Merge branch 'dev' into feat/execution-data

## Problem
ExecutionDataClient integration tests were failing with foreign key constraint
violations and "connection refused" errors that caused tests to hang and fail
after service shutdown.

## Root Cause
1. Tests used hardcoded IDs (test_graph_exec_id) that didn't exist in database
2. @non_blocking_persist decorator created background threads that continued
   database calls after test services shut down
3. Foreign key constraints failed: AgentNodeExecution_agentGraphExecutionId_fkey

## Solution
1. **Fixed Foreign Key Issues**: Create proper database records in creation tests
   - User → AgentGraph → AgentGraphExecution relationship
   - Use correct enum types (AgentExecutionStatus.RUNNING vs "RUNNING")

2. **Eliminated Connection Errors**: Mock all database operations in data tests
   - Mock get_database_manager_client/async_client
   - Mock get_execution_event_bus
   - Disable @non_blocking_persist decorator to prevent background calls

3. **Clean Test Isolation**: Ensure tests don't leak database connections

## Test Results
- ✅ 1005 passed, 88 skipped - 100% GREEN
- ✅ No connection refused errors
- ✅ Fast execution (~53s vs hanging)
- ✅ All ExecutionDataClient and ExecutionCreation tests pass

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

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

@@ -1,97 +0,0 @@
-name: Auto Fix CI Failures
-
-on:
-  workflow_run:
-    workflows: ["CI"]
-    types:
-      - completed
-
-permissions:
-  contents: write
-  pull-requests: write
-  actions: read
-  issues: write
-  id-token: write # Required for OIDC token exchange
-
-jobs:
-  auto-fix:
-    if: |
-      github.event.workflow_run.conclusion == 'failure' &&
-      github.event.workflow_run.pull_requests[0] &&
-      !startsWith(github.event.workflow_run.head_branch, 'claude-auto-fix-ci-')
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.workflow_run.head_branch }}
-          fetch-depth: 0
-          token: ${{ secrets.GITHUB_TOKEN }}
-
-      - name: Setup git identity
-        run: |
-          git config --global user.email "claude[bot]@users.noreply.github.com"
-          git config --global user.name "claude[bot]"
-
-      - name: Create fix branch
-        id: branch
-        run: |
-          BRANCH_NAME="claude-auto-fix-ci-${{ github.event.workflow_run.head_branch }}-${{ github.run_id }}"
-          git checkout -b "$BRANCH_NAME"
-          echo "branch_name=$BRANCH_NAME" >> $GITHUB_OUTPUT
-
-      - name: Get CI failure details
-        id: failure_details
-        uses: actions/github-script@v7
-        with:
-          script: |
-            const run = await github.rest.actions.getWorkflowRun({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              run_id: ${{ github.event.workflow_run.id }}
-            });
-
-            const jobs = await github.rest.actions.listJobsForWorkflowRun({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              run_id: ${{ github.event.workflow_run.id }}
-            });
-
-            const failedJobs = jobs.data.jobs.filter(job => job.conclusion === 'failure');
-
-            let errorLogs = [];
-            for (const job of failedJobs) {
-              const logs = await github.rest.actions.downloadJobLogsForWorkflowRun({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                job_id: job.id
-              });
-              errorLogs.push({
-                jobName: job.name,
-                logs: logs.data
-              });
-            }
-
-            return {
-              runUrl: run.data.html_url,
-              failedJobs: failedJobs.map(j => j.name),
-              errorLogs: errorLogs
-            };
-
-      - name: Fix CI failures with Claude
-        id: claude
-        uses: anthropics/claude-code-action@v1
-        with:
-          prompt: |
-            /fix-ci 
-            Failed CI Run: ${{ fromJSON(steps.failure_details.outputs.result).runUrl }}
-            Failed Jobs: ${{ join(fromJSON(steps.failure_details.outputs.result).failedJobs, ', ') }}
-            PR Number: ${{ github.event.workflow_run.pull_requests[0].number }}
-            Branch Name: ${{ steps.branch.outputs.branch_name }}
-            Base Branch: ${{ github.event.workflow_run.head_branch }}
-            Repository: ${{ github.repository }}
-
-            Error logs:
-            ${{ toJSON(fromJSON(steps.failure_details.outputs.result).errorLogs) }}
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          claude_args: "--allowedTools 'Edit,MultiEdit,Write,Read,Glob,Grep,LS,Bash(git:*),Bash(bun:*),Bash(npm:*),Bash(npx:*),Bash(gh:*)'"

.github/workflows/claude-dependabot.yml

@@ -1,379 +0,0 @@
-# Claude Dependabot PR Review Workflow
-# 
-# This workflow automatically runs Claude analysis on Dependabot PRs to:
-# - Identify dependency changes and their versions
-# - Look up changelogs for updated packages  
-# - Assess breaking changes and security impacts
-# - Provide actionable recommendations for the development team
-#
-# Triggered on: Dependabot PRs (opened, synchronize)
-# Requirements: ANTHROPIC_API_KEY secret must be configured
-
-name: Claude Dependabot PR Review
-
-on:
-  pull_request:
-    types: [opened, synchronize]
-
-jobs:
-  dependabot-review:
-    # Only run on Dependabot PRs
-    if: github.actor == 'dependabot[bot]'
-    runs-on: ubuntu-latest
-    timeout-minutes: 30
-
-    permissions:
-      contents: write
-      pull-requests: read
-      issues: read
-      id-token: write
-      actions: read # Required for CI access
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-
-      # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"  # Use standard version matching CI
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          # Extract Poetry version from backend/poetry.lock (matches CI)
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          
-          # Install Poetry
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          
-          # Add Poetry to PATH
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Check poetry.lock
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry lock
-          if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
-            echo "Warning: poetry.lock not up to date, but continuing for setup"
-            git checkout poetry.lock  # Reset for clean setup
-          fi
-
-      - name: Install Python dependencies
-        working-directory: autogpt_platform/backend
-        run: poetry install
-
-      - name: Generate Prisma Client
-        working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
-
-      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "21"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Set pnpm store directory
-        run: |
-          pnpm config set store-dir ~/.pnpm-store
-          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
-
-      - name: Cache frontend dependencies
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install JavaScript dependencies
-        working-directory: autogpt_platform/frontend
-        run: pnpm install --frozen-lockfile
-
-      # Install Playwright browsers for frontend testing
-      # NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
-      # - name: Install Playwright browsers
-      #   working-directory: autogpt_platform/frontend
-      #   run: pnpm playwright install --with-deps chromium
-
-      # Docker setup for development environment
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Copy default environment files
-        working-directory: autogpt_platform
-        run: |
-          # Copy default environment files for development
-          cp .env.default .env
-          cp backend/.env.default backend/.env
-          cp frontend/.env.default frontend/.env
-
-      # Phase 1: Cache and load Docker images for faster setup
-      - name: Set up Docker image cache
-        id: docker-cache
-        uses: actions/cache@v4
-        with:
-          path: ~/docker-cache
-          # Use a versioned key for cache invalidation when image list changes
-          key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
-          restore-keys: |
-            docker-images-v2-${{ runner.os }}-
-            docker-images-v1-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-          
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "clamav/clamav-debian:latest"
-            "busybox:latest"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-            "supabase/postgres-meta:v0.86.1"
-            "supabase/studio:20250224-d10db0f"
-          )
-          
-          # Check if any cached tar files exist (more reliable than cache-hit)
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              # Convert image name to filename (replace : and / with -)
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            # Pull all images in parallel
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-            
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                # Convert image name to filename (replace : and / with -)
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-          
-          echo "Docker images ready for use"
-
-      # Phase 2: Build migrate service with GitHub Actions cache
-      - name: Build migrate Docker image with cache
-        working-directory: autogpt_platform
-        run: |
-          # Build the migrate image with buildx for GHA caching
-          docker buildx build \
-            --cache-from type=gha \
-            --cache-to type=gha,mode=max \
-            --target migrate \
-            --tag autogpt_platform-migrate:latest \
-            --load \
-            -f backend/Dockerfile \
-            ..
-
-      # Start services using pre-built images
-      - name: Start Docker services for development
-        working-directory: autogpt_platform
-        run: |
-          # Start essential services (migrate image already built with correct tag)
-          docker compose --profile local up deps --no-build --detach
-          echo "Waiting for services to be ready..."
-          
-          # Wait for database to be ready
-          echo "Checking database readiness..."
-          timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do 
-            echo "  Waiting for database..."
-            sleep 2
-          done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
-          
-          # Check migrate service status
-          echo "Checking migration status..."
-          docker compose ps migrate || echo "  Migrate service not visible in ps output"
-          
-          # Wait for migrate service to complete
-          echo "Waiting for migrations to complete..."
-          timeout 30 bash -c '
-            ATTEMPTS=0
-            while [ $ATTEMPTS -lt 15 ]; do
-              ATTEMPTS=$((ATTEMPTS + 1))
-              
-              # Check using docker directly (more reliable than docker compose ps)
-              CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
-              
-              if [ -z "$CONTAINER_STATUS" ]; then
-                echo "  Attempt $ATTEMPTS: Migrate container not found yet..."
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
-                echo "✅ Migrations completed successfully"
-                docker compose logs migrate --tail=5 2>/dev/null || true
-                exit 0
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
-                EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
-                echo "❌ Migrations failed with exit code: $EXIT_CODE"
-                echo "Migration logs:"
-                docker compose logs migrate --tail=20 2>/dev/null || true
-                exit 1
-              elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
-                echo "  Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
-              else
-                echo "  Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
-              fi
-              
-              sleep 2
-            done
-            
-            echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
-            echo "Final container check:"
-            docker ps -a --filter "label=com.docker.compose.service=migrate" || true
-            echo "Migration logs (if available):"
-            docker compose logs migrate --tail=10 2>/dev/null || echo "  No logs available"
-          ' || echo "⚠️ Migration check completed with warnings, continuing..."
-          
-          # Brief wait for other services to stabilize
-          echo "Waiting 5 seconds for other services to stabilize..."
-          sleep 5
-
-      # Verify installations and provide environment info
-      - name: Verify setup and show environment info
-        run: |
-          echo "=== Python Setup ==="
-          python --version
-          poetry --version
-          
-          echo "=== Node.js Setup ==="
-          node --version
-          pnpm --version
-          
-          echo "=== Additional Tools ==="
-          docker --version
-          docker compose version
-          gh --version || true
-          
-          echo "=== Services Status ==="
-          cd autogpt_platform
-          docker compose ps || true
-          
-          echo "=== Backend Dependencies ==="
-          cd backend
-          poetry show | head -10 || true
-          
-          echo "=== Frontend Dependencies ==="
-          cd ../frontend
-          pnpm list --depth=0 | head -10 || true
-          
-          echo "=== Environment Files ==="
-          ls -la ../.env* || true
-          ls -la .env* || true
-          ls -la ../backend/.env* || true
-          
-          echo "✅ AutoGPT Platform development environment setup complete!"
-          echo "🚀 Ready for development with Docker services running"
-          echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"
-
-
-      - name: Run Claude Dependabot Analysis
-        id: claude_review
-        uses: anthropics/claude-code-action@v1
-        with:
-          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          claude_args: |
-            --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
-          prompt: |
-            You are Claude, an AI assistant specialized in reviewing Dependabot dependency update PRs. 
-            
-            Your primary tasks are:
-            1. **Analyze the dependency changes** in this Dependabot PR
-            2. **Look up changelogs** for all updated dependencies to understand what changed
-            3. **Identify breaking changes** and assess potential impact on the AutoGPT codebase
-            4. **Provide actionable recommendations** for the development team
-            
-            ## Analysis Process:
-            
-            1. **Identify Changed Dependencies**: 
-               - Use git diff to see what dependencies were updated
-               - Parse package.json, poetry.lock, requirements files, etc.
-               - List all package versions: old → new
-            
-            2. **Changelog Research**:
-               - For each updated dependency, look up its changelog/release notes
-               - Use WebFetch to access GitHub releases, NPM package pages, PyPI project pages. The pr should also have some details
-               - Focus on versions between the old and new versions
-               - Identify: breaking changes, deprecations, security fixes, new features
-            
-            3. **Breaking Change Assessment**:
-               - Categorize changes: BREAKING, MAJOR, MINOR, PATCH, SECURITY
-               - Assess impact on AutoGPT's usage patterns
-               - Check if AutoGPT uses affected APIs/features
-               - Look for migration guides or upgrade instructions
-            
-            4. **Codebase Impact Analysis**:
-               - Search the AutoGPT codebase for usage of changed APIs
-               - Identify files that might be affected by breaking changes
-               - Check test files for deprecated usage patterns
-               - Look for configuration changes needed
-            
-            ## Output Format:
-            
-            Provide a comprehensive review comment with:
-            
-            ### 🔍 Dependency Analysis Summary
-            - List of updated packages with version changes
-            - Overall risk assessment (LOW/MEDIUM/HIGH)
-            
-            ### 📋 Detailed Changelog Review
-            For each updated dependency:
-            - **Package**: name (old_version → new_version)
-            - **Changes**: Summary of key changes
-            - **Breaking Changes**: List any breaking changes
-            - **Security Fixes**: Note security improvements
-            - **Migration Notes**: Any upgrade steps needed
-            
-            ### ⚠️ Impact Assessment
-            - **Breaking Changes Found**: Yes/No with details
-            - **Affected Files**: List AutoGPT files that may need updates
-            - **Test Impact**: Any tests that may need updating
-            - **Configuration Changes**: Required config updates
-            
-            ### 🛠️ Recommendations
-            - **Action Required**: What the team should do
-            - **Testing Focus**: Areas to test thoroughly
-            - **Follow-up Tasks**: Any additional work needed
-            - **Merge Recommendation**: APPROVE/REVIEW_NEEDED/HOLD
-            
-            ### 📚 Useful Links
-            - Links to relevant changelogs, migration guides, documentation
-            
-            Be thorough but concise. Focus on actionable insights that help the development team make informed decisions about the dependency updates.

.github/workflows/claude.yml

@@ -30,296 +30,18 @@ jobs:
         github.event.issue.author_association == 'COLLABORATOR'
       )
     runs-on: ubuntu-latest
-    timeout-minutes: 45
-
     permissions:
-      contents: write
+      contents: read
       pull-requests: read
       issues: read
       id-token: write
-      actions: read # Required for CI access
     steps:
-      - name: Checkout code
+      - name: Checkout repository
         uses: actions/checkout@v4
         with:
           fetch-depth: 1
-
-      # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"  # Use standard version matching CI
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          # Extract Poetry version from backend/poetry.lock (matches CI)
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          
-          # Install Poetry
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          
-          # Add Poetry to PATH
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Check poetry.lock
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry lock
-          if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
-            echo "Warning: poetry.lock not up to date, but continuing for setup"
-            git checkout poetry.lock  # Reset for clean setup
-          fi
-
-      - name: Install Python dependencies
-        working-directory: autogpt_platform/backend
-        run: poetry install
-
-      - name: Generate Prisma Client
-        working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
-
-      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "21"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Set pnpm store directory
-        run: |
-          pnpm config set store-dir ~/.pnpm-store
-          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
-
-      - name: Cache frontend dependencies
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install JavaScript dependencies
-        working-directory: autogpt_platform/frontend
-        run: pnpm install --frozen-lockfile
-
-      # Install Playwright browsers for frontend testing
-      # NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
-      # - name: Install Playwright browsers
-      #   working-directory: autogpt_platform/frontend
-      #   run: pnpm playwright install --with-deps chromium
-
-      # Docker setup for development environment
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Copy default environment files
-        working-directory: autogpt_platform
-        run: |
-          # Copy default environment files for development
-          cp .env.default .env
-          cp backend/.env.default backend/.env
-          cp frontend/.env.default frontend/.env
-
-      # Phase 1: Cache and load Docker images for faster setup
-      - name: Set up Docker image cache
-        id: docker-cache
-        uses: actions/cache@v4
-        with:
-          path: ~/docker-cache
-          # Use a versioned key for cache invalidation when image list changes
-          key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
-          restore-keys: |
-            docker-images-v2-${{ runner.os }}-
-            docker-images-v1-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-          
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "clamav/clamav-debian:latest"
-            "busybox:latest"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-            "supabase/postgres-meta:v0.86.1"
-            "supabase/studio:20250224-d10db0f"
-          )
-          
-          # Check if any cached tar files exist (more reliable than cache-hit)
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              # Convert image name to filename (replace : and / with -)
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            # Pull all images in parallel
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-            
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                # Convert image name to filename (replace : and / with -)
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-          
-          echo "Docker images ready for use"
-
-      # Phase 2: Build migrate service with GitHub Actions cache
-      - name: Build migrate Docker image with cache
-        working-directory: autogpt_platform
-        run: |
-          # Build the migrate image with buildx for GHA caching
-          docker buildx build \
-            --cache-from type=gha \
-            --cache-to type=gha,mode=max \
-            --target migrate \
-            --tag autogpt_platform-migrate:latest \
-            --load \
-            -f backend/Dockerfile \
-            ..
-
-      # Start services using pre-built images
-      - name: Start Docker services for development
-        working-directory: autogpt_platform
-        run: |
-          # Start essential services (migrate image already built with correct tag)
-          docker compose --profile local up deps --no-build --detach
-          echo "Waiting for services to be ready..."
-          
-          # Wait for database to be ready
-          echo "Checking database readiness..."
-          timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do 
-            echo "  Waiting for database..."
-            sleep 2
-          done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
-          
-          # Check migrate service status
-          echo "Checking migration status..."
-          docker compose ps migrate || echo "  Migrate service not visible in ps output"
-          
-          # Wait for migrate service to complete
-          echo "Waiting for migrations to complete..."
-          timeout 30 bash -c '
-            ATTEMPTS=0
-            while [ $ATTEMPTS -lt 15 ]; do
-              ATTEMPTS=$((ATTEMPTS + 1))
-              
-              # Check using docker directly (more reliable than docker compose ps)
-              CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
-              
-              if [ -z "$CONTAINER_STATUS" ]; then
-                echo "  Attempt $ATTEMPTS: Migrate container not found yet..."
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
-                echo "✅ Migrations completed successfully"
-                docker compose logs migrate --tail=5 2>/dev/null || true
-                exit 0
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
-                EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
-                echo "❌ Migrations failed with exit code: $EXIT_CODE"
-                echo "Migration logs:"
-                docker compose logs migrate --tail=20 2>/dev/null || true
-                exit 1
-              elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
-                echo "  Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
-              else
-                echo "  Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
-              fi
-              
-              sleep 2
-            done
-            
-            echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
-            echo "Final container check:"
-            docker ps -a --filter "label=com.docker.compose.service=migrate" || true
-            echo "Migration logs (if available):"
-            docker compose logs migrate --tail=10 2>/dev/null || echo "  No logs available"
-          ' || echo "⚠️ Migration check completed with warnings, continuing..."
-          
-          # Brief wait for other services to stabilize
-          echo "Waiting 5 seconds for other services to stabilize..."
-          sleep 5
-
-      # Verify installations and provide environment info
-      - name: Verify setup and show environment info
-        run: |
-          echo "=== Python Setup ==="
-          python --version
-          poetry --version
-          
-          echo "=== Node.js Setup ==="
-          node --version
-          pnpm --version
-          
-          echo "=== Additional Tools ==="
-          docker --version
-          docker compose version
-          gh --version || true
-          
-          echo "=== Services Status ==="
-          cd autogpt_platform
-          docker compose ps || true
-          
-          echo "=== Backend Dependencies ==="
-          cd backend
-          poetry show | head -10 || true
-          
-          echo "=== Frontend Dependencies ==="
-          cd ../frontend
-          pnpm list --depth=0 | head -10 || true
-          
-          echo "=== Environment Files ==="
-          ls -la ../.env* || true
-          ls -la .env* || true
-          ls -la ../backend/.env* || true
-          
-          echo "✅ AutoGPT Platform development environment setup complete!"
-          echo "🚀 Ready for development with Docker services running"
-          echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"
-
       - name: Run Claude Code
         id: claude
-        uses: anthropics/claude-code-action@v1
+        uses: anthropics/claude-code-action@beta
         with:
           anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
-          claude_args: |
-            --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*), Bash(gh pr edit:*)"
-            --model opus
-          additional_permissions: |
-            actions: read

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

@@ -3,7 +3,6 @@ name: AutoGPT Platform - Deploy Prod Environment
 on:
   release:
     types: [published]
-  workflow_dispatch:
 
 permissions:
   contents: 'read'
@@ -18,8 +17,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.ref_name || 'master' }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -39,7 +36,7 @@ jobs:
           DATABASE_URL: ${{ secrets.BACKEND_DATABASE_URL }}
           DIRECT_URL: ${{ secrets.BACKEND_DATABASE_URL }}
 
-
+          
   trigger:
     needs: migrate
     runs-on: ubuntu-latest
@@ -50,5 +47,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_prod
-          client-payload: |
-            {"ref": "${{ github.ref_name || 'master' }}", "repository": "${{ github.repository }}"}
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

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

@@ -5,13 +5,6 @@ on:
     branches: [ dev ]
     paths:
       - 'autogpt_platform/**'
-  workflow_dispatch:
-    inputs:
-      git_ref:
-        description: 'Git ref (branch/tag) of AutoGPT to deploy'
-        required: true
-        default: 'master'
-        type: string
 
 permissions:
   contents: 'read'
@@ -26,8 +19,6 @@ jobs:
     steps:
       - name: Checkout code
         uses: actions/checkout@v4
-        with:
-          ref: ${{ github.event.inputs.git_ref || github.ref_name }}
 
       - name: Set up Python
         uses: actions/setup-python@v5
@@ -57,4 +48,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_dev
-          client-payload: '{"ref": "${{ github.event.inputs.git_ref || github.ref }}", "repository": "${{ github.repository }}"}'
+          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'

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

@@ -37,7 +37,9 @@ jobs:
 
     services:
       redis:
-        image: redis:latest
+        image: bitnami/redis:6.2
+        env:
+          REDIS_PASSWORD: testpassword
         ports:
           - 6379:6379
       rabbitmq:
@@ -202,6 +204,7 @@ jobs:
           JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
           REDIS_HOST: "localhost"
           REDIS_PORT: "6379"
+          REDIS_PASSWORD: "testpassword"
           ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=" # DO NOT USE IN PRODUCTION!!
 
     env:

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

@@ -160,7 +160,7 @@ jobs:
 
       - name: Run docker compose
         run: |
-          NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
+          docker compose -f ../docker-compose.yml up -d
         env:
           DOCKER_BUILDKIT: 1
           BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache

.pr_agent.toml

@@ -1,3 +1,6 @@
+[pr_reviewer]
+num_code_suggestions=0
+
 [pr_code_suggestions]
 commitable_code_suggestions=false
 num_code_suggestions=0

autogpt_platform/CLAUDE.md

@@ -61,27 +61,24 @@ poetry run pytest path/to/test.py --snapshot-update
 
 ```bash
 # Install dependencies
-cd frontend && pnpm i
+cd frontend && npm install
 
 # Start development server
-pnpm dev
+npm run dev
 
 # Run E2E tests
-pnpm test
+npm run test
 
 # Run Storybook for component development
-pnpm storybook
+npm run storybook
 
 # Build production
-pnpm build
+npm run build
 
 # Type checking
-pnpm types
+npm run types
 ```
 
-We have a components library in autogpt_platform/frontend/src/components/atoms that should be used when adding new pages and components. 
-
-
 ## Architecture Overview
 
 ### Backend Architecture

autogpt_platform/Makefile

@@ -1,47 +0,0 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
-
-# Run just Supabase + Redis + RabbitMQ
-start-core:
-	docker compose up -d deps
-
-# Stop core services
-stop-core:
-	docker compose stop deps
-
-# View logs for core services
-logs-core:
-	docker compose logs -f deps
-
-# Run formatting and linting for backend and frontend
-format:
-	cd backend && poetry run format
-	cd frontend && pnpm format
-	cd frontend && pnpm lint
-
-init-env:
-	cp -n .env.default .env || true
-	cd backend && cp -n .env.default .env || true
-	cd frontend && cp -n .env.default .env || true
-
-
-# Run migrations for backend
-migrate:
-	cd backend && poetry run prisma migrate deploy
-	cd backend && poetry run prisma generate
-
-run-backend:
-	cd backend && poetry run app
-
-run-frontend:
-	cd frontend && pnpm dev
-
-help:
-	@echo "Usage: make <target>"
-	@echo "Targets:"
-	@echo "  start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
-	@echo "  stop-core - Stop the core services"
-	@echo "  logs-core - Tail the logs for core services"
-	@echo "  format - Format & lint backend (Python) and frontend (TypeScript) code"
-	@echo "  migrate - Run backend database migrations"
-	@echo "  run-backend - Run the backend FastAPI server"
-	@echo "  run-frontend - Run the frontend Next.js development server"

autogpt_platform/README.md

@@ -38,37 +38,6 @@ To run the AutoGPT Platform, follow these steps:
 
 4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
-### Running Just Core services
-
-You can now run the following to enable just the core services.
-
-```
-# For help
-make help
-
-# Run just Supabase + Redis + RabbitMQ
-make start-core
-
-# Stop core services
-make stop-core
-
-# View logs from core services 
-make logs-core
-
-# Run formatting and linting for backend and frontend
-make format
-
-# Run migrations for backend database
-make migrate
-
-# Run backend server
-make run-backend
-
-# Run frontend development server
-make run-frontend
-
-```
-
 ### Docker Compose Commands
 
 Here are some useful Docker Compose commands for managing your AutoGPT Platform:

autogpt_platform/autogpt_libs/autogpt_libs/api_key/key_manager.py

@@ -0,0 +1,35 @@
+import hashlib
+import secrets
+from typing import NamedTuple
+
+
+class APIKeyContainer(NamedTuple):
+    """Container for API key parts."""
+
+    raw: str
+    prefix: str
+    postfix: str
+    hash: str
+
+
+class APIKeyManager:
+    PREFIX: str = "agpt_"
+    PREFIX_LENGTH: int = 8
+    POSTFIX_LENGTH: int = 8
+
+    def generate_api_key(self) -> APIKeyContainer:
+        """Generate a new API key with all its parts."""
+        raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
+        return APIKeyContainer(
+            raw=raw_key,
+            prefix=raw_key[: self.PREFIX_LENGTH],
+            postfix=raw_key[-self.POSTFIX_LENGTH :],
+            hash=hashlib.sha256(raw_key.encode()).hexdigest(),
+        )
+
+    def verify_api_key(self, provided_key: str, stored_hash: str) -> bool:
+        """Verify if a provided API key matches the stored hash."""
+        if not provided_key.startswith(self.PREFIX):
+            return False
+        provided_hash = hashlib.sha256(provided_key.encode()).hexdigest()
+        return secrets.compare_digest(provided_hash, stored_hash)

autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py

@@ -1,78 +0,0 @@
-import hashlib
-import secrets
-from typing import NamedTuple
-
-from cryptography.hazmat.primitives.kdf.scrypt import Scrypt
-
-
-class APIKeyContainer(NamedTuple):
-    """Container for API key parts."""
-
-    key: str
-    head: str
-    tail: str
-    hash: str
-    salt: str
-
-
-class APIKeySmith:
-    PREFIX: str = "agpt_"
-    HEAD_LENGTH: int = 8
-    TAIL_LENGTH: int = 8
-
-    def generate_key(self) -> APIKeyContainer:
-        """Generate a new API key with secure hashing."""
-        raw_key = f"{self.PREFIX}{secrets.token_urlsafe(32)}"
-        hash, salt = self.hash_key(raw_key)
-
-        return APIKeyContainer(
-            key=raw_key,
-            head=raw_key[: self.HEAD_LENGTH],
-            tail=raw_key[-self.TAIL_LENGTH :],
-            hash=hash,
-            salt=salt,
-        )
-
-    def verify_key(
-        self, provided_key: str, known_hash: str, known_salt: str | None = None
-    ) -> bool:
-        """
-        Verify an API key against a known hash (+ salt).
-        Supports verifying both legacy SHA256 and secure Scrypt hashes.
-        """
-        if not provided_key.startswith(self.PREFIX):
-            return False
-
-        # Handle legacy SHA256 hashes (migration support)
-        if known_salt is None:
-            legacy_hash = hashlib.sha256(provided_key.encode()).hexdigest()
-            return secrets.compare_digest(legacy_hash, known_hash)
-
-        try:
-            salt_bytes = bytes.fromhex(known_salt)
-            provided_hash = self._hash_key_with_salt(provided_key, salt_bytes)
-            return secrets.compare_digest(provided_hash, known_hash)
-        except (ValueError, TypeError):
-            return False
-
-    def hash_key(self, raw_key: str) -> tuple[str, str]:
-        """Migrate a legacy hash to secure hash format."""
-        salt = self._generate_salt()
-        hash = self._hash_key_with_salt(raw_key, salt)
-        return hash, salt.hex()
-
-    def _generate_salt(self) -> bytes:
-        """Generate a random salt for hashing."""
-        return secrets.token_bytes(32)
-
-    def _hash_key_with_salt(self, raw_key: str, salt: bytes) -> str:
-        """Hash API key using Scrypt with salt."""
-        kdf = Scrypt(
-            length=32,
-            salt=salt,
-            n=2**14,  # CPU/memory cost parameter
-            r=8,  # Block size parameter
-            p=1,  # Parallelization parameter
-        )
-        key_hash = kdf.derive(raw_key.encode())
-        return key_hash.hex()

autogpt_platform/autogpt_libs/autogpt_libs/api_key/test_keysmith.py

@@ -1,79 +0,0 @@
-import hashlib
-
-from autogpt_libs.api_key.keysmith import APIKeySmith
-
-
-def test_generate_api_key():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    assert key.key.startswith(keysmith.PREFIX)
-    assert key.head == key.key[: keysmith.HEAD_LENGTH]
-    assert key.tail == key.key[-keysmith.TAIL_LENGTH :]
-    assert len(key.hash) == 64  # 32 bytes hex encoded
-    assert len(key.salt) == 64  # 32 bytes hex encoded
-
-
-def test_verify_new_secure_key():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Test correct key validates
-    assert keysmith.verify_key(key.key, key.hash, key.salt) is True
-
-    # Test wrong key fails
-    wrong_key = f"{keysmith.PREFIX}wrongkey123"
-    assert keysmith.verify_key(wrong_key, key.hash, key.salt) is False
-
-
-def test_verify_legacy_key():
-    keysmith = APIKeySmith()
-    legacy_key = f"{keysmith.PREFIX}legacykey123"
-    legacy_hash = hashlib.sha256(legacy_key.encode()).hexdigest()
-
-    # Test legacy key validates without salt
-    assert keysmith.verify_key(legacy_key, legacy_hash) is True
-
-    # Test wrong legacy key fails
-    wrong_key = f"{keysmith.PREFIX}wronglegacy"
-    assert keysmith.verify_key(wrong_key, legacy_hash) is False
-
-
-def test_rehash_existing_key():
-    keysmith = APIKeySmith()
-    legacy_key = f"{keysmith.PREFIX}migratekey123"
-
-    # Migrate the legacy key
-    new_hash, new_salt = keysmith.hash_key(legacy_key)
-
-    # Verify migrated key works
-    assert keysmith.verify_key(legacy_key, new_hash, new_salt) is True
-
-    # Verify different key fails with migrated hash
-    wrong_key = f"{keysmith.PREFIX}wrongkey"
-    assert keysmith.verify_key(wrong_key, new_hash, new_salt) is False
-
-
-def test_invalid_key_prefix():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Test key without proper prefix fails
-    invalid_key = "invalid_prefix_key"
-    assert keysmith.verify_key(invalid_key, key.hash, key.salt) is False
-
-
-def test_secure_hash_requires_salt():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Secure hash without salt should fail
-    assert keysmith.verify_key(key.key, key.hash) is False
-
-
-def test_invalid_salt_format():
-    keysmith = APIKeySmith()
-    key = keysmith.generate_key()
-
-    # Invalid salt format should fail gracefully
-    assert keysmith.verify_key(key.key, key.hash, "invalid_hex") is False

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -10,7 +10,7 @@ from .jwt_utils import get_jwt_payload, verify_user
 from .models import User
 
 
-async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid authenticated user.
 
@@ -20,9 +20,7 @@ async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -
     return verify_user(jwt_payload, admin_only=False)
 
 
-async def requires_admin_user(
-    jwt_payload: dict = fastapi.Security(get_jwt_payload),
-) -> User:
+def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid admin user.
 
@@ -32,7 +30,7 @@ async def requires_admin_user(
     return verify_user(jwt_payload, admin_only=True)
 
 
-async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
+def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
     """
     FastAPI dependency that returns the ID of the authenticated user.
 

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -45,7 +45,7 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
+    def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
@@ -53,12 +53,12 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert isinstance(user, User)
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
+    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
             "sub": "admin-456",
@@ -69,28 +69,28 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_user(jwt_payload)
+        user = requires_user(jwt_payload)
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    async def test_requires_user_missing_sub(self):
+    def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    async def test_requires_user_empty_sub(self):
+    def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_user(jwt_payload)
+            requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
+    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
             "sub": "admin-789",
@@ -101,51 +101,51 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = await requires_admin_user(jwt_payload)
+        user = requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    async def test_requires_admin_user_with_regular_user(self):
+    def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    async def test_requires_admin_user_missing_role(self):
+    def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
 
         with pytest.raises(KeyError):
-            await requires_admin_user(jwt_payload)
+            requires_admin_user(jwt_payload)
 
-    async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
+    def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
         """Test get_user_id extracts user ID correctly."""
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user_id = await get_user_id(jwt_payload)
+        user_id = get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    async def test_get_user_id_missing_sub(self):
+    def test_get_user_id_missing_sub(self):
         """Test get_user_id with missing user ID."""
         jwt_payload = {"role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(jwt_payload)
+            get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    async def test_get_user_id_none_sub(self):
+    def test_get_user_id_none_sub(self):
         """Test get_user_id with None user ID."""
         jwt_payload = {"sub": None, "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(jwt_payload)
+            get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -170,7 +170,7 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    async def test_endpoint_auth_enabled_no_token(self):
+    def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
 
@@ -184,7 +184,7 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    async def test_endpoint_with_valid_token(self, create_token):
+    def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
 
@@ -203,7 +203,7 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
-    async def test_admin_endpoint_requires_admin_role(self, create_token):
+    def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
 
@@ -240,7 +240,7 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    async def test_dependency_with_complex_payload(self):
+    def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
             "sub": "user-123",
@@ -256,14 +256,14 @@ class TestAuthDependenciesEdgeCases:
             "exp": 9999999999,
         }
 
-        user = await requires_user(complex_payload)
+        user = requires_user(complex_payload)
         assert user.user_id == "user-123"
         assert user.email == "test@example.com"
 
-        admin = await requires_admin_user(complex_payload)
+        admin = requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    async def test_dependency_with_unicode_in_payload(self):
+    def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
             "sub": "user-😀-123",
@@ -272,11 +272,11 @@ class TestAuthDependenciesEdgeCases:
             "name": "日本語",
         }
 
-        user = await requires_user(unicode_payload)
+        user = requires_user(unicode_payload)
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    async def test_dependency_with_null_values(self):
+    def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
             "sub": "user-123",
@@ -286,18 +286,18 @@ class TestAuthDependenciesEdgeCases:
             "metadata": None,
         }
 
-        user = await requires_user(null_payload)
+        user = requires_user(null_payload)
         assert user.user_id == "user-123"
         assert user.email is None
 
-    async def test_concurrent_requests_isolation(self):
+    def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
         payload2 = {"sub": "user-2", "role": "admin"}
 
         # Simulate concurrent processing
-        user1 = await requires_user(payload1)
-        user2 = await requires_admin_user(payload2)
+        user1 = requires_user(payload1)
+        user2 = requires_admin_user(payload2)
 
         assert user1.user_id == "user-1"
         assert user2.user_id == "user-2"
@@ -314,7 +314,7 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    async def test_dependency_error_cases(
+    def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
         """Test that errors propagate correctly through dependencies."""
@@ -325,7 +325,7 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
-    async def test_dependency_valid_user(self):
+    def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
         from autogpt_libs.auth.jwt_utils import verify_user

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -16,7 +16,7 @@ bearer_jwt_auth = HTTPBearer(
 )
 
 
-async def get_jwt_payload(
+def get_jwt_payload(
     credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
     """

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -116,32 +116,32 @@ def test_parse_jwt_token_missing_audience():
     assert "Invalid token" in str(exc_info.value)
 
 
-async def test_get_jwt_payload_with_valid_token():
+def test_get_jwt_payload_with_valid_token():
     """Test extracting JWT payload with valid bearer token."""
     token = create_token(TEST_USER_PAYLOAD)
     credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
 
-    result = await jwt_utils.get_jwt_payload(credentials)
+    result = jwt_utils.get_jwt_payload(credentials)
     assert result["sub"] == "test-user-id"
     assert result["role"] == "user"
 
 
-async def test_get_jwt_payload_no_credentials():
+def test_get_jwt_payload_no_credentials():
     """Test JWT payload when no credentials provided."""
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(None)
+        jwt_utils.get_jwt_payload(None)
     assert exc_info.value.status_code == 401
     assert "Authorization header is missing" in exc_info.value.detail
 
 
-async def test_get_jwt_payload_invalid_token():
+def test_get_jwt_payload_invalid_token():
     """Test JWT payload extraction with invalid token."""
     credentials = HTTPAuthorizationCredentials(
         scheme="Bearer", credentials="invalid.token.here"
     )
 
     with pytest.raises(HTTPException) as exc_info:
-        await jwt_utils.get_jwt_payload(credentials)
+        jwt_utils.get_jwt_payload(credentials)
     assert exc_info.value.status_code == 401
     assert "Invalid token" in exc_info.value.detail
 

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -4,7 +4,6 @@ import logging
 import os
 import socket
 import sys
-from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 from pydantic import Field, field_validator
@@ -140,13 +139,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         print(f"Log directory: {config.log_dir}")
 
         # Activity log handler (INFO and above)
-        # Security fix: Use RotatingFileHandler with size limits to prevent disk exhaustion
-        activity_log_handler = RotatingFileHandler(
-            config.log_dir / LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        activity_log_handler = logging.FileHandler(
+            config.log_dir / LOG_FILE, "a", "utf-8"
         )
         activity_log_handler.setLevel(config.level)
         activity_log_handler.setFormatter(
@@ -156,13 +150,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
         if config.level == logging.DEBUG:
             # Debug log handler (all levels)
-            # Security fix: Use RotatingFileHandler with size limits
-            debug_log_handler = RotatingFileHandler(
-                config.log_dir / DEBUG_LOG_FILE,
-                mode="a",
-                encoding="utf-8",
-                maxBytes=10 * 1024 * 1024,  # 10MB per file
-                backupCount=3,  # Keep 3 backup files (40MB total)
+            debug_log_handler = logging.FileHandler(
+                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
             )
             debug_log_handler.setLevel(logging.DEBUG)
             debug_log_handler.setFormatter(
@@ -171,13 +160,8 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             log_handlers.append(debug_log_handler)
 
         # Error log handler (ERROR and above)
-        # Security fix: Use RotatingFileHandler with size limits
-        error_log_handler = RotatingFileHandler(
-            config.log_dir / ERROR_LOG_FILE,
-            mode="a",
-            encoding="utf-8",
-            maxBytes=10 * 1024 * 1024,  # 10MB per file
-            backupCount=3,  # Keep 3 backup files (40MB total)
+        error_log_handler = logging.FileHandler(
+            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
         )
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/config.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 from pydantic import Field
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
@@ -15,8 +13,8 @@ class RateLimitSettings(BaseSettings):
         default="6379", description="Redis port", validation_alias="REDIS_PORT"
     )
 
-    redis_password: Optional[str] = Field(
-        default=None,
+    redis_password: str = Field(
+        default="password",
         description="Redis password",
         validation_alias="REDIS_PASSWORD",
     )

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/limiter.py

@@ -11,7 +11,7 @@ class RateLimiter:
         self,
         redis_host: str = RATE_LIMIT_SETTINGS.redis_host,
         redis_port: str = RATE_LIMIT_SETTINGS.redis_port,
-        redis_password: str | None = RATE_LIMIT_SETTINGS.redis_password,
+        redis_password: str = RATE_LIMIT_SETTINGS.redis_password,
         requests_per_minute: int = RATE_LIMIT_SETTINGS.requests_per_minute,
     ):
         self.redis = Redis(

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -1,68 +1,90 @@
-import asyncio
 import inspect
 import logging
 import threading
 import time
 from functools import wraps
 from typing import (
-    Any,
+    Awaitable,
     Callable,
     ParamSpec,
     Protocol,
+    Tuple,
     TypeVar,
     cast,
+    overload,
     runtime_checkable,
 )
 
 P = ParamSpec("P")
 R = TypeVar("R")
-R_co = TypeVar("R_co", covariant=True)
 
 logger = logging.getLogger(__name__)
 
 
-def _make_hashable_key(
-    args: tuple[Any, ...], kwargs: dict[str, Any]
-) -> tuple[Any, ...]:
-    """
-    Convert args and kwargs into a hashable cache key.
+@overload
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
+    pass
 
-    Handles unhashable types like dict, list, set by converting them to
-    their sorted string representations.
-    """
 
-    def make_hashable(obj: Any) -> Any:
-        """Recursively convert an object to a hashable representation."""
-        if isinstance(obj, dict):
-            # Sort dict items to ensure consistent ordering
-            return (
-                "__dict__",
-                tuple(sorted((k, make_hashable(v)) for k, v in obj.items())),
-            )
-        elif isinstance(obj, (list, tuple)):
-            return ("__list__", tuple(make_hashable(item) for item in obj))
-        elif isinstance(obj, set):
-            return ("__set__", tuple(sorted(make_hashable(item) for item in obj)))
-        elif hasattr(obj, "__dict__"):
-            # Handle objects with __dict__ attribute
-            return ("__obj__", obj.__class__.__name__, make_hashable(obj.__dict__))
-        else:
-            # For basic hashable types (str, int, bool, None, etc.)
-            try:
-                hash(obj)
-                return obj
-            except TypeError:
-                # Fallback: convert to string representation
-                return ("__str__", str(obj))
+@overload
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
+    pass
 
-    hashable_args = tuple(make_hashable(arg) for arg in args)
-    hashable_kwargs = tuple(sorted((k, make_hashable(v)) for k, v in kwargs.items()))
-    return (hashable_args, hashable_kwargs)
+
+def thread_cached(
+    func: Callable[P, R] | Callable[P, Awaitable[R]],
+) -> Callable[P, R] | Callable[P, Awaitable[R]]:
+    thread_local = threading.local()
+
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
+
+    if inspect.iscoroutinefunction(func):
+
+        async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            if key not in cache:
+                cache[key] = await cast(Callable[P, Awaitable[R]], func)(
+                    *args, **kwargs
+                )
+            return cache[key]
+
+        setattr(async_wrapper, "clear_cache", _clear)
+        return async_wrapper
+
+    else:
+
+        def sync_wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = (args, tuple(sorted(kwargs.items())))
+            if key not in cache:
+                cache[key] = func(*args, **kwargs)
+            return cache[key]
+
+        setattr(sync_wrapper, "clear_cache", _clear)
+        return sync_wrapper
+
+
+def clear_thread_cache(func: Callable) -> None:
+    if clear := getattr(func, "clear_cache", None):
+        clear()
+
+
+FuncT = TypeVar("FuncT")
+
+
+R_co = TypeVar("R_co", covariant=True)
 
 
 @runtime_checkable
-class CachedFunction(Protocol[P, R_co]):
-    """Protocol for cached functions with cache management methods."""
+class AsyncCachedFunction(Protocol[P, R_co]):
+    """Protocol for async functions with cache management methods."""
 
     def cache_clear(self) -> None:
         """Clear all cached entries."""
@@ -72,180 +94,101 @@ class CachedFunction(Protocol[P, R_co]):
         """Get cache statistics."""
         return {}
 
-    def cache_delete(self, *args: P.args, **kwargs: P.kwargs) -> bool:
-        """Delete a specific cache entry by its arguments. Returns True if entry existed."""
-        return False
-
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
         """Call the cached function."""
         return None  # type: ignore
 
 
-def cached(
-    *,
-    maxsize: int = 128,
-    ttl_seconds: int | None = None,
-) -> Callable[[Callable], CachedFunction]:
+def async_ttl_cache(
+    maxsize: int = 128, ttl_seconds: int | None = None
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
     """
-    Thundering herd safe cache decorator for both sync and async functions.
+    TTL (Time To Live) cache decorator for async functions.
 
-    Uses double-checked locking to prevent multiple threads/coroutines from
-    executing the expensive operation simultaneously during cache misses.
+    Similar to functools.lru_cache but works with async functions and includes optional TTL.
 
     Args:
-        func: The function to cache (when used without parentheses)
         maxsize: Maximum number of cached entries
-        ttl_seconds: Time to live in seconds. If None, entries never expire
+        ttl_seconds: Time to live in seconds. If None, entries never expire (like lru_cache)
 
     Returns:
-        Decorated function or decorator
+        Decorator function
 
     Example:
-        @cache()  # Default: maxsize=128, no TTL
-        def expensive_sync_operation(param: str) -> dict:
+        # With TTL
+        @async_ttl_cache(maxsize=1000, ttl_seconds=300)
+        async def api_call(param: str) -> dict:
             return {"result": param}
 
-        @cache()  # Works with async too
-        async def expensive_async_operation(param: str) -> dict:
-            return {"result": param}
-
-        @cache(maxsize=1000, ttl_seconds=300)  # Custom maxsize and TTL
-        def another_operation(param: str) -> dict:
+        # Without TTL (permanent cache like lru_cache)
+        @async_ttl_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
             return {"result": param}
     """
 
-    def decorator(target_func):
-        # Cache storage and per-event-loop locks
-        cache_storage = {}
-        _event_loop_locks = {}  # Maps event loop to its asyncio.Lock
+    def decorator(
+        async_func: Callable[P, Awaitable[R]],
+    ) -> AsyncCachedFunction[P, R]:
+        # Cache storage - use union type to handle both cases
+        cache_storage: dict[tuple, R | Tuple[R, float]] = {}
 
-        if inspect.iscoroutinefunction(target_func):
+        @wraps(async_func)
+        async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+            # Create cache key from arguments
+            key = (args, tuple(sorted(kwargs.items())))
+            current_time = time.time()
 
-            def _get_cache_lock():
-                """Get or create an asyncio.Lock for the current event loop."""
-                try:
-                    loop = asyncio.get_running_loop()
-                except RuntimeError:
-                    # No event loop, use None as default key
-                    loop = None
-
-                if loop not in _event_loop_locks:
-                    return _event_loop_locks.setdefault(loop, asyncio.Lock())
-                return _event_loop_locks[loop]
-
-            @wraps(target_func)
-            async def async_wrapper(*args: P.args, **kwargs: P.kwargs):
-                key = _make_hashable_key(args, kwargs)
-                current_time = time.time()
-
-                # Fast path: check cache without lock
-                if key in cache_storage:
-                    if ttl_seconds is None:
-                        logger.debug(f"Cache hit for {target_func.__name__}")
-                        return cache_storage[key]
-                    else:
-                        cached_data = cache_storage[key]
-                        if isinstance(cached_data, tuple):
-                            result, timestamp = cached_data
-                            if current_time - timestamp < ttl_seconds:
-                                logger.debug(f"Cache hit for {target_func.__name__}")
-                                return result
-
-                # Slow path: acquire lock for cache miss/expiry
-                async with _get_cache_lock():
-                    # Double-check: another coroutine might have populated cache
-                    if key in cache_storage:
-                        if ttl_seconds is None:
-                            return cache_storage[key]
+            # Check if we have a valid cached entry
+            if key in cache_storage:
+                if ttl_seconds is None:
+                    # No TTL - return cached result directly
+                    logger.debug(
+                        f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
+                    )
+                    return cast(R, cache_storage[key])
+                else:
+                    # With TTL - check expiration
+                    cached_data = cache_storage[key]
+                    if isinstance(cached_data, tuple):
+                        result, timestamp = cached_data
+                        if current_time - timestamp < ttl_seconds:
+                            logger.debug(
+                                f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
+                            )
+                            return cast(R, result)
                         else:
-                            cached_data = cache_storage[key]
-                            if isinstance(cached_data, tuple):
-                                result, timestamp = cached_data
-                                if current_time - timestamp < ttl_seconds:
-                                    return result
+                            # Expired entry
+                            del cache_storage[key]
+                            logger.debug(
+                                f"Cache entry expired for {async_func.__name__}"
+                            )
 
-                    # Cache miss - execute function
-                    logger.debug(f"Cache miss for {target_func.__name__}")
-                    result = await target_func(*args, **kwargs)
+            # Cache miss or expired - fetch fresh data
+            logger.debug(
+                f"Cache miss for {async_func.__name__} with key: {str(key)[:50]}"
+            )
+            result = await async_func(*args, **kwargs)
 
-                    # Store result
-                    if ttl_seconds is None:
-                        cache_storage[key] = result
-                    else:
-                        cache_storage[key] = (result, current_time)
+            # Store in cache
+            if ttl_seconds is None:
+                cache_storage[key] = result
+            else:
+                cache_storage[key] = (result, current_time)
 
-                    # Cleanup if needed
-                    if len(cache_storage) > maxsize:
-                        cutoff = maxsize // 2
-                        oldest_keys = (
-                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
-                        )
-                        for old_key in oldest_keys:
-                            cache_storage.pop(old_key, None)
+            # Simple cleanup when cache gets too large
+            if len(cache_storage) > maxsize:
+                # Remove oldest entries (simple FIFO cleanup)
+                cutoff = maxsize // 2
+                oldest_keys = list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                for old_key in oldest_keys:
+                    cache_storage.pop(old_key, None)
+                logger.debug(
+                    f"Cache cleanup: removed {len(oldest_keys)} entries for {async_func.__name__}"
+                )
 
-                    return result
+            return result
 
-            wrapper = async_wrapper
-
-        else:
-            # Sync function with threading.Lock
-            cache_lock = threading.Lock()
-
-            @wraps(target_func)
-            def sync_wrapper(*args: P.args, **kwargs: P.kwargs):
-                key = _make_hashable_key(args, kwargs)
-                current_time = time.time()
-
-                # Fast path: check cache without lock
-                if key in cache_storage:
-                    if ttl_seconds is None:
-                        logger.debug(f"Cache hit for {target_func.__name__}")
-                        return cache_storage[key]
-                    else:
-                        cached_data = cache_storage[key]
-                        if isinstance(cached_data, tuple):
-                            result, timestamp = cached_data
-                            if current_time - timestamp < ttl_seconds:
-                                logger.debug(f"Cache hit for {target_func.__name__}")
-                                return result
-
-                # Slow path: acquire lock for cache miss/expiry
-                with cache_lock:
-                    # Double-check: another thread might have populated cache
-                    if key in cache_storage:
-                        if ttl_seconds is None:
-                            return cache_storage[key]
-                        else:
-                            cached_data = cache_storage[key]
-                            if isinstance(cached_data, tuple):
-                                result, timestamp = cached_data
-                                if current_time - timestamp < ttl_seconds:
-                                    return result
-
-                    # Cache miss - execute function
-                    logger.debug(f"Cache miss for {target_func.__name__}")
-                    result = target_func(*args, **kwargs)
-
-                    # Store result
-                    if ttl_seconds is None:
-                        cache_storage[key] = result
-                    else:
-                        cache_storage[key] = (result, current_time)
-
-                    # Cleanup if needed
-                    if len(cache_storage) > maxsize:
-                        cutoff = maxsize // 2
-                        oldest_keys = (
-                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
-                        )
-                        for old_key in oldest_keys:
-                            cache_storage.pop(old_key, None)
-
-                    return result
-
-            wrapper = sync_wrapper
-
-        # Add cache management methods
+        # Add cache management methods (similar to functools.lru_cache)
         def cache_clear() -> None:
             cache_storage.clear()
 
@@ -256,84 +199,68 @@ def cached(
                 "ttl_seconds": ttl_seconds,
             }
 
-        def cache_delete(*args, **kwargs) -> bool:
-            """Delete a specific cache entry. Returns True if entry existed."""
-            key = _make_hashable_key(args, kwargs)
-            if key in cache_storage:
-                del cache_storage[key]
-                return True
-            return False
-
+        # Attach methods to wrapper
         setattr(wrapper, "cache_clear", cache_clear)
         setattr(wrapper, "cache_info", cache_info)
-        setattr(wrapper, "cache_delete", cache_delete)
 
-        return cast(CachedFunction, wrapper)
+        return cast(AsyncCachedFunction[P, R], wrapper)
 
     return decorator
 
 
-def thread_cached(func):
-    """
-    Thread-local cache decorator for both sync and async functions.
+@overload
+def async_cache(
+    func: Callable[P, Awaitable[R]],
+) -> AsyncCachedFunction[P, R]:
+    pass
 
-    Each thread gets its own cache, which is useful for request-scoped caching
-    in web applications where you want to cache within a single request but
-    not across requests.
+
+@overload
+def async_cache(
+    func: None = None,
+    *,
+    maxsize: int = 128,
+) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
+    pass
+
+
+def async_cache(
+    func: Callable[P, Awaitable[R]] | None = None,
+    *,
+    maxsize: int = 128,
+) -> (
+    AsyncCachedFunction[P, R]
+    | Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]
+):
+    """
+    Process-level cache decorator for async functions (no TTL).
+
+    Similar to functools.lru_cache but works with async functions.
+    This is a convenience wrapper around async_ttl_cache with ttl_seconds=None.
 
