
+    Decode base64 and validate it's a known binary format.
+
+    Tries multiple 4-byte aligned offsets to handle cases where marker text
+    (e.g., "START" from "PDF_BASE64_START") bleeds into the regex match.
+    Base64 works in 4-char chunks, so we only check aligned offsets.
+
+    Returns (content, extension) if valid binary, None otherwise.
+    """
+    # Strip whitespace for RFC 2045 line-wrapped base64
+    normalized = re.sub(r"\s+", "", b64_str)
+
+    # Try offsets 0, 4, 8, ... up to 32 chars (handles markers up to ~24 chars)
+    # This handles cases like "STARTJVBERi0..." where "START" bleeds into match
+    for char_offset in range(0, min(33, len(normalized)), 4):
+        candidate = normalized[char_offset:]
+
+        try:
+            content = base64.b64decode(candidate, validate=True)
+        except (ValueError, binascii.Error):
+            continue
+
+        # Must meet minimum size
+        if len(content) < MIN_DECODED_SIZE:
+            continue
+
+        # Check magic numbers
+        for magic, ext in MAGIC_SIGNATURES:
+            if content.startswith(magic):
+                # Special case for WebP: RIFF container, verify "WEBP" at offset 8
+                if magic == b"RIFF":
+                    if len(content) < 12 or content[8:12] != b"WEBP":
+                        continue
+                return content, ext
+
+    return None
+
+
+def _expand_to_markers(text: str, start: int, end: int) -> tuple[int, int]:
+    """
+    Expand replacement bounds to include surrounding markers if present.
+
+    Handles patterns like:
+    - ---BASE64_START---\\n{base64}\\n---BASE64_END---
+    - [BASE64]{base64}[/BASE64]
+    - Or just the raw base64
+    """
+    # Common marker patterns to strip (order matters - check longer patterns first)
+    start_markers = [
+        "PDF_BASE64_START",
+        "---BASE64_START---\n",
+        "---BASE64_START---",
+        "[BASE64]\n",
+        "[BASE64]",
+    ]
+    end_markers = [
+        "PDF_BASE64_END",
+        "\n---BASE64_END---",
+        "---BASE64_END---",
+        "\n[/BASE64]",
+        "[/BASE64]",
+    ]
+
+    # Check for start markers
+    for marker in start_markers:
+        marker_start = start - len(marker)
+        if marker_start >= 0 and text[marker_start:start] == marker:
+            start = marker_start
+            break
+
+    # Check for end markers
+    for marker in end_markers:
+        marker_end = end + len(marker)
+        if marker_end <= len(text) and text[end:marker_end] == marker:
+            end = marker_end
+            break
+
+    return start, end
+
+
+async def _save_binary(
+    content: bytes,
+    ext: str,
+    wm: WorkspaceManager,
+    block: str,
+    cache: dict[str, str],
+) -> Optional[str]:
+    """
+    Save binary content to workspace with deduplication.
+
+    Returns workspace://file-id reference, or None on failure.
+    """
+    content_hash = hashlib.sha256(content).hexdigest()
+
+    if content_hash in cache:
+        return cache[content_hash]
+
+    try:
+        safe_block = sanitize_filename(block)[:20].lower()
+        filename = f"{safe_block}_{uuid.uuid4().hex[:12]}.{ext}"
+
+        # Scan for viruses before saving
+        await scan_content_safe(content, filename=filename)
+
+        file = await wm.write_file(content, filename)
+        ref = f"workspace://{file.id}"
+        cache[content_hash] = ref
+        return ref
+    except Exception as e:
+        logger.warning("Failed to save binary output: %s", e)
+        return None

autogpt_platform/backend/backend/api/features/chat/tools/check_operation_status.py

@@ -1,127 +0,0 @@
-"""CheckOperationStatusTool — query the status of a long-running operation."""
-
-import logging
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool
-from backend.api.features.chat.tools.models import (
-    ErrorResponse,
-    ResponseType,
-    ToolResponseBase,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class OperationStatusResponse(ToolResponseBase):
-    """Response for check_operation_status tool."""
-
-    type: ResponseType = ResponseType.OPERATION_STATUS
-    task_id: str
-    operation_id: str
-    status: str  # "running", "completed", "failed"
-    tool_name: str | None = None
-    message: str = ""
-
-
-class CheckOperationStatusTool(BaseTool):
-    """Check the status of a long-running operation (create_agent, edit_agent, etc.).
-
-    The CoPilot uses this tool to report back to the user whether an
-    operation that was started earlier has completed, failed, or is still
-    running.
-    """
-
-    @property
-    def name(self) -> str:
-        return "check_operation_status"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Check the current status of a long-running operation such as "
-            "create_agent or edit_agent. Accepts either an operation_id or "
-            "task_id from a previous operation_started response. "
-            "Returns the current status: running, completed, or failed."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "operation_id": {
-                    "type": "string",
-                    "description": (
-                        "The operation_id from an operation_started response."
-                    ),
-                },
-                "task_id": {
-                    "type": "string",
-                    "description": (
-                        "The task_id from an operation_started response. "
-                        "Used as fallback if operation_id is not provided."
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        from backend.api.features.chat import stream_registry
-
-        operation_id = (kwargs.get("operation_id") or "").strip()
-        task_id = (kwargs.get("task_id") or "").strip()
-
-        if not operation_id and not task_id:
-            return ErrorResponse(
-                message="Please provide an operation_id or task_id.",
-                error="missing_parameter",
-            )
-
-        task = None
-        if operation_id:
-            task = await stream_registry.find_task_by_operation_id(operation_id)
-        if task is None and task_id:
-            task = await stream_registry.get_task(task_id)
-
-        if task is None:
-            # Task not in Redis — it may have already expired (TTL).
-            # Check conversation history for the result instead.
-            return ErrorResponse(
-                message=(
-                    "Operation not found — it may have already completed and "
-                    "expired from the status tracker. Check the conversation "
-                    "history for the result."
-                ),
-                error="not_found",
-            )
-
-        status_messages = {
-            "running": (
-                f"The {task.tool_name or 'operation'} is still running. "
-                "Please wait for it to complete."
-            ),
-            "completed": (
-                f"The {task.tool_name or 'operation'} has completed successfully."
-            ),
-            "failed": f"The {task.tool_name or 'operation'} has failed.",
-        }
-
-        return OperationStatusResponse(
-            task_id=task.task_id,
-            operation_id=task.operation_id,
-            status=task.status,
-            tool_name=task.tool_name,
-            message=status_messages.get(task.status, f"Status: {task.status}"),
-        )

autogpt_platform/backend/backend/api/features/chat/tools/find_block.py

@@ -7,13 +7,13 @@ from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
 from backend.api.features.chat.tools.models import (
     BlockInfoSummary,
+    BlockInputFieldInfo,
     BlockListResponse,
     ErrorResponse,
     NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.blocks import get_block
+from backend.data.block import BlockType, get_block
-from backend.blocks._base import BlockType
 
 logger = logging.getLogger(__name__)
 
@@ -54,8 +54,7 @@ class FindBlockTool(BaseTool):
             "Blocks are reusable components that perform specific tasks like "
             "sending emails, making API calls, processing text, etc. "
             "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
-            "The response includes each block's id, name, and description. "
+            "The response includes each block's id, required_inputs, and input_schema."
-            "Call run_block with the block's id **with no inputs** to see detailed inputs/outputs and execute it."
         )
 
     @property
@@ -124,7 +123,7 @@ class FindBlockTool(BaseTool):
                     session_id=session_id,
                 )
 
-            # Enrich results with block information
+            # Enrich results with full block information
             blocks: list[BlockInfoSummary] = []
             for result in results:
                 block_id = result["content_id"]
@@ -141,12 +140,65 @@ class FindBlockTool(BaseTool):
                 ):
                     continue
 
+                # Get input/output schemas
+                input_schema = {}
+                output_schema = {}
+                try:
+                    input_schema = block.input_schema.jsonschema()
+                except Exception as e:
+                    logger.debug(
+                        "Failed to generate input schema for block %s: %s",
+                        block_id,
+                        e,
+                    )
+                try:
+                    output_schema = block.output_schema.jsonschema()
+                except Exception as e:
+                    logger.debug(
+                        "Failed to generate output schema for block %s: %s",
+                        block_id,
+                        e,
+                    )
+
+                # Get categories from block instance
+                categories = []
+                if hasattr(block, "categories") and block.categories:
+                    categories = [cat.value for cat in block.categories]
+
+                # Extract required inputs for easier use
+                required_inputs: list[BlockInputFieldInfo] = []
+                if input_schema:
+                    properties = input_schema.get("properties", {})
+                    required_fields = set(input_schema.get("required", []))
+                    # Get credential field names to exclude from required inputs
+                    credentials_fields = set(
+                        block.input_schema.get_credentials_fields().keys()
+                    )
+
+                    for field_name, field_schema in properties.items():
+                        # Skip credential fields - they're handled separately
+                        if field_name in credentials_fields:
+                            continue
+
+                        required_inputs.append(
+                            BlockInputFieldInfo(
+                                name=field_name,
+                                type=field_schema.get("type", "string"),
+                                description=field_schema.get("description", ""),
+                                required=field_name in required_fields,
+                                default=field_schema.get("default"),
+                            )
+                        )
+
                 blocks.append(
                     BlockInfoSummary(
                         id=block_id,
                         name=block.name,
                         description=block.description or "",
-                        categories=[c.value for c in block.categories],
+                        categories=categories,
+                        input_schema=input_schema,
+                        output_schema=output_schema,
+                        required_inputs=required_inputs,
                     )
                 )
 
@@ -175,7 +227,8 @@ class FindBlockTool(BaseTool):
             return BlockListResponse(
                 message=(
                     f"Found {len(blocks)} block(s) matching '{query}'. "
-                    "To see a block's inputs/outputs and execute it, use run_block with the block's 'id' - providing no inputs."
+                    "To execute a block, use run_block with the block's 'id' field "
+                    "and provide 'input_data' matching the block's input_schema."
                 ),
                 blocks=blocks,
                 count=len(blocks),

autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py

@@ -10,7 +10,7 @@ from backend.api.features.chat.tools.find_block import (
     FindBlockTool,
 )
 from backend.api.features.chat.tools.models import BlockListResponse
-from backend.blocks._base import BlockType
+from backend.data.block import BlockType
 
 from ._test_data import make_session
 
@@ -18,13 +18,7 @@ _TEST_USER_ID = "test-user-find-block"
 
 
 def make_mock_block(
-    block_id: str,
+    block_id: str, name: str, block_type: BlockType, disabled: bool = False
-    name: str,
-    block_type: BlockType,
-    disabled: bool = False,
-    input_schema: dict | None = None,
-    output_schema: dict | None = None,
-    credentials_fields: dict | None = None,
 ):
     """Create a mock block for testing."""
     mock = MagicMock()
@@ -34,13 +28,10 @@ def make_mock_block(
     mock.block_type = block_type
     mock.disabled = disabled
     mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = input_schema or {
+    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
-        "properties": {},
+    mock.input_schema.get_credentials_fields.return_value = {}
-        "required": [],
-    }
-    mock.input_schema.get_credentials_fields.return_value = credentials_fields or {}
     mock.output_schema = MagicMock()
-    mock.output_schema.jsonschema.return_value = output_schema or {}
+    mock.output_schema.jsonschema.return_value = {}
     mock.categories = []
     return mock
 
@@ -146,241 +137,3 @@ class TestFindBlockFiltering:
         assert isinstance(response, BlockListResponse)
         assert len(response.blocks) == 1
         assert response.blocks[0].id == "normal-block-id"
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_response_size_average_chars_per_block(self):
-        """Measure average chars per block in the serialized response."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        # Realistic block definitions modeled after real blocks
-        block_defs = [
-            {
-                "id": "http-block-id",
-                "name": "Send Web Request",
-                "input_schema": {
-                    "properties": {
-                        "url": {
-                            "type": "string",
-                            "description": "The URL to send the request to",
-                        },
-                        "method": {
-                            "type": "string",
-                            "description": "The HTTP method to use",
-                        },
-                        "headers": {
-                            "type": "object",
-                            "description": "Headers to include in the request",
-                        },
-                        "json_format": {
-                            "type": "boolean",
-                            "description": "If true, send the body as JSON",
-                        },
-                        "body": {
-                            "type": "object",
-                            "description": "Form/JSON body payload",
-                        },
-                        "credentials": {
-                            "type": "object",
-                            "description": "HTTP credentials",
-                        },
-                    },
-                    "required": ["url", "method"],
-                },
-                "output_schema": {
-                    "properties": {
-                        "response": {
-                            "type": "object",
-                            "description": "The response from the server",
-                        },
-                        "client_error": {
-                            "type": "object",
-                            "description": "Errors on 4xx status codes",
-                        },
-                        "server_error": {
-                            "type": "object",
-                            "description": "Errors on 5xx status codes",
-                        },
-                        "error": {
-                            "type": "string",
-                            "description": "Errors for all other exceptions",
-                        },
-                    },
-                },
-                "credentials_fields": {"credentials": True},
-            },
-            {
-                "id": "email-block-id",
-                "name": "Send Email",
-                "input_schema": {
-                    "properties": {
-                        "to_email": {
-                            "type": "string",
-                            "description": "Recipient email address",
-                        },
-                        "subject": {
-                            "type": "string",
-                            "description": "Subject of the email",
-                        },
-                        "body": {
-                            "type": "string",
-                            "description": "Body of the email",
-                        },
-                        "config": {
-                            "type": "object",
-                            "description": "SMTP Config",
-                        },
-                        "credentials": {
-                            "type": "object",
-                            "description": "SMTP credentials",
-                        },
-                    },
-                    "required": ["to_email", "subject", "body", "credentials"],
-                },
-                "output_schema": {
-                    "properties": {
-                        "status": {
-                            "type": "string",
-                            "description": "Status of the email sending operation",
-                        },
-                        "error": {
-                            "type": "string",
-                            "description": "Error message if sending failed",
-                        },
-                    },
-                },
-                "credentials_fields": {"credentials": True},
-            },
-            {
-                "id": "claude-code-block-id",
-                "name": "Claude Code",
-                "input_schema": {
-                    "properties": {
-                        "e2b_credentials": {
-                            "type": "object",
-                            "description": "API key for E2B platform",
-                        },
-                        "anthropic_credentials": {
-                            "type": "object",
-                            "description": "API key for Anthropic",
-                        },
-                        "prompt": {
-                            "type": "string",
-                            "description": "Task or instruction for Claude Code",
-                        },
-                        "timeout": {
-                            "type": "integer",
-                            "description": "Sandbox timeout in seconds",
-                        },
-                        "setup_commands": {
-                            "type": "array",
-                            "description": "Shell commands to run before execution",
-                        },
-                        "working_directory": {
-                            "type": "string",
-                            "description": "Working directory for Claude Code",
-                        },
-                        "session_id": {
-                            "type": "string",
-                            "description": "Session ID to resume a conversation",
-                        },
-                        "sandbox_id": {
-                            "type": "string",
-                            "description": "Sandbox ID to reconnect to",
-                        },
-                        "conversation_history": {
-                            "type": "string",
-                            "description": "Previous conversation history",
-                        },
-                        "dispose_sandbox": {
-                            "type": "boolean",
-                            "description": "Whether to dispose sandbox after execution",
-                        },
-                    },
-                    "required": [
-                        "e2b_credentials",
-                        "anthropic_credentials",
-                        "prompt",
-                    ],
-                },
-                "output_schema": {
-                    "properties": {
-                        "response": {
-                            "type": "string",
-                            "description": "Output from Claude Code execution",
-                        },
-                        "files": {
-                            "type": "array",
-                            "description": "Files created/modified by Claude Code",
-                        },
-                        "conversation_history": {
-                            "type": "string",
-                            "description": "Full conversation history",
-                        },
-                        "session_id": {
-                            "type": "string",
-                            "description": "Session ID for this conversation",
-                        },
-                        "sandbox_id": {
-                            "type": "string",
-                            "description": "ID of the sandbox instance",
-                        },
-                        "error": {
-                            "type": "string",
-                            "description": "Error message if execution failed",
-                        },
-                    },
-                },
-                "credentials_fields": {
-                    "e2b_credentials": True,
-                    "anthropic_credentials": True,
-                },
-            },
-        ]
-
-        search_results = [
-            {"content_id": d["id"], "score": 0.9 - i * 0.1}
-            for i, d in enumerate(block_defs)
-        ]
-        mock_blocks = {
-            d["id"]: make_mock_block(
-                block_id=d["id"],
-                name=d["name"],
-                block_type=BlockType.STANDARD,
-                input_schema=d["input_schema"],
-                output_schema=d["output_schema"],
-                credentials_fields=d["credentials_fields"],
-            )
-            for d in block_defs
-        }
-
-        with patch(
-            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
-            new_callable=AsyncMock,
-            return_value=(search_results, len(search_results)),
-        ), patch(
-            "backend.api.features.chat.tools.find_block.get_block",
-            side_effect=lambda bid: mock_blocks.get(bid),
-        ):
-            tool = FindBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID, session=session, query="test"
-            )
-
-        assert isinstance(response, BlockListResponse)
-        assert response.count == len(block_defs)
-
-        total_chars = len(response.model_dump_json())
-        avg_chars = total_chars // response.count
-
-        # Print for visibility in test output
-        print(f"\nTotal response size: {total_chars} chars")
-        print(f"Number of blocks: {response.count}")
-        print(f"Average chars per block: {avg_chars}")
-
-        # The old response was ~90K for 10 blocks (~9K per block).
-        # Previous optimization reduced it to ~1.5K per block (no raw JSON schemas).
-        # Now with only id/name/description, we expect ~300 chars per block.
-        assert avg_chars < 500, (
-            f"Average chars per block ({avg_chars}) exceeds 500. "
-            f"Total response: {total_chars} chars for {response.count} blocks."
-        )

