Merge branch 'swiftyos/caching-pt2' into swiftyos/shared-cache

In this PR, I’ve added an API Key modal to the new builder so users can
add API key credentials.


https://github.com/user-attachments/assets/68da226c-3787-4950-abb0-7a715910355e

### Changes
- Updated the credential field to support API key.
- Added a modal for creating new API keys and improved the selection UI
for credentials.
- Refactored components for better modularity and maintainability.
- Enhanced styling and user experience in the FlowEditor components.
- Updated OpenAPI documentation for better clarity on credential
operations.

### Checklist 📋

- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Able to create API key perfectly.
  - [x] can select the correct credentials.

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

@@ -0,0 +1,97 @@
+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

@@ -0,0 +1,379 @@
+# 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

@@ -30,18 +30,296 @@ jobs:
         github.event.issue.author_association == 'COLLABORATOR'
       )
     runs-on: ubuntu-latest
+    timeout-minutes: 45
+
     permissions:
-      contents: read
+      contents: write
       pull-requests: read
       issues: read
       id-token: write
+      actions: read # Required for CI access
     steps:
-      - name: Checkout repository
+      - 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 Code
         id: claude
-        uses: anthropics/claude-code-action@beta
+        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:*), Bash(gh pr edit:*)"
+            --model opus
+          additional_permissions: |
+            actions: read

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

@@ -5,6 +5,13 @@ 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'
@@ -19,6 +26,8 @@ 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
@@ -48,4 +57,4 @@ jobs:
           token: ${{ secrets.DEPLOY_TOKEN }}
           repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
           event-type: build_deploy_dev
-          client-payload: '{"ref": "${{ github.ref }}", "sha": "${{ github.sha }}", "repository": "${{ github.repository }}"}'
+          client-payload: '{"ref": "${{ github.event.inputs.git_ref || github.ref }}", "repository": "${{ github.repository }}"}'

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

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

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

@@ -37,9 +37,7 @@ jobs:
 
     services:
       redis:
-        image: bitnami/redis:6.2
-        env:
-          REDIS_PASSWORD: testpassword
+        image: redis:latest
         ports:
           - 6379:6379
       rabbitmq:
@@ -204,7 +202,6 @@ 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: |
-          docker compose -f ../docker-compose.yml up -d
+          NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
         env:
           DOCKER_BUILDKIT: 1
           BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
@@ -217,6 +217,9 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
+      - name: Generate API client
+        run: pnpm generate:api
+
       - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 