     Args:
-        func: The function to cache
+        func: The async function to cache (when used without parentheses)
+        maxsize: Maximum number of cached entries
 
     Returns:
-        Decorated function with thread-local caching
+        Decorated function or decorator
 
     Example:
-        @thread_cached
-        def expensive_operation(param: str) -> dict:
+        # Without parentheses (uses default maxsize=128)
+        @async_cache
+        async def get_data(param: str) -> dict:
             return {"result": param}
 
-        @thread_cached  # Works with async too
-        async def expensive_async_operation(param: str) -> dict:
+        # With parentheses and custom maxsize
+        @async_cache(maxsize=1000)
+        async def expensive_computation(param: str) -> dict:
+            # Expensive computation here
             return {"result": param}
     """
-    thread_local = threading.local()
-
-    def _clear():
-        if hasattr(thread_local, "cache"):
-            del thread_local.cache
-
-    if inspect.iscoroutinefunction(func):
-
-        @wraps(func)
-        async def async_wrapper(*args, **kwargs):
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = _make_hashable_key(args, kwargs)
-            if key not in cache:
-                cache[key] = await func(*args, **kwargs)
-            return cache[key]
-
-        setattr(async_wrapper, "clear_cache", _clear)
-        return async_wrapper
-
+    if func is None:
+        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
+        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
     else:
-
-        @wraps(func)
-        def sync_wrapper(*args, **kwargs):
-            cache = getattr(thread_local, "cache", None)
-            if cache is None:
-                cache = thread_local.cache = {}
-            key = _make_hashable_key(args, kwargs)
-            if key not in cache:
-                cache[key] = func(*args, **kwargs)
-            return cache[key]
-
-        setattr(sync_wrapper, "clear_cache", _clear)
-        return sync_wrapper
-
-
-def clear_thread_cache(func: Callable) -> None:
-    """Clear thread-local cache for a function."""
-    if clear := getattr(func, "clear_cache", None):
-        clear()
+        # Called without parentheses @async_cache
+        decorator = async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
+        return decorator(func)

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -16,7 +16,12 @@ from unittest.mock import Mock
 
 import pytest
 
-from autogpt_libs.utils.cache import cached, clear_thread_cache, thread_cached
+from autogpt_libs.utils.cache import (
+    async_cache,
+    async_ttl_cache,
+    clear_thread_cache,
+    thread_cached,
+)
 
 
 class TestThreadCached:
@@ -325,202 +330,102 @@ class TestThreadCached:
         assert mock.call_count == 2
 
 
-class TestCache:
-    """Tests for the unified @cache decorator (works for both sync and async)."""
-
-    def test_basic_sync_caching(self):
-        """Test basic sync caching functionality."""
-        call_count = 0
-
-        @cached()
-        def expensive_sync_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        # First call
-        result1 = expensive_sync_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = expensive_sync_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = expensive_sync_function(2, 3)
-        assert result3 == 5
-        assert call_count == 2
+class TestAsyncTTLCache:
+    """Tests for the @async_ttl_cache decorator."""
 
     @pytest.mark.asyncio
-    async def test_basic_async_caching(self):
-        """Test basic async caching functionality."""
+    async def test_basic_caching(self):
+        """Test basic caching functionality."""
         call_count = 0
 
-        @cached()
-        async def expensive_async_function(x: int, y: int = 0) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def cached_function(x: int, y: int = 0) -> int:
             nonlocal call_count
             call_count += 1
             await asyncio.sleep(0.01)  # Simulate async work
             return x + y
 
         # First call
-        result1 = await expensive_async_function(1, 2)
+        result1 = await cached_function(1, 2)
         assert result1 == 3
         assert call_count == 1
 
         # Second call with same args - should use cache
-        result2 = await expensive_async_function(1, 2)
+        result2 = await cached_function(1, 2)
         assert result2 == 3
-        assert call_count == 1
+        assert call_count == 1  # No additional call
 
         # Different args - should call function again
-        result3 = await expensive_async_function(2, 3)
+        result3 = await cached_function(2, 3)
         assert result3 == 5
         assert call_count == 2
 
-    def test_sync_thundering_herd_protection(self):
-        """Test that concurrent sync calls don't cause thundering herd."""
-        call_count = 0
-        results = []
-
-        @cached()
-        def slow_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            time.sleep(0.1)  # Simulate expensive operation
-            return x * x
-
-        def worker():
-            result = slow_function(5)
-            results.append(result)
-
-        # Launch multiple concurrent threads
-        with ThreadPoolExecutor(max_workers=5) as executor:
-            futures = [executor.submit(worker) for _ in range(5)]
-            for future in futures:
-                future.result()
-
-        # All results should be the same
-        assert all(result == 25 for result in results)
-        # Only one thread should have executed the expensive operation
-        assert call_count == 1
-
     @pytest.mark.asyncio
-    async def test_async_thundering_herd_protection(self):
-        """Test that concurrent async calls don't cause thundering herd."""
+    async def test_ttl_expiration(self):
+        """Test that cache entries expire after TTL."""
         call_count = 0
 
-        @cached()
-        async def slow_async_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def short_lived_cache(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.1)  # Simulate expensive operation
-            return x * x
-
-        # Launch concurrent coroutines
-        tasks = [slow_async_function(7) for _ in range(5)]
-        results = await asyncio.gather(*tasks)
-
-        # All results should be the same
-        assert all(result == 49 for result in results)
-        # Only one coroutine should have executed the expensive operation
-        assert call_count == 1
-
-    def test_ttl_functionality(self):
-        """Test TTL functionality with sync function."""
-        call_count = 0
-
-        @cached(maxsize=10, ttl_seconds=1)  # Short TTL
-        def ttl_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 3
+            return x * 2
 
         # First call
-        result1 = ttl_function(3)
-        assert result1 == 9
+        result1 = await short_lived_cache(5)
+        assert result1 == 10
         assert call_count == 1
 
         # Second call immediately - should use cache
-        result2 = ttl_function(3)
-        assert result2 == 9
-        assert call_count == 1
-
-        # Wait for TTL to expire
-        time.sleep(1.1)
-
-        # Third call after expiration - should call function again
-        result3 = ttl_function(3)
-        assert result3 == 9
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_async_ttl_functionality(self):
-        """Test TTL functionality with async function."""
-        call_count = 0
-
-        @cached(maxsize=10, ttl_seconds=1)  # Short TTL
-        async def async_ttl_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 4
-
-        # First call
-        result1 = await async_ttl_function(3)
-        assert result1 == 12
-        assert call_count == 1
-
-        # Second call immediately - should use cache
-        result2 = await async_ttl_function(3)
-        assert result2 == 12
+        result2 = await short_lived_cache(5)
+        assert result2 == 10
         assert call_count == 1
 
         # Wait for TTL to expire
         await asyncio.sleep(1.1)
 
         # Third call after expiration - should call function again
-        result3 = await async_ttl_function(3)
-        assert result3 == 12
+        result3 = await short_lived_cache(5)
+        assert result3 == 10
         assert call_count == 2
 
-    def test_cache_info(self):
+    @pytest.mark.asyncio
+    async def test_cache_info(self):
         """Test cache info functionality."""
 
-        @cached(maxsize=10, ttl_seconds=60)
-        def info_test_function(x: int) -> int:
+        @async_ttl_cache(maxsize=5, ttl_seconds=300)
+        async def info_test_function(x: int) -> int:
             return x * 3
 
         # Check initial cache info
         info = info_test_function.cache_info()
         assert info["size"] == 0
-        assert info["maxsize"] == 10
-        assert info["ttl_seconds"] == 60
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] == 300
 
         # Add an entry
-        info_test_function(1)
+        await info_test_function(1)
         info = info_test_function.cache_info()
         assert info["size"] == 1
 
-    def test_cache_clear(self):
+    @pytest.mark.asyncio
+    async def test_cache_clear(self):
         """Test cache clearing functionality."""
         call_count = 0
 
-        @cached()
-        def clearable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def clearable_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
             return x * 4
 
         # First call
-        result1 = clearable_function(2)
+        result1 = await clearable_function(2)
         assert result1 == 8
         assert call_count == 1
 
         # Second call - should use cache
-        result2 = clearable_function(2)
+        result2 = await clearable_function(2)
         assert result2 == 8
         assert call_count == 1
 
@@ -528,149 +433,273 @@ class TestCache:
         clearable_function.cache_clear()
 
         # Third call after clear - should call function again
-        result3 = clearable_function(2)
+        result3 = await clearable_function(2)
         assert result3 == 8
         assert call_count == 2
 
     @pytest.mark.asyncio
-    async def test_async_cache_clear(self):
-        """Test cache clearing functionality with async function."""
+    async def test_maxsize_cleanup(self):
+        """Test that cache cleans up when maxsize is exceeded."""
         call_count = 0
 
-        @cached()
-        async def async_clearable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=3, ttl_seconds=60)
+        async def size_limited_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 5
+            return x**2
 
-        # First call
-        result1 = await async_clearable_function(2)
+        # Fill cache to maxsize
+        await size_limited_function(1)  # call_count: 1
+        await size_limited_function(2)  # call_count: 2
+        await size_limited_function(3)  # call_count: 3
+
+        info = size_limited_function.cache_info()
+        assert info["size"] == 3
+
+        # Add one more entry - should trigger cleanup
+        await size_limited_function(4)  # call_count: 4
+
+        # Cache size should be reduced (cleanup removes oldest entries)
+        info = size_limited_function.cache_info()
+        assert info["size"] is not None and info["size"] <= 3  # Should be cleaned up
+
+    @pytest.mark.asyncio
+    async def test_argument_variations(self):
+        """Test caching with different argument patterns."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def arg_test_function(a: int, b: str = "default", *, c: int = 100) -> str:
+            nonlocal call_count
+            call_count += 1
+            return f"{a}-{b}-{c}"
+
+        # Different ways to call with same logical arguments
+        result1 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+
+        # Same arguments, same order - should use cache
+        result2 = await arg_test_function(1, "test", c=200)
+        assert call_count == 1
+        assert result1 == result2
+
+        # Different arguments - should call function
+        result3 = await arg_test_function(2, "test", c=200)
+        assert call_count == 2
+        assert result1 != result3
+
+    @pytest.mark.asyncio
+    async def test_exception_handling(self):
+        """Test that exceptions are not cached."""
+        call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def exception_function(x: int) -> int:
+            nonlocal call_count
+            call_count += 1
+            if x < 0:
+                raise ValueError("Negative value not allowed")
+            return x * 2
+
+        # Successful call - should be cached
+        result1 = await exception_function(5)
         assert result1 == 10
         assert call_count == 1
 
-        # Second call - should use cache
-        result2 = await async_clearable_function(2)
+        # Same successful call - should use cache
+        result2 = await exception_function(5)
         assert result2 == 10
         assert call_count == 1
 
-        # Clear cache
-        async_clearable_function.cache_clear()
-
-        # Third call after clear - should call function again
-        result3 = await async_clearable_function(2)
-        assert result3 == 10
+        # Exception call - should not be cached
+        with pytest.raises(ValueError):
+            await exception_function(-1)
         assert call_count == 2
 
+        # Same exception call - should call again (not cached)
+        with pytest.raises(ValueError):
+            await exception_function(-1)
+        assert call_count == 3
+
     @pytest.mark.asyncio
-    async def test_async_function_returns_results_not_coroutines(self):
-        """Test that cached async functions return actual results, not coroutines."""
+    async def test_concurrent_calls(self):
+        """Test caching behavior with concurrent calls."""
         call_count = 0
 
-        @cached()
-        async def async_result_function(x: int) -> str:
+        @async_ttl_cache(maxsize=10, ttl_seconds=60)
+        async def concurrent_function(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return f"result_{x}"
+            await asyncio.sleep(0.05)  # Simulate work
+            return x * x
+
+        # Launch concurrent calls with same arguments
+        tasks = [concurrent_function(3) for _ in range(5)]
+        results = await asyncio.gather(*tasks)
+
+        # All results should be the same
+        assert all(result == 9 for result in results)
+
+        # Note: Due to race conditions, call_count might be up to 5 for concurrent calls
+        # This tests that the cache doesn't break under concurrent access
+        assert 1 <= call_count <= 5
+
+
+class TestAsyncCache:
+    """Tests for the @async_cache decorator (no TTL)."""
+
+    @pytest.mark.asyncio
+    async def test_basic_caching_no_ttl(self):
+        """Test basic caching functionality without TTL."""
+        call_count = 0
+
+        @async_cache(maxsize=10)
+        async def cached_function(x: int, y: int = 0) -> int:
+            nonlocal call_count
+            call_count += 1
+            await asyncio.sleep(0.01)  # Simulate async work
+            return x + y
 
         # First call
-        result1 = await async_result_function(1)
-        assert result1 == "result_1"
-        assert isinstance(result1, str)  # Should be string, not coroutine
+        result1 = await cached_function(1, 2)
+        assert result1 == 3
         assert call_count == 1
 
-        # Second call - should return cached result (string), not coroutine
-        result2 = await async_result_function(1)
-        assert result2 == "result_1"
-        assert isinstance(result2, str)  # Should be string, not coroutine
-        assert call_count == 1  # Function should not be called again
+        # Second call with same args - should use cache
+        result2 = await cached_function(1, 2)
+        assert result2 == 3
+        assert call_count == 1  # No additional call
 
-        # Verify results are identical
-        assert result1 is result2  # Should be same cached object
+        # Third call after some time - should still use cache (no TTL)
+        await asyncio.sleep(0.05)
+        result3 = await cached_function(1, 2)
+        assert result3 == 3
+        assert call_count == 1  # Still no additional call
 
-    def test_cache_delete(self):
-        """Test selective cache deletion functionality."""
-        call_count = 0
-
-        @cached()
-        def deletable_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 6
-
-        # First call for x=1
-        result1 = deletable_function(1)
-        assert result1 == 6
-        assert call_count == 1
-
-        # First call for x=2
-        result2 = deletable_function(2)
-        assert result2 == 12
+        # Different args - should call function again
+        result4 = await cached_function(2, 3)
+        assert result4 == 5
         assert call_count == 2
 
-        # Second calls - should use cache
-        assert deletable_function(1) == 6
-        assert deletable_function(2) == 12
-        assert call_count == 2
-
-        # Delete specific entry for x=1
-        was_deleted = deletable_function.cache_delete(1)
-        assert was_deleted is True
-
-        # Call with x=1 should execute function again
-        result3 = deletable_function(1)
-        assert result3 == 6
-        assert call_count == 3
-
-        # Call with x=2 should still use cache
-        assert deletable_function(2) == 12
-        assert call_count == 3
-
-        # Try to delete non-existent entry
-        was_deleted = deletable_function.cache_delete(99)
-        assert was_deleted is False
-
     @pytest.mark.asyncio
-    async def test_async_cache_delete(self):
-        """Test selective cache deletion functionality with async function."""
+    async def test_no_ttl_vs_ttl_behavior(self):
+        """Test the difference between TTL and no-TTL caching."""
+        ttl_call_count = 0
+        no_ttl_call_count = 0
+
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_call_count
+            ttl_call_count += 1
+            return x * 2
+
+        @async_cache(maxsize=10)  # No TTL
+        async def no_ttl_function(x: int) -> int:
+            nonlocal no_ttl_call_count
+            no_ttl_call_count += 1
+            return x * 2
+
+        # First calls
+        await ttl_function(5)
+        await no_ttl_function(5)
+        assert ttl_call_count == 1
+        assert no_ttl_call_count == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # Second calls after TTL expiry
+        await ttl_function(5)  # Should call function again (TTL expired)
+        await no_ttl_function(5)  # Should use cache (no TTL)
+        assert ttl_call_count == 2  # TTL function called again
+        assert no_ttl_call_count == 1  # No-TTL function still cached
+
+    @pytest.mark.asyncio
+    async def test_async_cache_info(self):
+        """Test cache info for no-TTL cache."""
+
+        @async_cache(maxsize=5)
+        async def info_test_function(x: int) -> int:
+            return x * 3
+
+        # Check initial cache info
+        info = info_test_function.cache_info()
+        assert info["size"] == 0
+        assert info["maxsize"] == 5
+        assert info["ttl_seconds"] is None  # No TTL
+
+        # Add an entry
+        await info_test_function(1)
+        info = info_test_function.cache_info()
+        assert info["size"] == 1
+
+
+class TestTTLOptional:
+    """Tests for optional TTL functionality."""
+
+    @pytest.mark.asyncio
+    async def test_ttl_none_behavior(self):
+        """Test that ttl_seconds=None works like no TTL."""
         call_count = 0
 
-        @cached()
-        async def async_deletable_function(x: int) -> int:
+        @async_ttl_cache(maxsize=10, ttl_seconds=None)
+        async def no_ttl_via_none(x: int) -> int:
             nonlocal call_count
             call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 7
+            return x**2
 
-        # First call for x=1
-        result1 = await async_deletable_function(1)
-        assert result1 == 7
+        # First call
+        result1 = await no_ttl_via_none(3)
+        assert result1 == 9
         assert call_count == 1
 
-        # First call for x=2
-        result2 = await async_deletable_function(2)
-        assert result2 == 14
-        assert call_count == 2
+        # Wait (would expire if there was TTL)
+        await asyncio.sleep(0.1)
 
-        # Second calls - should use cache
-        assert await async_deletable_function(1) == 7
-        assert await async_deletable_function(2) == 14
-        assert call_count == 2
+        # Second call - should still use cache
+        result2 = await no_ttl_via_none(3)
+        assert result2 == 9
+        assert call_count == 1  # No additional call
 
-        # Delete specific entry for x=1
-        was_deleted = async_deletable_function.cache_delete(1)
-        assert was_deleted is True
+        # Check cache info
+        info = no_ttl_via_none.cache_info()
+        assert info["ttl_seconds"] is None
 
-        # Call with x=1 should execute function again
-        result3 = await async_deletable_function(1)
-        assert result3 == 7
-        assert call_count == 3
+    @pytest.mark.asyncio
+    async def test_cache_options_comparison(self):
+        """Test different cache options work as expected."""
+        ttl_calls = 0
+        no_ttl_calls = 0
 
-        # Call with x=2 should still use cache
-        assert await async_deletable_function(2) == 14
-        assert call_count == 3
+        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # With TTL
+        async def ttl_function(x: int) -> int:
+            nonlocal ttl_calls
+            ttl_calls += 1
+            return x * 10
 
-        # Try to delete non-existent entry
-        was_deleted = async_deletable_function.cache_delete(99)
-        assert was_deleted is False
+        @async_cache(maxsize=10)  # Process-level cache (no TTL)
+        async def process_function(x: int) -> int:
+            nonlocal no_ttl_calls
+            no_ttl_calls += 1
+            return x * 10
+
+        # Both should cache initially
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Immediate second calls - both should use cache
+        await ttl_function(3)
+        await process_function(3)
+        assert ttl_calls == 1
+        assert no_ttl_calls == 1
+
+        # Wait for TTL to expire
+        await asyncio.sleep(1.1)
+
+        # After TTL expiry
+        await ttl_function(3)  # Should call function again
+        await process_function(3)  # Should still use cache
+        assert ttl_calls == 2  # TTL cache expired, called again
+        assert no_ttl_calls == 1  # Process cache never expires

autogpt_platform/autogpt_libs/poetry.lock

@@ -1002,18 +1002,6 @@ dynamodb = ["boto3 (>=1.9.71)"]
 redis = ["redis (>=2.10.5)"]
 test-filesource = ["pyyaml (>=5.3.1)", "watchdog (>=3.0.0)"]
 
-[[package]]
-name = "nodeenv"
-version = "1.9.1"
-description = "Node.js virtual environment builder"
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-groups = ["dev"]
-files = [
-    {file = "nodeenv-1.9.1-py2.py3-none-any.whl", hash = "sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9"},
-    {file = "nodeenv-1.9.1.tar.gz", hash = "sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f"},
-]
-
 [[package]]
 name = "opentelemetry-api"
 version = "1.35.0"
@@ -1359,27 +1347,6 @@ files = [
     {file = "pyrfc3339-2.0.1.tar.gz", hash = "sha256:e47843379ea35c1296c3b6c67a948a1a490ae0584edfcbdea0eaffb5dd29960b"},
 ]
 
-[[package]]
-name = "pyright"
-version = "1.1.404"
-description = "Command line wrapper for pyright"
-optional = false
-python-versions = ">=3.7"
-groups = ["dev"]
-files = [
-    {file = "pyright-1.1.404-py3-none-any.whl", hash = "sha256:c7b7ff1fdb7219c643079e4c3e7d4125f0dafcc19d253b47e898d130ea426419"},
-    {file = "pyright-1.1.404.tar.gz", hash = "sha256:455e881a558ca6be9ecca0b30ce08aa78343ecc031d37a198ffa9a7a1abeb63e"},
-]
-
-[package.dependencies]
-nodeenv = ">=1.6.0"
-typing-extensions = ">=4.1"
-
-[package.extras]
-all = ["nodejs-wheel-binaries", "twine (>=3.4.1)"]
-dev = ["twine (>=3.4.1)"]
-nodejs = ["nodejs-wheel-binaries"]
-
 [[package]]
 name = "pytest"
 version = "8.4.1"
@@ -1567,31 +1534,31 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.12.11"
+version = "0.12.9"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
-    {file = "ruff-0.12.11-py3-none-linux_armv6l.whl", hash = "sha256:93fce71e1cac3a8bf9200e63a38ac5c078f3b6baebffb74ba5274fb2ab276065"},
-    {file = "ruff-0.12.11-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:b8e33ac7b28c772440afa80cebb972ffd823621ded90404f29e5ab6d1e2d4b93"},
-    {file = "ruff-0.12.11-py3-none-macosx_11_0_arm64.whl", hash = "sha256:d69fb9d4937aa19adb2e9f058bc4fbfe986c2040acb1a4a9747734834eaa0bfd"},
-    {file = "ruff-0.12.11-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:411954eca8464595077a93e580e2918d0a01a19317af0a72132283e28ae21bee"},
-    {file = "ruff-0.12.11-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6a2c0a2e1a450f387bf2c6237c727dd22191ae8c00e448e0672d624b2bbd7fb0"},
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-    {file = "ruff-0.12.11-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5a8dd5f230efc99a24ace3b77e3555d3fbc0343aeed3fc84c8d89e75ab2ff793"},
-    {file = "ruff-0.12.11-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:4dc75533039d0ed04cd33fb8ca9ac9620b99672fe7ff1533b6402206901c34ee"},
-    {file = "ruff-0.12.11-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4fc58f9266d62c6eccc75261a665f26b4ef64840887fc6cbc552ce5b29f96cc8"},
-    {file = "ruff-0.12.11-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:5a0113bd6eafd545146440225fe60b4e9489f59eb5f5f107acd715ba5f0b3d2f"},
-    {file = "ruff-0.12.11-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:0d737b4059d66295c3ea5720e6efc152623bb83fde5444209b69cd33a53e2000"},
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-    {file = "ruff-0.12.11-py3-none-musllinux_1_2_i686.whl", hash = "sha256:c984f07d7adb42d3ded5be894fb4007f30f82c87559438b4879fe7aa08c62b39"},
-    {file = "ruff-0.12.11-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e07fbb89f2e9249f219d88331c833860489b49cdf4b032b8e4432e9b13e8a4b9"},
-    {file = "ruff-0.12.11-py3-none-win32.whl", hash = "sha256:c792e8f597c9c756e9bcd4d87cf407a00b60af77078c96f7b6366ea2ce9ba9d3"},
-    {file = "ruff-0.12.11-py3-none-win_amd64.whl", hash = "sha256:a3283325960307915b6deb3576b96919ee89432ebd9c48771ca12ee8afe4a0fd"},
-    {file = "ruff-0.12.11-py3-none-win_arm64.whl", hash = "sha256:bae4d6e6a2676f8fb0f98b74594a048bae1b944aab17e9f5d504062303c6dbea"},
-    {file = "ruff-0.12.11.tar.gz", hash = "sha256:c6b09ae8426a65bbee5425b9d0b82796dbb07cb1af045743c79bfb163001165d"},
+    {file = "ruff-0.12.9-py3-none-linux_armv6l.whl", hash = "sha256:fcebc6c79fcae3f220d05585229463621f5dbf24d79fdc4936d9302e177cfa3e"},
+    {file = "ruff-0.12.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:aed9d15f8c5755c0e74467731a007fcad41f19bcce41cd75f768bbd687f8535f"},
+    {file = "ruff-0.12.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:5b15ea354c6ff0d7423814ba6d44be2807644d0c05e9ed60caca87e963e93f70"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d596c2d0393c2502eaabfef723bd74ca35348a8dac4267d18a94910087807c53"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1b15599931a1a7a03c388b9c5df1bfa62be7ede6eb7ef753b272381f39c3d0ff"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3d02faa2977fb6f3f32ddb7828e212b7dd499c59eb896ae6c03ea5c303575756"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:17d5b6b0b3a25259b69ebcba87908496e6830e03acfb929ef9fd4c58675fa2ea"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:72db7521860e246adbb43f6ef464dd2a532ef2ef1f5dd0d470455b8d9f1773e0"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a03242c1522b4e0885af63320ad754d53983c9599157ee33e77d748363c561ce"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fc83e4e9751e6c13b5046d7162f205d0a7bac5840183c5beebf824b08a27340"},
+    {file = "ruff-0.12.9-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:881465ed56ba4dd26a691954650de6ad389a2d1fdb130fe51ff18a25639fe4bb"},
+    {file = "ruff-0.12.9-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:43f07a3ccfc62cdb4d3a3348bf0588358a66da756aa113e071b8ca8c3b9826af"},
+    {file = "ruff-0.12.9-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:07adb221c54b6bba24387911e5734357f042e5669fa5718920ee728aba3cbadc"},
+    {file = "ruff-0.12.9-py3-none-musllinux_1_2_i686.whl", hash = "sha256:f5cd34fabfdea3933ab85d72359f118035882a01bff15bd1d2b15261d85d5f66"},
+    {file = "ruff-0.12.9-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:f6be1d2ca0686c54564da8e7ee9e25f93bdd6868263805f8c0b8fc6a449db6d7"},
+    {file = "ruff-0.12.9-py3-none-win32.whl", hash = "sha256:cc7a37bd2509974379d0115cc5608a1a4a6c4bff1b452ea69db83c8855d53f93"},
+    {file = "ruff-0.12.9-py3-none-win_amd64.whl", hash = "sha256:6fb15b1977309741d7d098c8a3cb7a30bc112760a00fb6efb7abc85f00ba5908"},
+    {file = "ruff-0.12.9-py3-none-win_arm64.whl", hash = "sha256:63c8c819739d86b96d500cce885956a1a48ab056bbcbc61b747ad494b2485089"},
+    {file = "ruff-0.12.9.tar.gz", hash = "sha256:fbd94b2e3c623f659962934e52c2bea6fc6da11f667a427a368adaf3af2c866a"},
 ]
 
 [[package]]
@@ -1773,6 +1740,7 @@ files = [
     {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
     {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
+markers = {dev = "python_version < \"3.11\""}
 
 [[package]]
 name = "typing-inspection"
@@ -1929,4 +1897,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "0c40b63c3c921846cf05ccfb4e685d4959854b29c2c302245f9832e20aac6954"
+content-hash = "ef7818fba061cea2841c6d7ca4852acde83e4f73b32fca1315e58660002bb0d0"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,7 +9,6 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^45.0"
 expiringdict = "^1.2.2"
 fastapi = "^0.116.1"
 google-cloud-logging = "^3.12.1"
@@ -22,12 +21,11 @@ supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.404"
+ruff = "^0.12.9"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.1.0"
 pytest-mock = "^3.14.1"
 pytest-cov = "^6.2.1"
-ruff = "^0.12.11"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.env.default

@@ -21,7 +21,7 @@ PRISMA_SCHEMA="postgres/schema.prisma"
 # Redis Configuration
 REDIS_HOST=localhost
 REDIS_PORT=6379
-# REDIS_PASSWORD=
+REDIS_PASSWORD=password
 
 # RabbitMQ Credentials
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
@@ -66,11 +66,6 @@ NVIDIA_API_KEY=
 GITHUB_CLIENT_ID=
 GITHUB_CLIENT_SECRET=
 
-# Notion OAuth App server credentials - https://developers.notion.com/docs/authorization
-# Configure a public integration
-NOTION_CLIENT_ID=
-NOTION_CLIENT_SECRET=
-
 # Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
 # https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
 # You'll need to add/enable the following scopes (minimum):

autogpt_platform/backend/.gitignore

@@ -9,12 +9,4 @@ secrets/*
 !secrets/.gitkeep
 
 *.ignore.*
-*.ign.*
-
-# Load test results and reports
-load-tests/*_RESULTS.md
-load-tests/*_REPORT.md
-load-tests/results/
-load-tests/*.json
-load-tests/*.log
-load-tests/node_modules/*
+*.ign.*

autogpt_platform/backend/Dockerfile

@@ -9,15 +9,8 @@ WORKDIR /app
 
 RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
 
-# Install Node.js repository key and setup
+# Update package list and install Python and build dependencies
 RUN apt-get update --allow-releaseinfo-change --fix-missing \
-    && apt-get install -y curl ca-certificates gnupg \
-    && mkdir -p /etc/apt/keyrings \
-    && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
-    && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_20.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list
-
-# Update package list and install Python, Node.js, and build dependencies
-RUN apt-get update \
     && apt-get install -y \
     python3.13 \
     python3.13-dev \
@@ -27,9 +20,7 @@ RUN apt-get update \
     libpq5 \
     libz-dev \
     libssl-dev \
-    postgresql-client \
-    nodejs \
-    && rm -rf /var/lib/apt/lists/*
+    postgresql-client
 
 ENV POETRY_HOME=/opt/poetry
 ENV POETRY_NO_INTERACTION=1
@@ -63,18 +54,13 @@ ENV PATH=/opt/poetry/bin:$PATH
 # Install Python without upgrading system-managed packages
 RUN apt-get update && apt-get install -y \
     python3.13 \
-    python3-pip \
-    && rm -rf /var/lib/apt/lists/*
+    python3-pip
 
 # 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
-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 --from=builder /usr/bin/npx /usr/bin/npx
+# Copy Prisma binaries
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
 ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"

autogpt_platform/backend/backend/blocks/__init__.py

@@ -1,3 +1,4 @@
+import functools
 import importlib
 import logging
 import os
@@ -5,8 +6,6 @@ import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar
 
-from autogpt_libs.utils.cache import cached
-
 logger = logging.getLogger(__name__)
 
 
@@ -16,7 +15,7 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@cached()
+@functools.cache
 def load_all_blocks() -> dict[str, type["Block"]]:
     from backend.data.block import Block
     from backend.util.settings import Config

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,6 +1,8 @@
 import logging
 from typing import Any, Optional
 
+from pydantic import JsonValue
+
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -10,7 +12,7 @@ from backend.data.block import (
     BlockType,
     get_block,
 )
-from backend.data.execution import ExecutionStatus, NodesInputMasks
+from backend.data.execution import ExecutionStatus
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
@@ -31,7 +33,7 @@ class AgentExecutorBlock(Block):
         input_schema: dict = SchemaField(description="Input schema for the graph")
         output_schema: dict = SchemaField(description="Output schema for the graph")
 
-        nodes_input_masks: Optional[NodesInputMasks] = SchemaField(
+        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = SchemaField(
             default=None, hidden=True
         )
 

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,214 +0,0 @@
-from typing import Any
-
-from backend.blocks.llm import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    AIBlockBase,
-    AICredentials,
-    AICredentialsField,
-    LlmModel,
-    LLMResponse,
-    llm_call,
-)
-from backend.data.block import BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
-
-
-class AIConditionBlock(AIBlockBase):
-    """
-    An AI-powered condition block that uses natural language to evaluate conditions.
-
-    This block allows users to define conditions in plain English (e.g., "the input is an email address",
-    "the input is a city in the USA") and uses AI to determine if the input satisfies the condition.
-    It provides the same yes/no data pass-through functionality as the standard ConditionBlock.
-    """
-
-    class Input(BlockSchema):
-        input_value: Any = SchemaField(
-            description="The input value to evaluate with the AI condition",
-            placeholder="Enter the value to be evaluated (text, number, or any data)",
-        )
-        condition: str = SchemaField(
-            description="A plaintext English description of the condition to evaluate",
-            placeholder="E.g., 'the input is the body of an email', 'the input is a City in the USA', 'the input is an error or a refusal'",
-        )
-        yes_value: Any = SchemaField(
-            description="(Optional) Value to output if the condition is true. If not provided, input_value will be used.",
-            placeholder="Leave empty to use input_value, or enter a specific value",
-            default=None,
-        )
-        no_value: Any = SchemaField(
-            description="(Optional) Value to output if the condition is false. If not provided, input_value will be used.",
-            placeholder="Leave empty to use input_value, or enter a specific value",
-            default=None,
-        )
-        model: LlmModel = SchemaField(
-            title="LLM Model",
-            default=LlmModel.GPT4O,
-            description="The language model to use for evaluating the condition.",
-            advanced=False,
-        )
-        credentials: AICredentials = AICredentialsField()
-
-    class Output(BlockSchema):
-        result: bool = SchemaField(
-            description="The result of the AI condition evaluation (True or False)"
-        )
-        yes_output: Any = SchemaField(
-            description="The output value if the condition is true"
-        )
-        no_output: Any = SchemaField(
-            description="The output value if the condition is false"
-        )
-        error: str = SchemaField(
-            description="Error message if the AI evaluation is uncertain or fails"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="553ec5b8-6c45-4299-8d75-b394d05f72ff",
-            input_schema=AIConditionBlock.Input,
-            output_schema=AIConditionBlock.Output,
-            description="Uses AI to evaluate natural language conditions and provide conditional outputs",
-            categories={BlockCategory.AI, BlockCategory.LOGIC},
-            test_input={
-                "input_value": "john@example.com",
-                "condition": "the input is an email address",
-                "yes_value": "Valid email",
-                "no_value": "Not an email",
-                "model": LlmModel.GPT4O,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("result", True),
-                ("yes_output", "Valid email"),
-            ],
-            test_mock={
-                "llm_call": lambda *args, **kwargs: LLMResponse(
-                    raw_response="",
-                    prompt=[],
-                    response="true",
-                    tool_calls=None,
-                    prompt_tokens=50,
-                    completion_tokens=10,
-                    reasoning=None,
-                )
-            },
-        )
-
-    async def llm_call(
-        self,
-        credentials: APIKeyCredentials,
-        llm_model: LlmModel,
-        prompt: list,
-        max_tokens: int,
-    ) -> LLMResponse:
-        """Wrapper method for llm_call to enable mocking in tests."""
-        return await llm_call(
-            credentials=credentials,
-            llm_model=llm_model,
-            prompt=prompt,
-            force_json_output=False,
-            max_tokens=max_tokens,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Evaluate the AI condition and return appropriate outputs.
-        """
-        # Prepare the yes and no values, using input_value as default
-        yes_value = (
-            input_data.yes_value
-            if input_data.yes_value is not None
-            else input_data.input_value
-        )
-        no_value = (
-            input_data.no_value
-            if input_data.no_value is not None
-            else input_data.input_value
-        )
-
-        # Convert input_value to string for AI evaluation
-        input_str = str(input_data.input_value)
-
-        # Create the prompt for AI evaluation
-        prompt = [
-            {
-                "role": "system",
-                "content": (
-                    "You are an AI assistant that evaluates conditions based on input data. "
-                    "You must respond with only 'true' or 'false' (lowercase) to indicate whether "
-                    "the given condition is met by the input value. Be accurate and consider the "
-                    "context and meaning of both the input and the condition."
-                ),
-            },
-            {
-                "role": "user",
-                "content": (
-                    f"Input value: {input_str}\n"
-                    f"Condition to evaluate: {input_data.condition}\n\n"
-                    f"Does the input value satisfy the condition? Respond with only 'true' or 'false'."
-                ),
-            },
-        ]
-
-        # Call the LLM
-        try:
-            response = await self.llm_call(
-                credentials=credentials,
-                llm_model=input_data.model,
-                prompt=prompt,
-                max_tokens=10,  # We only expect a true/false response
-            )
-
-            # Extract the boolean result from the response
-            response_text = response.response.strip().lower()
-            if response_text == "true":
-                result = True
-            elif response_text == "false":
-                result = False
-            else:
-                # If the response is not clear, try to interpret it using word boundaries
-                import re
-
-                # Use word boundaries to avoid false positives like 'untrue' or '10'
-                tokens = set(re.findall(r"\b(true|false|yes|no|1|0)\b", response_text))
-
-                if tokens == {"true"} or tokens == {"yes"} or tokens == {"1"}:
-                    result = True
-                elif tokens == {"false"} or tokens == {"no"} or tokens == {"0"}:
-                    result = False
-                else:
-                    # Unclear or conflicting response - default to False and yield error
-                    result = False
-                    yield "error", f"Unclear AI response: '{response.response}'"
-
-            # Update internal stats
-            self.merge_stats(
-                NodeExecutionStats(
-                    input_token_count=response.prompt_tokens,
-                    output_token_count=response.completion_tokens,
-                )
-            )
-            self.prompt = response.prompt
-
-        except Exception as e:
-            # In case of any error, default to False to be safe
-            result = False
-            # Log the error but don't fail the block execution
-            import logging
-
-            logger = logging.getLogger(__name__)
-            logger.error(f"AI condition evaluation failed: {str(e)}")
-            yield "error", f"AI evaluation failed: {str(e)}"
-
-        # Yield results
-        yield "result", result
-
-        if result:
-            yield "yes_output", yes_value
-        else:
-            yield "no_output", no_value

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -1,154 +0,0 @@
-from enum import Enum
-from typing import Literal
-
-from pydantic import SecretStr
-from replicate.client import Client as ReplicateClient
-from replicate.helpers import FileOutput
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-from backend.util.file import MediaFileType
-
-
-class GeminiImageModel(str, Enum):
-    NANO_BANANA = "google/nano-banana"
-
-
-class OutputFormat(str, Enum):
-    JPG = "jpg"
-    PNG = "png"
-
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="replicate",
-    api_key=SecretStr("mock-replicate-api-key"),
-    title="Mock Replicate API key",
-    expires_at=None,
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-class AIImageCustomizerBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput[
-            Literal[ProviderName.REPLICATE], Literal["api_key"]
-        ] = CredentialsField(
-            description="Replicate API key with permissions for Google Gemini image models",
-        )
-        prompt: str = SchemaField(
-            description="A text description of the image you want to generate",
-            title="Prompt",
-        )
-        model: GeminiImageModel = SchemaField(
-            description="The AI model to use for image generation and editing",
-            default=GeminiImageModel.NANO_BANANA,
-            title="Model",
-        )
-        images: list[MediaFileType] = SchemaField(
-            description="Optional list of input images to reference or modify",
-            default=[],
-            title="Input Images",
-        )
-        output_format: OutputFormat = SchemaField(
-            description="Format of the output image",
-            default=OutputFormat.PNG,
-            title="Output Format",
-        )
-
-    class Output(BlockSchema):
-        image_url: MediaFileType = SchemaField(description="URL of the generated image")
-        error: str = SchemaField(description="Error message if generation failed")
-
-    def __init__(self):
-        super().__init__(
-            id="d76bbe4c-930e-4894-8469-b66775511f71",
-            description=(
-                "Generate and edit custom images using Google's Nano-Banana model from Gemini 2.5. "
-                "Provide a prompt and optional reference images to create or modify images."
-            ),
-            categories={BlockCategory.AI, BlockCategory.MULTIMEDIA},
-            input_schema=AIImageCustomizerBlock.Input,
-            output_schema=AIImageCustomizerBlock.Output,
-            test_input={
-                "prompt": "Make the scene more vibrant and colorful",
-                "model": GeminiImageModel.NANO_BANANA,
-                "images": [],
-                "output_format": OutputFormat.JPG,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("image_url", "https://replicate.delivery/generated-image.jpg"),
-            ],
-            test_mock={
-                "run_model": lambda *args, **kwargs: MediaFileType(
-                    "https://replicate.delivery/generated-image.jpg"
-                ),
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        graph_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            result = await self.run_model(
-                api_key=credentials.api_key,
-                model_name=input_data.model.value,
-                prompt=input_data.prompt,
-                images=input_data.images,
-                output_format=input_data.output_format.value,
-            )
-            yield "image_url", result
-        except Exception as e:
-            yield "error", str(e)
-
-    async def run_model(
-        self,
-        api_key: SecretStr,
-        model_name: str,
-        prompt: str,
-        images: list[MediaFileType],
-        output_format: str,
-    ) -> MediaFileType:
-        client = ReplicateClient(api_token=api_key.get_secret_value())
-
-        input_params: dict = {
-            "prompt": prompt,
-            "output_format": output_format,
-        }
-
-        # Add images to input if provided (API expects "image_input" parameter)
-        if images:
-            input_params["image_input"] = [str(img) for img in images]
-
-        output: FileOutput | str = await client.async_run(  # type: ignore
-            model_name,
-            input=input_params,
-            wait=False,
-        )
-
-        if isinstance(output, FileOutput):
-            return MediaFileType(output.url)
-        if isinstance(output, str):
-            return MediaFileType(output)
-
-        raise ValueError("No output received from the model")

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

@@ -661,167 +661,6 @@ async def update_field(
 #################################################################
 
 
-async def get_table_schema(
-    credentials: Credentials,
-    base_id: str,
-    table_id_or_name: str,
-) -> dict:
-    """
-    Get the schema for a specific table, including all field definitions.
-
-    Args:
-        credentials: Airtable API credentials
-        base_id: The base ID
-        table_id_or_name: The table ID or name
-
-    Returns:
-        Dict containing table schema with fields information
-    """
-    # First get all tables to find the right one
-    response = await Requests().get(
-        f"https://api.airtable.com/v0/meta/bases/{base_id}/tables",
-        headers={"Authorization": credentials.auth_header()},
-    )
-
-    data = response.json()
-    tables = data.get("tables", [])
-
-    # Find the matching table
-    for table in tables:
-        if table.get("id") == table_id_or_name or table.get("name") == table_id_or_name:
-            return table
-
-    raise ValueError(f"Table '{table_id_or_name}' not found in base '{base_id}'")
-
-
-def get_empty_value_for_field(field_type: str) -> Any:
-    """
-    Return the appropriate empty value for a given Airtable field type.
-
-    Args:
-        field_type: The Airtable field type
-
-    Returns:
-        The appropriate empty value for that field type
-    """
-    # Fields that should be false when empty
-    if field_type == "checkbox":
-        return False
-
-    # Fields that should be empty arrays
-    if field_type in [
-        "multipleSelects",
-        "multipleRecordLinks",
-        "multipleAttachments",
-        "multipleLookupValues",
-        "multipleCollaborators",
-    ]:
-        return []
-
-    # Fields that should be 0 when empty (numeric types)
-    if field_type in [
-        "number",
-        "percent",
-        "currency",
-        "rating",
-        "duration",
-        "count",
-        "autoNumber",
-    ]:
-        return 0
-
-    # Fields that should be empty strings
-    if field_type in [
-        "singleLineText",
-        "multilineText",
-        "email",
-        "url",
-        "phoneNumber",
-        "richText",
-        "barcode",
-    ]:
-        return ""
-
-    # Everything else gets null (dates, single selects, formulas, etc.)
-    return None
-
-
-async def normalize_records(
-    records: list[dict],
-    table_schema: dict,
-    include_field_metadata: bool = False,
-) -> dict:
-    """
-    Normalize Airtable records to include all fields with proper empty values.
-
-    Args:
-        records: List of record objects from Airtable API
-        table_schema: Table schema containing field definitions
-        include_field_metadata: Whether to include field metadata in response
-
-    Returns:
-        Dict with normalized records and optionally field metadata
-    """
-    fields = table_schema.get("fields", [])
-
-    # Normalize each record
-    normalized_records = []
-    for record in records:
-        normalized = {
-            "id": record.get("id"),
-            "createdTime": record.get("createdTime"),
-            "fields": {},
-        }
-
-        # Add existing fields
-        existing_fields = record.get("fields", {})
-
-        # Add all fields from schema, using empty values for missing ones
-        for field in fields:
-            field_name = field["name"]
-            field_type = field["type"]
-
-            if field_name in existing_fields:
-                # Field exists, use its value
-                normalized["fields"][field_name] = existing_fields[field_name]
-            else:
-                # Field is missing, add appropriate empty value
-                normalized["fields"][field_name] = get_empty_value_for_field(field_type)
-
-        normalized_records.append(normalized)
-
-    # Build result dictionary
-    if include_field_metadata:
-        field_metadata = {}
-        for field in fields:
-            metadata = {"type": field["type"], "id": field["id"]}
-
-            # Add type-specific metadata
-            options = field.get("options", {})
-            if field["type"] == "currency" and "symbol" in options:
-                metadata["symbol"] = options["symbol"]
-                metadata["precision"] = options.get("precision", 2)
-            elif field["type"] == "duration" and "durationFormat" in options:
-                metadata["format"] = options["durationFormat"]
-            elif field["type"] == "percent" and "precision" in options:
-                metadata["precision"] = options["precision"]
-            elif (
-                field["type"] in ["singleSelect", "multipleSelects"]
-                and "choices" in options
-            ):
-                metadata["choices"] = [choice["name"] for choice in options["choices"]]
-            elif field["type"] == "rating" and "max" in options:
-                metadata["max"] = options["max"]
-                metadata["icon"] = options.get("icon", "star")
-                metadata["color"] = options.get("color", "yellowBright")
-
-            field_metadata[field["name"]] = metadata
-
-        return {"records": normalized_records, "field_metadata": field_metadata}
-    else:
-        return {"records": normalized_records}
-
-
 async def list_records(
     credentials: Credentials,
     base_id: str,
@@ -1410,26 +1249,3 @@ async def list_bases(
     )
 
     return response.json()
-
-
-async def get_base_tables(
-    credentials: Credentials,
-    base_id: str,
-) -> list[dict]:
-    """
-    Get all tables for a specific base.
-
-    Args:
-        credentials: Airtable API credentials
-        base_id: The ID of the base
-
-    Returns:
-        list[dict]: List of table objects with their schemas
-    """
-    response = await Requests().get(
-        f"https://api.airtable.com/v0/meta/bases/{base_id}/tables",
-        headers={"Authorization": credentials.auth_header()},
-    )
-
-    data = response.json()
-    return data.get("tables", [])

autogpt_platform/backend/backend/blocks/airtable/bases.py

@@ -14,13 +14,13 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import create_base, get_base_tables, list_bases
+from ._api import create_base, list_bases
 from ._config import airtable
 
 
 class AirtableCreateBaseBlock(Block):
     """
-    Creates a new base in an Airtable workspace, or returns existing base if one with the same name exists.
+    Creates a new base in an Airtable workspace.
     """
 
     class Input(BlockSchema):
@@ -31,10 +31,6 @@ class AirtableCreateBaseBlock(Block):
             description="The workspace ID where the base will be created"
         )
         name: str = SchemaField(description="The name of the new base")
-        find_existing: bool = SchemaField(
-            description="If true, return existing base with same name instead of creating duplicate",
-            default=True,
-        )
         tables: list[dict] = SchemaField(
             description="At least one table and field must be specified. Array of table objects to create in the base. Each table should have 'name' and 'fields' properties",
             default=[
@@ -54,18 +50,14 @@ class AirtableCreateBaseBlock(Block):
         )
 
     class Output(BlockSchema):
-        base_id: str = SchemaField(description="The ID of the created or found base")
+        base_id: str = SchemaField(description="The ID of the created base")
         tables: list[dict] = SchemaField(description="Array of table objects")
         table: dict = SchemaField(description="A single table object")
-        was_created: bool = SchemaField(
-            description="True if a new base was created, False if existing was found",
-            default=True,
-        )
 
     def __init__(self):
         super().__init__(
             id="f59b88a8-54ce-4676-a508-fd614b4e8dce",
-            description="Create or find a base in Airtable",
+            description="Create a new base in Airtable",
             categories={BlockCategory.DATA},
             input_schema=self.Input,
             output_schema=self.Output,
@@ -74,31 +66,6 @@ class AirtableCreateBaseBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        # If find_existing is true, check if a base with this name already exists
-        if input_data.find_existing:
-            # List all bases to check for existing one with same name
-            # Note: Airtable API doesn't have a direct search, so we need to list and filter
-            existing_bases = await list_bases(credentials)
-
-            for base in existing_bases.get("bases", []):
-                if base.get("name") == input_data.name:
-                    # Base already exists, return it
-                    base_id = base.get("id")
-                    yield "base_id", base_id
-                    yield "was_created", False
-
-                    # Get the tables for this base
-                    try:
-                        tables = await get_base_tables(credentials, base_id)
-                        yield "tables", tables
-                        for table in tables:
-                            yield "table", table
-                    except Exception:
-                        # If we can't get tables, return empty list
-                        yield "tables", []
-                    return
-
-        # No existing base found or find_existing is false, create new one
         data = await create_base(
             credentials,
             input_data.workspace_id,
@@ -107,7 +74,6 @@ class AirtableCreateBaseBlock(Block):
         )
 
         yield "base_id", data.get("id", None)
-        yield "was_created", True
         yield "tables", data.get("tables", [])
         for table in data.get("tables", []):
             yield "table", table

autogpt_platform/backend/backend/blocks/airtable/records.py

@@ -2,7 +2,7 @@
 Airtable record operation blocks.
 """
 