autogpt_platform/backend/backend/api/features/chat/tools/helpers.py

@@ -1,29 +0,0 @@
-"""Shared helpers for chat tools."""
-
-from typing import Any
-
-
-def get_inputs_from_schema(
-    input_schema: dict[str, Any],
-    exclude_fields: set[str] | None = None,
-) -> list[dict[str, Any]]:
-    """Extract input field info from JSON schema."""
-    if not isinstance(input_schema, dict):
-        return []
-
-    exclude = exclude_fields or set()
-    properties = input_schema.get("properties", {})
-    required = set(input_schema.get("required", []))
-
-    return [
-        {
-            "name": name,
-            "title": schema.get("title", name),
-            "type": schema.get("type", "string"),
-            "description": schema.get("description", ""),
-            "required": name in required,
-            "default": schema.get("default"),
-        }
-        for name, schema in properties.items()
-        if name not in exclude
-    ]

autogpt_platform/backend/backend/api/features/chat/tools/models.py

@@ -25,7 +25,6 @@ class ResponseType(str, Enum):
     AGENT_SAVED = "agent_saved"
     CLARIFICATION_NEEDED = "clarification_needed"
     BLOCK_LIST = "block_list"
-    BLOCK_DETAILS = "block_details"
     BLOCK_OUTPUT = "block_output"
     DOC_SEARCH_RESULTS = "doc_search_results"
     DOC_PAGE = "doc_page"
@@ -41,12 +40,6 @@ class ResponseType(str, Enum):
     OPERATION_IN_PROGRESS = "operation_in_progress"
     # Input validation
     INPUT_VALIDATION_ERROR = "input_validation_error"
-    # Web fetch
-    WEB_FETCH = "web_fetch"
-    # Code execution
-    BASH_EXEC = "bash_exec"
-    # Operation status check
-    OPERATION_STATUS = "operation_status"
 
 
 # Base response model
@@ -342,17 +335,11 @@ class BlockInfoSummary(BaseModel):
     name: str
     description: str
     categories: list[str]
-    input_schema: dict[str, Any] = Field(
+    input_schema: dict[str, Any]
-        default_factory=dict,
+    output_schema: dict[str, Any]
-        description="Full JSON schema for block inputs",
-    )
-    output_schema: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Full JSON schema for block outputs",
-    )
     required_inputs: list[BlockInputFieldInfo] = Field(
         default_factory=list,
-        description="List of input fields for this block",
+        description="List of required input fields for this block",
     )
 
 
@@ -365,29 +352,10 @@ class BlockListResponse(ToolResponseBase):
     query: str
     usage_hint: str = Field(
         default="To execute a block, call run_block with block_id set to the block's "
-        "'id' field and input_data containing the fields listed in required_inputs."
+        "'id' field and input_data containing the required fields from input_schema."
     )
 
 
-class BlockDetails(BaseModel):
-    """Detailed block information."""
-
-    id: str
-    name: str
-    description: str
-    inputs: dict[str, Any] = {}
-    outputs: dict[str, Any] = {}
-    credentials: list[CredentialsMetaInput] = []
-
-
-class BlockDetailsResponse(ToolResponseBase):
-    """Response for block details (first run_block attempt)."""
-
-    type: ResponseType = ResponseType.BLOCK_DETAILS
-    block: BlockDetails
-    user_authenticated: bool = False
-
-
 class BlockOutputResponse(ToolResponseBase):
     """Response for run_block tool."""
 
@@ -453,24 +421,3 @@ class AsyncProcessingResponse(ToolResponseBase):
     status: str = "accepted"  # Must be "accepted" for detection
     operation_id: str | None = None
     task_id: str | None = None
-
-
-class WebFetchResponse(ToolResponseBase):
-    """Response for web_fetch tool."""
-
-    type: ResponseType = ResponseType.WEB_FETCH
-    url: str
-    status_code: int
-    content_type: str
-    content: str
-    truncated: bool = False
-
-
-class BashExecResponse(ToolResponseBase):
-    """Response for bash_exec tool."""
-
-    type: ResponseType = ResponseType.BASH_EXEC
-    stdout: str
-    stderr: str
-    exit_code: int
-    timed_out: bool = False

autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py

@@ -24,7 +24,6 @@ from backend.util.timezone_utils import (
 )
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
     AgentDetails,
     AgentDetailsResponse,
@@ -262,7 +261,7 @@ class RunAgentTool(BaseTool):
                         ),
                         requirements={
                             "credentials": requirements_creds_list,
-                            "inputs": get_inputs_from_schema(graph.input_schema),
+                            "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },
                     ),
@@ -370,6 +369,22 @@ class RunAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
+        """Extract inputs list from schema."""
+        inputs_list = []
+        if isinstance(input_schema, dict) and "properties" in input_schema:
+            for field_name, field_schema in input_schema["properties"].items():
+                inputs_list.append(
+                    {
+                        "name": field_name,
+                        "title": field_schema.get("title", field_name),
+                        "type": field_schema.get("type", "string"),
+                        "description": field_schema.get("description", ""),
+                        "required": field_name in input_schema.get("required", []),
+                    }
+                )
+        return inputs_list
+
     def _get_execution_modes(self, graph: GraphModel) -> list[str]:
         """Get available execution modes for the graph."""
         trigger_info = graph.trigger_setup_info
@@ -383,7 +398,7 @@ class RunAgentTool(BaseTool):
         suffix: str,
     ) -> str:
         """Build a message describing available inputs for an agent."""
-        inputs_list = get_inputs_from_schema(graph.input_schema)
+        inputs_list = self._get_inputs_list(graph.input_schema)
         required_names = [i["name"] for i in inputs_list if i["required"]]
         optional_names = [i["name"] for i in inputs_list if not i["required"]]
 

autogpt_platform/backend/backend/api/features/chat/tools/run_block.py

@@ -12,31 +12,25 @@ from backend.api.features.chat.tools.find_block import (
     COPILOT_EXCLUDED_BLOCK_IDS,
     COPILOT_EXCLUDED_BLOCK_TYPES,
 )
-from backend.blocks import get_block
+from backend.data.block import get_block
-from backend.blocks._base import AnyBlockSchema
 from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
+from backend.util.workspace import WorkspaceManager
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
+from .binary_output_processor import process_binary_outputs
 from .models import (
-    BlockDetails,
-    BlockDetailsResponse,
     BlockOutputResponse,
     ErrorResponse,
-    InputValidationErrorResponse,
     SetupInfo,
     SetupRequirementsResponse,
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import (
+from .utils import build_missing_credentials_from_field_info
-    build_missing_credentials_from_field_info,
-    match_credentials_to_requirements,
-)
 
 logger = logging.getLogger(__name__)
 
@@ -54,8 +48,8 @@ class RunBlockTool(BaseTool):
             "Execute a specific block with the provided input data. "
             "IMPORTANT: You MUST call find_block first to get the block's 'id' - "
             "do NOT guess or make up block IDs. "
-            "On first attempt (without input_data), returns detailed schema showing "
+            "Use the 'id' from find_block results and provide input_data "
-            "required inputs and outputs. Then call again with proper input_data to execute."
+            "matching the block's required_inputs."
         )
 
     @property
@@ -70,19 +64,11 @@ class RunBlockTool(BaseTool):
                         "NEVER guess this - always get it from find_block first."
                     ),
                 },
-                "block_name": {
-                    "type": "string",
-                    "description": (
-                        "The block's human-readable name from find_block results. "
-                        "Used for display purposes in the UI."
-                    ),
-                },
                 "input_data": {
                     "type": "object",
                     "description": (
-                        "Input values for the block. "
+                        "Input values for the block. Use the 'required_inputs' field "
-                        "First call with empty {} to see the block's schema, "
+                        "from find_block to see what fields are needed."
-                        "then call again with proper values to execute."
                     ),
                 },
             },
@@ -93,6 +79,91 @@ class RunBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    async def _check_block_credentials(
+        self,
+        user_id: str,
+        block: Any,
+        input_data: dict[str, Any] | None = None,
+    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
+        """
+        Check if user has required credentials for a block.
+
+        Args:
+            user_id: User ID
+            block: Block to check credentials for
+            input_data: Input data for the block (used to determine provider via discriminator)
+
+        Returns:
+            tuple[matched_credentials, missing_credentials]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+        input_data = input_data or {}
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            effective_field_info = field_info
+            if field_info.discriminator and field_info.discriminator_mapping:
+                # Get discriminator from input, falling back to schema default
+                discriminator_value = input_data.get(field_info.discriminator)
+                if discriminator_value is None:
+                    field = block.input_schema.model_fields.get(
+                        field_info.discriminator
+                    )
+                    if field and field.default is not PydanticUndefined:
+                        discriminator_value = field.default
+
+                if (
+                    discriminator_value
+                    and discriminator_value in field_info.discriminator_mapping
+                ):
+                    effective_field_info = field_info.discriminate(discriminator_value)
+                    logger.debug(
+                        f"Discriminated provider for {field_name}: "
+                        f"{discriminator_value} -> {effective_field_info.provider}"
+                    )
+
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in effective_field_info.provider
+                    and cred.type in effective_field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(effective_field_info.provider), "unknown")
+                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
     async def _execute(
         self,
         user_id: str | None,
@@ -163,38 +234,10 @@ class RunBlockTool(BaseTool):
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
         creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = (
+        matched_credentials, missing_credentials = await self._check_block_credentials(
-            await self._resolve_block_credentials(user_id, block, input_data)
+            user_id, block, input_data
         )
 
-        # Get block schemas for details/validation
-        try:
-            input_schema: dict[str, Any] = block.input_schema.jsonschema()
-        except Exception as e:
-            logger.warning(
-                "Failed to generate input schema for block %s: %s",
-                block_id,
-                e,
-            )
-            return ErrorResponse(
-                message=f"Block '{block.name}' has an invalid input schema",
-                error=str(e),
-                session_id=session_id,
-            )
-        try:
-            output_schema: dict[str, Any] = block.output_schema.jsonschema()
-        except Exception as e:
-            logger.warning(
-                "Failed to generate output schema for block %s: %s",
-                block_id,
-                e,
-            )
-            return ErrorResponse(
-                message=f"Block '{block.name}' has an invalid output schema",
-                error=str(e),
-                session_id=session_id,
-            )
-
         if missing_credentials:
             # Return setup requirements response with missing credentials
             credentials_fields_info = block.input_schema.get_credentials_fields_info()
@@ -227,53 +270,6 @@ class RunBlockTool(BaseTool):
                 graph_version=None,
             )
 
-        # Check if this is a first attempt (required inputs missing)
-        # Return block details so user can see what inputs are needed
-        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-        required_keys = set(input_schema.get("required", []))
-        required_non_credential_keys = required_keys - credentials_fields
-        provided_input_keys = set(input_data.keys()) - credentials_fields
-
-        # Check for unknown input fields
-        valid_fields = (
-            set(input_schema.get("properties", {}).keys()) - credentials_fields
-        )
-        unrecognized_fields = provided_input_keys - valid_fields
-        if unrecognized_fields:
-            return InputValidationErrorResponse(
-                message=(
-                    f"Unknown input field(s) provided: {', '.join(sorted(unrecognized_fields))}. "
-                    f"Block was not executed. Please use the correct field names from the schema."
-                ),
-                session_id=session_id,
-                unrecognized_fields=sorted(unrecognized_fields),
-                inputs=input_schema,
-            )
-
-        # Show details when not all required non-credential inputs are provided
-        if not (required_non_credential_keys <= provided_input_keys):
-            # Get credentials info for the response
-            credentials_meta = []
-            for field_name, cred_meta in matched_credentials.items():
-                credentials_meta.append(cred_meta)
-
-            return BlockDetailsResponse(
-                message=(
-                    f"Block '{block.name}' details. "
-                    "Provide input_data matching the inputs schema to execute the block."
-                ),
-                session_id=session_id,
-                block=BlockDetails(
-                    id=block_id,
-                    name=block.name,
-                    description=block.description or "",
-                    inputs=input_schema,
-                    outputs=output_schema,
-                    credentials=credentials_meta,
-                ),
-                user_authenticated=True,
-            )
-
         try:
             # Get or create user's workspace for CoPilot file operations
             workspace = await get_or_create_workspace(user_id)
@@ -344,6 +340,16 @@ class RunBlockTool(BaseTool):
             ):
                 outputs[output_name].append(output_data)
 
+            # Post-process outputs to save binary content to workspace
+            workspace_manager = WorkspaceManager(
+                user_id=user_id,
+                workspace_id=workspace.id,
+                session_id=session.session_id,
+            )
+            outputs = await process_binary_outputs(
+                dict(outputs), workspace_manager, block.name
+            )
+
             return BlockOutputResponse(
                 message=f"Block '{block.name}' executed successfully",
                 block_id=block_id,
@@ -368,75 +374,29 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-    async def _resolve_block_credentials(
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
-        self,
-        user_id: str,
-        block: AnyBlockSchema,
-        input_data: dict[str, Any] | None = None,
-    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-        """
-        Resolve credentials for a block by matching user's available credentials.
-
-        Args:
-            user_id: User ID
-            block: Block to resolve credentials for
-            input_data: Input data for the block (used to determine provider via discriminator)
-
-        Returns:
-            tuple of (matched_credentials, missing_credentials) - matched credentials
-            are used for block execution, missing ones indicate setup requirements.
-        """
-        input_data = input_data or {}
-        requirements = self._resolve_discriminated_credentials(block, input_data)
-
-        if not requirements:
-            return {}, []
-
-        return await match_credentials_to_requirements(user_id, requirements)
-
-    def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
         """Extract non-credential inputs from block schema."""
+        inputs_list = []
         schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
         credentials_fields = set(block.input_schema.get_credentials_fields().keys())
-        return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
 
-    def _resolve_discriminated_credentials(
+        for field_name, field_schema in properties.items():
-        self,
+            # Skip credential fields
-        block: AnyBlockSchema,
+            if field_name in credentials_fields:
-        input_data: dict[str, Any],
+                continue
-    ) -> dict[str, CredentialsFieldInfo]:
-        """Resolve credential requirements, applying discriminator logic where needed."""
-        credentials_fields_info = block.input_schema.get_credentials_fields_info()
-        if not credentials_fields_info:
-            return {}
 
-        resolved: dict[str, CredentialsFieldInfo] = {}
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )
 
-        for field_name, field_info in credentials_fields_info.items():
+        return inputs_list
-            effective_field_info = field_info
-
-            if field_info.discriminator and field_info.discriminator_mapping:
-                discriminator_value = input_data.get(field_info.discriminator)
-                if discriminator_value is None:
-                    field = block.input_schema.model_fields.get(
-                        field_info.discriminator
-                    )
-                    if field and field.default is not PydanticUndefined:
-                        discriminator_value = field.default
-
-                if (
-                    discriminator_value
-                    and discriminator_value in field_info.discriminator_mapping
-                ):
-                    effective_field_info = field_info.discriminate(discriminator_value)
-                    # For host-scoped credentials, add the discriminator value
-                    # (e.g., URL) so _credential_is_for_host can match it
-                    effective_field_info.discriminator_values.add(discriminator_value)
-                    logger.debug(
-                        f"Discriminated provider for {field_name}: "
-                        f"{discriminator_value} -> {effective_field_info.provider}"
-                    )
-
-            resolved[field_name] = effective_field_info
-
-        return resolved

autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py

@@ -1,17 +1,12 @@
-"""Tests for block execution guards and input validation in RunBlockTool."""
+"""Tests for block execution guards in RunBlockTool."""
 
-from unittest.mock import AsyncMock, MagicMock, patch
+from unittest.mock import MagicMock, patch
 
 import pytest
 
-from backend.api.features.chat.tools.models import (
+from backend.api.features.chat.tools.models import ErrorResponse
-    BlockDetailsResponse,
-    BlockOutputResponse,
-    ErrorResponse,
-    InputValidationErrorResponse,
-)
 from backend.api.features.chat.tools.run_block import RunBlockTool
-from backend.blocks._base import BlockType
+from backend.data.block import BlockType
 
 from ._test_data import make_session
 
@@ -33,39 +28,6 @@ def make_mock_block(
     return mock
 
 
-def make_mock_block_with_schema(
-    block_id: str,
-    name: str,
-    input_properties: dict,
-    required_fields: list[str],
-    output_properties: dict | None = None,
-):
-    """Create a mock block with a defined input/output schema for validation tests."""
-    mock = MagicMock()
-    mock.id = block_id
-    mock.name = name
-    mock.block_type = BlockType.STANDARD
-    mock.disabled = False
-    mock.description = f"Test block: {name}"
-
-    input_schema = {
-        "properties": input_properties,
-        "required": required_fields,
-    }
-    mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = input_schema
-    mock.input_schema.get_credentials_fields_info.return_value = {}
-    mock.input_schema.get_credentials_fields.return_value = {}
-
-    output_schema = {
-        "properties": output_properties or {"result": {"type": "string"}},
-    }
-    mock.output_schema = MagicMock()
-    mock.output_schema.jsonschema.return_value = output_schema
-
-    return mock
-
-
 class TestRunBlockFiltering:
     """Tests for block execution guards in RunBlockTool."""
 