.pr_agent.toml

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

autogpt_platform/CLAUDE.md

@@ -61,24 +61,27 @@ poetry run pytest path/to/test.py --snapshot-update
 
 ```bash
 # Install dependencies
-cd frontend && npm install
+cd frontend && pnpm i
 
 # Start development server
-npm run dev
+pnpm dev
 
 # Run E2E tests
-npm run test
+pnpm test
 
 # Run Storybook for component development
-npm run storybook
+pnpm storybook
 
 # Build production
-npm run build
+pnpm build
 
 # Type checking
-npm run types
+pnpm 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/autogpt_libs/autogpt_libs/api_key/key_manager.py

@@ -1,35 +0,0 @@
-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

@@ -0,0 +1,78 @@
+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

@@ -0,0 +1,79 @@
+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
 
 
-def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
     FastAPI dependency that requires a valid authenticated user.
 
@@ -20,7 +20,9 @@ def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User
     return verify_user(jwt_payload, admin_only=False)
 
 
-def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+async def requires_admin_user(
+    jwt_payload: dict = fastapi.Security(get_jwt_payload),
+) -> User:
     """
     FastAPI dependency that requires a valid admin user.
 
@@ -30,7 +32,7 @@ def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -
     return verify_user(jwt_payload, admin_only=True)
 
 
-def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
+async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
     """
     FastAPI dependency that returns the ID of the authenticated user.
 

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -45,7 +45,7 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
+    async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
@@ -53,12 +53,12 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = requires_user(jwt_payload)
+        user = await requires_user(jwt_payload)
         assert isinstance(user, User)
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
+    async 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 = requires_user(jwt_payload)
+        user = await requires_user(jwt_payload)
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    def test_requires_user_missing_sub(self):
+    async 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:
-            requires_user(jwt_payload)
+            await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    def test_requires_user_empty_sub(self):
+    async 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:
-            requires_user(jwt_payload)
+            await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
+    async 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 = requires_admin_user(jwt_payload)
+        user = await requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    def test_requires_admin_user_with_regular_user(self):
+    async def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
 
         with pytest.raises(HTTPException) as exc_info:
-            requires_admin_user(jwt_payload)
+            await requires_admin_user(jwt_payload)
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    def test_requires_admin_user_missing_role(self):
+    async def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
 
         with pytest.raises(KeyError):
-            requires_admin_user(jwt_payload)
+            await requires_admin_user(jwt_payload)
 
-    def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
+    async 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 = get_user_id(jwt_payload)
+        user_id = await get_user_id(jwt_payload)
         assert user_id == "user-id-xyz"
 
-    def test_get_user_id_missing_sub(self):
+    async 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:
-            get_user_id(jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    def test_get_user_id_none_sub(self):
+    async 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:
-            get_user_id(jwt_payload)
+            await get_user_id(jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -170,7 +170,7 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    def test_endpoint_auth_enabled_no_token(self):
+    async 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
 
-    def test_endpoint_with_valid_token(self, create_token):
+    async 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"
 
-    def test_admin_endpoint_requires_admin_role(self, create_token):
+    async 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."""
 
-    def test_dependency_with_complex_payload(self):
+    async 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 = requires_user(complex_payload)
+        user = await requires_user(complex_payload)
         assert user.user_id == "user-123"
         assert user.email == "test@example.com"
 
-        admin = requires_admin_user(complex_payload)
+        admin = await requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    def test_dependency_with_unicode_in_payload(self):
+    async 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 = requires_user(unicode_payload)
+        user = await requires_user(unicode_payload)
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    def test_dependency_with_null_values(self):
+    async 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 = requires_user(null_payload)
+        user = await requires_user(null_payload)
         assert user.user_id == "user-123"
         assert user.email is None
 
-    def test_concurrent_requests_isolation(self):
+    async 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 = requires_user(payload1)
-        user2 = requires_admin_user(payload2)
+        user1 = await requires_user(payload1)
+        user2 = await 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),
         ],
     )
-    def test_dependency_error_cases(
+    async 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
 
-    def test_dependency_valid_user(self):
+    async def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
         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(
 )
 
 
-def get_jwt_payload(
+async 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)
 
 
-def test_get_jwt_payload_with_valid_token():
+async 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 = jwt_utils.get_jwt_payload(credentials)
+    result = await jwt_utils.get_jwt_payload(credentials)
     assert result["sub"] == "test-user-id"
     assert result["role"] == "user"
 
 
-def test_get_jwt_payload_no_credentials():
+async def test_get_jwt_payload_no_credentials():
     """Test JWT payload when no credentials provided."""
     with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.get_jwt_payload(None)
+        await jwt_utils.get_jwt_payload(None)
     assert exc_info.value.status_code == 401
     assert "Authorization header is missing" in exc_info.value.detail
 
 
-def test_get_jwt_payload_invalid_token():
+async 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:
-        jwt_utils.get_jwt_payload(credentials)
+        await 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

@@ -4,6 +4,7 @@ import logging
 import os
 import socket
 import sys
+from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 from pydantic import Field, field_validator
@@ -139,8 +140,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         print(f"Log directory: {config.log_dir}")
 
         # Activity log handler (INFO and above)
-        activity_log_handler = logging.FileHandler(
-            config.log_dir / LOG_FILE, "a", "utf-8"
+        # 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.setLevel(config.level)
         activity_log_handler.setFormatter(
@@ -150,8 +156,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
         if config.level == logging.DEBUG:
             # Debug log handler (all levels)
-            debug_log_handler = logging.FileHandler(
-                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
+            # 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.setLevel(logging.DEBUG)
             debug_log_handler.setFormatter(
@@ -160,8 +171,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             log_handlers.append(debug_log_handler)
 
         # Error log handler (ERROR and above)
-        error_log_handler = logging.FileHandler(
-            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
+        # 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.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))

autogpt_platform/autogpt_libs/autogpt_libs/rate_limit/config.py

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

@@ -1,266 +0,0 @@
-import inspect
-import logging
-import threading
-import time
-from functools import wraps
-from typing import (
-    Awaitable,
-    Callable,
-    ParamSpec,
-    Protocol,
-    Tuple,
-    TypeVar,
-    cast,
-    overload,
-    runtime_checkable,
-)
-
-P = ParamSpec("P")
-R = TypeVar("R")
-
-logger = logging.getLogger(__name__)
-
-
-@overload
-def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
-    pass
-
-
-@overload
-def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
-    pass
-
-
-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 AsyncCachedFunction(Protocol[P, R_co]):
-    """Protocol for async functions with cache management methods."""
-
-    def cache_clear(self) -> None:
-        """Clear all cached entries."""
-        return None
-
-    def cache_info(self) -> dict[str, int | None]:
-        """Get cache statistics."""
-        return {}
-
-    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
-        """Call the cached function."""
-        return None  # type: ignore
-
-
-def async_ttl_cache(
-    maxsize: int = 128, ttl_seconds: int | None = None
-) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
-    """
-    TTL (Time To Live) cache decorator for async functions.
-
-    Similar to functools.lru_cache but works with async functions and includes optional TTL.
-
-    Args:
-        maxsize: Maximum number of cached entries
-        ttl_seconds: Time to live in seconds. If None, entries never expire (like lru_cache)
-
-    Returns:
-        Decorator function
-
-    Example:
-        # With TTL
-        @async_ttl_cache(maxsize=1000, ttl_seconds=300)
-        async def api_call(param: str) -> dict:
-            return {"result": param}
-
-        # Without TTL (permanent cache like lru_cache)
-        @async_ttl_cache(maxsize=1000)
-        async def expensive_computation(param: str) -> dict:
-            return {"result": param}
-    """
-
-    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]] = {}
-
-        @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()
-
-            # 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:
-                            # Expired entry
-                            del cache_storage[key]
-                            logger.debug(
-                                f"Cache entry expired for {async_func.__name__}"
-                            )
-
-            # 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 in cache
-            if ttl_seconds is None:
-                cache_storage[key] = result
-            else:
-                cache_storage[key] = (result, current_time)
-
-            # 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
-
-        # Add cache management methods (similar to functools.lru_cache)
-        def cache_clear() -> None:
-            cache_storage.clear()
-
-        def cache_info() -> dict[str, int | None]:
-            return {
-                "size": len(cache_storage),
-                "maxsize": maxsize,
-                "ttl_seconds": ttl_seconds,
-            }
-
-        # Attach methods to wrapper
-        setattr(wrapper, "cache_clear", cache_clear)
-        setattr(wrapper, "cache_info", cache_info)
-
-        return cast(AsyncCachedFunction[P, R], wrapper)
-
-    return decorator
-
-
-@overload
-def async_cache(
-    func: Callable[P, Awaitable[R]],
-) -> AsyncCachedFunction[P, R]:
-    pass
-
-
-@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 async function to cache (when used without parentheses)
-        maxsize: Maximum number of cached entries
-
-    Returns:
-        Decorated function or decorator
-
-    Example:
-        # Without parentheses (uses default maxsize=128)
-        @async_cache
-        async def get_data(param: str) -> dict:
-            return {"result": param}
-
-        # With parentheses and custom maxsize
-        @async_cache(maxsize=1000)
-        async def expensive_computation(param: str) -> dict:
-            # Expensive computation here
-            return {"result": param}
-    """
-    if func is None:
-        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
-        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
-    else:
-        # 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

@@ -1,705 +0,0 @@
-"""Tests for the @thread_cached decorator.
-
-This module tests the thread-local caching functionality including:
-- Basic caching for sync and async functions
-- Thread isolation (each thread has its own cache)
-- Cache clearing functionality
-- Exception handling (exceptions are not cached)
-- Argument handling (positional vs keyword arguments)
-"""
-
-import asyncio
-import threading
-import time
-from concurrent.futures import ThreadPoolExecutor
-from unittest.mock import Mock
-
-import pytest
-
-from autogpt_libs.utils.cache import (
-    async_cache,
-    async_ttl_cache,
-    clear_thread_cache,
-    thread_cached,
-)
-
-
-class TestThreadCached:
-    def test_sync_function_caching(self):
-        call_count = 0
-
-        @thread_cached
-        def expensive_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        assert expensive_function(1, 2) == 3
-        assert call_count == 1
-
-        assert expensive_function(1, 2) == 3
-        assert call_count == 1
-
-        assert expensive_function(1, y=2) == 3
-        assert call_count == 2
-
-        assert expensive_function(2, 3) == 5
-        assert call_count == 3
-
-        assert expensive_function(1) == 1
-        assert call_count == 4
-
-    @pytest.mark.asyncio
-    async def test_async_function_caching(self):
-        call_count = 0
-
-        @thread_cached
-        async def expensive_async_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x + y
-
-        assert await expensive_async_function(1, 2) == 3
-        assert call_count == 1
-
-        assert await expensive_async_function(1, 2) == 3
-        assert call_count == 1
-
-        assert await expensive_async_function(1, y=2) == 3
-        assert call_count == 2
-
-        assert await expensive_async_function(2, 3) == 5
-        assert call_count == 3
-
-    def test_thread_isolation(self):
-        call_count = 0
-        results = {}
-
-        @thread_cached
-        def thread_specific_function(x: int) -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"{threading.current_thread().name}-{x}"
-
-        def worker(thread_id: int):
-            result1 = thread_specific_function(1)
-            result2 = thread_specific_function(1)
-            result3 = thread_specific_function(2)
-            results[thread_id] = (result1, result2, result3)
-
-        with ThreadPoolExecutor(max_workers=3) as executor:
-            futures = [executor.submit(worker, i) for i in range(3)]
-            for future in futures:
-                future.result()
-
-        assert call_count >= 2
-
-        for thread_id, (r1, r2, r3) in results.items():
-            assert r1 == r2
-            assert r1 != r3
-
-    @pytest.mark.asyncio
-    async def test_async_thread_isolation(self):
-        call_count = 0
-        results = {}
-
-        @thread_cached
-        async def async_thread_specific_function(x: int) -> str:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return f"{threading.current_thread().name}-{x}"
-
-        async def async_worker(worker_id: int):
-            result1 = await async_thread_specific_function(1)
-            result2 = await async_thread_specific_function(1)
-            result3 = await async_thread_specific_function(2)
-            results[worker_id] = (result1, result2, result3)
-
-        tasks = [async_worker(i) for i in range(3)]
-        await asyncio.gather(*tasks)
-
-        for worker_id, (r1, r2, r3) in results.items():
-            assert r1 == r2
-            assert r1 != r3
-
-    def test_clear_cache_sync(self):
-        call_count = 0
-
-        @thread_cached
-        def clearable_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 2
-
-        assert clearable_function(5) == 10
-        assert call_count == 1
-
-        assert clearable_function(5) == 10
-        assert call_count == 1
-
-        clear_thread_cache(clearable_function)
-
-        assert clearable_function(5) == 10
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_clear_cache_async(self):
-        call_count = 0
-
-        @thread_cached
-        async def clearable_async_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 2
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 1
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 1
-
-        clear_thread_cache(clearable_async_function)
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 2
-
-    def test_simple_arguments(self):
-        call_count = 0
-
-        @thread_cached
-        def simple_function(a: str, b: int, c: str = "default") -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"{a}-{b}-{c}"
-
-        # First call with all positional args
-        result1 = simple_function("test", 42, "custom")
-        assert call_count == 1
-
-        # Same args, all positional - should hit cache
-        result2 = simple_function("test", 42, "custom")
-        assert call_count == 1
-        assert result1 == result2
-
-        # Same values but last arg as keyword - creates different cache key
-        result3 = simple_function("test", 42, c="custom")
-        assert call_count == 2
-        assert result1 == result3  # Same result, different cache entry
-
-        # Different value - new cache entry
-        result4 = simple_function("test", 43, "custom")
-        assert call_count == 3
-        assert result1 != result4
-
-    def test_positional_vs_keyword_args(self):
-        """Test that positional and keyword arguments create different cache entries."""
-        call_count = 0
-
-        @thread_cached
-        def func(a: int, b: int = 10) -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"result-{a}-{b}"
-
-        # All positional
-        result1 = func(1, 2)
-        assert call_count == 1
-        assert result1 == "result-1-2"
-
-        # Same values, but second arg as keyword
-        result2 = func(1, b=2)
-        assert call_count == 2  # Different cache key!
-        assert result2 == "result-1-2"  # Same result
-
-        # Verify both are cached separately
-        func(1, 2)  # Uses first cache entry
-        assert call_count == 2
-
-        func(1, b=2)  # Uses second cache entry
-        assert call_count == 2
-
-    def test_exception_handling(self):
-        call_count = 0
-
-        @thread_cached
-        def failing_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            if x < 0:
-                raise ValueError("Negative value")
-            return x * 2
-
-        assert failing_function(5) == 10
-        assert call_count == 1
-
-        with pytest.raises(ValueError):
-            failing_function(-1)
-        assert call_count == 2
-
-        with pytest.raises(ValueError):
-            failing_function(-1)
-        assert call_count == 3
-
-        assert failing_function(5) == 10
-        assert call_count == 3
-
-    @pytest.mark.asyncio
-    async def test_async_exception_handling(self):
-        call_count = 0
-
-        @thread_cached
-        async def async_failing_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            if x < 0:
-                raise ValueError("Negative value")
-            return x * 2
-
-        assert await async_failing_function(5) == 10
-        assert call_count == 1
-
-        with pytest.raises(ValueError):
-            await async_failing_function(-1)
-        assert call_count == 2
-
-        with pytest.raises(ValueError):
-            await async_failing_function(-1)
-        assert call_count == 3
-
-    def test_sync_caching_performance(self):
-        @thread_cached
-        def slow_function(x: int) -> int:
-            print(f"slow_function called with x={x}")
-            time.sleep(0.1)
-            return x * 2
-
-        start = time.time()
-        result1 = slow_function(5)
-        first_call_time = time.time() - start
-        print(f"First call took {first_call_time:.4f} seconds")
-
-        start = time.time()
-        result2 = slow_function(5)
-        second_call_time = time.time() - start
-        print(f"Second call took {second_call_time:.4f} seconds")
-
-        assert result1 == result2 == 10
-        assert first_call_time > 0.09
-        assert second_call_time < 0.01
-
-    @pytest.mark.asyncio
-    async def test_async_caching_performance(self):
-        @thread_cached
-        async def slow_async_function(x: int) -> int:
-            print(f"slow_async_function called with x={x}")
-            await asyncio.sleep(0.1)
-            return x * 2
-
-        start = time.time()
-        result1 = await slow_async_function(5)
-        first_call_time = time.time() - start
-        print(f"First async call took {first_call_time:.4f} seconds")
-
-        start = time.time()
-        result2 = await slow_async_function(5)
-        second_call_time = time.time() - start
-        print(f"Second async call took {second_call_time:.4f} seconds")
-
-        assert result1 == result2 == 10
-        assert first_call_time > 0.09
-        assert second_call_time < 0.01
-
-    def test_with_mock_objects(self):
-        mock = Mock(return_value=42)
-
-        @thread_cached
-        def function_using_mock(x: int) -> int:
-            return mock(x)
-
-        assert function_using_mock(1) == 42
-        assert mock.call_count == 1
-
-        assert function_using_mock(1) == 42
-        assert mock.call_count == 1
-
-        assert function_using_mock(2) == 42
-        assert mock.call_count == 2
-
-
-class TestAsyncTTLCache:
-    """Tests for the @async_ttl_cache decorator."""
-
-    @pytest.mark.asyncio
-    async def test_basic_caching(self):
-        """Test basic caching functionality."""
-        call_count = 0
-
-        @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 cached_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = await cached_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1  # No additional call
-
-        # Different args - should call function again
-        result3 = await cached_function(2, 3)
-        assert result3 == 5
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_ttl_expiration(self):
-        """Test that cache entries expire after TTL."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
-        async def short_lived_cache(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 2
-
-        # First call
-        result1 = await short_lived_cache(5)
-        assert result1 == 10
-        assert call_count == 1
-
-        # Second call immediately - should use cache
-        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 short_lived_cache(5)
-        assert result3 == 10
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_cache_info(self):
-        """Test cache info functionality."""
-
-        @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"] == 5
-        assert info["ttl_seconds"] == 300
-
-        # Add an entry
-        await info_test_function(1)
-        info = info_test_function.cache_info()
-        assert info["size"] == 1
-
-    @pytest.mark.asyncio
-    async def test_cache_clear(self):
-        """Test cache clearing functionality."""
-        call_count = 0
-
-        @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 = await clearable_function(2)
-        assert result1 == 8
-        assert call_count == 1
-
-        # Second call - should use cache
-        result2 = await clearable_function(2)
-        assert result2 == 8
-        assert call_count == 1
-
-        # Clear cache
-        clearable_function.cache_clear()
-
-        # Third call after clear - should call function again
-        result3 = await clearable_function(2)
-        assert result3 == 8
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_maxsize_cleanup(self):