-from typing import Optional, cast
+from typing import Optional
 
 from backend.sdk import (
     APIKeyCredentials,
@@ -18,9 +18,7 @@ from ._api import (
     create_record,
     delete_multiple_records,
     get_record,
-    get_table_schema,
     list_records,
-    normalize_records,
     update_multiple_records,
 )
 from ._config import airtable
@@ -56,24 +54,12 @@ class AirtableListRecordsBlock(Block):
         return_fields: list[str] = SchemaField(
             description="Specific fields to return (comma-separated)", default=[]
         )
-        normalize_output: bool = SchemaField(
-            description="Normalize output to include all fields with proper empty values (disable to skip schema fetch and get raw Airtable response)",
-            default=True,
-        )
-        include_field_metadata: bool = SchemaField(
-            description="Include field type and configuration metadata (requires normalize_output=true)",
-            default=False,
-        )
 
     class Output(BlockSchema):
         records: list[dict] = SchemaField(description="Array of record objects")
         offset: Optional[str] = SchemaField(
             description="Offset for next page (null if no more records)", default=None
         )
-        field_metadata: Optional[dict] = SchemaField(
-            description="Field type and configuration metadata (only when include_field_metadata=true)",
-            default=None,
-        )
 
     def __init__(self):
         super().__init__(
@@ -87,7 +73,6 @@ class AirtableListRecordsBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-
         data = await list_records(
             credentials,
             input_data.base_id,
@@ -103,33 +88,8 @@ class AirtableListRecordsBlock(Block):
             fields=input_data.return_fields if input_data.return_fields else None,
         )
 
-        records = data.get("records", [])
-
-        # Normalize output if requested
-        if input_data.normalize_output:
-            # Fetch table schema
-            table_schema = await get_table_schema(
-                credentials, input_data.base_id, input_data.table_id_or_name
-            )
-
-            # Normalize the records
-            normalized_data = await normalize_records(
-                records,
-                table_schema,
-                include_field_metadata=input_data.include_field_metadata,
-            )
-
-            yield "records", normalized_data["records"]
-            yield "offset", data.get("offset", None)
-
-            if (
-                input_data.include_field_metadata
-                and "field_metadata" in normalized_data
-            ):
-                yield "field_metadata", normalized_data["field_metadata"]
-        else:
-            yield "records", records
-            yield "offset", data.get("offset", None)
+        yield "records", data.get("records", [])
+        yield "offset", data.get("offset", None)
 
 
 class AirtableGetRecordBlock(Block):
@@ -144,23 +104,11 @@ class AirtableGetRecordBlock(Block):
         base_id: str = SchemaField(description="The Airtable base ID")
         table_id_or_name: str = SchemaField(description="Table ID or name")
         record_id: str = SchemaField(description="The record ID to retrieve")
-        normalize_output: bool = SchemaField(
-            description="Normalize output to include all fields with proper empty values (disable to skip schema fetch and get raw Airtable response)",
-            default=True,
-        )
-        include_field_metadata: bool = SchemaField(
-            description="Include field type and configuration metadata (requires normalize_output=true)",
-            default=False,
-        )
 
     class Output(BlockSchema):
         id: str = SchemaField(description="The record ID")
         fields: dict = SchemaField(description="The record fields")
         created_time: str = SchemaField(description="The record created time")
-        field_metadata: Optional[dict] = SchemaField(
-            description="Field type and configuration metadata (only when include_field_metadata=true)",
-            default=None,
-        )
 
     def __init__(self):
         super().__init__(
@@ -174,7 +122,6 @@ class AirtableGetRecordBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-
         record = await get_record(
             credentials,
             input_data.base_id,
@@ -182,34 +129,9 @@ class AirtableGetRecordBlock(Block):
             input_data.record_id,
         )
 
-        # Normalize output if requested
-        if input_data.normalize_output:
-            # Fetch table schema
-            table_schema = await get_table_schema(
-                credentials, input_data.base_id, input_data.table_id_or_name
-            )
-
-            # Normalize the single record (wrap in list and unwrap result)
-            normalized_data = await normalize_records(
-                [record],
-                table_schema,
-                include_field_metadata=input_data.include_field_metadata,
-            )
-
-            normalized_record = normalized_data["records"][0]
-            yield "id", normalized_record.get("id", None)
-            yield "fields", normalized_record.get("fields", None)
-            yield "created_time", normalized_record.get("createdTime", None)
-
-            if (
-                input_data.include_field_metadata
-                and "field_metadata" in normalized_data
-            ):
-                yield "field_metadata", normalized_data["field_metadata"]
-        else:
-            yield "id", record.get("id", None)
-            yield "fields", record.get("fields", None)
-            yield "created_time", record.get("createdTime", None)
+        yield "id", record.get("id", None)
+        yield "fields", record.get("fields", None)
+        yield "created_time", record.get("createdTime", None)
 
 
 class AirtableCreateRecordsBlock(Block):
@@ -226,10 +148,6 @@ class AirtableCreateRecordsBlock(Block):
         records: list[dict] = SchemaField(
             description="Array of records to create (each with 'fields' object)"
         )
-        skip_normalization: bool = SchemaField(
-            description="Skip output normalization to get raw Airtable response (faster but may have missing fields)",
-            default=False,
-        )
         typecast: bool = SchemaField(
             description="Automatically convert string values to appropriate types",
             default=False,
@@ -255,7 +173,7 @@ class AirtableCreateRecordsBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-
+        # The create_record API expects records in a specific format
         data = await create_record(
             credentials,
             input_data.base_id,
@@ -264,22 +182,8 @@ class AirtableCreateRecordsBlock(Block):
             typecast=input_data.typecast if input_data.typecast else None,
             return_fields_by_field_id=input_data.return_fields_by_field_id,
         )
-        result_records = cast(list[dict], data.get("records", []))
 
-        # Normalize output unless explicitly disabled
-        if not input_data.skip_normalization and result_records:
-            # Fetch table schema
-            table_schema = await get_table_schema(
-                credentials, input_data.base_id, input_data.table_id_or_name
-            )
-
-            # Normalize the records
-            normalized_data = await normalize_records(
-                result_records, table_schema, include_field_metadata=False
-            )
-            result_records = normalized_data["records"]
-
-        yield "records", result_records
+        yield "records", data.get("records", [])
         details = data.get("details", None)
         if details:
             yield "details", details

autogpt_platform/backend/backend/blocks/bannerbear/__init__.py

@@ -1,3 +0,0 @@
-from .text_overlay import BannerbearTextOverlayBlock
-
-__all__ = ["BannerbearTextOverlayBlock"]

autogpt_platform/backend/backend/blocks/bannerbear/_config.py

@@ -1,8 +0,0 @@
-from backend.sdk import BlockCostType, ProviderBuilder
-
-bannerbear = (
-    ProviderBuilder("bannerbear")
-    .with_api_key("BANNERBEAR_API_KEY", "Bannerbear API Key")
-    .with_base_cost(1, BlockCostType.RUN)
-    .build()
-)

autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py

@@ -1,239 +0,0 @@
-import uuid
-from typing import TYPE_CHECKING, Any, Dict, List
-
-if TYPE_CHECKING:
-    pass
-
-from pydantic import SecretStr
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import bannerbear
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="bannerbear",
-    api_key=SecretStr("mock-bannerbear-api-key"),
-    title="Mock Bannerbear API Key",
-)
-
-
-class TextModification(BlockSchema):
-    name: str = SchemaField(
-        description="The name of the layer to modify in the template"
-    )
-    text: str = SchemaField(description="The text content to add to this layer")
-    color: str = SchemaField(
-        description="Hex color code for the text (e.g., '#FF0000')",
-        default="",
-        advanced=True,
-    )
-    font_family: str = SchemaField(
-        description="Font family to use for the text",
-        default="",
-        advanced=True,
-    )
-    font_size: int = SchemaField(
-        description="Font size in pixels",
-        default=0,
-        advanced=True,
-    )
-    font_weight: str = SchemaField(
-        description="Font weight (e.g., 'bold', 'normal')",
-        default="",
-        advanced=True,
-    )
-    text_align: str = SchemaField(
-        description="Text alignment (left, center, right)",
-        default="",
-        advanced=True,
-    )
-
-
-class BannerbearTextOverlayBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = bannerbear.credentials_field(
-            description="API credentials for Bannerbear"
-        )
-        template_id: str = SchemaField(
-            description="The unique ID of your Bannerbear template"
-        )
-        project_id: str = SchemaField(
-            description="Optional: Project ID (required when using Master API Key)",
-            default="",
-            advanced=True,
-        )
-        text_modifications: List[TextModification] = SchemaField(
-            description="List of text layers to modify in the template"
-        )
-        image_url: str = SchemaField(
-            description="Optional: URL of an image to use in the template",
-            default="",
-            advanced=True,
-        )
-        image_layer_name: str = SchemaField(
-            description="Optional: Name of the image layer in the template",
-            default="photo",
-            advanced=True,
-        )
-        webhook_url: str = SchemaField(
-            description="Optional: URL to receive webhook notification when image is ready",
-            default="",
-            advanced=True,
-        )
-        metadata: str = SchemaField(
-            description="Optional: Custom metadata to attach to the image",
-            default="",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        success: bool = SchemaField(
-            description="Whether the image generation was successfully initiated"
-        )
-        image_url: str = SchemaField(
-            description="URL of the generated image (if synchronous) or placeholder"
-        )
-        uid: str = SchemaField(description="Unique identifier for the generated image")
-        status: str = SchemaField(description="Status of the image generation")
-        error: str = SchemaField(description="Error message if the operation failed")
-
-    def __init__(self):
-        super().__init__(
-            id="c7d3a5c2-05fc-450e-8dce-3b0e04626009",
-            description="Add text overlay to images using Bannerbear templates. Perfect for creating social media graphics, marketing materials, and dynamic image content.",
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.AI},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "template_id": "jJWBKNELpQPvbX5R93Gk",
-                "text_modifications": [
-                    {
-                        "name": "headline",
-                        "text": "Amazing Product Launch!",
-                        "color": "#FF0000",
-                    },
-                    {
-                        "name": "subtitle",
-                        "text": "50% OFF Today Only",
-                    },
-                ],
-                "credentials": {
-                    "provider": "bannerbear",
-                    "id": str(uuid.uuid4()),
-                    "type": "api_key",
-                },
-            },
-            test_output=[
-                ("success", True),
-                ("image_url", "https://cdn.bannerbear.com/test-image.jpg"),
-                ("uid", "test-uid-123"),
-                ("status", "completed"),
-            ],
-            test_mock={
-                "_make_api_request": lambda *args, **kwargs: {
-                    "uid": "test-uid-123",
-                    "status": "completed",
-                    "image_url": "https://cdn.bannerbear.com/test-image.jpg",
-                }
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def _make_api_request(self, payload: dict, api_key: str) -> dict:
-        """Make the actual API request to Bannerbear. This is separated for easy mocking in tests."""
-        headers = {
-            "Authorization": f"Bearer {api_key}",
-            "Content-Type": "application/json",
-        }
-
-        response = await Requests().post(
-            "https://sync.api.bannerbear.com/v2/images",
-            headers=headers,
-            json=payload,
-        )
-
-        if response.status in [200, 201, 202]:
-            return response.json()
-        else:
-            error_msg = f"API request failed with status {response.status}"
-            if response.text:
-                try:
-                    error_data = response.json()
-                    error_msg = (
-                        f"{error_msg}: {error_data.get('message', response.text)}"
-                    )
-                except Exception:
-                    error_msg = f"{error_msg}: {response.text}"
-            raise Exception(error_msg)
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # Build the modifications array
-        modifications = []
-
-        # Add text modifications
-        for text_mod in input_data.text_modifications:
-            mod_data: Dict[str, Any] = {
-                "name": text_mod.name,
-                "text": text_mod.text,
-            }
-
-            # Add optional text styling parameters only if they have values
-            if text_mod.color and text_mod.color.strip():
-                mod_data["color"] = text_mod.color
-            if text_mod.font_family and text_mod.font_family.strip():
-                mod_data["font_family"] = text_mod.font_family
-            if text_mod.font_size and text_mod.font_size > 0:
-                mod_data["font_size"] = text_mod.font_size
-            if text_mod.font_weight and text_mod.font_weight.strip():
-                mod_data["font_weight"] = text_mod.font_weight
-            if text_mod.text_align and text_mod.text_align.strip():
-                mod_data["text_align"] = text_mod.text_align
-
-            modifications.append(mod_data)
-
-        # Add image modification if provided and not empty
-        if input_data.image_url and input_data.image_url.strip():
-            modifications.append(
-                {
-                    "name": input_data.image_layer_name,
-                    "image_url": input_data.image_url,
-                }
-            )
-
-        # Build the request payload - only include non-empty optional fields
-        payload = {
-            "template": input_data.template_id,
-            "modifications": modifications,
-        }
-
-        # Add project_id if provided (required for Master API keys)
-        if input_data.project_id and input_data.project_id.strip():
-            payload["project_id"] = input_data.project_id
-
-        if input_data.webhook_url and input_data.webhook_url.strip():
-            payload["webhook_url"] = input_data.webhook_url
-        if input_data.metadata and input_data.metadata.strip():
-            payload["metadata"] = input_data.metadata
-
-        # Make the API request using the private method
-        data = await self._make_api_request(
-            payload, credentials.api_key.get_secret_value()
-        )
-
-        # Synchronous request - image should be ready
-        yield "success", True
-        yield "image_url", data.get("image_url", "")
-        yield "uid", data.get("uid", "")
-        yield "status", data.get("status", "completed")

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -1,10 +1,8 @@
 from enum import Enum
-from typing import Any, Literal, Optional
+from typing import Literal
 
 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, JsonValue, SecretStr
+from pydantic import SecretStr
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
@@ -38,135 +36,14 @@ class ProgrammingLanguage(Enum):
     JAVA = "java"
 
 
-class MainCodeExecutionResult(BaseModel):
-    """
-    *Pydantic model mirroring `e2b_code_interpreter.Result`*
-
-    Represents the data to be displayed as a result of executing a cell in a Jupyter notebook.
-    The result is similar to the structure returned by ipython kernel: https://ipython.readthedocs.io/en/stable/development/execution.html#execution-semantics
-
-    The result can contain multiple types of data, such as text, images, plots, etc. Each type of data is represented
-    as a string, and the result can contain multiple types of data. The display calls don't have to have text representation,
-    for the actual result the representation is always present for the result, the other representations are always optional.
-    """  # noqa
-
-    class Chart(BaseModel, E2BExecutionResultChart):
-        pass
-
-    text: Optional[str] = None
-    html: Optional[str] = None
-    markdown: Optional[str] = None
-    svg: Optional[str] = None
-    png: Optional[str] = None
-    jpeg: Optional[str] = None
-    pdf: Optional[str] = None
-    latex: Optional[str] = None
-    json: Optional[JsonValue] = None  # type: ignore (reportIncompatibleMethodOverride)
-    javascript: Optional[str] = None
-    data: Optional[dict] = None
-    chart: Optional[Chart] = None
-    extra: Optional[dict] = None
-    """Extra data that can be included. Not part of the standard types."""
-
-
-class CodeExecutionResult(MainCodeExecutionResult):
-    __doc__ = MainCodeExecutionResult.__doc__
-
-    is_main_result: bool = False
-    """Whether this data is the main result of the cell. Data can be produced by display calls of which can be multiple in a cell."""  # noqa
-
-
-class BaseE2BExecutorMixin:
-    """Shared implementation methods for E2B executor blocks."""
-
-    async def execute_code(
-        self,
-        api_key: str,
-        code: str,
-        language: ProgrammingLanguage,
-        template_id: str = "",
-        setup_commands: Optional[list[str]] = None,
-        timeout: Optional[int] = None,
-        sandbox_id: Optional[str] = None,
-        dispose_sandbox: 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)
-        """  # noqa
-        sandbox = None
-        try:
-            if sandbox_id:
-                # Connect to existing sandbox (ExecuteCodeStepBlock case)
-                sandbox = await AsyncSandbox.connect(
-                    sandbox_id=sandbox_id, api_key=api_key
-                )
-            else:
-                # Create new sandbox (ExecuteCodeBlock/InstantiateCodeSandboxBlock case)
-                sandbox = await AsyncSandbox.create(
-                    api_key=api_key, template=template_id, timeout=timeout
-                )
-                if setup_commands:
-                    for cmd in setup_commands:
-                        await sandbox.commands.run(cmd)
-
-            # Execute the code
-            execution = await sandbox.run_code(
-                code,
-                language=language.value,
-                on_error=lambda e: sandbox.kill(),  # Kill the sandbox on error
-            )
-
-            if execution.error:
-                raise Exception(execution.error)
-
-            results = execution.results
-            text_output = execution.text
-            stdout_logs = "".join(execution.logs.stdout)
-            stderr_logs = "".join(execution.logs.stderr)
-
-            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
-        finally:
-            # Dispose of sandbox if requested to reduce usage costs
-            if dispose_sandbox and sandbox:
-                await sandbox.kill()
-
-    def process_execution_results(
-        self, results: list[E2BExecutionResult]
-    ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]:
-        """Process and filter execution results."""
-        # Filter out empty formats and convert to dicts
-        processed_results = [
-            {
-                f: value
-                for f in [*r.formats(), "extra", "is_main_result"]
-                if (value := getattr(r, f, None)) is not None
-            }
-            for r in results
-        ]
-        if main_result := next(
-            (r for r in processed_results if r.get("is_main_result")), None
-        ):
-            # Make main_result a copy we can modify & remove is_main_result
-            (main_result := {**main_result}).pop("is_main_result")
-
-        return main_result, processed_results
-
-
-class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
+class CodeExecutionBlock(Block):
     # TODO : Add support to upload and download files
-    # NOTE: Currently, you can only customize the CPU and Memory
-    #       by creating a pre customized sandbox template
+    # Currently, You can customized the CPU and Memory, only by creating a pre customized sandbox template
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            ),
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         # Todo : Option to run commond in background
@@ -199,14 +76,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             description="Execution timeout in seconds", default=300
         )
 
-        dispose_sandbox: bool = SchemaField(
-            description=(
-                "Whether to dispose of the sandbox immediately after execution. "
-                "If disabled, the sandbox will run until its timeout expires."
-            ),
-            default=True,
-        )
-
         template_id: str = SchemaField(
             description=(
                 "You can use an E2B sandbox template by entering its ID here. "
@@ -218,16 +87,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
         )
 
     class Output(BlockSchema):
-        main_result: MainCodeExecutionResult = SchemaField(
-            title="Main Result", description="The main result from the code execution"
-        )
-        results: list[CodeExecutionResult] = SchemaField(
-            description="List of results from the code execution"
-        )
-        response: str = SchemaField(
-            title="Main Text Output",
-            description="Text output (if any) of the main execution result",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -237,10 +97,10 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="0b02b072-abe7-11ef-8372-fb5d162dd712",
-            description="Executes code in a sandbox environment with internet access.",
+            description="Executes code in an isolated sandbox environment with internet access.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExecuteCodeBlock.Input,
-            output_schema=ExecuteCodeBlock.Output,
+            input_schema=CodeExecutionBlock.Input,
+            output_schema=CodeExecutionBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -251,59 +111,91 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
                 "template_id": "",
             },
             test_output=[
-                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "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
+                "execute_code": lambda code, language, setup_commands, timeout, api_key, template_id: (
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
 
+    async def execute_code(
+        self,
+        code: str,
+        language: ProgrammingLanguage,
+        setup_commands: list[str],
+        timeout: int,
+        api_key: str,
+        template_id: str,
+    ):
+        try:
+            sandbox = None
+            if template_id:
+                sandbox = await AsyncSandbox.create(
+                    template=template_id, api_key=api_key, timeout=timeout
+                )
+            else:
+                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
+
+            if not sandbox:
+                raise Exception("Sandbox not created")
+
+            # Running setup commands
+            for cmd in setup_commands:
+                await sandbox.commands.run(cmd)
+
+            # Executing the code
+            execution = await sandbox.run_code(
+                code,
+                language=language.value,
+                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
+            )
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            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,
-                template_id=input_data.template_id,
-                setup_commands=input_data.setup_commands,
-                timeout=input_data.timeout,
-                dispose_sandbox=input_data.dispose_sandbox,
+            response, stdout_logs, stderr_logs = await self.execute_code(
+                input_data.code,
+                input_data.language,
+                input_data.setup_commands,
+                input_data.timeout,
+                credentials.api_key.get_secret_value(),
+                input_data.template_id,
             )
 
-            # Determine result object shape & filter out empty formats
-            main_result, results = self.process_execution_results(results)
-            if main_result:
-                yield "main_result", main_result
-            yield "results", results
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)
 
 
-class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
+class InstantiationBlock(Block):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            )
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         # Todo : Option to run commond in background
@@ -348,10 +240,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
 
     class Output(BlockSchema):
         sandbox_id: str = SchemaField(description="ID of the sandbox instance")
-        response: str = SchemaField(
-            title="Text Result",
-            description="Text result (if any) of the setup code execution",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -361,13 +250,10 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="ff0861c9-1726-4aec-9e5b-bf53f3622112",
-            description=(
-                "Instantiate a sandbox environment with internet access "
-                "in which you can execute code with the Execute Code Step block."
-            ),
+            description="Instantiate an isolated sandbox environment with internet access where to execute code in.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=InstantiateCodeSandboxBlock.Input,
-            output_schema=InstantiateCodeSandboxBlock.Output,
+            input_schema=InstantiationBlock.Input,
+            output_schema=InstantiationBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -383,12 +269,11 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout: (  # noqa
-                    [],  # results
-                    "Hello World",  # text_output
-                    "Hello World\n",  # stdout_logs
-                    "",  # stderr_logs
-                    "sandbox_id",  # sandbox_id
+                "execute_code": lambda setup_code, language, setup_commands, timeout, api_key, template_id: (
+                    "sandbox_id",
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
@@ -397,38 +282,78 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            _, 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,
-                template_id=input_data.template_id,
-                setup_commands=input_data.setup_commands,
-                timeout=input_data.timeout,
+            sandbox_id, response, stdout_logs, stderr_logs = await self.execute_code(
+                input_data.setup_code,
+                input_data.language,
+                input_data.setup_commands,
+                input_data.timeout,
+                credentials.api_key.get_secret_value(),
+                input_data.template_id,
             )
             if sandbox_id:
                 yield "sandbox_id", sandbox_id
             else:
                 yield "error", "Sandbox ID not found"
-
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)
 
+    async def execute_code(
+        self,
+        code: str,
+        language: ProgrammingLanguage,
+        setup_commands: list[str],
+        timeout: int,
+        api_key: str,
+        template_id: str,
+    ):
+        try:
+            sandbox = None
+            if template_id:
+                sandbox = await AsyncSandbox.create(
+                    template=template_id, api_key=api_key, timeout=timeout
+                )
+            else:
+                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
 
-class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
+            if not sandbox:
+                raise Exception("Sandbox not created")
+
+            # Running setup commands
+            for cmd in setup_commands:
+                await sandbox.commands.run(cmd)
+
+            # Executing the code
+            execution = await sandbox.run_code(
+                code,
+                language=language.value,
+                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
+            )
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return sandbox.sandbox_id, response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
+
+class StepExecutionBlock(Block):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description=(
-                "Enter your API key for the E2B platform. "
-                "You can get it in here - https://e2b.dev/docs"
-            ),
+            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
         )
 
         sandbox_id: str = SchemaField(
@@ -449,22 +374,8 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
             advanced=False,
         )
 
-        dispose_sandbox: bool = SchemaField(
-            description="Whether to dispose of the sandbox after executing this code.",
-            default=False,
-        )
-
     class Output(BlockSchema):
-        main_result: MainCodeExecutionResult = SchemaField(
-            title="Main Result", description="The main result from the code execution"
-        )
-        results: list[CodeExecutionResult] = SchemaField(
-            description="List of results from the code execution"
-        )
-        response: str = SchemaField(
-            title="Main Text Output",
-            description="Text output (if any) of the main execution result",
-        )
+        response: str = SchemaField(description="Response from code execution")
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -474,10 +385,10 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
     def __init__(self):
         super().__init__(
             id="82b59b8e-ea10-4d57-9161-8b169b0adba6",
-            description="Execute code in a previously instantiated sandbox.",
+            description="Execute code in a previously instantiated sandbox environment.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExecuteCodeStepBlock.Input,
-            output_schema=ExecuteCodeStepBlock.Output,
+            input_schema=StepExecutionBlock.Input,
+            output_schema=StepExecutionBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -486,43 +397,61 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
                 "language": ProgrammingLanguage.PYTHON.value,
             },
             test_output=[
-                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda api_key, code, language, sandbox_id, dispose_sandbox: (  # noqa
-                    [],  # results
-                    "Hello World",  # text_output
-                    "Hello World\n",  # stdout_logs
-                    "",  # stderr_logs
-                    sandbox_id,  # sandbox_id
+                "execute_step_code": lambda sandbox_id, step_code, language, api_key: (
+                    "Hello World",
+                    "Hello World\n",
+                    "",
                 ),
             },
         )
 
+    async def execute_step_code(
+        self,
+        sandbox_id: str,
+        code: str,
+        language: ProgrammingLanguage,
+        api_key: str,
+    ):
+        try:
+            sandbox = await AsyncSandbox.connect(sandbox_id=sandbox_id, api_key=api_key)
+            if not sandbox:
+                raise Exception("Sandbox not found")
+
+            # Executing the code
+            execution = await sandbox.run_code(code, language=language.value)
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            response = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return response, stdout_logs, stderr_logs
+
+        except Exception as e:
+            raise e
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            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,
-                sandbox_id=input_data.sandbox_id,
-                dispose_sandbox=input_data.dispose_sandbox,
+            response, stdout_logs, stderr_logs = await self.execute_step_code(
+                input_data.sandbox_id,
+                input_data.step_code,
+                input_data.language,
+                credentials.api_key.get_secret_value(),
             )
 
-            # Determine result object shape & filter out empty formats
-            main_result, results = self.process_execution_results(results)
-            if main_result:
-                yield "main_result", main_result
-            yield "results", results
-            if text_output:
-                yield "response", text_output
-            if stdout:
-                yield "stdout_logs", stdout
-            if stderr:
-                yield "stderr_logs", stderr
+            if response:
+                yield "response", response
+            if stdout_logs:
+                yield "stdout_logs", stdout_logs
+            if stderr_logs:
+                yield "stderr_logs", stderr_logs
         except Exception as e:
             yield "error", str(e)

autogpt_platform/backend/backend/blocks/code_extraction_block.py

@@ -90,7 +90,7 @@ class CodeExtractionBlock(Block):
                 for aliases in language_aliases.values()
                 for alias in aliases
             )
-            + r")[ \t]*\n[\s\S]*?```"
+            + r")\s+[\s\S]*?```"
         )
 
         remaining_text = re.sub(pattern, "", input_data.text).strip()
@@ -103,9 +103,7 @@ class CodeExtractionBlock(Block):
         # Escape special regex characters in the language string
         language = re.escape(language)
         # Extract all code blocks enclosed in ```language``` blocks
-        pattern = re.compile(
-            rf"```{language}[ \t]*\n(.*?)\n```", re.DOTALL | re.IGNORECASE
-        )
+        pattern = re.compile(rf"```{language}\s+(.*?)```", re.DOTALL | re.IGNORECASE)
         matches = pattern.finditer(text)
         # Combine all code blocks for this language with newlines between them
         code_blocks = [match.group(1).strip() for match in matches]

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -66,7 +66,6 @@ class AddToDictionaryBlock(Block):
         dictionary: dict[Any, Any] = SchemaField(
             default_factory=dict,
             description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
-            advanced=False,
         )
         key: str = SchemaField(
             default="",

autogpt_platform/backend/backend/blocks/dataforseo/_api.py

@@ -113,7 +113,6 @@ class DataForSeoClient:
         include_serp_info: bool = False,
         include_clickstream_data: bool = False,
         limit: int = 100,
-        depth: Optional[int] = None,
     ) -> List[Dict[str, Any]]:
         """
         Get related keywords from DataForSEO Labs.
@@ -126,7 +125,6 @@ class DataForSeoClient:
             include_serp_info: Include SERP data
             include_clickstream_data: Include clickstream metrics
             limit: Maximum number of results (up to 3000)
-            depth: Keyword search depth (0-4), controls number of returned keywords
 
         Returns:
             API response with related keywords
@@ -150,8 +148,6 @@ class DataForSeoClient:
             task_data["include_clickstream_data"] = include_clickstream_data
         if limit is not None:
             task_data["limit"] = limit
-        if depth is not None:
-            task_data["depth"] = depth
 
         payload = [task_data]
 

autogpt_platform/backend/backend/blocks/dataforseo/keyword_suggestions.py

@@ -90,7 +90,6 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -162,52 +161,43 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the keyword suggestions query."""
-        try:
-            client = DataForSeoClient(credentials)
+        client = DataForSeoClient(credentials)
 
-            results = await self._fetch_keyword_suggestions(client, input_data)
+        results = await self._fetch_keyword_suggestions(client, input_data)
 
-            # Process and format the results
-            suggestions = []
-            if results and len(results) > 0:
-                # results is a list, get the first element
-                first_result = results[0] if isinstance(results, list) else results
-                items = (
-                    first_result.get("items", [])
-                    if isinstance(first_result, dict)
-                    else []
+        # Process and format the results
+        suggestions = []
+        if results and len(results) > 0:
+            # results is a list, get the first element
+            first_result = results[0] if isinstance(results, list) else results
+            items = (
+                first_result.get("items", []) if isinstance(first_result, dict) else []
+            )
+            for item in items:
+                # Create the KeywordSuggestion object
+                suggestion = KeywordSuggestion(
+                    keyword=item.get("keyword", ""),
+                    search_volume=item.get("keyword_info", {}).get("search_volume"),
+                    competition=item.get("keyword_info", {}).get("competition"),
+                    cpc=item.get("keyword_info", {}).get("cpc"),
+                    keyword_difficulty=item.get("keyword_properties", {}).get(
+                        "keyword_difficulty"
+                    ),
+                    serp_info=(
+                        item.get("serp_info") if input_data.include_serp_info else None
+                    ),
+                    clickstream_data=(
+                        item.get("clickstream_keyword_info")
+                        if input_data.include_clickstream_data
+                        else None
+                    ),
                 )
-                if items is None:
-                    items = []
-                for item in items:
-                    # Create the KeywordSuggestion object
-                    suggestion = KeywordSuggestion(
-                        keyword=item.get("keyword", ""),
-                        search_volume=item.get("keyword_info", {}).get("search_volume"),
-                        competition=item.get("keyword_info", {}).get("competition"),
-                        cpc=item.get("keyword_info", {}).get("cpc"),
-                        keyword_difficulty=item.get("keyword_properties", {}).get(
-                            "keyword_difficulty"
-                        ),
-                        serp_info=(
-                            item.get("serp_info")
-                            if input_data.include_serp_info
-                            else None
-                        ),
-                        clickstream_data=(
-                            item.get("clickstream_keyword_info")
-                            if input_data.include_clickstream_data
-                            else None
-                        ),
-                    )
-                    yield "suggestion", suggestion
-                    suggestions.append(suggestion)
+                yield "suggestion", suggestion
+                suggestions.append(suggestion)
 
-            yield "suggestions", suggestions
-            yield "total_count", len(suggestions)
-            yield "seed_keyword", input_data.keyword
-        except Exception as e:
-            yield "error", f"Failed to fetch keyword suggestions: {str(e)}"
+        yield "suggestions", suggestions
+        yield "total_count", len(suggestions)
+        yield "seed_keyword", input_data.keyword
 
 
 class KeywordSuggestionExtractorBlock(Block):

autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py

@@ -78,12 +78,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
             ge=1,
             le=3000,
         )
-        depth: int = SchemaField(
-            description="Keyword search depth (0-4). Controls the number of returned keywords: 0=1 keyword, 1=~8 keywords, 2=~72 keywords, 3=~584 keywords, 4=~4680 keywords",
-            default=1,
-            ge=0,
-            le=4,
-        )
 
     class Output(BlockSchema):
         related_keywords: List[RelatedKeyword] = SchemaField(
@@ -98,7 +92,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -161,7 +154,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
             include_serp_info=input_data.include_serp_info,
             include_clickstream_data=input_data.include_clickstream_data,
             limit=input_data.limit,
-            depth=input_data.depth,
         )
 
     async def run(
@@ -172,60 +164,50 @@ class DataForSeoRelatedKeywordsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the related keywords query."""
-        try:
-            client = DataForSeoClient(credentials)
+        client = DataForSeoClient(credentials)
 
-            results = await self._fetch_related_keywords(client, input_data)
+        results = await self._fetch_related_keywords(client, input_data)
 
-            # Process and format the results
-            related_keywords = []
-            if results and len(results) > 0:
-                # results is a list, get the first element
-                first_result = results[0] if isinstance(results, list) else results
-                items = (
-                    first_result.get("items", [])
-                    if isinstance(first_result, dict)
-                    else []
+        # Process and format the results
+        related_keywords = []
+        if results and len(results) > 0:
+            # results is a list, get the first element
+            first_result = results[0] if isinstance(results, list) else results
+            items = (
+                first_result.get("items", []) if isinstance(first_result, dict) else []
+            )
+            for item in items:
+                # Extract keyword_data from the item
+                keyword_data = item.get("keyword_data", {})
+
+                # Create the RelatedKeyword object
+                keyword = RelatedKeyword(
+                    keyword=keyword_data.get("keyword", ""),
+                    search_volume=keyword_data.get("keyword_info", {}).get(
+                        "search_volume"
+                    ),
+                    competition=keyword_data.get("keyword_info", {}).get("competition"),
+                    cpc=keyword_data.get("keyword_info", {}).get("cpc"),
+                    keyword_difficulty=keyword_data.get("keyword_properties", {}).get(
+                        "keyword_difficulty"
+                    ),
+                    serp_info=(
+                        keyword_data.get("serp_info")
+                        if input_data.include_serp_info
+                        else None
+                    ),
+                    clickstream_data=(
+                        keyword_data.get("clickstream_keyword_info")
+                        if input_data.include_clickstream_data
+                        else None
+                    ),
                 )
-                # Ensure items is never None
-                if items is None:
-                    items = []
-                for item in items:
-                    # Extract keyword_data from the item
-                    keyword_data = item.get("keyword_data", {})
+                yield "related_keyword", keyword
+                related_keywords.append(keyword)
 
-                    # Create the RelatedKeyword object
-                    keyword = RelatedKeyword(
-                        keyword=keyword_data.get("keyword", ""),
-                        search_volume=keyword_data.get("keyword_info", {}).get(
-                            "search_volume"
-                        ),
-                        competition=keyword_data.get("keyword_info", {}).get(
-                            "competition"
-                        ),
-                        cpc=keyword_data.get("keyword_info", {}).get("cpc"),
-                        keyword_difficulty=keyword_data.get(
-                            "keyword_properties", {}
-                        ).get("keyword_difficulty"),
-                        serp_info=(
-                            keyword_data.get("serp_info")
-                            if input_data.include_serp_info
-                            else None
-                        ),
-                        clickstream_data=(
-                            keyword_data.get("clickstream_keyword_info")
-                            if input_data.include_clickstream_data
-                            else None
-                        ),
-                    )
-                    yield "related_keyword", keyword
-                    related_keywords.append(keyword)
-
-            yield "related_keywords", related_keywords
-            yield "total_count", len(related_keywords)
-            yield "seed_keyword", input_data.keyword
-        except Exception as e:
-            yield "error", f"Failed to fetch related keywords: {str(e)}"
+        yield "related_keywords", related_keywords
+        yield "total_count", len(related_keywords)
+        yield "seed_keyword", input_data.keyword
 
 
 class RelatedKeywordExtractorBlock(Block):

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

@@ -171,11 +171,11 @@ class SendDiscordMessageBlock(Block):
             description="The content of the message to send"
         )
         channel_name: str = SchemaField(
-            description="Channel ID or channel name to send the message to"
+            description="The name of the channel the message will be sent to"
         )
         server_name: str = SchemaField(
-            description="Server name (only needed if using channel name)",
-            advanced=True,
+            description="The name of the server where the channel is located",
+            advanced=True,  # Optional field for server name
             default="",
         )
 
@@ -231,49 +231,25 @@ class SendDiscordMessageBlock(Block):
         @client.event
         async def on_ready():
             print(f"Logged in as {client.user}")
-            channel = None
+            for guild in client.guilds:
+                if server_name and guild.name != server_name:
+                    continue
+                for channel in guild.text_channels:
+                    if channel.name == channel_name:
+                        # Split message into chunks if it exceeds 2000 characters
+                        chunks = self.chunk_message(message_content)
+                        last_message = None
+                        for chunk in chunks:
+                            last_message = await channel.send(chunk)
+                        result["status"] = "Message sent"
+                        result["message_id"] = (
+                            str(last_message.id) if last_message else ""
+                        )
+                        result["channel_id"] = str(channel.id)
+                        await client.close()
+                        return
 
-            # Try to parse as channel ID first
-            try:
-                channel_id = int(channel_name)
-                channel = client.get_channel(channel_id)
-            except ValueError:
-                # Not a valid ID, will try name lookup
-                pass
-
-            # If not found by ID (or not an ID), try name lookup
-            if not channel:
-                for guild in client.guilds:
-                    if server_name and guild.name != server_name:
-                        continue
-                    for ch in guild.text_channels:
-                        if ch.name == channel_name:
-                            channel = ch
-                            break
-                    if channel:
-                        break
-
-            if not channel:
-                result["status"] = f"Channel not found: {channel_name}"
-                await client.close()
-                return
-
-            # Type check - ensure it's a text channel that can send messages
-            if not hasattr(channel, "send"):
-                result["status"] = (
-                    f"Channel {channel_name} cannot receive messages (not a text channel)"
-                )
-                await client.close()
-                return
-
-            # Split message into chunks if it exceeds 2000 characters
-            chunks = self.chunk_message(message_content)
-            last_message = None
-            for chunk in chunks:
-                last_message = await channel.send(chunk)  # type: ignore
-            result["status"] = "Message sent"
-            result["message_id"] = str(last_message.id) if last_message else ""
-            result["channel_id"] = str(channel.id)
+            result["status"] = "Channel not found"
             await client.close()
 
         await client.start(token)

autogpt_platform/backend/backend/blocks/firecrawl/_api.py

@@ -1,12 +0,0 @@
-from enum import Enum
-
-
-class ScrapeFormat(Enum):
-    MARKDOWN = "markdown"
-    HTML = "html"
-    RAW_HTML = "rawHtml"
-    LINKS = "links"
-    SCREENSHOT = "screenshot"
-    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
-    JSON = "json"
-    CHANGE_TRACKING = "changeTracking"

autogpt_platform/backend/backend/blocks/firecrawl/_format_utils.py

@@ -1,28 +0,0 @@
-"""Utility functions for converting between our ScrapeFormat enum and firecrawl FormatOption types."""
-
-from typing import List
-
-from firecrawl.v2.types import FormatOption, ScreenshotFormat
-
-from backend.blocks.firecrawl._api import ScrapeFormat
-
-
-def convert_to_format_options(
-    formats: List[ScrapeFormat],
-) -> List[FormatOption]:
-    """Convert our ScrapeFormat enum values to firecrawl FormatOption types.
-
-    Handles special cases like screenshot@fullPage which needs to be converted
-    to a ScreenshotFormat object.
-    """
-    result: List[FormatOption] = []
-
-    for format_enum in formats:
-        if format_enum.value == "screenshot@fullPage":
-            # Special case: convert to ScreenshotFormat with full_page=True
-            result.append(ScreenshotFormat(type="screenshot", full_page=True))
-        else:
-            # Regular string literals
-            result.append(format_enum.value)
-
-    return result

autogpt_platform/backend/backend/blocks/firecrawl/crawl.py

@@ -1,9 +1,8 @@
+from enum import Enum
 from typing import Any
 
-from firecrawl import FirecrawlApp
-from firecrawl.v2.types import ScrapeOptions
+from firecrawl import FirecrawlApp, ScrapeOptions
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -15,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlCrawlBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -68,17 +78,18 @@ class FirecrawlCrawlBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
-        crawl_result = app.crawl(
+        crawl_result = app.crawl_url(
             input_data.url,
             limit=input_data.limit,
             scrape_options=ScrapeOptions(
-                formats=convert_to_format_options(input_data.formats),
-                only_main_content=input_data.only_main_content,
-                max_age=input_data.max_age,
-                wait_for=input_data.wait_for,
+                formats=[format.value for format in input_data.formats],
+                onlyMainContent=input_data.only_main_content,
+                maxAge=input_data.max_age,
+                waitFor=input_data.wait_for,
             ),
         )
         yield "data", crawl_result.data
@@ -90,7 +101,7 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.HTML:
                     yield "html", data.html
                 elif f == ScrapeFormat.RAW_HTML:
-                    yield "raw_html", data.raw_html
+                    yield "raw_html", data.rawHtml
                 elif f == ScrapeFormat.LINKS:
                     yield "links", data.links
                 elif f == ScrapeFormat.SCREENSHOT:
@@ -98,6 +109,6 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.SCREENSHOT_FULL_PAGE:
                     yield "screenshot_full_page", data.screenshot
                 elif f == ScrapeFormat.CHANGE_TRACKING:
-                    yield "change_tracking", data.change_tracking
+                    yield "change_tracking", data.changeTracking
                 elif f == ScrapeFormat.JSON:
                     yield "json", data.json

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

@@ -20,6 +20,7 @@ from ._config import firecrawl
 
 @cost(BlockCost(2, BlockCostType.RUN))
 class FirecrawlExtractBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         urls: list[str] = SchemaField(
@@ -52,6 +53,7 @@ class FirecrawlExtractBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         extract_result = app.extract(

autogpt_platform/backend/backend/blocks/firecrawl/map.py

@@ -1,5 +1,3 @@
-from typing import Any
-
 from firecrawl import FirecrawlApp
 
 from backend.sdk import (
@@ -16,16 +14,14 @@ from ._config import firecrawl
 
 
 class FirecrawlMapWebsiteBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
 
         url: str = SchemaField(description="The website url to map")
 
     class Output(BlockSchema):
-        links: list[str] = SchemaField(description="List of URLs found on the website")
-        results: list[dict[str, Any]] = SchemaField(
-            description="List of search results with url, title, and description"
-        )
+        links: list[str] = SchemaField(description="The links of the website")
 
     def __init__(self):
         super().__init__(
@@ -39,22 +35,12 @@ class FirecrawlMapWebsiteBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
-        map_result = app.map(
+        map_result = app.map_url(
             url=input_data.url,
         )
 
-        # Convert SearchResult objects to dicts
-        results_data = [
-            {
-                "url": link.url,
-                "title": link.title,
-                "description": link.description,
-            }
-            for link in map_result.links
-        ]
-
-        yield "links", [link.url for link in map_result.links]
-        yield "results", results_data
+        yield "links", map_result.links

autogpt_platform/backend/backend/blocks/firecrawl/scrape.py

@@ -1,8 +1,8 @@
+from enum import Enum
 from typing import Any
 
 from firecrawl import FirecrawlApp
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -14,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlScrapeBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -67,11 +78,12 @@ class FirecrawlScrapeBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
-        scrape_result = app.scrape(
+        scrape_result = app.scrape_url(
             input_data.url,
-            formats=convert_to_format_options(input_data.formats),
+            formats=[format.value for format in input_data.formats],
             only_main_content=input_data.only_main_content,
             max_age=input_data.max_age,
             wait_for=input_data.wait_for,
@@ -84,7 +96,7 @@ class FirecrawlScrapeBlock(Block):
             elif f == ScrapeFormat.HTML:
                 yield "html", scrape_result.html
             elif f == ScrapeFormat.RAW_HTML:
-                yield "raw_html", scrape_result.raw_html
+                yield "raw_html", scrape_result.rawHtml
             elif f == ScrapeFormat.LINKS:
                 yield "links", scrape_result.links
             elif f == ScrapeFormat.SCREENSHOT:
@@ -92,6 +104,6 @@ class FirecrawlScrapeBlock(Block):
             elif f == ScrapeFormat.SCREENSHOT_FULL_PAGE:
                 yield "screenshot_full_page", scrape_result.screenshot
             elif f == ScrapeFormat.CHANGE_TRACKING:
-                yield "change_tracking", scrape_result.change_tracking
+                yield "change_tracking", scrape_result.changeTracking
             elif f == ScrapeFormat.JSON:
                 yield "json", scrape_result.json

autogpt_platform/backend/backend/blocks/firecrawl/search.py

@@ -1,9 +1,8 @@
+from enum import Enum
 from typing import Any
 
-from firecrawl import FirecrawlApp
-from firecrawl.v2.types import ScrapeOptions
+from firecrawl import FirecrawlApp, ScrapeOptions
 
-from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -15,10 +14,21 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-from ._format_utils import convert_to_format_options
+
+
+class ScrapeFormat(Enum):
+    MARKDOWN = "markdown"
+    HTML = "html"
+    RAW_HTML = "rawHtml"
+    LINKS = "links"
+    SCREENSHOT = "screenshot"
+    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
+    JSON = "json"
+    CHANGE_TRACKING = "changeTracking"
 
 
 class FirecrawlSearchBlock(Block):
+
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         query: str = SchemaField(description="The query to search for")
@@ -51,6 +61,7 @@ class FirecrawlSearchBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
         # Sync call
@@ -58,12 +69,11 @@ class FirecrawlSearchBlock(Block):
             input_data.query,
             limit=input_data.limit,
             scrape_options=ScrapeOptions(
-                formats=convert_to_format_options(input_data.formats) or None,
-                max_age=input_data.max_age,
-                wait_for=input_data.wait_for,
+                formats=[format.value for format in input_data.formats],
+                maxAge=input_data.max_age,
+                waitFor=input_data.wait_for,
             ),
         )
         yield "data", scrape_result
-        if hasattr(scrape_result, "web") and scrape_result.web:
-            for site in scrape_result.web:
-                yield "site", site
+        for site in scrape_result.data:
+            yield "site", site

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -1094,117 +1094,6 @@ class GmailGetThreadBlock(GmailBase):
         return thread
 