@@ -142,221 +104,3 @@ class TestRunBlockFiltering:
         # (may be other errors like missing credentials, but not the exclusion guard)
         if isinstance(response, ErrorResponse):
             assert "cannot be run directly in CoPilot" not in response.message
-
-
-class TestRunBlockInputValidation:
-    """Tests for input field validation in RunBlockTool.
-
-    run_block rejects unknown input field names with InputValidationErrorResponse,
-    preventing silent failures where incorrect keys would be ignored and the block
-    would execute with default values instead of the caller's intended values.
-    """
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_unknown_input_fields_are_rejected(self):
-        """run_block rejects unknown input fields instead of silently ignoring them.
-
-        Scenario: The AI Text Generator block has a field called 'model' (for LLM model
-        selection), but the LLM calling the tool guesses wrong and sends 'LLM_Model'
-        instead. The block should reject the request and return the valid schema.
-        """
-        session = make_session(user_id=_TEST_USER_ID)
-
-        mock_block = make_mock_block_with_schema(
-            block_id="ai-text-gen-id",
-            name="AI Text Generator",
-            input_properties={
-                "prompt": {"type": "string", "description": "The prompt to send"},
-                "model": {
-                    "type": "string",
-                    "description": "The LLM model to use",
-                    "default": "gpt-4o-mini",
-                },
-                "sys_prompt": {
-                    "type": "string",
-                    "description": "System prompt",
-                    "default": "",
-                },
-            },
-            required_fields=["prompt"],
-            output_properties={"response": {"type": "string"}},
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=mock_block,
-        ):
-            tool = RunBlockTool()
-
-            # Provide 'prompt' (correct) but 'LLM_Model' instead of 'model' (wrong key)
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="ai-text-gen-id",
-                input_data={
-                    "prompt": "Write a haiku about coding",
-                    "LLM_Model": "claude-opus-4-6",  # WRONG KEY - should be 'model'
-                },
-            )
-
-        assert isinstance(response, InputValidationErrorResponse)
-        assert "LLM_Model" in response.unrecognized_fields
-        assert "Block was not executed" in response.message
-        assert "inputs" in response.model_dump()  # valid schema included
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_multiple_wrong_keys_are_all_reported(self):
-        """All unrecognized field names are reported in a single error response."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        mock_block = make_mock_block_with_schema(
-            block_id="ai-text-gen-id",
-            name="AI Text Generator",
-            input_properties={
-                "prompt": {"type": "string"},
-                "model": {"type": "string", "default": "gpt-4o-mini"},
-                "sys_prompt": {"type": "string", "default": ""},
-                "retry": {"type": "integer", "default": 3},
-            },
-            required_fields=["prompt"],
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=mock_block,
-        ):
-            tool = RunBlockTool()
-
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="ai-text-gen-id",
-                input_data={
-                    "prompt": "Hello",  # correct
-                    "llm_model": "claude-opus-4-6",  # WRONG - should be 'model'
-                    "system_prompt": "Be helpful",  # WRONG - should be 'sys_prompt'
-                    "retries": 5,  # WRONG - should be 'retry'
-                },
-            )
-
-        assert isinstance(response, InputValidationErrorResponse)
-        assert set(response.unrecognized_fields) == {
-            "llm_model",
-            "system_prompt",
-            "retries",
-        }
-        assert "Block was not executed" in response.message
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_unknown_fields_rejected_even_with_missing_required(self):
-        """Unknown fields are caught before the missing-required-fields check."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        mock_block = make_mock_block_with_schema(
-            block_id="ai-text-gen-id",
-            name="AI Text Generator",
-            input_properties={
-                "prompt": {"type": "string"},
-                "model": {"type": "string", "default": "gpt-4o-mini"},
-            },
-            required_fields=["prompt"],
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=mock_block,
-        ):
-            tool = RunBlockTool()
-
-            # 'prompt' is missing AND 'LLM_Model' is an unknown field
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="ai-text-gen-id",
-                input_data={
-                    "LLM_Model": "claude-opus-4-6",  # wrong key, and 'prompt' is missing
-                },
-            )
-
-        # Unknown fields are caught first
-        assert isinstance(response, InputValidationErrorResponse)
-        assert "LLM_Model" in response.unrecognized_fields
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_correct_inputs_still_execute(self):
-        """Correct input field names pass validation and the block executes."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        mock_block = make_mock_block_with_schema(
-            block_id="ai-text-gen-id",
-            name="AI Text Generator",
-            input_properties={
-                "prompt": {"type": "string"},
-                "model": {"type": "string", "default": "gpt-4o-mini"},
-            },
-            required_fields=["prompt"],
-        )
-
-        async def mock_execute(input_data, **kwargs):
-            yield "response", "Generated text"
-
-        mock_block.execute = mock_execute
-
-        with (
-            patch(
-                "backend.api.features.chat.tools.run_block.get_block",
-                return_value=mock_block,
-            ),
-            patch(
-                "backend.api.features.chat.tools.run_block.get_or_create_workspace",
-                new_callable=AsyncMock,
-                return_value=MagicMock(id="test-workspace-id"),
-            ),
-        ):
-            tool = RunBlockTool()
-
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="ai-text-gen-id",
-                input_data={
-                    "prompt": "Write a haiku",
-                    "model": "gpt-4o-mini",  # correct field name
-                },
-            )
-
-        assert isinstance(response, BlockOutputResponse)
-        assert response.success is True
-
-    @pytest.mark.asyncio(loop_scope="session")
-    async def test_missing_required_fields_returns_details(self):
-        """Missing required fields returns BlockDetailsResponse with schema."""
-        session = make_session(user_id=_TEST_USER_ID)
-
-        mock_block = make_mock_block_with_schema(
-            block_id="ai-text-gen-id",
-            name="AI Text Generator",
-            input_properties={
-                "prompt": {"type": "string"},
-                "model": {"type": "string", "default": "gpt-4o-mini"},
-            },
-            required_fields=["prompt"],
-        )
-
-        with patch(
-            "backend.api.features.chat.tools.run_block.get_block",
-            return_value=mock_block,
-        ):
-            tool = RunBlockTool()
-
-            # Only provide valid optional field, missing required 'prompt'
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="ai-text-gen-id",
-                input_data={
-                    "model": "gpt-4o-mini",  # valid but optional
-                },
-            )
-
-        assert isinstance(response, BlockDetailsResponse)

autogpt_platform/backend/backend/api/features/chat/tools/sandbox.py

@@ -1,265 +0,0 @@
-"""Sandbox execution utilities for code execution tools.
-
-Provides filesystem + network isolated command execution using **bubblewrap**
-(``bwrap``): whitelist-only filesystem (only system dirs visible read-only),
-writable workspace only, clean environment, network blocked.
-
-Tools that call :func:`run_sandboxed` must first check :func:`has_full_sandbox`
-and refuse to run if bubblewrap is not available.
-"""
-
-import asyncio
-import logging
-import os
-import platform
-import shutil
-
-logger = logging.getLogger(__name__)
-
-_DEFAULT_TIMEOUT = 30
-_MAX_TIMEOUT = 120
-
-
-# ---------------------------------------------------------------------------
-# Sandbox capability detection (cached at first call)
-# ---------------------------------------------------------------------------
-
-_BWRAP_AVAILABLE: bool | None = None
-
-
-def has_full_sandbox() -> bool:
-    """Return True if bubblewrap is available (filesystem + network isolation).
-
-    On non-Linux platforms (macOS), always returns False.
-    """
-    global _BWRAP_AVAILABLE
-    if _BWRAP_AVAILABLE is None:
-        _BWRAP_AVAILABLE = (
-            platform.system() == "Linux" and shutil.which("bwrap") is not None
-        )
-    return _BWRAP_AVAILABLE
-
-
-WORKSPACE_PREFIX = "/tmp/copilot-"
-
-
-def make_session_path(session_id: str) -> str:
-    """Build a sanitized, session-specific path under :data:`WORKSPACE_PREFIX`.
-
-    Shared by both the SDK working-directory setup and the sandbox tools so
-    they always resolve to the same directory for a given session.
-
-    Steps:
-        1. Strip all characters except ``[A-Za-z0-9-]``.
-        2. Construct ``/tmp/copilot-<safe_id>``.
-        3. Validate via ``os.path.normpath`` + ``startswith`` (CodeQL-recognised
-           sanitizer) to prevent path traversal.
-
-    Raises:
-        ValueError: If the resulting path escapes the prefix.
-    """
-    import re
-
-    safe_id = re.sub(r"[^A-Za-z0-9-]", "", session_id)
-    if not safe_id:
-        safe_id = "default"
-    path = os.path.normpath(f"{WORKSPACE_PREFIX}{safe_id}")
-    if not path.startswith(WORKSPACE_PREFIX):
-        raise ValueError(f"Session path escaped prefix: {path}")
-    return path
-
-
-def get_workspace_dir(session_id: str) -> str:
-    """Get or create the workspace directory for a session.
-
-    Uses :func:`make_session_path` — the same path the SDK uses — so that
-    bash_exec shares the workspace with the SDK file tools.
-    """
-    workspace = make_session_path(session_id)
-    os.makedirs(workspace, exist_ok=True)
-    return workspace
-
-
-# ---------------------------------------------------------------------------
-# Bubblewrap command builder
-# ---------------------------------------------------------------------------
-
-# System directories mounted read-only inside the sandbox.
-# ONLY these are visible — /app, /root, /home, /opt, /var etc. are NOT accessible.
-_SYSTEM_RO_BINDS = [
-    "/usr",  # binaries, libraries, Python interpreter
-    "/etc",  # system config: ld.so, locale, passwd, alternatives
-]
-
-# Compat paths: symlinks to /usr/* on modern Debian, real dirs on older systems.
-# On Debian 13 these are symlinks (e.g. /bin -> usr/bin).  bwrap --ro-bind
-# can't create a symlink target, so we detect and use --symlink instead.
-# /lib64 is critical: the ELF dynamic linker lives at /lib64/ld-linux-x86-64.so.2.
-_COMPAT_PATHS = [
-    ("/bin", "usr/bin"),  # -> /usr/bin on Debian 13
-    ("/sbin", "usr/sbin"),  # -> /usr/sbin on Debian 13
-    ("/lib", "usr/lib"),  # -> /usr/lib on Debian 13
-    ("/lib64", "usr/lib64"),  # 64-bit libraries / ELF interpreter
-]
-
-# Resource limits to prevent fork bombs, memory exhaustion, and disk abuse.
-# Applied via ulimit inside the sandbox before exec'ing the user command.
-_RESOURCE_LIMITS = (
-    "ulimit -u 64"  # max 64 processes  (prevents fork bombs)
-    " -v 524288"  # 512 MB virtual memory
-    " -f 51200"  # 50 MB max file size  (1024-byte blocks)
-    " -n 256"  # 256 open file descriptors
-    " 2>/dev/null"
-)
-
-
-def _build_bwrap_command(
-    command: list[str], cwd: str, env: dict[str, str]
-) -> list[str]:
-    """Build a bubblewrap command with strict filesystem + network isolation.
-
-    Security model:
-    - **Whitelist-only filesystem**: only system directories (``/usr``, ``/etc``,
-      ``/bin``, ``/lib``) are mounted read-only.  Application code (``/app``),
-      home directories, ``/var``, ``/opt``, etc. are NOT accessible at all.
-    - **Writable workspace only**: the per-session workspace is the sole
-      writable path.
-    - **Clean environment**: ``--clearenv`` wipes all inherited env vars.
-      Only the explicitly-passed safe env vars are set inside the sandbox.
-    - **Network isolation**: ``--unshare-net`` blocks all network access.
-    - **Resource limits**: ulimit caps on processes (64), memory (512MB),
-      file size (50MB), and open FDs (256) to prevent fork bombs and abuse.
-    - **New session**: prevents terminal control escape.
-    - **Die with parent**: prevents orphaned sandbox processes.
-    """
-    cmd = [
-        "bwrap",
-        # Create a new user namespace so bwrap can set up sandboxing
-        # inside unprivileged Docker containers (no CAP_SYS_ADMIN needed).
-        "--unshare-user",
-        # Wipe all inherited environment variables (API keys, secrets, etc.)
-        "--clearenv",
-    ]
-
-    # Set only the safe env vars inside the sandbox
-    for key, value in env.items():
-        cmd.extend(["--setenv", key, value])
-
-    # System directories: read-only
-    for path in _SYSTEM_RO_BINDS:
-        cmd.extend(["--ro-bind", path, path])
-
-    # Compat paths: use --symlink when host path is a symlink (Debian 13),
-    # --ro-bind when it's a real directory (older distros).
-    for path, symlink_target in _COMPAT_PATHS:
-        if os.path.islink(path):
-            cmd.extend(["--symlink", symlink_target, path])
-        elif os.path.exists(path):
-            cmd.extend(["--ro-bind", path, path])
-
-    # Wrap the user command with resource limits:
-    #   sh -c 'ulimit ...; exec "$@"' -- <original command>
-    # `exec "$@"` replaces the shell so there's no extra process overhead,
-    # and properly handles arguments with spaces.
-    limited_command = [
-        "sh",
-        "-c",
-        f'{_RESOURCE_LIMITS}; exec "$@"',
-        "--",
-        *command,
-    ]
-
-    cmd.extend(
-        [
-            # Fresh virtual filesystems
-            "--dev",
-            "/dev",
-            "--proc",
-            "/proc",
-            "--tmpfs",
-            "/tmp",
-            # Workspace bind AFTER --tmpfs /tmp so it's visible through the tmpfs.
-            # (workspace lives under /tmp/copilot-<session>)
-            "--bind",
-            cwd,
-            cwd,
-            # Isolation
-            "--unshare-net",
-            "--die-with-parent",
-            "--new-session",
-            "--chdir",
-            cwd,
-            "--",
-            *limited_command,
-        ]
-    )
-
-    return cmd
-
-
-# ---------------------------------------------------------------------------
-# Public API
-# ---------------------------------------------------------------------------
-
-
-async def run_sandboxed(
-    command: list[str],
-    cwd: str,
-    timeout: int = _DEFAULT_TIMEOUT,
-    env: dict[str, str] | None = None,
-) -> tuple[str, str, int, bool]:
-    """Run a command inside a bubblewrap sandbox.
-
-    Callers **must** check :func:`has_full_sandbox` before calling this
-    function.  If bubblewrap is not available, this function raises
-    :class:`RuntimeError` rather than running unsandboxed.
-
-    Returns:
-        (stdout, stderr, exit_code, timed_out)
-    """
-    if not has_full_sandbox():
-        raise RuntimeError(
-            "run_sandboxed() requires bubblewrap but bwrap is not available. "
-            "Callers must check has_full_sandbox() before calling this function."
-        )
-
-    timeout = min(max(timeout, 1), _MAX_TIMEOUT)
-
-    safe_env = {
-        "PATH": "/usr/local/bin:/usr/bin:/bin",
-        "HOME": cwd,
-        "TMPDIR": cwd,
-        "LANG": "en_US.UTF-8",
-        "PYTHONDONTWRITEBYTECODE": "1",
-        "PYTHONIOENCODING": "utf-8",
-    }
-    if env:
-        safe_env.update(env)
-
-    full_command = _build_bwrap_command(command, cwd, safe_env)
-
-    try:
-        proc = await asyncio.create_subprocess_exec(
-            *full_command,
-            stdout=asyncio.subprocess.PIPE,
-            stderr=asyncio.subprocess.PIPE,
-            cwd=cwd,
-            env=safe_env,
-        )
-
-        try:
-            stdout_bytes, stderr_bytes = await asyncio.wait_for(
-                proc.communicate(), timeout=timeout
-            )
-            stdout = stdout_bytes.decode("utf-8", errors="replace")
-            stderr = stderr_bytes.decode("utf-8", errors="replace")
-            return stdout, stderr, proc.returncode or 0, False
-        except asyncio.TimeoutError:
-            proc.kill()
-            await proc.communicate()
-            return "", f"Execution timed out after {timeout}s", -1, True
-
-    except RuntimeError:
-        raise
-    except Exception as e:
-        return "", f"Sandbox error: {e}", -1, False

autogpt_platform/backend/backend/api/features/chat/tools/test_binary_output_processor.py

