ci: Modify Claude's system prompt in dependabot workflow

- Resolves #10849

### Changes 🏗️

- Use `AGENT_PRESET_INCLUDE` in `INTEGRATION_WEBHOOK_INCLUDE` so the
`AgentPreset.from_db(..)` doesn't break

### Checklist 📋

#### For code changes:
- [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] Webhook ingress works

.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 +30,295 @@ 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:*)"
+          additional_permissions: |
+            actions: read

.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

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/poetry.lock

@@ -1534,31 +1534,31 @@ 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 = [
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-    {file = "ruff-0.12.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:5b15ea354c6ff0d7423814ba6d44be2807644d0c05e9ed60caca87e963e93f70"},
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-    {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"},
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+    {file = "ruff-0.12.11-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6a2c0a2e1a450f387bf2c6237c727dd22191ae8c00e448e0672d624b2bbd7fb0"},
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+    {file = "ruff-0.12.11-py3-none-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]]
@@ -1897,4 +1897,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "ef7818fba061cea2841c6d7ca4852acde83e4f73b32fca1315e58660002bb0d0"
+content-hash = "d841f62f95180f6ad63ce82ed8e62aa201b9bf89242cc9299ae0f26ff1f72136"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -21,7 +21,7 @@ supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.12.9"
+ruff = "^0.12.11"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.1.0"
 pytest-mock = "^3.14.1"

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_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/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/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")

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/block.py

@@ -8,6 +8,7 @@ from enum import Enum
 from typing import (
     TYPE_CHECKING,
     Any,
+    Callable,
     ClassVar,
     Generic,
     Optional,
@@ -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.
 
 
@@ -306,7 +308,7 @@ 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,

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 +89,8 @@ class BlockErrorStats(BaseModel):
 
 
 ExecutionStatus = AgentExecutionStatus
+NodeInputMask = Mapping[str, JsonValue]
+NodesInputMasks = Mapping[str, NodeInputMask]
 
 
 class GraphExecutionMeta(BaseDbModel):
@@ -94,7 +98,10 @@ 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
@@ -179,6 +186,18 @@ 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,
@@ -206,7 +225,7 @@ class GraphExecutionMeta(BaseDbModel):
 
 
 class GraphExecution(GraphExecutionMeta):
-    inputs: BlockInput
+    inputs: BlockInput  # type: ignore - incompatible override is intentional
     outputs: CompletedBlockOutput
 
     @staticmethod
@@ -226,15 +245,18 @@ 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 +274,13 @@ 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 +313,17 @@ 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,
         )
 
@@ -315,10 +340,9 @@ class NodeExecutionResult(BaseModel):
     input_data: BlockInput
     output_data: CompletedBlockOutput
     add_time: datetime
-    queue_time: datetime | None = None
-    start_time: datetime | None = None
-    end_time: datetime | None = None
-    stats: NodeExecutionStats | None = None
+    queue_time: datetime | None
+    start_time: datetime | None
+    end_time: datetime | None
 
     @staticmethod
     def from_db(_node_exec: AgentNodeExecution, user_id: Optional[str] = None):
@@ -336,7 +360,7 @@ 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)
 
@@ -345,7 +369,7 @@ 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:
@@ -370,7 +394,6 @@ class NodeExecutionResult(BaseModel):
             queue_time=_node_exec.queuedTime,
             start_time=_node_exec.startedTime,
             end_time=_node_exec.endedTime,
-            stats=stats,
         )
 
     def to_node_execution_entry(
@@ -541,9 +564,12 @@ 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.
@@ -551,11 +577,18 @@ 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.QUEUED,
+            "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,
@@ -571,9 +604,9 @@ 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,
     )
 
@@ -584,7 +617,7 @@ 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]:
     """
@@ -633,7 +666,7 @@ 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,
@@ -656,42 +689,6 @@ async def upsert_execution_input(
         )
 
 
-async def create_node_execution(
-    node_exec_id: str,
-    node_id: str,
-    graph_exec_id: str,
-    input_name: str,
-    input_data: Any,
-) -> None:
-    """Create a new node execution with the first input."""
-    json_input_data = SafeJson(input_data)
-    await AgentNodeExecution.prisma().create(
-        data=AgentNodeExecutionCreateInput(
-            id=node_exec_id,
-            agentNodeId=node_id,
-            agentGraphExecutionId=graph_exec_id,
-            executionStatus=ExecutionStatus.INCOMPLETE,
-            Input={"create": {"name": input_name, "data": json_input_data}},
-        )
-    )
-
-
-async def add_input_to_node_execution(
-    node_exec_id: str,
-    input_name: str,
-    input_data: Any,
-) -> None:
-    """Add an input to an existing node execution."""
-    json_input_data = SafeJson(input_data)
-    await AgentNodeExecutionInputOutput.prisma().create(
-        data=AgentNodeExecutionInputOutputCreateInput(
-            name=input_name,
-            data=json_input_data,
-            referencedByInputExecId=node_exec_id,
-        )
-    )
-
-
 async def upsert_execution_output(
     node_exec_id: str,
     output_name: str,
@@ -926,7 +923,7 @@ 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
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -12,7 +12,7 @@ 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
@@ -34,6 +34,7 @@ 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__)
@@ -205,6 +206,35 @@ 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 +268,14 @@ 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
 
@@ -414,7 +452,7 @@ 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 +466,7 @@ 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 +480,7 @@ 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 +502,7 @@ 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.

autogpt_platform/backend/backend/data/includes.py

@@ -59,9 +59,15 @@ def graph_execution_include(
     }
 
 
+AGENT_PRESET_INCLUDE: prisma.types.AgentPresetInclude = {
+    "InputPresets": True,
+    "Webhook": True,
+}
+
+
 INTEGRATION_WEBHOOK_INCLUDE: prisma.types.IntegrationWebhookInclude = {
     "AgentNodes": {"include": AGENT_NODE_INCLUDE},
-    "AgentPresets": {"include": {"InputPresets": True}},
+    "AgentPresets": {"include": AGENT_PRESET_INCLUDE},
 }
 
 

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -115,7 +115,7 @@ async def generate_activity_status_for_execution(
 
         # Get all node executions for this graph execution
         node_executions = await db_client.get_node_executions(
-            graph_exec_id=graph_exec_id, include_exec_data=True
+            graph_exec_id, include_exec_data=True
         )
 
         # Get graph metadata and full graph structure for name, description, and links

autogpt_platform/backend/backend/executor/database.py

@@ -4,13 +4,12 @@ from typing import Callable, Concatenate, ParamSpec, TypeVar, cast
 from backend.data import db
 from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
-    add_input_to_node_execution,
     create_graph_execution,
-    create_node_execution,
     get_block_error_stats,
     get_execution_kv_data,
     get_graph_execution_meta,
     get_graph_executions,
+    get_latest_node_execution,
     get_node_execution,
     get_node_executions,
     set_execution_kv_data,
@@ -18,6 +17,7 @@ from backend.data.execution import (
     update_graph_execution_stats,
     update_node_execution_status,
     update_node_execution_status_batch,
+    upsert_execution_input,
     upsert_execution_output,
 )
 from backend.data.generate_data import get_user_execution_summary_data
@@ -105,13 +105,13 @@ class DatabaseManager(AppService):
     create_graph_execution = _(create_graph_execution)
     get_node_execution = _(get_node_execution)
     get_node_executions = _(get_node_executions)
+    get_latest_node_execution = _(get_latest_node_execution)
     update_node_execution_status = _(update_node_execution_status)
     update_node_execution_status_batch = _(update_node_execution_status_batch)
     update_graph_execution_start_time = _(update_graph_execution_start_time)
     update_graph_execution_stats = _(update_graph_execution_stats)
+    upsert_execution_input = _(upsert_execution_input)
     upsert_execution_output = _(upsert_execution_output)
-    create_node_execution = _(create_node_execution)
-    add_input_to_node_execution = _(add_input_to_node_execution)
     get_execution_kv_data = _(get_execution_kv_data)
     set_execution_kv_data = _(set_execution_kv_data)
     get_block_error_stats = _(get_block_error_stats)
@@ -171,12 +171,10 @@ class DatabaseManagerClient(AppServiceClient):
     get_graph_executions = _(d.get_graph_executions)
     get_graph_execution_meta = _(d.get_graph_execution_meta)
     get_node_executions = _(d.get_node_executions)
-    create_node_execution = _(d.create_node_execution)
     update_node_execution_status = _(d.update_node_execution_status)
     update_graph_execution_start_time = _(d.update_graph_execution_start_time)
     update_graph_execution_stats = _(d.update_graph_execution_stats)
     upsert_execution_output = _(d.upsert_execution_output)
-    add_input_to_node_execution = _(d.add_input_to_node_execution)
 
     # Graphs
     get_graph_metadata = _(d.get_graph_metadata)
@@ -191,6 +189,14 @@ class DatabaseManagerClient(AppServiceClient):
     # User Emails
     get_user_email_by_id = _(d.get_user_email_by_id)
 
+    # Library
+    list_library_agents = _(d.list_library_agents)
+    add_store_agent_to_library = _(d.add_store_agent_to_library)
+
+    # Store
+    get_store_agents = _(d.get_store_agents)
+    get_store_agent_details = _(d.get_store_agent_details)
+
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager
@@ -201,12 +207,16 @@ class DatabaseManagerAsyncClient(AppServiceClient):
 
     create_graph_execution = d.create_graph_execution
     get_connected_output_nodes = d.get_connected_output_nodes
+    get_latest_node_execution = d.get_latest_node_execution
     get_graph = d.get_graph
     get_graph_metadata = d.get_graph_metadata
     get_graph_execution_meta = d.get_graph_execution_meta
     get_node = d.get_node
+    get_node_execution = d.get_node_execution
     get_node_executions = d.get_node_executions
     get_user_integrations = d.get_user_integrations
+    upsert_execution_input = d.upsert_execution_input
+    upsert_execution_output = d.upsert_execution_output
     update_graph_execution_stats = d.update_graph_execution_stats
     update_node_execution_status = d.update_node_execution_status
     update_node_execution_status_batch = d.update_node_execution_status_batch

autogpt_platform/backend/backend/executor/execution_cache.py

@@ -1,154 +0,0 @@
-import logging
-import threading
-from collections import OrderedDict
-from functools import wraps
-from typing import TYPE_CHECKING, Any, Optional
-
-from backend.data.execution import ExecutionStatus, NodeExecutionResult
-from backend.data.model import GraphExecutionStats, NodeExecutionStats
-
-if TYPE_CHECKING:
-    from backend.executor import DatabaseManagerClient
-
-logger = logging.getLogger(__name__)
-
-
-def with_lock(func):
-    @wraps(func)
-    def wrapper(self, *args, **kwargs):
-        with self._lock:
-            return func(self, *args, **kwargs)
-
-    return wrapper
-
-
-class ExecutionCache:
-    def __init__(self, graph_exec_id: str, db_client: "DatabaseManagerClient"):
-        self._lock = threading.RLock()
-        self._graph_exec_id = graph_exec_id
-        self._graph_stats: GraphExecutionStats = GraphExecutionStats()
-        self._node_executions: OrderedDict[str, NodeExecutionResult] = OrderedDict()
-
-        for execution in db_client.get_node_executions(self._graph_exec_id):
-            self._node_executions[execution.node_exec_id] = execution
-
-    @with_lock
-    def get_node_execution(self, node_exec_id: str) -> Optional[NodeExecutionResult]:
-        execution = self._node_executions.get(node_exec_id)
-        return execution.model_copy(deep=True) if execution else None
-
-    @with_lock
-    def get_latest_node_execution(self, node_id: str) -> Optional[NodeExecutionResult]:
-        for execution in reversed(self._node_executions.values()):
-            if (
-                execution.node_id == node_id
-                and execution.status != ExecutionStatus.INCOMPLETE
-            ):
-                return execution.model_copy(deep=True)
-        return None
-
-    @with_lock
-    def get_node_executions(
-        self,
-        *,
-        statuses: Optional[list] = None,
-        block_ids: Optional[list] = None,
-        node_id: Optional[str] = None,
-    ):
-        results = []
-        for execution in self._node_executions.values():
-            if statuses and execution.status not in statuses:
-                continue
-            if block_ids and execution.block_id not in block_ids:
-                continue
-            if node_id and execution.node_id != node_id:
-                continue
-            results.append(execution.model_copy(deep=True))
-        return results
-
-    @with_lock
-    def update_node_execution_status(
-        self,
-        exec_id: str,
-        status: ExecutionStatus,
-        execution_data: Optional[dict] = None,
-        stats: Optional[dict] = None,
-    ):
-        if exec_id not in self._node_executions:
-            raise RuntimeError(f"Execution {exec_id} not found in cache")
-
-        execution = self._node_executions[exec_id]
-        execution.status = status
-
-        if execution_data:
-            execution.input_data.update(execution_data)
-
-        if stats:
-            execution.stats = execution.stats or NodeExecutionStats()
-            current_stats = execution.stats.model_dump()
-            current_stats.update(stats)
-            execution.stats = NodeExecutionStats.model_validate(current_stats)
-
-    @with_lock
-    def upsert_execution_output(
-        self, node_exec_id: str, output_name: str, output_data: Any
-    ) -> NodeExecutionResult:
-        if node_exec_id not in self._node_executions:
-            raise RuntimeError(f"Execution {node_exec_id} not found in cache")
-
-        execution = self._node_executions[node_exec_id]
-        if output_name not in execution.output_data:
-            execution.output_data[output_name] = []
-        execution.output_data[output_name].append(output_data)
-
-        return execution
-
-    @with_lock
-    def update_graph_stats(
-        self, status: Optional[ExecutionStatus] = None, stats: Optional[dict] = None
-    ):
-        if status is not None:
-            pass
-        if stats is not None:
-            current_stats = self._graph_stats.model_dump()
-            current_stats.update(stats)
-            self._graph_stats = GraphExecutionStats.model_validate(current_stats)
-
-    @with_lock
-    def update_graph_start_time(self):
-        """Update graph start time (handled by database persistence)."""
-        pass
-
-    @with_lock
-    def find_incomplete_execution_for_input(
-        self, node_id: str, input_name: str
-    ) -> tuple[str, NodeExecutionResult] | None:
-        for exec_id, execution in self._node_executions.items():
-            if (
-                execution.node_id == node_id
-                and execution.status == ExecutionStatus.INCOMPLETE
-                and input_name not in execution.input_data
-            ):
-                return exec_id, execution
-        return None
-
-    @with_lock
-    def add_node_execution(
-        self, node_exec_id: str, execution: NodeExecutionResult
-    ) -> None:
-        self._node_executions[node_exec_id] = execution
-
-    @with_lock
-    def update_execution_input(
-        self, exec_id: str, input_name: str, input_data: Any
-    ) -> dict:
-        if exec_id not in self._node_executions:
-            raise RuntimeError(f"Execution {exec_id} not found in cache")
-        execution = self._node_executions[exec_id]
-        execution.input_data[input_name] = input_data
-        return execution.input_data.copy()
-
-    def finalize(self) -> None:
-        with self._lock:
-            self._node_executions.clear()
-            self._graph_stats = GraphExecutionStats()