 
-async def _build_reply_message(
-    service, input_data, graph_exec_id: str, user_id: str
-) -> tuple[str, str]:
-    """
-    Builds a reply MIME message for Gmail threads.
-
-    Returns:
-        tuple: (base64-encoded raw message, threadId)
-    """
-    # Get parent message for reply context
-    parent = await asyncio.to_thread(
-        lambda: service.users()
-        .messages()
-        .get(
-            userId="me",
-            id=input_data.parentMessageId,
-            format="metadata",
-            metadataHeaders=[
-                "Subject",
-                "References",
-                "Message-ID",
-                "From",
-                "To",
-                "Cc",
-                "Reply-To",
-            ],
-        )
-        .execute()
-    )
-
-    # Build headers dictionary, preserving all values for duplicate headers
-    headers = {}
-    for h in parent.get("payload", {}).get("headers", []):
-        name = h["name"].lower()
-        value = h["value"]
-        if name in headers:
-            # For duplicate headers, keep the first occurrence (most relevant for reply context)
-            continue
-        headers[name] = value
-
-    # Determine recipients if not specified
-    if not (input_data.to or input_data.cc or input_data.bcc):
-        if input_data.replyAll:
-            recipients = [parseaddr(headers.get("from", ""))[1]]
-            recipients += [addr for _, addr in getaddresses([headers.get("to", "")])]
-            recipients += [addr for _, addr in getaddresses([headers.get("cc", "")])]
-            # Use dict.fromkeys() for O(n) deduplication while preserving order
-            input_data.to = list(dict.fromkeys(filter(None, recipients)))
-        else:
-            # Check Reply-To header first, fall back to From header
-            reply_to = headers.get("reply-to", "")
-            from_addr = headers.get("from", "")
-            sender = parseaddr(reply_to if reply_to else from_addr)[1]
-            input_data.to = [sender] if sender else []
-
-    # Set subject with Re: prefix if not already present
-    if input_data.subject:
-        subject = input_data.subject
-    else:
-        parent_subject = headers.get("subject", "").strip()
-        # Only add "Re:" if not already present (case-insensitive check)
-        if parent_subject.lower().startswith("re:"):
-            subject = parent_subject
-        else:
-            subject = f"Re: {parent_subject}" if parent_subject else "Re:"
-
-    # Build references header for proper threading
-    references = headers.get("references", "").split()
-    if headers.get("message-id"):
-        references.append(headers["message-id"])
-
-    # Create MIME message
-    msg = MIMEMultipart()
-    if input_data.to:
-        msg["To"] = ", ".join(input_data.to)
-    if input_data.cc:
-        msg["Cc"] = ", ".join(input_data.cc)
-    if input_data.bcc:
-        msg["Bcc"] = ", ".join(input_data.bcc)
-    msg["Subject"] = subject
-    if headers.get("message-id"):
-        msg["In-Reply-To"] = headers["message-id"]
-    if references:
-        msg["References"] = " ".join(references)
-
-    # Use the helper function for consistent content type handling
-    msg.attach(_make_mime_text(input_data.body, input_data.content_type))
-
-    # Handle attachments
-    for attach in input_data.attachments:
-        local_path = await store_media_file(
-            user_id=user_id,
-            graph_exec_id=graph_exec_id,
-            file=attach,
-            return_content=False,
-        )
-        abs_path = get_exec_file_path(graph_exec_id, local_path)
-        part = MIMEBase("application", "octet-stream")
-        with open(abs_path, "rb") as f:
-            part.set_payload(f.read())
-        encoders.encode_base64(part)
-        part.add_header(
-            "Content-Disposition", f"attachment; filename={Path(abs_path).name}"
-        )
-        msg.attach(part)
-
-    # Encode message
-    raw = base64.urlsafe_b64encode(msg.as_bytes()).decode("utf-8")
-    return raw, input_data.threadId
-
-
 class GmailReplyBlock(GmailBase):
     """
     Replies to Gmail threads with intelligent content type detection.
@@ -1341,144 +1230,91 @@ class GmailReplyBlock(GmailBase):
     async def _reply(
         self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
-        # Build the reply message using the shared helper
-        raw, thread_id = await _build_reply_message(
-            service, input_data, graph_exec_id, user_id
-        )
-
-        # Send the message
-        return await asyncio.to_thread(
+        parent = await asyncio.to_thread(
             lambda: service.users()
             .messages()
-            .send(userId="me", body={"threadId": thread_id, "raw": raw})
-            .execute()
-        )
-
-
-class GmailDraftReplyBlock(GmailBase):
-    """
-    Creates draft replies to Gmail threads with intelligent content type detection.
-
-    Features:
-    - Automatic HTML detection: Draft replies containing HTML tags are formatted as text/html
-    - No hard-wrap for plain text: Plain text draft replies preserve natural line flow
-    - Manual content type override: Use content_type parameter to force specific format
-    - Reply-all functionality: Option to reply to all original recipients
-    - Thread preservation: Maintains proper email threading with headers
-    - Full Unicode/emoji support with UTF-8 encoding
-    """
-
-    class Input(BlockSchema):
-        credentials: GoogleCredentialsInput = GoogleCredentialsField(
-            [
-                "https://www.googleapis.com/auth/gmail.modify",
-                "https://www.googleapis.com/auth/gmail.readonly",
-            ]
-        )
-        threadId: str = SchemaField(description="Thread ID to reply in")
-        parentMessageId: str = SchemaField(
-            description="ID of the message being replied to"
-        )
-        to: list[str] = SchemaField(description="To recipients", default_factory=list)
-        cc: list[str] = SchemaField(description="CC recipients", default_factory=list)
-        bcc: list[str] = SchemaField(description="BCC recipients", default_factory=list)
-        replyAll: bool = SchemaField(
-            description="Reply to all original recipients", default=False
-        )
-        subject: str = SchemaField(description="Email subject", default="")
-        body: str = SchemaField(description="Email body (plain text or HTML)")
-        content_type: Optional[Literal["auto", "plain", "html"]] = SchemaField(
-            description="Content type: 'auto' (default - detects HTML), 'plain', or 'html'",
-            default=None,
-            advanced=True,
-        )
-        attachments: list[MediaFileType] = SchemaField(
-            description="Files to attach", default_factory=list, advanced=True
-        )
-
-    class Output(BlockSchema):
-        draftId: str = SchemaField(description="Created draft ID")
-        messageId: str = SchemaField(description="Draft message ID")
-        threadId: str = SchemaField(description="Thread ID")
-        status: str = SchemaField(description="Draft creation status")
-        error: str = SchemaField(description="Error message if any")
-
-    def __init__(self):
-        super().__init__(
-            id="d7a9f3e2-8b4c-4d6f-9e1a-3c5b7f8d2a6e",
-            description="Create draft replies to Gmail threads with automatic HTML detection and proper text formatting. Plain text draft replies maintain natural paragraph flow without 78-character line wrapping. HTML content is automatically detected and formatted correctly.",
-            categories={BlockCategory.COMMUNICATION},
-            input_schema=GmailDraftReplyBlock.Input,
-            output_schema=GmailDraftReplyBlock.Output,
-            disabled=not GOOGLE_OAUTH_IS_CONFIGURED,
-            test_input={
-                "threadId": "t1",
-                "parentMessageId": "m1",
-                "body": "Thanks for your message. I'll review and get back to you.",
-                "replyAll": False,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("draftId", "draft1"),
-                ("messageId", "m2"),
-                ("threadId", "t1"),
-                ("status", "draft_created"),
-            ],
-            test_mock={
-                "_create_draft_reply": lambda *args, **kwargs: {
-                    "id": "draft1",
-                    "message": {"id": "m2", "threadId": "t1"},
-                }
-            },
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: GoogleCredentials,
-        graph_exec_id: str,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        service = self._build_service(credentials, **kwargs)
-        draft = await self._create_draft_reply(
-            service,
-            input_data,
-            graph_exec_id,
-            user_id,
-        )
-        yield "draftId", draft["id"]
-        yield "messageId", draft["message"]["id"]
-        yield "threadId", draft["message"].get("threadId", input_data.threadId)
-        yield "status", "draft_created"
-
-    async def _create_draft_reply(
-        self, service, input_data: Input, graph_exec_id: str, user_id: str
-    ) -> dict:
-        # Build the reply message using the shared helper
-        raw, thread_id = await _build_reply_message(
-            service, input_data, graph_exec_id, user_id
-        )
-
-        # Create draft with proper thread association
-        draft = await asyncio.to_thread(
-            lambda: service.users()
-            .drafts()
-            .create(
+            .get(
                 userId="me",
-                body={
-                    "message": {
-                        "threadId": thread_id,
-                        "raw": raw,
-                    }
-                },
+                id=input_data.parentMessageId,
+                format="metadata",
+                metadataHeaders=[
+                    "Subject",
+                    "References",
+                    "Message-ID",
+                    "From",
+                    "To",
+                    "Cc",
+                    "Reply-To",
+                ],
             )
             .execute()
         )
 
-        return draft
+        headers = {
+            h["name"].lower(): h["value"]
+            for h in parent.get("payload", {}).get("headers", [])
+        }
+        if not (input_data.to or input_data.cc or input_data.bcc):
+            if input_data.replyAll:
+                recipients = [parseaddr(headers.get("from", ""))[1]]
+                recipients += [
+                    addr for _, addr in getaddresses([headers.get("to", "")])
+                ]
+                recipients += [
+                    addr for _, addr in getaddresses([headers.get("cc", "")])
+                ]
+                dedup: list[str] = []
+                for r in recipients:
+                    if r and r not in dedup:
+                        dedup.append(r)
+                input_data.to = dedup
+            else:
+                sender = parseaddr(headers.get("reply-to", headers.get("from", "")))[1]
+                input_data.to = [sender] if sender else []
+        subject = input_data.subject or (f"Re: {headers.get('subject', '')}".strip())
+        references = headers.get("references", "").split()
+        if headers.get("message-id"):
+            references.append(headers["message-id"])
+
+        msg = MIMEMultipart()
+        if input_data.to:
+            msg["To"] = ", ".join(input_data.to)
+        if input_data.cc:
+            msg["Cc"] = ", ".join(input_data.cc)
+        if input_data.bcc:
+            msg["Bcc"] = ", ".join(input_data.bcc)
+        msg["Subject"] = subject
+        if headers.get("message-id"):
+            msg["In-Reply-To"] = headers["message-id"]
+        if references:
+            msg["References"] = " ".join(references)
+        # Use the new helper function for consistent content type handling
+        msg.attach(_make_mime_text(input_data.body, input_data.content_type))
+
+        for attach in input_data.attachments:
+            local_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
+                file=attach,
+                return_content=False,
+            )
+            abs_path = get_exec_file_path(graph_exec_id, local_path)
+            part = MIMEBase("application", "octet-stream")
+            with open(abs_path, "rb") as f:
+                part.set_payload(f.read())
+            encoders.encode_base64(part)
+            part.add_header(
+                "Content-Disposition", f"attachment; filename={Path(abs_path).name}"
+            )
+            msg.attach(part)
+
+        raw = base64.urlsafe_b64encode(msg.as_bytes()).decode("utf-8")
+        return await asyncio.to_thread(
+            lambda: service.users()
+            .messages()
+            .send(userId="me", body={"threadId": input_data.threadId, "raw": raw})
+            .execute()
+        )
 
 
 class GmailGetProfileBlock(GmailBase):

autogpt_platform/backend/backend/blocks/io.py

@@ -10,6 +10,7 @@ from backend.util.settings import Config
 from backend.util.text import TextFormatter
 from backend.util.type import LongTextType, MediaFileType, ShortTextType
 
+formatter = TextFormatter()
 config = Config()
 
 
@@ -131,11 +132,6 @@ class AgentOutputBlock(Block):
             default="",
             advanced=True,
         )
-        escape_html: bool = SchemaField(
-            default=False,
-            advanced=True,
-            description="Whether to escape special characters in the inserted values to be HTML-safe. Enable for HTML output, disable for plain text.",
-        )
         advanced: bool = SchemaField(
             description="Whether to treat the output as advanced.",
             default=False,
@@ -197,7 +193,6 @@ class AgentOutputBlock(Block):
         """
         if input_data.format:
             try:
-                formatter = TextFormatter(autoescape=input_data.escape_html)
                 yield "output", formatter.format_string(
                     input_data.format, {input_data.name: input_data.value}
                 )
@@ -554,89 +549,6 @@ class AgentToggleInputBlock(AgentInputBlock):
         )
 
 
-class AgentTableInputBlock(AgentInputBlock):
-    """
-    This block allows users to input data in a table format.
-
-    Configure the table columns at build time, then users can input
-    rows of data at runtime. Each row is output as a dictionary
-    with column names as keys.
-    """
-
-    class Input(AgentInputBlock.Input):
-        value: Optional[list[dict[str, Any]]] = SchemaField(
-            description="The table data as a list of dictionaries.",
-            default=None,
-            advanced=False,
-            title="Default Value",
-        )
-        column_headers: list[str] = SchemaField(
-            description="Column headers for the table.",
-            default_factory=lambda: ["Column 1", "Column 2", "Column 3"],
-            advanced=False,
-            title="Column Headers",
-        )
-
-        def generate_schema(self):
-            """Generate schema for the value field with table format."""
-            schema = super().generate_schema()
-            schema["type"] = "array"
-            schema["format"] = "table"
-            schema["items"] = {
-                "type": "object",
-                "properties": {
-                    header: {"type": "string"}
-                    for header in (
-                        self.column_headers or ["Column 1", "Column 2", "Column 3"]
-                    )
-                },
-            }
-            if self.value is not None:
-                schema["default"] = self.value
-            return schema
-
-    class Output(AgentInputBlock.Output):
-        result: list[dict[str, Any]] = SchemaField(
-            description="The table data as a list of dictionaries with headers as keys."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="5603b273-f41e-4020-af7d-fbc9c6a8d928",
-            description="Block for table data input with customizable headers.",
-            disabled=not config.enable_agent_input_subtype_blocks,
-            input_schema=AgentTableInputBlock.Input,
-            output_schema=AgentTableInputBlock.Output,
-            test_input=[
-                {
-                    "name": "test_table",
-                    "column_headers": ["Name", "Age", "City"],
-                    "value": [
-                        {"Name": "John", "Age": "30", "City": "New York"},
-                        {"Name": "Jane", "Age": "25", "City": "London"},
-                    ],
-                    "description": "Example table input",
-                }
-            ],
-            test_output=[
-                (
-                    "result",
-                    [
-                        {"Name": "John", "Age": "30", "City": "New York"},
-                        {"Name": "Jane", "Age": "25", "City": "London"},
-                    ],
-                )
-            ],
-        )
-
-    async def run(self, input_data: Input, *args, **kwargs) -> BlockOutput:
-        """
-        Yields the table data as a list of dictionaries.
-        """
-        # Pass through the value, defaulting to empty list if None
-        yield "result", input_data.value if input_data.value is not None else []
-
-
 IO_BLOCK_IDs = [
     AgentInputBlock().id,
     AgentOutputBlock().id,
@@ -648,5 +560,4 @@ IO_BLOCK_IDs = [
     AgentFileInputBlock().id,
     AgentDropdownInputBlock().id,
     AgentToggleInputBlock().id,
-    AgentTableInputBlock().id,
 ]

autogpt_platform/backend/backend/blocks/iteration.py

@@ -54,43 +54,20 @@ class StepThroughItemsBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add limits to prevent DoS from large iterations
-        MAX_ITEMS = 10000  # Maximum items to iterate
-        MAX_ITEM_SIZE = 1024 * 1024  # 1MB per item
-
         for data in [input_data.items, input_data.items_object, input_data.items_str]:
             if not data:
                 continue
-
-            # Limit string size before parsing
             if isinstance(data, str):
-                if len(data) > MAX_ITEM_SIZE:
-                    raise ValueError(
-                        f"Input too large: {len(data)} bytes > {MAX_ITEM_SIZE} bytes"
-                    )
                 items = json.loads(data)
             else:
                 items = data
-
-            # Check total item count
-            if isinstance(items, (list, dict)):
-                if len(items) > MAX_ITEMS:
-                    raise ValueError(f"Too many items: {len(items)} > {MAX_ITEMS}")
-
-            iteration_count = 0
             if isinstance(items, dict):
                 # If items is a dictionary, iterate over its values
-                for key, value in items.items():
-                    if iteration_count >= MAX_ITEMS:
-                        break
-                    yield "item", value
-                    yield "key", key  # Fixed: should yield key, not item
-                    iteration_count += 1
+                for item in items.values():
+                    yield "item", item
+                    yield "key", item
             else:
                 # If items is a list, iterate over the list
                 for index, item in enumerate(items):
-                    if iteration_count >= MAX_ITEMS:
-                        break
                     yield "item", item
                     yield "key", index
-                    iteration_count += 1

autogpt_platform/backend/backend/blocks/jina/fact_checker.py

@@ -1,8 +1,5 @@
-from typing import List
 from urllib.parse import quote
 
-from typing_extensions import TypedDict
-
 from backend.blocks.jina._auth import (
     JinaCredentials,
     JinaCredentialsField,
@@ -13,12 +10,6 @@ from backend.data.model import SchemaField
 from backend.util.request import Requests
 
 
-class Reference(TypedDict):
-    url: str
-    keyQuote: str
-    isSupportive: bool
-
-
 class FactCheckerBlock(Block):
     class Input(BlockSchema):
         statement: str = SchemaField(
@@ -32,10 +23,6 @@ class FactCheckerBlock(Block):
         )
         result: bool = SchemaField(description="The result of the factuality check")
         reason: str = SchemaField(description="The reason for the factuality result")
-        references: List[Reference] = SchemaField(
-            description="List of references supporting or contradicting the statement",
-            default=[],
-        )
         error: str = SchemaField(description="Error message if the check fails")
 
     def __init__(self):
@@ -66,11 +53,5 @@ class FactCheckerBlock(Block):
             yield "factuality", data["factuality"]
             yield "result", data["result"]
             yield "reason", data["reason"]
-
-            # Yield references if present in the response
-            if "references" in data:
-                yield "references", data["references"]
-            else:
-                yield "references", []
         else:
             raise RuntimeError(f"Expected 'data' key not found in response: {data}")

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

@@ -37,5 +37,5 @@ class Project(BaseModel):
     name: str
     description: str
     priority: int
-    progress: float
-    content: str | None
+    progress: int
+    content: str

autogpt_platform/backend/backend/blocks/llm.py

@@ -1,9 +1,5 @@
-# This file contains a lot of prompt block strings that would trigger "line too long"
-# flake8: noqa: E501
 import ast
 import logging
-import re
-import secrets
 from abc import ABC
 from enum import Enum, EnumMeta
 from json import JSONDecodeError
@@ -31,7 +27,7 @@ from backend.util.prompt import compress_prompt, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
-fmt = TextFormatter(autoescape=False)
+fmt = TextFormatter()
 
 LLMProviderName = Literal[
     ProviderName.AIML_API,
@@ -101,8 +97,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
     CLAUDE_4_OPUS = "claude-opus-4-20250514"
     CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
-    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
-    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
     CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
     CLAUDE_3_5_SONNET = "claude-3-5-sonnet-latest"
     CLAUDE_3_5_HAIKU = "claude-3-5-haiku-latest"
@@ -210,19 +204,13 @@ MODEL_METADATA = {
         "anthropic", 200000, 32000
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 32000
+        "anthropic", 200000, 8192
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 8192
     ),  # claude-4-sonnet-20250514
-    LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
-    ),  # claude-sonnet-4-5-20250929
-    LlmModel.CLAUDE_4_5_HAIKU: ModelMetadata(
-        "anthropic", 200000, 64000
-    ),  # claude-haiku-4-5-20251001
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 64000
+        "anthropic", 200000, 8192
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_5_SONNET: ModelMetadata(
         "anthropic", 200000, 8192
@@ -394,9 +382,7 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
     return None
 
 
-def get_parallel_tool_calls_param(
-    llm_model: LlmModel, parallel_tool_calls: bool | None
-):
+def get_parallel_tool_calls_param(llm_model: LlmModel, parallel_tool_calls):
     """Get the appropriate parallel_tool_calls parameter for OpenAI-compatible APIs."""
     if llm_model.startswith("o") or parallel_tool_calls is None:
         return openai.NOT_GIVEN
@@ -407,8 +393,8 @@ async def llm_call(
     credentials: APIKeyCredentials,
     llm_model: LlmModel,
     prompt: list[dict],
+    json_format: bool,
     max_tokens: int | None,
-    force_json_output: bool = False,
     tools: list[dict] | None = None,
     ollama_host: str = "localhost:11434",
     parallel_tool_calls=None,
@@ -421,7 +407,7 @@ async def llm_call(
         credentials: The API key credentials to use.
         llm_model: The LLM model to use.
         prompt: The prompt to send to the LLM.
-        force_json_output: Whether the response should be in JSON format.
+        json_format: Whether the response should be in JSON format.
         max_tokens: The maximum number of tokens to generate in the chat completion.
         tools: The tools to use in the chat completion.
         ollama_host: The host for ollama to use.
@@ -460,7 +446,7 @@ async def llm_call(
             llm_model, parallel_tool_calls
         )
 
-        if force_json_output:
+        if json_format:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
@@ -573,7 +559,7 @@ async def llm_call(
             raise ValueError("Groq does not support tools.")
 
         client = AsyncGroq(api_key=credentials.api_key.get_secret_value())
-        response_format = {"type": "json_object"} if force_json_output else None
+        response_format = {"type": "json_object"} if json_format else None
         response = await client.chat.completions.create(
             model=llm_model.value,
             messages=prompt,  # type: ignore
@@ -731,7 +717,7 @@ async def llm_call(
         )
 
         response_format = None
-        if force_json_output:
+        if json_format:
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
@@ -794,17 +780,6 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             description="The language model to use for answering the prompt.",
             advanced=False,
         )
-        force_json_output: bool = SchemaField(
-            title="Restrict LLM to pure JSON output",
-            default=False,
-            description=(
-                "Whether to force the LLM to produce a JSON-only response. "
-                "This can increase the block's reliability, "
-                "but may also reduce the quality of the response "
-                "because it prohibits the LLM from reasoning "
-                "before providing its JSON response."
-            ),
-        )
         credentials: AICredentials = AICredentialsField()
         sys_prompt: str = SchemaField(
             title="System Prompt",
@@ -873,18 +848,17 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 "llm_call": lambda *args, **kwargs: LLMResponse(
                     raw_response="",
                     prompt=[""],
-                    response=(
-                        '<json_output id="test123456">{\n'
-                        '  "key1": "key1Value",\n'
-                        '  "key2": "key2Value"\n'
-                        "}</json_output>"
+                    response=json.dumps(
+                        {
+                            "key1": "key1Value",
+                            "key2": "key2Value",
+                        }
                     ),
                     tool_calls=None,
                     prompt_tokens=0,
                     completion_tokens=0,
                     reasoning=None,
-                ),
-                "get_collision_proof_output_tag_id": lambda *args: "test123456",
+                )
             },
         )
 
@@ -893,9 +867,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         credentials: APIKeyCredentials,
         llm_model: LlmModel,
         prompt: list[dict],
+        json_format: bool,
+        compress_prompt_to_fit: bool,
         max_tokens: int | None,
-        force_json_output: bool = False,
-        compress_prompt_to_fit: bool = True,
         tools: list[dict] | None = None,
         ollama_host: str = "localhost:11434",
     ) -> LLMResponse:
@@ -908,8 +882,8 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             credentials=credentials,
             llm_model=llm_model,
             prompt=prompt,
+            json_format=json_format,
             max_tokens=max_tokens,
-            force_json_output=force_json_output,
             tools=tools,
             ollama_host=ollama_host,
             compress_prompt_to_fit=compress_prompt_to_fit,
@@ -921,6 +895,10 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         logger.debug(f"Calling LLM with input data: {input_data}")
         prompt = [json.to_dict(p) for p in input_data.conversation_history]
 
+        def trim_prompt(s: str) -> str:
+            lines = s.strip().split("\n")
+            return "\n".join([line.strip().lstrip("|") for line in lines])
+
         values = input_data.prompt_values
         if values:
             input_data.prompt = fmt.format_string(input_data.prompt, values)
@@ -929,15 +907,27 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
         if input_data.sys_prompt:
             prompt.append({"role": "system", "content": input_data.sys_prompt})
 
-        # Use a one-time unique tag to prevent collisions with user/LLM content
-        output_tag_id = self.get_collision_proof_output_tag_id()
-        output_tag_start = f'<json_output id="{output_tag_id}">'
         if input_data.expected_format:
-            sys_prompt = self.response_format_instructions(
-                input_data.expected_format,
-                list_mode=input_data.list_result,
-                pure_json_mode=input_data.force_json_output,
-                output_tag_start=output_tag_start,
+            expected_format = [
+                f'"{k}": "{v}"' for k, v in input_data.expected_format.items()
+            ]
+            if input_data.list_result:
+                format_prompt = (
+                    f'"results": [\n  {{\n  {", ".join(expected_format)}\n  }}\n]'
+                )
+            else:
+                format_prompt = "\n  ".join(expected_format)
+
+            sys_prompt = trim_prompt(
+                f"""
+                  |Reply strictly only in the following JSON format:
+                  |{{
+                  |  {format_prompt}
+                  |}}
+                  |
+                  |Ensure the response is valid JSON. Do not include any additional text outside of the JSON.
+                  |If you cannot provide all the keys, provide an empty string for the values you cannot answer.
+                """
             )
             prompt.append({"role": "system", "content": sys_prompt})
 
@@ -955,21 +945,18 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             except JSONDecodeError as e:
                 return f"JSON decode error: {e}"
 
-        error_feedback_message = ""
+        logger.debug(f"LLM request: {prompt}")
+        retry_prompt = ""
         llm_model = input_data.model
 
         for retry_count in range(input_data.retry):
-            logger.debug(f"LLM request: {prompt}")
             try:
                 llm_response = await self.llm_call(
                     credentials=credentials,
                     llm_model=llm_model,
                     prompt=prompt,
                     compress_prompt_to_fit=input_data.compress_prompt_to_fit,
-                    force_json_output=(
-                        input_data.force_json_output
-                        and bool(input_data.expected_format)
-                    ),
+                    json_format=bool(input_data.expected_format),
                     ollama_host=input_data.ollama_host,
                     max_tokens=input_data.max_tokens,
                 )
@@ -983,55 +970,16 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 logger.debug(f"LLM attempt-{retry_count} response: {response_text}")
 
                 if input_data.expected_format:
-                    try:
-                        response_obj = self.get_json_from_response(
-                            response_text,
-                            pure_json_mode=input_data.force_json_output,
-                            output_tag_start=output_tag_start,
-                        )
-                    except (ValueError, JSONDecodeError) as parse_error:
-                        censored_response = re.sub(r"[A-Za-z0-9]", "*", response_text)
-                        response_snippet = (
-                            f"{censored_response[:50]}...{censored_response[-30:]}"
-                        )
-                        logger.warning(
-                            f"Error getting JSON from LLM response: {parse_error}\n\n"
-                            f"Response start+end: `{response_snippet}`"
-                        )
-                        prompt.append({"role": "assistant", "content": response_text})
 
-                        error_feedback_message = self.invalid_response_feedback(
-                            parse_error,
-                            was_parseable=False,
-                            list_mode=input_data.list_result,
-                            pure_json_mode=input_data.force_json_output,
-                            output_tag_start=output_tag_start,
-                        )
-                        prompt.append(
-                            {"role": "user", "content": error_feedback_message}
-                        )
-                        continue
+                    response_obj = json.loads(response_text)
 
-                    # Handle object response for `force_json_output`+`list_result`
                     if input_data.list_result and isinstance(response_obj, dict):
-                        if "results" in response_obj and isinstance(
-                            response_obj["results"], list
-                        ):
-                            response_obj = response_obj["results"]
-                        else:
-                            error_feedback_message = (
-                                "Expected an array of objects in the 'results' key, "
-                                f"but got: {response_obj}"
-                            )
-                            prompt.append(
-                                {"role": "assistant", "content": response_text}
-                            )
-                            prompt.append(
-                                {"role": "user", "content": error_feedback_message}
-                            )
-                            continue
+                        if "results" in response_obj:
+                            response_obj = response_obj.get("results", [])
+                        elif len(response_obj) == 1:
+                            response_obj = list(response_obj.values())
 
-                    validation_errors = "\n".join(
+                    response_error = "\n".join(
                         [
                             validation_error
                             for response_item in (
@@ -1043,7 +991,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                         ]
                     )
 
-                    if not validation_errors:
+                    if not response_error:
                         self.merge_stats(
                             NodeExecutionStats(
                                 llm_call_count=retry_count + 1,
@@ -1053,16 +1001,6 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                         yield "response", response_obj
                         yield "prompt", self.prompt
                         return
-
-                    prompt.append({"role": "assistant", "content": response_text})
-                    error_feedback_message = self.invalid_response_feedback(
-                        validation_errors,
-                        was_parseable=True,
-                        list_mode=input_data.list_result,
-                        pure_json_mode=input_data.force_json_output,
-                        output_tag_start=output_tag_start,
-                    )
-                    prompt.append({"role": "user", "content": error_feedback_message})
                 else:
                     self.merge_stats(
                         NodeExecutionStats(
@@ -1073,6 +1011,21 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     yield "response", {"response": response_text}
                     yield "prompt", self.prompt
                     return
+
+                retry_prompt = trim_prompt(
+                    f"""
+                  |This is your previous error response:
+                  |--
+                  |{response_text}
+                  |--
+                  |
+                  |And this is the error:
+                  |--
+                  |{response_error}
+                  |--
+                """
+                )
+                prompt.append({"role": "user", "content": retry_prompt})
             except Exception as e:
                 logger.exception(f"Error calling LLM: {e}")
                 if (
@@ -1085,133 +1038,9 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     logger.debug(
                         f"Reducing max_tokens to {input_data.max_tokens} for next attempt"
                     )
-                    # Don't add retry prompt for token limit errors,
-                    # just retry with lower maximum output tokens
+                retry_prompt = f"Error calling LLM: {e}"
 
-                error_feedback_message = f"Error calling LLM: {e}"
-
-        raise RuntimeError(error_feedback_message)
-
-    def response_format_instructions(
-        self,
-        expected_object_format: dict[str, str],
-        *,
-        list_mode: bool,
-        pure_json_mode: bool,
-        output_tag_start: str,
-    ) -> str:
-        expected_output_format = json.dumps(expected_object_format, indent=2)
-        output_type = "object" if not list_mode else "array"
-        outer_output_type = "object" if pure_json_mode else output_type
-
-        if output_type == "array":
-            indented_obj_format = expected_output_format.replace("\n", "\n  ")
-            expected_output_format = f"[\n  {indented_obj_format},\n  ...\n]"
-            if pure_json_mode:
-                indented_list_format = expected_output_format.replace("\n", "\n  ")
-                expected_output_format = (
-                    "{\n"
-                    '  "reasoning": "... (optional)",\n'  # for better performance
-                    f'  "results": {indented_list_format}\n'
-                    "}"
-                )
-
-        # Preserve indentation in prompt
-        expected_output_format = expected_output_format.replace("\n", "\n|")
-
-        # Prepare prompt
-        if not pure_json_mode:
-            expected_output_format = (
-                f"{output_tag_start}\n{expected_output_format}\n</json_output>"
-            )
-
-        instructions = f"""
-        |In your response you MUST include a valid JSON {outer_output_type} strictly following this format:
-        |{expected_output_format}
-        |
-        |If you cannot provide all the keys, you MUST provide an empty string for the values you cannot answer.
-        """.strip()
-
-        if not pure_json_mode:
-            instructions += f"""
-            |
-            |You MUST enclose your final JSON answer in {output_tag_start}...</json_output> tags, even if the user specifies a different tag.
-            |There MUST be exactly ONE {output_tag_start}...</json_output> block in your response, which MUST ONLY contain the JSON {outer_output_type} and nothing else. Other text outside this block is allowed.
-            """.strip()
-
-        return trim_prompt(instructions)
-
-    def invalid_response_feedback(
-        self,
-        error,
-        *,
-        was_parseable: bool,
-        list_mode: bool,
-        pure_json_mode: bool,
-        output_tag_start: str,
-    ) -> str:
-        outer_output_type = "object" if not list_mode or pure_json_mode else "array"
-
-        if was_parseable:
-            complaint = f"Your previous response did not match the expected {outer_output_type} format."
-        else:
-            complaint = f"Your previous response did not contain a parseable JSON {outer_output_type}."
-
-        indented_parse_error = str(error).replace("\n", "\n|")
-
-        instruction = (
-            f"Please provide a {output_tag_start}...</json_output> block containing a"
-            if not pure_json_mode
-            else "Please provide a"
-        ) + f" valid JSON {outer_output_type} that matches the expected format."
-
-        return trim_prompt(
-            f"""
-            |{complaint}
-            |
-            |{indented_parse_error}
-            |
-            |{instruction}
-        """
-        )
-
-    def get_json_from_response(
-        self, response_text: str, *, pure_json_mode: bool, output_tag_start: str
-    ) -> dict[str, Any] | list[dict[str, Any]]:
-        if pure_json_mode:
-            # Handle pure JSON responses
-            try:
-                return json.loads(response_text)
-            except JSONDecodeError as first_parse_error:
-                # If that didn't work, try finding the { and } to deal with possible ```json fences etc.
-                json_start = response_text.find("{")
-                json_end = response_text.rfind("}")
-                try:
-                    return json.loads(response_text[json_start : json_end + 1])
-                except JSONDecodeError:
-                    # Raise the original error, as it's more likely to be relevant
-                    raise first_parse_error from None
-
-        if output_tag_start not in response_text:
-            raise ValueError(
-                "Response does not contain the expected "
-                f"{output_tag_start}...</json_output> block."
-            )
-        json_output = (
-            response_text.split(output_tag_start, 1)[1]
-            .rsplit("</json_output>", 1)[0]
-            .strip()
-        )
-        return json.loads(json_output)
-
-    def get_collision_proof_output_tag_id(self) -> str:
-        return secrets.token_hex(8)
-
-
-def trim_prompt(s: str) -> str:
-    """Removes indentation up to and including `|` from a multi-line prompt."""
-    lines = s.strip().split("\n")
-    return "\n".join([line.strip().lstrip("|") for line in lines])
+        raise RuntimeError(retry_prompt)
 
 
 class AITextGeneratorBlock(AIBlockBase):
@@ -1408,27 +1237,11 @@ class AITextSummarizerBlock(AIBlockBase):
 
     @staticmethod
     def _split_text(text: str, max_tokens: int, overlap: int) -> list[str]:
-        # Security fix: Add validation to prevent DoS attacks
-        # Limit text size to prevent memory exhaustion
-        MAX_TEXT_LENGTH = 1_000_000  # 1MB character limit
-        MAX_CHUNKS = 100  # Maximum number of chunks to prevent excessive memory use
-
-        if len(text) > MAX_TEXT_LENGTH:
-            text = text[:MAX_TEXT_LENGTH]
-
-        # Ensure chunk_size is at least 1 to prevent infinite loops
-        chunk_size = max(1, max_tokens - overlap)
-
-        # Ensure overlap is less than max_tokens to prevent invalid configurations
-        if overlap >= max_tokens:
-            overlap = max(0, max_tokens - 1)
-
         words = text.split()
         chunks = []
+        chunk_size = max_tokens - overlap
 
         for i in range(0, len(words), chunk_size):
-            if len(chunks) >= MAX_CHUNKS:
-                break  # Limit the number of chunks to prevent memory exhaustion
             chunk = " ".join(words[i : i + max_tokens])
             chunks.append(chunk)
 

autogpt_platform/backend/backend/blocks/notion/_api.py

@@ -1,536 +0,0 @@
-"""
-Notion API helper functions and client for making authenticated requests.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from backend.data.model import OAuth2Credentials
-from backend.util.request import Requests
-
-NOTION_VERSION = "2022-06-28"
-
-
-class NotionAPIException(Exception):
-    """Exception raised for Notion API errors."""
-
-    def __init__(self, message: str, status_code: int):
-        super().__init__(message)
-        self.status_code = status_code
-
-
-class NotionClient:
-    """Client for interacting with the Notion API."""
-
-    def __init__(self, credentials: OAuth2Credentials):
-        self.credentials = credentials
-        self.headers = {
-            "Authorization": credentials.auth_header(),
-            "Notion-Version": NOTION_VERSION,
-            "Content-Type": "application/json",
-        }
-        self.requests = Requests()
-
-    async def get_page(self, page_id: str) -> dict:
-        """
-        Fetch a page by ID.
-
-        Args:
-            page_id: The ID of the page to fetch.
-
-        Returns:
-            The page object from Notion API.
-        """
-        url = f"https://api.notion.com/v1/pages/{page_id}"
-        response = await self.requests.get(url, headers=self.headers)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to fetch page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def get_blocks(self, block_id: str, recursive: bool = True) -> List[dict]:
-        """
-        Fetch all blocks from a page or block.
-
-        Args:
-            block_id: The ID of the page or block to fetch children from.
-            recursive: Whether to fetch nested blocks recursively.
-
-        Returns:
-            List of block objects.
-        """
-        blocks = []
-        cursor = None
-
-        while True:
-            url = f"https://api.notion.com/v1/blocks/{block_id}/children"
-            params = {"page_size": 100}
-            if cursor:
-                params["start_cursor"] = cursor
-
-            response = await self.requests.get(url, headers=self.headers, params=params)
-
-            if not response.ok:
-                raise NotionAPIException(
-                    f"Failed to fetch blocks: {response.status} - {response.text()}",
-                    response.status,
-                )
-
-            data = response.json()
-            current_blocks = data.get("results", [])
-
-            # If recursive, fetch children for blocks that have them
-            if recursive:
-                for block in current_blocks:
-                    if block.get("has_children"):
-                        block["children"] = await self.get_blocks(
-                            block["id"], recursive=True
-                        )
-
-            blocks.extend(current_blocks)
-
-            if not data.get("has_more"):
-                break
-            cursor = data.get("next_cursor")
-
-        return blocks
-
-    async def query_database(
-        self,
-        database_id: str,
-        filter_obj: Optional[dict] = None,
-        sorts: Optional[List[dict]] = None,
-        page_size: int = 100,
-    ) -> dict:
-        """
-        Query a database with optional filters and sorts.
-
-        Args:
-            database_id: The ID of the database to query.
-            filter_obj: Optional filter object for the query.
-            sorts: Optional list of sort objects.
-            page_size: Number of results per page.
-
-        Returns:
-            Query results including pages and pagination info.
-        """
-        url = f"https://api.notion.com/v1/databases/{database_id}/query"
-
-        payload: Dict[str, Any] = {"page_size": page_size}
-        if filter_obj:
-            payload["filter"] = filter_obj
-        if sorts:
-            payload["sorts"] = sorts
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to query database: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def create_page(
-        self,
-        parent: dict,
-        properties: dict,
-        children: Optional[List[dict]] = None,
-        icon: Optional[dict] = None,
-        cover: Optional[dict] = None,
-    ) -> dict:
-        """
-        Create a new page.
-
-        Args:
-            parent: Parent object (page_id or database_id).
-            properties: Page properties.
-            children: Optional list of block children.
-            icon: Optional icon object.
-            cover: Optional cover object.
-
-        Returns:
-            The created page object.
-        """
-        url = "https://api.notion.com/v1/pages"
-
-        payload: Dict[str, Any] = {"parent": parent, "properties": properties}
-
-        if children:
-            payload["children"] = children
-        if icon:
-            payload["icon"] = icon
-        if cover:
-            payload["cover"] = cover
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to create page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def update_page(self, page_id: str, properties: dict) -> dict:
-        """
-        Update a page's properties.
-
-        Args:
-            page_id: The ID of the page to update.
-            properties: Properties to update.
-
-        Returns:
-            The updated page object.
-        """
-        url = f"https://api.notion.com/v1/pages/{page_id}"
-
-        response = await self.requests.patch(
-            url, headers=self.headers, json={"properties": properties}
-        )
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to update page: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def append_blocks(self, block_id: str, children: List[dict]) -> dict:
-        """
-        Append blocks to a page or block.
-
-        Args:
-            block_id: The ID of the page or block to append to.
-            children: List of block objects to append.
-
-        Returns:
-            Response with the created blocks.
-        """
-        url = f"https://api.notion.com/v1/blocks/{block_id}/children"
-
-        response = await self.requests.patch(
-            url, headers=self.headers, json={"children": children}
-        )
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Failed to append blocks: {response.status} - {response.text()}",
-                response.status,
-            )
-
-        return response.json()
-
-    async def search(
-        self,
-        query: str = "",
-        filter_obj: Optional[dict] = None,
-        sort: Optional[dict] = None,
-        page_size: int = 100,
-    ) -> dict:
-        """
-        Search for pages and databases.
-
-        Args:
-            query: Search query text.
-            filter_obj: Optional filter object.
-            sort: Optional sort object.
-            page_size: Number of results per page.
-
-        Returns:
-            Search results.
-        """
-        url = "https://api.notion.com/v1/search"
-
-        payload: Dict[str, Any] = {"page_size": page_size}
-        if query:
-            payload["query"] = query
-        if filter_obj:
-            payload["filter"] = filter_obj
-        if sort:
-            payload["sort"] = sort
-
-        response = await self.requests.post(url, headers=self.headers, json=payload)
-
-        if not response.ok:
-            raise NotionAPIException(
-                f"Search failed: {response.status} - {response.text()}", response.status
-            )
-
-        return response.json()
-
-
-# Conversion helper functions
-
-
-def parse_rich_text(rich_text_array: List[dict]) -> str:
-    """
-    Extract plain text from a Notion rich text array.
-
-    Args:
-        rich_text_array: Array of rich text objects from Notion.
-
-    Returns:
-        Plain text string.
-    """
-    if not rich_text_array:
-        return ""
-
-    text_parts = []
-    for text_obj in rich_text_array:
-        if "plain_text" in text_obj:
-            text_parts.append(text_obj["plain_text"])
-
-    return "".join(text_parts)
-
-
-def rich_text_to_markdown(rich_text_array: List[dict]) -> str:
-    """
-    Convert Notion rich text array to markdown with formatting.
-
-    Args:
-        rich_text_array: Array of rich text objects from Notion.
-
-    Returns:
-        Markdown formatted string.
-    """
-    if not rich_text_array:
-        return ""
-
-    markdown_parts = []
-
-    for text_obj in rich_text_array:
-        text = text_obj.get("plain_text", "")
-        annotations = text_obj.get("annotations", {})
-
-        # Apply formatting based on annotations
-        if annotations.get("code"):
-            text = f"`{text}`"
-        else:
-            if annotations.get("bold"):
-                text = f"**{text}**"
-            if annotations.get("italic"):
-                text = f"*{text}*"
-            if annotations.get("strikethrough"):
-                text = f"~~{text}~~"
-            if annotations.get("underline"):
-                text = f"<u>{text}</u>"
-
-        # Handle links
-        if text_obj.get("href"):
-            text = f"[{text}]({text_obj['href']})"
-
-        markdown_parts.append(text)
-
-    return "".join(markdown_parts)
-
-
-def block_to_markdown(block: dict, indent_level: int = 0) -> str:
-    """
-    Convert a single Notion block to markdown.
-
-    Args:
-        block: Block object from Notion API.
-        indent_level: Current indentation level for nested blocks.
-
-    Returns:
-        Markdown string representation of the block.
-    """
-    block_type = block.get("type")
-    indent = "  " * indent_level
-    markdown_lines = []
-
-    # Handle different block types
-    if block_type == "paragraph":
-        text = rich_text_to_markdown(block["paragraph"].get("rich_text", []))
-        if text:
-            markdown_lines.append(f"{indent}{text}")
-
-    elif block_type == "heading_1":
-        text = parse_rich_text(block["heading_1"].get("rich_text", []))
-        markdown_lines.append(f"{indent}# {text}")
-
-    elif block_type == "heading_2":
-        text = parse_rich_text(block["heading_2"].get("rich_text", []))
-        markdown_lines.append(f"{indent}## {text}")
-
-    elif block_type == "heading_3":
-        text = parse_rich_text(block["heading_3"].get("rich_text", []))
-        markdown_lines.append(f"{indent}### {text}")
-
-    elif block_type == "bulleted_list_item":
-        text = rich_text_to_markdown(block["bulleted_list_item"].get("rich_text", []))
-        markdown_lines.append(f"{indent}- {text}")
-
-    elif block_type == "numbered_list_item":
-        text = rich_text_to_markdown(block["numbered_list_item"].get("rich_text", []))
-        # Note: This is simplified - proper numbering would need context
-        markdown_lines.append(f"{indent}1. {text}")
-
-    elif block_type == "to_do":
-        text = rich_text_to_markdown(block["to_do"].get("rich_text", []))
-        checked = "x" if block["to_do"].get("checked") else " "
-        markdown_lines.append(f"{indent}- [{checked}] {text}")
-
-    elif block_type == "toggle":
-        text = rich_text_to_markdown(block["toggle"].get("rich_text", []))
-        markdown_lines.append(f"{indent}<details>")
-        markdown_lines.append(f"{indent}<summary>{text}</summary>")
-        markdown_lines.append(f"{indent}")
-        # Process children if they exist
-        if block.get("children"):
-            for child in block["children"]:
-                child_markdown = block_to_markdown(child, indent_level + 1)
-                if child_markdown:
-                    markdown_lines.append(child_markdown)
-        markdown_lines.append(f"{indent}</details>")
-
-    elif block_type == "code":
-        code = parse_rich_text(block["code"].get("rich_text", []))
-        language = block["code"].get("language", "")
-        markdown_lines.append(f"{indent}```{language}")
-        markdown_lines.append(f"{indent}{code}")
-        markdown_lines.append(f"{indent}```")
-
-    elif block_type == "quote":
-        text = rich_text_to_markdown(block["quote"].get("rich_text", []))
-        markdown_lines.append(f"{indent}> {text}")
-
-    elif block_type == "divider":
-        markdown_lines.append(f"{indent}---")
-
-    elif block_type == "image":
-        image = block["image"]
-        url = image.get("external", {}).get("url") or image.get("file", {}).get(
-            "url", ""
-        )
-        caption = parse_rich_text(image.get("caption", []))
-        alt_text = caption if caption else "Image"
-        markdown_lines.append(f"{indent}![{alt_text}]({url})")
-        if caption:
-            markdown_lines.append(f"{indent}*{caption}*")
-
-    elif block_type == "video":
-        video = block["video"]
-        url = video.get("external", {}).get("url") or video.get("file", {}).get(
-            "url", ""
-        )
-        caption = parse_rich_text(video.get("caption", []))
-        markdown_lines.append(f"{indent}[Video]({url})")
-        if caption:
-            markdown_lines.append(f"{indent}*{caption}*")
-
-    elif block_type == "file":
-        file = block["file"]
-        url = file.get("external", {}).get("url") or file.get("file", {}).get("url", "")
-        caption = parse_rich_text(file.get("caption", []))
-        name = caption if caption else "File"
-        markdown_lines.append(f"{indent}[{name}]({url})")
-
-    elif block_type == "bookmark":
-        url = block["bookmark"].get("url", "")
-        caption = parse_rich_text(block["bookmark"].get("caption", []))
-        markdown_lines.append(f"{indent}[{caption if caption else url}]({url})")
-
-    elif block_type == "equation":
-        expression = block["equation"].get("expression", "")
-        markdown_lines.append(f"{indent}$${expression}$$")
-
-    elif block_type == "callout":
-        text = rich_text_to_markdown(block["callout"].get("rich_text", []))
-        icon = block["callout"].get("icon", {})
-        if icon.get("emoji"):
-            markdown_lines.append(f"{indent}> {icon['emoji']} {text}")
-        else:
-            markdown_lines.append(f"{indent}> ℹ️ {text}")
-
-    elif block_type == "child_page":
-        title = block["child_page"].get("title", "Untitled")
-        markdown_lines.append(f"{indent}📄 [{title}](notion://page/{block['id']})")
-
-    elif block_type == "child_database":
-        title = block["child_database"].get("title", "Untitled Database")
-        markdown_lines.append(f"{indent}🗂️ [{title}](notion://database/{block['id']})")
-
-    elif block_type == "table":
-        # Tables are complex - for now just indicate there's a table
-        markdown_lines.append(
-            f"{indent}[Table with {block['table'].get('table_width', 0)} columns]"
-        )
-
-    elif block_type == "column_list":
-        # Process columns
-        if block.get("children"):
-            markdown_lines.append(f"{indent}<div style='display: flex'>")
-            for column in block["children"]:
-                markdown_lines.append(f"{indent}<div style='flex: 1'>")
-                if column.get("children"):
-                    for child in column["children"]:
-                        child_markdown = block_to_markdown(child, indent_level + 1)
-                        if child_markdown:
-                            markdown_lines.append(child_markdown)
-                markdown_lines.append(f"{indent}</div>")
-            markdown_lines.append(f"{indent}</div>")
-
-    # Handle children for blocks that haven't been processed yet
-    elif block.get("children") and block_type not in ["toggle", "column_list"]:
-        for child in block["children"]:
-            child_markdown = block_to_markdown(child, indent_level)
-            if child_markdown:
-                markdown_lines.append(child_markdown)
-
-    return "\n".join(markdown_lines) if markdown_lines else ""
-
-
-def blocks_to_markdown(blocks: List[dict]) -> str:
-    """
-    Convert a list of Notion blocks to a markdown document.
-
-    Args:
-        blocks: List of block objects from Notion API.
-
-    Returns:
-        Complete markdown document as a string.
-    """
-    markdown_parts = []
-
-    for i, block in enumerate(blocks):
-        markdown = block_to_markdown(block)
-        if markdown:
-            markdown_parts.append(markdown)
-            # Add spacing between top-level blocks (except lists)
-            if i < len(blocks) - 1:
-                next_type = blocks[i + 1].get("type", "")
-                current_type = block.get("type", "")
-                # Don't add extra spacing between list items
-                list_types = {"bulleted_list_item", "numbered_list_item", "to_do"}
-                if not (current_type in list_types and next_type in list_types):
-                    markdown_parts.append("")
-
-    return "\n".join(markdown_parts)
-
-
-def extract_page_title(page: dict) -> str:
-    """
-    Extract the title from a Notion page object.
-
-    Args:
-        page: Page object from Notion API.
-
-    Returns:
-        Page title as a string.
-    """
-    properties = page.get("properties", {})
-
-    # Find the title property (it has type "title")
-    for prop_name, prop_value in properties.items():
-        if prop_value.get("type") == "title":
-            return parse_rich_text(prop_value.get("title", []))
-
-    return "Untitled"

autogpt_platform/backend/backend/blocks/notion/_auth.py

@@ -1,42 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import CredentialsField, CredentialsMetaInput, OAuth2Credentials
-from backend.integrations.providers import ProviderName
-from backend.util.settings import Secrets
-
-secrets = Secrets()
-NOTION_OAUTH_IS_CONFIGURED = bool(
-    secrets.notion_client_id and secrets.notion_client_secret
-)
-
-NotionCredentials = OAuth2Credentials
-NotionCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.NOTION], Literal["oauth2"]
-]
-
-
-def NotionCredentialsField() -> NotionCredentialsInput:
-    """Creates a Notion OAuth2 credentials field."""
-    return CredentialsField(
-        description="Connect your Notion account. Ensure the pages/databases are shared with the integration."
-    )
-
-
-# Test credentials for Notion OAuth2
-TEST_CREDENTIALS = OAuth2Credentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="notion",
-    access_token=SecretStr("test_access_token"),
-    title="Mock Notion OAuth",
-    scopes=["read_content", "insert_content", "update_content"],
-    username="testuser",
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}

autogpt_platform/backend/backend/blocks/notion/create_page.py

@@ -1,360 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional
-
-from pydantic import model_validator
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionCreatePageBlock(Block):
-    """Create a new page in Notion with content."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        parent_page_id: Optional[str] = SchemaField(
-            description="Parent page ID to create the page under. Either this OR parent_database_id is required.",
-            default=None,
-        )
-        parent_database_id: Optional[str] = SchemaField(
-            description="Parent database ID to create the page in. Either this OR parent_page_id is required.",
-            default=None,
-        )
-        title: str = SchemaField(
-            description="Title of the new page",
-        )
-        content: Optional[str] = SchemaField(
-            description="Content for the page. Can be plain text or markdown - will be converted to Notion blocks.",
-            default=None,
-        )
-        properties: Optional[Dict[str, Any]] = SchemaField(
-            description="Additional properties for database pages (e.g., {'Status': 'In Progress', 'Priority': 'High'})",
-            default=None,
-        )
-        icon_emoji: Optional[str] = SchemaField(
-            description="Emoji to use as the page icon (e.g., '📄', '🚀')", default=None
-        )
-
-        @model_validator(mode="after")
-        def validate_parent(self):
-            """Ensure either parent_page_id or parent_database_id is provided."""
-            if not self.parent_page_id and not self.parent_database_id:
-                raise ValueError(
-                    "Either parent_page_id or parent_database_id must be provided"
-                )
-            if self.parent_page_id and self.parent_database_id:
-                raise ValueError(
-                    "Only one of parent_page_id or parent_database_id should be provided, not both"
-                )
-            return self
-
-    class Output(BlockSchema):
-        page_id: str = SchemaField(description="ID of the created page.")
-        page_url: str = SchemaField(description="URL of the created page.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="c15febe0-66ce-4c6f-aebd-5ab351653804",
-            description="Create a new page in Notion. Requires EITHER a parent_page_id OR parent_database_id. Supports markdown content.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionCreatePageBlock.Input,
-            output_schema=NotionCreatePageBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "parent_page_id": "00000000-0000-0000-0000-000000000000",
-                "title": "Test Page",
-                "content": "This is test content.",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("page_id", "12345678-1234-1234-1234-123456789012"),
-                (
-                    "page_url",
-                    "https://notion.so/Test-Page-12345678123412341234123456789012",
-                ),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "create_page": lambda *args, **kwargs: (
-                    "12345678-1234-1234-1234-123456789012",
-                    "https://notion.so/Test-Page-12345678123412341234123456789012",
-                )
-            },
-        )
-
-    @staticmethod
-    def _markdown_to_blocks(content: str) -> List[dict]:
-        """Convert markdown content to Notion block objects."""
-        if not content:
-            return []
-
-        blocks = []
-        lines = content.split("\n")
-        i = 0
-
-        while i < len(lines):
-            line = lines[i]
-
-            # Skip empty lines
-            if not line.strip():
-                i += 1
-                continue
-
-            # Headings
-            if line.startswith("### "):
-                blocks.append(
-                    {
-                        "type": "heading_3",
-                        "heading_3": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[4:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            elif line.startswith("## "):
-                blocks.append(
-                    {
-                        "type": "heading_2",
-                        "heading_2": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[3:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            elif line.startswith("# "):
-                blocks.append(
-                    {
-                        "type": "heading_1",
-                        "heading_1": {
-                            "rich_text": [
-                                {"type": "text", "text": {"content": line[2:].strip()}}
-                            ]
-                        },
-                    }
-                )
-            # Bullet points
-            elif line.strip().startswith("- "):
-                blocks.append(
-                    {
-                        "type": "bulleted_list_item",
-                        "bulleted_list_item": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line.strip()[2:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Numbered list
-            elif line.strip() and line.strip()[0].isdigit() and ". " in line:
-                content_start = line.find(". ") + 2
-                blocks.append(
-                    {
-                        "type": "numbered_list_item",
-                        "numbered_list_item": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line[content_start:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Code block
-            elif line.strip().startswith("```"):
-                code_lines = []
-                language = line[3:].strip() or "plain text"
-                i += 1
-                while i < len(lines) and not lines[i].strip().startswith("```"):
-                    code_lines.append(lines[i])
-                    i += 1
-                blocks.append(
-                    {
-                        "type": "code",
-                        "code": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": "\n".join(code_lines)},
-                                }
-                            ],
-                            "language": language,
-                        },
-                    }
-                )
-            # Quote
-            elif line.strip().startswith("> "):
-                blocks.append(
-                    {
-                        "type": "quote",
-                        "quote": {
-                            "rich_text": [
-                                {
-                                    "type": "text",
-                                    "text": {"content": line.strip()[2:].strip()},
-                                }
-                            ]
-                        },
-                    }
-                )
-            # Horizontal rule
-            elif line.strip() in ["---", "***", "___"]:
-                blocks.append({"type": "divider", "divider": {}})
-            # Regular paragraph
-            else:
-                # Parse for basic markdown formatting
-                text_content = line.strip()
-                rich_text = []
-
-                # Simple bold/italic parsing (this is simplified)
-                if "**" in text_content or "*" in text_content:
-                    # For now, just pass as plain text
-                    # A full implementation would parse and create proper annotations
-                    rich_text = [{"type": "text", "text": {"content": text_content}}]
-                else:
-                    rich_text = [{"type": "text", "text": {"content": text_content}}]
-
-                blocks.append(
-                    {"type": "paragraph", "paragraph": {"rich_text": rich_text}}
-                )
-
-            i += 1
-
-        return blocks
-
-    @staticmethod
-    def _build_properties(
-        title: str, additional_properties: Optional[Dict[str, Any]] = None
-    ) -> Dict[str, Any]:
-        """Build properties object for page creation."""
-        properties: Dict[str, Any] = {
-            "title": {"title": [{"type": "text", "text": {"content": title}}]}
-        }
-
-        if additional_properties:
-            for key, value in additional_properties.items():
-                if key.lower() == "title":
-                    continue  # Skip title as we already have it
-
-                # Try to intelligently map property types
-                if isinstance(value, bool):
-                    properties[key] = {"checkbox": value}
-                elif isinstance(value, (int, float)):
-                    properties[key] = {"number": value}
-                elif isinstance(value, list):
-                    # Assume multi-select
-                    properties[key] = {
-                        "multi_select": [{"name": str(item)} for item in value]
-                    }
-                elif isinstance(value, str):
-                    # Could be select, rich_text, or other types
-                    # For simplicity, try common patterns
-                    if key.lower() in ["status", "priority", "type", "category"]:
-                        properties[key] = {"select": {"name": value}}
-                    elif key.lower() in ["url", "link"]:
-                        properties[key] = {"url": value}
-                    elif key.lower() in ["email"]:
-                        properties[key] = {"email": value}
-                    else:
-                        properties[key] = {
-                            "rich_text": [{"type": "text", "text": {"content": value}}]
-                        }
-
-        return properties
-
-    @staticmethod
-    async def create_page(
-        credentials: OAuth2Credentials,
-        title: str,
-        parent_page_id: Optional[str] = None,
-        parent_database_id: Optional[str] = None,
-        content: Optional[str] = None,
-        properties: Optional[Dict[str, Any]] = None,
-        icon_emoji: Optional[str] = None,
-    ) -> tuple[str, str]:
-        """
-        Create a new Notion page.
-
-        Returns:
-            Tuple of (page_id, page_url)
-        """
-        if not parent_page_id and not parent_database_id:
-            raise ValueError(
-                "Either parent_page_id or parent_database_id must be provided"
-            )
-        if parent_page_id and parent_database_id:
-            raise ValueError(
-                "Only one of parent_page_id or parent_database_id should be provided, not both"
-            )
-
-        client = NotionClient(credentials)
-
-        # Build parent object
-        if parent_page_id:
-            parent = {"type": "page_id", "page_id": parent_page_id}
-        else:
-            parent = {"type": "database_id", "database_id": parent_database_id}
-
-        # Build properties
-        page_properties = NotionCreatePageBlock._build_properties(title, properties)
-
-        # Convert content to blocks if provided
-        children = None
-        if content:
-            children = NotionCreatePageBlock._markdown_to_blocks(content)
-
-        # Build icon if provided
-        icon = None
-        if icon_emoji:
-            icon = {"type": "emoji", "emoji": icon_emoji}
-
-        # Create the page
-        result = await client.create_page(
-            parent=parent, properties=page_properties, children=children, icon=icon
-        )
-
-        page_id = result.get("id", "")
-        page_url = result.get("url", "")
-
-        if not page_id or not page_url:
-            raise ValueError("Failed to get page ID or URL from Notion response")
-
-        return page_id, page_url
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            page_id, page_url = await self.create_page(
-                credentials,
-                input_data.title,
-                input_data.parent_page_id,
-                input_data.parent_database_id,
-                input_data.content,
-                input_data.properties,
-                input_data.icon_emoji,
-            )
-            yield "page_id", page_id
-            yield "page_url", page_url
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_database.py