@@ -0,0 +1,518 @@
+"""Tests for embedded binary detection in block outputs."""
+
+import base64
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from .binary_output_processor import (
+    _decode_and_validate,
+    _expand_to_markers,
+    process_binary_outputs,
+)
+
+
+@pytest.fixture
+def mock_workspace_manager():
+    """Create a mock workspace manager that returns predictable file IDs."""
+    wm = MagicMock()
+
+    async def mock_write_file(content, filename):
+        file = MagicMock()
+        file.id = f"file-{filename[:10]}"
+        return file
+
+    wm.write_file = AsyncMock(side_effect=mock_write_file)
+    return wm
+
+
+def _make_pdf_base64(size: int = 2000) -> str:
+    """Create a valid PDF base64 string of specified size."""
+    pdf_content = b"%PDF-1.4 " + b"x" * size
+    return base64.b64encode(pdf_content).decode()
+
+
+def _make_png_base64(size: int = 2000) -> str:
+    """Create a valid PNG base64 string of specified size."""
+    png_content = b"\x89PNG\r\n\x1a\n" + b"\x00" * size
+    return base64.b64encode(png_content).decode()
+
+
+# =============================================================================
+# Decode and Validate Tests
+# =============================================================================
+
+
+class TestDecodeAndValidate:
+    """Tests for _decode_and_validate function."""
+
+    def test_detects_pdf_magic_number(self):
+        """Should detect valid PDF by magic number."""
+        pdf_b64 = _make_pdf_base64()
+        result = _decode_and_validate(pdf_b64)
+        assert result is not None
+        content, ext = result
+        assert ext == "pdf"
+        assert content.startswith(b"%PDF-")
+
+    def test_detects_png_magic_number(self):
+        """Should detect valid PNG by magic number."""
+        png_b64 = _make_png_base64()
+        result = _decode_and_validate(png_b64)
+        assert result is not None
+        content, ext = result
+        assert ext == "png"
+
+    def test_detects_jpeg_magic_number(self):
+        """Should detect valid JPEG by magic number."""
+        jpeg_content = b"\xff\xd8\xff\xe0" + b"\x00" * 2000
+        jpeg_b64 = base64.b64encode(jpeg_content).decode()
+        result = _decode_and_validate(jpeg_b64)
+        assert result is not None
+        _, ext = result
+        assert ext == "jpg"
+
+    def test_detects_gif_magic_number(self):
+        """Should detect valid GIF by magic number."""
+        gif_content = b"GIF89a" + b"\x00" * 2000
+        gif_b64 = base64.b64encode(gif_content).decode()
+        result = _decode_and_validate(gif_b64)
+        assert result is not None
+        _, ext = result
+        assert ext == "gif"
+
+    def test_detects_webp_magic_number(self):
+        """Should detect valid WebP by magic number."""
+        webp_content = b"RIFF\x00\x00\x00\x00WEBP" + b"\x00" * 2000
+        webp_b64 = base64.b64encode(webp_content).decode()
+        result = _decode_and_validate(webp_b64)
+        assert result is not None
+        _, ext = result
+        assert ext == "webp"
+
+    def test_rejects_small_content(self):
+        """Should reject content smaller than threshold."""
+        small_pdf = b"%PDF-1.4 small"
+        small_b64 = base64.b64encode(small_pdf).decode()
+        result = _decode_and_validate(small_b64)
+        assert result is None
+
+    def test_rejects_no_magic_number(self):
+        """Should reject content without recognized magic number."""
+        random_content = b"This is just random text" * 100
+        random_b64 = base64.b64encode(random_content).decode()
+        result = _decode_and_validate(random_b64)
+        assert result is None
+
+    def test_rejects_invalid_base64(self):
+        """Should reject invalid base64."""
+        result = _decode_and_validate("not-valid-base64!!!")
+        assert result is None
+
+    def test_rejects_riff_without_webp(self):
+        """Should reject RIFF files that aren't WebP (e.g., WAV)."""
+        wav_content = b"RIFF\x00\x00\x00\x00WAVE" + b"\x00" * 2000
+        wav_b64 = base64.b64encode(wav_content).decode()
+        result = _decode_and_validate(wav_b64)
+        assert result is None
+
+    def test_handles_line_wrapped_base64(self):
+        """Should handle RFC 2045 line-wrapped base64."""
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+        # Simulate line wrapping at 76 chars
+        wrapped = "\n".join(pdf_b64[i : i + 76] for i in range(0, len(pdf_b64), 76))
+        result = _decode_and_validate(wrapped)
+        assert result is not None
+        content, ext = result
+        assert ext == "pdf"
+        assert content == pdf_content
+
+
+# =============================================================================
+# Marker Expansion Tests
+# =============================================================================
+
+
+class TestExpandToMarkers:
+    """Tests for _expand_to_markers function."""
+
+    def test_expands_base64_start_end_markers(self):
+        """Should expand to include ---BASE64_START--- and ---BASE64_END---."""
+        text = "prefix\n---BASE64_START---\nABCDEF\n---BASE64_END---\nsuffix"
+        # Base64 "ABCDEF" is at position 26-32
+        start, end = _expand_to_markers(text, 26, 32)
+        assert text[start:end] == "---BASE64_START---\nABCDEF\n---BASE64_END---"
+
+    def test_expands_bracket_markers(self):
+        """Should expand to include [BASE64] and [/BASE64] markers."""
+        text = "prefix[BASE64]ABCDEF[/BASE64]suffix"
+        # Base64 is at position 14-20
+        start, end = _expand_to_markers(text, 14, 20)
+        assert text[start:end] == "[BASE64]ABCDEF[/BASE64]"
+
+    def test_no_expansion_without_markers(self):
+        """Should not expand if no markers present."""
+        text = "prefix ABCDEF suffix"
+        start, end = _expand_to_markers(text, 7, 13)
+        assert start == 7
+        assert end == 13
+
+
+# =============================================================================
+# Process Binary Outputs Tests
+# =============================================================================
+
+
+@pytest.fixture
+def mock_scan():
+    """Patch virus scanner for tests."""
+    with patch(
+        "backend.api.features.chat.tools.binary_output_processor.scan_content_safe",
+        new_callable=AsyncMock,
+    ) as mock:
+        yield mock
+
+
+class TestProcessBinaryOutputs:
+    """Tests for process_binary_outputs function."""
+
+    @pytest.mark.asyncio
+    async def test_detects_embedded_pdf_in_stdout_logs(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should detect and replace embedded PDF in stdout_logs."""
+        pdf_b64 = _make_pdf_base64()
+        stdout = f"PDF generated!\n---BASE64_START---\n{pdf_b64}\n---BASE64_END---\n"
+
+        outputs = {"stdout_logs": [stdout]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "ExecuteCodeBlock"
+        )
+
+        # Should contain workspace reference, not base64
+        assert "workspace://" in result["stdout_logs"][0]
+        assert pdf_b64 not in result["stdout_logs"][0]
+        assert "PDF generated!" in result["stdout_logs"][0]
+        mock_workspace_manager.write_file.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_detects_embedded_png_without_markers(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should detect embedded PNG even without markers."""
+        png_b64 = _make_png_base64()
+        stdout = f"Image created: {png_b64} done"
+
+        outputs = {"stdout_logs": [stdout]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "ExecuteCodeBlock"
+        )
+
+        assert "workspace://" in result["stdout_logs"][0]
+        assert "Image created:" in result["stdout_logs"][0]
+        assert "done" in result["stdout_logs"][0]
+
+    @pytest.mark.asyncio
+    async def test_preserves_small_strings(self, mock_workspace_manager, mock_scan):
+        """Should not process small strings."""
+        outputs = {"stdout_logs": ["small output"]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        assert result["stdout_logs"][0] == "small output"
+        mock_workspace_manager.write_file.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_preserves_non_binary_large_strings(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should preserve large strings that don't contain valid binary."""
+        large_text = "A" * 5000  # Large string - decodes to nulls, no magic number
+
+        outputs = {"stdout_logs": [large_text]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        assert result["stdout_logs"][0] == large_text
+        mock_workspace_manager.write_file.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_deduplicates_identical_content(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should save identical content only once."""
+        pdf_b64 = _make_pdf_base64()
+        stdout1 = f"First: {pdf_b64}"
+        stdout2 = f"Second: {pdf_b64}"
+
+        outputs = {"stdout_logs": [stdout1, stdout2]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        # Both should have references
+        assert "workspace://" in result["stdout_logs"][0]
+        assert "workspace://" in result["stdout_logs"][1]
+        # But only one write
+        assert mock_workspace_manager.write_file.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_handles_multiple_binaries_in_one_string(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should handle multiple embedded binaries in a single string."""
+        pdf_b64 = _make_pdf_base64()
+        png_b64 = _make_png_base64()
+        stdout = f"PDF: {pdf_b64}\nPNG: {png_b64}"
+
+        outputs = {"stdout_logs": [stdout]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        # Should have two workspace references
+        assert result["stdout_logs"][0].count("workspace://") == 2
+        assert mock_workspace_manager.write_file.call_count == 2
+
+    @pytest.mark.asyncio
+    async def test_processes_nested_structures(self, mock_workspace_manager, mock_scan):
+        """Should recursively process nested dicts and lists."""
+        pdf_b64 = _make_pdf_base64()
+
+        outputs = {"result": [{"nested": {"deep": f"data: {pdf_b64}"}}]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        assert "workspace://" in result["result"][0]["nested"]["deep"]
+
+    @pytest.mark.asyncio
+    async def test_graceful_degradation_on_save_failure(
+        self, mock_workspace_manager, mock_scan
+    ):
+        """Should preserve original on save failure."""
+        mock_workspace_manager.write_file = AsyncMock(
+            side_effect=Exception("Storage error")
+        )
+
+        pdf_b64 = _make_pdf_base64()
+        stdout = f"PDF: {pdf_b64}"
+
+        outputs = {"stdout_logs": [stdout]}
+
+        result = await process_binary_outputs(
+            outputs, mock_workspace_manager, "TestBlock"
+        )
+
+        # Should keep original since save failed
+        assert pdf_b64 in result["stdout_logs"][0]
+
+
+# =============================================================================
+# Offset Loop Tests (handling marker bleed-in)
+# =============================================================================
+
+
+class TestOffsetLoopHandling:
+    """Tests for the offset-aligned decoding that handles marker bleed-in."""
+
+    def test_handles_4char_aligned_prefix(self):
+        """Should detect base64 when a 4-char aligned prefix bleeds into match.
+
+        When 'TEST' (4 chars, aligned) bleeds in, offset 4 finds valid base64.
+        """
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+        # 4-char prefix (aligned)
+        with_prefix = f"TEST{pdf_b64}"
+
+        result = _decode_and_validate(with_prefix)
+        assert result is not None
+        content, ext = result
+        assert ext == "pdf"
+        assert content == pdf_content
+
+    def test_handles_8char_aligned_prefix(self):
+        """Should detect base64 when an 8-char prefix bleeds into match."""
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+        # 8-char prefix (aligned)
+        with_prefix = f"TESTTEST{pdf_b64}"
+
+        result = _decode_and_validate(with_prefix)
+        assert result is not None
+        content, ext = result
+        assert ext == "pdf"
+
+    def test_handles_misaligned_prefix(self):
+        """Should handle misaligned prefix by finding a valid aligned offset.
+
+        'START' is 5 chars (misaligned). The loop tries offsets 0, 4, 8...
+        Since characters 0-4 include 'START' which is invalid base64 on its own,
+        we need the full PDF base64 to eventually decode correctly at some offset.
+        """
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+        # 5-char prefix - misaligned, but offset 4 should start mid-'START'
+        # and offset 8 will be past the prefix
+        with_prefix = f"START{pdf_b64}"
+
+        result = _decode_and_validate(with_prefix)
+        # Should find valid PDF at some offset (8 in this case)
+        assert result is not None
+        _, ext = result
+        assert ext == "pdf"
+
+    def test_handles_pdf_base64_start_marker_bleed(self):
+        """Should handle PDF_BASE64_START marker bleeding into regex match.
+
+        This is the real-world case: regex matches 'STARTJVBERi0...' because
+        'START' chars are in the base64 alphabet. Offset loop skips past it.
+        PDF_BASE64_START is 16 chars (4-aligned), so offset 16 finds valid base64.
+        """
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+        # Simulate regex capturing 'PDF_BASE64_START' + base64 together
+        # This happens when there's no delimiter between marker and content
+        with_full_marker = f"PDF_BASE64_START{pdf_b64}"
+
+        result = _decode_and_validate(with_full_marker)
+        assert result is not None
+        _, ext = result
+        assert ext == "pdf"
+
+    def test_clean_base64_works_at_offset_zero(self):
+        """Should detect clean base64 at offset 0 without issues."""
+        pdf_content = b"%PDF-1.4 " + b"x" * 2000
+        pdf_b64 = base64.b64encode(pdf_content).decode()
+
+        result = _decode_and_validate(pdf_b64)
+        assert result is not None
+        content, ext = result
+        assert ext == "pdf"
+        assert content == pdf_content
+
+
+# =============================================================================
+# PDF Marker Tests
+# =============================================================================
+
+
+class TestPdfMarkerExpansion:
+    """Tests for PDF_BASE64_START/END marker handling."""
+
+    def test_expands_pdf_base64_start_marker(self):
+        """Should expand to include PDF_BASE64_START marker."""
+        text = "prefixPDF_BASE64_STARTABCDEF"
+        # Base64 'ABCDEF' is at position 22-28
+        start, end = _expand_to_markers(text, 22, 28)
+        assert text[start:end] == "PDF_BASE64_STARTABCDEF"
+
+    def test_expands_pdf_base64_end_marker(self):
+        """Should expand to include PDF_BASE64_END marker."""
+        text = "ABCDEFPDF_BASE64_ENDsuffix"
+        # Base64 'ABCDEF' is at position 0-6
+        start, end = _expand_to_markers(text, 0, 6)
+        assert text[start:end] == "ABCDEFPDF_BASE64_END"
+
+    def test_expands_both_pdf_markers(self):
+        """Should expand to include both PDF_BASE64_START and END."""
+        text = "xPDF_BASE64_STARTABCDEFPDF_BASE64_ENDy"
+        # Base64 'ABCDEF' is at position 17-23
+        start, end = _expand_to_markers(text, 17, 23)
+        assert text[start:end] == "PDF_BASE64_STARTABCDEFPDF_BASE64_END"
+
+    def test_partial_marker_not_expanded(self):
+        """Should not expand if only partial marker present."""
+        text = "BASE64_STARTABCDEF"  # Missing 'PDF_' prefix
+        start, end = _expand_to_markers(text, 12, 18)
+        # Should not expand since it's not the full marker
+        assert start == 12
+        assert end == 18
+
+    @pytest.mark.asyncio
+    async def test_full_pipeline_with_pdf_markers(self, mock_workspace_manager):
+        """Test full pipeline with PDF_BASE64_START/END markers."""
+        pdf_b64 = _make_pdf_base64()
+        stdout = f"Output: PDF_BASE64_START{pdf_b64}PDF_BASE64_END done"
+
+        outputs = {"stdout_logs": [stdout]}
+
+        with patch(
+            "backend.api.features.chat.tools.binary_output_processor.scan_content_safe",
+            new_callable=AsyncMock,
+        ):
+            result = await process_binary_outputs(
+                outputs, mock_workspace_manager, "TestBlock"
+            )
+
+        # Should have workspace reference
+        assert "workspace://" in result["stdout_logs"][0]
+        # Markers should be consumed along with base64
+        assert "PDF_BASE64_START" not in result["stdout_logs"][0]
+        assert "PDF_BASE64_END" not in result["stdout_logs"][0]
+        # Surrounding text preserved
+        assert "Output:" in result["stdout_logs"][0]
+        assert "done" in result["stdout_logs"][0]
+
+
+# =============================================================================
+# Virus Scanning Tests
+# =============================================================================
+
+
+class TestVirusScanning:
+    """Tests for virus scanning integration."""
+
+    @pytest.mark.asyncio
+    async def test_calls_virus_scanner_before_save(self, mock_workspace_manager):
+        """Should call scan_content_safe before writing file."""
+        pdf_b64 = _make_pdf_base64()
+        stdout = f"PDF: {pdf_b64}"
+        outputs = {"stdout_logs": [stdout]}
+
+        with patch(
+            "backend.api.features.chat.tools.binary_output_processor.scan_content_safe",
+            new_callable=AsyncMock,
+        ) as mock_scan:
+            result = await process_binary_outputs(
+                outputs, mock_workspace_manager, "TestBlock"
+            )
+
+            # Verify scanner was called
+            mock_scan.assert_called_once()
+            # Verify file was written after scan
+            mock_workspace_manager.write_file.assert_called_once()
+            # Verify result has workspace reference
+            assert "workspace://" in result["stdout_logs"][0]
+
+    @pytest.mark.asyncio
+    async def test_virus_scan_failure_preserves_original(self, mock_workspace_manager):
+        """Should preserve original if virus scan fails."""
+        pdf_b64 = _make_pdf_base64()
+        stdout = f"PDF: {pdf_b64}"
+        outputs = {"stdout_logs": [stdout]}
+
+        with patch(
+            "backend.api.features.chat.tools.binary_output_processor.scan_content_safe",
+            new_callable=AsyncMock,
+            side_effect=Exception("Virus detected"),
+        ):
+            result = await process_binary_outputs(
+                outputs, mock_workspace_manager, "TestBlock"
+            )
+
+            # Should keep original since scan failed
+            assert pdf_b64 in result["stdout_logs"][0]
+            # File should not have been written
+            mock_workspace_manager.write_file.assert_not_called()

autogpt_platform/backend/backend/api/features/chat/tools/test_run_block_details.py