-        """Test that cache cleans up when maxsize is exceeded."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=3, ttl_seconds=60)
-        async def size_limited_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x**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
-
-        # Same successful call - should use cache
-        result2 = await exception_function(5)
-        assert result2 == 10
-        assert call_count == 1
-
-        # 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_concurrent_calls(self):
-        """Test caching behavior with concurrent calls."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def concurrent_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.05)  # 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 cached_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = await cached_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1  # No additional call
-
-        # 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
-
-        # Different args - should call function again
-        result4 = await cached_function(2, 3)
-        assert result4 == 5
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    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
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=None)
-        async def no_ttl_via_none(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x**2
-
-        # First call
-        result1 = await no_ttl_via_none(3)
-        assert result1 == 9
-        assert call_count == 1
-
-        # Wait (would expire if there was TTL)
-        await asyncio.sleep(0.1)
-
-        # Second call - should still use cache
-        result2 = await no_ttl_via_none(3)
-        assert result2 == 9
-        assert call_count == 1  # No additional call
-
-        # Check cache info
-        info = no_ttl_via_none.cache_info()
-        assert info["ttl_seconds"] is None
-
-    @pytest.mark.asyncio
-    async def test_cache_options_comparison(self):
-        """Test different cache options work as expected."""
-        ttl_calls = 0
-        no_ttl_calls = 0
-
-        @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
-
-        @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

@@ -1002,6 +1002,18 @@ 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"
@@ -1347,6 +1359,27 @@ 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"
@@ -1534,31 +1567,31 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.12.9"
+version = "0.12.11"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
-    {file = "ruff-0.12.9-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"},
+    {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"},
+    {file = "ruff-0.12.11-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:8ca4c3a7f937725fd2413c0e884b5248a19369ab9bdd850b5781348ba283f644"},
+    {file = "ruff-0.12.11-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:4d1df0098124006f6a66ecf3581a7f7e754c4df7644b2e6704cd7ca80ff95211"},
+    {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"},
+    {file = "ruff-0.12.11-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:916fc5defee32dbc1fc1650b576a8fed68f5e8256e2180d4d9855aea43d6aab2"},
+    {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"},
 ]
 
 [[package]]
@@ -1686,6 +1719,22 @@ files = [
 httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
 strenum = ">=0.4.15,<0.5.0"
 
+[[package]]
+name = "tenacity"
+version = "9.1.2"
+description = "Retry code until it succeeds"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138"},
+    {file = "tenacity-9.1.2.tar.gz", hash = "sha256:1169d376c297e7de388d18b4481760d478b0e99a777cad3a9c86e556f4b697cb"},
+]
+
+[package.extras]
+doc = ["reno", "sphinx"]
+test = ["pytest", "tornado (>=4.5)", "typeguard"]
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -1740,7 +1789,6 @@ 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"
@@ -1897,4 +1945,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "ef7818fba061cea2841c6d7ca4852acde83e4f73b32fca1315e58660002bb0d0"
+content-hash = "5ec9e6cd2ef7524a356586354755215699e7b37b9bbdfbabc9c73b43085915f4"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -9,6 +9,7 @@ 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"
@@ -18,14 +19,16 @@ pydantic-settings = "^2.10.1"
 pyjwt = { version = "^2.10.1", extras = ["crypto"] }
 redis = "^6.2.0"
 supabase = "^2.16.0"
+tenacity = "^9.1.2"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.12.9"
+pyright = "^1.1.404"
 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=password
+# REDIS_PASSWORD=
 
 # RabbitMQ Credentials
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
@@ -66,6 +66,11 @@ 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

@@ -9,4 +9,12 @@ secrets/*
 !secrets/.gitkeep
 
 *.ignore.*
-*.ign.*
+*.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/*

autogpt_platform/backend/Dockerfile

@@ -9,8 +9,15 @@ WORKDIR /app
 
 RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
 
-# Update package list and install Python and build dependencies
+# Install Node.js repository key and setup
 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 \
@@ -20,7 +27,9 @@ RUN apt-get update --allow-releaseinfo-change --fix-missing \
     libpq5 \
     libz-dev \
     libssl-dev \
-    postgresql-client
+    postgresql-client \
+    nodejs \
+    && rm -rf /var/lib/apt/lists/*
 
 ENV POETRY_HOME=/opt/poetry
 ENV POETRY_NO_INTERACTION=1
@@ -54,13 +63,18 @@ 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
+    python3-pip \
+    && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder
 COPY --from=builder /app /app
 COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
 COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
-# Copy Prisma binaries
+# 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 --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

@@ -1,4 +1,3 @@
-import functools
 import importlib
 import logging
 import os
@@ -6,6 +5,8 @@ import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar
 
+from backend.util.cache import cached
+
 logger = logging.getLogger(__name__)
 
 
@@ -15,7 +16,7 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@functools.cache
+@cached(ttl_seconds=3600)  # Cache blocks for 1 hour
 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

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

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -0,0 +1,214 @@
+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

@@ -0,0 +1,154 @@
+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

@@ -661,6 +661,167 @@ 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,
@@ -1249,3 +1410,26 @@ 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, list_bases
+from ._api import create_base, get_base_tables, list_bases
 from ._config import airtable
 
 
 class AirtableCreateBaseBlock(Block):
     """
-    Creates a new base in an Airtable workspace.
+    Creates a new base in an Airtable workspace, or returns existing base if one with the same name exists.
     """
 
     class Input(BlockSchema):
@@ -31,6 +31,10 @@ 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=[
@@ -50,14 +54,18 @@ class AirtableCreateBaseBlock(Block):
         )
 
     class Output(BlockSchema):
-        base_id: str = SchemaField(description="The ID of the created base")
+        base_id: str = SchemaField(description="The ID of the created or found 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 a new base in Airtable",
+            description="Create or find a base in Airtable",
             categories={BlockCategory.DATA},
             input_schema=self.Input,
             output_schema=self.Output,
@@ -66,6 +74,31 @@ 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,
@@ -74,6 +107,7 @@ 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
+from typing import Optional, cast
 
 from backend.sdk import (
     APIKeyCredentials,
@@ -18,7 +18,9 @@ from ._api import (
     create_record,
     delete_multiple_records,
     get_record,
+    get_table_schema,
     list_records,
+    normalize_records,
     update_multiple_records,
 )
 from ._config import airtable
@@ -54,12 +56,24 @@ 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__(
@@ -73,6 +87,7 @@ class AirtableListRecordsBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         data = await list_records(
             credentials,
             input_data.base_id,
@@ -88,8 +103,33 @@ class AirtableListRecordsBlock(Block):
             fields=input_data.return_fields if input_data.return_fields else None,
         )
 
-        yield "records", data.get("records", [])
-        yield "offset", data.get("offset", 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)
 
 
 class AirtableGetRecordBlock(Block):
@@ -104,11 +144,23 @@ 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__(
@@ -122,6 +174,7 @@ class AirtableGetRecordBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
+
         record = await get_record(
             credentials,
             input_data.base_id,
@@ -129,9 +182,34 @@ class AirtableGetRecordBlock(Block):
             input_data.record_id,
         )
 
-        yield "id", record.get("id", None)
-        yield "fields", record.get("fields", None)
-        yield "created_time", record.get("createdTime", None)
+        # 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)
 
 
 class AirtableCreateRecordsBlock(Block):
@@ -148,6 +226,10 @@ 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,
@@ -173,7 +255,7 @@ 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,
@@ -182,8 +264,22 @@ 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", []))
 
-        yield "records", 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
         details = data.get("details", None)
         if details:
             yield "details", details

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

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

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

@@ -0,0 +1,8 @@
+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

@@ -0,0 +1,239 @@
+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

@@ -1,8 +1,10 @@
 from enum import Enum
-from typing import Literal
+from typing import Any, Literal, Optional
 
 from e2b_code_interpreter import AsyncSandbox
-from pydantic import SecretStr
+from e2b_code_interpreter import Result as E2BExecutionResult
+from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
+from pydantic import BaseModel, JsonValue, SecretStr
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
@@ -36,14 +38,135 @@ class ProgrammingLanguage(Enum):
     JAVA = "java"
 
 
-class CodeExecutionBlock(Block):
+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):
     # TODO : Add support to upload and download files
-    # Currently, You can customized the CPU and Memory, only by creating a pre customized sandbox template
+    # NOTE: Currently, you can only customize the CPU and Memory
+    #       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 Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            ),
         )
 
         # Todo : Option to run commond in background
@@ -76,6 +199,14 @@ class CodeExecutionBlock(Block):
             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. "
@@ -87,7 +218,16 @@ class CodeExecutionBlock(Block):
         )
 
     class Output(BlockSchema):
-        response: str = SchemaField(description="Response from code execution")
+        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",
+        )
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -97,10 +237,10 @@ class CodeExecutionBlock(Block):
     def __init__(self):
         super().__init__(
             id="0b02b072-abe7-11ef-8372-fb5d162dd712",
-            description="Executes code in an isolated sandbox environment with internet access.",
+            description="Executes code in a sandbox environment with internet access.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=CodeExecutionBlock.Input,
-            output_schema=CodeExecutionBlock.Output,
+            input_schema=ExecuteCodeBlock.Input,
+            output_schema=ExecuteCodeBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -111,91 +251,59 @@ class CodeExecutionBlock(Block):
                 "template_id": "",
             },
             test_output=[
+                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda code, language, setup_commands, timeout, api_key, template_id: (
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "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
                 ),
             },
         )
 
-    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:
-            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,
+            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,
             )
 
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+            # 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
         except Exception as e:
             yield "error", str(e)
 
 
-class InstantiationBlock(Block):
+class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            )
         )
 
         # Todo : Option to run commond in background
@@ -240,7 +348,10 @@ class InstantiationBlock(Block):
 
     class Output(BlockSchema):
         sandbox_id: str = SchemaField(description="ID of the sandbox instance")
-        response: str = SchemaField(description="Response from code execution")
+        response: str = SchemaField(
+            title="Text Result",
+            description="Text result (if any) of the setup code execution",
+        )
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -250,10 +361,13 @@ class InstantiationBlock(Block):
     def __init__(self):
         super().__init__(
             id="ff0861c9-1726-4aec-9e5b-bf53f3622112",
-            description="Instantiate an isolated sandbox environment with internet access where to execute code in.",
+            description=(
+                "Instantiate a sandbox environment with internet access "
+                "in which you can execute code with the Execute Code Step block."
+            ),
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=InstantiationBlock.Input,
-            output_schema=InstantiationBlock.Output,
+            input_schema=InstantiateCodeSandboxBlock.Input,
+            output_schema=InstantiateCodeSandboxBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -269,11 +383,12 @@ class InstantiationBlock(Block):
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda setup_code, language, setup_commands, timeout, api_key, template_id: (
-                    "sandbox_id",
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "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
                 ),
             },
         )
@@ -282,78 +397,38 @@ class InstantiationBlock(Block):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            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,
+            _, 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,
             )
             if sandbox_id:
                 yield "sandbox_id", sandbox_id
             else:
                 yield "error", "Sandbox ID not found"
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+
+            if text_output:
+                yield "response", text_output
+            if stdout:
+                yield "stdout_logs", stdout
+            if stderr:
+                yield "stderr_logs", stderr
         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)
 
-            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 ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            ),
         )
 
         sandbox_id: str = SchemaField(
@@ -374,8 +449,22 @@ class StepExecutionBlock(Block):
             advanced=False,
         )
 
+        dispose_sandbox: bool = SchemaField(
+            description="Whether to dispose of the sandbox after executing this code.",
+            default=False,
+        )
+
     class Output(BlockSchema):
-        response: str = SchemaField(description="Response from code execution")
+        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",
+        )
         stdout_logs: str = SchemaField(
             description="Standard output logs from execution"
         )
@@ -385,10 +474,10 @@ class StepExecutionBlock(Block):
     def __init__(self):
         super().__init__(
             id="82b59b8e-ea10-4d57-9161-8b169b0adba6",
-            description="Execute code in a previously instantiated sandbox environment.",
+            description="Execute code in a previously instantiated sandbox.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=StepExecutionBlock.Input,
-            output_schema=StepExecutionBlock.Output,
+            input_schema=ExecuteCodeStepBlock.Input,
+            output_schema=ExecuteCodeStepBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -397,61 +486,43 @@ class StepExecutionBlock(Block):
                 "language": ProgrammingLanguage.PYTHON.value,
             },
             test_output=[
+                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_step_code": lambda sandbox_id, step_code, language, api_key: (
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "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
                 ),
             },
         )
 
-    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:
-            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(),
+            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,
             )
 
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+            # 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
         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")\s+[\s\S]*?```"
+            + r")[ \t]*\n[\s\S]*?```"
         )
 
         remaining_text = re.sub(pattern, "", input_data.text).strip()
@@ -103,7 +103,9 @@ 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}\s+(.*?)```", re.DOTALL | re.IGNORECASE)
+        pattern = re.compile(
+            rf"```{language}[ \t]*\n(.*?)\n```", 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/dataforseo/_api.py

@@ -113,6 +113,7 @@ 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.
@@ -125,6 +126,7 @@ 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
@@ -148,6 +150,8 @@ 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

@@ -90,6 +90,7 @@ 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__(
@@ -161,43 +162,52 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the keyword suggestions query."""
-        client = DataForSeoClient(credentials)
+        try:
+            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 []
-            )
-            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
-                    ),
+            # 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 []
                 )
-                yield "suggestion", suggestion
-                suggestions.append(suggestion)
+                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 "suggestions", suggestions
-        yield "total_count", len(suggestions)
-        yield "seed_keyword", input_data.keyword
+            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)}"
 
 
 class KeywordSuggestionExtractorBlock(Block):

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

@@ -78,6 +78,12 @@ 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(
@@ -92,6 +98,7 @@ 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__(
@@ -154,6 +161,7 @@ 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(
@@ -164,50 +172,60 @@ class DataForSeoRelatedKeywordsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the related keywords query."""
-        client = DataForSeoClient(credentials)
+        try:
+            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 []
-            )
-            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
-                    ),
+            # 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 []
                 )
-                yield "related_keyword", keyword
-                related_keywords.append(keyword)
+                # 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_keywords", related_keywords
-        yield "total_count", len(related_keywords)
-        yield "seed_keyword", input_data.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)}"
 
 
 class RelatedKeywordExtractorBlock(Block):

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

@@ -0,0 +1,12 @@
+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

@@ -0,0 +1,28 @@
+"""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

@@ -1,8 +1,9 @@
-from enum import Enum
 from typing import Any
 
-from firecrawl import FirecrawlApp, ScrapeOptions
+from firecrawl import FirecrawlApp
+from firecrawl.v2.types import ScrapeOptions
 
+from backend.blocks.firecrawl._api import ScrapeFormat
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -14,21 +15,10 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-
-
-class ScrapeFormat(Enum):
-    MARKDOWN = "markdown"
-    HTML = "html"
-    RAW_HTML = "rawHtml"
-    LINKS = "links"
-    SCREENSHOT = "screenshot"
-    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
-    JSON = "json"
-    CHANGE_TRACKING = "changeTracking"
+from ._format_utils import convert_to_format_options
 
 
 class FirecrawlCrawlBlock(Block):
-
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -78,18 +68,17 @@ 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_url(
+        crawl_result = app.crawl(
             input_data.url,
             limit=input_data.limit,
             scrape_options=ScrapeOptions(
-                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,
+                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,
             ),
         )
         yield "data", crawl_result.data
@@ -101,7 +90,7 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.HTML:
                     yield "html", data.html
                 elif f == ScrapeFormat.RAW_HTML:
-                    yield "raw_html", data.rawHtml
+                    yield "raw_html", data.raw_html
                 elif f == ScrapeFormat.LINKS:
                     yield "links", data.links
                 elif f == ScrapeFormat.SCREENSHOT:
@@ -109,6 +98,6 @@ class FirecrawlCrawlBlock(Block):
                 elif f == ScrapeFormat.SCREENSHOT_FULL_PAGE:
                     yield "screenshot_full_page", data.screenshot
                 elif f == ScrapeFormat.CHANGE_TRACKING:
-                    yield "change_tracking", data.changeTracking
+                    yield "change_tracking", data.change_tracking
                 elif f == ScrapeFormat.JSON:
                     yield "json", data.json

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

@@ -20,7 +20,6 @@ from ._config import firecrawl
 
 @cost(BlockCost(2, BlockCostType.RUN))
 class FirecrawlExtractBlock(Block):
-
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         urls: list[str] = SchemaField(
@@ -53,7 +52,6 @@ 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

@@ -1,3 +1,5 @@
+from typing import Any
+
 from firecrawl import FirecrawlApp
 
 from backend.sdk import (
@@ -14,14 +16,16 @@ 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="The links of the website")
+        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"
+        )
 
     def __init__(self):
         super().__init__(
@@ -35,12 +39,22 @@ 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_url(
+        map_result = app.map(
             url=input_data.url,
         )
 
-        yield "links", map_result.links
+        # 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

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,
@@ -14,21 +14,10 @@ from backend.sdk import (
 )
 
 from ._config import firecrawl
-
-
-class ScrapeFormat(Enum):
-    MARKDOWN = "markdown"
-    HTML = "html"
-    RAW_HTML = "rawHtml"
-    LINKS = "links"
-    SCREENSHOT = "screenshot"
-    SCREENSHOT_FULL_PAGE = "screenshot@fullPage"
-    JSON = "json"
-    CHANGE_TRACKING = "changeTracking"
+from ._format_utils import convert_to_format_options
 
 
 class FirecrawlScrapeBlock(Block):
-
     class Input(BlockSchema):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
@@ -78,12 +67,11 @@ 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_url(
+        scrape_result = app.scrape(
             input_data.url,
-            formats=[format.value for format in input_data.formats],
+            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,