autogpt_platform/backend/backend/executor/execution_creation_test.py

@@ -1,355 +0,0 @@
-"""Test execution creation with proper ID generation and persistence."""
-
-import asyncio
-import threading
-import uuid
-from datetime import datetime
-
-import pytest
-
-from backend.data.execution import ExecutionStatus
-from backend.executor.execution_data import ExecutionDataClient
-
-
-@pytest.fixture
-def event_loop():
-    loop = asyncio.new_event_loop()
-    yield loop
-    loop.close()
-
-
-@pytest.fixture
-async def execution_client_with_mock_db(event_loop):
-    """Create an ExecutionDataClient with proper database records."""
-    from datetime import datetime, timezone
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from prisma.models import AgentGraph, AgentGraphExecution, User
-
-    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
-
-    # Create test database records to satisfy foreign key constraints
-    try:
-        await User.prisma().create(
-            data={
-                "id": "test_user_123",
-                "email": "test@example.com",
-                "name": "Test User",
-            }
-        )
-
-        await AgentGraph.prisma().create(
-            data={
-                "id": "test_graph_456",
-                "version": 1,
-                "userId": "test_user_123",
-                "name": "Test Graph",
-                "description": "Test graph for execution tests",
-            }
-        )
-
-        from prisma.enums import AgentExecutionStatus
-
-        await AgentGraphExecution.prisma().create(
-            data={
-                "id": "test_graph_exec_id",
-                "userId": "test_user_123",
-                "agentGraphId": "test_graph_456",
-                "agentGraphVersion": 1,
-                "executionStatus": AgentExecutionStatus.RUNNING,
-            }
-        )
-    except Exception:
-        # Records might already exist, that's fine
-        pass
-
-    # Mock the graph execution metadata - align with assertions below
-    mock_graph_meta = GraphExecutionMeta(
-        id="test_graph_exec_id",
-        user_id="test_user_123",
-        graph_id="test_graph_456",
-        graph_version=1,
-        status=ExecutionStatus.RUNNING,
-        started_at=datetime.now(timezone.utc),
-        ended_at=datetime.now(timezone.utc),
-        stats=None,
-    )
-
-    # Create client with ThreadPoolExecutor and graph metadata (constructed inside patch)
-    from concurrent.futures import ThreadPoolExecutor
-
-    executor = ThreadPoolExecutor(max_workers=1)
-
-    # Storage for tracking created executions
-    created_executions = []
-
-    async def mock_create_node_execution(
-        node_exec_id, node_id, graph_exec_id, input_name, input_data
-    ):
-        """Mock execution creation that records what was created."""
-        created_executions.append(
-            {
-                "node_exec_id": node_exec_id,
-                "node_id": node_id,
-                "graph_exec_id": graph_exec_id,
-                "input_name": input_name,
-                "input_data": input_data,
-            }
-        )
-        return node_exec_id
-
-    def sync_mock_create_node_execution(
-        node_exec_id, node_id, graph_exec_id, input_name, input_data
-    ):
-        """Mock sync execution creation that records what was created."""
-        created_executions.append(
-            {
-                "node_exec_id": node_exec_id,
-                "node_id": node_id,
-                "graph_exec_id": graph_exec_id,
-                "input_name": input_name,
-                "input_data": input_data,
-            }
-        )
-        return node_exec_id
-
-    # Prepare mock async and sync DB clients
-    async_mock_client = AsyncMock()
-    async_mock_client.create_node_execution = mock_create_node_execution
-
-    sync_mock_client = MagicMock()
-    sync_mock_client.create_node_execution = sync_mock_create_node_execution
-    # Mock graph execution for return values
-    from backend.data.execution import GraphExecutionMeta
-
-    mock_graph_update = GraphExecutionMeta(
-        id="test_graph_exec_id",
-        user_id="test_user_123",
-        graph_id="test_graph_456",
-        graph_version=1,
-        status=ExecutionStatus.RUNNING,
-        started_at=datetime.now(timezone.utc),
-        ended_at=datetime.now(timezone.utc),
-        stats=None,
-    )
-
-    # No-ops for other sync methods used by the client during tests
-    sync_mock_client.add_input_to_node_execution.side_effect = lambda **kwargs: None
-    sync_mock_client.update_node_execution_status.side_effect = (
-        lambda *args, **kwargs: None
-    )
-    sync_mock_client.upsert_execution_output.side_effect = lambda **kwargs: None
-    sync_mock_client.update_graph_execution_stats.side_effect = (
-        lambda *args, **kwargs: mock_graph_update
-    )
-    sync_mock_client.update_graph_execution_start_time.side_effect = (
-        lambda *args, **kwargs: mock_graph_update
-    )
-
-    thread = threading.Thread(target=event_loop.run_forever, daemon=True)
-    thread.start()
-    with patch(
-        "backend.executor.execution_data.get_database_manager_async_client",
-        return_value=async_mock_client,
-    ), patch(
-        "backend.executor.execution_data.get_database_manager_client",
-        return_value=sync_mock_client,
-    ), patch(
-        "backend.executor.execution_data.get_execution_event_bus"
-    ), patch(
-        "backend.executor.execution_data.non_blocking_persist", lambda func: func
-    ):
-        # Now construct the client under the patch so it captures the mocked clients
-        client = ExecutionDataClient(executor, "test_graph_exec_id", mock_graph_meta)
-        # Store the mocks for the test to access if needed
-        setattr(client, "_test_async_client", async_mock_client)
-        setattr(client, "_test_sync_client", sync_mock_client)
-        setattr(client, "_created_executions", created_executions)
-        yield client
-
-    # Cleanup test database records
-    try:
-        await AgentGraphExecution.prisma().delete_many(
-            where={"id": "test_graph_exec_id"}
-        )
-        await AgentGraph.prisma().delete_many(where={"id": "test_graph_456"})
-        await User.prisma().delete_many(where={"id": "test_user_123"})
-    except Exception:
-        # Cleanup may fail if records don't exist
-        pass
-
-    # Cleanup
-    event_loop.call_soon_threadsafe(event_loop.stop)
-    thread.join(timeout=1)
-
-
-class TestExecutionCreation:
-    """Test execution creation with proper ID generation and persistence."""
-
-    async def test_execution_creation_with_valid_ids(
-        self, execution_client_with_mock_db
-    ):
-        """Test that execution creation generates and persists valid IDs."""
-        client = execution_client_with_mock_db
-
-        node_id = "test_node_789"
-        input_name = "test_input"
-        input_data = "test_value"
-        block_id = "test_block_abc"
-
-        # This should trigger execution creation since cache is empty
-        exec_id, input_dict = client.upsert_execution_input(
-            node_id=node_id,
-            input_name=input_name,
-            input_data=input_data,
-            block_id=block_id,
-        )
-
-        # Verify execution ID is valid UUID
-        try:
-            uuid.UUID(exec_id)
-        except ValueError:
-            pytest.fail(f"Generated execution ID '{exec_id}' is not a valid UUID")
-
-        # Verify execution was created in cache with complete data
-        assert exec_id in client._cache._node_executions
-        cached_execution = client._cache._node_executions[exec_id]
-
-        # Check all required fields have valid values
-        assert cached_execution.user_id == "test_user_123"
-        assert cached_execution.graph_id == "test_graph_456"
-        assert cached_execution.graph_version == 1
-        assert cached_execution.graph_exec_id == "test_graph_exec_id"
-        assert cached_execution.node_exec_id == exec_id
-        assert cached_execution.node_id == node_id
-        assert cached_execution.block_id == block_id
-        assert cached_execution.status == ExecutionStatus.INCOMPLETE
-        assert cached_execution.input_data == {input_name: input_data}
-        assert isinstance(cached_execution.add_time, datetime)
-
-        # Verify execution was persisted to database with our generated ID
-        created_executions = getattr(client, "_created_executions", [])
-        assert len(created_executions) == 1
-        created = created_executions[0]
-        assert created["node_exec_id"] == exec_id  # Our generated ID was used
-        assert created["node_id"] == node_id
-        assert created["graph_exec_id"] == "test_graph_exec_id"
-        assert created["input_name"] == input_name
-        assert created["input_data"] == input_data
-
-        # Verify input dict returned correctly
-        assert input_dict == {input_name: input_data}
-
-    async def test_execution_reuse_vs_creation(self, execution_client_with_mock_db):
-        """Test that execution reuse works and creation only happens when needed."""
-        client = execution_client_with_mock_db
-
-        node_id = "reuse_test_node"
-        block_id = "reuse_test_block"
-
-        # Create first execution
-        exec_id_1, input_dict_1 = client.upsert_execution_input(
-            node_id=node_id,
-            input_name="input_1",
-            input_data="value_1",
-            block_id=block_id,
-        )
-
-        # This should reuse the existing INCOMPLETE execution
-        exec_id_2, input_dict_2 = client.upsert_execution_input(
-            node_id=node_id,
-            input_name="input_2",
-            input_data="value_2",
-            block_id=block_id,
-        )
-
-        # Should reuse the same execution
-        assert exec_id_1 == exec_id_2
-        assert input_dict_2 == {"input_1": "value_1", "input_2": "value_2"}
-
-        # Only one execution should be created in database
-        created_executions = getattr(client, "_created_executions", [])
-        assert len(created_executions) == 1
-
-        # Verify cache has the merged inputs
-        cached_execution = client._cache._node_executions[exec_id_1]
-        assert cached_execution.input_data == {
-            "input_1": "value_1",
-            "input_2": "value_2",
-        }
-
-        # Now complete the execution and try to add another input
-        client.update_node_status_and_publish(
-            exec_id=exec_id_1, status=ExecutionStatus.COMPLETED
-        )
-
-        # Verify the execution status was actually updated in the cache
-        updated_execution = client._cache._node_executions[exec_id_1]
-        assert (
-            updated_execution.status == ExecutionStatus.COMPLETED
-        ), f"Expected COMPLETED but got {updated_execution.status}"
-
-        # This should create a NEW execution since the first is no longer INCOMPLETE
-        exec_id_3, input_dict_3 = client.upsert_execution_input(
-            node_id=node_id,
-            input_name="input_3",
-            input_data="value_3",
-            block_id=block_id,
-        )
-
-        # Should be a different execution
-        assert exec_id_3 != exec_id_1
-        assert input_dict_3 == {"input_3": "value_3"}
-
-        # Verify cache behavior: should have two different executions in cache now
-        cached_executions = client._cache._node_executions
-        assert len(cached_executions) == 2
-        assert exec_id_1 in cached_executions
-        assert exec_id_3 in cached_executions
-
-        # First execution should be COMPLETED
-        assert cached_executions[exec_id_1].status == ExecutionStatus.COMPLETED
-        # Third execution should be INCOMPLETE (newly created)
-        assert cached_executions[exec_id_3].status == ExecutionStatus.INCOMPLETE
-
-    async def test_multiple_nodes_get_different_execution_ids(
-        self, execution_client_with_mock_db
-    ):
-        """Test that different nodes get different execution IDs."""
-        client = execution_client_with_mock_db
-
-        # Create executions for different nodes
-        exec_id_a, _ = client.upsert_execution_input(
-            node_id="node_a",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="block_a",
-        )
-
-        exec_id_b, _ = client.upsert_execution_input(
-            node_id="node_b",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="block_b",
-        )
-
-        # Should be different executions with different IDs
-        assert exec_id_a != exec_id_b
-
-        # Both should be valid UUIDs
-        uuid.UUID(exec_id_a)
-        uuid.UUID(exec_id_b)
-
-        # Both should be in cache
-        cached_executions = client._cache._node_executions
-        assert len(cached_executions) == 2
-        assert exec_id_a in cached_executions
-        assert exec_id_b in cached_executions
-
-        # Both should have correct node IDs
-        assert cached_executions[exec_id_a].node_id == "node_a"
-        assert cached_executions[exec_id_b].node_id == "node_b"
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

autogpt_platform/backend/backend/executor/execution_data.py

@@ -1,338 +0,0 @@
-import logging
-import threading
-import uuid
-from concurrent.futures import Executor
-from datetime import datetime, timezone
-from typing import TYPE_CHECKING, Any, Callable, ParamSpec, TypeVar, cast
-
-from backend.data import redis_client as redis
-from backend.data.credit import UsageTransactionMetadata
-from backend.data.execution import (
-    ExecutionStatus,
-    GraphExecutionMeta,
-    NodeExecutionResult,
-)
-from backend.data.graph import Node
-from backend.data.model import GraphExecutionStats
-from backend.executor.execution_cache import ExecutionCache
-from backend.util.clients import (
-    get_database_manager_async_client,
-    get_database_manager_client,
-    get_execution_event_bus,
-)
-from backend.util.settings import Settings
-
-if TYPE_CHECKING:
-    from backend.executor import DatabaseManagerAsyncClient, DatabaseManagerClient
-
-settings = Settings()
-logger = logging.getLogger(__name__)
-
-P = ParamSpec("P")
-T = TypeVar("T")
-
-
-def non_blocking_persist(func: Callable[P, T]) -> Callable[P, None]:
-    from functools import wraps
-
-    @wraps(func)
-    def wrapper(*args: P.args, **kwargs: P.kwargs) -> None:
-        # First argument is always self for methods - access through cast for typing
-        self = cast("ExecutionDataClient", args[0])
-        future = self._executor.submit(func, *args, **kwargs)
-        self._pending_tasks.add(future)
-
-    return wrapper
-
-
-class ExecutionDataClient:
-    def __init__(
-        self, executor: Executor, graph_exec_id: str, graph_metadata: GraphExecutionMeta
-    ):
-        self._executor = executor
-        self._graph_exec_id = graph_exec_id
-        self._cache = ExecutionCache(graph_exec_id, self.db_client_sync)
-        self._pending_tasks = set()
-        self._graph_metadata = graph_metadata
-        self.graph_lock = threading.RLock()
-
-    def finalize_execution(self, timeout: float = 30.0):
-        logger.info(f"Flushing db writes for execution {self._graph_exec_id}")
-        exceptions = []
-
-        # Wait for all pending database operations to complete
-        logger.debug(
-            f"Waiting for {len(self._pending_tasks)} pending database operations"
-        )
-        for future in list(self._pending_tasks):
-            try:
-                future.result(timeout=timeout)
-            except Exception as e:
-                logger.error(f"Background database operation failed: {e}")
-                exceptions.append(e)
-            finally:
-                self._pending_tasks.discard(future)
-
-        self._cache.finalize()
-
-        if exceptions:
-            logger.error(f"Background persistence failed with {len(exceptions)} errors")
-            raise RuntimeError(
-                f"Background persistence failed with {len(exceptions)} errors: {exceptions}"
-            )
-
-    @property
-    def db_client_async(self) -> "DatabaseManagerAsyncClient":
-        return get_database_manager_async_client()
-
-    @property
-    def db_client_sync(self) -> "DatabaseManagerClient":
-        return get_database_manager_client()
-
-    @property
-    def event_bus(self):
-        return get_execution_event_bus()
-
-    async def get_node(self, node_id: str) -> Node:
-        return await self.db_client_async.get_node(node_id)
-
-    def spend_credits(
-        self,
-        user_id: str,
-        cost: int,
-        metadata: UsageTransactionMetadata,
-    ) -> int:
-        return self.db_client_sync.spend_credits(
-            user_id=user_id, cost=cost, metadata=metadata
-        )
-
-    def get_graph_execution_meta(
-        self, user_id: str, execution_id: str
-    ) -> GraphExecutionMeta | None:
-        return self.db_client_sync.get_graph_execution_meta(
-            user_id=user_id, execution_id=execution_id
-        )
-
-    def get_graph_metadata(
-        self, graph_id: str, graph_version: int | None = None
-    ) -> Any:
-        return self.db_client_sync.get_graph_metadata(graph_id, graph_version)
-
-    def get_credits(self, user_id: str) -> int:
-        return self.db_client_sync.get_credits(user_id)
-
-    def get_user_email_by_id(self, user_id: str) -> str | None:
-        return self.db_client_sync.get_user_email_by_id(user_id)
-
-    def get_latest_node_execution(self, node_id: str) -> NodeExecutionResult | None:
-        return self._cache.get_latest_node_execution(node_id)
-
-    def get_node_execution(self, node_exec_id: str) -> NodeExecutionResult | None:
-        return self._cache.get_node_execution(node_exec_id)
-
-    def get_node_executions(
-        self,
-        *,
-        node_id: str | None = None,
-        statuses: list[ExecutionStatus] | None = None,
-        block_ids: list[str] | None = None,
-    ) -> list[NodeExecutionResult]:
-        return self._cache.get_node_executions(
-            statuses=statuses, block_ids=block_ids, node_id=node_id
-        )
-
-    def update_node_status_and_publish(
-        self,
-        exec_id: str,
-        status: ExecutionStatus,
-        execution_data: dict | None = None,
-        stats: dict[str, Any] | None = None,
-    ):
-        self._cache.update_node_execution_status(exec_id, status, execution_data, stats)
-        self._persist_node_status_to_db(exec_id, status, execution_data, stats)
-
-    def upsert_execution_input(
-        self, node_id: str, input_name: str, input_data: Any, block_id: str
-    ) -> tuple[str, dict]:
-        # Validate input parameters to prevent foreign key constraint errors
-        if not node_id or not isinstance(node_id, str):
-            raise ValueError(f"Invalid node_id: {node_id}")
-        if not self._graph_exec_id or not isinstance(self._graph_exec_id, str):
-            raise ValueError(f"Invalid graph_exec_id: {self._graph_exec_id}")
-        if not block_id or not isinstance(block_id, str):
-            raise ValueError(f"Invalid block_id: {block_id}")
-
-        # UPDATE: Try to find an existing incomplete execution for this node and input
-        if result := self._cache.find_incomplete_execution_for_input(
-            node_id, input_name
-        ):
-            exec_id, _ = result
-            updated_input_data = self._cache.update_execution_input(
-                exec_id, input_name, input_data
-            )
-            self._persist_add_input_to_db(exec_id, input_name, input_data)
-            return exec_id, updated_input_data
-
-        # CREATE: No suitable execution found, create new one
-        node_exec_id = str(uuid.uuid4())
-        logger.debug(
-            f"Creating new execution {node_exec_id} for node {node_id} "
-            f"in graph execution {self._graph_exec_id}"
-        )
-
-        new_execution = NodeExecutionResult(
-            user_id=self._graph_metadata.user_id,
-            graph_id=self._graph_metadata.graph_id,
-            graph_version=self._graph_metadata.graph_version,
-            graph_exec_id=self._graph_exec_id,
-            node_exec_id=node_exec_id,
-            node_id=node_id,
-            block_id=block_id,
-            status=ExecutionStatus.INCOMPLETE,
-            input_data={input_name: input_data},
-            output_data={},
-            add_time=datetime.now(timezone.utc),
-        )
-        self._cache.add_node_execution(node_exec_id, new_execution)
-        self._persist_new_node_execution_to_db(
-            node_exec_id, node_id, input_name, input_data
-        )
-
-        return node_exec_id, {input_name: input_data}
-
-    def upsert_execution_output(
-        self, node_exec_id: str, output_name: str, output_data: Any
-    ):
-        self._cache.upsert_execution_output(node_exec_id, output_name, output_data)
-        self._persist_execution_output_to_db(node_exec_id, output_name, output_data)
-
-    def update_graph_stats_and_publish(
-        self,
-        status: ExecutionStatus | None = None,
-        stats: GraphExecutionStats | None = None,
-    ) -> None:
-        stats_dict = stats.model_dump() if stats else None
-        self._cache.update_graph_stats(status=status, stats=stats_dict)
-        self._persist_graph_stats_to_db(status=status, stats=stats)
-
-    def update_graph_start_time_and_publish(self) -> None:
-        self._cache.update_graph_start_time()
-        self._persist_graph_start_time_to_db()
-
-    @non_blocking_persist
-    def _persist_node_status_to_db(
-        self,
-        exec_id: str,
-        status: ExecutionStatus,
-        execution_data: dict | None = None,
-        stats: dict[str, Any] | None = None,
-    ):
-        exec_update = self.db_client_sync.update_node_execution_status(
-            exec_id, status, execution_data, stats
-        )
-        self.event_bus.publish(exec_update)
-
-    @non_blocking_persist
-    def _persist_add_input_to_db(
-        self, node_exec_id: str, input_name: str, input_data: Any
-    ):
-        self.db_client_sync.add_input_to_node_execution(
-            node_exec_id=node_exec_id,
-            input_name=input_name,
-            input_data=input_data,
-        )
-
-    @non_blocking_persist
-    def _persist_execution_output_to_db(
-        self, node_exec_id: str, output_name: str, output_data: Any
-    ):
-        self.db_client_sync.upsert_execution_output(
-            node_exec_id=node_exec_id,
-            output_name=output_name,
-            output_data=output_data,
-        )
-        if exec_update := self.get_node_execution(node_exec_id):
-            self.event_bus.publish(exec_update)
-
-    @non_blocking_persist
-    def _persist_graph_stats_to_db(
-        self,
-        status: ExecutionStatus | None = None,
-        stats: GraphExecutionStats | None = None,
-    ):
-        graph_update = self.db_client_sync.update_graph_execution_stats(
-            self._graph_exec_id, status, stats
-        )
-        if not graph_update:
-            raise RuntimeError(
-                f"Failed to update graph execution stats for {self._graph_exec_id}"
-            )
-        self.event_bus.publish(graph_update)
-
-    @non_blocking_persist
-    def _persist_graph_start_time_to_db(self):
-        graph_update = self.db_client_sync.update_graph_execution_start_time(
-            self._graph_exec_id
-        )
-        if not graph_update:
-            raise RuntimeError(
-                f"Failed to update graph execution start time for {self._graph_exec_id}"
-            )
-        self.event_bus.publish(graph_update)
-
-    async def generate_activity_status(
-        self,
-        graph_id: str,
-        graph_version: int,
-        execution_stats: GraphExecutionStats,
-        user_id: str,
-        execution_status: ExecutionStatus,
-    ) -> str | None:
-        from backend.executor.activity_status_generator import (
-            generate_activity_status_for_execution,
-        )
-
-        return await generate_activity_status_for_execution(
-            graph_exec_id=self._graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_stats=execution_stats,
-            db_client=self.db_client_async,
-            user_id=user_id,
-            execution_status=execution_status,
-        )
-
-    @non_blocking_persist
-    def _send_execution_update(self, execution: NodeExecutionResult):
-        """Send execution update to event bus."""
-        try:
-            self.event_bus.publish(execution)
-        except Exception as e:
-            logger.warning(f"Failed to send execution update: {e}")
-
-    @non_blocking_persist
-    def _persist_new_node_execution_to_db(
-        self, node_exec_id: str, node_id: str, input_name: str, input_data: Any
-    ):
-        try:
-            self.db_client_sync.create_node_execution(
-                node_exec_id=node_exec_id,
-                node_id=node_id,
-                graph_exec_id=self._graph_exec_id,
-                input_name=input_name,
-                input_data=input_data,
-            )
-        except Exception as e:
-            logger.error(
-                f"Failed to create node execution {node_exec_id} for node {node_id} "
-                f"in graph execution {self._graph_exec_id}: {e}"
-            )
-            raise
-
-    def increment_execution_count(self, user_id: str) -> int:
-        r = redis.get_redis()
-        k = f"uec:{user_id}"
-        counter = cast(int, r.incr(k))
-        if counter == 1:
-            r.expire(k, settings.config.execution_counter_expiration_time)
-        return counter