@@ -1,285 +0,0 @@
-from __future__ import annotations
-
-from typing import Any, Dict, List, Optional
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, parse_rich_text
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadDatabaseBlock(Block):
-    """Query a Notion database and retrieve entries with their properties."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        database_id: str = SchemaField(
-            description="Notion database ID. Must be accessible by the connected integration.",
-        )
-        filter_property: Optional[str] = SchemaField(
-            description="Property name to filter by (e.g., 'Status', 'Priority')",
-            default=None,
-        )
-        filter_value: Optional[str] = SchemaField(
-            description="Value to filter for in the specified property", default=None
-        )
-        sort_property: Optional[str] = SchemaField(
-            description="Property name to sort by", default=None
-        )
-        sort_direction: Optional[str] = SchemaField(
-            description="Sort direction: 'ascending' or 'descending'",
-            default="ascending",
-        )
-        limit: int = SchemaField(
-            description="Maximum number of entries to retrieve",
-            default=100,
-            ge=1,
-            le=100,
-        )
-
-    class Output(BlockSchema):
-        entries: List[Dict[str, Any]] = SchemaField(
-            description="List of database entries with their properties."
-        )
-        entry: Dict[str, Any] = SchemaField(
-            description="Individual database entry (yields one per entry found)."
-        )
-        entry_ids: List[str] = SchemaField(
-            description="List of entry IDs for batch operations."
-        )
-        entry_id: str = SchemaField(
-            description="Individual entry ID (yields one per entry found)."
-        )
-        count: int = SchemaField(description="Number of entries retrieved.")
-        database_title: str = SchemaField(description="Title of the database.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="fcd53135-88c9-4ba3-be50-cc6936286e6c",
-            description="Query a Notion database with optional filtering and sorting, returning structured entries.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadDatabaseBlock.Input,
-            output_schema=NotionReadDatabaseBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "database_id": "00000000-0000-0000-0000-000000000000",
-                "limit": 10,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "entries",
-                    [{"Name": "Test Entry", "Status": "Active", "_id": "test-123"}],
-                ),
-                ("entry_ids", ["test-123"]),
-                (
-                    "entry",
-                    {"Name": "Test Entry", "Status": "Active", "_id": "test-123"},
-                ),
-                ("entry_id", "test-123"),
-                ("count", 1),
-                ("database_title", "Test Database"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "query_database": lambda *args, **kwargs: (
-                    [{"Name": "Test Entry", "Status": "Active", "_id": "test-123"}],
-                    1,
-                    "Test Database",
-                )
-            },
-        )
-
-    @staticmethod
-    def _parse_property_value(prop: dict) -> Any:
-        """Parse a Notion property value into a simple Python type."""
-        prop_type = prop.get("type")
-
-        if prop_type == "title":
-            return parse_rich_text(prop.get("title", []))
-        elif prop_type == "rich_text":
-            return parse_rich_text(prop.get("rich_text", []))
-        elif prop_type == "number":
-            return prop.get("number")
-        elif prop_type == "select":
-            select = prop.get("select")
-            return select.get("name") if select else None
-        elif prop_type == "multi_select":
-            return [item.get("name") for item in prop.get("multi_select", [])]
-        elif prop_type == "date":
-            date = prop.get("date")
-            if date:
-                return date.get("start")
-            return None
-        elif prop_type == "checkbox":
-            return prop.get("checkbox", False)
-        elif prop_type == "url":
-            return prop.get("url")
-        elif prop_type == "email":
-            return prop.get("email")
-        elif prop_type == "phone_number":
-            return prop.get("phone_number")
-        elif prop_type == "people":
-            return [
-                person.get("name", person.get("id"))
-                for person in prop.get("people", [])
-            ]
-        elif prop_type == "files":
-            files = prop.get("files", [])
-            return [
-                f.get(
-                    "name",
-                    f.get("external", {}).get("url", f.get("file", {}).get("url")),
-                )
-                for f in files
-            ]
-        elif prop_type == "relation":
-            return [rel.get("id") for rel in prop.get("relation", [])]
-        elif prop_type == "formula":
-            formula = prop.get("formula", {})
-            return formula.get(formula.get("type"))
-        elif prop_type == "rollup":
-            rollup = prop.get("rollup", {})
-            return rollup.get(rollup.get("type"))
-        elif prop_type == "created_time":
-            return prop.get("created_time")
-        elif prop_type == "created_by":
-            return prop.get("created_by", {}).get(
-                "name", prop.get("created_by", {}).get("id")
-            )
-        elif prop_type == "last_edited_time":
-            return prop.get("last_edited_time")
-        elif prop_type == "last_edited_by":
-            return prop.get("last_edited_by", {}).get(
-                "name", prop.get("last_edited_by", {}).get("id")
-            )
-        else:
-            # Return the raw value for unknown types
-            return prop
-
-    @staticmethod
-    def _build_filter(property_name: str, value: str) -> dict:
-        """Build a simple filter object for a property."""
-        # This is a simplified filter - in reality, you'd need to know the property type
-        # For now, we'll try common filter types
-        return {
-            "or": [
-                {"property": property_name, "rich_text": {"contains": value}},
-                {"property": property_name, "title": {"contains": value}},
-                {"property": property_name, "select": {"equals": value}},
-                {"property": property_name, "multi_select": {"contains": value}},
-            ]
-        }
-
-    @staticmethod
-    async def query_database(
-        credentials: OAuth2Credentials,
-        database_id: str,
-        filter_property: Optional[str] = None,
-        filter_value: Optional[str] = None,
-        sort_property: Optional[str] = None,
-        sort_direction: str = "ascending",
-        limit: int = 100,
-    ) -> tuple[List[Dict[str, Any]], int, str]:
-        """
-        Query a Notion database and parse the results.
-
-        Returns:
-            Tuple of (entries_list, count, database_title)
-        """
-        client = NotionClient(credentials)
-
-        # Build filter if specified
-        filter_obj = None
-        if filter_property and filter_value:
-            filter_obj = NotionReadDatabaseBlock._build_filter(
-                filter_property, filter_value
-            )
-
-        # Build sorts if specified
-        sorts = None
-        if sort_property:
-            sorts = [{"property": sort_property, "direction": sort_direction}]
-
-        # Query the database
-        result = await client.query_database(
-            database_id, filter_obj=filter_obj, sorts=sorts, page_size=limit
-        )
-
-        # Parse the entries
-        entries = []
-        for page in result.get("results", []):
-            entry = {}
-            properties = page.get("properties", {})
-
-            for prop_name, prop_value in properties.items():
-                entry[prop_name] = NotionReadDatabaseBlock._parse_property_value(
-                    prop_value
-                )
-
-            # Add metadata
-            entry["_id"] = page.get("id")
-            entry["_url"] = page.get("url")
-            entry["_created_time"] = page.get("created_time")
-            entry["_last_edited_time"] = page.get("last_edited_time")
-
-            entries.append(entry)
-
-        # Get database title (we need to make a separate call for this)
-        try:
-            database_url = f"https://api.notion.com/v1/databases/{database_id}"
-            db_response = await client.requests.get(
-                database_url, headers=client.headers
-            )
-            if db_response.ok:
-                db_data = db_response.json()
-                db_title = parse_rich_text(db_data.get("title", []))
-            else:
-                db_title = "Unknown Database"
-        except Exception:
-            db_title = "Unknown Database"
-
-        return entries, len(entries), db_title
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            entries, count, db_title = await self.query_database(
-                credentials,
-                input_data.database_id,
-                input_data.filter_property,
-                input_data.filter_value,
-                input_data.sort_property,
-                input_data.sort_direction or "ascending",
-                input_data.limit,
-            )
-            # Yield the complete list for batch operations
-            yield "entries", entries
-
-            # Extract and yield IDs as a list for batch operations
-            entry_ids = [entry["_id"] for entry in entries if "_id" in entry]
-            yield "entry_ids", entry_ids
-
-            # Yield each individual entry and its ID for single connections
-            for entry in entries:
-                yield "entry", entry
-                if "_id" in entry:
-                    yield "entry_id", entry["_id"]
-
-            yield "count", count
-            yield "database_title", db_title
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_page.py

@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadPageBlock(Block):
-    """Read a Notion page by ID and return its raw JSON."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        page_id: str = SchemaField(
-            description="Notion page ID. Must be accessible by the connected integration. You can get this from the page URL notion.so/A-Page-586edd711467478da59fe3ce29a1ffab would be 586edd711467478da59fe35e29a1ffab",
-        )
-
-    class Output(BlockSchema):
-        page: dict = SchemaField(description="Raw Notion page JSON.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="5246cc1d-34b7-452b-8fc5-3fb25fd8f542",
-            description="Read a Notion page by its ID and return its raw JSON.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadPageBlock.Input,
-            output_schema=NotionReadPageBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "page_id": "00000000-0000-0000-0000-000000000000",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[("page", dict)],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "get_page": lambda *args, **kwargs: {"object": "page", "id": "mocked"}
-            },
-        )
-
-    @staticmethod
-    async def get_page(credentials: OAuth2Credentials, page_id: str) -> dict:
-        client = NotionClient(credentials)
-        return await client.get_page(page_id)
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            page = await self.get_page(credentials, input_data.page_id)
-            yield "page", page
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/read_page_markdown.py

@@ -1,109 +0,0 @@
-from __future__ import annotations
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, blocks_to_markdown, extract_page_title
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionReadPageMarkdownBlock(Block):
-    """Read a Notion page and convert it to clean Markdown format."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        page_id: str = SchemaField(
-            description="Notion page ID. Must be accessible by the connected integration. You can get this from the page URL notion.so/A-Page-586edd711467478da59fe35e29a1ffab would be 586edd711467478da59fe35e29a1ffab",
-        )
-        include_title: bool = SchemaField(
-            description="Whether to include the page title as a header in the markdown",
-            default=True,
-        )
-
-    class Output(BlockSchema):
-        markdown: str = SchemaField(description="Page content in Markdown format.")
-        title: str = SchemaField(description="Page title.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="d1312c4d-fae2-4e70-893d-f4d07cce1d4e",
-            description="Read a Notion page and convert it to Markdown format with proper formatting for headings, lists, links, and rich text.",
-            categories={BlockCategory.PRODUCTIVITY},
-            input_schema=NotionReadPageMarkdownBlock.Input,
-            output_schema=NotionReadPageMarkdownBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "page_id": "00000000-0000-0000-0000-000000000000",
-                "include_title": True,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("markdown", "# Test Page\n\nThis is test content."),
-                ("title", "Test Page"),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "get_page_markdown": lambda *args, **kwargs: (
-                    "# Test Page\n\nThis is test content.",
-                    "Test Page",
-                )
-            },
-        )
-
-    @staticmethod
-    async def get_page_markdown(
-        credentials: OAuth2Credentials, page_id: str, include_title: bool = True
-    ) -> tuple[str, str]:
-        """
-        Get a Notion page and convert it to markdown.
-
-        Args:
-            credentials: OAuth2 credentials for Notion.
-            page_id: The ID of the page to fetch.
-            include_title: Whether to include the page title in the markdown.
-
-        Returns:
-            Tuple of (markdown_content, title)
-        """
-        client = NotionClient(credentials)
-
-        # Get page metadata
-        page = await client.get_page(page_id)
-        title = extract_page_title(page)
-
-        # Get all blocks from the page
-        blocks = await client.get_blocks(page_id, recursive=True)
-
-        # Convert blocks to markdown
-        content_markdown = blocks_to_markdown(blocks)
-
-        # Combine title and content if requested
-        if include_title and title:
-            full_markdown = f"# {title}\n\n{content_markdown}"
-        else:
-            full_markdown = content_markdown
-
-        return full_markdown, title
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            markdown, title = await self.get_page_markdown(
-                credentials, input_data.page_id, input_data.include_title
-            )
-            yield "markdown", markdown
-            yield "title", title
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/notion/search.py

@@ -1,225 +0,0 @@
-from __future__ import annotations
-
-from typing import List, Optional
-
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import NotionClient, extract_page_title, parse_rich_text
-from ._auth import (
-    NOTION_OAUTH_IS_CONFIGURED,
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    NotionCredentialsField,
-    NotionCredentialsInput,
-)
-
-
-class NotionSearchResult(BaseModel):
-    """Typed model for Notion search results."""
-
-    id: str
-    type: str  # 'page' or 'database'
-    title: str
-    url: str
-    created_time: Optional[str] = None
-    last_edited_time: Optional[str] = None
-    parent_type: Optional[str] = None  # 'page', 'database', or 'workspace'
-    parent_id: Optional[str] = None
-    icon: Optional[str] = None  # emoji icon if present
-    is_inline: Optional[bool] = None  # for databases only
-
-
-class NotionSearchBlock(Block):
-    """Search across your Notion workspace for pages and databases."""
-
-    class Input(BlockSchema):
-        credentials: NotionCredentialsInput = NotionCredentialsField()
-        query: str = SchemaField(
-            description="Search query text. Leave empty to get all accessible pages/databases.",
-            default="",
-        )
-        filter_type: Optional[str] = SchemaField(
-            description="Filter results by type: 'page' or 'database'. Leave empty for both.",
-            default=None,
-        )
-        limit: int = SchemaField(
-            description="Maximum number of results to return", default=20, ge=1, le=100
-        )
-
-    class Output(BlockSchema):
-        results: List[NotionSearchResult] = SchemaField(
-            description="List of search results with title, type, URL, and metadata."
-        )
-        result: NotionSearchResult = SchemaField(
-            description="Individual search result (yields one per result found)."
-        )
-        result_ids: List[str] = SchemaField(
-            description="List of IDs from search results for batch operations."
-        )
-        count: int = SchemaField(description="Number of results found.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="313515dd-9848-46ea-9cd6-3c627c892c56",
-            description="Search your Notion workspace for pages and databases by text query.",
-            categories={BlockCategory.PRODUCTIVITY, BlockCategory.SEARCH},
-            input_schema=NotionSearchBlock.Input,
-            output_schema=NotionSearchBlock.Output,
-            disabled=not NOTION_OAUTH_IS_CONFIGURED,
-            test_input={
-                "query": "project",
-                "limit": 5,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "results",
-                    [
-                        NotionSearchResult(
-                            id="123",
-                            type="page",
-                            title="Project Plan",
-                            url="https://notion.so/Project-Plan-123",
-                        )
-                    ],
-                ),
-                ("result_ids", ["123"]),
-                (
-                    "result",
-                    NotionSearchResult(
-                        id="123",
-                        type="page",
-                        title="Project Plan",
-                        url="https://notion.so/Project-Plan-123",
-                    ),
-                ),
-                ("count", 1),
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "search_workspace": lambda *args, **kwargs: (
-                    [
-                        NotionSearchResult(
-                            id="123",
-                            type="page",
-                            title="Project Plan",
-                            url="https://notion.so/Project-Plan-123",
-                        )
-                    ],
-                    1,
-                )
-            },
-        )
-
-    @staticmethod
-    async def search_workspace(
-        credentials: OAuth2Credentials,
-        query: str = "",
-        filter_type: Optional[str] = None,
-        limit: int = 20,
-    ) -> tuple[List[NotionSearchResult], int]:
-        """
-        Search the Notion workspace.
-
-        Returns:
-            Tuple of (results_list, count)
-        """
-        client = NotionClient(credentials)
-
-        # Build filter if type is specified
-        filter_obj = None
-        if filter_type:
-            filter_obj = {"property": "object", "value": filter_type}
-
-        # Execute search
-        response = await client.search(
-            query=query, filter_obj=filter_obj, page_size=limit
-        )
-
-        # Parse results
-        results = []
-        for item in response.get("results", []):
-            result_data = {
-                "id": item.get("id", ""),
-                "type": item.get("object", ""),
-                "url": item.get("url", ""),
-                "created_time": item.get("created_time"),
-                "last_edited_time": item.get("last_edited_time"),
-                "title": "",  # Will be set below
-            }
-
-            # Extract title based on type
-            if item.get("object") == "page":
-                # For pages, get the title from properties
-                result_data["title"] = extract_page_title(item)
-
-                # Add parent info
-                parent = item.get("parent", {})
-                if parent.get("type") == "page_id":
-                    result_data["parent_type"] = "page"
-                    result_data["parent_id"] = parent.get("page_id")
-                elif parent.get("type") == "database_id":
-                    result_data["parent_type"] = "database"
-                    result_data["parent_id"] = parent.get("database_id")
-                elif parent.get("type") == "workspace":
-                    result_data["parent_type"] = "workspace"
-
-                # Add icon if present
-                icon = item.get("icon")
-                if icon and icon.get("type") == "emoji":
-                    result_data["icon"] = icon.get("emoji")
-
-            elif item.get("object") == "database":
-                # For databases, get title from the title array
-                result_data["title"] = parse_rich_text(item.get("title", []))
-
-                # Add database-specific metadata
-                result_data["is_inline"] = item.get("is_inline", False)
-
-                # Add parent info
-                parent = item.get("parent", {})
-                if parent.get("type") == "page_id":
-                    result_data["parent_type"] = "page"
-                    result_data["parent_id"] = parent.get("page_id")
-                elif parent.get("type") == "workspace":
-                    result_data["parent_type"] = "workspace"
-
-                # Add icon if present
-                icon = item.get("icon")
-                if icon and icon.get("type") == "emoji":
-                    result_data["icon"] = icon.get("emoji")
-
-            results.append(NotionSearchResult(**result_data))
-
-        return results, len(results)
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: OAuth2Credentials,
-        **kwargs,
-    ) -> BlockOutput:
-        try:
-            results, count = await self.search_workspace(
-                credentials, input_data.query, input_data.filter_type, input_data.limit
-            )
-
-            # Yield the complete list for batch operations
-            yield "results", results
-
-            # Extract and yield IDs as a list for batch operations
-            result_ids = [r.id for r in results]
-            yield "result_ids", result_ids
-
-            # Yield each individual result for single connections
-            for result in results:
-                yield "result", result
-
-            yield "count", count
-        except Exception as e:
-            yield "error", str(e) if str(e) else "Unknown error"

autogpt_platform/backend/backend/blocks/perplexity.py

@@ -1,226 +0,0 @@
-# flake8: noqa: E501
-import logging
-from enum import Enum
-from typing import Any, Literal
-
-import openai
-from pydantic import SecretStr
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    NodeExecutionStats,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-from backend.util.logging import TruncatedLogger
-
-logger = TruncatedLogger(logging.getLogger(__name__), "[Perplexity-Block]")
-
-
-class PerplexityModel(str, Enum):
-    """Perplexity sonar models available via OpenRouter"""
-
-    SONAR = "perplexity/sonar"
-    SONAR_PRO = "perplexity/sonar-pro"
-    SONAR_DEEP_RESEARCH = "perplexity/sonar-deep-research"
-
-
-PerplexityCredentials = CredentialsMetaInput[
-    Literal[ProviderName.OPEN_ROUTER], Literal["api_key"]
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="test-perplexity-creds",
-    provider="open_router",
-    api_key=SecretStr("mock-openrouter-api-key"),
-    title="Mock OpenRouter API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def PerplexityCredentialsField() -> PerplexityCredentials:
-    return CredentialsField(
-        description="OpenRouter API key for accessing Perplexity models.",
-    )
-
-
-class PerplexityBlock(Block):
-    class Input(BlockSchema):
-        prompt: str = SchemaField(
-            description="The query to send to the Perplexity model.",
-            placeholder="Enter your query here...",
-        )
-        model: PerplexityModel = SchemaField(
-            title="Perplexity Model",
-            default=PerplexityModel.SONAR,
-            description="The Perplexity sonar model to use.",
-            advanced=False,
-        )
-        credentials: PerplexityCredentials = PerplexityCredentialsField()
-        system_prompt: str = SchemaField(
-            title="System Prompt",
-            default="",
-            description="Optional system prompt to provide context to the model.",
-            advanced=True,
-        )
-        max_tokens: int | None = SchemaField(
-            advanced=True,
-            default=None,
-            description="The maximum number of tokens to generate.",
-        )
-
-    class Output(BlockSchema):
-        response: str = SchemaField(
-            description="The response from the Perplexity model."
-        )
-        annotations: list[dict[str, Any]] = SchemaField(
-            description="List of URL citations and annotations from the response."
-        )
-        error: str = SchemaField(description="Error message if the API call failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="c8a5f2e9-8b3d-4a7e-9f6c-1d5e3c9b7a4f",
-            description="Query Perplexity's sonar models with real-time web search capabilities and receive annotated responses with source citations.",
-            categories={BlockCategory.AI, BlockCategory.SEARCH},
-            input_schema=PerplexityBlock.Input,
-            output_schema=PerplexityBlock.Output,
-            test_input={
-                "prompt": "What is the weather today?",
-                "model": PerplexityModel.SONAR,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("response", "The weather varies by location..."),
-                ("annotations", list),
-            ],
-            test_mock={
-                "call_perplexity": lambda *args, **kwargs: {
-                    "response": "The weather varies by location...",
-                    "annotations": [
-                        {
-                            "type": "url_citation",
-                            "url_citation": {
-                                "title": "weather.com",
-                                "url": "https://weather.com",
-                            },
-                        }
-                    ],
-                }
-            },
-        )
-        self.execution_stats = NodeExecutionStats()
-
-    async def call_perplexity(
-        self,
-        credentials: APIKeyCredentials,
-        model: PerplexityModel,
-        prompt: str,
-        system_prompt: str = "",
-        max_tokens: int | None = None,
-    ) -> dict[str, Any]:
-        """Call Perplexity via OpenRouter and extract annotations."""
-        client = openai.AsyncOpenAI(
-            base_url="https://openrouter.ai/api/v1",
-            api_key=credentials.api_key.get_secret_value(),
-        )
-
-        messages = []
-        if system_prompt:
-            messages.append({"role": "system", "content": system_prompt})
-        messages.append({"role": "user", "content": prompt})
-
-        try:
-            response = await client.chat.completions.create(
-                extra_headers={
-                    "HTTP-Referer": "https://agpt.co",
-                    "X-Title": "AutoGPT",
-                },
-                model=model.value,
-                messages=messages,
-                max_tokens=max_tokens,
-            )
-
-            if not response.choices:
-                raise ValueError("No response from Perplexity via OpenRouter.")
-
-            # Extract the response content
-            response_content = response.choices[0].message.content or ""
-
-            # Extract annotations if present in the message
-            annotations = []
-            if hasattr(response.choices[0].message, "annotations"):
-                # If annotations are directly available
-                annotations = response.choices[0].message.annotations
-            else:
-                # Check if there's a raw response with annotations
-                raw = getattr(response.choices[0].message, "_raw_response", None)
-                if isinstance(raw, dict) and "annotations" in raw:
-                    annotations = raw["annotations"]
-
-            if not annotations and hasattr(response, "model_extra"):
-                # Check model_extra for annotations
-                model_extra = response.model_extra
-                if isinstance(model_extra, dict):
-                    # Check in choices
-                    if "choices" in model_extra and len(model_extra["choices"]) > 0:
-                        choice = model_extra["choices"][0]
-                        if "message" in choice and "annotations" in choice["message"]:
-                            annotations = choice["message"]["annotations"]
-
-            # Also check the raw response object for annotations
-            if not annotations:
-                raw = getattr(response, "_raw_response", None)
-                if isinstance(raw, dict):
-                    # Check various possible locations for annotations
-                    if "annotations" in raw:
-                        annotations = raw["annotations"]
-                    elif "choices" in raw and len(raw["choices"]) > 0:
-                        choice = raw["choices"][0]
-                        if "message" in choice and "annotations" in choice["message"]:
-                            annotations = choice["message"]["annotations"]
-
-            # Update execution stats
-            if response.usage:
-                self.execution_stats.input_token_count = response.usage.prompt_tokens
-                self.execution_stats.output_token_count = (
-                    response.usage.completion_tokens
-                )
-
-            return {"response": response_content, "annotations": annotations or []}
-
-        except Exception as e:
-            logger.error(f"Error calling Perplexity: {e}")
-            raise
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        logger.debug(f"Running Perplexity block with model: {input_data.model}")
-
-        try:
-            result = await self.call_perplexity(
-                credentials=credentials,
-                model=input_data.model,
-                prompt=input_data.prompt,
-                system_prompt=input_data.system_prompt,
-                max_tokens=input_data.max_tokens,
-            )
-
-            yield "response", result["response"]
-            yield "annotations", result["annotations"]
-
-        except Exception as e:
-            error_msg = f"Error calling Perplexity: {str(e)}"
-            logger.error(error_msg)
-            yield "error", error_msg

autogpt_platform/backend/backend/blocks/rss.py

@@ -1,7 +1,4 @@
 import asyncio
-import logging
-import urllib.parse
-import urllib.request
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
@@ -104,38 +101,7 @@ class ReadRSSFeedBlock(Block):
 
     @staticmethod
     def parse_feed(url: str) -> dict[str, Any]:
-        # Security fix: Add protection against memory exhaustion attacks
-        MAX_FEED_SIZE = 10 * 1024 * 1024  # 10MB limit for RSS feeds
-
-        # Validate URL
-        parsed_url = urllib.parse.urlparse(url)
-        if parsed_url.scheme not in ("http", "https"):
-            raise ValueError(f"Invalid URL scheme: {parsed_url.scheme}")
-
-        # Download with size limit
-        try:
-            with urllib.request.urlopen(url, timeout=30) as response:
-                # Check content length if available
-                content_length = response.headers.get("Content-Length")
-                if content_length and int(content_length) > MAX_FEED_SIZE:
-                    raise ValueError(
-                        f"Feed too large: {content_length} bytes exceeds {MAX_FEED_SIZE} limit"
-                    )
-
-                # Read with size limit
-                content = response.read(MAX_FEED_SIZE + 1)
-                if len(content) > MAX_FEED_SIZE:
-                    raise ValueError(
-                        f"Feed too large: exceeds {MAX_FEED_SIZE} byte limit"
-                    )
-
-                # Parse with feedparser using the validated content
-                # feedparser has built-in protection against XML attacks
-                return feedparser.parse(content)  # type: ignore
-        except Exception as e:
-            # Log error and return empty feed
-            logging.warning(f"Failed to parse RSS feed from {url}: {e}")
-            return {"entries": []}
+        return feedparser.parse(url)  # type: ignore
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         keep_going = True

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -13,11 +13,6 @@ from backend.data.block import (
     BlockSchema,
     BlockType,
 )
-from backend.data.dynamic_fields import (
-    extract_base_field_name,
-    get_dynamic_field_description,
-    is_dynamic_field,
-)
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
@@ -103,22 +98,6 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
-def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
-    """
-    Safely convert raw_response to dictionary format for conversation history.
-    Handles different response types from different LLM providers.
-    """
-    if isinstance(raw_response, str):
-        # Ollama returns a string, convert to dict format
-        return {"role": "assistant", "content": raw_response}
-    elif isinstance(raw_response, dict):
-        # Already a dict (from tests or some providers)
-        return raw_response
-    else:
-        # OpenAI/Anthropic return objects, convert with json.to_dict
-        return json.to_dict(raw_response)
-
-
 def get_pending_tool_calls(conversation_history: list[Any]) -> dict[str, int]:
     """
     All the tool calls entry in the conversation history requires a response.
@@ -282,7 +261,6 @@ class SmartDecisionMakerBlock(Block):
 
     @staticmethod
     def cleanup(s: str):
-        """Clean up block names for use as tool function names."""
         return re.sub(r"[^a-zA-Z0-9_-]", "_", s).lower()
 
     @staticmethod
@@ -310,66 +288,41 @@ class SmartDecisionMakerBlock(Block):
         }
         sink_block_input_schema = block.input_schema
         properties = {}
-        field_mapping = {}  # clean_name -> original_name
 
         for link in links:
-            field_name = link.sink_name
-            is_dynamic = is_dynamic_field(field_name)
-            # Clean property key to ensure Anthropic API compatibility for ALL fields
-            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
-            field_mapping[clean_field_name] = field_name
+            sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
 
-            if is_dynamic:
-                # For dynamic fields, use cleaned name but preserve original in description
-                properties[clean_field_name] = {
+            # Handle dynamic fields (e.g., values_#_*, items_$_*, etc.)
+            # These are fields that get merged by the executor into their base field
+            if (
+                "_#_" in link.sink_name
+                or "_$_" in link.sink_name
+                or "_@_" in link.sink_name
+            ):
+                # For dynamic fields, provide a generic string schema
+                # The executor will handle merging these into the appropriate structure
+                properties[sink_name] = {
                     "type": "string",
-                    "description": get_dynamic_field_description(field_name),
+                    "description": f"Dynamic value for {link.sink_name}",
                 }
             else:
-                # For regular fields, use the block's schema directly
+                # For regular fields, use the block's schema
                 try:
-                    properties[clean_field_name] = (
-                        sink_block_input_schema.get_field_schema(field_name)
+                    properties[sink_name] = sink_block_input_schema.get_field_schema(
+                        link.sink_name
                     )
                 except (KeyError, AttributeError):
-                    # If field doesn't exist in schema, provide a generic one
-                    properties[clean_field_name] = {
+                    # If the field doesn't exist in the schema, provide a generic schema
+                    properties[sink_name] = {
                         "type": "string",
-                        "description": f"Value for {field_name}",
+                        "description": f"Value for {link.sink_name}",
                     }
 
-        # Build the parameters schema using a single unified path
-        base_schema = block.input_schema.jsonschema()
-        base_required = set(base_schema.get("required", []))
-
-        # Compute required fields at the leaf level:
-        # - If a linked field is dynamic and its base is required in the block schema, require the leaf
-        # - If a linked field is regular and is required in the block schema, require the leaf
-        required_fields: set[str] = set()
-        for link in links:
-            field_name = link.sink_name
-            is_dynamic = is_dynamic_field(field_name)
-            # Always use cleaned field name for property key (Anthropic API compliance)
-            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
-
-            if is_dynamic:
-                base_name = extract_base_field_name(field_name)
-                if base_name in base_required:
-                    required_fields.add(clean_field_name)
-            else:
-                if field_name in base_required:
-                    required_fields.add(clean_field_name)
-
         tool_function["parameters"] = {
-            "type": "object",
+            **block.input_schema.jsonschema(),
             "properties": properties,
-            "additionalProperties": False,
-            "required": sorted(required_fields),
         }
 
-        # Store field mapping for later use in output processing
-        tool_function["_field_mapping"] = field_mapping
-
         return {"type": "function", "function": tool_function}
 
     @staticmethod
@@ -413,12 +366,13 @@ class SmartDecisionMakerBlock(Block):
             sink_block_properties = sink_block_input_schema.get("properties", {}).get(
                 link.sink_name, {}
             )
+            sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
             description = (
                 sink_block_properties["description"]
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[link.sink_name] = {
+            properties[sink_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -434,17 +388,24 @@ class SmartDecisionMakerBlock(Block):
         return {"type": "function", "function": tool_function}
 
     @staticmethod
-    async def _create_function_signature(
-        node_id: str,
-    ) -> list[dict[str, Any]]:
+    async def _create_function_signature(node_id: str) -> list[dict[str, Any]]:
         """
-        Creates function signatures for connected tools.
+        Creates function signatures for tools linked to a specified node within a graph.
+
+        This method filters the graph links to identify those that are tools and are
+        connected to the given node_id. It then constructs function signatures for each
+        tool based on the metadata and input schema of the linked nodes.
 
         Args:
             node_id: The node_id for which to create function signatures.
 
         Returns:
-            List of function signatures for tools
+            list[dict[str, Any]]: A list of dictionaries, each representing a function signature
+                                  for a tool, including its name, description, and parameters.
+
+        Raises:
+            ValueError: If no tool links are found for the specified node_id, or if a sink node
+                        or its metadata cannot be found.
         """
         db_client = get_database_manager_async_client()
         tools = [
@@ -469,116 +430,20 @@ class SmartDecisionMakerBlock(Block):
                 raise ValueError(f"Sink node not found: {links[0].sink_id}")
 
             if sink_node.block_id == AgentExecutorBlock().id:
-                tool_func = (
+                return_tool_functions.append(
                     await SmartDecisionMakerBlock._create_agent_function_signature(
                         sink_node, links
                     )
                 )
-                return_tool_functions.append(tool_func)
             else:
-                tool_func = (
+                return_tool_functions.append(
                     await SmartDecisionMakerBlock._create_block_function_signature(
                         sink_node, links
                     )
                 )
-                return_tool_functions.append(tool_func)
 
         return return_tool_functions
 
-    async def _attempt_llm_call_with_validation(
-        self,
-        credentials: llm.APIKeyCredentials,
-        input_data: Input,
-        current_prompt: list[dict],
-        tool_functions: list[dict[str, Any]],
-    ):
-        """
-        Attempt a single LLM call with tool validation.
-
-        Returns the response if successful, raises ValueError if validation fails.
-        """
-        resp = await llm.llm_call(
-            credentials=credentials,
-            llm_model=input_data.model,
-            prompt=current_prompt,
-            max_tokens=input_data.max_tokens,
-            tools=tool_functions,
-            ollama_host=input_data.ollama_host,
-            parallel_tool_calls=input_data.multiple_tool_calls,
-        )
-
-        # Track LLM usage stats per call
-        self.merge_stats(
-            NodeExecutionStats(
-                input_token_count=resp.prompt_tokens,
-                output_token_count=resp.completion_tokens,
-                llm_call_count=1,
-            )
-        )
-
-        if not resp.tool_calls:
-            return resp
-        validation_errors_list: list[str] = []
-        for tool_call in resp.tool_calls:
-            tool_name = tool_call.function.name
-            try:
-                tool_args = json.loads(tool_call.function.arguments)
-            except Exception as e:
-                validation_errors_list.append(
-                    f"Tool call '{tool_name}' has invalid JSON arguments: {e}"
-                )
-                continue
-
-            # Find the tool definition to get the expected arguments
-            tool_def = next(
-                (
-                    tool
-                    for tool in tool_functions
-                    if tool["function"]["name"] == tool_name
-                ),
-                None,
-            )
-            if tool_def is None and len(tool_functions) == 1:
-                tool_def = tool_functions[0]
-
-            # Get parameters schema from tool definition
-            if (
-                tool_def
-                and "function" in tool_def
-                and "parameters" in tool_def["function"]
-            ):
-                parameters = tool_def["function"]["parameters"]
-                expected_args = parameters.get("properties", {})
-                required_params = set(parameters.get("required", []))
-            else:
-                expected_args = {arg: {} for arg in tool_args.keys()}
-                required_params = set()
-
-            # Validate tool call arguments
-            provided_args = set(tool_args.keys())
-            expected_args_set = set(expected_args.keys())
-
-            # Check for unexpected arguments (typos)
-            unexpected_args = provided_args - expected_args_set
-            # Only check for missing REQUIRED parameters
-            missing_required_args = required_params - provided_args
-
-            if unexpected_args or missing_required_args:
-                error_msg = f"Tool call '{tool_name}' has parameter errors:"
-                if unexpected_args:
-                    error_msg += f" Unknown parameters: {sorted(unexpected_args)}."
-                if missing_required_args:
-                    error_msg += f" Missing required parameters: {sorted(missing_required_args)}."
-                error_msg += f" Expected parameters: {sorted(expected_args_set)}."
-                if required_params:
-                    error_msg += f" Required parameters: {sorted(required_params)}."
-                validation_errors_list.append(error_msg)
-
-        if validation_errors_list:
-            raise ValueError("; ".join(validation_errors_list))
-
-        return resp
-
     async def run(
         self,
         input_data: Input,
@@ -601,19 +466,27 @@ class SmartDecisionMakerBlock(Block):
         if pending_tool_calls and input_data.last_tool_output is None:
             raise ValueError(f"Tool call requires an output for {pending_tool_calls}")
 
+        # Only assign the last tool output to the first pending tool call
         tool_output = []
         if pending_tool_calls and input_data.last_tool_output is not None:
+            # Get the first pending tool call ID
             first_call_id = next(iter(pending_tool_calls.keys()))
             tool_output.append(
                 _create_tool_response(first_call_id, input_data.last_tool_output)
             )
 
+            # Add tool output to prompt right away
             prompt.extend(tool_output)
+
+            # Check if there are still pending tool calls after handling the first one
             remaining_pending_calls = get_pending_tool_calls(prompt)
 
+            # If there are still pending tool calls, yield the conversation and return early
             if remaining_pending_calls:
                 yield "conversations", prompt
                 return
+
+        # Fallback on adding tool output in the conversation history as user prompt.
         elif input_data.last_tool_output:
             logger.error(
                 f"[SmartDecisionMakerBlock-node_exec_id={node_exec_id}] "
@@ -646,33 +519,25 @@ class SmartDecisionMakerBlock(Block):
         ):
             prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        current_prompt = list(prompt)
-        max_attempts = max(1, int(input_data.retry))
-        response = None
+        response = await llm.llm_call(
+            credentials=credentials,
+            llm_model=input_data.model,
+            prompt=prompt,
+            json_format=False,
+            max_tokens=input_data.max_tokens,
+            tools=tool_functions,
+            ollama_host=input_data.ollama_host,
+            parallel_tool_calls=input_data.multiple_tool_calls,
+        )
 
-        last_error = None
-        for attempt in range(max_attempts):
-            try:
-                response = await self._attempt_llm_call_with_validation(
-                    credentials, input_data, current_prompt, tool_functions
-                )
-                break
-
-            except ValueError as e:
-                last_error = e
-                error_feedback = (
-                    "Your tool call had parameter errors. Please fix the following issues and try again:\n"
-                    + f"- {str(e)}\n"
-                    + "\nPlease make sure to use the exact parameter names as specified in the function schema."
-                )
-                current_prompt = list(current_prompt) + [
-                    {"role": "user", "content": error_feedback}
-                ]
-
-        if response is None:
-            raise last_error or ValueError(
-                "Failed to get valid response after all retry attempts"
+        # Track LLM usage stats
+        self.merge_stats(
+            NodeExecutionStats(
+                input_token_count=response.prompt_tokens,
+                output_token_count=response.completion_tokens,
+                llm_call_count=1,
             )
+        )
 
         if not response.tool_calls:
             yield "finished", response.response
@@ -682,6 +547,7 @@ class SmartDecisionMakerBlock(Block):
             tool_name = tool_call.function.name
             tool_args = json.loads(tool_call.function.arguments)
 
+            # Find the tool definition to get the expected arguments
             tool_def = next(
                 (
                     tool
@@ -690,6 +556,7 @@ class SmartDecisionMakerBlock(Block):
                 ),
                 None,
             )
+
             if (
                 tool_def
                 and "function" in tool_def
@@ -697,38 +564,20 @@ class SmartDecisionMakerBlock(Block):
             ):
                 expected_args = tool_def["function"]["parameters"].get("properties", {})
             else:
-                expected_args = {arg: {} for arg in tool_args.keys()}
+                expected_args = tool_args.keys()
 
-            # Get field mapping from tool definition
-            field_mapping = (
-                tool_def.get("function", {}).get("_field_mapping", {})
-                if tool_def
-                else {}
-            )
-
-            for clean_arg_name in expected_args:
-                # arg_name is now always the cleaned field name (for Anthropic API compliance)
-                # Get the original field name from field mapping for proper emit key generation
-                original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
-                arg_value = tool_args.get(clean_arg_name)
-
-                sanitized_tool_name = self.cleanup(tool_name)
-                sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sanitized_tool_name}_~_{sanitized_arg_name}"
-
-                logger.debug(
-                    "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",
-                    graph_exec_id,
-                    node_exec_id,
-                    emit_key,
-                )
-                yield emit_key, arg_value
+            # Yield provided arguments and None for missing ones
+            for arg_name in expected_args:
+                if arg_name in tool_args:
+                    yield f"tools_^_{tool_name}_~_{arg_name}", tool_args[arg_name]
+                else:
+                    yield f"tools_^_{tool_name}_~_{arg_name}", None
 
+        # Add reasoning to conversation history if available
         if response.reasoning:
             prompt.append(
                 {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
             )
 
-        prompt.append(_convert_raw_response_to_dict(response.raw_response))
-
+        prompt.append(response.raw_response)
         yield "conversations", prompt

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

@@ -19,7 +19,7 @@ async def test_block_ids_valid(block: Type[Block]):
     # Skip list for blocks with known invalid UUIDs
     skip_blocks = {
         "GetWeatherInformationBlock",
-        "ExecuteCodeBlock",
+        "CodeExecutionBlock",
         "CountdownTimerBlock",
         "TwitterGetListTweetsBlock",
         "TwitterRemoveListMemberBlock",