@@ -96,7 +84,7 @@ class FirecrawlScrapeBlock(Block):
             elif f == ScrapeFormat.HTML:
                 yield "html", scrape_result.html
             elif f == ScrapeFormat.RAW_HTML:
-                yield "raw_html", scrape_result.rawHtml
+                yield "raw_html", scrape_result.raw_html
             elif f == ScrapeFormat.LINKS:
                 yield "links", scrape_result.links
             elif f == ScrapeFormat.SCREENSHOT:
@@ -104,6 +92,6 @@ 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.changeTracking
+                yield "change_tracking", scrape_result.change_tracking
             elif f == ScrapeFormat.JSON:
                 yield "json", scrape_result.json

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

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

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

@@ -1094,6 +1094,117 @@ 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.
@@ -1230,93 +1341,146 @@ class GmailReplyBlock(GmailBase):
     async def _reply(
         self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
-        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 the reply message using the shared helper
+        raw, thread_id = await _build_reply_message(
+            service, input_data, graph_exec_id, user_id
         )
 
-        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")
+        # Send the message
         return await asyncio.to_thread(
             lambda: service.users()
             .messages()
-            .send(userId="me", body={"threadId": input_data.threadId, "raw": raw})
+            .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(
+                userId="me",
+                body={
+                    "message": {
+                        "threadId": thread_id,
+                        "raw": raw,
+                    }
+                },
+            )
+            .execute()
+        )
+
+        return draft
+
+
 class GmailGetProfileBlock(GmailBase):
     class Input(BlockSchema):
         credentials: GoogleCredentialsInput = GoogleCredentialsField(

autogpt_platform/backend/backend/blocks/io.py

@@ -10,7 +10,6 @@ from backend.util.settings import Config
 from backend.util.text import TextFormatter
 from backend.util.type import LongTextType, MediaFileType, ShortTextType
 
-formatter = TextFormatter()
 config = Config()
 
 
@@ -132,6 +131,11 @@ 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,
@@ -193,6 +197,7 @@ 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}
                 )
@@ -549,6 +554,89 @@ 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,
@@ -560,4 +648,5 @@ IO_BLOCK_IDs = [
     AgentFileInputBlock().id,
     AgentDropdownInputBlock().id,
     AgentToggleInputBlock().id,
+    AgentTableInputBlock().id,
 ]

autogpt_platform/backend/backend/blocks/iteration.py

@@ -54,20 +54,43 @@ 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 item in items.values():
-                    yield "item", item
-                    yield "key", item
+                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
             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/llm.py

@@ -1,5 +1,9 @@
+# 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
@@ -27,7 +31,7 @@ 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()
+fmt = TextFormatter(autoescape=False)
 
 LLMProviderName = Literal[
     ProviderName.AIML_API,
@@ -97,6 +101,7 @@ 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_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"
@@ -204,13 +209,16 @@ MODEL_METADATA = {
         "anthropic", 200000, 32000
     ),  # claude-opus-4-1-20250805
     LlmModel.CLAUDE_4_OPUS: ModelMetadata(
-        "anthropic", 200000, 8192
+        "anthropic", 200000, 32000
     ),  # claude-4-opus-20250514
     LlmModel.CLAUDE_4_SONNET: ModelMetadata(
-        "anthropic", 200000, 8192
+        "anthropic", 200000, 64000
     ),  # claude-4-sonnet-20250514
+    LlmModel.CLAUDE_4_5_SONNET: ModelMetadata(
+        "anthropic", 200000, 64000
+    ),  # claude-sonnet-4-5-20250929
     LlmModel.CLAUDE_3_7_SONNET: ModelMetadata(
-        "anthropic", 200000, 8192
+        "anthropic", 200000, 64000
     ),  # claude-3-7-sonnet-20250219
     LlmModel.CLAUDE_3_5_SONNET: ModelMetadata(
         "anthropic", 200000, 8192
@@ -382,7 +390,9 @@ def extract_openai_tool_calls(response) -> list[ToolContentBlock] | None:
     return None
 
 
-def get_parallel_tool_calls_param(llm_model: LlmModel, parallel_tool_calls):
+def get_parallel_tool_calls_param(
+    llm_model: LlmModel, parallel_tool_calls: bool | None
+):
     """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
@@ -393,8 +403,8 @@ 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,
@@ -407,7 +417,7 @@ 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.
-        json_format: Whether the response should be in JSON format.
+        force_json_output: 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.
@@ -446,7 +456,7 @@ async def llm_call(
             llm_model, parallel_tool_calls
         )
 
-        if json_format:
+        if force_json_output:
             response_format = {"type": "json_object"}
 
         response = await oai_client.chat.completions.create(
@@ -559,7 +569,7 @@ 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 json_format else None
+        response_format = {"type": "json_object"} if force_json_output else None
         response = await client.chat.completions.create(
             model=llm_model.value,
             messages=prompt,  # type: ignore
@@ -717,7 +727,7 @@ async def llm_call(
         )
 
         response_format = None
-        if json_format:
+        if force_json_output:
             response_format = {"type": "json_object"}
 
         parallel_tool_calls_param = get_parallel_tool_calls_param(
@@ -780,6 +790,17 @@ 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",
@@ -848,17 +869,18 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                 "llm_call": lambda *args, **kwargs: LLMResponse(
                     raw_response="",
                     prompt=[""],
-                    response=json.dumps(
-                        {
-                            "key1": "key1Value",
-                            "key2": "key2Value",
-                        }
+                    response=(
+                        '<json_output id="test123456">{\n'
+                        '  "key1": "key1Value",\n'
+                        '  "key2": "key2Value"\n'
+                        "}</json_output>"
                     ),
                     tool_calls=None,
                     prompt_tokens=0,
                     completion_tokens=0,
                     reasoning=None,
-                )
+                ),
+                "get_collision_proof_output_tag_id": lambda *args: "test123456",
             },
         )
 
@@ -867,9 +889,9 @@ 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:
@@ -882,8 +904,8 @@ 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,
@@ -895,10 +917,6 @@ 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)
@@ -907,27 +925,15 @@ 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:
-            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.
-                """
+            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,
             )
             prompt.append({"role": "system", "content": sys_prompt})
 
@@ -945,18 +951,21 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
             except JSONDecodeError as e:
                 return f"JSON decode error: {e}"
 
-        logger.debug(f"LLM request: {prompt}")
-        retry_prompt = ""
+        error_feedback_message = ""
         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,
-                    json_format=bool(input_data.expected_format),
+                    force_json_output=(
+                        input_data.force_json_output
+                        and bool(input_data.expected_format)
+                    ),
                     ollama_host=input_data.ollama_host,
                     max_tokens=input_data.max_tokens,
                 )
@@ -970,16 +979,55 @@ 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})
 
-                    response_obj = json.loads(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
 
+                    # Handle object response for `force_json_output`+`list_result`
                     if input_data.list_result and isinstance(response_obj, dict):
-                        if "results" in response_obj:
-                            response_obj = response_obj.get("results", [])
-                        elif len(response_obj) == 1:
-                            response_obj = list(response_obj.values())
+                        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
 
-                    response_error = "\n".join(
+                    validation_errors = "\n".join(
                         [
                             validation_error
                             for response_item in (
@@ -991,7 +1039,7 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                         ]
                     )
 
-                    if not response_error:
+                    if not validation_errors:
                         self.merge_stats(
                             NodeExecutionStats(
                                 llm_call_count=retry_count + 1,
@@ -1001,6 +1049,16 @@ 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(
@@ -1011,21 +1069,6 @@ 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 (
@@ -1038,9 +1081,133 @@ class AIStructuredResponseGeneratorBlock(AIBlockBase):
                     logger.debug(
                         f"Reducing max_tokens to {input_data.max_tokens} for next attempt"
                     )
-                retry_prompt = f"Error calling LLM: {e}"
+                    # Don't add retry prompt for token limit errors,
+                    # just retry with lower maximum output tokens
 
-        raise RuntimeError(retry_prompt)
+                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])
 
 
 class AITextGeneratorBlock(AIBlockBase):
@@ -1237,11 +1404,27 @@ 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

@@ -0,0 +1,536 @@
+"""
+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

@@ -0,0 +1,42 @@
+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

@@ -0,0 +1,360 @@
+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

@@ -0,0 +1,285 @@
+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

@@ -0,0 +1,64 @@
+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

@@ -0,0 +1,109 @@
+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

@@ -0,0 +1,225 @@
+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/rss.py

@@ -1,4 +1,7 @@
 import asyncio
+import logging
+import urllib.parse
+import urllib.request
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
@@ -101,7 +104,38 @@ class ReadRSSFeedBlock(Block):
 
     @staticmethod
     def parse_feed(url: str) -> dict[str, Any]:
-        return feedparser.parse(url)  # type: ignore
+        # 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": []}
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         keep_going = True

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -13,6 +13,11 @@ 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
@@ -98,6 +103,22 @@ 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.
@@ -261,6 +282,7 @@ 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
@@ -288,41 +310,66 @@ class SmartDecisionMakerBlock(Block):
         }
         sink_block_input_schema = block.input_schema
         properties = {}
+        field_mapping = {}  # clean_name -> original_name
 
         for link in links:
-            sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
+            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
 
-            # 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] = {
+            if is_dynamic:
+                # For dynamic fields, use cleaned name but preserve original in description
+                properties[clean_field_name] = {
                     "type": "string",
-                    "description": f"Dynamic value for {link.sink_name}",
+                    "description": get_dynamic_field_description(field_name),
                 }
             else:
-                # For regular fields, use the block's schema
+                # For regular fields, use the block's schema directly
                 try:
-                    properties[sink_name] = sink_block_input_schema.get_field_schema(
-                        link.sink_name
+                    properties[clean_field_name] = (
+                        sink_block_input_schema.get_field_schema(field_name)
                     )
                 except (KeyError, AttributeError):
-                    # If the field doesn't exist in the schema, provide a generic schema
-                    properties[sink_name] = {
+                    # If field doesn't exist in schema, provide a generic one
+                    properties[clean_field_name] = {
                         "type": "string",
-                        "description": f"Value for {link.sink_name}",
+                        "description": f"Value for {field_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"] = {
-            **block.input_schema.jsonschema(),
+            "type": "object",
             "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
@@ -366,13 +413,12 @@ 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[sink_name] = {
+            properties[link.sink_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -388,24 +434,17 @@ 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 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.
+        Creates function signatures for connected tools.
 
         Args:
             node_id: The node_id for which to create function signatures.
 
         Returns:
-            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.
+            List of function signatures for tools
         """
         db_client = get_database_manager_async_client()
         tools = [
@@ -430,20 +469,116 @@ class SmartDecisionMakerBlock(Block):
                 raise ValueError(f"Sink node not found: {links[0].sink_id}")
 
             if sink_node.block_id == AgentExecutorBlock().id:
-                return_tool_functions.append(
+                tool_func = (
                     await SmartDecisionMakerBlock._create_agent_function_signature(
                         sink_node, links
                     )
                 )
+                return_tool_functions.append(tool_func)
             else:
-                return_tool_functions.append(
+                tool_func = (
                     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,
@@ -466,27 +601,19 @@ 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}] "
@@ -519,25 +646,33 @@ class SmartDecisionMakerBlock(Block):
         ):
             prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        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,
-        )
+        current_prompt = list(prompt)
+        max_attempts = max(1, int(input_data.retry))
+        response = None
 
-        # Track LLM usage stats
-        self.merge_stats(
-            NodeExecutionStats(
-                input_token_count=response.prompt_tokens,
-                output_token_count=response.completion_tokens,
-                llm_call_count=1,
+        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"
             )
-        )
 
         if not response.tool_calls:
             yield "finished", response.response
@@ -547,7 +682,6 @@ 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
@@ -556,7 +690,6 @@ class SmartDecisionMakerBlock(Block):
                 ),
                 None,
             )
-
             if (
                 tool_def
                 and "function" in tool_def
@@ -564,20 +697,38 @@ class SmartDecisionMakerBlock(Block):
             ):
                 expected_args = tool_def["function"]["parameters"].get("properties", {})
             else:
-                expected_args = tool_args.keys()
+                expected_args = {arg: {} for arg in tool_args.keys()}
 
-            # 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
+            # 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
 
-        # Add reasoning to conversation history if available
         if response.reasoning:
             prompt.append(
                 {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
             )
 
-        prompt.append(response.raw_response)
+        prompt.append(_convert_raw_response_to_dict(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",
-        "CodeExecutionBlock",
+        "ExecuteCodeBlock",
         "CountdownTimerBlock",
         "TwitterGetListTweetsBlock",
         "TwitterRemoveListMemberBlock",

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

@@ -0,0 +1,269 @@
+"""
+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

@@ -30,7 +30,6 @@ class TestLLMStatsTracking:
                 credentials=llm.TEST_CREDENTIALS,
                 llm_model=llm.LlmModel.GPT4O,
                 prompt=[{"role": "user", "content": "Hello"}],
-                json_format=False,
                 max_tokens=100,
             )
 
@@ -42,6 +41,8 @@ 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()
@@ -51,7 +52,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='{"key1": "value1", "key2": "value2"}',
+                response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
                 tool_calls=None,
                 prompt_tokens=15,
                 completion_tokens=25,
@@ -69,10 +70,12 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        async for output_name, output_data in block.run(
-            input_data, credentials=llm.TEST_CREDENTIALS
-        ):
-            outputs[output_name] = output_data
+        # 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
 
         # Check stats
         assert block.execution_stats.input_token_count == 15
@@ -143,7 +146,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='{"wrong": "format"}',
+                    response='<json_output id="test123456">{"wrong": "format"}</json_output>',
                     tool_calls=None,
                     prompt_tokens=10,
                     completion_tokens=15,
@@ -154,7 +157,7 @@ class TestLLMStatsTracking:
                 return llm.LLMResponse(
                     raw_response="",
                     prompt=[],
-                    response='{"key1": "value1", "key2": "value2"}',
+                    response='<json_output id="test123456">{"key1": "value1", "key2": "value2"}</json_output>',
                     tool_calls=None,
                     prompt_tokens=20,
                     completion_tokens=25,
@@ -173,10 +176,12 @@ class TestLLMStatsTracking:
         )
 
         outputs = {}
-        async for output_name, output_data in block.run(
-            input_data, credentials=llm.TEST_CREDENTIALS
-        ):
-            outputs[output_name] = output_data
+        # 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
 
         # Check stats - should accumulate both calls
         # For 2 attempts: attempt 1 (failed) + attempt 2 (success) = 2 total
@@ -269,7 +274,8 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='{"summary": "Test chunk summary"}', tool_calls=None
+                            content='<json_output id="test123456">{"summary": "Test chunk summary"}</json_output>',
+                            tool_calls=None,
                         )
                     )
                 ]
@@ -277,7 +283,7 @@ class TestLLMStatsTracking:
                 mock_response.choices = [
                     MagicMock(
                         message=MagicMock(
-                            content='{"final_summary": "Test final summary"}',
+                            content='<json_output id="test123456">{"final_summary": "Test final summary"}</json_output>',
                             tool_calls=None,
                         )
                     )
@@ -298,11 +304,13 @@ class TestLLMStatsTracking:
                 max_tokens=1000,  # Large enough to avoid chunking
             )
 
-            outputs = {}
-            async for output_name, output_data in block.run(
-                input_data, credentials=llm.TEST_CREDENTIALS
-            ):
-                outputs[output_name] = output_data
+            # 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
 
             print(f"Actual calls made: {call_count}")
             print(f"Block stats: {block.execution_stats}")
@@ -457,7 +465,7 @@ class TestLLMStatsTracking:
             return llm.LLMResponse(
                 raw_response="",
                 prompt=[],
-                response='{"result": "test"}',
+                response='<json_output id="test123456">{"result": "test"}</json_output>',
                 tool_calls=None,
                 prompt_tokens=10,
                 completion_tokens=20,
@@ -476,10 +484,12 @@ class TestLLMStatsTracking:
 
         # Run the block
         outputs = {}
-        async for output_name, output_data in block.run(
-            input_data, credentials=llm.TEST_CREDENTIALS
-        ):
-            outputs[output_name] = output_data
+        # 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
 
         # 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

@@ -35,20 +35,19 @@ async def execute_graph(
     logger.info("Input data: %s", input_data)
 
     # --- Test adding new executions --- #
-    response = await agent_server.test_execute_graph(
+    graph_exec = 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,
     )
-    graph_exec_id = response.graph_exec_id
-    logger.info("Created execution with ID: %s", 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")
@@ -217,8 +216,17 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_function_signature method to avoid database calls
-    with patch("backend.blocks.llm.llm_call", return_value=mock_response), patch.object(
-        SmartDecisionMakerBlock, "_create_function_signature", return_value=[]
+    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=[],
     ):
 
         # Create test input
@@ -250,3 +258,471 @@ 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

@@ -48,16 +48,24 @@ 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
+    # Check that dynamic fields are handled with original names
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 3  # Should have all three fields
 
-    # Each dynamic field should have proper schema
-    for prop_value in properties.values():
+    # 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():
         assert "type" in prop_value
         assert prop_value["type"] == "string"  # Dynamic fields get string type
         assert "description" in prop_value
-        assert "Dynamic value for" in prop_value["description"]
+        # 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"]
 
 
 @pytest.mark.asyncio
@@ -96,10 +104,18 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     properties = signature["function"]["parameters"]["properties"]
     assert len(properties) == 2  # Should have both list items
 
-    # Each dynamic field should have proper schema
-    for prop_value in properties.values():
+    # 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():
         assert prop_value["type"] == "string"
-        assert "Dynamic value for" in prop_value["description"]
+        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"]
 
 
 @pytest.mark.asyncio

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

@@ -0,0 +1,553 @@
+"""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

@@ -0,0 +1,131 @@
+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

@@ -2,6 +2,8 @@ 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
@@ -137,6 +139,11 @@ 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
@@ -148,20 +155,85 @@ class ExtractTextInformationBlock(Block):
         else:
             txt = json.dumps(input_data.text)
 
-        matches = [
-            match.group(input_data.group)
-            for match in re.finditer(input_data.pattern, txt, flags)
-            if input_data.group <= len(match.groups())
-        ]
-        if not input_data.find_all:
-            matches = matches[:1]
-        for match in matches:
-            yield "positive", match
-        if not matches:
-            yield "negative", input_data.text
+        # Limit text size to prevent DoS