autogpt_platform/backend/backend/executor/execution_data_test.py

@@ -1,668 +0,0 @@
-"""Test suite for ExecutionDataClient."""
-
-import asyncio
-import threading
-
-import pytest
-
-from backend.data.execution import ExecutionStatus
-from backend.executor.execution_data import ExecutionDataClient
-
-
-@pytest.fixture
-def event_loop():
-    loop = asyncio.new_event_loop()
-    yield loop
-    loop.close()
-
-
-@pytest.fixture
-def execution_client(event_loop):
-    from datetime import datetime, timezone
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from backend.data.execution import ExecutionStatus, GraphExecutionMeta
-
-    mock_graph_meta = GraphExecutionMeta(
-        id="test_graph_exec_id",
-        user_id="test_user_id",
-        graph_id="test_graph_id",
-        graph_version=1,
-        status=ExecutionStatus.RUNNING,
-        started_at=datetime.now(timezone.utc),
-        ended_at=datetime.now(timezone.utc),
-        stats=None,
-    )
-
-    from concurrent.futures import ThreadPoolExecutor
-
-    executor = ThreadPoolExecutor(max_workers=1)
-
-    # Mock all database operations to prevent connection attempts
-    async_mock_client = AsyncMock()
-    sync_mock_client = MagicMock()
-
-    # Mock all database methods to return None or empty results
-    sync_mock_client.get_node_executions.return_value = []
-    sync_mock_client.create_node_execution.return_value = None
-    sync_mock_client.add_input_to_node_execution.return_value = None
-    sync_mock_client.update_node_execution_status.return_value = None
-    sync_mock_client.upsert_execution_output.return_value = None
-    sync_mock_client.update_graph_execution_stats.return_value = mock_graph_meta
-    sync_mock_client.update_graph_execution_start_time.return_value = mock_graph_meta
-
-    # Mock event bus to prevent connection attempts
-    mock_event_bus = MagicMock()
-    mock_event_bus.publish.return_value = None
-
-    thread = threading.Thread(target=event_loop.run_forever, daemon=True)
-    thread.start()
-
-    with patch(
-        "backend.executor.execution_data.get_database_manager_async_client",
-        return_value=async_mock_client,
-    ), patch(
-        "backend.executor.execution_data.get_database_manager_client",
-        return_value=sync_mock_client,
-    ), patch(
-        "backend.executor.execution_data.get_execution_event_bus",
-        return_value=mock_event_bus,
-    ), patch(
-        "backend.executor.execution_data.non_blocking_persist", lambda func: func
-    ):
-
-        client = ExecutionDataClient(executor, "test_graph_exec_id", mock_graph_meta)
-        yield client
-
-    event_loop.call_soon_threadsafe(event_loop.stop)
-    thread.join(timeout=1)
-
-
-class TestExecutionDataClient:
-
-    async def test_update_node_status_writes_to_cache_immediately(
-        self, execution_client
-    ):
-        """Test that node status updates are immediately visible in cache."""
-        # First create an execution to update
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        status = ExecutionStatus.RUNNING
-        execution_data = {"step": "processing"}
-        stats = {"duration": 5.2}
-
-        # Update status of existing execution
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=status,
-            execution_data=execution_data,
-            stats=stats,
-        )
-
-        # Verify immediate visibility in cache
-        cached_exec = execution_client.get_node_execution(node_exec_id)
-        assert cached_exec is not None
-        assert cached_exec.status == status
-        # execution_data should be merged with existing input_data, not replace it
-        expected_input_data = {"test_input": "test_value", "step": "processing"}
-        assert cached_exec.input_data == expected_input_data
-
-    def test_update_node_status_execution_not_found_raises_error(
-        self, execution_client
-    ):
-        """Test that updating non-existent execution raises error instead of creating it."""
-        non_existent_id = "does-not-exist"
-
-        with pytest.raises(
-            RuntimeError, match="Execution does-not-exist not found in cache"
-        ):
-            execution_client.update_node_status_and_publish(
-                exec_id=non_existent_id, status=ExecutionStatus.COMPLETED
-            )
-
-    async def test_upsert_execution_output_writes_to_cache_immediately(
-        self, execution_client
-    ):
-        """Test that output updates are immediately visible in cache."""
-        # First create an execution
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        output_name = "result"
-        output_data = {"answer": 42, "confidence": 0.95}
-
-        # Update to RUNNING status first
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"input": "test"},
-        )
-
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id, output_name=output_name, output_data=output_data
-        )
-        # Check output through the node execution
-        cached_exec = execution_client.get_node_execution(node_exec_id)
-        assert cached_exec is not None
-        assert output_name in cached_exec.output_data
-        assert cached_exec.output_data[output_name] == [output_data]
-
-    async def test_get_node_execution_reads_from_cache(self, execution_client):
-        """Test that get_node_execution returns cached data immediately."""
-        # First create an execution to work with
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        # Then update its status
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.COMPLETED,
-            execution_data={"result": "success"},
-        )
-
-        result = execution_client.get_node_execution(node_exec_id)
-
-        assert result is not None
-        assert result.status == ExecutionStatus.COMPLETED
-        # execution_data gets merged with existing input_data
-        expected_input_data = {"test_input": "test_value", "result": "success"}
-        assert result.input_data == expected_input_data
-
-    async def test_get_latest_node_execution_reads_from_cache(self, execution_client):
-        """Test that get_latest_node_execution returns cached data."""
-        node_id = "node-1"
-
-        # First create an execution for this node
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id=node_id,
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        # Then update its status to make it non-INCOMPLETE (so it's returned by get_latest)
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"from": "cache"},
-        )
-
-        result = execution_client.get_latest_node_execution(node_id)
-
-        assert result is not None
-        assert result.status == ExecutionStatus.RUNNING
-        # execution_data gets merged with existing input_data
-        expected_input_data = {"test_input": "test_value", "from": "cache"}
-        assert result.input_data == expected_input_data
-
-    async def test_get_node_executions_sync_filters_correctly(self, execution_client):
-        # Create executions with different statuses
-        executions = [
-            (ExecutionStatus.RUNNING, "block-a"),
-            (ExecutionStatus.COMPLETED, "block-a"),
-            (ExecutionStatus.FAILED, "block-b"),
-            (ExecutionStatus.RUNNING, "block-b"),
-        ]
-
-        exec_ids = []
-        for i, (status, block_id) in enumerate(executions):
-            # First create the execution
-            exec_id, _ = execution_client.upsert_execution_input(
-                node_id=f"node-{i}",
-                input_name="test_input",
-                input_data="test_value",
-                block_id=block_id,
-            )
-            exec_ids.append(exec_id)
-
-            # Then update its status and metadata
-            execution_client.update_node_status_and_publish(
-                exec_id=exec_id, status=status, execution_data={"block": block_id}
-            )
-            # Update cached execution with graph_exec_id and block_id for filtering
-            # Note: In real implementation, these would be set during creation
-            # For test purposes, we'll skip this manual update since the filtering
-            # logic should work with the data as created
-
-        # Test status filtering
-        running_execs = execution_client.get_node_executions(
-            statuses=[ExecutionStatus.RUNNING]
-        )
-        assert len(running_execs) == 2
-        assert all(e.status == ExecutionStatus.RUNNING for e in running_execs)
-
-        # Test block_id filtering
-        block_a_execs = execution_client.get_node_executions(block_ids=["block-a"])
-        assert len(block_a_execs) == 2
-        assert all(e.block_id == "block-a" for e in block_a_execs)
-
-        # Test combined filtering
-        running_block_b = execution_client.get_node_executions(
-            statuses=[ExecutionStatus.RUNNING], block_ids=["block-b"]
-        )
-        assert len(running_block_b) == 1
-        assert running_block_b[0].status == ExecutionStatus.RUNNING
-        assert running_block_b[0].block_id == "block-b"
-
-    async def test_write_then_read_consistency(self, execution_client):
-        """Test critical race condition scenario: immediate read after write."""
-        # First create an execution to work with
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="consistency-test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        # Write status
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"step": 1},
-        )
-
-        # Write output
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id,
-            output_name="intermediate",
-            output_data={"progress": 50},
-        )
-
-        # Update status again
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.COMPLETED,
-            execution_data={"step": 2},
-        )
-
-        # All changes should be immediately visible
-        cached_exec = execution_client.get_node_execution(node_exec_id)
-        assert cached_exec is not None
-        assert cached_exec.status == ExecutionStatus.COMPLETED
-        # execution_data gets merged with existing input_data - step 2 overwrites step 1
-        expected_input_data = {"test_input": "test_value", "step": 2}
-        assert cached_exec.input_data == expected_input_data
-
-        # Output should be visible in execution record
-        assert cached_exec.output_data["intermediate"] == [{"progress": 50}]
-
-    async def test_concurrent_operations_are_thread_safe(self, execution_client):
-        """Test that concurrent operations don't corrupt cache."""
-        num_threads = 3  # Reduced for simpler test
-        operations_per_thread = 5  # Reduced for simpler test
-
-        # Create all executions upfront
-        created_exec_ids = []
-        for thread_id in range(num_threads):
-            for i in range(operations_per_thread):
-                exec_id, _ = execution_client.upsert_execution_input(
-                    node_id=f"node-{thread_id}-{i}",
-                    input_name="test_input",
-                    input_data="test_value",
-                    block_id=f"block-{thread_id}-{i}",
-                )
-                created_exec_ids.append((exec_id, thread_id, i))
-
-        def worker(thread_data):
-            """Perform multiple operations from a thread."""
-            thread_id, ops = thread_data
-            for i, (exec_id, _, _) in enumerate(ops):
-                # Status updates
-                execution_client.update_node_status_and_publish(
-                    exec_id=exec_id,
-                    status=ExecutionStatus.RUNNING,
-                    execution_data={"thread": thread_id, "op": i},
-                )
-
-                # Output updates (use just one exec_id per thread for outputs)
-                if i == 0:  # Only add outputs to first execution of each thread
-                    execution_client.upsert_execution_output(
-                        node_exec_id=exec_id,
-                        output_name=f"output_{i}",
-                        output_data={"thread": thread_id, "value": i},
-                    )
-
-        # Organize executions by thread
-        thread_data = []
-        for tid in range(num_threads):
-            thread_ops = [
-                exec_data for exec_data in created_exec_ids if exec_data[1] == tid
-            ]
-            thread_data.append((tid, thread_ops))
-
-        # Start multiple threads
-        threads = []
-        for data in thread_data:
-            thread = threading.Thread(target=worker, args=(data,))
-            threads.append(thread)
-            thread.start()
-
-        # Wait for completion
-        for thread in threads:
-            thread.join()
-
-        # Verify data integrity
-        expected_executions = num_threads * operations_per_thread
-        all_executions = execution_client.get_node_executions()
-        assert len(all_executions) == expected_executions
-
-        # Verify outputs - only first execution of each thread should have outputs
-        output_count = 0
-        for execution in all_executions:
-            if execution.output_data:
-                output_count += 1
-        assert output_count == num_threads  # One output per thread
-
-    async def test_sync_and_async_versions_consistent(self, execution_client):
-        """Test that sync and async versions of output operations behave the same."""
-        # First create the execution
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="sync-async-test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"input": "test"},
-        )
-
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id,
-            output_name="sync_result",
-            output_data={"method": "sync"},
-        )
-
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id,
-            output_name="async_result",
-            output_data={"method": "async"},
-        )
-
-        cached_exec = execution_client.get_node_execution(node_exec_id)
-        assert cached_exec is not None
-        assert "sync_result" in cached_exec.output_data
-        assert "async_result" in cached_exec.output_data
-        assert cached_exec.output_data["sync_result"] == [{"method": "sync"}]
-        assert cached_exec.output_data["async_result"] == [{"method": "async"}]
-
-    async def test_finalize_execution_completes_and_clears_cache(
-        self, execution_client
-    ):
-        """Test that finalize_execution waits for background tasks and clears cache."""
-        # First create the execution
-        node_exec_id, _ = execution_client.upsert_execution_input(
-            node_id="pending-test-node",
-            input_name="test_input",
-            input_data="test_value",
-            block_id="test-block",
-        )
-
-        # Trigger some background operations
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id, status=ExecutionStatus.RUNNING
-        )
-
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id, output_name="test", output_data={"value": 1}
-        )
-
-        # Wait for background tasks - may fail in test environment due to DB issues
-        try:
-            execution_client.finalize_execution(timeout=5.0)
-        except RuntimeError as e:
-            # In test environment, background DB operations may fail, but cache should still be cleared
-            assert "Background persistence failed" in str(e)
-
-        # Cache should be cleared regardless of background task failures
-        all_executions = execution_client.get_node_executions()
-        assert len(all_executions) == 0  # Cache should be cleared
-
-    async def test_manager_usage_pattern(self, execution_client):
-        # Create executions first
-        node_exec_id_1, _ = execution_client.upsert_execution_input(
-            node_id="node-1",
-            input_name="input1",
-            input_data="data1",
-            block_id="block-1",
-        )
-
-        node_exec_id_2, _ = execution_client.upsert_execution_input(
-            node_id="node-2",
-            input_name="input_from_node1",
-            input_data="value1",
-            block_id="block-2",
-        )
-
-        # Simulate manager.py workflow
-
-        # 1. Start execution
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id_1,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"input": "data1"},
-        )
-
-        # 2. Node produces output
-        execution_client.upsert_execution_output(
-            node_exec_id=node_exec_id_1,
-            output_name="result",
-            output_data={"output": "value1"},
-        )
-
-        # 3. Complete first node
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id_1, status=ExecutionStatus.COMPLETED
-        )
-
-        # 4. Start second node (would read output from first)
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id_2,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"input_from_node1": "value1"},
-        )
-
-        # 5. Manager queries for executions
-
-        all_executions = execution_client.get_node_executions()
-        running_executions = execution_client.get_node_executions(
-            statuses=[ExecutionStatus.RUNNING]
-        )
-        completed_executions = execution_client.get_node_executions(
-            statuses=[ExecutionStatus.COMPLETED]
-        )
-
-        # Verify manager can see all data immediately
-        assert len(all_executions) == 2
-        assert len(running_executions) == 1
-        assert len(completed_executions) == 1
-
-        # Verify output is accessible
-        exec_1 = execution_client.get_node_execution(node_exec_id_1)
-        assert exec_1 is not None
-        assert exec_1.output_data["result"] == [{"output": "value1"}]
-
-    def test_stats_handling_in_update_node_status(self, execution_client):
-        """Test that stats parameter is properly handled in update_node_status_and_publish."""
-        # Create a fake execution directly in cache to avoid database issues
-        from datetime import datetime, timezone
-
-        from backend.data.execution import NodeExecutionResult
-
-        node_exec_id = "test-stats-exec-id"
-        fake_execution = NodeExecutionResult(
-            user_id="test-user",
-            graph_id="test-graph",
-            graph_version=1,
-            graph_exec_id="test-graph-exec",
-            node_exec_id=node_exec_id,
-            node_id="stats-test-node",
-            block_id="test-block",
-            status=ExecutionStatus.INCOMPLETE,
-            input_data={"test_input": "test_value"},
-            output_data={},
-            add_time=datetime.now(timezone.utc),
-            queue_time=None,
-            start_time=None,
-            end_time=None,
-            stats=None,
-        )
-
-        # Add directly to cache
-        execution_client._cache.add_node_execution(node_exec_id, fake_execution)
-
-        stats = {"token_count": 150, "processing_time": 2.5}
-
-        # Update status with stats
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.RUNNING,
-            execution_data={"input": "test"},
-            stats=stats,
-        )
-
-        # Verify execution was updated and stats are stored
-        execution = execution_client.get_node_execution(node_exec_id)
-        assert execution is not None
-        assert execution.status == ExecutionStatus.RUNNING
-
-        # Stats should be stored in proper stats field
-        assert execution.stats is not None
-        stats_dict = execution.stats.model_dump()
-        # Only check the fields we set, ignore defaults
-        assert stats_dict["token_count"] == 150
-        assert stats_dict["processing_time"] == 2.5
-
-        # Update with additional stats
-        additional_stats = {"error_count": 0}
-        execution_client.update_node_status_and_publish(
-            exec_id=node_exec_id,
-            status=ExecutionStatus.COMPLETED,
-            stats=additional_stats,
-        )
-
-        # Stats should be merged
-        execution = execution_client.get_node_execution(node_exec_id)
-        assert execution is not None
-        assert execution.status == ExecutionStatus.COMPLETED
-        stats_dict = execution.stats.model_dump()
-        # Check the merged stats
-        assert stats_dict["token_count"] == 150
-        assert stats_dict["processing_time"] == 2.5
-        assert stats_dict["error_count"] == 0
-
-    async def test_upsert_execution_input_scenarios(self, execution_client):
-        """Test different scenarios of upsert_execution_input - create vs update."""
-        node_id = "test-node"
-        graph_exec_id = (
-            "test_graph_exec_id"  # Must match the ExecutionDataClient's scope
-        )
-
-        # Scenario 1: Create new execution when none exists
-        exec_id_1, input_data_1 = execution_client.upsert_execution_input(
-            node_id=node_id,
-            input_name="first_input",
-            input_data="value1",
-            block_id="test-block",
-        )
-
-        # Should create new execution
-        execution = execution_client.get_node_execution(exec_id_1)
-        assert execution is not None
-        assert execution.status == ExecutionStatus.INCOMPLETE
-        assert execution.node_id == node_id
-        assert execution.graph_exec_id == graph_exec_id
-        assert input_data_1 == {"first_input": "value1"}
-
-        # Scenario 2: Add input to existing INCOMPLETE execution
-        exec_id_2, input_data_2 = execution_client.upsert_execution_input(
-            node_id=node_id,
-            input_name="second_input",
-            input_data="value2",
-            block_id="test-block",
-        )
-
-        # Should use same execution
-        assert exec_id_2 == exec_id_1
-        assert input_data_2 == {"first_input": "value1", "second_input": "value2"}
-
-        # Verify execution has both inputs
-        execution = execution_client.get_node_execution(exec_id_1)
-        assert execution is not None
-        assert execution.input_data == {
-            "first_input": "value1",
-            "second_input": "value2",
-        }
-
-        # Scenario 3: Create new execution when existing is not INCOMPLETE
-        execution_client.update_node_status_and_publish(
-            exec_id=exec_id_1, status=ExecutionStatus.RUNNING
-        )
-
-        exec_id_3, input_data_3 = execution_client.upsert_execution_input(
-            node_id=node_id,
-            input_name="third_input",
-            input_data="value3",
-            block_id="test-block",
-        )
-
-        # Should create new execution
-        assert exec_id_3 != exec_id_1
-        execution_3 = execution_client.get_node_execution(exec_id_3)
-        assert execution_3 is not None
-        assert input_data_3 == {"third_input": "value3"}
-
-        # Verify we now have 2 executions
-        all_executions = execution_client.get_node_executions()
-        assert len(all_executions) == 2
-
-    def test_graph_stats_operations(self, execution_client):
-        """Test graph-level stats and start time operations."""
-
-        # Test update_graph_stats_and_publish
-        from backend.data.model import GraphExecutionStats
-
-        stats = GraphExecutionStats(
-            walltime=10.5, cputime=8.2, node_count=5, node_error_count=1
-        )
-
-        execution_client.update_graph_stats_and_publish(
-            status=ExecutionStatus.RUNNING, stats=stats
-        )
-
-        # Verify stats are stored in cache
-        cached_stats = execution_client._cache._graph_stats
-        assert cached_stats.walltime == 10.5
-
-        execution_client.update_graph_start_time_and_publish()
-        cached_stats = execution_client._cache._graph_stats
-        assert cached_stats.walltime == 10.5
-
-    def test_public_methods_accessible(self, execution_client):
-        """Test that public methods are accessible."""
-        assert hasattr(execution_client._cache, "update_node_execution_status")
-        assert hasattr(execution_client._cache, "upsert_execution_output")
-        assert hasattr(execution_client._cache, "add_node_execution")
-        assert hasattr(execution_client._cache, "find_incomplete_execution_for_input")
-        assert hasattr(execution_client._cache, "update_execution_input")
-        assert hasattr(execution_client, "upsert_execution_input")
-        assert hasattr(execution_client, "update_node_status_and_publish")
-
-
-if __name__ == "__main__":
-    pytest.main([__file__, "-v"])

autogpt_platform/backend/backend/executor/manager.py

@@ -5,31 +5,14 @@ import threading
 import time
 from collections import defaultdict
 from concurrent.futures import Future, ThreadPoolExecutor
-from typing import Any, Optional, TypeVar, cast
+from contextlib import asynccontextmanager
+from typing import TYPE_CHECKING, Any, Optional, TypeVar, cast
 
 from pika.adapters.blocking_connection import BlockingChannel
 from pika.spec import Basic, BasicProperties
-from prometheus_client import Gauge, start_http_server
-from pydantic import JsonValue
+from redis.asyncio.lock import Lock as RedisLock
 