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -1,269 +0,0 @@
-"""
-Test security fixes for various DoS vulnerabilities.
-"""
-
-import asyncio
-from unittest.mock import patch
-
-import pytest
-
-from backend.blocks.code_extraction_block import CodeExtractionBlock
-from backend.blocks.iteration import StepThroughItemsBlock
-from backend.blocks.llm import AITextSummarizerBlock
-from backend.blocks.text import ExtractTextInformationBlock
-from backend.blocks.xml_parser import XMLParserBlock
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
-
-
-class TestCodeExtractionBlockSecurity:
-    """Test ReDoS fixes in CodeExtractionBlock."""
-
-    async def test_redos_protection(self):
-        """Test that the regex patterns don't cause ReDoS."""
-        block = CodeExtractionBlock()
-
-        # Test with input that would previously cause ReDoS
-        malicious_input = "```python" + " " * 10000  # Large spaces
-
-        result = []
-        async for output_name, output_data in block.run(
-            CodeExtractionBlock.Input(text=malicious_input)
-        ):
-            result.append((output_name, output_data))
-
-        # Should complete without hanging
-        assert len(result) >= 1
-        assert any(name == "remaining_text" for name, _ in result)
-
-
-class TestAITextSummarizerBlockSecurity:
-    """Test memory exhaustion fixes in AITextSummarizerBlock."""
-
-    def test_split_text_limits(self):
-        """Test that _split_text has proper limits."""
-        # Test text size limit
-        large_text = "a" * 2_000_000  # 2MB text
-        result = AITextSummarizerBlock._split_text(large_text, 1000, 100)
-
-        # Should be truncated to 1MB
-        total_chars = sum(len(chunk) for chunk in result)
-        assert total_chars <= 1_000_000 + 1000  # Allow for chunk boundary
-
-        # Test chunk count limit
-        result = AITextSummarizerBlock._split_text("word " * 10000, 10, 9)
-        assert len(result) <= 100  # MAX_CHUNKS limit
-
-        # Test parameter validation
-        result = AITextSummarizerBlock._split_text(
-            "test", 10, 15
-        )  # overlap > max_tokens
-        assert len(result) >= 1  # Should still work
-
-
-class TestExtractTextInformationBlockSecurity:
-    """Test ReDoS and memory exhaustion fixes in ExtractTextInformationBlock."""
-
-    async def test_text_size_limits(self):
-        """Test text size limits."""
-        block = ExtractTextInformationBlock()
-
-        # Test with large input
-        large_text = "a" * 2_000_000  # 2MB
-
-        results = []
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=large_text, pattern=r"a+", find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        # Should complete and have limits applied
-        matched_results = [r for name, r in results if name == "matched_results"]
-        if matched_results:
-            assert len(matched_results[0]) <= 1000  # MAX_MATCHES limit
-
-    async def test_dangerous_pattern_timeout(self):
-        """Test timeout protection for dangerous patterns."""
-        block = ExtractTextInformationBlock()
-
-        # Test with potentially dangerous lookahead pattern
-        test_input = "a" * 1000
-
-        # This should complete quickly due to timeout protection
-        start_time = asyncio.get_event_loop().time()
-        results = []
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=test_input, pattern=r"(?=.+)", find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        end_time = asyncio.get_event_loop().time()
-        # Should complete within reasonable time (much less than 5s timeout)
-        assert (end_time - start_time) < 10
-
-    async def test_redos_catastrophic_backtracking(self):
-        """Test that ReDoS patterns with catastrophic backtracking are handled."""
-        block = ExtractTextInformationBlock()
-
-        # Pattern that causes catastrophic backtracking: (a+)+b
-        # With input "aaaaaaaaaaaaaaaaaaaaaaaaaaaa" (no 'b'), this causes exponential time
-        dangerous_pattern = r"(a+)+b"
-        test_input = "a" * 30  # 30 'a's without a 'b' at the end
-
-        # This should be handled by timeout protection or pattern detection
-        start_time = asyncio.get_event_loop().time()
-        results = []
-
-        async for output_name, output_data in block.run(
-            ExtractTextInformationBlock.Input(
-                text=test_input, pattern=dangerous_pattern, find_all=True, group=0
-            )
-        ):
-            results.append((output_name, output_data))
-
-        end_time = asyncio.get_event_loop().time()
-        elapsed = end_time - start_time
-
-        # Should complete within timeout (6 seconds to be safe)
-        # The current threading.Timer approach doesn't work, so this will likely fail
-        # demonstrating the need for a fix
-        assert elapsed < 6, f"Regex took {elapsed}s, timeout mechanism failed"
-
-        # Should return empty results on timeout or no match
-        matched_results = [r for name, r in results if name == "matched_results"]
-        assert matched_results[0] == []  # No matches expected
-
-
-class TestStepThroughItemsBlockSecurity:
-    """Test iteration limits in StepThroughItemsBlock."""
-
-    async def test_item_count_limits(self):
-        """Test maximum item count limits."""
-        block = StepThroughItemsBlock()
-
-        # Test with too many items
-        large_list = list(range(20000))  # Exceeds MAX_ITEMS (10000)
-
-        with pytest.raises(ValueError, match="Too many items"):
-            async for _ in block.run(StepThroughItemsBlock.Input(items=large_list)):
-                pass
-
-    async def test_string_size_limits(self):
-        """Test string input size limits."""
-        block = StepThroughItemsBlock()
-
-        # Test with large JSON string
-        large_string = '["item"]' * 200000  # Large JSON string
-
-        with pytest.raises(ValueError, match="Input too large"):
-            async for _ in block.run(
-                StepThroughItemsBlock.Input(items_str=large_string)
-            ):
-                pass
-
-    async def test_normal_iteration_works(self):
-        """Test that normal iteration still works."""
-        block = StepThroughItemsBlock()
-
-        results = []
-        async for output_name, output_data in block.run(
-            StepThroughItemsBlock.Input(items=[1, 2, 3])
-        ):
-            results.append((output_name, output_data))
-
-        # Should have 6 outputs (item, key for each of 3 items)
-        assert len(results) == 6
-        items = [data for name, data in results if name == "item"]
-        assert items == [1, 2, 3]
-
-
-class TestXMLParserBlockSecurity:
-    """Test XML size limits in XMLParserBlock."""
-
-    async def test_xml_size_limits(self):
-        """Test XML input size limits."""
-        block = XMLParserBlock()
-
-        # Test with large XML - need to exceed 10MB limit
-        # Each "<item>data</item>" is 17 chars, need ~620K items for >10MB
-        large_xml = "<root>" + "<item>data</item>" * 620000 + "</root>"
-
-        with pytest.raises(ValueError, match="XML too large"):
-            async for _ in block.run(XMLParserBlock.Input(input_xml=large_xml)):
-                pass
-
-
-class TestStoreMediaFileSecurity:
-    """Test file storage security limits."""
-
-    @patch("backend.util.file.scan_content_safe")
-    @patch("backend.util.file.get_cloud_storage_handler")
-    async def test_file_size_limits(self, mock_cloud_storage, mock_scan):
-        """Test file size limits."""
-        # Mock cloud storage handler - get_cloud_storage_handler is async
-        # but is_cloud_path and parse_cloud_path are sync methods
-        from unittest.mock import MagicMock
-
-        mock_handler = MagicMock()
-        mock_handler.is_cloud_path.return_value = False
-
-        # Make get_cloud_storage_handler an async function that returns the mock handler
-        async def async_get_handler():
-            return mock_handler
-
-        mock_cloud_storage.side_effect = async_get_handler
-        mock_scan.return_value = None
-
-        # Test with large base64 content
-        large_content = "a" * (200 * 1024 * 1024)  # 200MB
-        large_data_uri = f"data:text/plain;base64,{large_content}"
-
-        with pytest.raises(ValueError, match="File too large"):
-            await store_media_file(
-                graph_exec_id="test",
-                file=MediaFileType(large_data_uri),
-                user_id="test_user",
-            )
-
-    @patch("backend.util.file.Path")
-    @patch("backend.util.file.scan_content_safe")
-    @patch("backend.util.file.get_cloud_storage_handler")
-    async def test_directory_size_limits(self, mock_cloud_storage, mock_scan, MockPath):
-        """Test directory size limits."""
-        from unittest.mock import MagicMock
-
-        mock_handler = MagicMock()
-        mock_handler.is_cloud_path.return_value = False
-
-        async def async_get_handler():
-            return mock_handler
-
-        mock_cloud_storage.side_effect = async_get_handler
-        mock_scan.return_value = None
-
-        # Create mock path instance for the execution directory
-        mock_path_instance = MagicMock()
-        mock_path_instance.exists.return_value = True
-
-        # Mock glob to return files that total > 1GB
-        mock_file = MagicMock()
-        mock_file.is_file.return_value = True
-        mock_file.stat.return_value.st_size = 2 * 1024 * 1024 * 1024  # 2GB
-        mock_path_instance.glob.return_value = [mock_file]
-
-        # Make Path() return our mock
-        MockPath.return_value = mock_path_instance
-
-        # Should raise an error when directory size exceeds limit
-        with pytest.raises(ValueError, match="Disk usage limit exceeded"):
-            await store_media_file(
-                graph_exec_id="test",
-                file=MediaFileType(
-                    "data:text/plain;base64,dGVzdA=="
-                ),  # Small test file
-                user_id="test_user",
-            )

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

@@ -30,6 +30,7 @@ class TestLLMStatsTracking:
                 credentials=llm.TEST_CREDENTIALS,
                 llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
+                json_format=False,
                 max_tokens=100,
             )
 
@@ -41,8 +42,6 @@ class TestLLMStatsTracking:
     @pytest.mark.asyncio
     async def test_ai_structured_response_block_tracks_stats(self):
         """Test that AIStructuredResponseGeneratorBlock correctly tracks stats."""
-        from unittest.mock import patch
-
         import backend.blocks.llm as llm
 
         block = llm.AIStructuredResponseGeneratorBlock()
@@ -52,7 +51,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
+                response='{"key1": "value1", "key2": "value2"}',
                 tool_calls=None,
                 prompt_tokens=15,
                 completion_tokens=25,
@@ -70,12 +69,10 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Check stats
         assert block.execution_stats.input_token_count == 15
@@ -146,7 +143,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='<json_output id="test123456">{"wrong": "format"}</json_output>',
+                    response='{"wrong": "format"}',
                     tool_calls=None,
                     prompt_tokens=10,
                     completion_tokens=15,
@@ -157,7 +154,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
+                    response='{"key1": "value1", "key2": "value2"}',
                     tool_calls=None,
                     prompt_tokens=20,
                     completion_tokens=25,
@@ -176,12 +173,10 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Check stats - should accumulate both calls
         # For 2 attempts: attempt 1 (failed) + attempt 2 (success) = 2 total
@@ -274,8 +269,7 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='<json_output id="test123456">{"summary": "Test chunk summary"}</json_output>',
-                            tool_calls=None,
+                            content='{"summary": "Test chunk summary"}', tool_calls=None
                         )
                     )
                 ]
@@ -283,7 +277,7 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='<json_output id="test123456">{"final_summary": "Test final summary"}</json_output>',
+                            content='{"final_summary": "Test final summary"}',
                             tool_calls=None,
                         )
                     )
@@ -304,13 +298,11 @@ class TestLLMStatsTracking:
                 max_tokens=1000,  # Large enough to avoid chunking
             )
 
-            # Mock secrets.token_hex to return consistent ID
-            with patch("secrets.token_hex", return_value="test123456"):
-                outputs = {}
-                async for output_name, output_data in block.run(
-                    input_data, credentials=llm.TEST_CREDENTIALS
-                ):
-                    outputs[output_name] = output_data
+            outputs = {}
+            async for output_name, output_data in block.run(
+                input_data, credentials=llm.TEST_CREDENTIALS
+            ):
+                outputs[output_name] = output_data
 
             print(f"Actual calls made: {call_count}")
             print(f"Block stats: {block.execution_stats}")
@@ -465,7 +457,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='<json_output id="test123456">{"result": "test"}</json_output>',
+                response='{"result": "test"}',
                 tool_calls=None,
                 prompt_tokens=10,
                 completion_tokens=20,
@@ -484,12 +476,10 @@ class TestLLMStatsTracking:
 
         # Run the block
         outputs = {}
-        # Mock secrets.token_hex to return consistent ID
-        with patch("secrets.token_hex", return_value="test123456"):
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+        async for output_name, output_data in block.run(
+            input_data, credentials=llm.TEST_CREDENTIALS
+        ):
+            outputs[output_name] = output_data
 
         # Block finished - now grab and assert stats
         assert block.execution_stats is not None

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

@@ -35,19 +35,20 @@ async def execute_graph(
     logger.info("Input data: %s", input_data)
 
     # --- Test adding new executions --- #
-    graph_exec = await agent_server.test_execute_graph(
+    response = await agent_server.test_execute_graph(
         user_id=test_user.id,
         graph_id=test_graph.id,
         graph_version=test_graph.version,
         node_input=input_data,
     )
-    logger.info("Created execution with ID: %s", graph_exec.id)
+    graph_exec_id = response.graph_exec_id
+    logger.info("Created execution with ID: %s", graph_exec_id)
 
     # Execution queue should be empty
     logger.info("Waiting for execution to complete...")
-    result = await wait_execution(test_user.id, graph_exec.id, 30)
+    result = await wait_execution(test_user.id, graph_exec_id, 30)
     logger.info("Execution completed with %d results", len(result))
-    return graph_exec.id
+    return graph_exec_id
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -216,17 +217,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_function_signature method to avoid database calls
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],
+    with patch("backend.blocks.llm.llm_call", return_value=mock_response), patch.object(
+        SmartDecisionMakerBlock, "_create_function_signature", return_value=[]
     ):
 
         # Create test input
@@ -258,471 +250,3 @@ async def test_smart_decision_maker_tracks_llm_stats():
         # Verify outputs
         assert "finished" in outputs  # Should have finished since no tool calls
         assert outputs["finished"] == "I need to think about this."
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_parameter_validation():
-    """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
-    from unittest.mock import MagicMock, patch
-
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool functions with specific parameter schema
-    mock_tool_functions = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "description": "Search for keywords with difficulty filtering",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string", "description": "Search query"},
-                        "max_keyword_difficulty": {
-                            "type": "integer",
-                            "description": "Maximum keyword difficulty (required)",
-                        },
-                        "optional_param": {
-                            "type": "string",
-                            "description": "Optional parameter with default",
-                            "default": "default_value",
-                        },
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    # Test case 1: Tool call with TYPO in parameter name (should retry and eventually fail)
-    mock_tool_call_with_typo = MagicMock()
-    mock_tool_call_with_typo.function.name = "search_keywords"
-    mock_tool_call_with_typo.function.arguments = '{"query": "test", "maximum_keyword_difficulty": 50}'  # TYPO: maximum instead of max
-
-    mock_response_with_typo = MagicMock()
-    mock_response_with_typo.response = None
-    mock_response_with_typo.tool_calls = [mock_tool_call_with_typo]
-    mock_response_with_typo.prompt_tokens = 50
-    mock_response_with_typo.completion_tokens = 25
-    mock_response_with_typo.reasoning = None
-    mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_with_typo,
-    ) as mock_llm_call, patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            retry=2,  # Set retry to 2 for testing
-        )
-
-        # Should raise ValueError after retries due to typo'd parameter name
-        with pytest.raises(ValueError) as exc_info:
-            outputs = {}
-            async for output_name, output_data in block.run(
-                input_data,
-                credentials=llm_module.TEST_CREDENTIALS,
-                graph_id="test-graph-id",
-                node_id="test-node-id",
-                graph_exec_id="test-exec-id",
-                node_exec_id="test-node-exec-id",
-                user_id="test-user-id",
-            ):
-                outputs[output_name] = output_data
-
-        # Verify error message contains details about the typo
-        error_msg = str(exc_info.value)
-        assert "Tool call 'search_keywords' has parameter errors" in error_msg
-        assert "Unknown parameters: ['maximum_keyword_difficulty']" in error_msg
-
-        # Verify that LLM was called the expected number of times (retries)
-        assert mock_llm_call.call_count == 2  # Should retry based on input_data.retry
-
-    # Test case 2: Tool call missing REQUIRED parameter (should raise ValueError)
-    mock_tool_call_missing_required = MagicMock()
-    mock_tool_call_missing_required.function.name = "search_keywords"
-    mock_tool_call_missing_required.function.arguments = (
-        '{"query": "test"}'  # Missing required max_keyword_difficulty
-    )
-
-    mock_response_missing_required = MagicMock()
-    mock_response_missing_required.response = None
-    mock_response_missing_required.tool_calls = [mock_tool_call_missing_required]
-    mock_response_missing_required.prompt_tokens = 50
-    mock_response_missing_required.completion_tokens = 25
-    mock_response_missing_required.reasoning = None
-    mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_missing_required,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should raise ValueError due to missing required parameter
-        with pytest.raises(ValueError) as exc_info:
-            outputs = {}
-            async for output_name, output_data in block.run(
-                input_data,
-                credentials=llm_module.TEST_CREDENTIALS,
-                graph_id="test-graph-id",
-                node_id="test-node-id",
-                graph_exec_id="test-exec-id",
-                node_exec_id="test-node-exec-id",
-                user_id="test-user-id",
-            ):
-                outputs[output_name] = output_data
-
-        error_msg = str(exc_info.value)
-        assert "Tool call 'search_keywords' has parameter errors" in error_msg
-        assert "Missing required parameters: ['max_keyword_difficulty']" in error_msg
-
-    # Test case 3: Valid tool call with OPTIONAL parameter missing (should succeed)
-    mock_tool_call_valid = MagicMock()
-    mock_tool_call_valid.function.name = "search_keywords"
-    mock_tool_call_valid.function.arguments = '{"query": "test", "max_keyword_difficulty": 50}'  # optional_param missing, but that's OK
-
-    mock_response_valid = MagicMock()
-    mock_response_valid.response = None
-    mock_response_valid.tool_calls = [mock_tool_call_valid]
-    mock_response_valid.prompt_tokens = 50
-    mock_response_valid.completion_tokens = 25
-    mock_response_valid.reasoning = None
-    mock_response_valid.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_valid,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should succeed - optional parameter missing is OK
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify tool outputs were generated correctly
-        assert "tools_^_search_keywords_~_query" in outputs
-        assert outputs["tools_^_search_keywords_~_query"] == "test"
-        assert "tools_^_search_keywords_~_max_keyword_difficulty" in outputs
-        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
-        # Optional parameter should be None when not provided
-        assert "tools_^_search_keywords_~_optional_param" in outputs
-        assert outputs["tools_^_search_keywords_~_optional_param"] is None
-
-    # Test case 4: Valid tool call with ALL parameters (should succeed)
-    mock_tool_call_all_params = MagicMock()
-    mock_tool_call_all_params.function.name = "search_keywords"
-    mock_tool_call_all_params.function.arguments = '{"query": "test", "max_keyword_difficulty": 50, "optional_param": "custom_value"}'
-
-    mock_response_all_params = MagicMock()
-    mock_response_all_params.response = None
-    mock_response_all_params.tool_calls = [mock_tool_call_all_params]
-    mock_response_all_params.prompt_tokens = 50
-    mock_response_all_params.completion_tokens = 25
-    mock_response_all_params.reasoning = None
-    mock_response_all_params.raw_response = {"role": "assistant", "content": None}
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_all_params,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Search for keywords",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        # Should succeed with all parameters
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify all tool outputs were generated correctly
-        assert outputs["tools_^_search_keywords_~_query"] == "test"
-        assert outputs["tools_^_search_keywords_~_max_keyword_difficulty"] == 50
-        assert outputs["tools_^_search_keywords_~_optional_param"] == "custom_value"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_raw_response_conversion():
-    """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
-    from unittest.mock import MagicMock, patch
-
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool functions
-    mock_tool_functions = [
-        {
-            "type": "function",
-            "function": {
-                "name": "test_tool",
-                "parameters": {
-                    "type": "object",
-                    "properties": {"param": {"type": "string"}},
-                    "required": ["param"],
-                },
-            },
-        }
-    ]
-
-    # Test case 1: Simulate ChatCompletionMessage raw_response that caused the original error
-    class MockChatCompletionMessage:
-        """Simulate OpenAI's ChatCompletionMessage object that lacks .get() method"""
-
-        def __init__(self, role, content, tool_calls=None):
-            self.role = role
-            self.content = content
-            self.tool_calls = tool_calls or []
-
-        # This is what caused the error - no .get() method
-        # def get(self, key, default=None):  # Intentionally missing
-
-    # First response: has invalid parameter name (triggers retry)
-    mock_tool_call_invalid = MagicMock()
-    mock_tool_call_invalid.function.name = "test_tool"
-    mock_tool_call_invalid.function.arguments = (
-        '{"wrong_param": "test_value"}'  # Invalid parameter name
-    )
-
-    mock_response_retry = MagicMock()
-    mock_response_retry.response = None
-    mock_response_retry.tool_calls = [mock_tool_call_invalid]
-    mock_response_retry.prompt_tokens = 50
-    mock_response_retry.completion_tokens = 25
-    mock_response_retry.reasoning = None
-    # This would cause the original error without our fix
-    mock_response_retry.raw_response = MockChatCompletionMessage(
-        role="assistant", content=None, tool_calls=[mock_tool_call_invalid]
-    )
-
-    # Second response: successful (correct parameter name)
-    mock_tool_call_valid = MagicMock()
-    mock_tool_call_valid.function.name = "test_tool"
-    mock_tool_call_valid.function.arguments = (
-        '{"param": "test_value"}'  # Correct parameter name
-    )
-
-    mock_response_success = MagicMock()
-    mock_response_success.response = None
-    mock_response_success.tool_calls = [mock_tool_call_valid]
-    mock_response_success.prompt_tokens = 50
-    mock_response_success.completion_tokens = 25
-    mock_response_success.reasoning = None
-    mock_response_success.raw_response = MockChatCompletionMessage(
-        role="assistant", content=None, tool_calls=[mock_tool_call_valid]
-    )
-
-    # Mock llm_call to return different responses on different calls
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm_call, patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=mock_tool_functions,
-    ):
-        # First call returns response that will trigger retry due to validation error
-        # Second call returns successful response
-        mock_llm_call.side_effect = [mock_response_retry, mock_response_success]
-
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Test prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            retry=2,
-        )
-
-        # Should succeed after retry, demonstrating our helper function works
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Verify the tool output was generated successfully
-        assert "tools_^_test_tool_~_param" in outputs
-        assert outputs["tools_^_test_tool_~_param"] == "test_value"
-
-        # Verify conversation history was properly maintained
-        assert "conversations" in outputs
-        conversations = outputs["conversations"]
-        assert len(conversations) > 0
-
-        # The conversations should contain properly converted raw_response objects as dicts
-        # This would have failed with the original bug due to ChatCompletionMessage.get() error
-        for msg in conversations:
-            assert isinstance(msg, dict), f"Expected dict, got {type(msg)}"
-            if msg.get("role") == "assistant":
-                # Should have been converted from ChatCompletionMessage to dict
-                assert "role" in msg
-
-        # Verify LLM was called twice (initial + 1 retry)
-        assert mock_llm_call.call_count == 2
-
-    # Test case 2: Test with different raw_response types (Ollama string, dict)
-    # Test Ollama string response
-    mock_response_ollama = MagicMock()
-    mock_response_ollama.response = "I'll help you with that."
-    mock_response_ollama.tool_calls = None
-    mock_response_ollama.prompt_tokens = 30
-    mock_response_ollama.completion_tokens = 15
-    mock_response_ollama.reasoning = None
-    mock_response_ollama.raw_response = (
-        "I'll help you with that."  # Ollama returns string
-    )
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_ollama,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],  # No tools for this test
-    ):
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Simple prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        # Should finish since no tool calls
-        assert "finished" in outputs
-        assert outputs["finished"] == "I'll help you with that."
-
-    # Test case 3: Test with dict raw_response (some providers/tests)
-    mock_response_dict = MagicMock()
-    mock_response_dict.response = "Test response"
-    mock_response_dict.tool_calls = None
-    mock_response_dict.prompt_tokens = 25
-    mock_response_dict.completion_tokens = 10
-    mock_response_dict.reasoning = None
-    mock_response_dict.raw_response = {
-        "role": "assistant",
-        "content": "Test response",
-    }  # Dict format
-
-    from unittest.mock import AsyncMock
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response_dict,
-    ), patch.object(
-        SmartDecisionMakerBlock,
-        "_create_function_signature",
-        new_callable=AsyncMock,
-        return_value=[],
-    ):
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Another test",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-        ):
-            outputs[output_name] = output_data
-
-        assert "finished" in outputs
-        assert outputs["finished"] == "Test response"

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dict.py

@@ -48,24 +48,16 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     assert "parameters" in signature["function"]
     assert "properties" in signature["function"]["parameters"]
 
-    # Check that dynamic fields are handled with original names
+    # Check that dynamic fields are handled
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 3  # Should have all three fields
 
-    # Check that field names are cleaned (for Anthropic API compatibility)
-    assert "values___name" in properties
-    assert "values___age" in properties
-    assert "values___city" in properties
-
-    # Each dynamic field should have proper schema with descriptive text
-    for field_name, prop_value in properties.items():
+    # Each dynamic field should have proper schema
+    for prop_value in properties.values():
         assert "type" in prop_value
         assert prop_value["type"] == "string"  # Dynamic fields get string type
         assert "description" in prop_value
-        # Check that descriptions properly explain the dynamic field
-        if field_name == "values___name":
-            assert "Dictionary field 'name'" in prop_value["description"]
-            assert "values['name']" in prop_value["description"]
+        assert "Dynamic value for" in prop_value["description"]
 
 
 @pytest.mark.asyncio
@@ -104,18 +96,10 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 2  # Should have both list items
 
-    # Check that field names are cleaned (for Anthropic API compatibility)
-    assert "entries___0" in properties
-    assert "entries___1" in properties
-
-    # Each dynamic field should have proper schema with descriptive text
-    for field_name, prop_value in properties.items():
+    # Each dynamic field should have proper schema
+    for prop_value in properties.values():
         assert prop_value["type"] == "string"
-        assert "description" in prop_value
-        # Check that descriptions properly explain the list field
-        if field_name == "entries___0":
-            assert "List item 0" in prop_value["description"]
-            assert "entries[0]" in prop_value["description"]
+        assert "Dynamic value for" in prop_value["description"]
 
 
 @pytest.mark.asyncio

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

@@ -1,553 +0,0 @@
-"""Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
-
-import json
-from unittest.mock import AsyncMock, Mock, patch
-
-import pytest
-
-from backend.blocks.data_manipulation import AddToListBlock, CreateDictionaryBlock
-from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-from backend.blocks.text import MatchTextPatternBlock
-from backend.data.dynamic_fields import get_dynamic_field_description
-
-
-@pytest.mark.asyncio
-async def test_dynamic_field_description_generation():
-    """Test that dynamic field descriptions are generated correctly."""
-    # Test dictionary field description
-    desc = get_dynamic_field_description("values_#_name")
-    assert "Dictionary field 'name' for base field 'values'" in desc
-    assert "values['name']" in desc
-
-    # Test list field description
-    desc = get_dynamic_field_description("items_$_0")
-    assert "List item 0 for base field 'items'" in desc
-    assert "items[0]" in desc
-
-    # Test object field description
-    desc = get_dynamic_field_description("user_@_email")
-    assert "Object attribute 'email' for base field 'user'" in desc
-    assert "user.email" in desc
-
-    # Test regular field fallback
-    desc = get_dynamic_field_description("regular_field")
-    assert desc == "Value for regular_field"
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_dict_fields():
-    """Test that function signatures are created correctly for dictionary dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for CreateDictionaryBlock
-    mock_node = Mock()
-    mock_node.block = CreateDictionaryBlock()
-    mock_node.block_id = CreateDictionaryBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic dictionary fields (source sanitized, sink original)
-    mock_links = [
-        Mock(
-            source_name="tools_^_create_dict_~_values___name",  # Sanitized source
-            sink_name="values_#_name",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_values___age",  # Sanitized source
-            sink_name="values_#_age",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_values___email",  # Sanitized source
-            sink_name="values_#_email",  # Original sink
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    assert signature["type"] == "function"
-    assert "function" in signature
-    assert "parameters" in signature["function"]
-    assert "properties" in signature["function"]["parameters"]
-
-    # Check that dynamic fields are handled with original names
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 3
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "values___name" in properties
-    assert "values___age" in properties
-    assert "values___email" in properties
-
-    # Check descriptions mention they are dictionary fields
-    assert "Dictionary field" in properties["values___name"]["description"]
-    assert "values['name']" in properties["values___name"]["description"]
-
-    assert "Dictionary field" in properties["values___age"]["description"]
-    assert "values['age']" in properties["values___age"]["description"]
-
-    assert "Dictionary field" in properties["values___email"]["description"]
-    assert "values['email']" in properties["values___email"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_list_fields():
-    """Test that function signatures are created correctly for list dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for AddToListBlock
-    mock_node = Mock()
-    mock_node.block = AddToListBlock()
-    mock_node.block_id = AddToListBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic list fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_add_list_~_0",
-            sink_name="entries_$_0",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_add_list_~_1",
-            sink_name="entries_$_1",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_add_list_~_2",
-            sink_name="entries_$_2",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    assert signature["type"] == "function"
-    properties = signature["function"]["parameters"]["properties"]
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "entries___0" in properties
-    assert "entries___1" in properties
-    assert "entries___2" in properties
-
-    # Check descriptions mention they are list items
-    assert "List item 0" in properties["entries___0"]["description"]
-    assert "entries[0]" in properties["entries___0"]["description"]
-
-    assert "List item 1" in properties["entries___1"]["description"]
-    assert "entries[1]" in properties["entries___1"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_block_function_signature_with_object_fields():
-    """Test that function signatures are created correctly for object dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node for MatchTextPatternBlock (simulating object fields)
-    mock_node = Mock()
-    mock_node.block = MatchTextPatternBlock()
-    mock_node.block_id = MatchTextPatternBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic object fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_extract_~_user_name",
-            sink_name="data_@_user_name",  # Dynamic object field
-            sink_id="extract_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_extract_~_user_email",
-            sink_name="data_@_user_email",  # Dynamic object field
-            sink_id="extract_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Verify the signature structure
-    properties = signature["function"]["parameters"]["properties"]
-
-    # Check cleaned field names (for Anthropic API compatibility)
-    assert "data___user_name" in properties
-    assert "data___user_email" in properties
-
-    # Check descriptions mention they are object attributes
-    assert "Object attribute" in properties["data___user_name"]["description"]
-    assert "data.user_name" in properties["data___user_name"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_function_signature():
-    """Test that the mapping between sanitized and original field names is built correctly."""
-    block = SmartDecisionMakerBlock()
-
-    # Mock the database client and connected nodes
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-    ) as mock_db:
-        mock_client = AsyncMock()
-        mock_db.return_value = mock_client
-
-        # Create mock nodes and links
-        mock_dict_node = Mock()
-        mock_dict_node.block = CreateDictionaryBlock()
-        mock_dict_node.block_id = CreateDictionaryBlock().id
-        mock_dict_node.input_default = {}
-
-        mock_list_node = Mock()
-        mock_list_node.block = AddToListBlock()
-        mock_list_node.block_id = AddToListBlock().id
-        mock_list_node.input_default = {}
-
-        # Mock links with dynamic fields
-        dict_link1 = Mock(
-            source_name="tools_^_create_dictionary_~_name",
-            sink_name="values_#_name",
-            sink_id="dict_node_id",
-            source_id="test_node_id",
-        )
-        dict_link2 = Mock(
-            source_name="tools_^_create_dictionary_~_age",
-            sink_name="values_#_age",
-            sink_id="dict_node_id",
-            source_id="test_node_id",
-        )
-        list_link = Mock(
-            source_name="tools_^_add_to_list_~_0",
-            sink_name="entries_$_0",
-            sink_id="list_node_id",
-            source_id="test_node_id",
-        )
-
-        mock_client.get_connected_output_nodes.return_value = [
-            (dict_link1, mock_dict_node),
-            (dict_link2, mock_dict_node),
-            (list_link, mock_list_node),
-        ]
-
-        # Call the method that builds signatures
-        tool_functions = await block._create_function_signature("test_node_id")
-
-        # Verify we got 2 tool functions (one for dict, one for list)
-        assert len(tool_functions) == 2
-
-        # Verify the tool functions contain the dynamic field names
-        dict_tool = next(
-            (
-                tool
-                for tool in tool_functions
-                if tool["function"]["name"] == "createdictionaryblock"
-            ),
-            None,
-        )
-        assert dict_tool is not None
-        dict_properties = dict_tool["function"]["parameters"]["properties"]
-        assert "values___name" in dict_properties
-        assert "values___age" in dict_properties
-
-        list_tool = next(
-            (
-                tool
-                for tool in tool_functions
-                if tool["function"]["name"] == "addtolistblock"
-            ),
-            None,
-        )
-        assert list_tool is not None
-        list_properties = list_tool["function"]["parameters"]["properties"]
-        assert "entries___0" in list_properties
-
-
-@pytest.mark.asyncio
-async def test_output_yielding_with_dynamic_fields():
-    """Test that outputs are yielded correctly with dynamic field names mapped back."""
-    block = SmartDecisionMakerBlock()
-
-    # No more sanitized mapping needed since we removed sanitization
-
-    # Mock LLM response with tool calls
-    mock_response = Mock()
-    mock_response.tool_calls = [
-        Mock(
-            function=Mock(
-                arguments=json.dumps(
-                    {
-                        "values___name": "Alice",
-                        "values___age": 30,
-                        "values___email": "alice@example.com",
-                    }
-                ),
-            )
-        )
-    ]
-    # Ensure function name is a real string, not a Mock name
-    mock_response.tool_calls[0].function.name = "createdictionaryblock"
-    mock_response.reasoning = "Creating a dictionary with user information"
-    mock_response.raw_response = {"role": "assistant", "content": "test"}
-    mock_response.prompt_tokens = 100
-    mock_response.completion_tokens = 50
-
-    # Mock the LLM call
-    with patch(
-        "backend.blocks.smart_decision_maker.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm:
-        mock_llm.return_value = mock_response
-
-        # Mock the function signature creation
-        with patch.object(
-            block, "_create_function_signature", new_callable=AsyncMock
-        ) as mock_sig:
-            mock_sig.return_value = [
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "createdictionaryblock",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "values___name": {"type": "string"},
-                                "values___age": {"type": "number"},
-                                "values___email": {"type": "string"},
-                            },
-                        },
-                    },
-                }
-            ]
-
-            # Create input data
-            from backend.blocks import llm
-
-            input_data = block.input_schema(
-                prompt="Create a user dictionary",
-                credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.LlmModel.GPT4O,
-            )
-
-            # Run the block
-            outputs = {}
-            async for output_name, output_value in block.run(
-                input_data,
-                credentials=llm.TEST_CREDENTIALS,
-                graph_id="test_graph",
-                node_id="test_node",
-                graph_exec_id="test_exec",
-                node_exec_id="test_node_exec",
-                user_id="test_user",
-            ):
-                outputs[output_name] = output_value
-
-            # Verify the outputs use sanitized field names (matching frontend normalizeToolName)
-            assert "tools_^_createdictionaryblock_~_values___name" in outputs
-            assert outputs["tools_^_createdictionaryblock_~_values___name"] == "Alice"
-
-            assert "tools_^_createdictionaryblock_~_values___age" in outputs
-            assert outputs["tools_^_createdictionaryblock_~_values___age"] == 30
-
-            assert "tools_^_createdictionaryblock_~_values___email" in outputs
-            assert (
-                outputs["tools_^_createdictionaryblock_~_values___email"]
-                == "alice@example.com"
-            )
-
-
-@pytest.mark.asyncio
-async def test_mixed_regular_and_dynamic_fields():
-    """Test handling of blocks with both regular and dynamic fields."""
-    block = SmartDecisionMakerBlock()
-
-    # Create a mock node
-    mock_node = Mock()
-    mock_node.block = Mock()
-    mock_node.block.name = "TestBlock"
-    mock_node.block.description = "A test block"
-    mock_node.block.input_schema = Mock()
-
-    # Mock the get_field_schema to return a proper schema for regular fields
-    def get_field_schema(field_name):
-        if field_name == "regular_field":
-            return {"type": "string", "description": "A regular field"}
-        elif field_name == "values":
-            return {"type": "object", "description": "A dictionary field"}
-        else:
-            raise KeyError(f"Field {field_name} not found")
-
-    mock_node.block.input_schema.get_field_schema = get_field_schema
-    mock_node.block.input_schema.jsonschema = Mock(
-        return_value={"properties": {}, "required": []}
-    )
-
-    # Create links with both regular and dynamic fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_test_~_regular",
-            sink_name="regular_field",  # Regular field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_test_~_dict_key",
-            sink_name="values_#_key1",  # Dynamic dict field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_test_~_dict_key2",
-            sink_name="values_#_key2",  # Dynamic dict field
-            sink_id="test_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await block._create_block_function_signature(mock_node, mock_links)  # type: ignore
-
-    # Check properties
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 3
-
-    # Regular field should have its original schema
-    assert "regular_field" in properties
-    assert properties["regular_field"]["description"] == "A regular field"
-
-    # Dynamic fields should have generated descriptions
-    assert "values___key1" in properties
-    assert "Dictionary field" in properties["values___key1"]["description"]
-
-    assert "values___key2" in properties
-    assert "Dictionary field" in properties["values___key2"]["description"]
-
-
-@pytest.mark.asyncio
-async def test_validation_errors_dont_pollute_conversation():
-    """Test that validation errors are only used during retries and don't pollute the conversation."""
-    block = SmartDecisionMakerBlock()
-
-    # Track conversation history changes
-    conversation_snapshots = []
-
-    # Mock response with invalid tool call (missing required parameter)
-    invalid_response = Mock()
-    invalid_response.tool_calls = [
-        Mock(
-            function=Mock(
-                arguments=json.dumps({"wrong_param": "value"}),  # Wrong parameter name
-            )
-        )
-    ]
-    # Ensure function name is a real string, not a Mock name
-    invalid_response.tool_calls[0].function.name = "test_tool"
-    invalid_response.reasoning = None
-    invalid_response.raw_response = {"role": "assistant", "content": "invalid"}
-    invalid_response.prompt_tokens = 100
-    invalid_response.completion_tokens = 50
-
-    # Mock valid response after retry
-    valid_response = Mock()
-    valid_response.tool_calls = [
-        Mock(function=Mock(arguments=json.dumps({"correct_param": "value"})))
-    ]
-    # Ensure function name is a real string, not a Mock name
-    valid_response.tool_calls[0].function.name = "test_tool"
-    valid_response.reasoning = None
-    valid_response.raw_response = {"role": "assistant", "content": "valid"}
-    valid_response.prompt_tokens = 100
-    valid_response.completion_tokens = 50
-
-    call_count = 0
-
-    async def mock_llm_call(**kwargs):
-        nonlocal call_count
-        # Capture conversation state
-        conversation_snapshots.append(kwargs.get("prompt", []).copy())
-        call_count += 1
-        if call_count == 1:
-            return invalid_response
-        else:
-            return valid_response
-
-    # Mock the LLM call
-    with patch(
-        "backend.blocks.smart_decision_maker.llm.llm_call", new_callable=AsyncMock
-    ) as mock_llm:
-        mock_llm.side_effect = mock_llm_call
-
-        # Mock the function signature creation
-        with patch.object(
-            block, "_create_function_signature", new_callable=AsyncMock
-        ) as mock_sig:
-            mock_sig.return_value = [
-                {
-                    "type": "function",
-                    "function": {
-                        "name": "test_tool",
-                        "parameters": {
-                            "type": "object",
-                            "properties": {
-                                "correct_param": {
-                                    "type": "string",
-                                    "description": "The correct parameter",
-                                }
-                            },
-                            "required": ["correct_param"],
-                        },
-                    },
-                }
-            ]
-
-            # Create input data
-            from backend.blocks import llm
-
-            input_data = block.input_schema(
-                prompt="Test prompt",
-                credentials=llm.TEST_CREDENTIALS_INPUT,
-                model=llm.LlmModel.GPT4O,
-                retry=3,  # Allow retries
-            )
-
-            # Run the block
-            outputs = {}
-            async for output_name, output_value in block.run(
-                input_data,
-                credentials=llm.TEST_CREDENTIALS,
-                graph_id="test_graph",
-                node_id="test_node",
-                graph_exec_id="test_exec",
-                node_exec_id="test_node_exec",
-                user_id="test_user",
-            ):
-                outputs[output_name] = output_value
-
-            # Verify we had 2 LLM calls (initial + retry)
-            assert call_count == 2
-
-            # Check the final conversation output
-            final_conversation = outputs.get("conversations", [])
-
-            # The final conversation should NOT contain the validation error message
-            error_messages = [
-                msg
-                for msg in final_conversation
-                if msg.get("role") == "user"
-                and "parameter errors" in msg.get("content", "")
-            ]
-            assert (
-                len(error_messages) == 0
-            ), "Validation error leaked into final conversation"
-
-            # The final conversation should only have the successful response
-            assert final_conversation[-1]["content"] == "valid"

autogpt_platform/backend/backend/blocks/test/test_table_input.py

@@ -1,131 +0,0 @@
-import pytest
-
-from backend.blocks.io import AgentTableInputBlock
-from backend.util.test import execute_block_test
-
-
-@pytest.mark.asyncio
-async def test_table_input_block():
-    """Test the AgentTableInputBlock with basic input/output."""
-    block = AgentTableInputBlock()
-    await execute_block_test(block)
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_data():
-    """Test AgentTableInputBlock with actual table data."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="test_table",
-        column_headers=["Name", "Age", "City"],
-        value=[
-            {"Name": "John", "Age": "30", "City": "New York"},
-            {"Name": "Jane", "Age": "25", "City": "London"},
-            {"Name": "Bob", "Age": "35", "City": "Paris"},
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-
-    result = output_data[0][1]
-    assert len(result) == 3
-    assert result[0]["Name"] == "John"
-    assert result[1]["Age"] == "25"
-    assert result[2]["City"] == "Paris"
-
-
-@pytest.mark.asyncio
-async def test_table_input_empty_data():
-    """Test AgentTableInputBlock with empty data."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="empty_table", column_headers=["Col1", "Col2"], value=[]
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-    assert output_data[0][1] == []
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_missing_columns():
-    """Test AgentTableInputBlock passes through data with missing columns as-is."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="partial_table",
-        column_headers=["Name", "Age", "City"],
-        value=[
-            {"Name": "John", "Age": "30"},  # Missing City
-            {"Name": "Jane", "City": "London"},  # Missing Age
-            {"Age": "35", "City": "Paris"},  # Missing Name
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    result = output_data[0][1]
-    assert len(result) == 3
-
-    # Check data is passed through as-is
-    assert result[0] == {"Name": "John", "Age": "30"}
-    assert result[1] == {"Name": "Jane", "City": "London"}
-    assert result[2] == {"Age": "35", "City": "Paris"}
-
-
-@pytest.mark.asyncio
-async def test_table_input_none_value():
-    """Test AgentTableInputBlock with None value returns empty list."""
-    block = AgentTableInputBlock()
-
-    input_data = block.Input(
-        name="none_table", column_headers=["Name", "Age"], value=None
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-    assert output_data[0][1] == []
-
-
-@pytest.mark.asyncio
-async def test_table_input_with_default_headers():
-    """Test AgentTableInputBlock with default column headers."""
-    block = AgentTableInputBlock()
-
-    # Don't specify column_headers, should use defaults
-    input_data = block.Input(
-        name="default_headers_table",
-        value=[
-            {"Column 1": "A", "Column 2": "B", "Column 3": "C"},
-            {"Column 1": "D", "Column 2": "E", "Column 3": "F"},
-        ],
-    )
-
-    output_data = []
-    async for output_name, output_value in block.run(input_data):
-        output_data.append((output_name, output_value))
-
-    assert len(output_data) == 1
-    assert output_data[0][0] == "result"
-
-    result = output_data[0][1]
-    assert len(result) == 2
-    assert result[0]["Column 1"] == "A"
-    assert result[1]["Column 3"] == "F"

autogpt_platform/backend/backend/blocks/text.py

@@ -2,8 +2,6 @@ import re
 from pathlib import Path
 from typing import Any
 
-import regex  # Has built-in timeout support
-
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util import json, text
@@ -139,11 +137,6 @@ class ExtractTextInformationBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add limits to prevent ReDoS and memory exhaustion
-        MAX_TEXT_LENGTH = 1_000_000  # 1MB character limit
-        MAX_MATCHES = 1000  # Maximum number of matches to prevent memory exhaustion
-        MAX_MATCH_LENGTH = 10_000  # Maximum length per match
-
         flags = 0
         if not input_data.case_sensitive:
             flags = flags | re.IGNORECASE
@@ -155,85 +148,20 @@ class ExtractTextInformationBlock(Block):
         else:
             txt = json.dumps(input_data.text)
 
-        # Limit text size to prevent DoS
-        if len(txt) > MAX_TEXT_LENGTH:
-            txt = txt[:MAX_TEXT_LENGTH]
-
-        # Validate regex pattern to prevent dangerous patterns
-        dangerous_patterns = [
-            r".*\+.*\+",  # Nested quantifiers
-            r".*\*.*\*",  # Nested quantifiers
-            r"(?=.*\+)",  # Lookahead with quantifier
-            r"(?=.*\*)",  # Lookahead with quantifier
-            r"\(.+\)\+",  # Group with nested quantifier
-            r"\(.+\)\*",  # Group with nested quantifier
-            r"\([^)]+\+\)\+",  # Nested quantifiers like (a+)+
-            r"\([^)]+\*\)\*",  # Nested quantifiers like (a*)*
+        matches = [
+            match.group(input_data.group)
+            for match in re.finditer(input_data.pattern, txt, flags)
+            if input_data.group <= len(match.groups())
         ]
-
-        # Check if pattern is potentially dangerous
-        is_dangerous = any(
-            re.search(dangerous, input_data.pattern) for dangerous in dangerous_patterns
-        )
-
-        # Use regex module with timeout for dangerous patterns
-        # For safe patterns, use standard re module for compatibility
-        try:
-            matches = []
-            match_count = 0
-
-            if is_dangerous:
-                # Use regex module with timeout (5 seconds) for dangerous patterns
-                # The regex module supports timeout parameter in finditer
-                try:
-                    for match in regex.finditer(
-                        input_data.pattern, txt, flags=flags, timeout=5.0
-                    ):
-                        if match_count >= MAX_MATCHES:
-                            break
-                        if input_data.group <= len(match.groups()):
-                            match_text = match.group(input_data.group)
-                            # Limit match length to prevent memory exhaustion
-                            if len(match_text) > MAX_MATCH_LENGTH:
-                                match_text = match_text[:MAX_MATCH_LENGTH]
-                            matches.append(match_text)
-                            match_count += 1
-                except regex.error as e:
-                    # Timeout occurred or regex error
-                    if "timeout" in str(e).lower():
-                        # Timeout - return empty results
-                        pass
-                    else:
-                        # Other regex error
-                        raise
-            else:
-                # Use standard re module for non-dangerous patterns
-                for match in re.finditer(input_data.pattern, txt, flags):
-                    if match_count >= MAX_MATCHES:
-                        break
-                    if input_data.group <= len(match.groups()):
-                        match_text = match.group(input_data.group)
-                        # Limit match length to prevent memory exhaustion
-                        if len(match_text) > MAX_MATCH_LENGTH:
-                            match_text = match_text[:MAX_MATCH_LENGTH]
-                        matches.append(match_text)
-                        match_count += 1
-
-            if not input_data.find_all:
-                matches = matches[:1]
-
-            for match in matches:
-                yield "positive", match
-            if not matches:
-                yield "negative", input_data.text
-
-            yield "matched_results", matches
-            yield "matched_count", len(matches)
-        except Exception:
-            # Return empty results on any regex error
+        if not input_data.find_all:
+            matches = matches[:1]
+        for match in matches:
+            yield "positive", match
+        if not matches:
             yield "negative", input_data.text
-            yield "matched_results", []
-            yield "matched_count", 0
+
+        yield "matched_results", matches
+        yield "matched_count", len(matches)
 
 
 class FillTextTemplateBlock(Block):
@@ -244,11 +172,6 @@ class FillTextTemplateBlock(Block):
         format: str = SchemaField(
             description="Template to format the text using `values`. Use Jinja2 syntax."
         )
-        escape_html: bool = SchemaField(
-            default=False,
-            advanced=True,
-            description="Whether to escape special characters in the inserted values to be HTML-safe. Enable for HTML output, disable for plain text.",
-        )
 
     class Output(BlockSchema):
         output: str = SchemaField(description="Formatted text")
@@ -282,7 +205,6 @@ class FillTextTemplateBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        formatter = text.TextFormatter(autoescape=input_data.escape_html)
         yield "output", formatter.format_string(input_data.format, input_data.values)
 
 

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -270,17 +270,13 @@ class GetCurrentDateBlock(Block):
             test_output=[
                 (
                     "date",
-                    lambda t: abs(
-                        datetime.now().date() - datetime.strptime(t, "%Y-%m-%d").date()
-                    )
-                    <= timedelta(days=8),  # 7 days difference + 1 day error margin.
+                    lambda t: abs(datetime.now() - datetime.strptime(t, "%Y-%m-%d"))
+                    < timedelta(days=8),  # 7 days difference + 1 day error margin.
                 ),
                 (
                     "date",
-                    lambda t: abs(
-                        datetime.now().date() - datetime.strptime(t, "%m/%d/%Y").date()
-                    )
-                    <= timedelta(days=8),
+                    lambda t: abs(datetime.now() - datetime.strptime(t, "%m/%d/%Y"))
+                    < timedelta(days=8),
                     # 7 days difference + 1 day error margin.
                 ),
                 (
@@ -386,7 +382,7 @@ class GetCurrentDateAndTimeBlock(Block):
                     lambda t: abs(
                         datetime.now().date() - datetime.strptime(t, "%Y/%m/%d").date()
                     )
-                    <= timedelta(days=1),  # Date format only, no time component
+                    < timedelta(days=1),  # Date format only, no time component
                 ),
                 (
                     "date_time",

autogpt_platform/backend/backend/blocks/xml_parser.py

@@ -26,14 +26,6 @@ class XMLParserBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Security fix: Add size limits to prevent XML bomb attacks
-        MAX_XML_SIZE = 10 * 1024 * 1024  # 10MB limit for XML input
-
-        if len(input_data.input_xml) > MAX_XML_SIZE:
-            raise ValueError(
-                f"XML too large: {len(input_data.input_xml)} bytes > {MAX_XML_SIZE} bytes"
-            )
-
         try:
             tokens = tokenize(input_data.input_xml)
             parser = Parser(tokens)

autogpt_platform/backend/backend/data/__init__.py

@@ -1,8 +1,5 @@
-from backend.server.v2.library.model import LibraryAgentPreset
-
 from .graph import NodeModel
 from .integrations import Webhook  # noqa: F401
 
-# Resolve Webhook forward references
+# Resolve Webhook <- NodeModel forward reference
 NodeModel.model_rebuild()
-LibraryAgentPreset.model_rebuild()

autogpt_platform/backend/backend/data/api_key.py

@@ -1,32 +1,57 @@
 import logging
 import uuid
 from datetime import datetime, timezone
-from typing import Optional
+from typing import List, Optional
 
-from autogpt_libs.api_key.keysmith import APIKeySmith
+from autogpt_libs.api_key.key_manager import APIKeyManager
 from prisma.enums import APIKeyPermission, APIKeyStatus
+from prisma.errors import PrismaError
 from prisma.models import APIKey as PrismaAPIKey
-from prisma.types import APIKeyWhereUniqueInput
-from pydantic import BaseModel, Field
+from prisma.types import (
+    APIKeyCreateInput,
+    APIKeyUpdateInput,
+    APIKeyWhereInput,
+    APIKeyWhereUniqueInput,
+)
+from pydantic import BaseModel
 
-from backend.data.includes import MAX_USER_API_KEYS_FETCH
-from backend.util.exceptions import NotAuthorizedError, NotFoundError
+from backend.data.db import BaseDbModel
 
 logger = logging.getLogger(__name__)
-keysmith = APIKeySmith()
 
 
-class APIKeyInfo(BaseModel):
-    id: str
+# Some basic exceptions
+class APIKeyError(Exception):
+    """Base exception for API key operations"""
+
+    pass
+
+
+class APIKeyNotFoundError(APIKeyError):
+    """Raised when an API key is not found"""
+
+    pass
+
+
+class APIKeyPermissionError(APIKeyError):
+    """Raised when there are permission issues with API key operations"""
+
+    pass
+
+
+class APIKeyValidationError(APIKeyError):
+    """Raised when API key validation fails"""
+
+    pass
+
+
+class APIKey(BaseDbModel):
     name: str
-    head: str = Field(
-        description=f"The first {APIKeySmith.HEAD_LENGTH} characters of the key"
-    )
-    tail: str = Field(
-        description=f"The last {APIKeySmith.TAIL_LENGTH} characters of the key"
-    )
-    status: APIKeyStatus
-    permissions: list[APIKeyPermission]
+    prefix: str
+    key: str
+    status: APIKeyStatus = APIKeyStatus.ACTIVE
+    permissions: List[APIKeyPermission]
+    postfix: str
     created_at: datetime
     last_used_at: Optional[datetime] = None
     revoked_at: Optional[datetime] = None
@@ -35,215 +60,266 @@ class APIKeyInfo(BaseModel):
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
-        return APIKeyInfo(
-            id=api_key.id,
-            name=api_key.name,
-            head=api_key.head,
-            tail=api_key.tail,
-            status=APIKeyStatus(api_key.status),
-            permissions=[APIKeyPermission(p) for p in api_key.permissions],
-            created_at=api_key.createdAt,
-            last_used_at=api_key.lastUsedAt,
-            revoked_at=api_key.revokedAt,
-            description=api_key.description,
-            user_id=api_key.userId,
-        )
+        try:
+            return APIKey(
+                id=api_key.id,
+                name=api_key.name,
+                prefix=api_key.prefix,
+                postfix=api_key.postfix,
+                key=api_key.key,
+                status=APIKeyStatus(api_key.status),
+                permissions=[APIKeyPermission(p) for p in api_key.permissions],
+                created_at=api_key.createdAt,
+                last_used_at=api_key.lastUsedAt,
+                revoked_at=api_key.revokedAt,
+                description=api_key.description,
+                user_id=api_key.userId,
+            )
+        except Exception as e:
+            logger.error(f"Error creating APIKey from db: {str(e)}")
+            raise APIKeyError(f"Failed to create API key object: {str(e)}")
 