@@ -1,153 +0,0 @@
-"""Tests for BlockDetailsResponse in RunBlockTool."""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from backend.api.features.chat.tools.models import BlockDetailsResponse
-from backend.api.features.chat.tools.run_block import RunBlockTool
-from backend.blocks._base import BlockType
-from backend.data.model import CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-from ._test_data import make_session
-
-_TEST_USER_ID = "test-user-run-block-details"
-
-
-def make_mock_block_with_inputs(
-    block_id: str, name: str, description: str = "Test description"
-):
-    """Create a mock block with input/output schemas for testing."""
-    mock = MagicMock()
-    mock.id = block_id
-    mock.name = name
-    mock.description = description
-    mock.block_type = BlockType.STANDARD
-    mock.disabled = False
-
-    # Input schema with non-credential fields
-    mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = {
-        "properties": {
-            "url": {"type": "string", "description": "URL to fetch"},
-            "method": {"type": "string", "description": "HTTP method"},
-        },
-        "required": ["url"],
-    }
-    mock.input_schema.get_credentials_fields.return_value = {}
-    mock.input_schema.get_credentials_fields_info.return_value = {}
-
-    # Output schema
-    mock.output_schema = MagicMock()
-    mock.output_schema.jsonschema.return_value = {
-        "properties": {
-            "response": {"type": "object", "description": "HTTP response"},
-            "error": {"type": "string", "description": "Error message"},
-        }
-    }
-
-    return mock
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_run_block_returns_details_when_no_input_provided():
-    """When run_block is called without input_data, it should return BlockDetailsResponse."""
-    session = make_session(user_id=_TEST_USER_ID)
-
-    # Create a block with inputs
-    http_block = make_mock_block_with_inputs(
-        "http-block-id", "HTTP Request", "Send HTTP requests"
-    )
-
-    with patch(
-        "backend.api.features.chat.tools.run_block.get_block",
-        return_value=http_block,
-    ):
-        # Mock credentials check to return no missing credentials
-        with patch.object(
-            RunBlockTool,
-            "_resolve_block_credentials",
-            new_callable=AsyncMock,
-            return_value=({}, []),  # (matched_credentials, missing_credentials)
-        ):
-            tool = RunBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="http-block-id",
-                input_data={},  # Empty input data
-            )
-
-    # Should return BlockDetailsResponse showing the schema
-    assert isinstance(response, BlockDetailsResponse)
-    assert response.block.id == "http-block-id"
-    assert response.block.name == "HTTP Request"
-    assert response.block.description == "Send HTTP requests"
-    assert "url" in response.block.inputs["properties"]
-    assert "method" in response.block.inputs["properties"]
-    assert "response" in response.block.outputs["properties"]
-    assert response.user_authenticated is True
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_run_block_returns_details_when_only_credentials_provided():
-    """When only credentials are provided (no actual input), should return details."""
-    session = make_session(user_id=_TEST_USER_ID)
-
-    # Create a block with both credential and non-credential inputs
-    mock = MagicMock()
-    mock.id = "api-block-id"
-    mock.name = "API Call"
-    mock.description = "Make API calls"
-    mock.block_type = BlockType.STANDARD
-    mock.disabled = False
-
-    mock.input_schema = MagicMock()
-    mock.input_schema.jsonschema.return_value = {
-        "properties": {
-            "credentials": {"type": "object", "description": "API credentials"},
-            "endpoint": {"type": "string", "description": "API endpoint"},
-        },
-        "required": ["credentials", "endpoint"],
-    }
-    mock.input_schema.get_credentials_fields.return_value = {"credentials": True}
-    mock.input_schema.get_credentials_fields_info.return_value = {}
-
-    mock.output_schema = MagicMock()
-    mock.output_schema.jsonschema.return_value = {
-        "properties": {"result": {"type": "object"}}
-    }
-
-    with patch(
-        "backend.api.features.chat.tools.run_block.get_block",
-        return_value=mock,
-    ):
-        with patch.object(
-            RunBlockTool,
-            "_resolve_block_credentials",
-            new_callable=AsyncMock,
-            return_value=(
-                {
-                    "credentials": CredentialsMetaInput(
-                        id="cred-id",
-                        provider=ProviderName("test_provider"),
-                        type="api_key",
-                        title="Test Credential",
-                    )
-                },
-                [],
-            ),
-        ):
-            tool = RunBlockTool()
-            response = await tool._execute(
-                user_id=_TEST_USER_ID,
-                session=session,
-                block_id="api-block-id",
-                input_data={"credentials": {"some": "cred"}},  # Only credential
-            )
-
-    # Should return details because no non-credential inputs provided
-    assert isinstance(response, BlockDetailsResponse)
-    assert response.block.id == "api-block-id"
-    assert response.block.name == "API Call"

autogpt_platform/backend/backend/api/features/chat/tools/utils.py

@@ -8,7 +8,6 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
-    Credentials,
     CredentialsFieldInfo,
     CredentialsMetaInput,
     HostScopedCredentials,
@@ -224,99 +223,6 @@ async def get_or_create_library_agent(
     return library_agents[0]
 
 
-async def match_credentials_to_requirements(
-    user_id: str,
-    requirements: dict[str, CredentialsFieldInfo],
-) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
-    """
-    Match user's credentials against a dictionary of credential requirements.
-
-    This is the core matching logic shared by both graph and block credential matching.
-    """
-    matched: dict[str, CredentialsMetaInput] = {}
-    missing: list[CredentialsMetaInput] = []
-
-    if not requirements:
-        return matched, missing
-
-    available_creds = await get_user_credentials(user_id)
-
-    for field_name, field_info in requirements.items():
-        matching_cred = find_matching_credential(available_creds, field_info)
-
-        if matching_cred:
-            try:
-                matched[field_name] = create_credential_meta_from_match(matching_cred)
-            except Exception as e:
-                logger.error(
-                    f"Failed to create CredentialsMetaInput for field '{field_name}': "
-                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
-                    f"credential_id={matching_cred.id}",
-                    exc_info=True,
-                )
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=f"{field_name} (validation failed: {e})",
-                    )
-                )
-        else:
-            provider = next(iter(field_info.provider), "unknown")
-            cred_type = next(iter(field_info.supported_types), "api_key")
-            missing.append(
-                CredentialsMetaInput(
-                    id=field_name,
-                    provider=provider,  # type: ignore
-                    type=cred_type,  # type: ignore
-                    title=field_name.replace("_", " ").title(),
-                )
-            )
-
-    return matched, missing
-
-
-async def get_user_credentials(user_id: str) -> list[Credentials]:
-    """Get all available credentials for a user."""
-    creds_manager = IntegrationCredentialsManager()
-    return await creds_manager.store.get_all_creds(user_id)
-
-
-def find_matching_credential(
-    available_creds: list[Credentials],
-    field_info: CredentialsFieldInfo,
-) -> Credentials | None:
-    """Find a credential that matches the required provider, type, scopes, and host."""
-    for cred in available_creds:
-        if cred.provider not in field_info.provider:
-            continue
-        if cred.type not in field_info.supported_types:
-            continue
-        if cred.type == "oauth2" and not _credential_has_required_scopes(
-            cred, field_info
-        ):
-            continue
-        if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
-            continue
-        return cred
-    return None
-
-
-def create_credential_meta_from_match(
-    matching_cred: Credentials,
-) -> CredentialsMetaInput:
-    """Create a CredentialsMetaInput from a matched credential."""
-    return CredentialsMetaInput(
-        id=matching_cred.id,
-        provider=matching_cred.provider,  # type: ignore
-        type=matching_cred.type,
-        title=matching_cred.title,
-    )
-
-
 async def match_user_credentials_to_graph(
     user_id: str,
     graph: GraphModel,
@@ -425,6 +331,8 @@ def _credential_has_required_scopes(
     # If no scopes are required, any credential matches
     if not requirements.required_scopes:
         return True
+
+    # Check that credential scopes are a superset of required scopes
     return set(credential.scopes).issuperset(requirements.required_scopes)
 
 

autogpt_platform/backend/backend/api/features/chat/tools/web_fetch.py

@@ -1,151 +0,0 @@
-"""Web fetch tool — safely retrieve public web page content."""
-
-import logging
-from typing import Any
-
-import aiohttp
-import html2text
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.base import BaseTool
-from backend.api.features.chat.tools.models import (
-    ErrorResponse,
-    ToolResponseBase,
-    WebFetchResponse,
-)
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-# Limits
-_MAX_CONTENT_BYTES = 102_400  # 100 KB download cap
-_REQUEST_TIMEOUT = aiohttp.ClientTimeout(total=15)
-
-# Content types we'll read as text
-_TEXT_CONTENT_TYPES = {
-    "text/html",
-    "text/plain",
-    "text/xml",
-    "text/csv",
-    "text/markdown",
-    "application/json",
-    "application/xml",
-    "application/xhtml+xml",
-    "application/rss+xml",
-    "application/atom+xml",
-}
-
-
-def _is_text_content(content_type: str) -> bool:
-    base = content_type.split(";")[0].strip().lower()
-    return base in _TEXT_CONTENT_TYPES or base.startswith("text/")
-
-
-def _html_to_text(html: str) -> str:
-    h = html2text.HTML2Text()
-    h.ignore_links = False
-    h.ignore_images = True
-    h.body_width = 0
-    return h.handle(html)
-
-
-class WebFetchTool(BaseTool):
-    """Safely fetch content from a public URL using SSRF-protected HTTP."""
-
-    @property
-    def name(self) -> str:
-        return "web_fetch"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Fetch the content of a public web page by URL. "
-            "Returns readable text extracted from HTML by default. "
-            "Useful for reading documentation, articles, and API responses. "
-            "Only supports HTTP/HTTPS GET requests to public URLs "
-            "(private/internal network addresses are blocked)."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "url": {
-                    "type": "string",
-                    "description": "The public HTTP/HTTPS URL to fetch.",
-                },
-                "extract_text": {
-                    "type": "boolean",
-                    "description": (
-                        "If true (default), extract readable text from HTML. "
-                        "If false, return raw content."
-                    ),
-                    "default": True,
-                },
-            },
-            "required": ["url"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return False
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs: Any,
-    ) -> ToolResponseBase:
-        url: str = (kwargs.get("url") or "").strip()
-        extract_text: bool = kwargs.get("extract_text", True)
-        session_id = session.session_id if session else None
-
-        if not url:
-            return ErrorResponse(
-                message="Please provide a URL to fetch.",
-                error="missing_url",
-                session_id=session_id,
-            )
-
-        try:
-            client = Requests(raise_for_status=False, retry_max_attempts=1)
-            response = await client.get(url, timeout=_REQUEST_TIMEOUT)
-        except ValueError as e:
-            # validate_url raises ValueError for SSRF / blocked IPs
-            return ErrorResponse(
-                message=f"URL blocked: {e}",
-                error="url_blocked",
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.warning(f"[web_fetch] Request failed for {url}: {e}")
-            return ErrorResponse(
-                message=f"Failed to fetch URL: {e}",
-                error="fetch_failed",
-                session_id=session_id,
-            )
-
-        content_type = response.headers.get("content-type", "")
-        if not _is_text_content(content_type):
-            return ErrorResponse(
-                message=f"Non-text content type: {content_type.split(';')[0]}",
-                error="unsupported_content_type",
-                session_id=session_id,
-            )
-
-        raw = response.content[:_MAX_CONTENT_BYTES]
-        text = raw.decode("utf-8", errors="replace")
-
-        if extract_text and "html" in content_type.lower():
-            text = _html_to_text(text)
-
-        return WebFetchResponse(
-            message=f"Fetched {url}",
-            url=response.url,
-            status_code=response.status,
-            content_type=content_type.split(";")[0].strip(),
-            content=text,
-            truncated=False,
-            session_id=session_id,
-        )

autogpt_platform/backend/backend/api/features/chat/tools/workspace_files.py

@@ -88,9 +88,7 @@ class ListWorkspaceFilesTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "List files in the user's persistent workspace (cloud storage). "
+            "List files in the user's workspace. "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Read/Glob tools instead. "
             "Returns file names, paths, sizes, and metadata. "
             "Optionally filter by path prefix."
         )
@@ -206,9 +204,7 @@ class ReadWorkspaceFileTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Read a file from the user's persistent workspace (cloud storage). "
+            "Read a file from the user's workspace. "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Read tool instead. "
             "Specify either file_id or path to identify the file. "
             "For small text files, returns content directly. "
             "For large or binary files, returns metadata and a download URL. "
@@ -382,9 +378,7 @@ class WriteWorkspaceFileTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Write or create a file in the user's persistent workspace (cloud storage). "
+            "Write or create a file in the user's workspace. "
-            "These files survive across sessions. "
-            "For ephemeral session files, use the SDK Write tool instead. "
             "Provide the content as a base64-encoded string. "
             f"Maximum file size is {Config().max_file_size_mb}MB. "
             "Files are saved to the current session's folder by default. "
@@ -529,7 +523,7 @@ class DeleteWorkspaceFileTool(BaseTool):
     @property
     def description(self) -> str:
         return (
-            "Delete a file from the user's persistent workspace (cloud storage). "
+            "Delete a file from the user's workspace. "
             "Specify either file_id or path to identify the file. "
             "Paths are scoped to the current session by default. "
             "Use /sessions/<session_id>/... for cross-session access."

autogpt_platform/backend/backend/api/features/library/db.py

@@ -12,11 +12,12 @@ import backend.api.features.store.image_gen as store_image_gen
 import backend.api.features.store.media as store_media
 import backend.data.graph as graph_db
 import backend.data.integrations as integrations_db
+from backend.data.block import BlockInput
 from backend.data.db import transaction
 from backend.data.execution import get_graph_execution
 from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
-from backend.data.model import CredentialsMetaInput, GraphInput
+from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import (
     on_graph_activate,
@@ -1129,7 +1130,7 @@ async def create_preset_from_graph_execution(
 async def update_preset(
     user_id: str,
     preset_id: str,
-    inputs: Optional[GraphInput] = None,
+    inputs: Optional[BlockInput] = None,
     credentials: Optional[dict[str, CredentialsMetaInput]] = None,
     name: Optional[str] = None,
     description: Optional[str] = None,

autogpt_platform/backend/backend/api/features/library/model.py

@@ -6,12 +6,9 @@ import prisma.enums
 import prisma.models
 import pydantic
 
+from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
-from backend.data.model import (
+from backend.data.model import CredentialsMetaInput, is_credentials_field_name
-    CredentialsMetaInput,
-    GraphInput,
-    is_credentials_field_name,
-)
 from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
 
@@ -326,7 +323,7 @@ class LibraryAgentPresetCreatable(pydantic.BaseModel):
     graph_id: str
     graph_version: int
 
-    inputs: GraphInput
+    inputs: BlockInput
     credentials: dict[str, CredentialsMetaInput]
 
     name: str
@@ -355,7 +352,7 @@ class LibraryAgentPresetUpdatable(pydantic.BaseModel):
     Request model used when updating a preset for a library agent.
     """
 
-    inputs: Optional[GraphInput] = None
+    inputs: Optional[BlockInput] = None
     credentials: Optional[dict[str, CredentialsMetaInput]] = None
 
     name: Optional[str] = None
@@ -398,7 +395,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
                 "Webhook must be included in AgentPreset query when webhookId is set"
             )
 
-        input_data: GraphInput = {}
+        input_data: BlockInput = {}
         input_credentials: dict[str, CredentialsMetaInput] = {}
 
         for preset_input in preset.InputPresets:

autogpt_platform/backend/backend/api/features/otto/service.py

@@ -5,8 +5,8 @@ from typing import Optional
 import aiohttp
 from fastapi import HTTPException
 
-from backend.blocks import get_block
 from backend.data import graph as graph_db
+from backend.data.block import get_block
 from backend.util.settings import Settings
 
 from .models import ApiResponse, ChatRequest, GraphData

autogpt_platform/backend/backend/api/features/store/content_handlers.py

@@ -152,7 +152,7 @@ class BlockHandler(ContentHandler):
 
     async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
         """Fetch blocks without embeddings."""
-        from backend.blocks import get_blocks
+        from backend.data.block import get_blocks
 