-from backend.blocks.agent import AgentExecutorBlock
 from backend.blocks.io import AgentOutputBlock
-from backend.data.block import (
-    BlockData,
-    BlockInput,
-    BlockOutput,
-    BlockSchema,
-    get_block,
-)
-from backend.data.credit import UsageTransactionMetadata
-from backend.data.execution import (
-    ExecutionQueue,
-    ExecutionStatus,
-    GraphExecutionEntry,
-    NodeExecutionEntry,
-    UserContext,
-)
-from backend.data.graph import Link, Node
 from backend.data.model import GraphExecutionStats, NodeExecutionStats
 from backend.data.notifications import (
     AgentRunData,
@@ -39,14 +22,45 @@ from backend.data.notifications import (
     ZeroBalanceData,
 )
 from backend.data.rabbitmq import SyncRabbitMQ
-from backend.executor.execution_data import ExecutionDataClient
+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 (
+    BlockInput,
+    BlockOutput,
+    BlockOutputEntry,
+    BlockSchema,
+    get_block,
+)
+from backend.data.credit import UsageTransactionMetadata
+from backend.data.execution import (
+    ExecutionQueue,
+    ExecutionStatus,
+    GraphExecution,
+    GraphExecutionEntry,
+    NodeExecutionEntry,
+    NodeExecutionResult,
+    NodesInputMasks,
+    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,
@@ -55,17 +69,21 @@ from backend.executor.utils import (
     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 get_notification_manager_client
+from backend.util.clients import (
+    get_async_execution_event_bus,
+    get_database_manager_async_client,
+    get_database_manager_client,
+    get_execution_event_bus,
+    get_notification_manager_client,
+)
 from backend.util.decorator import (
     async_error_logged,
     async_time_measured,
     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
@@ -113,13 +131,14 @@ 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,
     persist the execution result, and return the subsequent node to be executed.
 
     Args:
+        db_client: The client to send execution updates to the server.
         creds_manager: The manager to acquire and release credentials.
         data: The execution data for executing the current node.
         execution_stats: The execution statistics to be updated.
@@ -216,20 +235,21 @@ async def execute_node(
 
 
 async def _enqueue_next_nodes(
-    execution_data_client: ExecutionDataClient,
+    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(
         node_exec_id: str, node_id: str, block_id: str, data: BlockInput
     ) -> NodeExecutionEntry:
-        execution_data_client.update_node_status_and_publish(
+        await async_update_node_execution_status(
+            db_client=db_client,
             exec_id=node_exec_id,
             status=ExecutionStatus.QUEUED,
             execution_data=data,
@@ -262,22 +282,21 @@ async def _enqueue_next_nodes(
         next_data = parse_execution_output(output, next_output_name)
         if next_data is None and output_name != next_output_name:
             return enqueued_executions
-        next_node = await execution_data_client.get_node(next_node_id)
+        next_node = await db_client.get_node(next_node_id)
 
         # Multiple node can register the same next node, we need this to be atomic
         # To avoid same execution to be enqueued multiple times,
         # Or the same input to be consumed multiple times.
-        with execution_data_client.graph_lock:
+        async with synchronized(f"upsert_input-{next_node_id}-{graph_exec_id}"):
             # Add output data to the earliest incomplete execution, or create a new one.
-            next_node_exec_id, next_node_input = (
-                execution_data_client.upsert_execution_input(
-                    node_id=next_node_id,
-                    input_name=next_input_name,
-                    input_data=next_data,
-                    block_id=next_node.block_id,
-                )
+            next_node_exec_id, next_node_input = await db_client.upsert_execution_input(
+                node_id=next_node_id,
+                graph_exec_id=graph_exec_id,
+                input_name=next_input_name,
+                input_data=next_data,
             )
-            execution_data_client.update_node_status_and_publish(
+            await async_update_node_execution_status(
+                db_client=db_client,
                 exec_id=next_node_exec_id,
                 status=ExecutionStatus.INCOMPLETE,
             )
@@ -289,8 +308,8 @@ async def _enqueue_next_nodes(
                 if link.is_static and link.sink_name not in next_node_input
             }
             if static_link_names and (
-                latest_execution := execution_data_client.get_latest_node_execution(
-                    next_node_id
+                latest_execution := await db_client.get_latest_node_execution(
+                    next_node_id, graph_exec_id
                 )
             ):
                 for name in static_link_names:
@@ -329,8 +348,9 @@ async def _enqueue_next_nodes(
 
             # If link is static, there could be some incomplete executions waiting for it.
             # Load and complete the input missing input data, and try to re-enqueue them.
-            for iexec in execution_data_client.get_node_executions(
+            for iexec in await db_client.get_node_executions(
                 node_id=next_node_id,
+                graph_exec_id=graph_exec_id,
                 statuses=[ExecutionStatus.INCOMPLETE],
             ):
                 idata = iexec.input_data
@@ -394,15 +414,12 @@ class ExecutionProcessor:
         9. Node executor enqueues the next executed nodes to the node execution queue.
     """
 
-    # Current execution data client (scoped to current graph execution)
-    execution_data: ExecutionDataClient
-
     @async_error_logged(swallow=True)
     async def on_node_execution(
         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(
@@ -414,7 +431,8 @@ class ExecutionProcessor:
             node_id=node_exec.node_id,
             block_name="-",
         )
-        node = await self.execution_data.get_node(node_exec.node_id)
+        db_client = get_db_async_client()
+        node = await db_client.get_node(node_exec.node_id)
         execution_stats = NodeExecutionStats()
 
         timing_info, status = await self._on_node_execution(
@@ -422,6 +440,7 @@ class ExecutionProcessor:
             node_exec=node_exec,
             node_exec_progress=node_exec_progress,
             stats=execution_stats,
+            db_client=db_client,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
         )
@@ -445,12 +464,15 @@ class ExecutionProcessor:
         if node_error and not isinstance(node_error, str):
             node_stats["error"] = str(node_error) or node_stats.__class__.__name__
 
-        self.execution_data.update_node_status_and_publish(
+        await async_update_node_execution_status(
+            db_client=db_client,
             exec_id=node_exec.node_exec_id,
             status=status,
             stats=node_stats,
         )
-        self.execution_data.update_graph_stats_and_publish(
+        await async_update_graph_execution_state(
+            db_client=db_client,
+            graph_exec_id=node_exec.graph_exec_id,
             stats=graph_stats,
         )
 
@@ -463,17 +485,22 @@ class ExecutionProcessor:
         node_exec: NodeExecutionEntry,
         node_exec_progress: NodeExecutionProgress,
         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
 
         async def persist_output(output_name: str, output_data: Any) -> None:
-            self.execution_data.upsert_execution_output(
+            await db_client.upsert_execution_output(
                 node_exec_id=node_exec.node_exec_id,
                 output_name=output_name,
                 output_data=output_data,
             )
+            if exec_update := await db_client.get_node_execution(
+                node_exec.node_exec_id
+            ):
+                await send_async_execution_update(exec_update)
 
             node_exec_progress.add_output(
                 ExecutionOutputEntry(
@@ -485,7 +512,8 @@ class ExecutionProcessor:
 
         try:
             log_metadata.info(f"Start node execution {node_exec.node_exec_id}")
-            self.execution_data.update_node_status_and_publish(
+            await async_update_node_execution_status(
+                db_client=db_client,
                 exec_id=node_exec.node_exec_id,
                 status=ExecutionStatus.RUNNING,
             )
@@ -546,8 +574,6 @@ class ExecutionProcessor:
         self.node_evaluation_thread = threading.Thread(
             target=self.node_evaluation_loop.run_forever, daemon=True
         )
-        # single thread executor
-        self.execution_data_executor = ThreadPoolExecutor(max_workers=1)
         self.node_execution_thread.start()
         self.node_evaluation_thread.start()
         logger.info(f"[GraphExecutor] {self.tid} started")
@@ -567,13 +593,9 @@ class ExecutionProcessor:
             node_eid="*",
             block_name="-",
         )
+        db_client = get_db_client()
 
-        # Get graph execution metadata first via sync client
-        from backend.util.clients import get_database_manager_client
-
-        db_client_sync = get_database_manager_client()
-
-        exec_meta = db_client_sync.get_graph_execution_meta(
+        exec_meta = db_client.get_graph_execution_meta(
             user_id=graph_exec.user_id,
             execution_id=graph_exec.graph_exec_id,
         )
@@ -583,15 +605,12 @@ class ExecutionProcessor:
             )
             return
 
-        # Create scoped ExecutionDataClient for this graph execution with metadata
-        self.execution_data = ExecutionDataClient(
-            self.execution_data_executor, graph_exec.graph_exec_id, exec_meta
-        )
-
         if exec_meta.status == ExecutionStatus.QUEUED:
             log_metadata.info(f"⚙️ Starting graph execution #{graph_exec.graph_exec_id}")
             exec_meta.status = ExecutionStatus.RUNNING
-            self.execution_data.update_graph_start_time_and_publish()
+            send_execution_update(
+                db_client.update_graph_execution_start_time(graph_exec.graph_exec_id)
+            )
         elif exec_meta.status == ExecutionStatus.RUNNING:
             log_metadata.info(
                 f"⚙️ Graph execution #{graph_exec.graph_exec_id} is already running, continuing where it left off."
@@ -601,7 +620,9 @@ class ExecutionProcessor:
             log_metadata.info(
                 f"⚙️ Graph execution #{graph_exec.graph_exec_id} was disturbed, continuing where it left off."
             )
-            self.execution_data.update_graph_stats_and_publish(
+            update_graph_execution_state(
+                db_client=db_client,
+                graph_exec_id=graph_exec.graph_exec_id,
                 status=ExecutionStatus.RUNNING,
             )
         else:
@@ -632,10 +653,12 @@ class ExecutionProcessor:
 
             # Activity status handling
             activity_status = asyncio.run_coroutine_threadsafe(
-                self.execution_data.generate_activity_status(
+                generate_activity_status_for_execution(
+                    graph_exec_id=graph_exec.graph_exec_id,
                     graph_id=graph_exec.graph_id,
                     graph_version=graph_exec.graph_version,
                     execution_stats=exec_stats,
+                    db_client=get_db_async_client(),
                     user_id=graph_exec.user_id,
                     execution_status=status,
                 ),
@@ -650,14 +673,15 @@ class ExecutionProcessor:
                 )
 
             # Communication handling
-            self._handle_agent_run_notif(graph_exec, exec_stats)
+            self._handle_agent_run_notif(db_client, graph_exec, exec_stats)
 
         finally:
-            self.execution_data.update_graph_stats_and_publish(
+            update_graph_execution_state(
+                db_client=db_client,
+                graph_exec_id=graph_exec.graph_exec_id,
                 status=exec_meta.status,
                 stats=exec_stats,
             )
-            self.execution_data.finalize_execution()
 
     def _charge_usage(
         self,
@@ -666,6 +690,7 @@ class ExecutionProcessor:
     ) -> tuple[int, int]:
         total_cost = 0
         remaining_balance = 0
+        db_client = get_db_client()
         block = get_block(node_exec.block_id)
         if not block:
             logger.error(f"Block {node_exec.block_id} not found.")
@@ -675,7 +700,7 @@ class ExecutionProcessor:
             block=block, input_data=node_exec.inputs
         )
         if cost > 0:
-            remaining_balance = self.execution_data.spend_credits(
+            remaining_balance = db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -693,7 +718,7 @@ class ExecutionProcessor:
 
         cost, usage_count = execution_usage_cost(execution_count)
         if cost > 0:
-            remaining_balance = self.execution_data.spend_credits(
+            remaining_balance = db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -726,6 +751,7 @@ class ExecutionProcessor:
         """
         execution_status: ExecutionStatus = ExecutionStatus.RUNNING
         error: Exception | None = None
+        db_client = get_db_client()
         execution_stats_lock = threading.Lock()
 
         # State holders ----------------------------------------------------
@@ -736,7 +762,7 @@ class ExecutionProcessor:
         execution_queue = ExecutionQueue[NodeExecutionEntry]()
 
         try:
-            if self.execution_data.get_credits(graph_exec.user_id) <= 0:
+            if db_client.get_credits(graph_exec.user_id) <= 0:
                 raise InsufficientBalanceError(
                     user_id=graph_exec.user_id,
                     message="You have no credits left to run an agent.",
@@ -748,7 +774,7 @@ class ExecutionProcessor:
             try:
                 if moderation_error := asyncio.run_coroutine_threadsafe(
                     automod_manager.moderate_graph_execution_inputs(
-                        db_client=self.execution_data.db_client_async,
+                        db_client=get_db_async_client(),
                         graph_exec=graph_exec,
                     ),
                     self.node_evaluation_loop,
@@ -763,34 +789,16 @@ class ExecutionProcessor:
             # ------------------------------------------------------------
             # Pre‑populate queue ---------------------------------------
             # ------------------------------------------------------------
-
-            queued_executions = self.execution_data.get_node_executions(
+            for node_exec in db_client.get_node_executions(
+                graph_exec.graph_exec_id,
                 statuses=[
                     ExecutionStatus.RUNNING,
                     ExecutionStatus.QUEUED,
                     ExecutionStatus.TERMINATED,
                 ],
-            )
-            log_metadata.info(
-                f"Pre-populating queue with {len(queued_executions)} executions from cache"
-            )
-
-            for i, node_exec in enumerate(queued_executions):
-                log_metadata.info(
-                    f"  [{i}] {node_exec.node_exec_id}: status={node_exec.status}, node={node_exec.node_id}"
-                )
-                try:
-                    node_entry = node_exec.to_node_execution_entry(
-                        graph_exec.user_context
-                    )
-                    execution_queue.add(node_entry)
-                    log_metadata.info("    Added to execution queue successfully")
-                except Exception as e:
-                    log_metadata.error(f"    Failed to add to execution queue: {e}")
-
-            log_metadata.info(
-                f"Execution queue populated with {len(queued_executions)} executions"
-            )
+            ):
+                node_entry = node_exec.to_node_execution_entry(graph_exec.user_context)
+                execution_queue.add(node_entry)
 
             # ------------------------------------------------------------
             # Main dispatch / polling loop -----------------------------
@@ -810,14 +818,13 @@ class ExecutionProcessor:
                 try:
                     cost, remaining_balance = self._charge_usage(
                         node_exec=queued_node_exec,
-                        execution_count=self.execution_data.increment_execution_count(
-                            graph_exec.user_id
-                        ),
+                        execution_count=increment_execution_count(graph_exec.user_id),
                     )
                     with execution_stats_lock:
                         execution_stats.cost += cost
                     # Check if we crossed the low balance threshold
                     self._handle_low_balance(
+                        db_client=db_client,
                         user_id=graph_exec.user_id,
                         current_balance=remaining_balance,
                         transaction_cost=cost,
@@ -825,17 +832,19 @@ class ExecutionProcessor:
                 except InsufficientBalanceError as balance_error:
                     error = balance_error  # Set error to trigger FAILED status
                     node_exec_id = queued_node_exec.node_exec_id
-                    self.execution_data.upsert_execution_output(
+                    db_client.upsert_execution_output(
                         node_exec_id=node_exec_id,
                         output_name="error",
                         output_data=str(error),
                     )
-                    self.execution_data.update_node_status_and_publish(
+                    update_node_execution_status(
+                        db_client=db_client,
                         exec_id=node_exec_id,
                         status=ExecutionStatus.FAILED,
                     )
 
                     self._handle_insufficient_funds_notif(
+                        db_client,
                         graph_exec.user_id,
                         graph_exec.graph_id,
                         error,
@@ -922,13 +931,12 @@ class ExecutionProcessor:
                         time.sleep(0.1)
 
             # loop done --------------------------------------------------
-            # Background task finalization moved to finally block
 
             # Output moderation
             try:
                 if moderation_error := asyncio.run_coroutine_threadsafe(
                     automod_manager.moderate_graph_execution_outputs(
-                        db_client=self.execution_data.db_client_async,
+                        db_client=get_db_async_client(),
                         graph_exec_id=graph_exec.graph_exec_id,
                         user_id=graph_exec.user_id,
                         graph_id=graph_exec.graph_id,
@@ -982,6 +990,7 @@ class ExecutionProcessor:
                 error=error,
                 graph_exec_id=graph_exec.graph_exec_id,
                 log_metadata=log_metadata,
+                db_client=db_client,
             )
 
     @error_logged(swallow=True)
@@ -994,6 +1003,7 @@ class ExecutionProcessor:
         error: Exception | None,
         graph_exec_id: str,
         log_metadata: LogMetadata,
+        db_client: "DatabaseManagerClient",
     ) -> None:
         """
         Clean up running node executions and evaluations when graph execution ends.
@@ -1027,7 +1037,8 @@ class ExecutionProcessor:
                 )
 
         while queued_execution := execution_queue.get_or_none():
-            self.execution_data.update_node_status_and_publish(
+            update_node_execution_status(
+                db_client=db_client,
                 exec_id=queued_execution.node_exec_id,
                 status=execution_status,
                 stats={"error": str(error)} if error else None,
@@ -1042,7 +1053,7 @@ 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.
@@ -1055,10 +1066,12 @@ class ExecutionProcessor:
             nodes_input_masks: Optional map of node input overrides
             execution_queue: Queue to add next executions to
         """
+        db_client = get_db_async_client()
+
         log_metadata.debug(f"Enqueue nodes for {node_id}: {output}")
 
         for next_execution in await _enqueue_next_nodes(
-            execution_data_client=self.execution_data,
+            db_client=db_client,
             node=output.node,
             output=output.data,
             user_id=graph_exec.user_id,
@@ -1072,13 +1085,15 @@ class ExecutionProcessor:
 
     def _handle_agent_run_notif(
         self,
+        db_client: "DatabaseManagerClient",
         graph_exec: GraphExecutionEntry,
         exec_stats: GraphExecutionStats,
     ):
-        metadata = self.execution_data.get_graph_metadata(
+        metadata = db_client.get_graph_metadata(
             graph_exec.graph_id, graph_exec.graph_version
         )
-        outputs = self.execution_data.get_node_executions(
+        outputs = db_client.get_node_executions(
+            graph_exec.graph_exec_id,
             block_ids=[AgentOutputBlock().id],
         )
 