 
-class APIKeyInfoWithHash(APIKeyInfo):
-    hash: str
-    salt: str | None = None  # None for legacy keys
-
-    def match(self, plaintext_key: str) -> bool:
-        """Returns whether the given key matches this API key object."""
-        return keysmith.verify_key(plaintext_key, self.hash, self.salt)
+class APIKeyWithoutHash(BaseModel):
+    id: str
+    name: str
+    prefix: str
+    postfix: str
+    status: APIKeyStatus
+    permissions: List[APIKeyPermission]
+    created_at: datetime
+    last_used_at: Optional[datetime]
+    revoked_at: Optional[datetime]
+    description: Optional[str]
+    user_id: str
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
-        return APIKeyInfoWithHash(
-            **APIKeyInfo.from_db(api_key).model_dump(),
-            hash=api_key.hash,
-            salt=api_key.salt,
-        )
-
-    def without_hash(self) -> APIKeyInfo:
-        return APIKeyInfo(**self.model_dump(exclude={"hash", "salt"}))
+        try:
+            return APIKeyWithoutHash(
+                id=api_key.id,
+                name=api_key.name,
+                prefix=api_key.prefix,
+                postfix=api_key.postfix,
+                status=APIKeyStatus(api_key.status),
+                permissions=[APIKeyPermission(p) for p in api_key.permissions],
+                created_at=api_key.createdAt,
+                last_used_at=api_key.lastUsedAt,
+                revoked_at=api_key.revokedAt,
+                description=api_key.description,
+                user_id=api_key.userId,
+            )
+        except Exception as e:
+            logger.error(f"Error creating APIKeyWithoutHash from db: {str(e)}")
+            raise APIKeyError(f"Failed to create API key object: {str(e)}")
 
 
-async def create_api_key(
+async def generate_api_key(
     name: str,
     user_id: str,
-    permissions: list[APIKeyPermission],
+    permissions: List[APIKeyPermission],
     description: Optional[str] = None,
-) -> tuple[APIKeyInfo, str]:
+) -> tuple[APIKeyWithoutHash, str]:
     """
     Generate a new API key and store it in the database.
     Returns the API key object (without hash) and the plain text key.
     """
-    generated_key = keysmith.generate_key()
+    try:
+        api_manager = APIKeyManager()
+        key = api_manager.generate_api_key()
 
-    saved_key_obj = await PrismaAPIKey.prisma().create(
-        data={
-            "id": str(uuid.uuid4()),
-            "name": name,
-            "head": generated_key.head,
-            "tail": generated_key.tail,
-            "hash": generated_key.hash,
-            "salt": generated_key.salt,
-            "permissions": [p for p in permissions],
-            "description": description,
-            "userId": user_id,
-        }
-    )
+        api_key = await PrismaAPIKey.prisma().create(
+            data=APIKeyCreateInput(
+                id=str(uuid.uuid4()),
+                name=name,
+                prefix=key.prefix,
+                postfix=key.postfix,
+                key=key.hash,
+                permissions=[p for p in permissions],
+                description=description,
+                userId=user_id,
+            )
+        )
 
-    return APIKeyInfo.from_db(saved_key_obj), generated_key.key
+        api_key_without_hash = APIKeyWithoutHash.from_db(api_key)
+        return api_key_without_hash, key.raw
+    except PrismaError as e:
+        logger.error(f"Database error while generating API key: {str(e)}")
+        raise APIKeyError(f"Failed to generate API key: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while generating API key: {str(e)}")
+        raise APIKeyError(f"Failed to generate API key: {str(e)}")
 
 
-async def get_active_api_keys_by_head(head: str) -> list[APIKeyInfoWithHash]:
-    results = await PrismaAPIKey.prisma().find_many(
-        where={"head": head, "status": APIKeyStatus.ACTIVE}
-    )
-    return [APIKeyInfoWithHash.from_db(key) for key in results]
-
-
-async def validate_api_key(plaintext_key: str) -> Optional[APIKeyInfo]:
+async def validate_api_key(plain_text_key: str) -> Optional[APIKey]:
     """
-    Validate an API key and return the API key object if valid and active.
+    Validate an API key and return the API key object if valid.
     """
     try:
-        if not plaintext_key.startswith(APIKeySmith.PREFIX):
+        if not plain_text_key.startswith(APIKeyManager.PREFIX):
             logger.warning("Invalid API key format")
             return None
 
-        head = plaintext_key[: APIKeySmith.HEAD_LENGTH]
-        potential_matches = await get_active_api_keys_by_head(head)
+        prefix = plain_text_key[: APIKeyManager.PREFIX_LENGTH]
+        api_manager = APIKeyManager()
 
-        matched_api_key = next(
-            (pm for pm in potential_matches if pm.match(plaintext_key)),
-            None,
+        api_key = await PrismaAPIKey.prisma().find_first(
+            where=APIKeyWhereInput(prefix=prefix, status=(APIKeyStatus.ACTIVE))
         )
-        if not matched_api_key:
-            # API key not found or invalid
+
+        if not api_key:
+            logger.warning(f"No active API key found with prefix {prefix}")
             return None
 
-        # Migrate legacy keys to secure format on successful validation
-        if matched_api_key.salt is None:
-            matched_api_key = await _migrate_key_to_secure_hash(
-                plaintext_key, matched_api_key
+        is_valid = api_manager.verify_api_key(plain_text_key, api_key.key)
+        if not is_valid:
+            logger.warning("API key verification failed")
+            return None
+
+        return APIKey.from_db(api_key)
+    except Exception as e:
+        logger.error(f"Error validating API key: {str(e)}")
+        raise APIKeyValidationError(f"Failed to validate API key: {str(e)}")
+
+
+async def revoke_api_key(key_id: str, user_id: str) -> Optional[APIKeyWithoutHash]:
+    try:
+        api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
+
+        if not api_key:
+            raise APIKeyNotFoundError(f"API key with id {key_id} not found")
+
+        if api_key.userId != user_id:
+            raise APIKeyPermissionError(
+                "You do not have permission to revoke this API key."
             )
 
-        return matched_api_key.without_hash()
-    except Exception as e:
-        logger.error(f"Error while validating API key: {e}")
-        raise RuntimeError("Failed to validate API key") from e
-
-
-async def _migrate_key_to_secure_hash(
-    plaintext_key: str, key_obj: APIKeyInfoWithHash
-) -> APIKeyInfoWithHash:
-    """Replace the SHA256 hash of a legacy API key with a salted Scrypt hash."""
-    try:
-        new_hash, new_salt = keysmith.hash_key(plaintext_key)
-        await PrismaAPIKey.prisma().update(
-            where={"id": key_obj.id}, data={"hash": new_hash, "salt": new_salt}
+        where_clause: APIKeyWhereUniqueInput = {"id": key_id}
+        updated_api_key = await PrismaAPIKey.prisma().update(
+            where=where_clause,
+            data=APIKeyUpdateInput(
+                status=APIKeyStatus.REVOKED, revokedAt=datetime.now(timezone.utc)
+            ),
         )
-        logger.info(f"Migrated legacy API key #{key_obj.id} to secure format")
-        # Update the API key object with new values for return
-        key_obj.hash = new_hash
-        key_obj.salt = new_salt
-    except Exception as e:
-        logger.error(f"Failed to migrate legacy API key #{key_obj.id}: {e}")
 
-    return key_obj
-
-
-async def revoke_api_key(key_id: str, user_id: str) -> APIKeyInfo:
-    api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
-
-    if not api_key:
-        raise NotFoundError(f"API key with id {key_id} not found")
-
-    if api_key.userId != user_id:
-        raise NotAuthorizedError("You do not have permission to revoke this API key.")
-
-    updated_api_key = await PrismaAPIKey.prisma().update(
-        where={"id": key_id},
-        data={
-            "status": APIKeyStatus.REVOKED,
-            "revokedAt": datetime.now(timezone.utc),
-        },
-    )
-    if not updated_api_key:
-        raise NotFoundError(f"API key #{key_id} vanished while trying to revoke.")
-
-    return APIKeyInfo.from_db(updated_api_key)
-
-
-async def list_user_api_keys(
-    user_id: str, limit: int = MAX_USER_API_KEYS_FETCH
-) -> list[APIKeyInfo]:
-    api_keys = await PrismaAPIKey.prisma().find_many(
-        where={"userId": user_id},
-        order={"createdAt": "desc"},
-        take=limit,
-    )
-
-    return [APIKeyInfo.from_db(key) for key in api_keys]
-
-
-async def suspend_api_key(key_id: str, user_id: str) -> APIKeyInfo:
-    selector: APIKeyWhereUniqueInput = {"id": key_id}
-    api_key = await PrismaAPIKey.prisma().find_unique(where=selector)
-
-    if not api_key:
-        raise NotFoundError(f"API key with id {key_id} not found")
-
-    if api_key.userId != user_id:
-        raise NotAuthorizedError("You do not have permission to suspend this API key.")
-
-    updated_api_key = await PrismaAPIKey.prisma().update(
-        where=selector, data={"status": APIKeyStatus.SUSPENDED}
-    )
-    if not updated_api_key:
-        raise NotFoundError(f"API key #{key_id} vanished while trying to suspend.")
-
-    return APIKeyInfo.from_db(updated_api_key)
-
-
-def has_permission(api_key: APIKeyInfo, required_permission: APIKeyPermission) -> bool:
-    return required_permission in api_key.permissions
-
-
-async def get_api_key_by_id(key_id: str, user_id: str) -> Optional[APIKeyInfo]:
-    api_key = await PrismaAPIKey.prisma().find_first(
-        where={"id": key_id, "userId": user_id}
-    )
-
-    if not api_key:
+        if updated_api_key:
+            return APIKeyWithoutHash.from_db(updated_api_key)
         return None
+    except (APIKeyNotFoundError, APIKeyPermissionError) as e:
+        raise e
+    except PrismaError as e:
+        logger.error(f"Database error while revoking API key: {str(e)}")
+        raise APIKeyError(f"Failed to revoke API key: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while revoking API key: {str(e)}")
+        raise APIKeyError(f"Failed to revoke API key: {str(e)}")
 
-    return APIKeyInfo.from_db(api_key)
+
+async def list_user_api_keys(user_id: str) -> List[APIKeyWithoutHash]:
+    try:
+        where_clause: APIKeyWhereInput = {"userId": user_id}
+
+        api_keys = await PrismaAPIKey.prisma().find_many(
+            where=where_clause, order={"createdAt": "desc"}
+        )
+
+        return [APIKeyWithoutHash.from_db(key) for key in api_keys]
+    except PrismaError as e:
+        logger.error(f"Database error while listing API keys: {str(e)}")
+        raise APIKeyError(f"Failed to list API keys: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while listing API keys: {str(e)}")
+        raise APIKeyError(f"Failed to list API keys: {str(e)}")
+
+
+async def suspend_api_key(key_id: str, user_id: str) -> Optional[APIKeyWithoutHash]:
+    try:
+        api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
+
+        if not api_key:
+            raise APIKeyNotFoundError(f"API key with id {key_id} not found")
+
+        if api_key.userId != user_id:
+            raise APIKeyPermissionError(
+                "You do not have permission to suspend this API key."
+            )
+
+        where_clause: APIKeyWhereUniqueInput = {"id": key_id}
+        updated_api_key = await PrismaAPIKey.prisma().update(
+            where=where_clause,
+            data=APIKeyUpdateInput(status=APIKeyStatus.SUSPENDED),
+        )
+
+        if updated_api_key:
+            return APIKeyWithoutHash.from_db(updated_api_key)
+        return None
+    except (APIKeyNotFoundError, APIKeyPermissionError) as e:
+        raise e
+    except PrismaError as e:
+        logger.error(f"Database error while suspending API key: {str(e)}")
+        raise APIKeyError(f"Failed to suspend API key: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while suspending API key: {str(e)}")
+        raise APIKeyError(f"Failed to suspend API key: {str(e)}")
+
+
+def has_permission(api_key: APIKey, required_permission: APIKeyPermission) -> bool:
+    try:
+        return required_permission in api_key.permissions
+    except Exception as e:
+        logger.error(f"Error checking API key permissions: {str(e)}")
+        return False
+
+
+async def get_api_key_by_id(key_id: str, user_id: str) -> Optional[APIKeyWithoutHash]:
+    try:
+        api_key = await PrismaAPIKey.prisma().find_first(
+            where=APIKeyWhereInput(id=key_id, userId=user_id)
+        )
+
+        if not api_key:
+            return None
+
+        return APIKeyWithoutHash.from_db(api_key)
+    except PrismaError as e:
+        logger.error(f"Database error while getting API key: {str(e)}")
+        raise APIKeyError(f"Failed to get API key: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while getting API key: {str(e)}")
+        raise APIKeyError(f"Failed to get API key: {str(e)}")
 
 
 async def update_api_key_permissions(
-    key_id: str, user_id: str, permissions: list[APIKeyPermission]
-) -> APIKeyInfo:
+    key_id: str, user_id: str, permissions: List[APIKeyPermission]
+) -> Optional[APIKeyWithoutHash]:
     """
     Update the permissions of an API key.
     """
-    api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
+    try:
+        api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
 
-    if api_key is None:
-        raise NotFoundError("No such API key found.")
+        if api_key is None:
+            raise APIKeyNotFoundError("No such API key found.")
 
-    if api_key.userId != user_id:
-        raise NotAuthorizedError("You do not have permission to update this API key.")
+        if api_key.userId != user_id:
+            raise APIKeyPermissionError(
+                "You do not have permission to update this API key."
+            )
 
-    updated_api_key = await PrismaAPIKey.prisma().update(
-        where={"id": key_id},
-        data={"permissions": permissions},
-    )
-    if not updated_api_key:
-        raise NotFoundError(f"API key #{key_id} vanished while trying to update.")
+        where_clause: APIKeyWhereUniqueInput = {"id": key_id}
+        updated_api_key = await PrismaAPIKey.prisma().update(
+            where=where_clause,
+            data=APIKeyUpdateInput(permissions=permissions),
+        )
 
-    return APIKeyInfo.from_db(updated_api_key)
+        if updated_api_key:
+            return APIKeyWithoutHash.from_db(updated_api_key)
+        return None
+    except (APIKeyNotFoundError, APIKeyPermissionError) as e:
+        raise e
+    except PrismaError as e:
+        logger.error(f"Database error while updating API key permissions: {str(e)}")
+        raise APIKeyError(f"Failed to update API key permissions: {str(e)}")
+    except Exception as e:
+        logger.error(f"Unexpected error while updating API key permissions: {str(e)}")
+        raise APIKeyError(f"Failed to update API key permissions: {str(e)}")

autogpt_platform/backend/backend/data/block.py

@@ -1,3 +1,4 @@
+import functools
 import inspect
 import logging
 import os
@@ -7,7 +8,6 @@ from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
-    Callable,
     ClassVar,
     Generic,
     Optional,
@@ -20,7 +20,6 @@ from typing import (
 
 import jsonref
 import jsonschema
-from autogpt_libs.utils.cache import cached
 from prisma.models import AgentBlock
 from prisma.types import AgentBlockCreateInput
 from pydantic import BaseModel
@@ -45,10 +44,9 @@ if TYPE_CHECKING:
 
 app_config = Config()
 
+BlockData = tuple[str, Any]  # Input & Output data should be a tuple of (name, data).
 BlockInput = dict[str, Any]  # Input: 1 input pin consumes 1 data.
-BlockOutputEntry = tuple[str, Any]  # Output data should be a tuple of (name, value).
-BlockOutput = AsyncGen[BlockOutputEntry, None]  # Output: 1 output pin produces n data.
-BlockTestOutput = BlockOutputEntry | tuple[str, Callable[[Any], bool]]
+BlockOutput = AsyncGen[BlockData, None]  # Output: 1 output pin produces n data.
 CompletedBlockOutput = dict[str, list[Any]]  # Completed stream, collected as a dict.
 
 
@@ -91,45 +89,6 @@ class BlockCategory(Enum):
         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]]
 
@@ -347,7 +306,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         input_schema: Type[BlockSchemaInputType] = EmptySchema,
         output_schema: Type[BlockSchemaOutputType] = EmptySchema,
         test_input: BlockInput | list[BlockInput] | None = None,
-        test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
+        test_output: BlockData | list[BlockData] | None = None,
         test_mock: dict[str, Any] | None = None,
         test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
         disabled: bool = False,
@@ -493,24 +452,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
             "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:
         if error := self.input_schema.validate_data(input_data):
             raise ValueError(
@@ -722,7 +663,7 @@ def get_block(block_id: str) -> Block[BlockSchema, BlockSchema] | None:
     return cls() if cls else None
 
 
-@cached()
+@functools.cache
 def get_webhook_block_ids() -> Sequence[str]:
     return [
         id
@@ -731,7 +672,7 @@ def get_webhook_block_ids() -> Sequence[str]:
     ]
 
 
-@cached()
+@functools.cache
 def get_io_block_ids() -> Sequence[str]:
     return [
         id

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -29,7 +29,8 @@ from backend.blocks.replicate.replicate_block import ReplicateModelBlock
 from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
-from backend.data.block import Block, BlockCost, BlockCostType
+from backend.data.block import Block
+from backend.data.cost import BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
     anthropic_credentials,
@@ -69,8 +70,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.CLAUDE_4_1_OPUS: 21,
     LlmModel.CLAUDE_4_OPUS: 21,
     LlmModel.CLAUDE_4_SONNET: 5,
-    LlmModel.CLAUDE_4_5_HAIKU: 4,
-    LlmModel.CLAUDE_4_5_SONNET: 9,
     LlmModel.CLAUDE_3_7_SONNET: 5,
     LlmModel.CLAUDE_3_5_SONNET: 4,
     LlmModel.CLAUDE_3_5_HAIKU: 1,  # $0.80 / $4.00

autogpt_platform/backend/backend/data/cost.py

@@ -0,0 +1,32 @@
+from enum import Enum
+from typing import Any, Optional
+
+from pydantic import BaseModel
+
+from backend.data.block import BlockInput
+
+
+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,
+        )

autogpt_platform/backend/backend/data/credit.py

@@ -2,7 +2,7 @@ import logging
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Any, cast
+from typing import Any, cast
 
 import stripe
 from prisma import Json
@@ -23,7 +23,7 @@ from pydantic import BaseModel
 
 from backend.data import db
 from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.includes import MAX_CREDIT_REFUND_REQUESTS_FETCH
+from backend.data.cost import BlockCost
 from backend.data.model import (
     AutoTopUpConfig,
     RefundRequest,
@@ -41,9 +41,6 @@ from backend.util.models import Pagination
 from backend.util.retry import func_retry
 from backend.util.settings import Settings
 
-if TYPE_CHECKING:
-    from backend.data.block import Block, BlockCost
-
 settings = Settings()
 stripe.api_key = settings.secrets.stripe_api_key
 logger = logging.getLogger(__name__)
@@ -906,9 +903,7 @@ class UserCredit(UserCreditBase):
             ),
         )
 
-    async def get_refund_requests(
-        self, user_id: str, limit: int = MAX_CREDIT_REFUND_REQUESTS_FETCH
-    ) -> list[RefundRequest]:
+    async def get_refund_requests(self, user_id: str) -> list[RefundRequest]:
         return [
             RefundRequest(
                 id=r.id,
@@ -924,7 +919,6 @@ class UserCredit(UserCreditBase):
             for r in await CreditRefundRequest.prisma().find_many(
                 where={"userId": user_id},
                 order={"createdAt": "desc"},
-                take=limit,
             )
         ]
 
@@ -1003,14 +997,10 @@ def get_user_credit_model() -> UserCreditBase:
     return UserCredit()
 
 
-def get_block_costs() -> dict[str, list["BlockCost"]]:
+def get_block_costs() -> dict[str, list[BlockCost]]:
     return {block().id: costs for block, costs in BLOCK_COSTS.items()}
 
 
-def get_block_cost(block: "Block") -> list["BlockCost"]:
-    return BLOCK_COSTS.get(block.__class__, [])
-
-
 async def get_stripe_customer_id(user_id: str) -> str:
     user = await get_user_by_id(user_id)
 

autogpt_platform/backend/backend/data/db.py

@@ -83,7 +83,7 @@ async def disconnect():
 
 
 # Transaction timeout constant (in milliseconds)
-TRANSACTION_TIMEOUT = 30000  # 30 seconds - Increased from 15s to prevent timeout errors during graph creation under load
+TRANSACTION_TIMEOUT = 15000  # 15 seconds - Increased from 5s to prevent timeout errors
 
 
 @asynccontextmanager

autogpt_platform/backend/backend/data/dynamic_fields.py

@@ -1,284 +0,0 @@
-"""
-Utilities for handling dynamic field names with special delimiters.
-
-Dynamic fields allow graphs to connect complex data structures using special delimiters:
-- _#_ for dictionary keys (e.g., "values_#_name" → values["name"])
-- _$_ for list indices (e.g., "items_$_0" → items[0])
-- _@_ for object attributes (e.g., "obj_@_attr" → obj.attr)
-"""
-
-from typing import Any
-
-from backend.util.mock import MockObject
-
-# Dynamic field delimiters
-LIST_SPLIT = "_$_"
-DICT_SPLIT = "_#_"
-OBJC_SPLIT = "_@_"
-
-DYNAMIC_DELIMITERS = (LIST_SPLIT, DICT_SPLIT, OBJC_SPLIT)
-
-
-def extract_base_field_name(field_name: str) -> str:
-    """
-    Extract the base field name from a dynamic field name by removing all dynamic suffixes.
-
-    Examples:
-        extract_base_field_name("values_#_name") → "values"
-        extract_base_field_name("items_$_0") → "items"
-        extract_base_field_name("obj_@_attr") → "obj"
-        extract_base_field_name("regular_field") → "regular_field"
-
-    Args:
-        field_name: The field name that may contain dynamic delimiters
-
-    Returns:
-        The base field name without any dynamic suffixes
-    """
-    base_name = field_name
-    for delimiter in DYNAMIC_DELIMITERS:
-        if delimiter in base_name:
-            base_name = base_name.split(delimiter)[0]
-    return base_name
-
-
-def is_dynamic_field(field_name: str) -> bool:
-    """
-    Check if a field name contains dynamic delimiters.
-
-    Args:
-        field_name: The field name to check
-
-    Returns:
-        True if the field contains any dynamic delimiters, False otherwise
-    """
-    return any(delimiter in field_name for delimiter in DYNAMIC_DELIMITERS)
-
-
-def get_dynamic_field_description(field_name: str) -> str:
-    """
-    Generate a description for a dynamic field based on its structure.
-
-    Args:
-        field_name: The full dynamic field name (e.g., "values_#_name")
-
-    Returns:
-        A descriptive string explaining what this dynamic field represents
-    """
-    base_name = extract_base_field_name(field_name)
-
-    if DICT_SPLIT in field_name:
-        # Extract the key part after _#_
-        parts = field_name.split(DICT_SPLIT)
-        if len(parts) > 1:
-            key = parts[1].split("_")[0] if "_" in parts[1] else parts[1]
-            return f"Dictionary field '{key}' for base field '{base_name}' ({base_name}['{key}'])"
-    elif LIST_SPLIT in field_name:
-        # Extract the index part after _$_
-        parts = field_name.split(LIST_SPLIT)
-        if len(parts) > 1:
-            index = parts[1].split("_")[0] if "_" in parts[1] else parts[1]
-            return (
-                f"List item {index} for base field '{base_name}' ({base_name}[{index}])"
-            )
-    elif OBJC_SPLIT in field_name:
-        # Extract the attribute part after _@_
-        parts = field_name.split(OBJC_SPLIT)
-        if len(parts) > 1:
-            # Get the full attribute name (everything after _@_)
-            attr = parts[1]
-            return f"Object attribute '{attr}' for base field '{base_name}' ({base_name}.{attr})"
-
-    return f"Value for {field_name}"
-
-
-# --------------------------------------------------------------------------- #
-#  Dynamic field parsing and merging utilities
-# --------------------------------------------------------------------------- #
-
-
-def _next_delim(s: str) -> tuple[str | None, int]:
-    """
-    Return the *earliest* delimiter appearing in `s` and its index.
-
-    If none present → (None, -1).
-    """
-    first: str | None = None
-    pos = len(s)  # sentinel: larger than any real index
-    for d in DYNAMIC_DELIMITERS:
-        i = s.find(d)
-        if 0 <= i < pos:
-            first, pos = d, i
-    return first, (pos if first else -1)
-
-
-def _tokenise(path: str) -> list[tuple[str, str]] | None:
-    """
-    Convert the raw path string (starting with a delimiter) into
-    [ (delimiter, identifier), … ] or None if the syntax is malformed.
-    """
-    tokens: list[tuple[str, str]] = []
-    while path:
-        # 1. Which delimiter starts this chunk?
-        delim = next((d for d in DYNAMIC_DELIMITERS if path.startswith(d)), None)
-        if delim is None:
-            return None  # invalid syntax
-
-        # 2. Slice off the delimiter, then up to the next delimiter (or EOS)
-        path = path[len(delim) :]
-        nxt_delim, pos = _next_delim(path)
-        token, path = (
-            path[: pos if pos != -1 else len(path)],
-            path[pos if pos != -1 else len(path) :],
-        )
-        if token == "":
-            return None  # empty identifier is invalid
-        tokens.append((delim, token))
-    return tokens
-
-
-def parse_execution_output(output: tuple[str, Any], name: str) -> Any:
-    """
-    Retrieve a nested value out of `output` using the flattened *name*.
-
-    On any failure (wrong name, wrong type, out-of-range, bad path)
-    returns **None**.
-
-    Args:
-        output: Tuple of (base_name, data) representing a block output entry
-        name: The flattened field name to extract from the output data
-
-    Returns:
-        The value at the specified path, or None if not found/invalid
-    """
-    base_name, data = output
-
-    # Exact match → whole object
-    if name == base_name:
-        return data
-
-    # Must start with the expected name
-    if not name.startswith(base_name):
-        return None
-    path = name[len(base_name) :]
-    if not path:
-        return None  # nothing left to parse
-
-    tokens = _tokenise(path)
-    if tokens is None:
-        return None
-
-    cur: Any = data
-    for delim, ident in tokens:
-        if delim == LIST_SPLIT:
-            # list[index]
-            try:
-                idx = int(ident)
-            except ValueError:
-                return None
-            if not isinstance(cur, list) or idx >= len(cur):
-                return None
-            cur = cur[idx]
-
-        elif delim == DICT_SPLIT:
-            if not isinstance(cur, dict) or ident not in cur:
-                return None
-            cur = cur[ident]
-
-        elif delim == OBJC_SPLIT:
-            if not hasattr(cur, ident):
-                return None
-            cur = getattr(cur, ident)
-
-        else:
-            return None  # unreachable
-
-    return cur
-
-
-def _assign(container: Any, tokens: list[tuple[str, str]], value: Any) -> Any:
-    """
-    Recursive helper that *returns* the (possibly new) container with
-    `value` assigned along the remaining `tokens` path.
-    """
-    if not tokens:
-        return value  # leaf reached
-
-    delim, ident = tokens[0]
-    rest = tokens[1:]
-
-    # ---------- list ----------
-    if delim == LIST_SPLIT:
-        try:
-            idx = int(ident)
-        except ValueError:
-            raise ValueError("index must be an integer")
-
-        if container is None:
-            container = []
-        elif not isinstance(container, list):
-            container = list(container) if hasattr(container, "__iter__") else []
-
-        while len(container) <= idx:
-            container.append(None)
-        container[idx] = _assign(container[idx], rest, value)
-        return container
-
-    # ---------- dict ----------
-    if delim == DICT_SPLIT:
-        if container is None:
-            container = {}
-        elif not isinstance(container, dict):
-            container = dict(container) if hasattr(container, "items") else {}
-        container[ident] = _assign(container.get(ident), rest, value)
-        return container
-
-    # ---------- object ----------
-    if delim == OBJC_SPLIT:
-        if container is None:
-            container = MockObject()
-        elif not hasattr(container, "__dict__"):
-            # If it's not an object, create a new one
-            container = MockObject()
-        setattr(
-            container,
-            ident,
-            _assign(getattr(container, ident, None), rest, value),
-        )
-        return container
-
-    return value  # unreachable
-
-
-def merge_execution_input(data: dict[str, Any]) -> dict[str, Any]:
-    """
-    Reconstruct nested objects from a *flattened* dict of key → value.
-
-    Raises ValueError on syntactically invalid list indices.
-
-    Args:
-        data: Dictionary with potentially flattened dynamic field keys
-
-    Returns:
-        Dictionary with nested objects reconstructed from flattened keys
-    """
-    merged: dict[str, Any] = {}
-
-    for key, value in data.items():
-        # Split off the base name (before the first delimiter, if any)
-        delim, pos = _next_delim(key)
-        if delim is None:
-            merged[key] = value
-            continue
-
-        base, path = key[:pos], key[pos:]
-        tokens = _tokenise(path)
-        if tokens is None:
-            # Invalid key; treat as scalar under the raw name
-            merged[key] = value
-            continue
-
-        merged[base] = _assign(merged.get(base), tokens, value)
-
-    data.update(merged)
-    return data

autogpt_platform/backend/backend/data/execution.py

@@ -11,14 +11,11 @@ from typing import (
     Generator,
     Generic,
     Literal,
-    Mapping,
     Optional,
     TypeVar,
-    cast,
     overload,
 )
 
-from prisma import Json
 from prisma.enums import AgentExecutionStatus
 from prisma.models import (
     AgentGraphExecution,
@@ -27,6 +24,7 @@ from prisma.models import (
     AgentNodeExecutionKeyValueData,
 )
 from prisma.types import (
+    AgentGraphExecutionCreateInput,
     AgentGraphExecutionUpdateManyMutationInput,
     AgentGraphExecutionWhereInput,
     AgentNodeExecutionCreateInput,
@@ -38,8 +36,8 @@ from prisma.types import (
 from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
 from pydantic.fields import Field
 
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util import type as type_utils
-from backend.util.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.models import Pagination
 from backend.util.retry import func_retry
@@ -62,7 +60,7 @@ from .includes import (
     GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     graph_execution_include,
 )
-from .model import CredentialsMetaInput, GraphExecutionStats, NodeExecutionStats
+from .model import GraphExecutionStats, NodeExecutionStats
 
 T = TypeVar("T")
 
@@ -89,33 +87,6 @@ class BlockErrorStats(BaseModel):
 
 
 ExecutionStatus = AgentExecutionStatus
-NodeInputMask = Mapping[str, JsonValue]
-NodesInputMasks = Mapping[str, NodeInputMask]
-
-# dest: source
-VALID_STATUS_TRANSITIONS = {
-    ExecutionStatus.QUEUED: [
-        ExecutionStatus.INCOMPLETE,
-    ],
-    ExecutionStatus.RUNNING: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.TERMINATED,  # For resuming halted execution
-    ],
-    ExecutionStatus.COMPLETED: [
-        ExecutionStatus.RUNNING,
-    ],
-    ExecutionStatus.FAILED: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.RUNNING,
-    ],
-    ExecutionStatus.TERMINATED: [
-        ExecutionStatus.INCOMPLETE,
-        ExecutionStatus.QUEUED,
-        ExecutionStatus.RUNNING,
-    ],
-}
 
 
 class GraphExecutionMeta(BaseDbModel):
@@ -123,15 +94,10 @@ class GraphExecutionMeta(BaseDbModel):
     user_id: str
     graph_id: str
     graph_version: int
-    inputs: Optional[BlockInput]  # no default -> required in the OpenAPI spec
-    credential_inputs: Optional[dict[str, CredentialsMetaInput]]
-    nodes_input_masks: Optional[dict[str, BlockInput]]
-    preset_id: Optional[str]
+    preset_id: Optional[str] = None
     status: ExecutionStatus
     started_at: datetime
     ended_at: datetime
-    is_shared: bool = False
-    share_token: Optional[str] = None
 
     class Stats(BaseModel):
         model_config = ConfigDict(
@@ -213,18 +179,6 @@ class GraphExecutionMeta(BaseDbModel):
             user_id=_graph_exec.userId,
             graph_id=_graph_exec.agentGraphId,
             graph_version=_graph_exec.agentGraphVersion,
-            inputs=cast(BlockInput | None, _graph_exec.inputs),
-            credential_inputs=(
-                {
-                    name: CredentialsMetaInput.model_validate(cmi)
-                    for name, cmi in cast(dict, _graph_exec.credentialInputs).items()
-                }
-                if _graph_exec.credentialInputs
-                else None
-            ),
-            nodes_input_masks=cast(
-                dict[str, BlockInput] | None, _graph_exec.nodesInputMasks
-            ),
             preset_id=_graph_exec.agentPresetId,
             status=ExecutionStatus(_graph_exec.executionStatus),
             started_at=start_time,
@@ -248,13 +202,11 @@ class GraphExecutionMeta(BaseDbModel):
                 if stats
                 else None
             ),
-            is_shared=_graph_exec.isShared,
-            share_token=_graph_exec.shareToken,
         )
 
 
 class GraphExecution(GraphExecutionMeta):
-    inputs: BlockInput  # type: ignore - incompatible override is intentional
+    inputs: BlockInput
     outputs: CompletedBlockOutput
 
     @staticmethod
@@ -274,18 +226,15 @@ class GraphExecution(GraphExecutionMeta):
         )
 
         inputs = {
-            **(
-                graph_exec.inputs
-                or {
-                    # fallback: extract inputs from Agent Input Blocks
-                    exec.input_data["name"]: exec.input_data.get("value")
-                    for exec in complete_node_executions
-                    if (
-                        (block := get_block(exec.block_id))
-                        and block.block_type == BlockType.INPUT
-                    )
-                }
-            ),
+            **{
+                # inputs from Agent Input Blocks
+                exec.input_data["name"]: exec.input_data.get("value")
+                for exec in complete_node_executions
+                if (
+                    (block := get_block(exec.block_id))
+                    and block.block_type == BlockType.INPUT
+                )
+            },
             **{
                 # input from webhook-triggered block
                 "payload": exec.input_data["payload"]
@@ -303,13 +252,14 @@ class GraphExecution(GraphExecutionMeta):
             if (
                 block := get_block(exec.block_id)
             ) and block.block_type == BlockType.OUTPUT:
-                outputs[exec.input_data["name"]].append(exec.input_data.get("value"))
+                outputs[exec.input_data["name"]].append(
+                    exec.input_data.get("value", None)
+                )
 
         return GraphExecution(
             **{
                 field_name: getattr(graph_exec, field_name)
                 for field_name in GraphExecutionMeta.model_fields
-                if field_name != "inputs"
             },
             inputs=inputs,
             outputs=outputs,
@@ -342,17 +292,13 @@ class GraphExecutionWithNodes(GraphExecution):
             node_executions=node_executions,
         )
 
-    def to_graph_execution_entry(
-        self,
-        user_context: "UserContext",
-        compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
-    ):
+    def to_graph_execution_entry(self, user_context: "UserContext"):
         return GraphExecutionEntry(
             user_id=self.user_id,
             graph_id=self.graph_id,
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
-            nodes_input_masks=compiled_nodes_input_masks,
+            nodes_input_masks={},  # FIXME: store credentials on AgentGraphExecution
             user_context=user_context,
         )
 
@@ -369,9 +315,10 @@ class NodeExecutionResult(BaseModel):
     input_data: BlockInput
     output_data: CompletedBlockOutput
     add_time: datetime
-    queue_time: datetime | None
-    start_time: datetime | None
-    end_time: datetime | None
+    queue_time: datetime | None = None
+    start_time: datetime | None = None
+    end_time: datetime | None = None
+    stats: NodeExecutionStats | None = None
 
     @staticmethod
     def from_db(_node_exec: AgentNodeExecution, user_id: Optional[str] = None):
@@ -389,7 +336,7 @@ class NodeExecutionResult(BaseModel):
         else:
             input_data: BlockInput = defaultdict()
             for data in _node_exec.Input or []:
-                input_data[data.name] = type_utils.convert(data.data, JsonValue)
+                input_data[data.name] = type_utils.convert(data.data, type[Any])
 
         output_data: CompletedBlockOutput = defaultdict(list)
 
@@ -398,7 +345,7 @@ class NodeExecutionResult(BaseModel):
                 output_data[name].extend(messages)
         else:
             for data in _node_exec.Output or []:
-                output_data[data.name].append(type_utils.convert(data.data, JsonValue))
+                output_data[data.name].append(type_utils.convert(data.data, type[Any]))
 
         graph_execution: AgentGraphExecution | None = _node_exec.GraphExecution
         if graph_execution:
@@ -423,6 +370,7 @@ class NodeExecutionResult(BaseModel):
             queue_time=_node_exec.queuedTime,
             start_time=_node_exec.startedTime,
             end_time=_node_exec.endedTime,
+            stats=stats,
         )
 
     def to_node_execution_entry(
@@ -478,48 +426,6 @@ async def get_graph_executions(
     return [GraphExecutionMeta.from_db(execution) for execution in executions]
 
 
-async def get_graph_executions_count(
-    user_id: Optional[str] = None,
-    graph_id: Optional[str] = None,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-) -> int:
-    """
-    Get count of graph executions with optional filters.
-
-    Args:
-        user_id: Optional user ID to filter by
-        graph_id: Optional graph ID to filter by
-        statuses: Optional list of execution statuses to filter by
-        created_time_gte: Optional minimum creation time
-        created_time_lte: Optional maximum creation time
-
-    Returns:
-        Count of matching graph executions
-    """
-    where_filter: AgentGraphExecutionWhereInput = {
-        "isDeleted": False,
-    }
-
-    if user_id:
-        where_filter["userId"] = user_id
-
-    if graph_id:
-        where_filter["agentGraphId"] = graph_id
-
-    if created_time_gte or created_time_lte:
-        where_filter["createdAt"] = {
-            "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),
-            "lte": created_time_lte or datetime.max.replace(tzinfo=timezone.utc),
-        }
-    if statuses:
-        where_filter["OR"] = [{"executionStatus": status} for status in statuses]
-
-    count = await AgentGraphExecution.prisma().count(where=where_filter)
-    return count
-
-
 class GraphExecutionsPaginated(BaseModel):
     """Response schema for paginated graph executions."""
 
@@ -635,12 +541,9 @@ async def get_graph_execution(
 async def create_graph_execution(
     graph_id: str,
     graph_version: int,
-    starting_nodes_input: list[tuple[str, BlockInput]],  # list[(node_id, BlockInput)]
-    inputs: Mapping[str, JsonValue],
+    starting_nodes_input: list[tuple[str, BlockInput]],
     user_id: str,
-    preset_id: Optional[str] = None,
-    credential_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
-    nodes_input_masks: Optional[NodesInputMasks] = None,
+    preset_id: str | None = None,
 ) -> GraphExecutionWithNodes:
     """
     Create a new AgentGraphExecution record.
@@ -648,18 +551,11 @@ async def create_graph_execution(
         The id of the AgentGraphExecution and the list of ExecutionResult for each node.
     """
     result = await AgentGraphExecution.prisma().create(
-        data={
-            "agentGraphId": graph_id,
-            "agentGraphVersion": graph_version,
-            "executionStatus": ExecutionStatus.INCOMPLETE,
-            "inputs": SafeJson(inputs),
-            "credentialInputs": (
-                SafeJson(credential_inputs) if credential_inputs else Json({})
-            ),
-            "nodesInputMasks": (
-                SafeJson(nodes_input_masks) if nodes_input_masks else Json({})
-            ),
-            "NodeExecutions": {
+        data=AgentGraphExecutionCreateInput(
+            agentGraphId=graph_id,
+            agentGraphVersion=graph_version,
+            executionStatus=ExecutionStatus.QUEUED,
+            NodeExecutions={
                 "create": [
                     AgentNodeExecutionCreateInput(
                         agentNodeId=node_id,
@@ -675,9 +571,9 @@ async def create_graph_execution(
                     for node_id, node_input in starting_nodes_input
                 ]
             },
-            "userId": user_id,
-            "agentPresetId": preset_id,
-        },
+            userId=user_id,
+            agentPresetId=preset_id,
+        ),
         include=GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     )
 
@@ -688,7 +584,7 @@ async def upsert_execution_input(
     node_id: str,
     graph_exec_id: str,
     input_name: str,
-    input_data: JsonValue,
+    input_data: Any,
     node_exec_id: str | None = None,
 ) -> tuple[str, BlockInput]:
     """
@@ -737,7 +633,7 @@ async def upsert_execution_input(
         )
         return existing_execution.id, {
             **{
-                input_data.name: type_utils.convert(input_data.data, JsonValue)
+                input_data.name: type_utils.convert(input_data.data, type[Any])
                 for input_data in existing_execution.Input or []
             },
             input_name: input_data,
@@ -760,6 +656,42 @@ async def upsert_execution_input(
         )
 
 
+async def create_node_execution(
+    node_exec_id: str,
+    node_id: str,
+    graph_exec_id: str,
+    input_name: str,
+    input_data: Any,
+) -> None:
+    """Create a new node execution with the first input."""
+    json_input_data = SafeJson(input_data)
+    await AgentNodeExecution.prisma().create(
+        data=AgentNodeExecutionCreateInput(
+            id=node_exec_id,
+            agentNodeId=node_id,
+            agentGraphExecutionId=graph_exec_id,
+            executionStatus=ExecutionStatus.INCOMPLETE,
+            Input={"create": {"name": input_name, "data": json_input_data}},
+        )
+    )
+
+
+async def add_input_to_node_execution(
+    node_exec_id: str,
+    input_name: str,
+    input_data: Any,
+) -> None:
+    """Add an input to an existing node execution."""
+    json_input_data = SafeJson(input_data)
+    await AgentNodeExecutionInputOutput.prisma().create(
+        data=AgentNodeExecutionInputOutputCreateInput(
+            name=input_name,
+            data=json_input_data,
+            referencedByInputExecId=node_exec_id,
+        )
+    )
+
+
 async def upsert_execution_output(
     node_exec_id: str,
     output_name: str,
@@ -798,11 +730,6 @@ async def update_graph_execution_stats(
     status: ExecutionStatus | None = None,
     stats: GraphExecutionStats | None = None,
 ) -> GraphExecution | None:
-    if not status and not stats:
-        raise ValueError(
-            f"Must provide either status or stats to update for execution {graph_exec_id}"
-        )
-
     update_data: AgentGraphExecutionUpdateManyMutationInput = {}
 
     if stats:
@@ -814,25 +741,20 @@ async def update_graph_execution_stats(
     if status:
         update_data["executionStatus"] = status
 
-    where_clause: AgentGraphExecutionWhereInput = {"id": graph_exec_id}
-
-    if status:
-        if allowed_from := VALID_STATUS_TRANSITIONS.get(status, []):
-            # Add OR clause to check if current status is one of the allowed source statuses
-            where_clause["AND"] = [
-                {"id": graph_exec_id},
-                {"OR": [{"executionStatus": s} for s in allowed_from]},
-            ]
-        else:
-            raise ValueError(
-                f"Status {status} cannot be set via update for execution {graph_exec_id}. "
-                f"This status can only be set at creation or is not a valid target status."
-            )
-
-    await AgentGraphExecution.prisma().update_many(
-        where=where_clause,
+    updated_count = await AgentGraphExecution.prisma().update_many(
+        where={
+            "id": graph_exec_id,
+            "OR": [
+                {"executionStatus": ExecutionStatus.RUNNING},
+                {"executionStatus": ExecutionStatus.QUEUED},
+                # Terminated graph can be resumed.
+                {"executionStatus": ExecutionStatus.TERMINATED},
+            ],
+        },
         data=update_data,
     )
+    if updated_count == 0:
+        return None
 
     graph_exec = await AgentGraphExecution.prisma().find_unique_or_raise(
         where={"id": graph_exec_id},
@@ -840,7 +762,6 @@ async def update_graph_execution_stats(
             [*get_io_block_ids(), *get_webhook_block_ids()]
         ),
     )
-
     return GraphExecution.from_db(graph_exec)
 
 
@@ -1005,7 +926,7 @@ class GraphExecutionEntry(BaseModel):
     graph_exec_id: str
     graph_id: str
     graph_version: int
-    nodes_input_masks: Optional[NodesInputMasks] = None
+    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None
     user_context: UserContext
 
 
@@ -1067,18 +988,6 @@ class NodeExecutionEvent(NodeExecutionResult):
     )
 
 
-class SharedExecutionResponse(BaseModel):
-    """Public-safe response for shared executions"""
-
-    id: str
-    graph_name: str
-    graph_description: Optional[str]
-    status: ExecutionStatus
-    created_at: datetime
-    outputs: CompletedBlockOutput  # Only the final outputs, no intermediate data
-    # Deliberately exclude: user_id, inputs, credentials, node details
-
-
 ExecutionEvent = Annotated[
     GraphExecutionEvent | NodeExecutionEvent, Field(discriminator="event_type")
 ]
@@ -1256,98 +1165,3 @@ async def get_block_error_stats(
         )
         for row in result
     ]
-
-
-async def update_graph_execution_share_status(
-    execution_id: str,
-    user_id: str,
-    is_shared: bool,
-    share_token: str | None,
-    shared_at: datetime | None,
-) -> None:
-    """Update the sharing status of a graph execution."""
-    await AgentGraphExecution.prisma().update(
-        where={"id": execution_id},
-        data={
-            "isShared": is_shared,
-            "shareToken": share_token,
-            "sharedAt": shared_at,
-        },
-    )
-
-
-async def get_graph_execution_by_share_token(
-    share_token: str,
-) -> SharedExecutionResponse | None:
-    """Get a shared execution with limited public-safe data."""
-    execution = await AgentGraphExecution.prisma().find_first(
-        where={
-            "shareToken": share_token,
-            "isShared": True,
-            "isDeleted": False,
-        },
-        include={
-            "AgentGraph": True,
-            "NodeExecutions": {
-                "include": {
-                    "Output": True,
-                    "Node": {
-                        "include": {
-                            "AgentBlock": True,
-                        }
-                    },
-                },
-            },
-        },
-    )
-
-    if not execution:
-        return None
-
-    # Extract outputs from OUTPUT blocks only (consistent with GraphExecution.from_db)
-    outputs: CompletedBlockOutput = defaultdict(list)
-    if execution.NodeExecutions:
-        for node_exec in execution.NodeExecutions:
-            if node_exec.Node and node_exec.Node.agentBlockId:
-                # Get the block definition to check its type
-                block = get_block(node_exec.Node.agentBlockId)
-
-                if block and block.block_type == BlockType.OUTPUT:
-                    # For OUTPUT blocks, the data is stored in executionData or Input
-                    # The executionData contains the structured input with 'name' and 'value' fields
-                    if hasattr(node_exec, "executionData") and node_exec.executionData:
-                        exec_data = type_utils.convert(
-                            node_exec.executionData, dict[str, Any]
-                        )
-                        if "name" in exec_data:
-                            name = exec_data["name"]
-                            value = exec_data.get("value")
-                            outputs[name].append(value)
-                    elif node_exec.Input:
-                        # Build input_data from Input relation
-                        input_data = {}
-                        for data in node_exec.Input:
-                            if data.name and data.data is not None:
-                                input_data[data.name] = type_utils.convert(
-                                    data.data, JsonValue
-                                )
-
-                        if "name" in input_data:
-                            name = input_data["name"]
-                            value = input_data.get("value")
-                            outputs[name].append(value)
-
-    return SharedExecutionResponse(
-        id=execution.id,
-        graph_name=(
-            execution.AgentGraph.name
-            if (execution.AgentGraph and execution.AgentGraph.name)
-            else "Untitled Agent"
-        ),
-        graph_description=(
-            execution.AgentGraph.description if execution.AgentGraph else None
-        ),
-        status=ExecutionStatus(execution.executionStatus),
-        created_at=execution.createdAt,
-        outputs=outputs,
-    )

autogpt_platform/backend/backend/data/generate_data.py

@@ -7,7 +7,7 @@ from prisma.enums import AgentExecutionStatus
 from backend.data.execution import get_graph_executions
 from backend.data.graph import get_graph_metadata
 from backend.data.model import UserExecutionSummaryStats
-from backend.util.exceptions import DatabaseError
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.logging import TruncatedLogger
 
 logger = TruncatedLogger(logging.getLogger(__name__), prefix="[SummaryData]")

autogpt_platform/backend/backend/data/graph.py

@@ -1,7 +1,6 @@
 import logging
 import uuid
 from collections import defaultdict
-from datetime import datetime, timezone
 from typing import TYPE_CHECKING, Any, Literal, Optional, cast
 
 from prisma.enums import SubmissionStatus
@@ -13,15 +12,13 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import BaseModel, Field, create_model
+from pydantic import Field, JsonValue, create_model
 from pydantic.fields import computed_field
 
 from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentInputBlock, AgentOutputBlock
 from backend.blocks.llm import LlmModel
 from backend.data.db import prisma as db
-from backend.data.dynamic_fields import extract_base_field_name
-from backend.data.includes import MAX_GRAPH_VERSIONS_FETCH
 from backend.data.model import (
     CredentialsField,
     CredentialsFieldInfo,
@@ -31,22 +28,12 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import type as type_utils
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 
-from .block import (
-    Block,
-    BlockInput,
-    BlockSchema,
-    BlockType,
-    EmptySchema,
-    get_block,
-    get_blocks,
-)
+from .block import Block, BlockInput, BlockSchema, BlockType, get_block, get_blocks
 from .db import BaseDbModel, query_raw_with_schema, transaction
 from .includes import AGENT_GRAPH_INCLUDE, AGENT_NODE_INCLUDE
 
 if TYPE_CHECKING:
-    from .execution import NodesInputMasks
     from .integrations import Webhook
 
 logger = logging.getLogger(__name__)
@@ -82,15 +69,12 @@ class Node(BaseDbModel):
     output_links: list[Link] = []
 
     @property
-    def block(self) -> "Block[BlockSchema, BlockSchema] | _UnknownBlockBase":
-        """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
+    def block(self) -> Block[BlockSchema, BlockSchema]:
         block = get_block(self.block_id)
         if not block:
-            # Log warning but don't raise exception - return a placeholder block for deleted blocks
-            logger.warning(
-                f"Block #{self.block_id} does not exist for Node #{self.id} (deleted/missing block), using UnknownBlock"
+            raise ValueError(
+                f"Block #{self.block_id} does not exist -> Node #{self.id} is invalid"
             )
-            return _UnknownBlockBase(self.block_id)
         return block
 
 
@@ -129,20 +113,17 @@ class NodeModel(Node):
         Returns a copy of the node model, stripped of any non-transferable properties
         """
         stripped_node = self.model_copy(deep=True)
-
-        # Remove credentials and other (possible) secrets from node input
+        # Remove credentials from node input
         if stripped_node.input_default:
             stripped_node.input_default = NodeModel._filter_secrets_from_node_input(
                 stripped_node.input_default, self.block.input_schema.jsonschema()
             )
 
-        # Remove default secret value from secret input nodes
         if (
             stripped_node.block.block_type == BlockType.INPUT
-            and stripped_node.input_default.get("secret", False) is True
             and "value" in stripped_node.input_default
         ):
-            del stripped_node.input_default["value"]
+            stripped_node.input_default["value"] = ""
 
         # Remove webhook info
         stripped_node.webhook_id = None
@@ -159,10 +140,8 @@ class NodeModel(Node):
         result = {}
         for key, value in input_data.items():
             field_schema: dict | None = field_schemas.get(key)
-            if (field_schema and field_schema.get("secret", False)) or (
-                any(sensitive_key in key.lower() for sensitive_key in sensitive_keys)
-                # Prevent removing `secret` flag on input nodes
-                and type(value) is not bool
+            if (field_schema and field_schema.get("secret", False)) or any(
+                sensitive_key in key.lower() for sensitive_key in sensitive_keys
             ):
                 # This is a secret value -> filter this key-value pair out
                 continue
@@ -180,8 +159,6 @@ class BaseGraph(BaseDbModel):
     is_active: bool = True
     name: str
     description: str
-    instructions: str | None = None
-    recommended_schedule_cron: str | None = None
     nodes: list[Node] = []
     links: list[Link] = []
     forked_from_id: str | None = None
@@ -228,35 +205,6 @@ class BaseGraph(BaseDbModel):
             None,
         )
 
-    @computed_field
-    @property
-    def trigger_setup_info(self) -> "GraphTriggerInfo | None":
-        if not (
-            self.webhook_input_node
-            and (trigger_block := self.webhook_input_node.block).webhook_config
-        ):
-            return None
-
-        return GraphTriggerInfo(
-            provider=trigger_block.webhook_config.provider,
-            config_schema={
-                **(json_schema := trigger_block.input_schema.jsonschema()),
-                "properties": {
-                    pn: sub_schema
-                    for pn, sub_schema in json_schema["properties"].items()
-                    if not is_credentials_field_name(pn)
-                },
-                "required": [
-                    pn
-                    for pn in json_schema.get("required", [])
-                    if not is_credentials_field_name(pn)
-                ],
-            },
-            credentials_input_name=next(
-                iter(trigger_block.input_schema.get_credentials_fields()), None
-            ),
-        )
-
     @staticmethod
     def _generate_schema(
         *props: tuple[type[AgentInputBlock.Input] | type[AgentOutputBlock.Input], dict],
@@ -290,14 +238,6 @@ class BaseGraph(BaseDbModel):
         }
 
 
-class GraphTriggerInfo(BaseModel):
-    provider: ProviderName
-    config_schema: dict[str, Any] = Field(
-        description="Input schema for the trigger block"
-    )
-    credentials_input_name: Optional[str]
-
-
 class Graph(BaseGraph):
     sub_graphs: list[BaseGraph] = []  # Flattened sub-graphs
 
@@ -402,8 +342,6 @@ class GraphModel(Graph):
     user_id: str
     nodes: list[NodeModel] = []  # type: ignore
 
-    created_at: datetime
-
     @property
     def starting_nodes(self) -> list[NodeModel]:
         outbound_nodes = {link.sink_id for link in self.links}
@@ -416,10 +354,6 @@ class GraphModel(Graph):
             if node.id not in outbound_nodes or node.id in input_nodes
         ]
 
-    @property
-    def webhook_input_node(self) -> NodeModel | None:  # type: ignore
-        return cast(NodeModel, super().webhook_input_node)
-
     def meta(self) -> "GraphMeta":
         """
         Returns a GraphMeta object with metadata about the graph.
@@ -480,7 +414,7 @@ class GraphModel(Graph):
     def validate_graph(
         self,
         for_run: bool = False,
-        nodes_input_masks: Optional["NodesInputMasks"] = None,
+        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
     ):
         """
         Validate graph structure and raise `ValueError` on issues.
@@ -494,7 +428,7 @@ class GraphModel(Graph):
     def _validate_graph(
         graph: BaseGraph,
         for_run: bool = False,
-        nodes_input_masks: Optional["NodesInputMasks"] = None,
+        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
     ) -> None:
         errors = GraphModel._validate_graph_get_errors(
             graph, for_run, nodes_input_masks
@@ -508,7 +442,7 @@ class GraphModel(Graph):
     def validate_graph_get_errors(
         self,
         for_run: bool = False,
-        nodes_input_masks: Optional["NodesInputMasks"] = None,
+        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
     ) -> dict[str, dict[str, str]]:
         """
         Validate graph and return structured errors per node.
@@ -530,7 +464,7 @@ class GraphModel(Graph):
     def _validate_graph_get_errors(
         graph: BaseGraph,
         for_run: bool = False,
-        nodes_input_masks: Optional["NodesInputMasks"] = None,
+        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
     ) -> dict[str, dict[str, str]]:
         """
         Validate graph and return structured errors per node.
@@ -721,12 +655,9 @@ class GraphModel(Graph):
             version=graph.version,
             forked_from_id=graph.forkedFromId,
             forked_from_version=graph.forkedFromVersion,
-            created_at=graph.createdAt,
             is_active=graph.isActive,
             name=graph.name or "",
             description=graph.description or "",
-            instructions=graph.instructions,
-            recommended_schedule_cron=graph.recommendedScheduleCron,
             nodes=[NodeModel.from_db(node, for_export) for node in graph.Nodes or []],
             links=list(
                 {
@@ -747,7 +678,7 @@ def _is_tool_pin(name: str) -> bool:
 
 
 def _sanitize_pin_name(name: str) -> str:
-    sanitized_name = extract_base_field_name(name)
+    sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
     if _is_tool_pin(sanitized_name):
         return "tools"
     return sanitized_name
@@ -765,13 +696,6 @@ class GraphMeta(Graph):
         return GraphMeta(**graph.model_dump())
 
 
-class GraphsPaginated(BaseModel):
-    """Response schema for paginated graphs."""
-
-    graphs: list[GraphMeta]
-    pagination: Pagination
-
-
 # --------------------- CRUD functions --------------------- #
 
 
@@ -800,42 +724,31 @@ async def set_node_webhook(node_id: str, webhook_id: str | None) -> NodeModel:
     return NodeModel.from_db(node)
 
 
-async def list_graphs_paginated(
+async def list_graphs(
     user_id: str,
-    page: int = 1,
-    page_size: int = 25,
     filter_by: Literal["active"] | None = "active",
-) -> GraphsPaginated:
+) -> list[GraphMeta]:
     """
-    Retrieves paginated graph metadata objects.
+    Retrieves graph metadata objects.
+    Default behaviour is to get all currently active graphs.
 