         # Get all available blocks
         all_blocks = get_blocks()
@@ -249,7 +249,7 @@ class BlockHandler(ContentHandler):
 
     async def get_stats(self) -> dict[str, int]:
         """Get statistics about block embedding coverage."""
-        from backend.blocks import get_blocks
+        from backend.data.block import get_blocks
 
         all_blocks = get_blocks()
 

autogpt_platform/backend/backend/api/features/store/content_handlers_test.py

@@ -93,7 +93,7 @@ async def test_block_handler_get_missing_items(mocker):
     mock_existing = []
 
     with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -135,7 +135,7 @@ async def test_block_handler_get_stats(mocker):
     mock_embedded = [{"count": 2}]
 
     with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -327,7 +327,7 @@ async def test_block_handler_handles_missing_attributes():
     mock_blocks = {"block-minimal": mock_block_class}
 
     with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -360,7 +360,7 @@ async def test_block_handler_skips_failed_blocks():
     mock_blocks = {"good-block": good_block, "bad-block": bad_block}
 
     with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -662,7 +662,7 @@ async def cleanup_orphaned_embeddings() -> dict[str, Any]:
                 )
                 current_ids = {row["id"] for row in valid_agents}
             elif content_type == ContentType.BLOCK:
-                from backend.blocks import get_blocks
+                from backend.data.block import get_blocks
 
                 current_ids = set(get_blocks().keys())
             elif content_type == ContentType.DOCUMENTATION:

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.
 
 import logging
 import re
-import time
 from dataclasses import dataclass
 from typing import Any, Literal
 
@@ -363,11 +362,7 @@ async def unified_hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
+    results = await query_raw_with_schema(sql_query, *params)
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
 
     total = results[0]["total_count"] if results else 0
     # Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
         LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    try:
+    results = await query_raw_with_schema(sql_query, *params)
-        results = await query_raw_with_schema(sql_query, *params)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
 
     total = results[0]["total_count"] if results else 0
 
@@ -727,87 +718,6 @@ async def hybrid_search_simple(
     return await hybrid_search(query=query, page=page, page_size=page_size)
 
 
-# ============================================================================
-# Diagnostics
-# ============================================================================
-
-# Rate limit: only log vector error diagnostics once per this interval
-_VECTOR_DIAG_INTERVAL_SECONDS = 60
-_last_vector_diag_time: float = 0
-
-
-async def _log_vector_error_diagnostics(error: Exception) -> None:
-    """Log diagnostic info when 'type vector does not exist' error occurs.
-
-    Note: Diagnostic queries use query_raw_with_schema which may run on a different
-    pooled connection than the one that failed. Session-level search_path can differ,
-    so these diagnostics show cluster-wide state, not necessarily the failed session.
-
-    Includes rate limiting to avoid log spam - only logs once per minute.
-    Caller should re-raise the error after calling this function.
-    """
-    global _last_vector_diag_time
-
-    # Check if this is the vector type error
-    error_str = str(error).lower()
-    if not (
-        "type" in error_str and "vector" in error_str and "does not exist" in error_str
-    ):
-        return
-
-    # Rate limit: only log once per interval
-    now = time.time()
-    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
-        return
-    _last_vector_diag_time = now
-
-    try:
-        diagnostics: dict[str, object] = {}
-
-        try:
-            search_path_result = await query_raw_with_schema("SHOW search_path")
-            diagnostics["search_path"] = search_path_result
-        except Exception as e:
-            diagnostics["search_path"] = f"Error: {e}"
-
-        try:
-            schema_result = await query_raw_with_schema("SELECT current_schema()")
-            diagnostics["current_schema"] = schema_result
-        except Exception as e:
-            diagnostics["current_schema"] = f"Error: {e}"
-
-        try:
-            user_result = await query_raw_with_schema(
-                "SELECT current_user, session_user, current_database()"
-            )
-            diagnostics["user_info"] = user_result
-        except Exception as e:
-            diagnostics["user_info"] = f"Error: {e}"
-
-        try:
-            # Check pgvector extension installation (cluster-wide, stable info)
-            ext_result = await query_raw_with_schema(
-                "SELECT extname, extversion, nspname as schema "
-                "FROM pg_extension e "
-                "JOIN pg_namespace n ON e.extnamespace = n.oid "
-                "WHERE extname = 'vector'"
-            )
-            diagnostics["pgvector_extension"] = ext_result
-        except Exception as e:
-            diagnostics["pgvector_extension"] = f"Error: {e}"
-
-        logger.error(
-            f"Vector type error diagnostics:\n"
-            f"  Error: {error}\n"
-            f"  search_path: {diagnostics.get('search_path')}\n"
-            f"  current_schema: {diagnostics.get('current_schema')}\n"
-            f"  user_info: {diagnostics.get('user_info')}\n"
-            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
-        )
-    except Exception as diag_error:
-        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
-
-
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights

autogpt_platform/backend/backend/api/features/store/image_gen.py

@@ -7,6 +7,15 @@ from replicate.client import Client as ReplicateClient
 from replicate.exceptions import ReplicateError
 from replicate.helpers import FileOutput
 
+from backend.blocks.ideogram import (
+    AspectRatio,
+    ColorPalettePreset,
+    IdeogramModelBlock,
+    IdeogramModelName,
+    MagicPromptOption,
+    StyleType,
+    UpscaleOption,
+)
 from backend.data.graph import GraphBaseMeta
 from backend.data.model import CredentialsMetaInput, ProviderName
 from backend.integrations.credentials_store import ideogram_credentials
@@ -41,16 +50,6 @@ async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.Bytes
     if not ideogram_credentials.api_key:
         raise ValueError("Missing Ideogram API key")
 
-    from backend.blocks.ideogram import (
-        AspectRatio,
-        ColorPalettePreset,
-        IdeogramModelBlock,
-        IdeogramModelName,
-        MagicPromptOption,
-        StyleType,
-        UpscaleOption,
-    )
-
     name = graph.name
     description = f"{name} ({graph.description})" if graph.description else name
 

autogpt_platform/backend/backend/api/features/v1.py

@@ -40,11 +40,10 @@ from backend.api.model import (
     UpdateTimezoneRequest,
     UploadFileResponse,
 )
-from backend.blocks import get_block, get_blocks
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
 from backend.data.auth import api_key as api_key_db
-from backend.data.block import BlockInput, CompletedBlockOutput
+from backend.data.block import BlockInput, CompletedBlockOutput, get_block, get_blocks
 from backend.data.credit import (
     AutoTopUpConfig,
     RefundRequest,

autogpt_platform/backend/backend/blocks/__init__.py

@@ -3,19 +3,22 @@ import logging
 import os
 import re
 from pathlib import Path
-from typing import Sequence, Type, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 
-from backend.blocks._base import AnyBlockSchema, BlockType
 from backend.util.cache import cached
 
 logger = logging.getLogger(__name__)
 
+
+if TYPE_CHECKING:
+    from backend.data.block import Block
+
 T = TypeVar("T")
 
 
 @cached(ttl_seconds=3600)
-def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
+def load_all_blocks() -> dict[str, type["Block"]]:
-    from backend.blocks._base import Block
+    from backend.data.block import Block
     from backend.util.settings import Config
 
     # Check if example blocks should be loaded from settings
@@ -47,8 +50,8 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
         importlib.import_module(f".{module}", package=__name__)
 
     # Load all Block instances from the available modules
-    available_blocks: dict[str, type["AnyBlockSchema"]] = {}
+    available_blocks: dict[str, type["Block"]] = {}
-    for block_cls in _all_subclasses(Block):
+    for block_cls in all_subclasses(Block):
         class_name = block_cls.__name__
 
         if class_name.endswith("Base"):
@@ -61,7 +64,7 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
                 "please name the class with 'Base' at the end"
             )
 
-        block = block_cls()  # pyright: ignore[reportAbstractUsage]
+        block = block_cls.create()
 
         if not isinstance(block.id, str) or len(block.id) != 36:
             raise ValueError(
@@ -102,7 +105,7 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
         available_blocks[block.id] = block_cls
 
     # Filter out blocks with incomplete auth configs, e.g. missing OAuth server secrets
-    from ._utils import is_block_auth_configured
+    from backend.data.block import is_block_auth_configured
 
     filtered_blocks = {}
     for block_id, block_cls in available_blocks.items():
@@ -112,48 +115,11 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
     return filtered_blocks
 
 
-def _all_subclasses(cls: type[T]) -> list[type[T]]:
+__all__ = ["load_all_blocks"]
+
+
+def all_subclasses(cls: type[T]) -> list[type[T]]:
     subclasses = cls.__subclasses__()
     for subclass in subclasses:
-        subclasses += _all_subclasses(subclass)
+        subclasses += all_subclasses(subclass)
     return subclasses
-
-
-# ============== Block access helper functions ============== #
-
-
-def get_blocks() -> dict[str, Type["AnyBlockSchema"]]:
-    return load_all_blocks()
-
-
-# Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
-def get_block(block_id: str) -> "AnyBlockSchema | None":
-    cls = get_blocks().get(block_id)
-    return cls() if cls else None
-
-
-@cached(ttl_seconds=3600)
-def get_webhook_block_ids() -> Sequence[str]:
-    return [
-        id
-        for id, B in get_blocks().items()
-        if B().block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
-    ]
-
-
-@cached(ttl_seconds=3600)
-def get_io_block_ids() -> Sequence[str]:
-    return [
-        id
-        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/blocks/_base.py