@@ -1107,12 +1122,13 @@ class ExecutionProcessor:
 
     def _handle_insufficient_funds_notif(
         self,
+        db_client: "DatabaseManagerClient",
         user_id: str,
         graph_id: str,
         e: InsufficientBalanceError,
     ):
         shortfall = abs(e.amount) - e.balance
-        metadata = self.execution_data.get_graph_metadata(graph_id)
+        metadata = db_client.get_graph_metadata(graph_id)
         base_url = (
             settings.config.frontend_base_url or settings.config.platform_base_url
         )
@@ -1131,7 +1147,7 @@ class ExecutionProcessor:
         )
 
         try:
-            user_email = self.execution_data.get_user_email_by_id(user_id)
+            user_email = db_client.get_user_email_by_id(user_id)
 
             alert_message = (
                 f"❌ **Insufficient Funds Alert**\n"
@@ -1153,6 +1169,7 @@ class ExecutionProcessor:
 
     def _handle_low_balance(
         self,
+        db_client: "DatabaseManagerClient",
         user_id: str,
         current_balance: int,
         transaction_cost: int,
@@ -1181,7 +1198,7 @@ class ExecutionProcessor:
             )
 
             try:
-                user_email = self.execution_data.get_user_email_by_id(user_id)
+                user_email = db_client.get_user_email_by_id(user_id)
                 alert_message = (
                     f"⚠️ **Low Balance Alert**\n"
                     f"User: {user_email or user_id}\n"
@@ -1559,3 +1576,117 @@ class ExecutionManager(AppProcess):
         )
 
         logger.info(f"{prefix} ✅ Finished GraphExec cleanup")
+
+
+# ------- UTILITIES ------- #
+
+
+def get_db_client() -> "DatabaseManagerClient":
+    return get_database_manager_client()
+
+
+def get_db_async_client() -> "DatabaseManagerAsyncClient":
+    return get_database_manager_async_client()
+
+
+@func_retry
+async def send_async_execution_update(
+    entry: GraphExecution | NodeExecutionResult | None,
+) -> None:
+    if entry is None:
+        return
+    await get_async_execution_event_bus().publish(entry)
+
+
+@func_retry
+def send_execution_update(entry: GraphExecution | NodeExecutionResult | None):
+    if entry is None:
+        return
+    return get_execution_event_bus().publish(entry)
+
+
+async def async_update_node_execution_status(
+    db_client: "DatabaseManagerAsyncClient",
+    exec_id: str,
+    status: ExecutionStatus,
+    execution_data: BlockInput | None = None,
+    stats: dict[str, Any] | None = None,
+) -> NodeExecutionResult:
+    """Sets status and fetches+broadcasts the latest state of the node execution"""
+    exec_update = await db_client.update_node_execution_status(
+        exec_id, status, execution_data, stats
+    )
+    await send_async_execution_update(exec_update)
+    return exec_update
+
+
+def update_node_execution_status(
+    db_client: "DatabaseManagerClient",
+    exec_id: str,
+    status: ExecutionStatus,
+    execution_data: BlockInput | None = None,
+    stats: dict[str, Any] | None = None,
+) -> NodeExecutionResult:
+    """Sets status and fetches+broadcasts the latest state of the node execution"""
+    exec_update = db_client.update_node_execution_status(
+        exec_id, status, execution_data, stats
+    )
+    send_execution_update(exec_update)
+    return exec_update
+
+
+async def async_update_graph_execution_state(
+    db_client: "DatabaseManagerAsyncClient",
+    graph_exec_id: str,
+    status: ExecutionStatus | None = None,
+    stats: GraphExecutionStats | None = None,
+) -> GraphExecution | None:
+    """Sets status and fetches+broadcasts the latest state of the graph execution"""
+    graph_update = await db_client.update_graph_execution_stats(
+        graph_exec_id, status, stats
+    )
+    if graph_update:
+        await send_async_execution_update(graph_update)
+    else:
+        logger.error(f"Failed to update graph execution stats for {graph_exec_id}")
+    return graph_update
+
+
+def update_graph_execution_state(
+    db_client: "DatabaseManagerClient",
+    graph_exec_id: str,
+    status: ExecutionStatus | None = None,
+    stats: GraphExecutionStats | None = None,
+) -> GraphExecution | None:
+    """Sets status and fetches+broadcasts the latest state of the graph execution"""
+    graph_update = db_client.update_graph_execution_stats(graph_exec_id, status, stats)
+    if graph_update:
+        send_execution_update(graph_update)
+    else:
+        logger.error(f"Failed to update graph execution stats for {graph_exec_id}")
+    return graph_update
+
+
+@asynccontextmanager
+async def synchronized(key: str, timeout: int = 60):
+    r = await redis.get_redis_async()
+    lock: RedisLock = r.lock(f"lock:{key}", timeout=timeout)
+    try:
+        await lock.acquire()
+        yield
+    finally:
+        if await lock.locked() and await lock.owned():
+            await lock.release()
+
+
+def increment_execution_count(user_id: str) -> int:
+    """
+    Increment the execution count for a given user,
+    this will be used to charge the user for the execution cost.
+    """
+    r = redis.get_redis()
+    k = f"uec:{user_id}"  # User Execution Count global key
+    counter = cast(int, r.incr(k))
+    if counter == 1:
+        r.expire(k, settings.config.execution_counter_expiration_time)
+    return counter

autogpt_platform/backend/backend/executor/manager_low_balance_test.py

@@ -32,17 +32,13 @@ async def test_handle_low_balance_threshold_crossing(server: SpinTestServer):
         mock_settings.config.low_balance_threshold = 500  # $5 threshold
         mock_settings.config.frontend_base_url = "https://test.com"
 
-        # Initialize the execution processor and mock its execution_data
-        execution_processor.on_graph_executor_start()
-
-        # Mock the execution_data attribute since it's created in on_graph_execution
-        mock_execution_data = MagicMock()
-        execution_processor.execution_data = mock_execution_data
-
-        mock_execution_data.get_user_email_by_id.return_value = "test@example.com"
+        # Create mock database client
+        mock_db_client = MagicMock()
+        mock_db_client.get_user_email_by_id.return_value = "test@example.com"
 
         # Test the low balance handler
         execution_processor._handle_low_balance(
+            db_client=mock_db_client,
             user_id=user_id,
             current_balance=current_balance,
             transaction_cost=transaction_cost,
@@ -66,19 +62,6 @@ async def test_handle_low_balance_threshold_crossing(server: SpinTestServer):
         assert "$4.00" in discord_message
         assert "$6.00" in discord_message
 
-    # Cleanup execution processor threads
-    try:
-        execution_processor.node_execution_loop.call_soon_threadsafe(
-            execution_processor.node_execution_loop.stop
-        )
-        execution_processor.node_evaluation_loop.call_soon_threadsafe(
-            execution_processor.node_evaluation_loop.stop
-        )
-        execution_processor.node_execution_thread.join(timeout=1)
-        execution_processor.node_evaluation_thread.join(timeout=1)
-    except Exception:
-        pass  # Ignore cleanup errors
-
 
 @pytest.mark.asyncio(loop_scope="session")
 async def test_handle_low_balance_no_notification_when_not_crossing(
@@ -107,17 +90,12 @@ async def test_handle_low_balance_no_notification_when_not_crossing(
         mock_get_client.return_value = mock_client
         mock_settings.config.low_balance_threshold = 500  # $5 threshold
 
-        # Initialize the execution processor and mock its execution_data
-        execution_processor.on_graph_executor_start()
-
-        # Mock the execution_data attribute since it's created in on_graph_execution
-        mock_execution_data = MagicMock()
-        execution_processor.execution_data = mock_execution_data
-
-        mock_execution_data.get_user_email_by_id.return_value = "test@example.com"
+        # Create mock database client
+        mock_db_client = MagicMock()
 
         # Test the low balance handler
         execution_processor._handle_low_balance(
+            db_client=mock_db_client,
             user_id=user_id,
             current_balance=current_balance,
             transaction_cost=transaction_cost,
@@ -127,19 +105,6 @@ async def test_handle_low_balance_no_notification_when_not_crossing(
         mock_queue_notif.assert_not_called()
         mock_client.discord_system_alert.assert_not_called()
 
-    # Cleanup execution processor threads
-    try:
-        execution_processor.node_execution_loop.call_soon_threadsafe(
-            execution_processor.node_execution_loop.stop
-        )
-        execution_processor.node_evaluation_loop.call_soon_threadsafe(
-            execution_processor.node_evaluation_loop.stop
-        )
-        execution_processor.node_execution_thread.join(timeout=1)
-        execution_processor.node_evaluation_thread.join(timeout=1)
-    except Exception:
-        pass  # Ignore cleanup errors
-
 
 @pytest.mark.asyncio(loop_scope="session")
 async def test_handle_low_balance_no_duplicate_when_already_below(
@@ -168,17 +133,12 @@ async def test_handle_low_balance_no_duplicate_when_already_below(
         mock_get_client.return_value = mock_client
         mock_settings.config.low_balance_threshold = 500  # $5 threshold
 
-        # Initialize the execution processor and mock its execution_data
-        execution_processor.on_graph_executor_start()
-
-        # Mock the execution_data attribute since it's created in on_graph_execution
-        mock_execution_data = MagicMock()
-        execution_processor.execution_data = mock_execution_data
-
-        mock_execution_data.get_user_email_by_id.return_value = "test@example.com"
+        # Create mock database client
+        mock_db_client = MagicMock()
 
         # Test the low balance handler
         execution_processor._handle_low_balance(
+            db_client=mock_db_client,
             user_id=user_id,
             current_balance=current_balance,
             transaction_cost=transaction_cost,
@@ -187,16 +147,3 @@ async def test_handle_low_balance_no_duplicate_when_already_below(
         # Verify no notification was sent (user was already below threshold)
         mock_queue_notif.assert_not_called()
         mock_client.discord_system_alert.assert_not_called()
-
-    # Cleanup execution processor threads
-    try:
-        execution_processor.node_execution_loop.call_soon_threadsafe(
-            execution_processor.node_execution_loop.stop
-        )
-        execution_processor.node_evaluation_loop.call_soon_threadsafe(
-            execution_processor.node_evaluation_loop.stop
-        )
-        execution_processor.node_execution_thread.join(timeout=1)
-        execution_processor.node_evaluation_thread.join(timeout=1)
-    except Exception:
-        pass  # Ignore cleanup errors

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/utils.py

@@ -4,13 +4,13 @@ import threading
 import time
 from collections import defaultdict
 from concurrent.futures import Future
-from typing import Any, Optional
+from typing import Any, 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, 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
@@ -18,6 +18,7 @@ from backend.data.execution import (
     ExecutionStatus,
     GraphExecutionStats,
     GraphExecutionWithNodes,
+    NodesInputMasks,
     UserContext,
 )
 from backend.data.graph import GraphModel, Node
@@ -239,7 +240,7 @@ def _tokenise(path: str) -> list[tuple[str, str]] | None:
 # --------------------------------------------------------------------------- #
 
 
-def parse_execution_output(output: BlockData, name: str) -> Any | None:
+def parse_execution_output(output: BlockOutputEntry, name: str) -> JsonValue | None:
     """
     Retrieve a nested value out of `output` using the flattened *name*.
 
@@ -263,7 +264,7 @@ def parse_execution_output(output: BlockData, name: str) -> Any | None:
     if tokens is None:
         return None
 
-    cur: Any = data
+    cur: JsonValue = data
     for delim, ident in tokens:
         if delim == LIST_SPLIT:
             # list[index]
@@ -428,7 +429,7 @@ 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 +509,8 @@ 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 +545,8 @@ 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 +576,7 @@ 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 +617,7 @@ 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 +644,9 @@ 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 +660,9 @@ 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 +703,11 @@ 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 +857,8 @@ 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 +882,7 @@ 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,
@@ -892,10 +895,15 @@ 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,
         )
@@ -904,9 +912,9 @@ async def add_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, compiled_nodes_input_masks
+        )
 
         logger.info(
             f"Created graph execution #{graph_exec.id} for graph "
@@ -952,7 +960,7 @@ 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

@@ -1,6 +1,7 @@
 from typing import cast
 
 import pytest
+from pytest_mock import MockerFixture
 
 from backend.executor.utils import merge_execution_input, parse_execution_output
 from backend.util.mock import MockObject
@@ -276,3 +277,142 @@ 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.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_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/server/conn_manager_test.py

@@ -88,6 +88,7 @@ async def test_send_graph_execution_result(
         user_id="user-1",
         graph_id="test_graph",
         graph_version=1,
+        preset_id=None,
         status=ExecutionStatus.COMPLETED,
         started_at=datetime.now(tz=timezone.utc),
         ended_at=datetime.now(tz=timezone.utc),
@@ -101,6 +102,8 @@ async def test_send_graph_execution_result(
             "input_1": "some input value :)",
             "input_2": "some *other* input value",
         },
+        credential_inputs=None,
+        nodes_input_masks=None,
         outputs={
             "the_output": ["some output value"],
             "other_output": ["sike there was another output"],

autogpt_platform/backend/backend/server/external/middleware.py

@@ -1,16 +1,14 @@
-from fastapi import Depends, HTTPException, Request
+from fastapi import HTTPException, Security
 from fastapi.security import APIKeyHeader
 from prisma.enums import APIKeyPermission
 
-from backend.data.api_key import has_permission, validate_api_key
+from backend.data.api_key import APIKey, has_permission, validate_api_key
 
-api_key_header = APIKeyHeader(name="X-API-Key")
+api_key_header = APIKeyHeader(name="X-API-Key", auto_error=False)
 
 
-async def require_api_key(request: Request):
+async def require_api_key(api_key: str | None = Security(api_key_header)) -> APIKey:
     """Base middleware for API key authentication"""
-    api_key = await api_key_header(request)
-
     if api_key is None:
         raise HTTPException(status_code=401, detail="Missing API key")
 
@@ -19,18 +17,17 @@ async def require_api_key(request: Request):
     if not api_key_obj:
         raise HTTPException(status_code=401, detail="Invalid API key")
 
-    request.state.api_key = api_key_obj
     return api_key_obj
 
 
 def require_permission(permission: APIKeyPermission):
     """Dependency function for checking specific permissions"""
 
-    async def check_permission(api_key=Depends(require_api_key)):
+    async def check_permission(api_key: APIKey = Security(require_api_key)):
         if not has_permission(api_key, permission):
             raise HTTPException(
                 status_code=403,
-                detail=f"API key missing required permission: {permission}",
+                detail=f"API key lacks the required permission '{permission}'",
             )
         return api_key
 

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

@@ -2,7 +2,7 @@ import logging
 from collections import defaultdict
 from typing import Annotated, Any, Optional, Sequence
 
-from fastapi import APIRouter, Body, Depends, HTTPException
+from fastapi import APIRouter, Body, HTTPException, Security
 from prisma.enums import AgentExecutionStatus, APIKeyPermission
 from typing_extensions import TypedDict
 
@@ -47,7 +47,7 @@ class GraphExecutionResult(TypedDict):
 @v1_router.get(
     path="/blocks",
     tags=["blocks"],
-    dependencies=[Depends(require_permission(APIKeyPermission.READ_BLOCK))],
+    dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
 def get_graph_blocks() -> Sequence[dict[Any, Any]]:
     blocks = [block() for block in backend.data.block.get_blocks().values()]
@@ -57,12 +57,12 @@ def get_graph_blocks() -> Sequence[dict[Any, Any]]:
 @v1_router.post(
     path="/blocks/{block_id}/execute",
     tags=["blocks"],
-    dependencies=[Depends(require_permission(APIKeyPermission.EXECUTE_BLOCK))],
+    dependencies=[Security(require_permission(APIKeyPermission.EXECUTE_BLOCK))],
 )
 async def execute_graph_block(
     block_id: str,
     data: BlockInput,
-    api_key: APIKey = Depends(require_permission(APIKeyPermission.EXECUTE_BLOCK)),
+    api_key: APIKey = Security(require_permission(APIKeyPermission.EXECUTE_BLOCK)),
 ) -> CompletedBlockOutput:
     obj = backend.data.block.get_block(block_id)
     if not obj:
@@ -82,7 +82,7 @@ async def execute_graph(
     graph_id: str,
     graph_version: int,
     node_input: Annotated[dict[str, Any], Body(..., embed=True, default_factory=dict)],
-    api_key: APIKey = Depends(require_permission(APIKeyPermission.EXECUTE_GRAPH)),
+    api_key: APIKey = Security(require_permission(APIKeyPermission.EXECUTE_GRAPH)),
 ) -> dict[str, Any]:
     try:
         graph_exec = await add_graph_execution(
@@ -104,7 +104,7 @@ async def execute_graph(
 async def get_graph_execution_results(
     graph_id: str,
     graph_exec_id: str,
-    api_key: APIKey = Depends(require_permission(APIKeyPermission.READ_GRAPH)),
+    api_key: APIKey = Security(require_permission(APIKeyPermission.READ_GRAPH)),
 ) -> GraphExecutionResult:
     graph = await graph_db.get_graph(graph_id, user_id=api_key.user_id)
     if not graph:

autogpt_platform/backend/backend/server/model.py

@@ -34,10 +34,6 @@ class WSSubscribeGraphExecutionsRequest(pydantic.BaseModel):
     graph_id: str
 
 
-class ExecuteGraphResponse(pydantic.BaseModel):
-    graph_exec_id: str
-
-
 class CreateGraph(pydantic.BaseModel):
     graph: Graph
 

autogpt_platform/backend/backend/server/rest_api.py

@@ -363,6 +363,7 @@ class AgentServer(backend.util.service.AppProcess):
             preset_id=preset_id,
             user_id=user_id,
             inputs=inputs or {},
+            credential_inputs={},
         )
 