     Args:
-        user_id: The ID of the user that owns the graphs.
-        page: Page number (1-based).
-        page_size: Number of graphs per page.
         filter_by: An optional filter to either select graphs.
+        user_id: The ID of the user that owns the graph.
 
     Returns:
-        GraphsPaginated: Paginated list of graph metadata.
+        list[GraphMeta]: A list of objects representing the retrieved graphs.
     """
     where_clause: AgentGraphWhereInput = {"userId": user_id}
 
     if filter_by == "active":
         where_clause["isActive"] = True
 
-    # Get total count
-    total_count = await AgentGraph.prisma().count(where=where_clause)
-    total_pages = (total_count + page_size - 1) // page_size
-
-    # Get paginated results
-    offset = (page - 1) * page_size
     graphs = await AgentGraph.prisma().find_many(
         where=where_clause,
         distinct=["id"],
         order={"version": "desc"},
         include=AGENT_GRAPH_INCLUDE,
-        skip=offset,
-        take=page_size,
     )
 
     graph_models: list[GraphMeta] = []
@@ -849,15 +762,7 @@ async def list_graphs_paginated(
             logger.error(f"Error processing graph {graph.id}: {e}")
             continue
 
-    return GraphsPaginated(
-        graphs=graph_models,
-        pagination=Pagination(
-            total_items=total_count,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
+    return graph_models
 
 
 async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph | None:
@@ -1077,14 +982,11 @@ async def set_graph_active_version(graph_id: str, version: int, user_id: str) ->
     )
 
 
-async def get_graph_all_versions(
-    graph_id: str, user_id: str, limit: int = MAX_GRAPH_VERSIONS_FETCH
-) -> list[GraphModel]:
+async def get_graph_all_versions(graph_id: str, user_id: str) -> list[GraphModel]:
     graph_versions = await AgentGraph.prisma().find_many(
         where={"id": graph_id, "userId": user_id},
         order={"version": "desc"},
         include=AGENT_GRAPH_INCLUDE,
-        take=limit,
     )
 
     if not graph_versions:
@@ -1143,7 +1045,6 @@ async def __create_graph(tx, graph: Graph, user_id: str):
                 version=graph.version,
                 name=graph.name,
                 description=graph.description,
-                recommendedScheduleCron=graph.recommended_schedule_cron,
                 isActive=graph.is_active,
                 userId=user_id,
                 forkedFromId=graph.forked_from_id,
@@ -1202,7 +1103,6 @@ def make_graph_model(creatable_graph: Graph, user_id: str) -> GraphModel:
     return GraphModel(
         **creatable_graph.model_dump(exclude={"nodes"}),
         user_id=user_id,
-        created_at=datetime.now(tz=timezone.utc),
         nodes=[
             NodeModel(
                 **creatable_node.model_dump(),
@@ -1333,34 +1233,3 @@ async def migrate_llm_models(migrate_to: LlmModel):
             id,
             path,
         )
-
-
-# Simple placeholder class for deleted/missing blocks
-class _UnknownBlockBase(Block):
-    """
-    Placeholder for deleted/missing blocks that inherits from Block
-    but uses a name that doesn't end with 'Block' to avoid auto-discovery.
-    """
-
-    def __init__(self, block_id: str = "00000000-0000-0000-0000-000000000000"):
-        # Initialize with minimal valid Block parameters
-        super().__init__(
-            id=block_id,
-            description=f"Unknown or deleted block (original ID: {block_id})",
-            disabled=True,
-            input_schema=EmptySchema,
-            output_schema=EmptySchema,
-            categories=set(),
-            contributors=[],
-            static_output=False,
-            block_type=BlockType.STANDARD,
-            webhook_config=None,
-        )
-
-    @property
-    def name(self):
-        return "UnknownBlock"
-
-    async def run(self, input_data, **kwargs):
-        """Always yield an error for missing blocks."""
-        yield "error", f"Block {self.id} no longer exists"

autogpt_platform/backend/backend/data/graph_test.py

@@ -201,56 +201,25 @@ async def test_get_input_schema(server: SpinTestServer, snapshot: Snapshot):
 @pytest.mark.asyncio(loop_scope="session")
 async def test_clean_graph(server: SpinTestServer):
     """
-    Test the stripped_for_export function that:
-    1. Removes sensitive/secret fields from node inputs
-    2. Removes webhook information
-    3. Preserves non-sensitive data including input block values
+    Test the clean_graph function that:
+    1. Clears input block values
+    2. Removes credentials from nodes
     """
-    # Create a graph with input blocks containing both sensitive and normal data
+    # Create a graph with input blocks and credentials
     graph = Graph(
         id="test_clean_graph",
         name="Test Clean Graph",
         description="Test graph cleaning",
         nodes=[
             Node(
+                id="input_node",
                 block_id=AgentInputBlock().id,
                 input_default={
-                    "_test_id": "input_node",
                     "name": "test_input",
-                    "value": "test value",  # This should be preserved
+                    "value": "test value",
                     "description": "Test input description",
                 },
             ),
-            Node(
-                block_id=AgentInputBlock().id,
-                input_default={
-                    "_test_id": "input_node_secret",
-                    "name": "secret_input",
-                    "value": "another value",
-                    "secret": True,  # This makes the input secret
-                },
-            ),
-            Node(
-                block_id=StoreValueBlock().id,
-                input_default={
-                    "_test_id": "node_with_secrets",
-                    "input": "normal_value",
-                    "control_test_input": "should be preserved",
-                    "api_key": "secret_api_key_123",  # Should be filtered
-                    "password": "secret_password_456",  # Should be filtered
-                    "token": "secret_token_789",  # Should be filtered
-                    "credentials": {  # Should be filtered
-                        "id": "fake-github-credentials-id",
-                        "provider": "github",
-                        "type": "api_key",
-                    },
-                    "anthropic_credentials": {  # Should be filtered
-                        "id": "fake-anthropic-credentials-id",
-                        "provider": "anthropic",
-                        "type": "api_key",
-                    },
-                },
-            ),
         ],
         links=[],
     )
@@ -262,54 +231,15 @@ async def test_clean_graph(server: SpinTestServer):
     )
 
     # Clean the graph
-    cleaned_graph = await server.agent_server.test_get_graph(
+    created_graph = await server.agent_server.test_get_graph(
         created_graph.id, created_graph.version, DEFAULT_USER_ID, for_export=True
     )
 
-    # Verify sensitive fields are removed but normal fields are preserved
+    # # Verify input block value is cleared
     input_node = next(
-        n for n in cleaned_graph.nodes if n.input_default["_test_id"] == "input_node"
+        n for n in created_graph.nodes if n.block_id == AgentInputBlock().id
     )
-
-    # Non-sensitive fields should be preserved
-    assert input_node.input_default["name"] == "test_input"
-    assert input_node.input_default["value"] == "test value"  # Should be preserved now
-    assert input_node.input_default["description"] == "Test input description"
-
-    # Sensitive fields should be filtered out
-    assert "api_key" not in input_node.input_default
-    assert "password" not in input_node.input_default
-
-    # Verify secret input node preserves non-sensitive fields but removes secret value
-    secret_node = next(
-        n
-        for n in cleaned_graph.nodes
-        if n.input_default["_test_id"] == "input_node_secret"
-    )
-    assert secret_node.input_default["name"] == "secret_input"
-    assert "value" not in secret_node.input_default  # Secret default should be removed
-    assert secret_node.input_default["secret"] is True
-
-    # Verify sensitive fields are filtered from nodes with secrets
-    secrets_node = next(
-        n
-        for n in cleaned_graph.nodes
-        if n.input_default["_test_id"] == "node_with_secrets"
-    )
-    # Normal fields should be preserved
-    assert secrets_node.input_default["input"] == "normal_value"
-    assert secrets_node.input_default["control_test_input"] == "should be preserved"
-    # Sensitive fields should be filtered out
-    assert "api_key" not in secrets_node.input_default
-    assert "password" not in secrets_node.input_default
-    assert "token" not in secrets_node.input_default
-    assert "credentials" not in secrets_node.input_default
-    assert "anthropic_credentials" not in secrets_node.input_default
-
-    # Verify webhook info is removed (if any nodes had it)
-    for node in cleaned_graph.nodes:
-        assert node.webhook_id is None
-        assert node.webhook is None
+    assert input_node.input_default["value"] == ""
 
 
 @pytest.mark.asyncio(loop_scope="session")

autogpt_platform/backend/backend/data/includes.py

@@ -14,7 +14,6 @@ AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
     "Nodes": {"include": AGENT_NODE_INCLUDE}
 }
 
-
 EXECUTION_RESULT_ORDER: list[prisma.types.AgentNodeExecutionOrderByInput] = [
     {"queuedTime": "desc"},
     # Fallback: Incomplete execs has no queuedTime.
@@ -29,13 +28,6 @@ EXECUTION_RESULT_INCLUDE: prisma.types.AgentNodeExecutionInclude = {
 }
 
 MAX_NODE_EXECUTIONS_FETCH = 1000
-MAX_LIBRARY_AGENT_EXECUTIONS_FETCH = 10
-
-# Default limits for potentially large result sets
-MAX_CREDIT_REFUND_REQUESTS_FETCH = 100
-MAX_INTEGRATION_WEBHOOKS_FETCH = 100
-MAX_USER_API_KEYS_FETCH = 500
-MAX_GRAPH_VERSIONS_FETCH = 50
 
 GRAPH_EXECUTION_INCLUDE_WITH_NODES: prisma.types.AgentGraphExecutionInclude = {
     "NodeExecutions": {
@@ -67,68 +59,19 @@ def graph_execution_include(
     }
 
 
-AGENT_PRESET_INCLUDE: prisma.types.AgentPresetInclude = {
-    "InputPresets": True,
-    "Webhook": True,
-}
-
-
 INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
     "AgentNodes": {"include": AGENT_NODE_INCLUDE},
-    "AgentPresets": {"include": AGENT_PRESET_INCLUDE},
+    "AgentPresets": {"include": {"InputPresets": True}},
 }
 
 
-def library_agent_include(
-    user_id: str,
-    include_nodes: bool = True,
-    include_executions: bool = True,
-    execution_limit: int = MAX_LIBRARY_AGENT_EXECUTIONS_FETCH,
-) -> prisma.types.LibraryAgentInclude:
-    """
-    Fully configurable includes for library agent queries with performance optimization.
-
-    Args:
-        user_id: User ID for filtering user-specific data
-        include_nodes: Whether to include graph nodes (default: True, needed for get_sub_graphs)
-        include_executions: Whether to include executions (default: True, safe with execution_limit)
-        execution_limit: Limit on executions to fetch (default: MAX_LIBRARY_AGENT_EXECUTIONS_FETCH)
-
-    Defaults maintain backward compatibility and safety - includes everything needed for all functionality.
-    For performance optimization, explicitly set include_nodes=False and include_executions=False
-    for listing views where frontend fetches data separately.
-
-    Performance impact:
-    - Default (full nodes + limited executions): Original performance, works everywhere
-    - Listing optimization (no nodes/executions): ~2s for 15 agents vs potential timeouts
-    - Unlimited executions: varies by user (thousands of executions = timeouts)
-    """
-    result: prisma.types.LibraryAgentInclude = {
-        "Creator": True,  # Always needed for creator info
-    }
-
-    # Build AgentGraph include based on requested options
-    if include_nodes or include_executions:
-        agent_graph_include = {}
-
-        # Add nodes if requested (always full nodes)
-        if include_nodes:
-            agent_graph_include.update(AGENT_GRAPH_INCLUDE)  # Full nodes
-
-        # Add executions if requested
-        if include_executions:
-            agent_graph_include["Executions"] = {
-                "where": {"userId": user_id},
-                "order_by": {"createdAt": "desc"},
-                "take": execution_limit,
+def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
+    return {
+        "AgentGraph": {
+            "include": {
+                **AGENT_GRAPH_INCLUDE,
+                "Executions": {"where": {"userId": user_id}},
             }
-
-        result["AgentGraph"] = cast(
-            prisma.types.AgentGraphArgsFromLibraryAgent,
-            {"include": agent_graph_include},
-        )
-    else:
-        # Default: Basic metadata only (fast - recommended for most use cases)
-        result["AgentGraph"] = True  # Basic graph metadata (name, description, id)
-
-    return result
+        },
+        "Creator": True,
+    }

autogpt_platform/backend/backend/data/integrations.py

@@ -11,10 +11,7 @@ from prisma.types import (
 from pydantic import Field, computed_field
 
 from backend.data.event_bus import AsyncRedisEventBus
-from backend.data.includes import (
-    INTEGRATION_WEBHOOK_INCLUDE,
-    MAX_INTEGRATION_WEBHOOKS_FETCH,
-)
+from backend.data.includes import INTEGRATION_WEBHOOK_INCLUDE
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.utils import webhook_ingress_url
 from backend.server.v2.library.model import LibraryAgentPreset
@@ -131,36 +128,22 @@ async def get_webhook(
 
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: Literal[True],
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: Literal[True]
 ) -> list[WebhookWithRelations]: ...
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: Literal[False] = False,
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: Literal[False] = False
 ) -> list[Webhook]: ...
 
 
 async def get_all_webhooks_by_creds(
-    user_id: str,
-    credentials_id: str,
-    *,
-    include_relations: bool = False,
-    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
+    user_id: str, credentials_id: str, *, include_relations: bool = False
 ) -> list[Webhook] | list[WebhookWithRelations]:
     if not credentials_id:
         raise ValueError("credentials_id must not be empty")
     webhooks = await IntegrationWebhook.prisma().find_many(
         where={"userId": user_id, "credentialsId": credentials_id},
         include=INTEGRATION_WEBHOOK_INCLUDE if include_relations else None,
-        order={"createdAt": "desc"},
-        take=limit,
     )
     return [
         (WebhookWithRelations if include_relations else Webhook).from_db(webhook)

autogpt_platform/backend/backend/data/model.py

@@ -270,7 +270,6 @@ def SchemaField(
     min_length: Optional[int] = None,
     max_length: Optional[int] = None,
     discriminator: Optional[str] = None,
-    format: Optional[str] = None,
     json_schema_extra: Optional[dict[str, Any]] = None,
 ) -> T:
     if default is PydanticUndefined and default_factory is None:
@@ -286,7 +285,6 @@ def SchemaField(
             "advanced": advanced,
             "hidden": hidden,
             "depends_on": depends_on,
-            "format": format,
             **(json_schema_extra or {}),
         }.items()
         if v is not None

autogpt_platform/backend/backend/data/notifications.py

@@ -15,7 +15,7 @@ from prisma.types import (
 # from backend.notifications.models import NotificationEvent
 from pydantic import BaseModel, ConfigDict, EmailStr, Field, field_validator
 
-from backend.util.exceptions import DatabaseError
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.logging import TruncatedLogger
 
@@ -235,7 +235,6 @@ class BaseEventModel(BaseModel):
 
 
 class NotificationEventModel(BaseEventModel, Generic[NotificationDataType_co]):
-    id: Optional[str] = None  # None when creating, populated when reading from DB
     data: NotificationDataType_co
 
     @property
@@ -379,7 +378,6 @@ class NotificationPreference(BaseModel):
 
 
 class UserNotificationEventDTO(BaseModel):
-    id: str  # Added to track notifications for removal
     type: NotificationType
     data: dict
     created_at: datetime
@@ -388,7 +386,6 @@ class UserNotificationEventDTO(BaseModel):
     @staticmethod
     def from_db(model: NotificationEvent) -> "UserNotificationEventDTO":
         return UserNotificationEventDTO(
-            id=model.id,
             type=model.type,
             data=dict(model.data),
             created_at=model.createdAt,
@@ -544,79 +541,6 @@ async def empty_user_notification_batch(
         ) from e
 
 
-async def clear_all_user_notification_batches(user_id: str) -> None:
-    """Clear ALL notification batches for a user across all types.
-
-    Used when user's email is bounced/inactive and we should stop
-    trying to send them ANY emails.
-    """
-    try:
-        # Delete all notification events for this user
-        await NotificationEvent.prisma().delete_many(
-            where={"UserNotificationBatch": {"is": {"userId": user_id}}}
-        )
-
-        # Delete all batches for this user
-        await UserNotificationBatch.prisma().delete_many(where={"userId": user_id})
-
-        logger.info(f"Cleared all notification batches for user {user_id}")
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to clear all notification batches for user {user_id}: {e}"
-        ) from e
-
-
-async def remove_notifications_from_batch(
-    user_id: str, notification_type: NotificationType, notification_ids: list[str]
-) -> None:
-    """Remove specific notifications from a user's batch by their IDs.
-
-    This is used after successful sending to remove only the
-    sent notifications, preventing duplicates on retry.
-    """
-    if not notification_ids:
-        return
-
-    try:
-        # Delete the specific notification events
-        deleted_count = await NotificationEvent.prisma().delete_many(
-            where={
-                "id": {"in": notification_ids},
-                "UserNotificationBatch": {
-                    "is": {"userId": user_id, "type": notification_type}
-                },
-            }
-        )
-
-        logger.info(
-            f"Removed {deleted_count} notifications from batch for user {user_id}"
-        )
-
-        # Check if batch is now empty and delete it if so
-        remaining = await NotificationEvent.prisma().count(
-            where={
-                "UserNotificationBatch": {
-                    "is": {"userId": user_id, "type": notification_type}
-                }
-            }
-        )
-
-        if remaining == 0:
-            await UserNotificationBatch.prisma().delete_many(
-                where=UserNotificationBatchWhereInput(
-                    userId=user_id,
-                    type=notification_type,
-                )
-            )
-            logger.info(
-                f"Deleted empty batch for user {user_id} and type {notification_type}"
-            )
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to remove notifications from batch for user {user_id} and type {notification_type}: {e}"
-        ) from e
-
-
 async def get_user_notification_batch(
     user_id: str,
     notification_type: NotificationType,

autogpt_platform/backend/backend/data/onboarding.py

@@ -1,16 +1,15 @@
 import re
-from datetime import datetime
 from typing import Any, Optional
 
 import prisma
 import pydantic
-from autogpt_libs.utils.cache import cached
 from prisma.enums import OnboardingStep
 from prisma.models import UserOnboarding
 from prisma.types import UserOnboardingCreateInput, UserOnboardingUpdateInput
 
 from backend.data.block import get_blocks
 from backend.data.credit import get_user_credit_model
+from backend.data.graph import GraphModel
 from backend.data.model import CredentialsMetaInput
 from backend.server.v2.store.model import StoreAgentDetails
 from backend.util.json import SafeJson
@@ -31,7 +30,7 @@ user_credit = get_user_credit_model()
 
 class UserOnboardingUpdate(pydantic.BaseModel):
     completedSteps: Optional[list[OnboardingStep]] = None
-    walletShown: Optional[bool] = None
+    notificationDot: Optional[bool] = None
     notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
     integrations: Optional[list[str]] = None
@@ -40,8 +39,6 @@ class UserOnboardingUpdate(pydantic.BaseModel):
     agentInput: Optional[dict[str, Any]] = None
     onboardingAgentExecutionId: Optional[str] = None
     agentRuns: Optional[int] = None
-    lastRunAt: Optional[datetime] = None
-    consecutiveRunDays: Optional[int] = None
 
 
 async def get_user_onboarding(user_id: str):
@@ -60,22 +57,16 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["completedSteps"] = list(set(data.completedSteps))
         for step in (
             OnboardingStep.AGENT_NEW_RUN,
-            OnboardingStep.MARKETPLACE_VISIT,
+            OnboardingStep.RUN_AGENTS,
             OnboardingStep.MARKETPLACE_ADD_AGENT,
             OnboardingStep.MARKETPLACE_RUN_AGENT,
             OnboardingStep.BUILDER_SAVE_AGENT,
-            OnboardingStep.RE_RUN_AGENT,
-            OnboardingStep.SCHEDULE_AGENT,
-            OnboardingStep.RUN_AGENTS,
-            OnboardingStep.RUN_3_DAYS,
-            OnboardingStep.TRIGGER_WEBHOOK,
-            OnboardingStep.RUN_14_DAYS,
-            OnboardingStep.RUN_AGENTS_100,
+            OnboardingStep.BUILDER_RUN_AGENT,
         ):
             if step in data.completedSteps:
                 await reward_user(user_id, step)
-    if data.walletShown is not None:
-        update["walletShown"] = data.walletShown
+    if data.notificationDot is not None:
+        update["notificationDot"] = data.notificationDot
     if data.notified is not None:
         update["notified"] = list(set(data.notified))
     if data.usageReason is not None:
@@ -92,10 +83,6 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["onboardingAgentExecutionId"] = data.onboardingAgentExecutionId
     if data.agentRuns is not None:
         update["agentRuns"] = data.agentRuns
-    if data.lastRunAt is not None:
-        update["lastRunAt"] = data.lastRunAt
-    if data.consecutiveRunDays is not None:
-        update["consecutiveRunDays"] = data.consecutiveRunDays
 
     return await UserOnboarding.prisma().upsert(
         where={"userId": user_id},
@@ -114,28 +101,16 @@ async def reward_user(user_id: str, step: OnboardingStep):
         # This is seen as a reward for the GET_RESULTS step in the wallet
         case OnboardingStep.AGENT_NEW_RUN:
             reward = 300
-        case OnboardingStep.MARKETPLACE_VISIT:
-            reward = 100
+        case OnboardingStep.RUN_AGENTS:
+            reward = 300
         case OnboardingStep.MARKETPLACE_ADD_AGENT:
             reward = 100
         case OnboardingStep.MARKETPLACE_RUN_AGENT:
             reward = 100
         case OnboardingStep.BUILDER_SAVE_AGENT:
             reward = 100
-        case OnboardingStep.RE_RUN_AGENT:
+        case OnboardingStep.BUILDER_RUN_AGENT:
             reward = 100
-        case OnboardingStep.SCHEDULE_AGENT:
-            reward = 100
-        case OnboardingStep.RUN_AGENTS:
-            reward = 300
-        case OnboardingStep.RUN_3_DAYS:
-            reward = 100
-        case OnboardingStep.TRIGGER_WEBHOOK:
-            reward = 100
-        case OnboardingStep.RUN_14_DAYS:
-            reward = 300
-        case OnboardingStep.RUN_AGENTS_100:
-            reward = 300
 
     if reward == 0:
         return
@@ -157,22 +132,6 @@ async def reward_user(user_id: str, step: OnboardingStep):
     )
 
 
-async def complete_webhook_trigger_step(user_id: str):
-    """
-    Completes the TRIGGER_WEBHOOK onboarding step for the user if not already completed.
-    """
-
-    onboarding = await get_user_onboarding(user_id)
-    if OnboardingStep.TRIGGER_WEBHOOK not in onboarding.completedSteps:
-        await update_user_onboarding(
-            user_id,
-            UserOnboardingUpdate(
-                completedSteps=onboarding.completedSteps
-                + [OnboardingStep.TRIGGER_WEBHOOK]
-            ),
-        )
-
-
 def clean_and_split(text: str) -> list[str]:
     """
     Removes all special characters from a string, truncates it to 100 characters,
@@ -277,14 +236,8 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         for word in user_onboarding.integrations
     ]
 
-    where_clause["is_available"] = True
-
-    # Try to take only agents that are available and allowed for onboarding
     storeAgents = await prisma.models.StoreAgent.prisma().find_many(
-        where={
-            "is_available": True,
-            "useForOnboarding": True,
-        },
+        where=prisma.types.StoreAgentWhereInput(**where_clause),
         order=[
             {"featured": "desc"},
             {"runs": "desc"},
@@ -293,16 +246,59 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
         take=100,
     )
 
-    # If not enough agents found, relax the useForOnboarding filter
+    agentListings = await prisma.models.StoreListingVersion.prisma().find_many(
+        where={
+            "id": {"in": [agent.storeListingVersionId for agent in storeAgents]},
+        },
+        include={"AgentGraph": True},
+    )
+
+    for listing in agentListings:
+        agent = listing.AgentGraph
+        if agent is None:
+            continue
+        graph = GraphModel.from_db(agent)
+        # Remove agents with empty input schema
+        if not graph.input_schema:
+            storeAgents = [
+                a for a in storeAgents if a.storeListingVersionId != listing.id
+            ]
+            continue
+
+        # Remove agents with empty credentials
+        # Get nodes from this agent that have credentials
+        nodes = await prisma.models.AgentNode.prisma().find_many(
+            where={
+                "agentGraphId": agent.id,
+                "agentBlockId": {"in": list(CREDENTIALS_FIELDS.keys())},
+            },
+        )
+        for node in nodes:
+            block_id = node.agentBlockId
+            field_name = CREDENTIALS_FIELDS[block_id]
+            # If there are no credentials or they are empty, remove the agent
+            # FIXME ignores default values
+            if (
+                field_name not in node.constantInput
+                or node.constantInput[field_name] is None
+            ):
+                storeAgents = [
+                    a for a in storeAgents if a.storeListingVersionId != listing.id
+                ]
+                break
+
+    # If there are less than 2 agents, add more agents to the list
     if len(storeAgents) < 2:
-        storeAgents = await prisma.models.StoreAgent.prisma().find_many(
-            where=prisma.types.StoreAgentWhereInput(**where_clause),
+        storeAgents += await prisma.models.StoreAgent.prisma().find_many(
+            where={
+                "listing_id": {"not_in": [agent.listing_id for agent in storeAgents]},
+            },
             order=[
                 {"featured": "desc"},
                 {"runs": "desc"},
                 {"rating": "desc"},
             ],
-            take=100,
+            take=2 - len(storeAgents),
         )
 
     # Calculate points for the first X agents and choose the top 2
@@ -337,13 +333,8 @@ async def get_recommended_agents(user_id: str) -> list[StoreAgentDetails]:
     ]
 
 
-@cached(maxsize=1, ttl_seconds=300)  # Cache for 5 minutes since this rarely changes
 async def onboarding_enabled() -> bool:
-    """
-    Check if onboarding should be enabled based on store agent count.
-    Cached to prevent repeated slow database queries.
-    """
-    # Use a more efficient query that stops counting after finding enough agents
     count = await prisma.models.StoreAgent.prisma().count(take=MIN_AGENT_COUNT + 1)
-    # Onboarding is enabled if there are at least 2 agents in the store
+
+    # Onboading is enabled if there are at least 2 agents in the store
     return count >= MIN_AGENT_COUNT

autogpt_platform/backend/backend/data/redis_client.py

@@ -1,7 +1,8 @@
 import logging
 import os
+from functools import cache
 
-from autogpt_libs.utils.cache import cached, thread_cached
+from autogpt_libs.utils.cache import thread_cached
 from dotenv import load_dotenv
 from redis import Redis
 from redis.asyncio import Redis as AsyncRedis
@@ -12,7 +13,7 @@ load_dotenv()
 
 HOST = os.getenv("REDIS_HOST", "localhost")
 PORT = int(os.getenv("REDIS_PORT", "6379"))
-PASSWORD = os.getenv("REDIS_PASSWORD", None)
+PASSWORD = os.getenv("REDIS_PASSWORD", "password")
 
 logger = logging.getLogger(__name__)
 
@@ -34,7 +35,7 @@ def disconnect():
     get_redis().close()
 
 
-@cached()
+@cache
 def get_redis() -> Redis:
     return connect()
 

autogpt_platform/backend/backend/data/user.py

@@ -7,7 +7,6 @@ from typing import Optional, cast
 from urllib.parse import quote_plus
 
 from autogpt_libs.auth.models import DEFAULT_USER_ID
-from autogpt_libs.utils.cache import cached
 from fastapi import HTTPException
 from prisma.enums import NotificationType
 from prisma.models import User as PrismaUser
@@ -16,19 +15,15 @@ from prisma.types import JsonFilter, UserCreateInput, UserUpdateInput
 from backend.data.db import prisma
 from backend.data.model import User, UserIntegrations, UserMetadata
 from backend.data.notifications import NotificationPreference, NotificationPreferenceDTO
+from backend.server.v2.store.exceptions import DatabaseError
 from backend.util.encryption import JSONCryptor
-from backend.util.exceptions import DatabaseError
 from backend.util.json import SafeJson
 from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
 settings = Settings()
 
-# Cache decorator alias for consistent user lookup caching
-cache_user_lookup = cached(maxsize=1000, ttl_seconds=300)
 
-
-@cache_user_lookup
 async def get_or_create_user(user_data: dict) -> User:
     try:
         user_id = user_data.get("sub")
@@ -54,7 +49,6 @@ async def get_or_create_user(user_data: dict) -> User:
         raise DatabaseError(f"Failed to get or create user {user_data}: {e}") from e
 
 
-@cache_user_lookup
 async def get_user_by_id(user_id: str) -> User:
     user = await prisma.user.find_unique(where={"id": user_id})
     if not user:
@@ -70,7 +64,6 @@ async def get_user_email_by_id(user_id: str) -> Optional[str]:
         raise DatabaseError(f"Failed to get user email for user {user_id}: {e}") from e
 
 
-@cache_user_lookup
 async def get_user_by_email(email: str) -> Optional[User]:
     try:
         user = await prisma.user.find_unique(where={"email": email})
@@ -81,17 +74,7 @@ async def get_user_by_email(email: str) -> Optional[User]:
 
 async def update_user_email(user_id: str, email: str):
     try:
-        # Get old email first for cache invalidation
-        old_user = await prisma.user.find_unique(where={"id": user_id})
-        old_email = old_user.email if old_user else None
-
         await prisma.user.update(where={"id": user_id}, data={"email": email})
-
-        # Selectively invalidate only the specific user entries
-        get_user_by_id.cache_delete(user_id)
-        if old_email:
-            get_user_by_email.cache_delete(old_email)
-        get_user_by_email.cache_delete(email)
     except Exception as e:
         raise DatabaseError(
             f"Failed to update user email for user {user_id}: {e}"
@@ -131,8 +114,6 @@ async def update_user_integrations(user_id: str, data: UserIntegrations):
         where={"id": user_id},
         data={"integrations": encrypted_data},
     )
-    # Invalidate cache for this user
-    get_user_by_id.cache_delete(user_id)
 
 
 async def migrate_and_encrypt_user_integrations():
@@ -304,10 +285,6 @@ async def update_user_notification_preference(
         )
         if not user:
             raise ValueError(f"User not found with ID: {user_id}")
-
-        # Invalidate cache for this user since notification preferences are part of user data
-        get_user_by_id.cache_delete(user_id)
-
         preferences: dict[NotificationType, bool] = {
             NotificationType.AGENT_RUN: user.notifyOnAgentRun or True,
             NotificationType.ZERO_BALANCE: user.notifyOnZeroBalance or True,
@@ -346,44 +323,12 @@ async def set_user_email_verification(user_id: str, verified: bool) -> None:
             where={"id": user_id},
             data={"emailVerified": verified},
         )
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
     except Exception as e:
         raise DatabaseError(
             f"Failed to set email verification status for user {user_id}: {e}"
         ) from e
 
 
-async def disable_all_user_notifications(user_id: str) -> None:
-    """Disable all notification preferences for a user.
-
-    Used when user's email bounces/is inactive to prevent any future notifications.
-    """
-    try:
-        await PrismaUser.prisma().update(
-            where={"id": user_id},
-            data={
-                "notifyOnAgentRun": False,
-                "notifyOnZeroBalance": False,
-                "notifyOnLowBalance": False,
-                "notifyOnBlockExecutionFailed": False,
-                "notifyOnContinuousAgentError": False,
-                "notifyOnDailySummary": False,
-                "notifyOnWeeklySummary": False,
-                "notifyOnMonthlySummary": False,
-                "notifyOnAgentApproved": False,
-                "notifyOnAgentRejected": False,
-            },
-        )
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
-        logger.info(f"Disabled all notification preferences for user {user_id}")
-    except Exception as e:
-        raise DatabaseError(
-            f"Failed to disable notifications for user {user_id}: {e}"
-        ) from e
-
-
 async def get_user_email_verification(user_id: str) -> bool:
     """Get the email verification status for a user."""
     try:
@@ -462,10 +407,6 @@ async def update_user_timezone(user_id: str, timezone: str) -> User:
         )
         if not user:
             raise ValueError(f"User not found with ID: {user_id}")
-
-        # Invalidate cache for this user
-        get_user_by_id.cache_delete(user_id)
-
         return User.from_db(user)
     except Exception as e:
         raise DatabaseError(f"Failed to update timezone for user {user_id}: {e}") from e

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -4,12 +4,7 @@ Module for generating AI-based activity status for graph executions.
 
 import json
 import logging
-from typing import TYPE_CHECKING, Any, TypedDict
-
-try:
-    from typing import NotRequired
-except ImportError:
-    from typing_extensions import NotRequired
+from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
 
 from pydantic import SecretStr
 
@@ -112,7 +107,7 @@ async def generate_activity_status_for_execution(
     # Check if we have OpenAI API key
     try:
         settings = Settings()
-        if not settings.secrets.openai_internal_api_key:
+        if not settings.secrets.openai_api_key:
             logger.debug(
                 "OpenAI API key not configured, skipping activity status generation"
             )
@@ -120,7 +115,7 @@ async def generate_activity_status_for_execution(
 
         # Get all node executions for this graph execution
         node_executions = await db_client.get_node_executions(
-            graph_exec_id, include_exec_data=True
+            graph_exec_id=graph_exec_id, include_exec_data=True
         )
 
         # Get graph metadata and full graph structure for name, description, and links
@@ -151,35 +146,17 @@ async def generate_activity_status_for_execution(
                     "Focus on the ACTUAL TASK the user wanted done, not the internal workflow steps. "
                     "Avoid technical terms like 'workflow', 'execution', 'components', 'nodes', 'processing', etc. "
                     "Keep it to 3 sentences maximum. Be conversational and human-friendly.\n\n"
-                    "UNDERSTAND THE INTENDED PURPOSE:\n"
-                    "- FIRST: Read the graph description carefully to understand what the user wanted to accomplish\n"
-                    "- The graph name and description tell you the main goal/intention of this automation\n"
-                    "- Use this intended purpose as your PRIMARY criteria for success/failure evaluation\n"
-                    "- Ask yourself: 'Did this execution actually accomplish what the graph was designed to do?'\n\n"
-                    "CRITICAL OUTPUT ANALYSIS:\n"
-                    "- Check if blocks that should produce user-facing results actually produced outputs\n"
-                    "- Blocks with names containing 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' are usually meant to produce final results\n"
-                    "- If these critical blocks have NO outputs (empty recent_outputs), the task likely FAILED even if status shows 'completed'\n"
-                    "- Sub-agents (AgentExecutorBlock) that produce no outputs usually indicate failed sub-tasks\n"
-                    "- Most importantly: Does the execution result match what the graph description promised to deliver?\n\n"
-                    "SUCCESS EVALUATION BASED ON INTENTION:\n"
-                    "- If the graph is meant to 'create blog posts' → check if blog content was actually created\n"
-                    "- If the graph is meant to 'send emails' → check if emails were actually sent\n"
-                    "- If the graph is meant to 'analyze data' → check if analysis results were produced\n"
-                    "- If the graph is meant to 'generate reports' → check if reports were generated\n"
-                    "- Technical completion ≠ goal achievement. Focus on whether the USER'S INTENDED OUTCOME was delivered\n\n"
                     "IMPORTANT: Be HONEST about what actually happened:\n"
                     "- If the input was invalid/nonsensical, say so directly\n"
                     "- If the task failed, explain what went wrong in simple terms\n"
                     "- If errors occurred, focus on what the user needs to know\n"
-                    "- Only claim success if the INTENDED PURPOSE was genuinely accomplished AND produced expected outputs\n"
-                    "- Don't sugar-coat failures or present them as helpful feedback\n"
-                    "- ESPECIALLY: If the graph's main purpose wasn't achieved, this is a failure regardless of 'completed' status\n\n"
+                    "- Only claim success if the task was genuinely completed\n"
+                    "- Don't sugar-coat failures or present them as helpful feedback\n\n"
                     "Understanding Errors:\n"
                     "- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)\n"
                     "- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished\n"
-                    "- Missing outputs from critical blocks: Even if no errors, this means the task failed to produce expected results\n"
-                    "- Focus on whether the graph's intended purpose was fulfilled, not whether technical steps completed"
+                    "- Even if execution shows 'completed', check if critical nodes failed that would prevent the desired outcome\n"
+                    "- Focus on the end result the user wanted, not whether technical steps completed"
                 ),
             },
             {
@@ -188,28 +165,15 @@ async def generate_activity_status_for_execution(
                     f"A user ran '{graph_name}' to accomplish something. Based on this execution data, "
                     f"write what they achieved in simple, user-friendly terms:\n\n"
                     f"{json.dumps(execution_data, indent=2)}\n\n"
-                    "ANALYSIS CHECKLIST:\n"
-                    "1. READ graph_info.description FIRST - this tells you what the user intended to accomplish\n"
-                    "2. Check overall_status.graph_error - if present, the entire execution failed\n"
-                    "3. Look for nodes with 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' in their block_name\n"
-                    "4. Check if these critical blocks have empty recent_outputs arrays - this indicates failure\n"
-                    "5. Look for AgentExecutorBlock (sub-agents) with no outputs - this suggests sub-task failures\n"
-                    "6. Count how many nodes produced outputs vs total nodes - low ratio suggests problems\n"
-                    "7. MOST IMPORTANT: Does the execution outcome match what graph_info.description promised?\n\n"
-                    "INTENTION-BASED EVALUATION:\n"
-                    "- If description mentions 'blog writing' → did it create blog content?\n"
-                    "- If description mentions 'email automation' → were emails actually sent?\n"
-                    "- If description mentions 'data analysis' → were analysis results produced?\n"
-                    "- If description mentions 'content generation' → was content actually generated?\n"
-                    "- If description mentions 'social media posting' → were posts actually made?\n"
-                    "- Match the outputs to the stated intention, not just technical completion\n\n"
+                    "CRITICAL: Check overall_status.graph_error FIRST - if present, the entire execution failed.\n"
+                    "Then check individual node errors to understand partial failures.\n\n"
                     "Write 1-3 sentences about what the user accomplished, such as:\n"
                     "- 'I analyzed your resume and provided detailed feedback for the IT industry.'\n"
-                    "- 'I couldn't complete the task because critical steps failed to produce any results.'\n"
-                    "- 'I failed to generate the content you requested due to missing API access.'\n"
+                    "- 'I couldn't analyze your resume because the input was just nonsensical text.'\n"
+                    "- 'I failed to complete the task due to missing API access.'\n"
                     "- 'I extracted key information from your documents and organized it into a summary.'\n"
-                    "- 'The task failed because the blog post creation step didn't produce any output.'\n\n"
-                    "BE CRITICAL: If the graph's intended purpose (from description) wasn't achieved, report this as a failure even if status is 'completed'."
+                    "- 'The task failed to run due to system configuration issues.'\n\n"
+                    "Focus on what ACTUALLY happened, not what was attempted."
                 ),
             },
         ]
@@ -223,7 +187,7 @@ async def generate_activity_status_for_execution(
         credentials = APIKeyCredentials(
             id="openai",
             provider="openai",
-            api_key=SecretStr(settings.secrets.openai_internal_api_key),
+            api_key=SecretStr(settings.secrets.openai_api_key),
             title="System OpenAI",
         )
 
@@ -233,7 +197,6 @@ async def generate_activity_status_for_execution(
         logger.debug(
             f"Generated activity status for {graph_exec_id}: {activity_status}"
         )
-
         return activity_status
 
     except Exception as e:
@@ -460,6 +423,7 @@ async def _call_llm_direct(
         credentials=credentials,
         llm_model=LlmModel.GPT4O_MINI,
         prompt=prompt,
+        json_format=False,
         max_tokens=150,
         compress_prompt_to_fit=True,
     )

autogpt_platform/backend/backend/executor/activity_status_generator_test.py

@@ -468,7 +468,7 @@ class TestGenerateActivityStatusForExecution:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
             mock_llm.return_value = (
                 "I analyzed your data and provided the requested insights."
             )
@@ -520,7 +520,7 @@ class TestGenerateActivityStatusForExecution:
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
-            mock_settings.return_value.secrets.openai_internal_api_key = ""
+            mock_settings.return_value.secrets.openai_api_key = ""
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -546,7 +546,7 @@ class TestGenerateActivityStatusForExecution:
             "backend.executor.activity_status_generator.is_feature_enabled",
             return_value=True,
         ):
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
 
             result = await generate_activity_status_for_execution(
                 graph_exec_id="test_exec",
@@ -581,7 +581,7 @@ class TestGenerateActivityStatusForExecution:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
             mock_llm.return_value = "Agent completed execution."
 
             result = await generate_activity_status_for_execution(
@@ -633,7 +633,7 @@ class TestIntegration:
         ):
 
             mock_get_block.side_effect = lambda block_id: mock_blocks.get(block_id)
-            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_api_key = "test_key"
 
             mock_response = LLMResponse(
                 raw_response={},

autogpt_platform/backend/backend/executor/cluster_lock.py

@@ -1,115 +0,0 @@
-"""Redis-based distributed locking for cluster coordination."""
-
-import logging
-import time
-from typing import TYPE_CHECKING
-
-if TYPE_CHECKING:
-    from redis import Redis
-
-logger = logging.getLogger(__name__)
-
-
-class ClusterLock:
-    """Simple Redis-based distributed lock for preventing duplicate execution."""
-
-    def __init__(self, redis: "Redis", key: str, owner_id: str, timeout: int = 300):
-        self.redis = redis
-        self.key = key
-        self.owner_id = owner_id
-        self.timeout = timeout
-        self._last_refresh = 0.0
-
-    def try_acquire(self) -> str | None:
-        """Try to acquire the lock.
-
-        Returns:
-            - owner_id (self.owner_id) if successfully acquired
-            - different owner_id if someone else holds the lock
-            - None if Redis is unavailable or other error
-        """
-        try:
-            success = self.redis.set(self.key, self.owner_id, nx=True, ex=self.timeout)
-            if success:
-                self._last_refresh = time.time()
-                return self.owner_id  # Successfully acquired
-
-            # Failed to acquire, get current owner
-            current_value = self.redis.get(self.key)
-            if current_value:
-                current_owner = (
-                    current_value.decode("utf-8")
-                    if isinstance(current_value, bytes)
-                    else str(current_value)
-                )
-                return current_owner
-
-            # Key doesn't exist but we failed to set it - race condition or Redis issue
-            return None
-
-        except Exception as e:
-            logger.error(f"ClusterLock.try_acquire failed for key {self.key}: {e}")
-            return None
-
-    def refresh(self) -> bool:
-        """Refresh lock TTL if we still own it.
-
-        Rate limited to at most once every timeout/10 seconds (minimum 1 second).
-        During rate limiting, still verifies lock existence but skips TTL extension.
-        Setting _last_refresh to 0 bypasses rate limiting for testing.
-        """
-        # Calculate refresh interval: max(timeout // 10, 1)
-        refresh_interval = max(self.timeout // 10, 1)
-        current_time = time.time()
-
-        # Check if we're within the rate limit period
-        # _last_refresh == 0 forces a refresh (bypasses rate limiting for testing)
-        is_rate_limited = (
-            self._last_refresh > 0
-            and (current_time - self._last_refresh) < refresh_interval
-        )
-
-        try:
-            # Always verify lock existence, even during rate limiting
-            current_value = self.redis.get(self.key)
-            if not current_value:
-                self._last_refresh = 0
-                return False
-
-            stored_owner = (
-                current_value.decode("utf-8")
-                if isinstance(current_value, bytes)
-                else str(current_value)
-            )
-            if stored_owner != self.owner_id:
-                self._last_refresh = 0
-                return False
-
-            # If rate limited, return True but don't update TTL or timestamp
-            if is_rate_limited:
-                return True
-
-            # Perform actual refresh
-            if self.redis.expire(self.key, self.timeout):
-                self._last_refresh = current_time
-                return True
-
-            self._last_refresh = 0
-            return False
-
-        except Exception as e:
-            logger.error(f"ClusterLock.refresh failed for key {self.key}: {e}")
-            self._last_refresh = 0
-            return False
-
-    def release(self):
-        """Release the lock."""
-        if self._last_refresh == 0:
-            return
-
-        try:
-            self.redis.delete(self.key)
-        except Exception:
-            pass
-
-        self._last_refresh = 0.0