+        if len(txt) > MAX_TEXT_LENGTH:
+            txt = txt[:MAX_TEXT_LENGTH]
 
-        yield "matched_results", matches
-        yield "matched_count", len(matches)
+        # 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*)*
+        ]
+
+        # 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
+            yield "negative", input_data.text
+            yield "matched_results", []
+            yield "matched_count", 0
 
 
 class FillTextTemplateBlock(Block):
@@ -172,6 +244,11 @@ 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")
@@ -205,6 +282,7 @@ 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

@@ -270,13 +270,17 @@ class GetCurrentDateBlock(Block):
             test_output=[
                 (
                     "date",
-                    lambda t: abs(datetime.now() - datetime.strptime(t, "%Y-%m-%d"))
-                    < timedelta(days=8),  # 7 days difference + 1 day error margin.
+                    lambda t: abs(
+                        datetime.now().date() - datetime.strptime(t, "%Y-%m-%d").date()
+                    )
+                    <= timedelta(days=8),  # 7 days difference + 1 day error margin.
                 ),
                 (
                     "date",
-                    lambda t: abs(datetime.now() - datetime.strptime(t, "%m/%d/%Y"))
-                    < timedelta(days=8),
+                    lambda t: abs(
+                        datetime.now().date() - datetime.strptime(t, "%m/%d/%Y").date()
+                    )
+                    <= timedelta(days=8),
                     # 7 days difference + 1 day error margin.
                 ),
                 (
@@ -382,7 +386,7 @@ 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

@@ -26,6 +26,14 @@ 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

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

autogpt_platform/backend/backend/data/api_key.py

@@ -1,57 +1,32 @@
 import logging
 import uuid
 from datetime import datetime, timezone
-from typing import List, Optional
+from typing import Optional
 
-from autogpt_libs.api_key.key_manager import APIKeyManager
+from autogpt_libs.api_key.keysmith import APIKeySmith
 from prisma.enums import APIKeyPermission, APIKeyStatus
-from prisma.errors import PrismaError
 from prisma.models import APIKey as PrismaAPIKey
-from prisma.types import (
-    APIKeyCreateInput,
-    APIKeyUpdateInput,
-    APIKeyWhereInput,
-    APIKeyWhereUniqueInput,
-)
-from pydantic import BaseModel
+from prisma.types import APIKeyWhereUniqueInput
+from pydantic import BaseModel, Field
 
-from backend.data.db import BaseDbModel
+from backend.data.includes import MAX_USER_API_KEYS_FETCH
+from backend.util.exceptions import NotAuthorizedError, NotFoundError
 
 logger = logging.getLogger(__name__)
+keysmith = APIKeySmith()
 
 
-# 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):
+class APIKeyInfo(BaseModel):
+    id: str
     name: str
-    prefix: str
-    key: str
-    status: APIKeyStatus = APIKeyStatus.ACTIVE
-    permissions: List[APIKeyPermission]
-    postfix: 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]
     created_at: datetime
     last_used_at: Optional[datetime] = None
     revoked_at: Optional[datetime] = None
@@ -60,266 +35,215 @@ class APIKey(BaseDbModel):
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
-        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)}")
+        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,
+        )
 
 
-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
+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)
 
     @staticmethod
     def from_db(api_key: PrismaAPIKey):
-        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)}")
+        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"}))
 
 
-async def generate_api_key(
+async def create_api_key(
     name: str,
     user_id: str,
-    permissions: List[APIKeyPermission],
+    permissions: list[APIKeyPermission],
     description: Optional[str] = None,
-) -> tuple[APIKeyWithoutHash, str]:
+) -> tuple[APIKeyInfo, str]:
     """
     Generate a new API key and store it in the database.
     Returns the API key object (without hash) and the plain text key.
     """
-    try:
-        api_manager = APIKeyManager()
-        key = api_manager.generate_api_key()
+    generated_key = keysmith.generate_key()
 
-        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,
-            )
-        )
+    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_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)}")
+    return APIKeyInfo.from_db(saved_key_obj), generated_key.key
 
 
-async def validate_api_key(plain_text_key: str) -> Optional[APIKey]:
+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]:
     """
-    Validate an API key and return the API key object if valid.
+    Validate an API key and return the API key object if valid and active.
     """
     try:
-        if not plain_text_key.startswith(APIKeyManager.PREFIX):
+        if not plaintext_key.startswith(APIKeySmith.PREFIX):
             logger.warning("Invalid API key format")
             return None
 
-        prefix = plain_text_key[: APIKeyManager.PREFIX_LENGTH]
-        api_manager = APIKeyManager()
+        head = plaintext_key[: APIKeySmith.HEAD_LENGTH]
+        potential_matches = await get_active_api_keys_by_head(head)
 
-        api_key = await PrismaAPIKey.prisma().find_first(
-            where=APIKeyWhereInput(prefix=prefix, status=(APIKeyStatus.ACTIVE))
+        matched_api_key = next(
+            (pm for pm in potential_matches if pm.match(plaintext_key)),
+            None,
         )
-
-        if not api_key:
-            logger.warning(f"No active API key found with prefix {prefix}")
+        if not matched_api_key:
+            # API key not found or invalid
             return None
 
-        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."
+        # 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
             )
 
-        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)
-            ),
-        )
+        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
 
-        if updated_api_key:
-            return APIKeyWithoutHash.from_db(updated_api_key)
+
+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}
+        )
+        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:
         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)}")
 
-
-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)}")
+    return APIKeyInfo.from_db(api_key)
 
 
 async def update_api_key_permissions(
-    key_id: str, user_id: str, permissions: List[APIKeyPermission]
-) -> Optional[APIKeyWithoutHash]:
+    key_id: str, user_id: str, permissions: list[APIKeyPermission]
+) -> APIKeyInfo:
     """
     Update the permissions of an API key.
     """
-    try:
-        api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
+    api_key = await PrismaAPIKey.prisma().find_unique(where={"id": key_id})
 
-        if api_key is None:
-            raise APIKeyNotFoundError("No such API key found.")
+    if api_key is None:
+        raise NotFoundError("No such API key found.")
 
-        if api_key.userId != user_id:
-            raise APIKeyPermissionError(
-                "You do not have permission to update this API key."
-            )
+    if api_key.userId != user_id:
+        raise NotAuthorizedError("You do not have permission to update this API key.")
 
-        where_clause: APIKeyWhereUniqueInput = {"id": key_id}
-        updated_api_key = await PrismaAPIKey.prisma().update(
-            where=where_clause,
-            data=APIKeyUpdateInput(permissions=permissions),
-        )
+    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.")
 
-        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)}")
+    return APIKeyInfo.from_db(updated_api_key)

autogpt_platform/backend/backend/data/block.py

@@ -1,4 +1,3 @@
-import functools
 import inspect
 import logging
 import os
@@ -8,6 +7,7 @@ from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
+    Callable,
     ClassVar,
     Generic,
     Optional,
@@ -27,6 +27,7 @@ from pydantic import BaseModel
 from backend.data.model import NodeExecutionStats
 from backend.integrations.providers import ProviderName
 from backend.util import json
+from backend.util.cache import cached
 from backend.util.settings import Config
 
 from .model import (
@@ -44,9 +45,10 @@ 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.
-BlockOutput = AsyncGen[BlockData, None]  # Output: 1 output pin produces n 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]]
 CompletedBlockOutput = dict[str, list[Any]]  # Completed stream, collected as a dict.
 
 
@@ -89,6 +91,45 @@ 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]]
 
@@ -306,7 +347,7 @@ 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: BlockData | list[BlockData] | None = None,
+        test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
         test_mock: dict[str, Any] | None = None,
         test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
         disabled: bool = False,
@@ -452,6 +493,24 @@ 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(
@@ -663,7 +722,7 @@ def get_block(block_id: str) -> Block[BlockSchema, BlockSchema] | None:
     return cls() if cls else None
 
 
-@functools.cache
+@cached(ttl_seconds=3600)
 def get_webhook_block_ids() -> Sequence[str]:
     return [
         id
@@ -672,7 +731,7 @@ def get_webhook_block_ids() -> Sequence[str]:
     ]
 
 
-@functools.cache
+@cached(ttl_seconds=3600)
 def get_io_block_ids() -> Sequence[str]:
     return [
         id

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -29,8 +29,7 @@ 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
-from backend.data.cost import BlockCost, BlockCostType
+from backend.data.block import Block, BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
     aiml_api_credentials,
     anthropic_credentials,
@@ -70,6 +69,7 @@ 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_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

@@ -1,32 +0,0 @@
-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 Any, cast
+from typing import TYPE_CHECKING, 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.cost import BlockCost
+from backend.data.includes import MAX_CREDIT_REFUND_REQUESTS_FETCH
 from backend.data.model import (
     AutoTopUpConfig,
     RefundRequest,
@@ -41,6 +41,9 @@ 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__)
@@ -903,7 +906,9 @@ class UserCredit(UserCreditBase):
             ),
         )
 
-    async def get_refund_requests(self, user_id: str) -> list[RefundRequest]:
+    async def get_refund_requests(
+        self, user_id: str, limit: int = MAX_CREDIT_REFUND_REQUESTS_FETCH
+    ) -> list[RefundRequest]:
         return [
             RefundRequest(
                 id=r.id,
@@ -919,6 +924,7 @@ class UserCredit(UserCreditBase):
             for r in await CreditRefundRequest.prisma().find_many(
                 where={"userId": user_id},
                 order={"createdAt": "desc"},
+                take=limit,
             )
         ]
 
@@ -997,10 +1003,14 @@ 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 = 15000  # 15 seconds - Increased from 5s to prevent timeout errors
+TRANSACTION_TIMEOUT = 30000  # 30 seconds - Increased from 15s to prevent timeout errors during graph creation under load
 
 
 @asynccontextmanager

autogpt_platform/backend/backend/data/dynamic_fields.py

@@ -0,0 +1,284 @@
+"""
+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

@@ -11,11 +11,14 @@ 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,
@@ -24,7 +27,6 @@ from prisma.models import (
     AgentNodeExecutionKeyValueData,
 )
 from prisma.types import (
-    AgentGraphExecutionCreateInput,
     AgentGraphExecutionUpdateManyMutationInput,
     AgentGraphExecutionWhereInput,
     AgentNodeExecutionCreateInput,
@@ -60,7 +62,7 @@ from .includes import (
     GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     graph_execution_include,
 )
-from .model import GraphExecutionStats, NodeExecutionStats
+from .model import CredentialsMetaInput, GraphExecutionStats, NodeExecutionStats
 
 T = TypeVar("T")
 
@@ -87,6 +89,33 @@ 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):
@@ -94,10 +123,15 @@ class GraphExecutionMeta(BaseDbModel):
     user_id: str
     graph_id: str
     graph_version: int
-    preset_id: Optional[str] = None
+    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]
     status: ExecutionStatus
     started_at: datetime
     ended_at: datetime
+    is_shared: bool = False
+    share_token: Optional[str] = None
 
     class Stats(BaseModel):
         model_config = ConfigDict(
@@ -179,6 +213,18 @@ 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,
@@ -202,11 +248,13 @@ class GraphExecutionMeta(BaseDbModel):
                 if stats
                 else None
             ),
+            is_shared=_graph_exec.isShared,
+            share_token=_graph_exec.shareToken,
         )
 
 
 class GraphExecution(GraphExecutionMeta):
-    inputs: BlockInput
+    inputs: BlockInput  # type: ignore - incompatible override is intentional
     outputs: CompletedBlockOutput
 
     @staticmethod
@@ -226,15 +274,18 @@ class GraphExecution(GraphExecutionMeta):
         )
 
         inputs = {
-            **{
-                # 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
-                )
-            },
+            **(
+                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
+                    )
+                }
+            ),
             **{
                 # input from webhook-triggered block
                 "payload": exec.input_data["payload"]
@@ -252,14 +303,13 @@ 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", None)
-                )
+                outputs[exec.input_data["name"]].append(exec.input_data.get("value"))
 
         return GraphExecution(
             **{
                 field_name: getattr(graph_exec, field_name)
                 for field_name in GraphExecutionMeta.model_fields
+                if field_name != "inputs"
             },
             inputs=inputs,
             outputs=outputs,
@@ -292,13 +342,17 @@ class GraphExecutionWithNodes(GraphExecution):
             node_executions=node_executions,
         )
 
-    def to_graph_execution_entry(self, user_context: "UserContext"):
+    def to_graph_execution_entry(
+        self,
+        user_context: "UserContext",
+        compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
+    ):
         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={},  # FIXME: store credentials on AgentGraphExecution
+            nodes_input_masks=compiled_nodes_input_masks,
             user_context=user_context,
         )
 
@@ -335,7 +389,7 @@ class NodeExecutionResult(BaseModel):
         else:
             input_data: BlockInput = defaultdict()
             for data in _node_exec.Input or []:
-                input_data[data.name] = type_utils.convert(data.data, type[Any])
+                input_data[data.name] = type_utils.convert(data.data, JsonValue)
 
         output_data: CompletedBlockOutput = defaultdict(list)
 
@@ -344,7 +398,7 @@ 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, type[Any]))
+                output_data[data.name].append(type_utils.convert(data.data, JsonValue))
 
         graph_execution: AgentGraphExecution | None = _node_exec.GraphExecution
         if graph_execution:
@@ -539,9 +593,12 @@ async def get_graph_execution(
 async def create_graph_execution(
     graph_id: str,
     graph_version: int,
-    starting_nodes_input: list[tuple[str, BlockInput]],
+    starting_nodes_input: list[tuple[str, BlockInput]],  # list[(node_id, BlockInput)]
+    inputs: Mapping[str, JsonValue],
     user_id: str,
-    preset_id: str | None = None,
+    preset_id: Optional[str] = None,
+    credential_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> GraphExecutionWithNodes:
     """
     Create a new AgentGraphExecution record.
@@ -549,11 +606,18 @@ async def create_graph_execution(
         The id of the AgentGraphExecution and the list of ExecutionResult for each node.
     """
     result = await AgentGraphExecution.prisma().create(
-        data=AgentGraphExecutionCreateInput(
-            agentGraphId=graph_id,
-            agentGraphVersion=graph_version,
-            executionStatus=ExecutionStatus.QUEUED,
-            NodeExecutions={
+        data={
+            "agentGraphId": graph_id,
+            "agentGraphVersion": graph_version,
+            "executionStatus": ExecutionStatus.INCOMPLETE,
+            "inputs": SafeJson(inputs),
+            "credentialInputs": (
+                SafeJson(credential_inputs) if credential_inputs else Json({})
+            ),
+            "nodesInputMasks": (
+                SafeJson(nodes_input_masks) if nodes_input_masks else Json({})
+            ),
+            "NodeExecutions": {
                 "create": [
                     AgentNodeExecutionCreateInput(
                         agentNodeId=node_id,
@@ -569,9 +633,9 @@ 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,
     )
 
@@ -582,7 +646,7 @@ async def upsert_execution_input(
     node_id: str,
     graph_exec_id: str,
     input_name: str,
-    input_data: Any,
+    input_data: JsonValue,
     node_exec_id: str | None = None,
 ) -> tuple[str, BlockInput]:
     """
@@ -631,7 +695,7 @@ async def upsert_execution_input(
         )
         return existing_execution.id, {
             **{
-                input_data.name: type_utils.convert(input_data.data, type[Any])
+                input_data.name: type_utils.convert(input_data.data, JsonValue)
                 for input_data in existing_execution.Input or []
             },
             input_name: input_data,
@@ -692,6 +756,11 @@ 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:
@@ -703,20 +772,25 @@ async def update_graph_execution_stats(
     if status:
         update_data["executionStatus"] = status
 
-    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},
-            ],
-        },
+    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,
         data=update_data,
     )
-    if updated_count == 0:
-        return None
 
     graph_exec = await AgentGraphExecution.prisma().find_unique_or_raise(
         where={"id": graph_exec_id},
@@ -724,6 +798,7 @@ async def update_graph_execution_stats(
             [*get_io_block_ids(), *get_webhook_block_ids()]
         ),
     )
+
     return GraphExecution.from_db(graph_exec)
 
 
@@ -888,7 +963,7 @@ class GraphExecutionEntry(BaseModel):
     graph_exec_id: str
     graph_id: str
     graph_version: int
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None
+    nodes_input_masks: Optional[NodesInputMasks] = None
     user_context: UserContext
 
 
@@ -950,6 +1025,18 @@ 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")
 ]
@@ -1127,3 +1214,98 @@ 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/graph.py

@@ -1,6 +1,7 @@
 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
@@ -12,13 +13,15 @@ from prisma.types import (
     AgentNodeLinkCreateInput,
     StoreListingVersionWhereInput,
 )
-from pydantic import Field, JsonValue, create_model
+from pydantic import BaseModel, Field, 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,
@@ -28,12 +31,22 @@ 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, get_block, get_blocks
+from .block import (
+    Block,
+    BlockInput,
+    BlockSchema,
+    BlockType,
+    EmptySchema,
+    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__)
@@ -69,12 +82,15 @@ class Node(BaseDbModel):
     output_links: list[Link] = []
 
     @property
-    def block(self) -> Block[BlockSchema, BlockSchema]:
+    def block(self) -> "Block[BlockSchema, BlockSchema] | _UnknownBlockBase":
+        """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
         block = get_block(self.block_id)
         if not block:
-            raise ValueError(
-                f"Block #{self.block_id} does not exist -> Node #{self.id} is invalid"
+            # 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"
             )
+            return _UnknownBlockBase(self.block_id)
         return block
 
 
@@ -159,6 +175,8 @@ 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
@@ -205,6 +223,35 @@ 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],
@@ -238,6 +285,14 @@ 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
 
@@ -342,6 +397,8 @@ 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}
@@ -354,6 +411,10 @@ 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.
@@ -414,7 +475,7 @@ class GraphModel(Graph):
     def validate_graph(
         self,
         for_run: bool = False,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+        nodes_input_masks: Optional["NodesInputMasks"] = None,
     ):
         """
         Validate graph structure and raise `ValueError` on issues.
@@ -428,7 +489,7 @@ class GraphModel(Graph):
     def _validate_graph(
         graph: BaseGraph,
         for_run: bool = False,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+        nodes_input_masks: Optional["NodesInputMasks"] = None,
     ) -> None:
         errors = GraphModel._validate_graph_get_errors(
             graph, for_run, nodes_input_masks
@@ -442,7 +503,7 @@ class GraphModel(Graph):
     def validate_graph_get_errors(
         self,
         for_run: bool = False,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+        nodes_input_masks: Optional["NodesInputMasks"] = None,
     ) -> dict[str, dict[str, str]]:
         """
         Validate graph and return structured errors per node.
@@ -464,7 +525,7 @@ class GraphModel(Graph):
     def _validate_graph_get_errors(
         graph: BaseGraph,
         for_run: bool = False,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+        nodes_input_masks: Optional["NodesInputMasks"] = None,
     ) -> dict[str, dict[str, str]]:
         """
         Validate graph and return structured errors per node.
@@ -655,9 +716,12 @@ 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(
                 {
@@ -678,7 +742,7 @@ def _is_tool_pin(name: str) -> bool:
 
 
 def _sanitize_pin_name(name: str) -> str:
-    sanitized_name = name.split("_#_")[0].split("_@_")[0].split("_$_")[0]
+    sanitized_name = extract_base_field_name(name)
     if _is_tool_pin(sanitized_name):
         return "tools"
     return sanitized_name
@@ -696,6 +760,13 @@ class GraphMeta(Graph):
         return GraphMeta(**graph.model_dump())
 
 
+class GraphsPaginated(BaseModel):
+    """Response schema for paginated graphs."""
+
+    graphs: list[GraphMeta]
+    pagination: Pagination
+
+
 # --------------------- CRUD functions --------------------- #
 
 
@@ -724,31 +795,42 @@ async def set_node_webhook(node_id: str, webhook_id: str | None) -> NodeModel:
     return NodeModel.from_db(node)
 
 
-async def list_graphs(
+async def list_graphs_paginated(
     user_id: str,
+    page: int = 1,
+    page_size: int = 25,
     filter_by: Literal["active"] | None = "active",
-) -> list[GraphMeta]:
+) -> GraphsPaginated:
     """
-    Retrieves graph metadata objects.
-    Default behaviour is to get all currently active graphs.
+    Retrieves paginated graph metadata objects.
 
     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:
-        list[GraphMeta]: A list of objects representing the retrieved graphs.
+        GraphsPaginated: Paginated list of graph metadata.
     """
     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] = []
@@ -762,7 +844,15 @@ async def list_graphs(
             logger.error(f"Error processing graph {graph.id}: {e}")
             continue
 
-    return graph_models
+    return GraphsPaginated(
+        graphs=graph_models,
+        pagination=Pagination(
+            total_items=total_count,
+            total_pages=total_pages,
+            current_page=page,
+            page_size=page_size,
+        ),
+    )
 
 
 async def get_graph_metadata(graph_id: str, version: int | None = None) -> Graph | None:
@@ -982,11 +1072,14 @@ 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) -> list[GraphModel]:
+async def get_graph_all_versions(
+    graph_id: str, user_id: str, limit: int = MAX_GRAPH_VERSIONS_FETCH
+) -> 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:
@@ -1045,6 +1138,7 @@ 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,
@@ -1103,6 +1197,7 @@ 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(),
@@ -1233,3 +1328,34 @@ 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/includes.py