     @staticmethod

autogpt_platform/backend/backend/server/routers/v1.py

@@ -78,7 +78,6 @@ from backend.server.model import (
     CreateAPIKeyRequest,
     CreateAPIKeyResponse,
     CreateGraph,
-    ExecuteGraphResponse,
     RequestTopUp,
     SetGraphActiveVersion,
     TimezoneResponse,
@@ -783,7 +782,7 @@ async def execute_graph(
     ],
     graph_version: Optional[int] = None,
     preset_id: Optional[str] = None,
-) -> ExecuteGraphResponse:
+) -> execution_db.GraphExecutionMeta:
     current_balance = await _user_credit_model.get_credits(user_id)
     if current_balance <= 0:
         raise HTTPException(
@@ -792,7 +791,7 @@ async def execute_graph(
         )
 
     try:
-        graph_exec = await execution_utils.add_graph_execution(
+        return await execution_utils.add_graph_execution(
             graph_id=graph_id,
             user_id=user_id,
             inputs=inputs,
@@ -800,7 +799,6 @@ async def execute_graph(
             graph_version=graph_version,
             graph_credentials_inputs=credentials_inputs,
         )
-        return ExecuteGraphResponse(graph_exec_id=graph_exec.id)
     except GraphValidationError as e:
         # Return structured validation errors that the frontend can parse
         raise HTTPException(

autogpt_platform/backend/backend/server/v2/AutoMod/manager.py

@@ -147,10 +147,8 @@ class AutoModManager:
             return None
 
         # Get completed executions and collect outputs
-        completed_executions = await db_client.get_node_executions(  # type: ignore
-            graph_exec_id=graph_exec_id,
-            statuses=[ExecutionStatus.COMPLETED],
-            include_exec_data=True,
+        completed_executions = await db_client.get_node_executions(
+            graph_exec_id, statuses=[ExecutionStatus.COMPLETED], include_exec_data=True
         )
 
         if not completed_executions:
@@ -220,7 +218,7 @@ class AutoModManager:
     ):
         """Update node execution statuses for frontend display when moderation fails"""
         # Import here to avoid circular imports
-        from backend.util.clients import get_async_execution_event_bus
+        from backend.executor.manager import send_async_execution_update
 
         if moderation_type == "input":
             # For input moderation, mark queued/running/incomplete nodes as failed
@@ -234,10 +232,8 @@ class AutoModManager:
             target_statuses = [ExecutionStatus.COMPLETED]
 
         # Get the executions that need to be updated
-        executions_to_update = await db_client.get_node_executions(  # type: ignore
-            graph_exec_id=graph_exec_id,
-            statuses=target_statuses,
-            include_exec_data=True,
+        executions_to_update = await db_client.get_node_executions(
+            graph_exec_id, statuses=target_statuses, include_exec_data=True
         )
 
         if not executions_to_update:
@@ -280,12 +276,10 @@ class AutoModManager:
         updated_execs = await asyncio.gather(*exec_updates)
 
         # Send all websocket updates in parallel
-        event_bus = get_async_execution_event_bus()
         await asyncio.gather(
             *[
-                event_bus.publish(updated_exec)
+                send_async_execution_update(updated_exec)
                 for updated_exec in updated_execs
-                if updated_exec is not None
             ]
         )
 

autogpt_platform/backend/backend/server/v2/library/db.py

@@ -16,7 +16,7 @@ import backend.server.v2.store.media as store_media
 from backend.data.block import BlockInput
 from backend.data.db import transaction
 from backend.data.execution import get_graph_execution
-from backend.data.includes import library_agent_include
+from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
@@ -617,7 +617,7 @@ async def list_presets(
             where=query_filter,
             skip=(page - 1) * page_size,
             take=page_size,
-            include={"InputPresets": True},
+            include=AGENT_PRESET_INCLUDE,
         )
         total_items = await prisma.models.AgentPreset.prisma().count(where=query_filter)
         total_pages = (total_items + page_size - 1) // page_size
@@ -662,7 +662,7 @@ async def get_preset(
     try:
         preset = await prisma.models.AgentPreset.prisma().find_unique(
             where={"id": preset_id},
-            include={"InputPresets": True},
+            include=AGENT_PRESET_INCLUDE,
         )
         if not preset or preset.userId != user_id or preset.isDeleted:
             return None
@@ -717,7 +717,7 @@ async def create_preset(
                     ]
                 },
             ),
-            include={"InputPresets": True},
+            include=AGENT_PRESET_INCLUDE,
         )
         return library_model.LibraryAgentPreset.from_db(new_preset)
     except prisma.errors.PrismaError as e:
@@ -747,6 +747,25 @@ async def create_preset_from_graph_execution(
     if not graph_execution:
         raise NotFoundError(f"Graph execution #{graph_exec_id} not found")
 
+    # Sanity check: credential inputs must be available if required for this preset
+    if graph_execution.credential_inputs is None:
+        graph = await graph_db.get_graph(
+            graph_id=graph_execution.graph_id,
+            version=graph_execution.graph_version,
+            user_id=graph_execution.user_id,
+            include_subgraphs=True,
+        )
+        if not graph:
+            raise NotFoundError(
+                f"Graph #{graph_execution.graph_id} not found or accessible"
+            )
+        elif len(graph.aggregate_credentials_inputs()) > 0:
+            raise ValueError(
+                f"Graph execution #{graph_exec_id} can't be turned into a preset "
+                "because it was run before this feature existed "
+                "and so the input credentials were not saved."
+            )
+
     logger.debug(
         f"Creating preset for user #{user_id} from graph execution #{graph_exec_id}",
     )
@@ -754,7 +773,7 @@ async def create_preset_from_graph_execution(
         user_id=user_id,
         preset=library_model.LibraryAgentPresetCreatable(
             inputs=graph_execution.inputs,
-            credentials={},  # FIXME
+            credentials=graph_execution.credential_inputs or {},
             graph_id=graph_execution.graph_id,
             graph_version=graph_execution.graph_version,
             name=create_request.name,
@@ -834,7 +853,7 @@ async def update_preset(
             updated = await prisma.models.AgentPreset.prisma(tx).update(
                 where={"id": preset_id},
                 data=update_data,
-                include={"InputPresets": True},
+                include=AGENT_PRESET_INCLUDE,
             )
         if not updated:
             raise RuntimeError(f"AgentPreset #{preset_id} vanished while updating")
@@ -849,7 +868,7 @@ async def set_preset_webhook(
 ) -> library_model.LibraryAgentPreset:
     current = await prisma.models.AgentPreset.prisma().find_unique(
         where={"id": preset_id},
-        include={"InputPresets": True},
+        include=AGENT_PRESET_INCLUDE,
     )
     if not current or current.userId != user_id:
         raise NotFoundError(f"Preset #{preset_id} not found")
@@ -861,7 +880,7 @@ async def set_preset_webhook(
             if webhook_id
             else {"Webhook": {"disconnect": True}}
         ),
-        include={"InputPresets": True},
+        include=AGENT_PRESET_INCLUDE,
     )
     if not updated:
         raise RuntimeError(f"AgentPreset #{preset_id} vanished while updating")

autogpt_platform/backend/backend/server/v2/library/model.py

@@ -1,6 +1,6 @@
 import datetime
 from enum import Enum
-from typing import Any, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
 import prisma.enums
 import prisma.models
@@ -9,9 +9,11 @@ import pydantic
 import backend.data.block as block_model
 import backend.data.graph as graph_model
 from backend.data.model import CredentialsMetaInput, is_credentials_field_name
-from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 
+if TYPE_CHECKING:
+    from backend.data.integrations import Webhook
+
 
 class LibraryAgentStatus(str, Enum):
     COMPLETED = "COMPLETED"  # All runs completed
@@ -20,14 +22,6 @@ class LibraryAgentStatus(str, Enum):
     ERROR = "ERROR"  # Agent is in an error state
 
 
-class LibraryAgentTriggerInfo(pydantic.BaseModel):
-    provider: ProviderName
-    config_schema: dict[str, Any] = pydantic.Field(
-        description="Input schema for the trigger block"
-    )
-    credentials_input_name: Optional[str]
-
-
 class LibraryAgent(pydantic.BaseModel):
     """
     Represents an agent in the library, including metadata for display and
@@ -59,7 +53,7 @@ class LibraryAgent(pydantic.BaseModel):
     has_external_trigger: bool = pydantic.Field(
         description="Whether the agent has an external trigger (e.g. webhook) node"
     )
-    trigger_setup_info: Optional[LibraryAgentTriggerInfo] = None
+    trigger_setup_info: Optional[graph_model.GraphTriggerInfo] = None
 
     # Indicates whether there's a new output (based on recent runs)
     new_output: bool
@@ -132,30 +126,7 @@ class LibraryAgent(pydantic.BaseModel):
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
             has_external_trigger=graph.has_external_trigger,
-            trigger_setup_info=(
-                LibraryAgentTriggerInfo(
-                    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
-                    ),
-                )
-                if graph.webhook_input_node
-                and (trigger_block := graph.webhook_input_node.block).webhook_config
-                else None
-            ),
+            trigger_setup_info=graph.trigger_setup_info,
             new_output=new_output,
             can_access_graph=can_access_graph,
             is_latest_version=is_latest_version,
@@ -284,10 +255,18 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
     user_id: str
     updated_at: datetime.datetime
 
+    webhook: "Webhook | None"
+
     @classmethod
     def from_db(cls, preset: prisma.models.AgentPreset) -> "LibraryAgentPreset":
+        from backend.data.integrations import Webhook
+
         if preset.InputPresets is None:
             raise ValueError("InputPresets must be included in AgentPreset query")
+        if preset.webhookId and preset.Webhook is None:
+            raise ValueError(
+                "Webhook must be included in AgentPreset query when webhookId is set"
+            )
 
         input_data: block_model.BlockInput = {}
         input_credentials: dict[str, CredentialsMetaInput] = {}
@@ -312,6 +291,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
             inputs=input_data,
             credentials=input_credentials,
             webhook_id=preset.webhookId,
+            webhook=Webhook.from_db(preset.Webhook) if preset.Webhook else None,
         )
 
 

autogpt_platform/backend/backend/server/v2/library/routes/presets.py

@@ -6,8 +6,10 @@ from fastapi import APIRouter, Body, HTTPException, Query, Security, status
 
 import backend.server.v2.library.db as db
 import backend.server.v2.library.model as models
+from backend.data.execution import GraphExecutionMeta
 from backend.data.graph import get_graph
 from backend.data.integrations import get_webhook
+from backend.data.model import CredentialsMetaInput
 from backend.executor.utils import add_graph_execution, make_node_credentials_input_map
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks import get_webhook_manager
@@ -369,48 +371,41 @@ async def execute_preset(
     preset_id: str,
     user_id: str = Security(autogpt_auth_lib.get_user_id),
     inputs: dict[str, Any] = Body(..., embed=True, default_factory=dict),
-) -> dict[str, Any]:  # FIXME: add proper return type
+    credential_inputs: dict[str, CredentialsMetaInput] = Body(
+        ..., embed=True, default_factory=dict
+    ),
+) -> GraphExecutionMeta:
     """
     Execute a preset given graph parameters, returning the execution ID on success.
 
     Args:
-        preset_id (str): ID of the preset to execute.
-        user_id (str): ID of the authenticated user.
-        inputs (dict[str, Any]): Optionally, additional input data for the graph execution.
+        preset_id: ID of the preset to execute.
+        user_id: ID of the authenticated user.
+        inputs: Optionally, inputs to override the preset for execution.
+        credential_inputs: Optionally, credentials to override the preset for execution.
 
     Returns:
-        {id: graph_exec_id}: A response containing the execution ID.
+        GraphExecutionMeta: Object representing the created execution.
 
     Raises:
         HTTPException: If the preset is not found or an error occurs while executing the preset.
     """
-    try:
-        preset = await db.get_preset(user_id, preset_id)
-        if not preset:
-            raise HTTPException(
-                status_code=status.HTTP_404_NOT_FOUND,
-                detail=f"Preset #{preset_id} not found",
-            )
-
-        # Merge input overrides with preset inputs
-        merged_node_input = preset.inputs | inputs
-
-        execution = await add_graph_execution(
-            user_id=user_id,
-            graph_id=preset.graph_id,
-            graph_version=preset.graph_version,
-            preset_id=preset_id,
-            inputs=merged_node_input,
-        )
-
-        logger.debug(f"Execution added: {execution} with input: {merged_node_input}")
-
-        return {"id": execution.id}
-    except HTTPException:
-        raise
-    except Exception as e:
-        logger.exception("Preset execution failed for user %s: %s", user_id, e)
+    preset = await db.get_preset(user_id, preset_id)
+    if not preset:
         raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=str(e),
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=f"Preset #{preset_id} not found",
         )
+
+    # Merge input overrides with preset inputs
+    merged_node_input = preset.inputs | inputs
+    merged_credential_inputs = preset.credentials | credential_inputs
+
+    return await add_graph_execution(
+        user_id=user_id,
+        graph_id=preset.graph_id,
+        graph_version=preset.graph_version,
+        preset_id=preset_id,
+        inputs=merged_node_input,
+        graph_credentials_inputs=merged_credential_inputs,
+    )

autogpt_platform/backend/backend/server/v2/store/db.py

@@ -1,3 +1,4 @@
+import asyncio
 import logging
 from datetime import datetime, timezone
 
@@ -9,6 +10,7 @@ import prisma.types
 
 import backend.server.v2.store.exceptions
 import backend.server.v2.store.model
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -70,7 +72,7 @@ async def get_store_agents(
     )
     sanitized_query = sanitize_query(search_query)
 
-    where_clause = {}
+    where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
     if featured:
         where_clause["featured"] = featured
     if creators:
@@ -94,15 +96,13 @@ async def get_store_agents(
 
     try:
         agents = await prisma.models.StoreAgent.prisma().find_many(
-            where=prisma.types.StoreAgentWhereInput(**where_clause),
+            where=where_clause,
             order=order_by,
             skip=(page - 1) * page_size,
             take=page_size,
         )
 
-        total = await prisma.models.StoreAgent.prisma().count(
-            where=prisma.types.StoreAgentWhereInput(**where_clause)
-        )
+        total = await prisma.models.StoreAgent.prisma().count(where=where_clause)
         total_pages = (total + page_size - 1) // page_size
 
         store_agents: list[backend.server.v2.store.model.StoreAgent] = []
@@ -190,8 +190,8 @@ async def get_store_agent_details(
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
             agent_image=agent.agent_image,
-            creator=agent.creator_username,
-            creator_avatar=agent.creator_avatar,
+            creator=agent.creator_username or "",
+            creator_avatar=agent.creator_avatar or "",
             sub_heading=agent.sub_heading,
             description=agent.description,
             categories=agent.categories,
@@ -263,8 +263,8 @@ async def get_store_agent_by_version_id(
             agent_name=agent.agent_name,
             agent_video=agent.agent_video or "",
             agent_image=agent.agent_image,
-            creator=agent.creator_username,
-            creator_avatar=agent.creator_avatar,
+            creator=agent.creator_username or "",
+            creator_avatar=agent.creator_avatar or "",
             sub_heading=agent.sub_heading,
             description=agent.description,
             categories=agent.categories,
@@ -1200,40 +1200,103 @@ async def get_agent(store_listing_version_id: str) -> GraphModel:
 #####################################################
 
 
-async def _get_missing_sub_store_listing(
-    graph: prisma.models.AgentGraph,
-) -> list[prisma.models.AgentGraph]:
-    """
-    Agent graph can have sub-graphs, and those sub-graphs also need to be store listed.
-    This method fetches the sub-graphs, and returns the ones not listed in the store.
-    """
-    sub_graphs = await get_sub_graphs(graph)
-    if not sub_graphs:
-        return []
+async def _approve_sub_agent(
+    tx,
+    sub_graph: prisma.models.AgentGraph,
+    main_agent_name: str,
+    main_agent_version: int,
+    main_agent_user_id: str,
+) -> None:
+    """Approve a single sub-agent by creating/updating store listings as needed"""
+    heading = f"Sub-agent of {main_agent_name} v{main_agent_version}"
 
-    # Fetch all the sub-graphs that are listed, and return the ones missing.
-    store_listed_sub_graphs = {
-        (listing.agentGraphId, listing.agentGraphVersion)
-        for listing in await prisma.models.StoreListingVersion.prisma().find_many(
-            where={
-                "OR": [
-                    {
-                        "agentGraphId": sub_graph.id,
-                        "agentGraphVersion": sub_graph.version,
-                    }
-                    for sub_graph in sub_graphs
-                ],
-                "submissionStatus": prisma.enums.SubmissionStatus.APPROVED,
-                "isDeleted": False,
-            }
+    # Find existing listing for this sub-agent
+    listing = await prisma.models.StoreListing.prisma(tx).find_first(
+        where={"agentGraphId": sub_graph.id, "isDeleted": False},
+        include={"Versions": True},
+    )
+
+    # Early return: Create new listing if none exists
+    if not listing:
+        await prisma.models.StoreListing.prisma(tx).create(
+            data=prisma.types.StoreListingCreateInput(
+                slug=f"sub-agent-{sub_graph.id[:8]}",
+                agentGraphId=sub_graph.id,
+                agentGraphVersion=sub_graph.version,
+                owningUserId=main_agent_user_id,
+                hasApprovedVersion=True,
+                Versions={
+                    "create": [
+                        _create_sub_agent_version_data(
+                            sub_graph, heading, main_agent_name
+                        )
+                    ]
+                },
+            )
         )
-    }
+        return
 
-    return [
-        sub_graph
-        for sub_graph in sub_graphs
-        if (sub_graph.id, sub_graph.version) not in store_listed_sub_graphs
-    ]
+    # Find version matching this sub-graph
+    matching_version = next(
+        (
+            v
+            for v in listing.Versions or []
+            if v.agentGraphId == sub_graph.id
+            and v.agentGraphVersion == sub_graph.version
+        ),
+        None,
+    )
+
+    # Early return: Approve existing version if found and not already approved
+    if matching_version:
+        if matching_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
+            return  # Already approved, nothing to do
+
+        await prisma.models.StoreListingVersion.prisma(tx).update(
+            where={"id": matching_version.id},
+            data={
+                "submissionStatus": prisma.enums.SubmissionStatus.APPROVED,
+                "reviewedAt": datetime.now(tz=timezone.utc),
+            },
+        )
+        await prisma.models.StoreListing.prisma(tx).update(
+            where={"id": listing.id}, data={"hasApprovedVersion": True}
+        )
+        return
+
+    # Create new version if no matching version found
+    next_version = max((v.version for v in listing.Versions or []), default=0) + 1
+    await prisma.models.StoreListingVersion.prisma(tx).create(
+        data={
+            **_create_sub_agent_version_data(sub_graph, heading, main_agent_name),
+            "version": next_version,
+            "storeListingId": listing.id,
+        }
+    )
+    await prisma.models.StoreListing.prisma(tx).update(
+        where={"id": listing.id}, data={"hasApprovedVersion": True}
+    )
+
+
+def _create_sub_agent_version_data(
+    sub_graph: prisma.models.AgentGraph, heading: str, main_agent_name: str
+) -> prisma.types.StoreListingVersionCreateInput:
+    """Create store listing version data for a sub-agent"""
+    return prisma.types.StoreListingVersionCreateInput(
+        agentGraphId=sub_graph.id,
+        agentGraphVersion=sub_graph.version,
+        name=sub_graph.name or heading,
+        submissionStatus=prisma.enums.SubmissionStatus.APPROVED,
+        subHeading=heading,
+        description=(
+            f"{heading}: {sub_graph.description}" if sub_graph.description else heading
+        ),
+        changesSummary=f"Auto-approved as sub-agent of {main_agent_name}",
+        isAvailable=False,
+        submittedAt=datetime.now(tz=timezone.utc),
+        imageUrls=[],  # Sub-agents don't need images
+        categories=[],  # Sub-agents don't need categories
+    )
 
 
 async def review_store_submission(
@@ -1271,33 +1334,30 @@ async def review_store_submission(
 