@@ -1,739 +0,0 @@
-import inspect
-import logging
-from abc import ABC, abstractmethod
-from enum import Enum
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    ClassVar,
-    Generic,
-    Optional,
-    Type,
-    TypeAlias,
-    TypeVar,
-    cast,
-    get_origin,
-)
-
-import jsonref
-import jsonschema
-from pydantic import BaseModel
-
-from backend.data.block import BlockInput, BlockOutput, BlockOutputEntry
-from backend.data.model import (
-    Credentials,
-    CredentialsFieldInfo,
-    CredentialsMetaInput,
-    SchemaField,
-    is_credentials_field_name,
-)
-from backend.integrations.providers import ProviderName
-from backend.util import json
-from backend.util.exceptions import (
-    BlockError,
-    BlockExecutionError,
-    BlockInputError,
-    BlockOutputError,
-    BlockUnknownError,
-)
-from backend.util.settings import Config
-
-logger = logging.getLogger(__name__)
-
-if TYPE_CHECKING:
-    from backend.data.execution import ExecutionContext
-    from backend.data.model import ContributorDetails, NodeExecutionStats
-
-    from ..data.graph import Link
-
-app_config = Config()
-
-
-BlockTestOutput = BlockOutputEntry | tuple[str, Callable[[Any], bool]]
-
-
-class BlockType(Enum):
-    STANDARD = "Standard"
-    INPUT = "Input"
-    OUTPUT = "Output"
-    NOTE = "Note"
-    WEBHOOK = "Webhook"
-    WEBHOOK_MANUAL = "Webhook (manual)"
-    AGENT = "Agent"
-    AI = "AI"
-    AYRSHARE = "Ayrshare"
-    HUMAN_IN_THE_LOOP = "Human In The Loop"
-
-
-class BlockCategory(Enum):
-    AI = "Block that leverages AI to perform a task."
-    SOCIAL = "Block that interacts with social media platforms."
-    TEXT = "Block that processes text data."
-    SEARCH = "Block that searches or extracts information from the internet."
-    BASIC = "Block that performs basic operations."
-    INPUT = "Block that interacts with input of the graph."
-    OUTPUT = "Block that interacts with output of the graph."
-    LOGIC = "Programming logic to control the flow of your agent"
-    COMMUNICATION = "Block that interacts with communication platforms."
-    DEVELOPER_TOOLS = "Developer tools such as GitHub blocks."
-    DATA = "Block that interacts with structured data."
-    HARDWARE = "Block that interacts with hardware."
-    AGENT = "Block that interacts with other agents."
-    CRM = "Block that interacts with CRM services."
-    SAFETY = (
-        "Block that provides AI safety mechanisms such as detecting harmful content"
-    )
-    PRODUCTIVITY = "Block that helps with productivity"
-    ISSUE_TRACKING = "Block that helps with issue tracking"
-    MULTIMEDIA = "Block that interacts with multimedia content"
-    MARKETING = "Block that helps with marketing"
-
-    def dict(self) -> dict[str, str]:
-        return {"category": self.name, "description": self.value}
-
-
-class BlockCostType(str, Enum):
-    RUN = "run"  # cost X credits per run
-    BYTE = "byte"  # cost X credits per byte
-    SECOND = "second"  # cost X credits per second
-
-
-class BlockCost(BaseModel):
-    cost_amount: int
-    cost_filter: BlockInput
-    cost_type: BlockCostType
-
-    def __init__(
-        self,
-        cost_amount: int,
-        cost_type: BlockCostType = BlockCostType.RUN,
-        cost_filter: Optional[BlockInput] = None,
-        **data: Any,
-    ) -> None:
-        super().__init__(
-            cost_amount=cost_amount,
-            cost_filter=cost_filter or {},
-            cost_type=cost_type,
-            **data,
-        )
-
-
-class BlockInfo(BaseModel):
-    id: str
-    name: str
-    inputSchema: dict[str, Any]
-    outputSchema: dict[str, Any]
-    costs: list[BlockCost]
-    description: str
-    categories: list[dict[str, str]]
-    contributors: list[dict[str, Any]]
-    staticOutput: bool
-    uiType: str
-
-
-class BlockSchema(BaseModel):
-    cached_jsonschema: ClassVar[dict[str, Any]]
-
-    @classmethod
-    def jsonschema(cls) -> dict[str, Any]:
-        if cls.cached_jsonschema:
-            return cls.cached_jsonschema
-
-        model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
-
-        def ref_to_dict(obj):
-            if isinstance(obj, dict):
-                # OpenAPI <3.1 does not support sibling fields that has a $ref key
-                # So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
-                keys = {"allOf", "anyOf", "oneOf"}
-                one_key = next((k for k in keys if k in obj and len(obj[k]) == 1), None)
-                if one_key:
-                    obj.update(obj[one_key][0])
-
-                return {
-                    key: ref_to_dict(value)
-                    for key, value in obj.items()
-                    if not key.startswith("$") and key != one_key
-                }
-            elif isinstance(obj, list):
-                return [ref_to_dict(item) for item in obj]
-
-            return obj
-
-        cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
-
-        return cls.cached_jsonschema
-
-    @classmethod
-    def validate_data(cls, data: BlockInput) -> str | None:
-        return json.validate_with_jsonschema(
-            schema=cls.jsonschema(),
-            data={k: v for k, v in data.items() if v is not None},
-        )
-
-    @classmethod
-    def get_mismatch_error(cls, data: BlockInput) -> str | None:
-        return cls.validate_data(data)
-
-    @classmethod
-    def get_field_schema(cls, field_name: str) -> dict[str, Any]:
-        model_schema = cls.jsonschema().get("properties", {})
-        if not model_schema:
-            raise ValueError(f"Invalid model schema {cls}")
-
-        property_schema = model_schema.get(field_name)
-        if not property_schema:
-            raise ValueError(f"Invalid property name {field_name}")
-
-        return property_schema
-
-    @classmethod
-    def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
-        """
-        Validate the data against a specific property (one of the input/output name).
-        Returns the validation error message if the data does not match the schema.
-        """
-        try:
-            property_schema = cls.get_field_schema(field_name)
-            jsonschema.validate(json.to_dict(data), property_schema)
-            return None
-        except jsonschema.ValidationError as e:
-            return str(e)
-
-    @classmethod
-    def get_fields(cls) -> set[str]:
-        return set(cls.model_fields.keys())
-
-    @classmethod
-    def get_required_fields(cls) -> set[str]:
-        return {
-            field
-            for field, field_info in cls.model_fields.items()
-            if field_info.is_required()
-        }
-
-    @classmethod
-    def __pydantic_init_subclass__(cls, **kwargs):
-        """Validates the schema definition. Rules:
-        - Fields with annotation `CredentialsMetaInput` MUST be
-          named `credentials` or `*_credentials`
-        - Fields named `credentials` or `*_credentials` MUST be
-          of type `CredentialsMetaInput`
-        """
-        super().__pydantic_init_subclass__(**kwargs)
-
-        # Reset cached JSON schema to prevent inheriting it from parent class
-        cls.cached_jsonschema = {}
-
-        credentials_fields = cls.get_credentials_fields()
-
-        for field_name in cls.get_fields():
-            if is_credentials_field_name(field_name):
-                if field_name not in credentials_fields:
-                    raise TypeError(
-                        f"Credentials field '{field_name}' on {cls.__qualname__} "
-                        f"is not of type {CredentialsMetaInput.__name__}"
-                    )
-
-                CredentialsMetaInput.validate_credentials_field_schema(
-                    cls.get_field_schema(field_name), field_name
-                )
-
-            elif field_name in credentials_fields:
-                raise KeyError(
-                    f"Credentials field '{field_name}' on {cls.__qualname__} "
-                    "has invalid name: must be 'credentials' or *_credentials"
-                )
-
-    @classmethod
-    def get_credentials_fields(cls) -> dict[str, type[CredentialsMetaInput]]:
-        return {
-            field_name: info.annotation
-            for field_name, info in cls.model_fields.items()
-            if (
-                inspect.isclass(info.annotation)
-                and issubclass(
-                    get_origin(info.annotation) or info.annotation,
-                    CredentialsMetaInput,
-                )
-            )
-        }
-
-    @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(
-                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
-
-    @classmethod
-    def get_input_defaults(cls, data: BlockInput) -> BlockInput:
-        return data  # Return as is, by default.
-
-    @classmethod
-    def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
-        input_fields_from_nodes = {link.sink_name for link in links}
-        return input_fields_from_nodes - set(data)
-
-    @classmethod
-    def get_missing_input(cls, data: BlockInput) -> set[str]:
-        return cls.get_required_fields() - set(data)
-
-
-class BlockSchemaInput(BlockSchema):
-    """
-    Base schema class for block inputs.
-    All block input schemas should extend this class for consistency.
-    """
-
-    pass
-
-
-class BlockSchemaOutput(BlockSchema):
-    """
-    Base schema class for block outputs that includes a standard error field.
-    All block output schemas should extend this class to ensure consistent error handling.
-    """
-
-    error: str = SchemaField(
-        description="Error message if the operation failed", default=""
-    )
-
-
-BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchemaInput)
-BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchemaOutput)
-
-
-class EmptyInputSchema(BlockSchemaInput):
-    pass
-
-
-class EmptyOutputSchema(BlockSchemaOutput):
-    pass
-
-
-# For backward compatibility - will be deprecated
-EmptySchema = EmptyOutputSchema
-
-
-# --8<-- [start:BlockWebhookConfig]
-class BlockManualWebhookConfig(BaseModel):
-    """
-    Configuration model for webhook-triggered blocks on which
-    the user has to manually set up the webhook at the provider.
-    """
-
-    provider: ProviderName
-    """The service provider that the webhook connects to"""
-
-    webhook_type: str
-    """
-    Identifier for the webhook type. E.g. GitHub has repo and organization level hooks.
-
-    Only for use in the corresponding `WebhooksManager`.
-    """
-
-    event_filter_input: str = ""
-    """
-    Name of the block's event filter input.
-    Leave empty if the corresponding webhook doesn't have distinct event/payload types.
-    """
-
-    event_format: str = "{event}"
-    """
-    Template string for the event(s) that a block instance subscribes to.
-    Applied individually to each event selected in the event filter input.
-
-    Example: `"pull_request.{event}"` -> `"pull_request.opened"`
-    """
-
-
-class BlockWebhookConfig(BlockManualWebhookConfig):
-    """
-    Configuration model for webhook-triggered blocks for which
-    the webhook can be automatically set up through the provider's API.
-    """
-
-    resource_format: str
-    """
-    Template string for the resource that a block instance subscribes to.
-    Fields will be filled from the block's inputs (except `payload`).
-
-    Example: `f"{repo}/pull_requests"` (note: not how it's actually implemented)
-
-    Only for use in the corresponding `WebhooksManager`.
-    """
-    # --8<-- [end:BlockWebhookConfig]
-
-
-class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
-    def __init__(
-        self,
-        id: str = "",
-        description: str = "",
-        contributors: list["ContributorDetails"] = [],
-        categories: set[BlockCategory] | None = None,
-        input_schema: Type[BlockSchemaInputType] = EmptyInputSchema,
-        output_schema: Type[BlockSchemaOutputType] = EmptyOutputSchema,
-        test_input: BlockInput | list[BlockInput] | None = None,
-        test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
-        test_mock: dict[str, Any] | None = None,
-        test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
-        disabled: bool = False,
-        static_output: bool = False,
-        block_type: BlockType = BlockType.STANDARD,
-        webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
-        is_sensitive_action: bool = False,
-    ):
-        """
-        Initialize the block with the given schema.
-
-        Args:
-            id: The unique identifier for the block, this value will be persisted in the
-                DB. So it should be a unique and constant across the application run.
-                Use the UUID format for the ID.
-            description: The description of the block, explaining what the block does.
-            contributors: The list of contributors who contributed to the block.
-            input_schema: The schema, defined as a Pydantic model, for the input data.
-            output_schema: The schema, defined as a Pydantic model, for the output data.
-            test_input: The list or single sample input data for the block, for testing.
-            test_output: The list or single expected output if the test_input is run.
-            test_mock: function names on the block implementation to mock on test run.
-            disabled: If the block is disabled, it will not be available for execution.
-            static_output: Whether the output links of the block are static by default.
-        """
-        from backend.data.model import NodeExecutionStats
-
-        self.id = id
-        self.input_schema = input_schema
-        self.output_schema = output_schema
-        self.test_input = test_input
-        self.test_output = test_output
-        self.test_mock = test_mock
-        self.test_credentials = test_credentials
-        self.description = description
-        self.categories = categories or set()
-        self.contributors = contributors or set()
-        self.disabled = disabled
-        self.static_output = static_output
-        self.block_type = block_type
-        self.webhook_config = webhook_config
-        self.is_sensitive_action = is_sensitive_action
-        self.execution_stats: "NodeExecutionStats" = NodeExecutionStats()
-
-        if self.webhook_config:
-            if isinstance(self.webhook_config, BlockWebhookConfig):
-                # Enforce presence of credentials field on auto-setup webhook blocks
-                if not (cred_fields := self.input_schema.get_credentials_fields()):
-                    raise TypeError(
-                        "credentials field is required on auto-setup webhook blocks"
-                    )
-                # Disallow multiple credentials inputs on webhook blocks
-                elif len(cred_fields) > 1:
-                    raise ValueError(
-                        "Multiple credentials inputs not supported on webhook blocks"
-                    )
-
-                self.block_type = BlockType.WEBHOOK
-            else:
-                self.block_type = BlockType.WEBHOOK_MANUAL
-
-            # Enforce shape of webhook event filter, if present
-            if self.webhook_config.event_filter_input:
-                event_filter_field = self.input_schema.model_fields[
-                    self.webhook_config.event_filter_input
-                ]
-                if not (
-                    isinstance(event_filter_field.annotation, type)
-                    and issubclass(event_filter_field.annotation, BaseModel)
-                    and all(
-                        field.annotation is bool
-                        for field in event_filter_field.annotation.model_fields.values()
-                    )
-                ):
-                    raise NotImplementedError(
-                        f"{self.name} has an invalid webhook event selector: "
-                        "field must be a BaseModel and all its fields must be boolean"
-                    )
-
-            # Enforce presence of 'payload' input
-            if "payload" not in self.input_schema.model_fields:
-                raise TypeError(
-                    f"{self.name} is webhook-triggered but has no 'payload' input"
-                )
-
-            # Disable webhook-triggered block if webhook functionality not available
-            if not app_config.platform_base_url:
-                self.disabled = True
-
-    @abstractmethod
-    async def run(self, input_data: BlockSchemaInputType, **kwargs) -> BlockOutput:
-        """
-        Run the block with the given input data.
-        Args:
-            input_data: The input data with the structure of input_schema.
-
-        Kwargs: Currently 14/02/2025 these include
-            graph_id: The ID of the graph.
-            node_id: The ID of the node.
-            graph_exec_id: The ID of the graph execution.
-            node_exec_id: The ID of the node execution.
-            user_id: The ID of the user.
-
-        Returns:
-            A Generator that yields (output_name, output_data).
-            output_name: One of the output name defined in Block's output_schema.
-            output_data: The data for the output_name, matching the defined schema.
-        """
-        # --- satisfy the type checker, never executed -------------
-        if False:  # noqa: SIM115
-            yield "name", "value"  # pyright: ignore[reportMissingYield]
-        raise NotImplementedError(f"{self.name} does not implement the run method.")
-
-    async def run_once(
-        self, input_data: BlockSchemaInputType, output: str, **kwargs
-    ) -> Any:
-        async for item in self.run(input_data, **kwargs):
-            name, data = item
-            if name == output:
-                return data
-        raise ValueError(f"{self.name} did not produce any output for {output}")
-
-    def merge_stats(self, stats: "NodeExecutionStats") -> "NodeExecutionStats":
-        self.execution_stats += stats
-        return self.execution_stats
-
-    @property
-    def name(self):
-        return self.__class__.__name__
-
-    def to_dict(self):
-        return {
-            "id": self.id,
-            "name": self.name,
-            "inputSchema": self.input_schema.jsonschema(),
-            "outputSchema": self.output_schema.jsonschema(),
-            "description": self.description,
-            "categories": [category.dict() for category in self.categories],
-            "contributors": [
-                contributor.model_dump() for contributor in self.contributors
-            ],
-            "staticOutput": self.static_output,
-            "uiType": self.block_type.value,
-        }
-
-    def get_info(self) -> BlockInfo:
-        from backend.data.credit import get_block_cost
-
-        return BlockInfo(
-            id=self.id,
-            name=self.name,
-            inputSchema=self.input_schema.jsonschema(),
-            outputSchema=self.output_schema.jsonschema(),
-            costs=get_block_cost(self),
-            description=self.description,
-            categories=[category.dict() for category in self.categories],
-            contributors=[
-                contributor.model_dump() for contributor in self.contributors
-            ],
-            staticOutput=self.static_output,
-            uiType=self.block_type.value,
-        )
-
-    async def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        try:
-            async for output_name, output_data in self._execute(input_data, **kwargs):
-                yield output_name, output_data
-        except Exception as ex:
-            if isinstance(ex, BlockError):
-                raise ex
-            else:
-                raise (
-                    BlockExecutionError
-                    if isinstance(ex, ValueError)
-                    else BlockUnknownError
-                )(
-                    message=str(ex),
-                    block_name=self.name,
-                    block_id=self.id,
-                ) from ex
-
-    async def is_block_exec_need_review(
-        self,
-        input_data: BlockInput,
-        *,
-        user_id: str,
-        node_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: "ExecutionContext",
-        **kwargs,
-    ) -> tuple[bool, BlockInput]:
-        """
-        Check if this block execution needs human review and handle the review process.
-
-        Returns:
-            Tuple of (should_pause, input_data_to_use)
-            - should_pause: True if execution should be paused for review
-            - input_data_to_use: The input data to use (may be modified by reviewer)
-        """
-        if not (
-            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
-        ):
-            return False, input_data
-
-        from backend.blocks.helpers.review import HITLReviewHelper
-
-        # Handle the review request and get decision
-        decision = await HITLReviewHelper.handle_review_decision(
-            input_data=input_data,
-            user_id=user_id,
-            node_id=node_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            block_name=self.name,
-            editable=True,
-        )
-
-        if decision is None:
-            # We're awaiting review - pause execution
-            return True, input_data
-
-        if not decision.should_proceed:
-            # Review was rejected, raise an error to stop execution
-            raise BlockExecutionError(
-                message=f"Block execution rejected by reviewer: {decision.message}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-
-        # Review was approved - use the potentially modified data
-        # ReviewResult.data must be a dict for block inputs
-        reviewed_data = decision.review_result.data
-        if not isinstance(reviewed_data, dict):
-            raise BlockExecutionError(
-                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-        return False, reviewed_data
-
-    async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        # Check for review requirement only if running within a graph execution context
-        # Direct block execution (e.g., from chat) skips the review process
-        has_graph_context = all(
-            key in kwargs
-            for key in (
-                "node_exec_id",
-                "graph_exec_id",
-                "graph_id",
-                "execution_context",
-            )
-        )
-        if has_graph_context:
-            should_pause, input_data = await self.is_block_exec_need_review(
-                input_data, **kwargs
-            )
-            if should_pause:
-                return
-
-        # Validate the input data (original or reviewer-modified) once
-        if error := self.input_schema.validate_data(input_data):
-            raise BlockInputError(
-                message=f"Unable to execute block with invalid input data: {error}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-
-        # Use the validated input data
-        async for output_name, output_data in self.run(
-            self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
-            **kwargs,
-        ):
-            if output_name == "error":
-                raise BlockExecutionError(
-                    message=output_data, block_name=self.name, block_id=self.id
-                )
-            if self.block_type == BlockType.STANDARD and (
-                error := self.output_schema.validate_field(output_name, output_data)
-            ):
-                raise BlockOutputError(
-                    message=f"Block produced an invalid output data: {error}",
-                    block_name=self.name,
-                    block_id=self.id,
-                )
-            yield output_name, output_data
-
-    def is_triggered_by_event_type(
-        self, trigger_config: dict[str, Any], event_type: str
-    ) -> bool:
-        if not self.webhook_config:
-            raise TypeError("This method can't be used on non-trigger blocks")
-        if not self.webhook_config.event_filter_input:
-            return True
-        event_filter = trigger_config.get(self.webhook_config.event_filter_input)
-        if not event_filter:
-            raise ValueError("Event filter is not configured on trigger")
-        return event_type in [
-            self.webhook_config.event_format.format(event=k)
-            for k in event_filter
-            if event_filter[k] is True
-        ]
-
-
-# Type alias for any block with standard input/output schemas
-AnyBlockSchema: TypeAlias = Block[BlockSchemaInput, BlockSchemaOutput]

autogpt_platform/backend/backend/blocks/_utils.py

@@ -1,122 +0,0 @@
-import logging
-import os
-
-from backend.integrations.providers import ProviderName
-
-from ._base import AnyBlockSchema
-
-logger = logging.getLogger(__name__)
-
-
-def is_block_auth_configured(
-    block_cls: type[AnyBlockSchema],
-) -> bool:
-    """
-    Check if a block has a valid authentication method configured at runtime.
-
-    For example if a block is an OAuth-only block and there env vars are not set,
-    do not show it in the UI.
-
-    """
-    from backend.sdk.registry import AutoRegistry
-
-    # Create an instance to access input_schema
-    try:
-        block = block_cls()
-    except Exception as e:
-        # If we can't create a block instance, assume it's not OAuth-only
-        logger.error(f"Error creating block instance for {block_cls.__name__}: {e}")
-        return True
-    logger.debug(
-        f"Checking if block {block_cls.__name__} has a valid provider configured"
-    )
-
-    # Get all credential inputs from input schema
-    credential_inputs = block.input_schema.get_credentials_fields_info()
-    required_inputs = block.input_schema.get_required_fields()
-    if not credential_inputs:
-        logger.debug(
-            f"Block {block_cls.__name__} has no credential inputs - Treating as valid"
-        )
-        return True
-
-    # Check credential inputs
-    if len(required_inputs.intersection(credential_inputs.keys())) == 0:
-        logger.debug(
-            f"Block {block_cls.__name__} has only optional credential inputs"
-            " - will work without credentials configured"
-        )
-
-    # Check if the credential inputs for this block are correctly configured
-    for field_name, field_info in credential_inputs.items():
-        provider_names = field_info.provider
-        if not provider_names:
-            logger.warning(
-                f"Block {block_cls.__name__} "
-                f"has credential input '{field_name}' with no provider options"
-                " - Disabling"
-            )
-            return False
-
-        # If a field has multiple possible providers, each one needs to be usable to
-        # prevent breaking the UX
-        for _provider_name in provider_names:
-            provider_name = _provider_name.value
-            if provider_name in ProviderName.__members__.values():
-                logger.debug(
-                    f"Block {block_cls.__name__} credential input '{field_name}' "
-                    f"provider '{provider_name}' is part of the legacy provider system"
-                    " - Treating as valid"
-                )
-                break
-
-            provider = AutoRegistry.get_provider(provider_name)
-            if not provider:
-                logger.warning(
-                    f"Block {block_cls.__name__} credential input '{field_name}' "
-                    f"refers to unknown provider '{provider_name}' - Disabling"
-                )
-                return False
-
-            # Check the provider's supported auth types
-            if field_info.supported_types != provider.supported_auth_types:
-                logger.warning(
-                    f"Block {block_cls.__name__} credential input '{field_name}' "
-                    f"has mismatched supported auth types (field <> Provider): "
-                    f"{field_info.supported_types} != {provider.supported_auth_types}"
-                )
-
-            if not (supported_auth_types := provider.supported_auth_types):
-                # No auth methods are been configured for this provider
-                logger.warning(
-                    f"Block {block_cls.__name__} credential input '{field_name}' "
-                    f"provider '{provider_name}' "
-                    "has no authentication methods configured - Disabling"
-                )
-                return False
-
-            # Check if provider supports OAuth
-            if "oauth2" in supported_auth_types:
-                # Check if OAuth environment variables are set
-                if (oauth_config := provider.oauth_config) and bool(
-                    os.getenv(oauth_config.client_id_env_var)
-                    and os.getenv(oauth_config.client_secret_env_var)
-                ):
-                    logger.debug(
-                        f"Block {block_cls.__name__} credential input '{field_name}' "
-                        f"provider '{provider_name}' is configured for OAuth"
-                    )
-                else:
-                    logger.error(
-                        f"Block {block_cls.__name__} credential input '{field_name}' "
-                        f"provider '{provider_name}' "
-                        "is missing OAuth client ID or secret - Disabling"
-                    )
-                    return False
-
-        logger.debug(
-            f"Block {block_cls.__name__} credential input '{field_name}' is valid; "
-            f"supported credential types: {', '.join(field_info.supported_types)}"
-        )
-
-    return True

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,7 +1,7 @@
 import logging
-from typing import TYPE_CHECKING, Any, Optional
+from typing import Any, Optional
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockInput,
@@ -9,15 +9,13 @@ from backend.blocks._base import (
     BlockSchema,
     BlockSchemaInput,
     BlockType,
+    get_block,
 )
 from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
 
-if TYPE_CHECKING:
-    from backend.executor.utils import LogMetadata
-
 _logger = logging.getLogger(__name__)
 