@@ -14,6 +14,7 @@ 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.
@@ -28,6 +29,13 @@ 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": {
@@ -59,19 +67,68 @@ def graph_execution_include(
     }
 
 
-INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
-    "AgentNodes": {"include": AGENT_NODE_INCLUDE},
-    "AgentPresets": {"include": {"InputPresets": True}},
+AGENT_PRESET_INCLUDE: prisma.types.AgentPresetInclude = {
+    "InputPresets": True,
+    "Webhook": True,
 }
 
 
-def library_agent_include(user_id: str) -> prisma.types.LibraryAgentInclude:
-    return {
-        "AgentGraph": {
-            "include": {
-                **AGENT_GRAPH_INCLUDE,
-                "Executions": {"where": {"userId": user_id}},
-            }
-        },
-        "Creator": True,
+INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
+    "AgentNodes": {"include": AGENT_NODE_INCLUDE},
+    "AgentPresets": {"include": AGENT_PRESET_INCLUDE},
+}
+
+
+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,
+            }
+
+        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

autogpt_platform/backend/backend/data/integrations.py

@@ -11,7 +11,10 @@ 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
+from backend.data.includes import (
+    INTEGRATION_WEBHOOK_INCLUDE,
+    MAX_INTEGRATION_WEBHOOKS_FETCH,
+)
 from backend.integrations.providers import ProviderName
 from backend.integrations.webhooks.utils import webhook_ingress_url
 from backend.server.v2.library.model import LibraryAgentPreset
@@ -128,22 +131,36 @@ async def get_webhook(
 
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str, credentials_id: str, *, include_relations: Literal[True]
+    user_id: str,
+    credentials_id: str,
+    *,
+    include_relations: Literal[True],
+    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
 ) -> list[WebhookWithRelations]: ...
 @overload
 async def get_all_webhooks_by_creds(
-    user_id: str, credentials_id: str, *, include_relations: Literal[False] = False
+    user_id: str,
+    credentials_id: str,
+    *,
+    include_relations: Literal[False] = False,
+    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
 ) -> list[Webhook]: ...
 
 
 async def get_all_webhooks_by_creds(
-    user_id: str, credentials_id: str, *, include_relations: bool = False
+    user_id: str,
+    credentials_id: str,
+    *,
+    include_relations: bool = False,
+    limit: int = MAX_INTEGRATION_WEBHOOKS_FETCH,
 ) -> 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

@@ -270,6 +270,7 @@ 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:
@@ -285,6 +286,7 @@ 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/onboarding.py

@@ -1,8 +1,10 @@
 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
@@ -30,7 +32,7 @@ user_credit = get_user_credit_model()
 
 class UserOnboardingUpdate(pydantic.BaseModel):
     completedSteps: Optional[list[OnboardingStep]] = None
-    notificationDot: Optional[bool] = None
+    walletShown: Optional[bool] = None
     notified: Optional[list[OnboardingStep]] = None
     usageReason: Optional[str] = None
     integrations: Optional[list[str]] = None
@@ -39,6 +41,8 @@ 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):
@@ -57,16 +61,22 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
         update["completedSteps"] = list(set(data.completedSteps))
         for step in (
             OnboardingStep.AGENT_NEW_RUN,
-            OnboardingStep.RUN_AGENTS,
+            OnboardingStep.MARKETPLACE_VISIT,
             OnboardingStep.MARKETPLACE_ADD_AGENT,
             OnboardingStep.MARKETPLACE_RUN_AGENT,
             OnboardingStep.BUILDER_SAVE_AGENT,
-            OnboardingStep.BUILDER_RUN_AGENT,
+            OnboardingStep.RE_RUN_AGENT,
+            OnboardingStep.SCHEDULE_AGENT,
+            OnboardingStep.RUN_AGENTS,
+            OnboardingStep.RUN_3_DAYS,
+            OnboardingStep.TRIGGER_WEBHOOK,
+            OnboardingStep.RUN_14_DAYS,
+            OnboardingStep.RUN_AGENTS_100,
         ):
             if step in data.completedSteps:
                 await reward_user(user_id, step)
-    if data.notificationDot is not None:
-        update["notificationDot"] = data.notificationDot
+    if data.walletShown is not None:
+        update["walletShown"] = data.walletShown
     if data.notified is not None:
         update["notified"] = list(set(data.notified))
     if data.usageReason is not None:
@@ -83,6 +93,10 @@ 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},
@@ -101,16 +115,28 @@ 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.RUN_AGENTS:
-            reward = 300
+        case OnboardingStep.MARKETPLACE_VISIT:
+            reward = 100
         case OnboardingStep.MARKETPLACE_ADD_AGENT:
             reward = 100
         case OnboardingStep.MARKETPLACE_RUN_AGENT:
             reward = 100
         case OnboardingStep.BUILDER_SAVE_AGENT:
             reward = 100
-        case OnboardingStep.BUILDER_RUN_AGENT:
+        case OnboardingStep.RE_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
@@ -132,6 +158,22 @@ 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,
@@ -333,8 +375,13 @@ 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)
-
-    # Onboading is enabled if there are at least 2 agents in the store
+    # Onboarding 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

@@ -1,30 +1,24 @@
 import logging
-import os
-from functools import cache
 
-from autogpt_libs.utils.cache import thread_cached
-from dotenv import load_dotenv
 from redis import Redis
 from redis.asyncio import Redis as AsyncRedis
 
+from backend.util.cache import cached, thread_cached
 from backend.util.retry import conn_retry
+from backend.util.settings import Settings
 
-load_dotenv()
-
-HOST = os.getenv("REDIS_HOST", "localhost")
-PORT = int(os.getenv("REDIS_PORT", "6379"))
-PASSWORD = os.getenv("REDIS_PASSWORD", "password")
+settings = Settings()
 
 logger = logging.getLogger(__name__)
 
 
 @conn_retry("Redis", "Acquiring connection")
-def connect() -> Redis:
+def connect(decode_responses: bool = True) -> Redis:
     c = Redis(
-        host=HOST,
-        port=PORT,
-        password=PASSWORD,
-        decode_responses=True,
+        host=settings.config.redis_host,
+        port=settings.config.redis_port,
+        password=settings.config.redis_password or None,
+        decode_responses=decode_responses,
     )
     c.ping()
     return c
@@ -35,7 +29,7 @@ def disconnect():
     get_redis().close()
 
 
-@cache
+@cached(ttl_seconds=3600)
 def get_redis() -> Redis:
     return connect()
 
@@ -43,9 +37,9 @@ def get_redis() -> Redis:
 @conn_retry("AsyncRedis", "Acquiring connection")
 async def connect_async() -> AsyncRedis:
     c = AsyncRedis(
-        host=HOST,
-        port=PORT,
-        password=PASSWORD,
+        host=settings.config.redis_host,
+        port=settings.config.redis_port,
+        password=settings.config.redis_password or None,
         decode_responses=True,
     )
     await c.ping()

autogpt_platform/backend/backend/data/user.py

@@ -16,6 +16,7 @@ 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.cache import cached
 from backend.util.encryption import JSONCryptor
 from backend.util.json import SafeJson
 from backend.util.settings import Settings
@@ -23,7 +24,11 @@ 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")
@@ -49,6 +54,7 @@ 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:
@@ -64,6 +70,7 @@ 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})
@@ -74,7 +81,17 @@ 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}"
@@ -114,6 +131,8 @@ 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():
@@ -285,6 +304,10 @@ 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,
@@ -323,6 +346,8 @@ 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}"
@@ -407,6 +432,10 @@ 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

@@ -4,7 +4,12 @@ Module for generating AI-based activity status for graph executions.
 
 import json
 import logging
-from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
+from typing import TYPE_CHECKING, Any, TypedDict
+
+try:
+    from typing import NotRequired
+except ImportError:
+    from typing_extensions import NotRequired
 
 from pydantic import SecretStr
 
@@ -107,7 +112,7 @@ async def generate_activity_status_for_execution(
     # Check if we have OpenAI API key
     try:
         settings = Settings()
-        if not settings.secrets.openai_api_key:
+        if not settings.secrets.openai_internal_api_key:
             logger.debug(
                 "OpenAI API key not configured, skipping activity status generation"
             )
@@ -146,17 +151,35 @@ 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 task was genuinely completed\n"
-                    "- Don't sugar-coat failures or present them as helpful feedback\n\n"
+                    "- Only claim success if the INTENDED PURPOSE was genuinely accomplished AND produced expected outputs\n"
+                    "- Don't sugar-coat failures or present them as helpful feedback\n"
+                    "- ESPECIALLY: If the graph's main purpose wasn't achieved, this is a failure regardless of 'completed' status\n\n"
                     "Understanding Errors:\n"
                     "- Node errors: Individual steps may fail but the overall task might still complete (e.g., one data source fails but others work)\n"
                     "- Graph error (in overall_status.graph_error): This means the entire execution failed and nothing was accomplished\n"
-                    "- 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"
+                    "- 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"
                 ),
             },
             {
@@ -165,15 +188,28 @@ 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"
-                    "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"
+                    "ANALYSIS CHECKLIST:\n"
+                    "1. READ graph_info.description FIRST - this tells you what the user intended to accomplish\n"
+                    "2. Check overall_status.graph_error - if present, the entire execution failed\n"
+                    "3. Look for nodes with 'Output', 'Post', 'Create', 'Send', 'Publish', 'Generate' in their block_name\n"
+                    "4. Check if these critical blocks have empty recent_outputs arrays - this indicates failure\n"
+                    "5. Look for AgentExecutorBlock (sub-agents) with no outputs - this suggests sub-task failures\n"
+                    "6. Count how many nodes produced outputs vs total nodes - low ratio suggests problems\n"
+                    "7. MOST IMPORTANT: Does the execution outcome match what graph_info.description promised?\n\n"
+                    "INTENTION-BASED EVALUATION:\n"
+                    "- If description mentions 'blog writing' → did it create blog content?\n"
+                    "- If description mentions 'email automation' → were emails actually sent?\n"
+                    "- If description mentions 'data analysis' → were analysis results produced?\n"
+                    "- If description mentions 'content generation' → was content actually generated?\n"
+                    "- If description mentions 'social media posting' → were posts actually made?\n"
+                    "- Match the outputs to the stated intention, not just technical completion\n\n"
                     "Write 1-3 sentences about what the user accomplished, such as:\n"
                     "- 'I analyzed your resume and provided detailed feedback for the IT industry.'\n"
-                    "- 'I couldn't analyze your resume because the input was just nonsensical text.'\n"
-                    "- 'I failed to complete the task due to missing API access.'\n"
+                    "- 'I couldn't complete the task because critical steps failed to produce any results.'\n"
+                    "- 'I failed to generate the content you requested due to missing API access.'\n"
                     "- 'I extracted key information from your documents and organized it into a summary.'\n"
-                    "- 'The task failed to run due to system configuration issues.'\n\n"
-                    "Focus on what ACTUALLY happened, not what was attempted."
+                    "- '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'."
                 ),
             },
         ]
@@ -187,7 +223,7 @@ async def generate_activity_status_for_execution(
         credentials = APIKeyCredentials(
             id="openai",
             provider="openai",
-            api_key=SecretStr(settings.secrets.openai_api_key),
+            api_key=SecretStr(settings.secrets.openai_internal_api_key),
             title="System OpenAI",
         )
 
@@ -197,6 +233,7 @@ 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:
@@ -423,7 +460,6 @@ 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_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_internal_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_api_key = ""
+            mock_settings.return_value.secrets.openai_internal_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_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_internal_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_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_internal_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_api_key = "test_key"
+            mock_settings.return_value.secrets.openai_internal_api_key = "test_key"
 
             mock_response = LLMResponse(
                 raw_response={},

autogpt_platform/backend/backend/executor/cluster_lock.py

@@ -0,0 +1,115 @@
+"""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

autogpt_platform/backend/backend/executor/cluster_lock_test.py

@@ -0,0 +1,509 @@
+"""
+Integration tests for ClusterLock - Redis-based distributed locking.
+
+Tests the complete lock lifecycle without mocking Redis to ensure
+real-world behavior is correct. Covers acquisition, refresh, expiry,
+contention, and error scenarios.
+"""
+
+import logging
+import time
+import uuid
+from threading import Thread
+
+import pytest
+import redis
+
+from .cluster_lock import ClusterLock
+
+logger = logging.getLogger(__name__)
+
+
+@pytest.fixture
+def redis_client():
+    """Get Redis client for testing using same config as backend."""
+    from backend.util.settings import Settings
+
+    settings = Settings()
+
+    # Use same config as backend but without decode_responses since ClusterLock needs raw bytes
+    client = redis.Redis(
+        host=settings.config.redis_host,
+        port=settings.config.redis_port,
+        password=settings.config.redis_password or None,
+        decode_responses=False,  # ClusterLock needs raw bytes for ownership verification
+    )
+
+    # Clean up any existing test keys
+    try:
+        for key in client.scan_iter(match="test_lock:*"):
+            client.delete(key)
+    except Exception:
+        pass  # Ignore cleanup errors
+
+    return client
+
+
+@pytest.fixture
+def lock_key():
+    """Generate unique lock key for each test."""
+    return f"test_lock:{uuid.uuid4()}"
+
+
+@pytest.fixture
+def owner_id():
+    """Generate unique owner ID for each test."""
+    return str(uuid.uuid4())
+
+
+class TestClusterLockBasic:
+    """Basic lock acquisition and release functionality."""
+
+    def test_lock_acquisition_success(self, redis_client, lock_key, owner_id):
+        """Test basic lock acquisition succeeds."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        # Lock should be acquired successfully
+        result = lock.try_acquire()
+        assert result == owner_id  # Returns our owner_id when successfully acquired
+        assert lock._last_refresh > 0
+
+        # Lock key should exist in Redis
+        assert redis_client.exists(lock_key) == 1
+        assert redis_client.get(lock_key).decode("utf-8") == owner_id
+
+    def test_lock_acquisition_contention(self, redis_client, lock_key):
+        """Test second acquisition fails when lock is held."""
+        owner1 = str(uuid.uuid4())
+        owner2 = str(uuid.uuid4())
+
+        lock1 = ClusterLock(redis_client, lock_key, owner1, timeout=60)
+        lock2 = ClusterLock(redis_client, lock_key, owner2, timeout=60)
+
+        # First lock should succeed
+        result1 = lock1.try_acquire()
+        assert result1 == owner1  # Successfully acquired, returns our owner_id
+
+        # Second lock should fail and return the first owner
+        result2 = lock2.try_acquire()
+        assert result2 == owner1  # Returns the current owner (first owner)
+        assert lock2._last_refresh == 0
+
+    def test_lock_release_deletes_redis_key(self, redis_client, lock_key, owner_id):
+        """Test lock release deletes Redis key and marks locally as released."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        lock.try_acquire()
+        assert lock._last_refresh > 0
+        assert redis_client.exists(lock_key) == 1
+
+        # Release should delete Redis key and mark locally as released
+        lock.release()
+        assert lock._last_refresh == 0
+        assert lock._last_refresh == 0.0
+
+        # Redis key should be deleted for immediate release
+        assert redis_client.exists(lock_key) == 0
+
+        # Another lock should be able to acquire immediately
+        new_owner_id = str(uuid.uuid4())
+        new_lock = ClusterLock(redis_client, lock_key, new_owner_id, timeout=60)
+        assert new_lock.try_acquire() == new_owner_id
+
+
+class TestClusterLockRefresh:
+    """Lock refresh and TTL management."""
+
+    def test_lock_refresh_success(self, redis_client, lock_key, owner_id):
+        """Test lock refresh extends TTL."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        lock.try_acquire()
+        original_ttl = redis_client.ttl(lock_key)
+
+        # Wait a bit then refresh
+        time.sleep(1)
+        lock._last_refresh = 0  # Force refresh past rate limit
+        assert lock.refresh() is True
+
+        # TTL should be reset to full timeout (allow for small timing differences)
+        new_ttl = redis_client.ttl(lock_key)
+        assert new_ttl >= original_ttl or new_ttl >= 58  # Allow for timing variance
+
+    def test_lock_refresh_rate_limiting(self, redis_client, lock_key, owner_id):
+        """Test refresh is rate-limited to timeout/10."""
+        lock = ClusterLock(
+            redis_client, lock_key, owner_id, timeout=100
+        )  # 100s timeout
+
+        lock.try_acquire()
+
+        # First refresh should work
+        assert lock.refresh() is True
+        first_refresh_time = lock._last_refresh
+
+        # Immediate second refresh should be skipped (rate limited) but verify key exists
+        assert lock.refresh() is True  # Returns True but skips actual refresh
+        assert lock._last_refresh == first_refresh_time  # Time unchanged
+
+    def test_lock_refresh_verifies_existence_during_rate_limit(
+        self, redis_client, lock_key, owner_id
+    ):
+        """Test refresh verifies lock existence even during rate limiting."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=100)
+
+        lock.try_acquire()
+
+        # Manually delete the key (simulates expiry or external deletion)
+        redis_client.delete(lock_key)
+
+        # Refresh should detect missing key even during rate limit period
+        assert lock.refresh() is False
+        assert lock._last_refresh == 0
+
+    def test_lock_refresh_ownership_lost(self, redis_client, lock_key, owner_id):
+        """Test refresh fails when ownership is lost."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        lock.try_acquire()
+
+        # Simulate another process taking the lock
+        different_owner = str(uuid.uuid4())
+        redis_client.set(lock_key, different_owner, ex=60)
+
+        # Force refresh past rate limit and verify it fails
+        lock._last_refresh = 0  # Force refresh past rate limit
+        assert lock.refresh() is False
+        assert lock._last_refresh == 0
+
+    def test_lock_refresh_when_not_acquired(self, redis_client, lock_key, owner_id):
+        """Test refresh fails when lock was never acquired."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        # Refresh without acquiring should fail
+        assert lock.refresh() is False
+
+
+class TestClusterLockExpiry:
+    """Lock expiry and timeout behavior."""
+
+    def test_lock_natural_expiry(self, redis_client, lock_key, owner_id):
+        """Test lock expires naturally via Redis TTL."""
+        lock = ClusterLock(
+            redis_client, lock_key, owner_id, timeout=2
+        )  # 2 second timeout
+
+        lock.try_acquire()
+        assert redis_client.exists(lock_key) == 1
+
+        # Wait for expiry
+        time.sleep(3)
+        assert redis_client.exists(lock_key) == 0
+
+        # New lock with same key should succeed
+        new_lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+        assert new_lock.try_acquire() == owner_id
+
+    def test_lock_refresh_prevents_expiry(self, redis_client, lock_key, owner_id):
+        """Test refreshing prevents lock from expiring."""
+        lock = ClusterLock(
+            redis_client, lock_key, owner_id, timeout=3
+        )  # 3 second timeout
+
+        lock.try_acquire()
+
+        # Wait and refresh before expiry
+        time.sleep(1)
+        lock._last_refresh = 0  # Force refresh past rate limit
+        assert lock.refresh() is True
+
+        # Wait beyond original timeout
+        time.sleep(2.5)
+        assert redis_client.exists(lock_key) == 1  # Should still exist
+
+
+class TestClusterLockConcurrency:
+    """Concurrent access patterns."""
+
+    def test_multiple_threads_contention(self, redis_client, lock_key):
+        """Test multiple threads competing for same lock."""
+        num_threads = 5
+        successful_acquisitions = []
+
+        def try_acquire_lock(thread_id):
+            owner_id = f"thread_{thread_id}"
+            lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+            if lock.try_acquire() == owner_id:
+                successful_acquisitions.append(thread_id)
+                time.sleep(0.1)  # Hold lock briefly
+                lock.release()
+
+        threads = []
+        for i in range(num_threads):
+            thread = Thread(target=try_acquire_lock, args=(i,))
+            threads.append(thread)
+            thread.start()
+
+        for thread in threads:
+            thread.join()
+
+        # Only one thread should have acquired the lock
+        assert len(successful_acquisitions) == 1
+
+    def test_sequential_lock_reuse(self, redis_client, lock_key):
+        """Test lock can be reused after natural expiry."""
+        owners = [str(uuid.uuid4()) for _ in range(3)]
+
+        for i, owner_id in enumerate(owners):
+            lock = ClusterLock(redis_client, lock_key, owner_id, timeout=1)  # 1 second
+
+            assert lock.try_acquire() == owner_id
+            time.sleep(1.5)  # Wait for expiry
+
+            # Verify lock expired
+            assert redis_client.exists(lock_key) == 0
+
+    def test_refresh_during_concurrent_access(self, redis_client, lock_key):
+        """Test lock refresh works correctly during concurrent access attempts."""
+        owner1 = str(uuid.uuid4())
+        owner2 = str(uuid.uuid4())
+
+        lock1 = ClusterLock(redis_client, lock_key, owner1, timeout=5)
+        lock2 = ClusterLock(redis_client, lock_key, owner2, timeout=5)
+
+        # Thread 1 holds lock and refreshes
+        assert lock1.try_acquire() == owner1
+
+        def refresh_continuously():
+            for _ in range(10):
+                lock1._last_refresh = 0  # Force refresh
+                lock1.refresh()
+                time.sleep(0.1)
+
+        def try_acquire_continuously():
+            attempts = 0
+            while attempts < 20:
+                if lock2.try_acquire() == owner2:
+                    return True
+                time.sleep(0.1)
+                attempts += 1
+            return False
+
+        refresh_thread = Thread(target=refresh_continuously)
+        acquire_thread = Thread(target=try_acquire_continuously)
+
+        refresh_thread.start()
+        acquire_thread.start()
+
+        refresh_thread.join()
+        acquire_thread.join()
+
+        # Lock1 should still own the lock due to refreshes
+        assert lock1._last_refresh > 0
+        assert lock2._last_refresh == 0
+
+
+class TestClusterLockErrorHandling:
+    """Error handling and edge cases."""
+
+    def test_redis_connection_failure_on_acquire(self, lock_key, owner_id):
+        """Test graceful handling when Redis is unavailable during acquisition."""