         # If approving, update the listing to indicate it has an approved version
         if is_approved and store_listing_version.AgentGraph:
-            heading = f"Sub-graph of {store_listing_version.name}v{store_listing_version.agentGraphVersion}"
-
-            sub_store_listing_versions = [
-                prisma.types.StoreListingVersionCreateWithoutRelationsInput(
-                    agentGraphId=sub_graph.id,
-                    agentGraphVersion=sub_graph.version,
-                    name=sub_graph.name or heading,
-                    submissionStatus=prisma.enums.SubmissionStatus.APPROVED,
-                    subHeading=heading,
-                    description=f"{heading}: {sub_graph.description}",
-                    changesSummary=f"This listing is added as a {heading} / #{store_listing_version.agentGraphId}.",
-                    isAvailable=False,  # Hide sub-graphs from the store by default.
-                    submittedAt=datetime.now(tz=timezone.utc),
+            async with transaction() as tx:
+                # Handle sub-agent approvals in transaction
+                await asyncio.gather(
+                    *[
+                        _approve_sub_agent(
+                            tx,
+                            sub_graph,
+                            store_listing_version.name,
+                            store_listing_version.agentGraphVersion,
+                            store_listing_version.StoreListing.owningUserId,
+                        )
+                        for sub_graph in await get_sub_graphs(
+                            store_listing_version.AgentGraph
+                        )
+                    ]
                 )
-                for sub_graph in await _get_missing_sub_store_listing(
-                    store_listing_version.AgentGraph
-                )
-            ]
 
-            await prisma.models.StoreListing.prisma().update(
-                where={"id": store_listing_version.StoreListing.id},
-                data={
-                    "hasApprovedVersion": True,
-                    "ActiveVersion": {"connect": {"id": store_listing_version_id}},
-                    "Versions": {"create": sub_store_listing_versions},
-                },
-            )
+                await prisma.models.StoreListing.prisma(tx).update(
+                    where={"id": store_listing_version.StoreListing.id},
+                    data={
+                        "hasApprovedVersion": True,
+                        "ActiveVersion": {"connect": {"id": store_listing_version_id}},
+                    },
+                )
 
         # If rejecting an approved agent, update the StoreListing accordingly
         if is_rejecting_approved:

autogpt_platform/backend/backend/server/v2/store/db_test.py

@@ -41,6 +41,7 @@ async def test_get_store_agents(mocker):
             rating=4.5,
             versions=["1.0"],
             updated_at=datetime.now(),
+            is_available=False,
         )
     ]
 
@@ -82,6 +83,7 @@ async def test_get_store_agent_details(mocker):
         rating=4.5,
         versions=["1.0"],
         updated_at=datetime.now(),
+        is_available=False,
     )
 
     # Create a mock StoreListing result

autogpt_platform/backend/backend/usecases/block_autogen.py

@@ -251,14 +251,14 @@ async def block_autogen_agent():
         test_user = await create_test_user()
         test_graph = await create_graph(create_test_graph(), user_id=test_user.id)
         input_data = {"input": "Write me a block that writes a string into a file."}
-        response = await server.agent_server.test_execute_graph(
+        graph_exec = await server.agent_server.test_execute_graph(
             graph_id=test_graph.id,
             user_id=test_user.id,
             node_input=input_data,
         )
-        print(response)
+        print(graph_exec)
         result = await wait_execution(
-            graph_exec_id=response.graph_exec_id,
+            graph_exec_id=graph_exec.id,
             timeout=1200,
             user_id=test_user.id,
         )

autogpt_platform/backend/backend/usecases/reddit_marketing.py

@@ -155,13 +155,13 @@ async def reddit_marketing_agent():
         test_user = await create_test_user()
         test_graph = await create_graph(create_test_graph(), user_id=test_user.id)
         input_data = {"subreddit": "AutoGPT"}
-        response = await server.agent_server.test_execute_graph(
+        graph_exec = await server.agent_server.test_execute_graph(
             graph_id=test_graph.id,
             user_id=test_user.id,
             node_input=input_data,
         )
-        print(response)
-        result = await wait_execution(test_user.id, response.graph_exec_id, 120)
+        print(graph_exec)
+        result = await wait_execution(test_user.id, graph_exec.id, 120)
         print(result)
 
 

autogpt_platform/backend/backend/usecases/sample.py

@@ -88,12 +88,12 @@ async def sample_agent():
         test_user = await create_test_user()
         test_graph = await create_graph(create_test_graph(), test_user.id)
         input_data = {"input_1": "Hello", "input_2": "World"}
-        response = await server.agent_server.test_execute_graph(
+        graph_exec = await server.agent_server.test_execute_graph(
             graph_id=test_graph.id,
             user_id=test_user.id,
             node_input=input_data,
         )
-        await wait_execution(test_user.id, response.graph_exec_id, 10)
+        await wait_execution(test_user.id, graph_exec.id, 10)
 
 
 if __name__ == "__main__":

autogpt_platform/backend/backend/util/exceptions.py

@@ -1,3 +1,6 @@
+from typing import Mapping
+
+
 class MissingConfigError(Exception):
     """The attempted operation requires configuration which is not available"""
 
@@ -69,7 +72,7 @@ class GraphValidationError(ValueError):
     """Structured validation error for graph validation failures"""
 
     def __init__(
-        self, message: str, node_errors: dict[str, dict[str, str]] | None = None
+        self, message: str, node_errors: Mapping[str, Mapping[str, str]] | None = None
     ):
         super().__init__(message)
         self.message = message

autogpt_platform/backend/migrations/20250902171554_add_credentials_to_graph_execution/migration.sql

@@ -0,0 +1,5 @@
+-- Add 'credentialInputs', 'inputs', and 'nodesInputMasks' columns to the AgentGraphExecution table
+ALTER TABLE "AgentGraphExecution"
+  ADD COLUMN "credentialInputs" JSONB,
+  ADD COLUMN "inputs"           JSONB,
+  ADD COLUMN "nodesInputMasks"  JSONB;

autogpt_platform/backend/migrations/20250904171522_update_store_agent_view_with_availability/migration.sql

@@ -0,0 +1,53 @@
+-- Update StoreAgent view to include is_available field and fix creator field nullability
+
+BEGIN;
+
+-- Drop and recreate the StoreAgent view with isAvailable field
+DROP VIEW IF EXISTS "StoreAgent";
+
+CREATE OR REPLACE VIEW "StoreAgent" AS
+WITH agent_versions AS (
+    SELECT 
+        "storeListingId",
+        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+)
+SELECT
+    sl.id AS listing_id,
+    slv.id AS "storeListingVersionId",
+    slv."createdAt" AS updated_at,
+    sl.slug,
+    COALESCE(slv.name, '') AS agent_name,
+    slv."videoUrl" AS agent_video,
+    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
+    slv."isFeatured" AS featured,
+    p.username AS creator_username,  -- Allow NULL for malformed sub-agents
+    p."avatarUrl" AS creator_avatar,  -- Allow NULL for malformed sub-agents
+    slv."subHeading" AS sub_heading,
+    slv.description,
+    slv.categories,
+    COALESCE(ar.run_count, 0::bigint) AS runs,
+    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
+    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
+    slv."isAvailable" AS is_available  -- Add isAvailable field to filter sub-agents
+FROM "StoreListing" sl
+INNER JOIN "StoreListingVersion" slv 
+    ON slv."storeListingId" = sl.id 
+    AND slv."submissionStatus" = 'APPROVED'
+JOIN "AgentGraph" a 
+    ON slv."agentGraphId" = a.id 
+    AND slv."agentGraphVersion" = a.version
+LEFT JOIN "Profile" p 
+    ON sl."owningUserId" = p."userId"
+LEFT JOIN "mv_review_stats" rs 
+    ON sl.id = rs."storeListingId"
+LEFT JOIN "mv_agent_run_counts" ar 
+    ON a.id = ar."agentGraphId"
+LEFT JOIN agent_versions av 
+    ON sl.id = av."storeListingId"
+WHERE sl."isDeleted" = false
+  AND sl."hasApprovedVersion" = true;
+
+COMMIT;

autogpt_platform/backend/poetry.lock

@@ -898,52 +898,62 @@ pytz = ">2021.1"
 
 [[package]]
 name = "cryptography"
-version = "43.0.3"
+version = "45.0.7"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
-python-versions = ">=3.7"
+python-versions = "!=3.9.0,!=3.9.1,>=3.7"
 groups = ["main"]
 files = [
-    {file = "cryptography-43.0.3-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:bf7a1932ac4176486eab36a19ed4c0492da5d97123f1406cf15e41b05e787d2e"},
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 ]
 
 [package.dependencies]
-cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
+cffi = {version = ">=1.14", markers = "platform_python_implementation != \"PyPy\""}
 
 [package.extras]
-docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
-nox = ["nox"]
-pep8test = ["check-sdist", "click", "mypy", "ruff"]
-sdist = ["build"]
+docs = ["sphinx (>=5.3.0)", "sphinx-inline-tabs ; python_full_version >= \"3.8.0\"", "sphinx-rtd-theme (>=3.0.0) ; python_full_version >= \"3.8.0\""]
+docstest = ["pyenchant (>=3)", "readme-renderer (>=30.0)", "sphinxcontrib-spelling (>=7.3.1)"]
+nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2) ; python_full_version >= \"3.8.0\""]
+pep8test = ["check-sdist ; python_full_version >= \"3.8.0\"", "click (>=8.0.1)", "mypy (>=1.4)", "ruff (>=0.3.6)"]
+sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["certifi", "cryptography-vectors (==43.0.3)", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi (>=2024)", "cryptography-vectors (==45.0.7)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -7132,4 +7142,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "892daa57d7126d9a9d5308005b07328a39b8c4cd7fe198f9b5ab10f957787c48"
+content-hash = "80d4dc2cbcd1ae33b2fa3920db5dcb1f82ad252d1e4a8bfeba8b2f2eebbdda0d"

autogpt_platform/backend/pyproject.toml

@@ -17,7 +17,7 @@ apscheduler = "^3.11.0"
 autogpt-libs = { path = "../autogpt_libs", develop = true }
 bleach = { extras = ["css"], version = "^6.2.0" }
 click = "^8.2.0"
-cryptography = "^43.0"
+cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
 fastapi = "^0.116.1"

autogpt_platform/backend/schema.prisma

@@ -36,7 +36,7 @@ model User {
   notifyOnAgentApproved        Boolean @default(true)
   notifyOnAgentRejected        Boolean @default(true)
 
-  timezone                     String  @default("not-set")
+  timezone String @default("not-set")
 
   // Relations
 
@@ -354,15 +354,20 @@ model AgentGraphExecution {
   agentGraphVersion Int        @default(1)
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version], onDelete: Cascade)
 
+  agentPresetId String?
+  AgentPreset   AgentPreset? @relation(fields: [agentPresetId], references: [id])
+
+  inputs           Json?
+  credentialInputs Json?
+  nodesInputMasks  Json?
+
   NodeExecutions AgentNodeExecution[]
 
   // Link to User model -- Executed by this user
   userId String
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
 
-  stats         Json?
-  agentPresetId String?
-  AgentPreset   AgentPreset? @relation(fields: [agentPresetId], references: [id])
+  stats Json?
 
   @@index([agentGraphId, agentGraphVersion])
   @@index([userId])
@@ -625,14 +630,15 @@ view StoreAgent {
   agent_image String[]
 
   featured         Boolean  @default(false)
-  creator_username String
-  creator_avatar   String
+  creator_username String?
+  creator_avatar   String?
   sub_heading      String
   description      String
   categories       String[]
   runs             Int
   rating           Float
   versions         String[]
+  is_available     Boolean  @default(true)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId

autogpt_platform/backend/snapshots/grph_single

@@ -24,6 +24,7 @@
     "type": "object"
   },
   "sub_graphs": [],
+  "trigger_setup_info": null,
   "user_id": "test-user-id",
   "version": 1
 }

autogpt_platform/backend/snapshots/grphs_all

@@ -23,6 +23,7 @@
       "type": "object"
     },
     "sub_graphs": [],
+    "trigger_setup_info": null,
     "user_id": "test-user-id",
     "version": 1
   }

autogpt_platform/db/docker/docker-compose.yml

@@ -25,83 +25,10 @@ x-supabase-env-files: &supabase-env-files
 
 # Common Supabase environment - hardcoded defaults to avoid variable substitution
 x-supabase-env: &supabase-env
-  # Core PostgreSQL settings
-  POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
-  POSTGRES_HOST: db
-  POSTGRES_PORT: "5432"
-  POSTGRES_DB: postgres
-  
-  # Authentication & Security
-  JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
-  ANON_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
-  SERVICE_ROLE_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
-  DASHBOARD_USERNAME: supabase
-  DASHBOARD_PASSWORD: this_password_is_insecure_and_should_be_updated
-  SECRET_KEY_BASE: UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq
-  VAULT_ENC_KEY: your-encryption-key-32-chars-min
-  
-  # URLs and Endpoints
-  SITE_URL: http://localhost:3000
-  API_EXTERNAL_URL: http://localhost:8000
-  SUPABASE_PUBLIC_URL: http://localhost:8000
-  ADDITIONAL_REDIRECT_URLS: ""
-  
-  # Feature Flags
-  DISABLE_SIGNUP: "false"
-  ENABLE_EMAIL_SIGNUP: "true"
-  ENABLE_EMAIL_AUTOCONFIRM: "false"
-  ENABLE_ANONYMOUS_USERS: "false"
-  ENABLE_PHONE_SIGNUP: "true"
-  ENABLE_PHONE_AUTOCONFIRM: "true"
-  FUNCTIONS_VERIFY_JWT: "false"
-  IMGPROXY_ENABLE_WEBP_DETECTION: "true"
-  
-  # Email/SMTP Configuration
-  SMTP_ADMIN_EMAIL: admin@example.com
-  SMTP_HOST: supabase-mail
-  SMTP_PORT: "2500"
-  SMTP_USER: fake_mail_user
-  SMTP_PASS: fake_mail_password
-  SMTP_SENDER_NAME: fake_sender
-  
-  # Mailer URLs
-  MAILER_URLPATHS_CONFIRMATION: /auth/v1/verify
-  MAILER_URLPATHS_INVITE: /auth/v1/verify
-  MAILER_URLPATHS_RECOVERY: /auth/v1/verify
-  MAILER_URLPATHS_EMAIL_CHANGE: /auth/v1/verify
-  
-  # JWT Settings
-  JWT_EXPIRY: "3600"
-  
-  # Database Schemas
-  PGRST_DB_SCHEMAS: public,storage,graphql_public
-  
-  # Studio Settings
-  STUDIO_DEFAULT_ORGANIZATION: Default Organization
-  STUDIO_DEFAULT_PROJECT: Default Project
-  
-  # Logging
-  LOGFLARE_API_KEY: your-super-secret-and-long-logflare-key
-  
-  # Pooler Settings
-  POOLER_DEFAULT_POOL_SIZE: "20"
-  POOLER_MAX_CLIENT_CONN: "100"
-  POOLER_TENANT_ID: your-tenant-id
-  POOLER_PROXY_PORT_TRANSACTION: "6543"
-  
-  # Kong Ports
-  KONG_HTTP_PORT: "8000"
-  KONG_HTTPS_PORT: "8443"
-  
-  # Docker
-  DOCKER_SOCKET_LOCATION: /var/run/docker.sock
-  
-  # Google Cloud (if needed)
-  GOOGLE_PROJECT_ID: GOOGLE_PROJECT_ID
-  GOOGLE_PROJECT_NUMBER: GOOGLE_PROJECT_NUMBER
+  SUPABASE_ANON_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
+  SUPABASE_SERVICE_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
 
 services:
-
   studio:
     container_name: supabase-studio
     image: supabase/studio:20250224-d10db0f
@@ -122,16 +49,9 @@ services:
       <<: *supabase-env
       # Keep any existing environment variables specific to that service
       STUDIO_PG_META_URL: http://meta:8080
-      POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
-
       DEFAULT_ORGANIZATION_NAME: Default Organization
       DEFAULT_PROJECT_NAME: Default Project
-      OPENAI_API_KEY: ""
-
       SUPABASE_URL: http://kong:8000
-      SUPABASE_PUBLIC_URL: http://localhost:8000
-      SUPABASE_ANON_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
-      SUPABASE_SERVICE_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
       AUTH_JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
 
       LOGFLARE_API_KEY: your-super-secret-and-long-logflare-key
@@ -163,10 +83,6 @@ services:
       KONG_PLUGINS: request-transformer,cors,key-auth,acl,basic-auth
       KONG_NGINX_PROXY_PROXY_BUFFER_SIZE: 160k
       KONG_NGINX_PROXY_PROXY_BUFFERS: 64 160k
-      SUPABASE_ANON_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
-      SUPABASE_SERVICE_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
-      DASHBOARD_USERNAME: supabase
-      DASHBOARD_PASSWORD: this_password_is_insecure_and_should_be_updated
     # https://unix.stackexchange.com/a/294837
     entrypoint: bash -c 'eval "echo \"$$(cat ~/temp.yml)\"" > ~/kong.yml && /docker-entrypoint.sh kong docker-start'
 
@@ -197,7 +113,6 @@ services:
       # Keep any existing environment variables specific to that service
       GOTRUE_API_HOST: 0.0.0.0
       GOTRUE_API_PORT: 9999
-      API_EXTERNAL_URL: http://localhost:8000
 
       GOTRUE_DB_DRIVER: postgres
       GOTRUE_DB_DATABASE_URL: postgres://supabase_auth_admin:your-super-secret-and-long-postgres-password@db:5432/postgres
@@ -425,9 +340,7 @@ services:
     environment:
       <<: *supabase-env
       # Keep any existing environment variables specific to that service
-      JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
       SUPABASE_URL: http://kong:8000
-      SUPABASE_ANON_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
       SUPABASE_SERVICE_ROLE_KEY: eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
       SUPABASE_DB_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres
       # TODO: Allow configuring VERIFY_JWT per function. This PR might help: https://github.com/supabase/cli/pull/786
@@ -531,12 +444,8 @@ services:
       # Keep any existing environment variables specific to that service
       POSTGRES_HOST: /var/run/postgresql
       PGPORT: 5432
-      POSTGRES_PORT: 5432
       PGPASSWORD: your-super-secret-and-long-postgres-password
-      POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
       PGDATABASE: postgres
-      POSTGRES_DB: postgres
-      JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
       JWT_EXP: 3600
     command:
       [
@@ -570,8 +479,6 @@ services:
     <<: *supabase-env-files
     environment:
       <<: *supabase-env
-      # Keep any existing environment variables specific to that service
-      LOGFLARE_API_KEY: your-super-secret-and-long-logflare-key
     command:
       [
         "--config",
@@ -612,9 +519,6 @@ services:
       <<: *supabase-env
       # Keep any existing environment variables specific to that service
       PORT: 4000
-      POSTGRES_PORT: 5432
-      POSTGRES_DB: postgres
-      POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
       DATABASE_URL: ecto://supabase_admin:your-super-secret-and-long-postgres-password@db:5432/_supabase
       CLUSTER_POSTGRES: true
       SECRET_KEY_BASE: UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq

autogpt_platform/docker-compose.platform.yml

@@ -6,8 +6,7 @@
 # 5. CLI arguments - docker compose run -e VAR=value
 
 # Common backend environment - Docker service names
-x-backend-env:
-  &backend-env # Docker internal service hostnames (override localhost defaults)
+x-backend-env: &backend-env # Docker internal service hostnames (override localhost defaults)
   PYRO_HOST: "0.0.0.0"
   AGENTSERVER_HOST: rest_server
   SCHEDULER_HOST: scheduler_server
@@ -20,6 +19,10 @@ x-backend-env:
   RABBITMQ_HOST: rabbitmq
   # Override Supabase URL for Docker network
   SUPABASE_URL: http://kong:8000
+  # Database connection string for Docker network
+  # This cannot be constructed like in .env because we cannot interpolate values set here (DB_HOST)
+  DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
+  DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
 