 
@@ -126,10 +124,9 @@ class AgentExecutorBlock(Block):
         graph_version: int,
         graph_exec_id: str,
         user_id: str,
-        logger: "LogMetadata",
+        logger,
     ) -> BlockOutput:
 
-        from backend.blocks import get_block
         from backend.data.execution import ExecutionEventType
         from backend.executor import utils as execution_utils
 
@@ -201,7 +198,7 @@ class AgentExecutorBlock(Block):
         self,
         graph_exec_id: str,
         user_id: str,
-        logger: "LogMetadata",
+        logger,
     ) -> None:
         from backend.executor import utils as execution_utils
 

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,11 +1,5 @@
 from typing import Any
 
-from backend.blocks._base import (
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.blocks.llm import (
     DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
@@ -17,6 +11,12 @@ from backend.blocks.llm import (
     LLMResponse,
     llm_call,
 )
+from backend.data.block import (
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
 
 

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -6,7 +6,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -5,12 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.blocks._base import (
+from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
-    Block,
-    BlockCategory,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -6,7 +6,7 @@ from typing import Literal
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -6,7 +6,7 @@ from typing import Literal
 
 from pydantic import SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -1,10 +1,3 @@
-from backend.blocks._base import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -17,6 +10,13 @@ from backend.blocks.apollo.models import (
     PrimaryPhone,
     SearchOrganizationsRequest,
 )
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/apollo/people.py

@@ -1,12 +1,5 @@
 import asyncio
 
-from backend.blocks._base import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -21,6 +14,13 @@ from backend.blocks.apollo.models import (
     SearchPeopleRequest,
     SenorityLevels,
 )
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/apollo/person.py

@@ -1,10 +1,3 @@
-from backend.blocks._base import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -13,6 +6,13 @@ from backend.blocks.apollo._auth import (
     ApolloCredentialsInput,
 )
 from backend.blocks.apollo.models import Contact, EnrichPersonRequest
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/ayrshare/_util.py

@@ -3,7 +3,7 @@ from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from backend.blocks._base import BlockSchemaInput
+from backend.data.block import BlockSchemaInput
 from backend.data.model import SchemaField, UserIntegrations
 from backend.integrations.ayrshare import AyrshareClient
 from backend.util.clients import get_database_manager_async_client

autogpt_platform/backend/backend/blocks/basic.py

@@ -1,7 +1,7 @@
 import enum
 from typing import Any
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/block.py

@@ -2,7 +2,7 @@ import os
 import re
 from typing import Type
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/branching.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/claude_code.py

@@ -1,12 +1,12 @@
 import json
 import shlex
 import uuid
-from typing import TYPE_CHECKING, Literal, Optional
+from typing import Literal, Optional
 
 from e2b import AsyncSandbox as BaseAsyncSandbox
-from pydantic import SecretStr
+from pydantic import BaseModel, SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
@@ -20,13 +20,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.sandbox_files import (
-    SandboxFileOutput,
-    extract_and_store_sandbox_files,
-)
-
-if TYPE_CHECKING:
-    from backend.executor.utils import ExecutionContext
 
 
 class ClaudeCodeExecutionError(Exception):
@@ -181,15 +174,22 @@ class ClaudeCodeBlock(Block):
             advanced=True,
         )
 
+    class FileOutput(BaseModel):
+        """A file extracted from the sandbox."""
+
+        path: str
+        relative_path: str  # Path relative to working directory (for GitHub, etc.)
+        name: str
+        content: str
+
     class Output(BlockSchemaOutput):
         response: str = SchemaField(
             description="The output/response from Claude Code execution"
         )
-        files: list[SandboxFileOutput] = SchemaField(
+        files: list["ClaudeCodeBlock.FileOutput"] = SchemaField(
             description=(
                 "List of text files created/modified by Claude Code during this execution. "
-                "Each file has 'path', 'relative_path', 'name', 'content', and 'workspace_ref' fields. "
+                "Each file has 'path', 'relative_path', 'name', and 'content' fields."
-                "workspace_ref contains a workspace:// URI if the file was stored to workspace."
             )
         )
         conversation_history: str = SchemaField(
@@ -252,7 +252,6 @@ class ClaudeCodeBlock(Block):
                             "relative_path": "index.html",
                             "name": "index.html",
                             "content": "<html>Hello World</html>",
-                            "workspace_ref": None,
                         }
                     ],
                 ),
@@ -268,12 +267,11 @@ class ClaudeCodeBlock(Block):
                 "execute_claude_code": lambda *args, **kwargs: (
                     "Created index.html with hello world content",  # response
                     [
-                        SandboxFileOutput(
+                        ClaudeCodeBlock.FileOutput(
                             path="/home/user/index.html",
                             relative_path="index.html",
                             name="index.html",
                             content="<html>Hello World</html>",
-                            workspace_ref=None,
                         )
                     ],  # files
                     "User: Create a hello world HTML file\n"
@@ -296,8 +294,7 @@ class ClaudeCodeBlock(Block):
         existing_sandbox_id: str,
         conversation_history: str,
         dispose_sandbox: bool,
-        execution_context: "ExecutionContext",
+    ) -> tuple[str, list["ClaudeCodeBlock.FileOutput"], str, str, str]:
-    ) -> tuple[str, list[SandboxFileOutput], str, str, str]:
         """
         Execute Claude Code in an E2B sandbox.
 
@@ -452,18 +449,14 @@ class ClaudeCodeBlock(Block):
                 else:
                     new_conversation_history = turn_entry
 
-            # Extract files created/modified during this run and store to workspace
+            # Extract files created/modified during this run
-            sandbox_files = await extract_and_store_sandbox_files(
+            files = await self._extract_files(
-                sandbox=sandbox,
+                sandbox, working_directory, start_timestamp
-                working_directory=working_directory,
-                execution_context=execution_context,
-                since_timestamp=start_timestamp,
-                text_only=True,
             )
 
             return (
                 response,
-                sandbox_files,  # Already SandboxFileOutput objects
+                files,
                 new_conversation_history,
                 current_session_id,
                 sandbox_id,
@@ -478,6 +471,140 @@ class ClaudeCodeBlock(Block):
             if dispose_sandbox and sandbox:
                 await sandbox.kill()
 
+    async def _extract_files(
+        self,
+        sandbox: BaseAsyncSandbox,
+        working_directory: str,
+        since_timestamp: str | None = None,
+    ) -> list["ClaudeCodeBlock.FileOutput"]:
+        """
+        Extract text files created/modified during this Claude Code execution.
+
+        Args:
+            sandbox: The E2B sandbox instance
+            working_directory: Directory to search for files
+            since_timestamp: ISO timestamp - only return files modified after this time
+
+        Returns:
+            List of FileOutput objects with path, relative_path, name, and content
+        """
+        files: list[ClaudeCodeBlock.FileOutput] = []
+
+        # Text file extensions we can safely read as text
+        text_extensions = {
+            ".txt",
+            ".md",
+            ".html",
+            ".htm",
+            ".css",
+            ".js",
+            ".ts",
+            ".jsx",
+            ".tsx",
+            ".json",
+            ".xml",
+            ".yaml",
+            ".yml",
+            ".toml",
+            ".ini",
+            ".cfg",
+            ".conf",
+            ".py",
+            ".rb",
+            ".php",
+            ".java",
+            ".c",
+            ".cpp",
+            ".h",
+            ".hpp",
+            ".cs",
+            ".go",
+            ".rs",
+            ".swift",
+            ".kt",
+            ".scala",
+            ".sh",
+            ".bash",
+            ".zsh",
+            ".sql",
+            ".graphql",
+            ".env",
+            ".gitignore",
+            ".dockerfile",
+            "Dockerfile",
+            ".vue",
+            ".svelte",
+            ".astro",
+            ".mdx",
+            ".rst",
+            ".tex",
+            ".csv",
+            ".log",
+        }
+
+        try:
+            # List files recursively using find command
+            # Exclude node_modules and .git directories, but allow hidden files
+            # like .env and .gitignore (they're filtered by text_extensions later)
+            # Filter by timestamp to only get files created/modified during this run
+            safe_working_dir = shlex.quote(working_directory)
+            timestamp_filter = ""
+            if since_timestamp:
+                timestamp_filter = f"-newermt {shlex.quote(since_timestamp)} "
+            find_result = await sandbox.commands.run(
+                f"find {safe_working_dir} -type f "
+                f"{timestamp_filter}"
+                f"-not -path '*/node_modules/*' "
+                f"-not -path '*/.git/*' "
+                f"2>/dev/null"
+            )
+
+            if find_result.stdout:
+                for file_path in find_result.stdout.strip().split("\n"):
+                    if not file_path:
+                        continue
+
+                    # Check if it's a text file we can read
+                    is_text = any(
+                        file_path.endswith(ext) for ext in text_extensions
+                    ) or file_path.endswith("Dockerfile")
+
+                    if is_text:
+                        try:
+                            content = await sandbox.files.read(file_path)
+                            # Handle bytes or string
+                            if isinstance(content, bytes):
+                                content = content.decode("utf-8", errors="replace")
+
+                            # Extract filename from path
+                            file_name = file_path.split("/")[-1]
+
+                            # Calculate relative path by stripping working directory
+                            relative_path = file_path
+                            if file_path.startswith(working_directory):
+                                relative_path = file_path[len(working_directory) :]
+                                # Remove leading slash if present
+                                if relative_path.startswith("/"):
+                                    relative_path = relative_path[1:]
+
+                            files.append(
+                                ClaudeCodeBlock.FileOutput(
+                                    path=file_path,
+                                    relative_path=relative_path,
+                                    name=file_name,
+                                    content=content,
+                                )
+                            )
+                        except Exception:
+                            # Skip files that can't be read
+                            pass
+
+        except Exception:
+            # If file extraction fails, return empty results
+            pass
+
+        return files
+
     def _escape_prompt(self, prompt: str) -> str:
         """Escape the prompt for safe shell execution."""
         # Use single quotes and escape any single quotes in the prompt
@@ -490,7 +617,6 @@ class ClaudeCodeBlock(Block):
         *,
         e2b_credentials: APIKeyCredentials,
         anthropic_credentials: APIKeyCredentials,
-        execution_context: "ExecutionContext",
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -511,7 +637,6 @@ class ClaudeCodeBlock(Block):
                 existing_sandbox_id=input_data.sandbox_id,
                 conversation_history=input_data.conversation_history,
                 dispose_sandbox=input_data.dispose_sandbox,
-                execution_context=execution_context,
             )
 
             yield "response", response

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -1,12 +1,12 @@
 from enum import Enum
-from typing import TYPE_CHECKING, Any, Literal, Optional
+from typing import Any, Literal, Optional
 
 from e2b_code_interpreter import AsyncSandbox
 from e2b_code_interpreter import Result as E2BExecutionResult
 from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
 from pydantic import BaseModel, Field, JsonValue, SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
@@ -20,13 +20,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.sandbox_files import (
-    SandboxFileOutput,
-    extract_and_store_sandbox_files,
-)
-
-if TYPE_CHECKING:
-    from backend.executor.utils import ExecutionContext
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -92,9 +85,6 @@ class CodeExecutionResult(MainCodeExecutionResult):
 class BaseE2BExecutorMixin:
     """Shared implementation methods for E2B executor blocks."""
 
-    # Default working directory in E2B sandboxes
-    WORKING_DIR = "/home/user"
-
     async def execute_code(
         self,
         api_key: str,
@@ -105,21 +95,14 @@ class BaseE2BExecutorMixin:
         timeout: Optional[int] = None,
         sandbox_id: Optional[str] = None,
         dispose_sandbox: bool = False,
-        execution_context: Optional["ExecutionContext"] = None,
-        extract_files: bool = False,
     ):
         """
         Unified code execution method that handles all three use cases:
         1. Create new sandbox and execute (ExecuteCodeBlock)
         2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
         3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
-
-        Args:
-            extract_files: If True and execution_context provided, extract files
-                           created/modified during execution and store to workspace.
         """  # noqa
         sandbox = None
-        files: list[SandboxFileOutput] = []
         try:
             if sandbox_id:
                 # Connect to existing sandbox (ExecuteCodeStepBlock case)
@@ -135,12 +118,6 @@ class BaseE2BExecutorMixin:
                     for cmd in setup_commands:
                         await sandbox.commands.run(cmd)
 
-            # Capture timestamp before execution to scope file extraction
-            start_timestamp = None
-            if extract_files:
-                ts_result = await sandbox.commands.run("date -u +%Y-%m-%dT%H:%M:%S")
-                start_timestamp = ts_result.stdout.strip() if ts_result.stdout else None
-
             # Execute the code
             execution = await sandbox.run_code(
                 code,
@@ -156,24 +133,7 @@ class BaseE2BExecutorMixin:
             stdout_logs = "".join(execution.logs.stdout)
             stderr_logs = "".join(execution.logs.stderr)
 
-            # Extract files created/modified during this execution
+            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
-            if extract_files and execution_context:
-                files = await extract_and_store_sandbox_files(
-                    sandbox=sandbox,
-                    working_directory=self.WORKING_DIR,
-                    execution_context=execution_context,
-                    since_timestamp=start_timestamp,
-                    text_only=False,  # Include binary files too
-                )
-
-            return (
-                results,
-                text_output,
-                stdout_logs,
-                stderr_logs,
-                sandbox.sandbox_id,
-                files,
-            )
         finally:
             # Dispose of sandbox if requested to reduce usage costs
             if dispose_sandbox and sandbox:
@@ -278,12 +238,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             description="Standard output logs from execution"
         )
         stderr_logs: str = SchemaField(description="Standard error logs from execution")
-        files: list[SandboxFileOutput] = SchemaField(
-            description=(
-                "Files created or modified during execution. "
-                "Each file has path, name, content, and workspace_ref (if stored)."
-            ),
-        )
 
     def __init__(self):
         super().__init__(
@@ -305,30 +259,23 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
-                ("files", []),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox, execution_context, extract_files: (  # noqa
+                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: (  # noqa
                     [],  # results
                     "Hello World",  # text_output
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     "sandbox_id",  # sandbox_id
-                    [],  # files
                 ),
             },
         )
 
     async def run(
-        self,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: "ExecutionContext",
-        **kwargs,
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _, files = await self.execute_code(
+            results, text_output, stdout, stderr, _ = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.code,
                 language=input_data.language,
@@ -336,8 +283,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 setup_commands=input_data.setup_commands,
                 timeout=input_data.timeout,
                 dispose_sandbox=input_data.dispose_sandbox,
-                execution_context=execution_context,
-                extract_files=True,
             )
 
             # Determine result object shape & filter out empty formats
@@ -351,8 +296,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 yield "stdout_logs", stdout
             if stderr:
                 yield "stderr_logs", stderr
-            # Always yield files (empty list if none)
-            yield "files", [f.model_dump() for f in files]
         except Exception as e:
             yield "error", str(e)
 
@@ -450,7 +393,6 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     "sandbox_id",  # sandbox_id
-                    [],  # files
                 ),
             },
         )
@@ -459,7 +401,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            _, text_output, stdout, stderr, sandbox_id, _ = await self.execute_code(
+            _, text_output, stdout, stderr, sandbox_id = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.setup_code,
                 language=input_data.language,
@@ -558,7 +500,6 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
                     "Hello World\n",  # stdout_logs
                     "",  # stderr_logs
                     sandbox_id,  # sandbox_id
-                    [],  # files
                 ),
             },
         )
@@ -567,7 +508,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            results, text_output, stdout, stderr, _, _ = await self.execute_code(
+            results, text_output, stdout, stderr, _ = await self.execute_code(
                 api_key=credentials.api_key.get_secret_value(),
                 code=input_data.step_code,
                 language=input_data.language,

autogpt_platform/backend/backend/blocks/code_extraction_block.py

@@ -1,6 +1,6 @@
 import re
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/codex.py

@@ -6,7 +6,7 @@ from openai import AsyncOpenAI
 from openai.types.responses import Response as OpenAIResponse
 from pydantic import SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/compass/triggers.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockManualWebhookConfig,

autogpt_platform/backend/backend/blocks/count_words_and_char_block.py

@@ -1,4 +1,4 @@
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -1,6 +1,6 @@
 from typing import Any, List
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/decoder_block.py

@@ -1,6 +1,6 @@
 import codecs
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

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

@@ -8,7 +8,7 @@ from typing import Any, Literal, cast
 import discord
 from pydantic import SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py

@@ -2,7 +2,7 @@
 Discord OAuth-based blocks.
 """
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/email_block.py

@@ -7,7 +7,7 @@ from typing import Literal
 
 from pydantic import BaseModel, ConfigDict, SecretStr
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/encoder_block.py

@@ -2,7 +2,7 @@
 
 import codecs
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py

@@ -8,7 +8,7 @@ which provides access to LinkedIn profile data and related information.
 import logging
 from typing import Optional
 
-from backend.blocks._base import (
+from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -3,13 +3,6 @@ import logging
 from enum import Enum
 from typing import Any
 
-from backend.blocks._base import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.blocks.fal._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
@@ -17,6 +10,13 @@ from backend.blocks.fal._auth import (
     FalCredentialsField,
     FalCredentialsInput,
 )
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file