+        # Use invalid Redis connection
+        bad_redis = redis.Redis(
+            host="invalid_host", port=1234, socket_connect_timeout=1
+        )
+        lock = ClusterLock(bad_redis, lock_key, owner_id, timeout=60)
+
+        # Should return None for Redis connection failures
+        result = lock.try_acquire()
+        assert result is None  # Returns None when Redis fails
+        assert lock._last_refresh == 0
+
+    def test_redis_connection_failure_on_refresh(
+        self, redis_client, lock_key, owner_id
+    ):
+        """Test graceful handling when Redis fails during refresh."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        # Acquire normally
+        assert lock.try_acquire() == owner_id
+
+        # Replace Redis client with failing one
+        lock.redis = redis.Redis(
+            host="invalid_host", port=1234, socket_connect_timeout=1
+        )
+
+        # Refresh should fail gracefully
+        lock._last_refresh = 0  # Force refresh
+        assert lock.refresh() is False
+        assert lock._last_refresh == 0
+
+    def test_invalid_lock_parameters(self, redis_client):
+        """Test validation of lock parameters."""
+        owner_id = str(uuid.uuid4())
+
+        # All parameters are now simple - no validation needed
+        # Just test basic construction works
+        lock = ClusterLock(redis_client, "test_key", owner_id, timeout=60)
+        assert lock.key == "test_key"
+        assert lock.owner_id == owner_id
+        assert lock.timeout == 60
+
+    def test_refresh_after_redis_key_deleted(self, redis_client, lock_key, owner_id):
+        """Test refresh behavior when Redis key is manually deleted."""
+        lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+
+        lock.try_acquire()
+
+        # Manually delete the key (simulates external deletion)
+        redis_client.delete(lock_key)
+
+        # Refresh should fail and mark as not acquired
+        lock._last_refresh = 0  # Force refresh
+        assert lock.refresh() is False
+        assert lock._last_refresh == 0
+
+
+class TestClusterLockDynamicRefreshInterval:
+    """Dynamic refresh interval based on timeout."""
+
+    def test_refresh_interval_calculation(self, redis_client, lock_key, owner_id):
+        """Test refresh interval is calculated as max(timeout/10, 1)."""
+        test_cases = [
+            (5, 1),  # 5/10 = 0, but minimum is 1
+            (10, 1),  # 10/10 = 1
+            (30, 3),  # 30/10 = 3
+            (100, 10),  # 100/10 = 10
+            (200, 20),  # 200/10 = 20
+            (1000, 100),  # 1000/10 = 100
+        ]
+
+        for timeout, expected_interval in test_cases:
+            lock = ClusterLock(
+                redis_client, f"{lock_key}_{timeout}", owner_id, timeout=timeout
+            )
+            lock.try_acquire()
+
+            # Calculate expected interval using same logic as implementation
+            refresh_interval = max(timeout // 10, 1)
+            assert refresh_interval == expected_interval
+
+            # Test rate limiting works with calculated interval
+            assert lock.refresh() is True
+            first_refresh_time = lock._last_refresh
+
+            # Sleep less than interval - should be rate limited
+            time.sleep(0.1)
+            assert lock.refresh() is True
+            assert lock._last_refresh == first_refresh_time  # No actual refresh
+
+
+class TestClusterLockRealWorldScenarios:
+    """Real-world usage patterns."""
+
+    def test_execution_coordination_simulation(self, redis_client):
+        """Simulate graph execution coordination across multiple pods."""
+        graph_exec_id = str(uuid.uuid4())
+        lock_key = f"execution:{graph_exec_id}"
+
+        # Simulate 3 pods trying to execute same graph
+        pods = [f"pod_{i}" for i in range(3)]
+        execution_results = {}
+
+        def execute_graph(pod_id):
+            """Simulate graph execution with cluster lock."""
+            lock = ClusterLock(redis_client, lock_key, pod_id, timeout=300)
+
+            if lock.try_acquire() == pod_id:
+                # Simulate execution work
+                execution_results[pod_id] = "executed"
+                time.sleep(0.1)
+                lock.release()
+            else:
+                execution_results[pod_id] = "rejected"
+
+        threads = []
+        for pod_id in pods:
+            thread = Thread(target=execute_graph, args=(pod_id,))
+            threads.append(thread)
+            thread.start()
+
+        for thread in threads:
+            thread.join()
+
+        # Only one pod should have executed
+        executed_count = sum(
+            1 for result in execution_results.values() if result == "executed"
+        )
+        rejected_count = sum(
+            1 for result in execution_results.values() if result == "rejected"
+        )
+
+        assert executed_count == 1
+        assert rejected_count == 2
+
+    def test_long_running_execution_with_refresh(
+        self, redis_client, lock_key, owner_id
+    ):
+        """Test lock maintains ownership during long execution with periodic refresh."""
+        lock = ClusterLock(
+            redis_client, lock_key, owner_id, timeout=30
+        )  # 30 second timeout, refresh interval = max(30//10, 1) = 3 seconds
+
+        def long_execution_with_refresh():
+            """Simulate long-running execution with periodic refresh."""
+            assert lock.try_acquire() == owner_id
+
+            # Simulate 10 seconds of work with refreshes every 2 seconds
+            # This respects rate limiting - actual refreshes will happen at 0s, 3s, 6s, 9s
+            try:
+                for i in range(5):  # 5 iterations * 2 seconds = 10 seconds total
+                    time.sleep(2)
+                    refresh_success = lock.refresh()
+                    assert refresh_success is True, f"Refresh failed at iteration {i}"
+                return "completed"
+            finally:
+                lock.release()
+
+        # Should complete successfully without losing lock
+        result = long_execution_with_refresh()
+        assert result == "completed"
+
+    def test_graceful_degradation_pattern(self, redis_client, lock_key):
+        """Test graceful degradation when Redis becomes unavailable."""
+        owner_id = str(uuid.uuid4())
+        lock = ClusterLock(
+            redis_client, lock_key, owner_id, timeout=3
+        )  # Use shorter timeout
+
+        # Normal operation
+        assert lock.try_acquire() == owner_id
+        lock._last_refresh = 0  # Force refresh past rate limit
+        assert lock.refresh() is True
+
+        # Simulate Redis becoming unavailable
+        original_redis = lock.redis
+        lock.redis = redis.Redis(
+            host="invalid_host",
+            port=1234,
+            socket_connect_timeout=1,
+            decode_responses=False,
+        )
+
+        # Should degrade gracefully
+        lock._last_refresh = 0  # Force refresh past rate limit
+        assert lock.refresh() is False
+        assert lock._last_refresh == 0
+
+        # Restore Redis and verify can acquire again
+        lock.redis = original_redis
+        # Wait for original lock to expire (use longer wait for 3s timeout)
+        time.sleep(4)
+
+        new_lock = ClusterLock(redis_client, lock_key, owner_id, timeout=60)
+        assert new_lock.try_acquire() == owner_id
+
+
+if __name__ == "__main__":
+    # Run specific test for quick validation
+    pytest.main([__file__, "-v"])

autogpt_platform/backend/backend/executor/database.py

@@ -85,6 +85,16 @@ class DatabaseManager(AppService):
     async def health_check(self) -> str:
         if not db.is_connected():
             raise UnhealthyServiceError("Database is not connected")
+
+        try:
+            # Test actual database connectivity by executing a simple query
+            # This will fail if Prisma query engine is not responding
+            result = await db.query_raw_with_schema("SELECT 1 as health_check")
+            if not result or result[0].get("health_check") != 1:
+                raise UnhealthyServiceError("Database query test failed")
+        except Exception as e:
+            raise UnhealthyServiceError(f"Database health check failed: {e}")
+
         return await super().health_check()
 
     @classmethod

autogpt_platform/backend/backend/executor/manager.py

@@ -3,6 +3,7 @@ import logging
 import os
 import threading
 import time
+import uuid
 from collections import defaultdict
 from concurrent.futures import Future, ThreadPoolExecutor
 from contextlib import asynccontextmanager
@@ -10,10 +11,32 @@ from typing import TYPE_CHECKING, Any, Optional, TypeVar, cast
 
 from pika.adapters.blocking_connection import BlockingChannel
 from pika.spec import Basic, BasicProperties
-from pydantic import JsonValue
-from redis.asyncio.lock import Lock as RedisLock
+from prometheus_client import Gauge, start_http_server
+from redis.asyncio.lock import Lock as AsyncRedisLock
 
+from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentOutputBlock
+from backend.data import redis_client as redis
+from backend.data.block import (
+    BlockInput,
+    BlockOutput,
+    BlockOutputEntry,
+    BlockSchema,
+    get_block,
+)
+from backend.data.credit import UsageTransactionMetadata
+from backend.data.dynamic_fields import parse_execution_output
+from backend.data.execution import (
+    ExecutionQueue,
+    ExecutionStatus,
+    GraphExecution,
+    GraphExecutionEntry,
+    NodeExecutionEntry,
+    NodeExecutionResult,
+    NodesInputMasks,
+    UserContext,
+)
+from backend.data.graph import Link, Node
 from backend.data.model import GraphExecutionStats, NodeExecutionStats
 from backend.data.notifications import (
     AgentRunData,
@@ -26,49 +49,21 @@ from backend.data.rabbitmq import SyncRabbitMQ
 from backend.executor.activity_status_generator import (
     generate_activity_status_for_execution,
 )
-from backend.executor.utils import LogMetadata
-from backend.notifications.notifications import queue_notification
-from backend.util.exceptions import InsufficientBalanceError, ModerationError
-
-if TYPE_CHECKING:
-    from backend.executor import DatabaseManagerClient, DatabaseManagerAsyncClient
-
-from prometheus_client import Gauge, start_http_server
-
-from backend.blocks.agent import AgentExecutorBlock
-from backend.data import redis_client as redis
-from backend.data.block import (
-    BlockData,
-    BlockInput,
-    BlockOutput,
-    BlockSchema,
-    get_block,
-)
-from backend.data.credit import UsageTransactionMetadata
-from backend.data.execution import (
-    ExecutionQueue,
-    ExecutionStatus,
-    GraphExecution,
-    GraphExecutionEntry,
-    NodeExecutionEntry,
-    NodeExecutionResult,
-    UserContext,
-)
-from backend.data.graph import Link, Node
 from backend.executor.utils import (
     GRACEFUL_SHUTDOWN_TIMEOUT_SECONDS,
     GRAPH_EXECUTION_CANCEL_QUEUE_NAME,
     GRAPH_EXECUTION_QUEUE_NAME,
     CancelExecutionEvent,
     ExecutionOutputEntry,
+    LogMetadata,
     NodeExecutionProgress,
     block_usage_cost,
     create_execution_queue_config,
     execution_usage_cost,
-    parse_execution_output,
     validate_exec,
 )
 from backend.integrations.creds_manager import IntegrationCredentialsManager
+from backend.notifications.notifications import queue_notification
 from backend.server.v2.AutoMod.manager import automod_manager
 from backend.util import json
 from backend.util.clients import (
@@ -84,6 +79,7 @@ from backend.util.decorator import (
     error_logged,
     time_measured,
 )
+from backend.util.exceptions import InsufficientBalanceError, ModerationError
 from backend.util.file import clean_exec_files
 from backend.util.logging import TruncatedLogger, configure_logging
 from backend.util.metrics import DiscordChannel
@@ -91,6 +87,12 @@ from backend.util.process import AppProcess, set_service_name
 from backend.util.retry import continuous_retry, func_retry
 from backend.util.settings import Settings
 
+from .cluster_lock import ClusterLock
+
+if TYPE_CHECKING:
+    from backend.executor import DatabaseManagerAsyncClient, DatabaseManagerClient
+
+
 _logger = logging.getLogger(__name__)
 logger = TruncatedLogger(_logger, prefix="[GraphExecutor]")
 settings = Settings()
@@ -106,6 +108,7 @@ utilization_gauge = Gauge(
     "Ratio of active graph runs to max graph workers",
 )
 
+
 # Thread-local storage for ExecutionProcessor instances
 _tls = threading.local()
 
@@ -117,10 +120,14 @@ def init_worker():
 
 
 def execute_graph(
-    graph_exec_entry: "GraphExecutionEntry", cancel_event: threading.Event
+    graph_exec_entry: "GraphExecutionEntry",
+    cancel_event: threading.Event,
+    cluster_lock: ClusterLock,
 ):
     """Execute graph using thread-local ExecutionProcessor instance"""
-    return _tls.processor.on_graph_execution(graph_exec_entry, cancel_event)
+    return _tls.processor.on_graph_execution(
+        graph_exec_entry, cancel_event, cluster_lock
+    )
 
 
 T = TypeVar("T")
@@ -131,7 +138,7 @@ async def execute_node(
     creds_manager: IntegrationCredentialsManager,
     data: NodeExecutionEntry,
     execution_stats: NodeExecutionStats | None = None,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> BlockOutput:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -237,12 +244,12 @@ async def execute_node(
 async def _enqueue_next_nodes(
     db_client: "DatabaseManagerAsyncClient",
     node: Node,
-    output: BlockData,
+    output: BlockOutputEntry,
     user_id: str,
     graph_exec_id: str,
     graph_id: str,
     log_metadata: LogMetadata,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]],
+    nodes_input_masks: Optional[NodesInputMasks],
     user_context: UserContext,
 ) -> list[NodeExecutionEntry]:
     async def add_enqueued_execution(
@@ -419,7 +426,7 @@ class ExecutionProcessor:
         self,
         node_exec: NodeExecutionEntry,
         node_exec_progress: NodeExecutionProgress,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]],
+        nodes_input_masks: Optional[NodesInputMasks],
         graph_stats_pair: tuple[GraphExecutionStats, threading.Lock],
     ) -> NodeExecutionStats:
         log_metadata = LogMetadata(
@@ -429,7 +436,7 @@ class ExecutionProcessor:
             graph_id=node_exec.graph_id,
             node_eid=node_exec.node_exec_id,
             node_id=node_exec.node_id,
-            block_name="-",
+            block_name=b.name if (b := get_block(node_exec.block_id)) else "-",
         )
         db_client = get_db_async_client()
         node = await db_client.get_node(node_exec.node_id)
@@ -487,7 +494,7 @@ class ExecutionProcessor:
         stats: NodeExecutionStats,
         db_client: "DatabaseManagerAsyncClient",
         log_metadata: LogMetadata,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+        nodes_input_masks: Optional[NodesInputMasks] = None,
     ) -> ExecutionStatus:
         status = ExecutionStatus.RUNNING
 
@@ -583,6 +590,7 @@ class ExecutionProcessor:
         self,
         graph_exec: GraphExecutionEntry,
         cancel: threading.Event,
+        cluster_lock: ClusterLock,
     ):
         log_metadata = LogMetadata(
             logger=_logger,
@@ -605,7 +613,7 @@ class ExecutionProcessor:
             )
             return
 
-        if exec_meta.status == ExecutionStatus.QUEUED:
+        if exec_meta.status in [ExecutionStatus.QUEUED, ExecutionStatus.INCOMPLETE]:
             log_metadata.info(f"⚙️ Starting graph execution #{graph_exec.graph_exec_id}")
             exec_meta.status = ExecutionStatus.RUNNING
             send_execution_update(
@@ -641,6 +649,7 @@ class ExecutionProcessor:
             cancel=cancel,
             log_metadata=log_metadata,
             execution_stats=exec_stats,
+            cluster_lock=cluster_lock,
         )
         exec_stats.walltime += timing_info.wall_time
         exec_stats.cputime += timing_info.cpu_time
@@ -742,6 +751,7 @@ class ExecutionProcessor:
         cancel: threading.Event,
         log_metadata: LogMetadata,
         execution_stats: GraphExecutionStats,
+        cluster_lock: ClusterLock,
     ) -> ExecutionStatus:
         """
         Returns:
@@ -927,7 +937,7 @@ class ExecutionProcessor:
                         and execution_queue.empty()
                         and (running_node_execution or running_node_evaluation)
                     ):
-                        # There is nothing to execute, and no output to process, let's relax for a while.
+                        cluster_lock.refresh()
                         time.sleep(0.1)
 
             # loop done --------------------------------------------------
@@ -1053,7 +1063,7 @@ class ExecutionProcessor:
         node_id: str,
         graph_exec: GraphExecutionEntry,
         log_metadata: LogMetadata,
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]],
+        nodes_input_masks: Optional[NodesInputMasks],
         execution_queue: ExecutionQueue[NodeExecutionEntry],
     ) -> None:
         """Process a node's output, update its status, and enqueue next nodes.
@@ -1219,6 +1229,7 @@ class ExecutionManager(AppProcess):
         super().__init__()
         self.pool_size = settings.config.num_graph_workers
         self.active_graph_runs: dict[str, tuple[Future, threading.Event]] = {}
+        self.executor_id = str(uuid.uuid4())
 
         self._executor = None
         self._stop_consuming = None
@@ -1228,6 +1239,8 @@ class ExecutionManager(AppProcess):
         self._run_thread = None
         self._run_client = None
 
+        self._execution_locks = {}
+
     @property
     def cancel_thread(self) -> threading.Thread:
         if self._cancel_thread is None:
@@ -1435,17 +1448,46 @@ class ExecutionManager(AppProcess):
         logger.info(
             f"[{self.service_name}] Received RUN for graph_exec_id={graph_exec_id}"
         )
+
+        # Check for local duplicate execution first
         if graph_exec_id in self.active_graph_runs:
-            # TODO: Make this check cluster-wide, prevent duplicate runs across executor pods.
-            logger.error(
-                f"[{self.service_name}] Graph {graph_exec_id} already running; rejecting duplicate run."
+            logger.warning(
+                f"[{self.service_name}] Graph {graph_exec_id} already running locally; rejecting duplicate."
             )
-            _ack_message(reject=True, requeue=False)
+            _ack_message(reject=True, requeue=True)
             return
 
+        # Try to acquire cluster-wide execution lock
+        cluster_lock = ClusterLock(
+            redis=redis.get_redis(),
+            key=f"exec_lock:{graph_exec_id}",
+            owner_id=self.executor_id,
+            timeout=settings.config.cluster_lock_timeout,
+        )
+        current_owner = cluster_lock.try_acquire()
+        if current_owner != self.executor_id:
+            # Either someone else has it or Redis is unavailable
+            if current_owner is not None:
+                logger.warning(
+                    f"[{self.service_name}] Graph {graph_exec_id} already running on pod {current_owner}"
+                )
+            else:
+                logger.warning(
+                    f"[{self.service_name}] Could not acquire lock for {graph_exec_id} - Redis unavailable"
+                )
+            _ack_message(reject=True, requeue=True)
+            return
+        self._execution_locks[graph_exec_id] = cluster_lock
+
+        logger.info(
+            f"[{self.service_name}] Acquired cluster lock for {graph_exec_id} with executor {self.executor_id}"
+        )
+
         cancel_event = threading.Event()
 
-        future = self.executor.submit(execute_graph, graph_exec_entry, cancel_event)
+        future = self.executor.submit(
+            execute_graph, graph_exec_entry, cancel_event, cluster_lock
+        )
         self.active_graph_runs[graph_exec_id] = (future, cancel_event)
         self._update_prompt_metrics()
 
@@ -1464,6 +1506,10 @@ class ExecutionManager(AppProcess):
                     f"[{self.service_name}] Error in run completion callback: {e}"
                 )
             finally:
+                # Release the cluster-wide execution lock
+                if graph_exec_id in self._execution_locks:
+                    self._execution_locks[graph_exec_id].release()
+                    del self._execution_locks[graph_exec_id]
                 self._cleanup_completed_runs()
 
         future.add_done_callback(_on_run_done)
@@ -1546,6 +1592,10 @@ class ExecutionManager(AppProcess):
                         f"{prefix} ⏳ Still waiting for {len(self.active_graph_runs)} executions: {ids}"
                     )
 
+                    for graph_exec_id in self.active_graph_runs:
+                        if lock := self._execution_locks.get(graph_exec_id):
+                            lock.refresh()
+
                 time.sleep(wait_interval)
                 waited += wait_interval
 
@@ -1563,6 +1613,15 @@ class ExecutionManager(AppProcess):
         except Exception as e:
             logger.error(f"{prefix} ⚠️ Error during executor shutdown: {type(e)} {e}")
 
+        # Release remaining execution locks
+        try:
+            for lock in self._execution_locks.values():
+                lock.release()
+            self._execution_locks.clear()
+            logger.info(f"{prefix} ✅ Released execution locks")
+        except Exception as e:
+            logger.warning(f"{prefix} ⚠️ Failed to release all locks: {e}")
+
         # Disconnect the run execution consumer
         self._stop_message_consumers(
             self.run_thread,
@@ -1668,15 +1727,18 @@ def update_graph_execution_state(
 
 
 @asynccontextmanager
-async def synchronized(key: str, timeout: int = 60):
+async def synchronized(key: str, timeout: int = settings.config.cluster_lock_timeout):
     r = await redis.get_redis_async()
-    lock: RedisLock = r.lock(f"lock:{key}", timeout=timeout)
+    lock: AsyncRedisLock = r.lock(f"lock:{key}", timeout=timeout)
     try:
         await lock.acquire()
         yield
     finally:
         if await lock.locked() and await lock.owned():
-            await lock.release()
+            try:
+                await lock.release()
+            except Exception as e:
+                logger.warning(f"Failed to release lock for key {key}: {e}")
 
 
 def increment_execution_count(user_id: str) -> int:

autogpt_platform/backend/backend/executor/manager_test.py

@@ -35,21 +35,20 @@ async def execute_graph(
     logger.info(f"Input data: {input_data}")
 
     # --- Test adding new executions --- #
-    response = await agent_server.test_execute_graph(
+    graph_exec = 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,
     )
-    graph_exec_id = response.graph_exec_id
-    logger.info(f"Created execution with ID: {graph_exec_id}")
+    logger.info(f"Created execution with ID: {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(f"Execution completed with {len(result)} results")
     assert len(result) == num_execs
-    return graph_exec_id
+    return graph_exec.id
 
 
 async def assert_sample_graph_executions(
@@ -379,7 +378,7 @@ async def test_execute_preset(server: SpinTestServer):
 
     # Verify execution
     assert result is not None
-    graph_exec_id = result["id"]
+    graph_exec_id = result.id
 
     # Wait for execution to complete
     executions = await wait_execution(test_user.id, graph_exec_id)
@@ -468,7 +467,7 @@ async def test_execute_preset_with_clash(server: SpinTestServer):
 
     # Verify execution
     assert result is not None, "Result must not be None"
-    graph_exec_id = result["id"]
+    graph_exec_id = result.id
 
     # Wait for execution to complete
     executions = await wait_execution(test_user.id, graph_exec_id)

autogpt_platform/backend/backend/executor/scheduler.py

@@ -191,15 +191,22 @@ class GraphExecutionJobInfo(GraphExecutionJobArgs):
     id: str
     name: str
     next_run_time: str
+    timezone: str = Field(default="UTC", description="Timezone used for scheduling")
 
     @staticmethod
     def from_db(
         job_args: GraphExecutionJobArgs, job_obj: JobObj
     ) -> "GraphExecutionJobInfo":
+        # Extract timezone from the trigger if it's a CronTrigger
+        timezone_str = "UTC"
+        if hasattr(job_obj.trigger, "timezone"):
+            timezone_str = str(job_obj.trigger.timezone)
+
         return GraphExecutionJobInfo(
             id=job_obj.id,
             name=job_obj.name,
             next_run_time=job_obj.next_run_time.isoformat(),
+            timezone=timezone_str,
             **job_args.model_dump(),
         )
 
@@ -395,6 +402,7 @@ class Scheduler(AppService):
         input_data: BlockInput,
         input_credentials: dict[str, CredentialsMetaInput],
         name: Optional[str] = None,
+        user_timezone: str | None = None,
     ) -> GraphExecutionJobInfo:
         # Validate the graph before scheduling to prevent runtime failures
         # We don't need the return value, just want the validation to run
@@ -408,7 +416,18 @@ class Scheduler(AppService):
             )
         )
 
-        logger.info(f"Scheduling job for user {user_id} in UTC (cron: {cron})")
+        # Use provided timezone or default to UTC
+        # Note: Timezone should be passed from the client to avoid database lookups
+        if not user_timezone:
+            user_timezone = "UTC"
+            logger.warning(
+                f"No timezone provided for user {user_id}, using UTC for scheduling. "
+                f"Client should pass user's timezone for correct scheduling."
+            )
+
+        logger.info(
+            f"Scheduling job for user {user_id} with timezone {user_timezone} (cron: {cron})"
+        )
 
         job_args = GraphExecutionJobArgs(
             user_id=user_id,
@@ -422,12 +441,12 @@ class Scheduler(AppService):
             execute_graph,
             kwargs=job_args.model_dump(),
             name=name,
-            trigger=CronTrigger.from_crontab(cron, timezone="UTC"),
+            trigger=CronTrigger.from_crontab(cron, timezone=user_timezone),
             jobstore=Jobstores.EXECUTION.value,
             replace_existing=True,
         )
         logger.info(
-            f"Added job {job.id} with cron schedule '{cron}' in UTC, input data: {input_data}"
+            f"Added job {job.id} with cron schedule '{cron}' in timezone {user_timezone}, input data: {input_data}"
         )
         return GraphExecutionJobInfo.from_db(job_args, job)
 

autogpt_platform/backend/backend/executor/utils.py

@@ -4,20 +4,30 @@ import threading
 import time
 from collections import defaultdict
 from concurrent.futures import Future
-from typing import Any, Optional
+from typing import Mapping, Optional, cast
 
 from pydantic import BaseModel, JsonValue, ValidationError
 
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
-from backend.data.block import Block, BlockData, BlockInput, BlockType, get_block
+from backend.data.block import (
+    Block,
+    BlockCostType,
+    BlockInput,
+    BlockOutputEntry,
+    BlockType,
+    get_block,
+)
 from backend.data.block_cost_config import BLOCK_COSTS
-from backend.data.cost import BlockCostType
 from backend.data.db import prisma
+
+# Import dynamic field utilities from centralized location
+from backend.data.dynamic_fields import merge_execution_input
 from backend.data.execution import (
     ExecutionStatus,
     GraphExecutionStats,
     GraphExecutionWithNodes,
+    NodesInputMasks,
     UserContext,
 )
 from backend.data.graph import GraphModel, Node
@@ -32,7 +42,6 @@ from backend.util.clients import (
 )
 from backend.util.exceptions import GraphValidationError, NotFoundError
 from backend.util.logging import TruncatedLogger
-from backend.util.mock import MockObject
 from backend.util.settings import Config
 from backend.util.type import convert
 
@@ -179,195 +188,7 @@ def _is_cost_filter_match(cost_filter: BlockInput, input_data: BlockInput) -> bo
 
 # ============ Execution Input Helpers ============ #
 
-# --------------------------------------------------------------------------- #
-#  Delimiters
-# --------------------------------------------------------------------------- #
-
-LIST_SPLIT = "_$_"
-DICT_SPLIT = "_#_"
-OBJC_SPLIT = "_@_"
-
-_DELIMS = (LIST_SPLIT, DICT_SPLIT, OBJC_SPLIT)
-
-# --------------------------------------------------------------------------- #
-#  Tokenisation 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 _DELIMS:
-        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 _DELIMS 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
-
-
-# --------------------------------------------------------------------------- #
-#  Public API – parsing (flattened ➜ concrete)
-# --------------------------------------------------------------------------- #
-
-
-def parse_execution_output(output: BlockData, name: str) -> Any | None:
-    """
-    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**.
-    """
-    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 or not isinstance(container, MockObject):
-            container = MockObject()
-        setattr(
-            container,
-            ident,
-            _assign(getattr(container, ident, None), rest, value),
-        )
-        return container
-
-    return value  # unreachable
-
-
-def merge_execution_input(data: BlockInput) -> BlockInput:
-    """
-    Reconstruct nested objects from a *flattened* dict of key → value.
-
-    Raises ValueError on syntactically invalid list indices.
-    """
-    merged: BlockInput = {}
-
-    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
+# Dynamic field utilities are now imported from backend.data.dynamic_fields
 
 
 def validate_exec(
@@ -428,7 +249,7 @@ def validate_exec(
 async def _validate_node_input_credentials(
     graph: GraphModel,
     user_id: str,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> dict[str, dict[str, str]]:
     """
     Checks all credentials for all nodes of the graph and returns structured errors.
@@ -508,8 +329,8 @@ async def _validate_node_input_credentials(
 
 def make_node_credentials_input_map(
     graph: GraphModel,
-    graph_credentials_input: dict[str, CredentialsMetaInput],
-) -> dict[str, dict[str, JsonValue]]:
+    graph_credentials_input: Mapping[str, CredentialsMetaInput],
+) -> NodesInputMasks:
     """
     Maps credentials for an execution to the correct nodes.
 
@@ -544,8 +365,8 @@ def make_node_credentials_input_map(
 async def validate_graph_with_credentials(
     graph: GraphModel,
     user_id: str,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
-) -> dict[str, dict[str, str]]:
+    nodes_input_masks: Optional[NodesInputMasks] = None,
+) -> Mapping[str, Mapping[str, str]]:
     """
     Validate graph including credentials and return structured errors per node.
 
@@ -575,7 +396,7 @@ async def _construct_starting_node_execution_input(
     graph: GraphModel,
     user_id: str,
     graph_inputs: BlockInput,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> list[tuple[str, BlockInput]]:
     """
     Validates and prepares the input data for executing a graph.
@@ -616,7 +437,7 @@ async def _construct_starting_node_execution_input(
 
         # Extract request input data, and assign it to the input pin.
         if block.block_type == BlockType.INPUT:
-            input_name = node.input_default.get("name")
+            input_name = cast(str | None, node.input_default.get("name"))
             if input_name and input_name in graph_inputs:
                 input_data = {"value": graph_inputs[input_name]}
 
@@ -643,9 +464,9 @@ async def validate_and_construct_node_execution_input(
     user_id: str,
     graph_inputs: BlockInput,
     graph_version: Optional[int] = None,
-    graph_credentials_inputs: Optional[dict[str, CredentialsMetaInput]] = None,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
-) -> tuple[GraphModel, list[tuple[str, BlockInput]], dict[str, dict[str, JsonValue]]]:
+    graph_credentials_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
+) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -659,7 +480,9 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks: Node inputs to use.
 
     Returns:
-        tuple[GraphModel, list[tuple[str, BlockInput]]]: Graph model and list of tuples for node execution input.
+        GraphModel: Full graph object for the given `graph_id`.
+        list[tuple[node_id, BlockInput]]: Starting node IDs with corresponding inputs.
+        dict[str, BlockInput]: Node input masks including all passed-in credentials.
 
     Raises:
         NotFoundError: If the graph is not found.
@@ -700,11 +523,11 @@ async def validate_and_construct_node_execution_input(
 
 
 def _merge_nodes_input_masks(
-    overrides_map_1: dict[str, dict[str, JsonValue]],
-    overrides_map_2: dict[str, dict[str, JsonValue]],
-) -> dict[str, dict[str, JsonValue]]:
+    overrides_map_1: NodesInputMasks,
+    overrides_map_2: NodesInputMasks,
+) -> NodesInputMasks:
     """Perform a per-node merge of input overrides"""
-    result = overrides_map_1.copy()
+    result = dict(overrides_map_1).copy()
     for node_id, overrides2 in overrides_map_2.items():
         if node_id in result:
             result[node_id] = {**result[node_id], **overrides2}
@@ -854,8 +677,8 @@ async def add_graph_execution(
     inputs: Optional[BlockInput] = None,
     preset_id: Optional[str] = None,
     graph_version: Optional[int] = None,
-    graph_credentials_inputs: Optional[dict[str, CredentialsMetaInput]] = None,
-    nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None,
+    graph_credentials_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
+    nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> GraphExecutionWithNodes:
     """
     Adds a graph execution to the queue and returns the execution entry.
@@ -879,7 +702,7 @@ async def add_graph_execution(
     else:
         edb = get_database_manager_async_client()
 
-    graph, starting_nodes_input, nodes_input_masks = (
+    graph, starting_nodes_input, compiled_nodes_input_masks = (
         await validate_and_construct_node_execution_input(
             graph_id=graph_id,
             user_id=user_id,
@@ -892,37 +715,43 @@ async def add_graph_execution(
     graph_exec = None
 
     try:
+        # Sanity check: running add_graph_execution with the properties of
+        # the graph_exec created here should create the same execution again.
         graph_exec = await edb.create_graph_execution(
             user_id=user_id,
             graph_id=graph_id,
             graph_version=graph.version,
+            inputs=inputs or {},
+            credential_inputs=graph_credentials_inputs,
+            nodes_input_masks=nodes_input_masks,
             starting_nodes_input=starting_nodes_input,
             preset_id=preset_id,
         )
 
-        # Fetch user context for the graph execution
-        user_context = await get_user_context(user_id)
-
-        queue = await get_async_execution_queue()
-        graph_exec_entry = graph_exec.to_graph_execution_entry(user_context)
-        if nodes_input_masks:
-            graph_exec_entry.nodes_input_masks = nodes_input_masks
-
+        graph_exec_entry = graph_exec.to_graph_execution_entry(
+            user_context=await get_user_context(user_id),
+            compiled_nodes_input_masks=compiled_nodes_input_masks,
+        )
         logger.info(
             f"Created graph execution #{graph_exec.id} for graph "
             f"#{graph_id} with {len(starting_nodes_input)} starting nodes. "
             f"Now publishing to execution queue."
         )
 
-        await queue.publish_message(
+        exec_queue = await get_async_execution_queue()
+        await exec_queue.publish_message(
             routing_key=GRAPH_EXECUTION_ROUTING_KEY,
             message=graph_exec_entry.model_dump_json(),
             exchange=GRAPH_EXECUTION_EXCHANGE,
         )
         logger.info(f"Published execution {graph_exec.id} to RabbitMQ queue")
 
-        bus = get_async_execution_event_bus()
-        await bus.publish(graph_exec)
+        graph_exec.status = ExecutionStatus.QUEUED
+        await edb.update_graph_execution_stats(
+            graph_exec_id=graph_exec.id,
+            status=graph_exec.status,
+        )
+        await get_async_execution_event_bus().publish(graph_exec)
 
         return graph_exec
     except BaseException as e:
@@ -952,7 +781,7 @@ async def add_graph_execution(
 class ExecutionOutputEntry(BaseModel):
     node: Node
     node_exec_id: str
-    data: BlockData
+    data: BlockOutputEntry
 
 
 class NodeExecutionProgress:

autogpt_platform/backend/backend/executor/utils_test.py

@@ -1,8 +1,9 @@
 from typing import cast
 
 import pytest
+from pytest_mock import MockerFixture
 
-from backend.executor.utils import merge_execution_input, parse_execution_output
+from backend.data.dynamic_fields import merge_execution_input, parse_execution_output
 from backend.util.mock import MockObject
 
 
@@ -276,3 +277,147 @@ def test_merge_execution_input():
     result = merge_execution_input(data)
     assert "mixed" in result
     assert result["mixed"].attr[0]["key"] == "value3"
+
+
+@pytest.mark.asyncio
+async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
+    """
+    Verify that calling the function with its own output creates the same execution again.
+    """
+    from backend.data.execution import GraphExecutionWithNodes
+    from backend.data.model import CredentialsMetaInput
+    from backend.executor.utils import add_graph_execution
+    from backend.integrations.providers import ProviderName
+
+    # Mock data
+    graph_id = "test-graph-id"
+    user_id = "test-user-id"
+    inputs = {"test_input": "test_value"}
+    preset_id = "test-preset-id"
+    graph_version = 1
+    graph_credentials_inputs = {
+        "cred_key": CredentialsMetaInput(
+            id="cred-id", provider=ProviderName("test_provider"), type="oauth2"
+        )
+    }
+    nodes_input_masks = {"node1": {"input1": "masked_value"}}
+
+    # Mock the graph object returned by validate_and_construct_node_execution_input
+    mock_graph = mocker.MagicMock()
+    mock_graph.version = graph_version
+
+    # Mock the starting nodes input and compiled nodes input masks
+    starting_nodes_input = [
+        ("node1", {"input1": "value1"}),
+        ("node2", {"input1": "value2"}),
+    ]
+    compiled_nodes_input_masks = {"node1": {"input1": "compiled_mask"}}
+
+    # Mock the graph execution object
+    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
+    mock_graph_exec.id = "execution-id-123"
+    mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
+    mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
+
+    # Mock user context
+    mock_user_context = {"user_id": user_id, "context": "test_context"}
+
+    # Mock the queue and event bus
+    mock_queue = mocker.AsyncMock()
+    mock_event_bus = mocker.MagicMock()
+    mock_event_bus.publish = mocker.AsyncMock()
+
+    # Setup mocks
+    mock_validate = mocker.patch(
+        "backend.executor.utils.validate_and_construct_node_execution_input"
+    )
+    mock_edb = mocker.patch("backend.executor.utils.execution_db")
+    mock_prisma = mocker.patch("backend.executor.utils.prisma")
+    mock_get_user_context = mocker.patch("backend.executor.utils.get_user_context")
+    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
+    mock_get_event_bus = mocker.patch(
+        "backend.executor.utils.get_async_execution_event_bus"
+    )
+
+    # Setup mock returns
+    mock_validate.return_value = (
+        mock_graph,
+        starting_nodes_input,
+        compiled_nodes_input_masks,
+    )
+    mock_prisma.is_connected.return_value = True
+    mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
+    mock_edb.update_graph_execution_stats = mocker.AsyncMock(
+        return_value=mock_graph_exec
+    )
+    mock_edb.update_node_execution_status_batch = mocker.AsyncMock()
+    mock_get_user_context.return_value = mock_user_context
+    mock_get_queue.return_value = mock_queue
+    mock_get_event_bus.return_value = mock_event_bus
+
+    # Call the function - first execution
+    result1 = await add_graph_execution(
+        graph_id=graph_id,
+        user_id=user_id,
+        inputs=inputs,
+        preset_id=preset_id,
+        graph_version=graph_version,
+        graph_credentials_inputs=graph_credentials_inputs,
+        nodes_input_masks=nodes_input_masks,
+    )
+
+    # Store the parameters used in the first call to create_graph_execution
+    first_call_kwargs = mock_edb.create_graph_execution.call_args[1]
+
+    # Verify the create_graph_execution was called with correct parameters
+    mock_edb.create_graph_execution.assert_called_once_with(
+        user_id=user_id,
+        graph_id=graph_id,
+        graph_version=mock_graph.version,
+        inputs=inputs,
+        credential_inputs=graph_credentials_inputs,
+        nodes_input_masks=nodes_input_masks,
+        starting_nodes_input=starting_nodes_input,
+        preset_id=preset_id,
+    )
+
+    # Set up the graph execution mock to have properties we can extract
+    mock_graph_exec.graph_id = graph_id
+    mock_graph_exec.user_id = user_id
+    mock_graph_exec.graph_version = graph_version
+    mock_graph_exec.inputs = inputs
+    mock_graph_exec.credential_inputs = graph_credentials_inputs
+    mock_graph_exec.nodes_input_masks = nodes_input_masks
+    mock_graph_exec.preset_id = preset_id
+
+    # Create a second mock execution for the sanity check
+    mock_graph_exec_2 = mocker.MagicMock(spec=GraphExecutionWithNodes)
+    mock_graph_exec_2.id = "execution-id-456"
+    mock_graph_exec_2.to_graph_execution_entry.return_value = mocker.MagicMock()
+
+    # Reset mocks and set up for second call
+    mock_edb.create_graph_execution.reset_mock()
+    mock_edb.create_graph_execution.return_value = mock_graph_exec_2
+    mock_validate.reset_mock()
+
+    # Sanity check: call add_graph_execution with properties from first result
+    # This should create the same execution parameters
+    result2 = await add_graph_execution(
+        graph_id=mock_graph_exec.graph_id,
+        user_id=mock_graph_exec.user_id,
+        inputs=mock_graph_exec.inputs,
+        preset_id=mock_graph_exec.preset_id,
+        graph_version=mock_graph_exec.graph_version,
+        graph_credentials_inputs=mock_graph_exec.credential_inputs,
+        nodes_input_masks=mock_graph_exec.nodes_input_masks,
+    )
+
+    # Verify that create_graph_execution was called with identical parameters
+    second_call_kwargs = mock_edb.create_graph_execution.call_args[1]
+
+    # The sanity check: both calls should use identical parameters
+    assert first_call_kwargs == second_call_kwargs
+
+    # Both executions should succeed (though they create different objects)
+    assert result1 == mock_graph_exec
+    assert result2 == mock_graph_exec_2

autogpt_platform/backend/backend/integrations/creds_manager.py

@@ -151,7 +151,10 @@ class IntegrationCredentialsManager:
                 fresh_credentials = await oauth_handler.refresh_tokens(credentials)
                 await self.store.update_creds(user_id, fresh_credentials)
                 if _lock and (await _lock.locked()) and (await _lock.owned()):
-                    await _lock.release()
+                    try:
+                        await _lock.release()
+                    except Exception as e:
+                        logger.warning(f"Failed to release OAuth refresh lock: {e}")
 
                 credentials = fresh_credentials
         return credentials
@@ -184,7 +187,10 @@ class IntegrationCredentialsManager:
             yield
         finally:
             if (await lock.locked()) and (await lock.owned()):
-                await lock.release()
+                try:
+                    await lock.release()
+                except Exception as e:
+                    logger.warning(f"Failed to release credentials lock: {e}")
 
     async def release_all_locks(self):
         """Call this on process termination to ensure all locks are released"""

autogpt_platform/backend/backend/integrations/webhooks/__init__.py

@@ -1,13 +1,14 @@
-import functools
 from typing import TYPE_CHECKING
 
+from backend.util.cache import cached
+
 if TYPE_CHECKING:
     from ..providers import ProviderName
     from ._base import BaseWebhooksManager
 
 
 # --8<-- [start:load_webhook_managers]
-@functools.cache
+@cached(ttl_seconds=3600)  # Cache webhook managers for 1 hour
 def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]:
     webhook_managers = {}
 

autogpt_platform/backend/backend/integrations/webhooks/graph_lifecycle_hooks.py

@@ -7,10 +7,9 @@ from backend.data.graph import set_node_webhook
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 
 from . import get_webhook_manager, supports_webhooks
-from .utils import setup_webhook_for_block
 
 if TYPE_CHECKING:
-    from backend.data.graph import BaseGraph, GraphModel, Node, NodeModel
+    from backend.data.graph import BaseGraph, GraphModel, NodeModel
     from backend.data.model import Credentials
 
     from ._base import BaseWebhooksManager
@@ -43,32 +42,19 @@ async def _on_graph_activate(graph: "BaseGraph", user_id: str) -> "BaseGraph": .
 
 async def _on_graph_activate(graph: "BaseGraph | GraphModel", user_id: str):
     get_credentials = credentials_manager.cached_getter(user_id)
-    updated_nodes = []
     for new_node in graph.nodes:
         block_input_schema = cast(BlockSchema, new_node.block.input_schema)
 
-        node_credentials = None
-        if (
-            # Webhook-triggered blocks are only allowed to have 1 credentials input
-            (
-                creds_field_name := next(
-                    iter(block_input_schema.get_credentials_fields()), None
+        for creds_field_name in block_input_schema.get_credentials_fields().keys():
+            # Prevent saving graph with non-existent credentials
+            if (
+                creds_meta := new_node.input_default.get(creds_field_name)
+            ) and not await get_credentials(creds_meta["id"]):
+                raise ValueError(
+                    f"Node #{new_node.id} input '{creds_field_name}' updated with "
+                    f"non-existent credentials #{creds_meta['id']}"
                 )
-            )
-            and (creds_meta := new_node.input_default.get(creds_field_name))
-            and not (node_credentials := await get_credentials(creds_meta["id"]))
-        ):
-            raise ValueError(
-                f"Node #{new_node.id} input '{creds_field_name}' updated with "
-                f"non-existent credentials #{creds_meta['id']}"
-            )
 
-        updated_node = await on_node_activate(
-            user_id, graph.id, new_node, credentials=node_credentials
-        )
-        updated_nodes.append(updated_node)
-
-    graph.nodes = updated_nodes
     return graph
 
 
@@ -85,20 +71,14 @@ async def on_graph_deactivate(graph: "GraphModel", user_id: str):
         block_input_schema = cast(BlockSchema, node.block.input_schema)
 
         node_credentials = None
-        if (
-            # Webhook-triggered blocks are only allowed to have 1 credentials input
-            (
-                creds_field_name := next(
-                    iter(block_input_schema.get_credentials_fields()), None
+        for creds_field_name in block_input_schema.get_credentials_fields().keys():
+            if (creds_meta := node.input_default.get(creds_field_name)) and not (
+                node_credentials := await get_credentials(creds_meta["id"])
+            ):
+                logger.warning(
+                    f"Node #{node.id} input '{creds_field_name}' referenced "
+                    f"non-existent credentials #{creds_meta['id']}"
                 )
-            )
-            and (creds_meta := node.input_default.get(creds_field_name))
-            and not (node_credentials := await get_credentials(creds_meta["id"]))
-        ):
-            logger.error(
-                f"Node #{node.id} input '{creds_field_name}' referenced non-existent "
-                f"credentials #{creds_meta['id']}"
-            )
 
         updated_node = await on_node_deactivate(
             user_id, node, credentials=node_credentials
@@ -109,32 +89,6 @@ async def on_graph_deactivate(graph: "GraphModel", user_id: str):
     return graph
 
 
-async def on_node_activate(
-    user_id: str,
-    graph_id: str,
-    node: "Node",
-    *,
-    credentials: Optional["Credentials"] = None,
-) -> "Node":
-    """Hook to be called when the node is activated/created"""
-
-    if node.block.webhook_config:
-        new_webhook, feedback = await setup_webhook_for_block(
-            user_id=user_id,
-            trigger_block=node.block,
-            trigger_config=node.input_default,
-            for_graph_id=graph_id,
-        )
-        if new_webhook:
-            node = await set_node_webhook(node.id, new_webhook.id)
-        else:
-            logger.debug(
-                f"Node #{node.id} does not have everything for a webhook: {feedback}"
-            )
-
-    return node
-
-
 async def on_node_deactivate(
     user_id: str,
     node: "NodeModel",