 # Common env_file configuration for backend services
 x-backend-env-files: &backend-env-files
@@ -43,14 +46,18 @@ services:
     depends_on:
       db:
         condition: service_healthy
+    <<: *backend-env-files
     environment:
-      - DATABASE_URL=postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      - DIRECT_URL=postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
+      <<: *backend-env
     networks:
       - app-network
     restart: on-failure
     healthcheck:
-      test: ["CMD-SHELL", "poetry run prisma migrate status | grep -q 'No pending migrations' || exit 1"]
+      test:
+        [
+          "CMD-SHELL",
+          "poetry run prisma migrate status | grep -q 'No pending migrations' || exit 1",
+        ]
       interval: 30s
       timeout: 10s
       retries: 3
@@ -77,9 +84,9 @@ services:
       timeout: 10s
       retries: 5
       start_period: 10s
+    <<: *backend-env-files
     environment:
-      - RABBITMQ_DEFAULT_USER=rabbitmq_user_default
-      - RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7 # CHANGE THIS TO A RANDOM PASSWORD IN PRODUCTION -- everywhere lol
+      <<: *backend-env
     ports:
       - "5672:5672"
       - "15672:15672"
@@ -107,9 +114,6 @@ services:
     <<: *backend-env-files
     environment:
       <<: *backend-env
-      # Service-specific overrides
-      DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
     ports:
       - "8006:8006"
     networks:
@@ -140,9 +144,6 @@ services:
     <<: *backend-env-files
     environment:
       <<: *backend-env
-      # Service-specific overrides
-      DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
     ports:
       - "8002:8002"
     networks:
@@ -171,9 +172,6 @@ services:
     <<: *backend-env-files
     environment:
       <<: *backend-env
-      # Service-specific overrides
-      DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
     ports:
       - "8001:8001"
     networks:
@@ -198,9 +196,6 @@ services:
     <<: *backend-env-files
     environment:
       <<: *backend-env
-      # Service-specific overrides
-      DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
     ports:
       - "8005:8005"
     networks:
@@ -244,9 +239,6 @@ services:
     <<: *backend-env-files
     environment:
       <<: *backend-env
-      # Service-specific overrides
-      DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
-      DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@db:5432/postgres?connect_timeout=60&schema=platform
     ports:
       - "8003:8003"
     networks:
@@ -284,6 +276,8 @@ services:
       context: ../
       dockerfile: autogpt_platform/frontend/Dockerfile
       target: prod
+      args:
+        NEXT_PUBLIC_PW_TEST: ${NEXT_PUBLIC_PW_TEST:-false}
     depends_on:
       db:
         condition: service_healthy

autogpt_platform/frontend/.env.default

@@ -16,5 +16,4 @@
     NEXT_PUBLIC_REACT_QUERY_DEVTOOL=true
 
     NEXT_PUBLIC_GA_MEASUREMENT_ID=G-FH2XK2W4GN
-    NEXT_PUBLIC_PW_TEST=true
     

autogpt_platform/frontend/Dockerfile

@@ -9,13 +9,17 @@ RUN --mount=type=cache,target=/root/.local/share/pnpm pnpm install --frozen-lock
 FROM base AS build
 
 COPY autogpt_platform/frontend/ .
+# Allow CI to opt-in to Playwright test build-time flags
+ARG NEXT_PUBLIC_PW_TEST="false"
+ENV NEXT_PUBLIC_PW_TEST=$NEXT_PUBLIC_PW_TEST
 RUN if [ -f .env ]; then \
       cat .env.default .env > .env.merged && mv .env.merged .env; \
     else \
       cp .env.default .env; \
     fi
 RUN pnpm run generate:api
-RUN pnpm build
+# In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
+RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true pnpm build; else pnpm build; fi
 
 # Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
 FROM node:21-alpine AS prod

autogpt_platform/frontend/package.json

@@ -10,8 +10,8 @@
     "lint": "next lint && prettier --check .",
     "format": "next lint --fix; prettier --write .",
     "types": "tsc --noEmit",
-    "test": "next build --turbo && playwright test",
-    "test-ui": "next build --turbo && playwright test --ui",
+    "test": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test",
+    "test-ui": "NEXT_PUBLIC_PW_TEST=true next build --turbo && playwright test --ui",
     "test:no-build": "playwright test",
     "gentests": "playwright codegen http://localhost:3000",
     "storybook": "storybook dev -p 6006",
@@ -66,12 +66,14 @@
     "embla-carousel-react": "8.6.0",
     "framer-motion": "12.23.12",
     "geist": "1.4.2",
+    "highlight.js": "11.11.1",
     "jaro-winkler": "0.2.8",
+    "katex": "0.16.22",
     "launchdarkly-react-client-sdk": "3.8.1",
     "lodash": "4.17.21",
     "lucide-react": "0.539.0",
     "moment": "2.30.1",
-    "next": "15.4.6",
+    "next": "15.4.7",
     "next-themes": "0.4.6",
     "nuqs": "2.4.3",
     "party-js": "2.2.0",
@@ -85,10 +87,17 @@
     "react-modal": "3.16.3",
     "react-shepherd": "6.1.9",
     "react-window": "1.8.11",
-    "recharts": "2.15.3",
+    "recharts": "3.1.2",
+    "rehype-autolink-headings": "7.1.0",
+    "rehype-highlight": "7.0.2",
+    "rehype-katex": "7.0.1",
+    "rehype-slug": "6.0.0",
+    "remark-gfm": "4.0.1",
+    "remark-math": "6.0.0",
     "shepherd.js": "14.5.1",
     "sonner": "2.0.7",
     "tailwind-merge": "2.6.0",
+    "tailwind-scrollbar": "4.0.2",
     "tailwindcss-animate": "1.0.7",
     "uuid": "11.1.0",
     "vaul": "1.1.2",

autogpt_platform/frontend/scripts/generate-api-queries.ts

@@ -4,6 +4,7 @@ import { getAgptServerBaseUrl } from "@/lib/env-config";
 import { execSync } from "child_process";
 import * as path from "path";
 import * as fs from "fs";
+import * as os from "os";
 
 function fetchOpenApiSpec(): void {
   const args = process.argv.slice(2);
@@ -42,15 +43,30 @@ function fetchOpenApiSpec(): void {
 
   console.log(`Fetching OpenAPI spec from: ${openApiUrl}`);
 
+  // Write to a temporary file first to avoid clearing the real file on failure
+  const tmpOutputPath = path.join(
+    os.tmpdir(),
+    `openapi-fetch-${Date.now()}.json`,
+  );
+
   try {
-    // Fetch the OpenAPI spec
-    execSync(`curl "${openApiUrl}" > "${outputPath}"`, { stdio: "inherit" });
+    // Fetch the OpenAPI spec to a temp file
+    execSync(`curl "${openApiUrl}" -o "${tmpOutputPath}"`, {
+      stdio: "inherit",
+    });
 
     // Format with prettier
-    execSync(`prettier --write "${outputPath}"`, { stdio: "inherit" });
+    execSync(`prettier --write "${tmpOutputPath}"`, { stdio: "inherit" });
+
+    // Move temp file to final output path
+    fs.copyFileSync(tmpOutputPath, outputPath);
+    fs.unlinkSync(tmpOutputPath);
 
     console.log("✅ OpenAPI spec fetched and formatted successfully");
   } catch (error) {
+    if (fs.existsSync(tmpOutputPath)) {
+      fs.unlinkSync(tmpOutputPath);
+    }
     console.error("❌ Failed to fetch OpenAPI spec:", error);
     process.exit(1);
   }

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/page.tsx

@@ -9,7 +9,6 @@ import {
 import { OnboardingText } from "@/components/onboarding/OnboardingText";
 import StarRating from "@/components/onboarding/StarRating";
 import SchemaTooltip from "@/components/SchemaTooltip";
-import { TypeBasedInput } from "@/components/type-based-input";
 import { Card, CardContent, CardHeader, CardTitle } from "@/components/ui/card";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { GraphMeta, StoreAgentDetails } from "@/lib/autogpt-server-api";
@@ -18,6 +17,7 @@ import { cn } from "@/lib/utils";
 import { Play } from "lucide-react";
 import { useRouter } from "next/navigation";
 import { useCallback, useEffect, useState } from "react";
+import { RunAgentInputs } from "@/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentInputs/RunAgentInputs";
 
 export default function Page() {
   const { state, updateState, setStep } = useOnboarding(
@@ -89,13 +89,13 @@ export default function Page() {
       const libraryAgent = await api.addMarketplaceAgentToLibrary(
         storeAgent?.store_listing_version_id || "",
       );
-      const { graph_exec_id } = await api.executeGraph(
+      const { id: runID } = await api.executeGraph(
         libraryAgent.graph_id,
         libraryAgent.graph_version,
         state?.agentInput || {},
       );
       updateState({
-        onboardingAgentExecutionId: graph_exec_id,
+        onboardingAgentExecutionId: runID,
         agentRuns: (state?.agentRuns || 0) + 1,
       });
       router.push("/onboarding/6-congrats");
@@ -233,7 +233,7 @@ export default function Page() {
                             description={inputSubSchema.description}
                           />
                         </label>
-                        <TypeBasedInput
+                        <RunAgentInputs
                           schema={inputSubSchema}
                           value={state?.agentInput?.[key]}
                           placeholder={inputSubSchema.description}

autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/route.ts

@@ -1,4 +1,4 @@
-import { OAuthPopupResultMessage } from "@/components/integrations/credentials-input";
+import { OAuthPopupResultMessage } from "@/app/(platform)/library/agents/[id]/components/AgentRunsView/components/CredentialsInputs/CredentialsInputs";
 import { NextResponse } from "next/server";
 
 // This route is intended to be used as the callback for integration OAuth flows,

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenu/BlockMenu.tsx

@@ -8,12 +8,13 @@ import { ToyBrick } from "lucide-react";
 import { BlockMenuContent } from "../BlockMenuContent/BlockMenuContent";
 import { ControlPanelButton } from "../ControlPanelButton";
 import { useBlockMenu } from "./useBlockMenu";
+import { BlockMenuStateProvider } from "../block-menu-provider";
 
 interface BlockMenuProps {
   pinBlocksPopover: boolean;
-  blockMenuSelected: "save" | "block" | "";
+  blockMenuSelected: "save" | "block" | "search" | "";
   setBlockMenuSelected: React.Dispatch<
-    React.SetStateAction<"" | "save" | "block">
+    React.SetStateAction<"" | "save" | "block" | "search">
   >;
 }
 
@@ -44,7 +45,10 @@ export const BlockMenu: React.FC<BlockMenuProps> = ({
         className="absolute h-[75vh] w-[46.625rem] overflow-hidden rounded-[1rem] border-none p-0 shadow-[0_2px_6px_0_rgba(0,0,0,0.05)]"
         data-id="blocks-control-popover-content"
       >
-          <BlockMenuContent />
+          <BlockMenuStateProvider>
+            <BlockMenuContent />
+          </BlockMenuStateProvider>
+
       </PopoverContent>
     </Popover>
   );

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenu/useBlockMenu.ts

@@ -3,7 +3,7 @@ import { useState } from "react";
 interface useBlockMenuProps {
     pinBlocksPopover: boolean;
     setBlockMenuSelected: React.Dispatch<
-    React.SetStateAction<"" | "save" | "block">
+    React.SetStateAction<"" | "save" | "block" | "search">
   >;
 }
 

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuContent/BlockMenuContent.tsx

@@ -1,10 +1,18 @@
 "use client";
 import React from "react";
+import { useBlockMenuContext } from "../block-menu-provider";
+import { BlockMenuSearchBar } from "../BlockMenuSearchBar/BlockMenuSearchBar";
+import { Separator } from "@/components/ui/separator";
+import { BlockMenuDefault } from "../BlockMenuDefault/BlockMenuDefault";
+import { BlockMenuSearch } from "../BlockMenuSearch/BlockMenuSearch";
 
 export const BlockMenuContent = () => {
+  const { searchQuery } = useBlockMenuContext();
   return (
-    <div className="flex h-full w-full flex-col items-center justify-center">
-        This is the block menu content
+    <div className="flex h-full w-full flex-col">
+      <BlockMenuSearchBar />
+      <Separator className="h-[1px] w-full text-zinc-300" />
+      {searchQuery ? <BlockMenuSearch /> : <BlockMenuDefault />}
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuDefault/BlockMenuDefault.tsx

@@ -0,0 +1,15 @@
+import React from "react";
+
+import { Separator } from "@/components/ui/separator";
+import { BlockMenuDefaultContent } from "../BlockMenuDefaultContent/BlockMenuDefaultContent";
+import { BlockMenuSidebar } from "../BlockMenuSidebar/BlockMenuSidebar";
+
+export const BlockMenuDefault = () => {
+  return (
+    <div className="flex flex-1 overflow-y-auto">
+      <BlockMenuSidebar />
+      <Separator className="h-full w-[1px] text-zinc-300" />
+      <BlockMenuDefaultContent />
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuDefaultContent/BlockMenuDefaultContent.tsx

@@ -0,0 +1,14 @@
+import { Text } from "@/components/atoms/Text/Text";
+import React from "react";
+
+export const BlockMenuDefaultContent = () => {
+
+  return (
+    <div className="h-full flex-1 overflow-hidden flex items-center justify-center">
+      {/* I have added temporary content here, will fillup it in follow up prs */}
+      <Text variant="body" className="text-green-300"> 
+        This is the block menu default content
+      </Text>
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuSearch/BlockMenuSearch.tsx

@@ -0,0 +1,12 @@
+import { Text } from "@/components/atoms/Text/Text";
+
+export const BlockMenuSearch = () => {
+  return (
+    // This is just a temporary text, will content inside in it [in follow-up prs]
+    <div className="flex items-center justify-center h-full w-full">
+      <Text variant="h3" className="text-green-300">
+        This is the block menu search
+      </Text>
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuSearchBar/BlockMenuSearchBar.tsx

@@ -0,0 +1,53 @@
+import { cn } from "@/lib/utils";
+import React from "react";
+import { Input } from "@/components/ui/input";
+import { useBlockMenuSearchBar } from "./useBlockMenuSearchBar";
+import { Button } from "@/components/ui/button";
+import { MagnifyingGlassIcon, XIcon } from "@phosphor-icons/react";
+
+interface BlockMenuSearchBarProps {
+  className?: string;
+}
+
+export const BlockMenuSearchBar: React.FC<BlockMenuSearchBarProps> = ({
+  className = "",
+}) => {
+  const { handleClear, inputRef, localQuery, setLocalQuery, debouncedSetSearchQuery } = useBlockMenuSearchBar();
+
+  return (
+    <div
+      className={cn(
+        "flex min-h-[3.5625rem] items-center gap-2.5 px-4",
+        className,
+      )}
+    >
+      <div className="flex h-6 w-6 items-center justify-center">
+        <MagnifyingGlassIcon className="h-6 w-6 text-zinc-700" strokeWidth={2} />
+      </div>
+      <Input
+        ref={inputRef}
+        type="text"
+        value={localQuery}
+        onChange={(e) => {
+          setLocalQuery(e.target.value);
+          debouncedSetSearchQuery(e.target.value);
+        }}
+        placeholder={"Blocks, Agents, Integrations or Keywords..."}
+        className={cn(
+          "m-0 border-none p-0 font-sans text-base font-normal text-zinc-800 shadow-none outline-none",
+          "placeholder:text-zinc-400 focus:shadow-none focus:outline-none focus:ring-0",
+        )}
+      />
+      {localQuery.length > 0 && (
+        <Button
+          variant="ghost"
+          size={"sm"}
+          onClick={handleClear}
+          className="p-0 hover:bg-transparent"
+        >
+          <XIcon className="h-6 w-6 text-zinc-700" strokeWidth={2} />
+        </Button>
+      )}
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts

@@ -0,0 +1,46 @@
+import { debounce } from "lodash";
+import { useEffect, useRef, useState } from "react";
+import { useBlockMenuContext } from "../block-menu-provider";
+
+const SEARCH_DEBOUNCE_MS = 300;
+
+export const useBlockMenuSearchBar = () => {
+    const inputRef = useRef<HTMLInputElement>(null);
+    const [localQuery, setLocalQuery] = useState("");
+    const { setSearchQuery, setSearchId, searchId } = useBlockMenuContext();
+    
+    const searchIdRef = useRef(searchId);
+    useEffect(() => {
+      searchIdRef.current = searchId;
+    }, [searchId]);
+
+    const debouncedSetSearchQuery = debounce((value: string) => {
+          setSearchQuery(value);
+          if (value.length === 0) {
+            setSearchId(undefined);
+          } else if (!searchIdRef.current) {
+            setSearchId(crypto.randomUUID());
+          }
+        }, SEARCH_DEBOUNCE_MS);
+  
+    useEffect(() => {
+      return () => {
+        debouncedSetSearchQuery.cancel();
+      };
+    }, [debouncedSetSearchQuery]);
+  
+    const handleClear = () => {
+      setLocalQuery("");
+      setSearchQuery("");
+      setSearchId(undefined);
+      debouncedSetSearchQuery.cancel();
+    };
+
+    return {
+        handleClear,
+        inputRef,
+        localQuery,
+        setLocalQuery,
+        debouncedSetSearchQuery,
+    }
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuSidebar/BlockMenuSidebar.tsx

@@ -0,0 +1,117 @@
+import React from "react";
+import { MenuItem } from "../MenuItem";
+import { DefaultStateType } from "../block-menu-provider";
+import { useBlockMenuSidebar } from "./useBlockMenuSidebar";
+import { Skeleton } from "@/components/ui/skeleton";
+import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+
+export const BlockMenuSidebar = () => {
+  const { blockCounts, setDefaultState, defaultState, isLoading, isError, error } = useBlockMenuSidebar();
+
+  if (isLoading) {
+    return (
+      <div className="w-fit space-y-2 px-4 pt-4">
+        <Skeleton className="h-12 w-[12.875rem]" />
+        <Skeleton className="h-12 w-[12.875rem]" />
+        <Skeleton className="h-12 w-[12.875rem]" />
+        <Skeleton className="h-12 w-[12.875rem]" />
+        <Skeleton className="h-12 w-[12.875rem]" />
+        <Skeleton className="h-12 w-[12.875rem]" />
+      </div>
+    );
+  }
+  if (isError) {
+    return <div className="w-fit space-y-2 px-4 pt-4">
+      <ErrorCard className="w-[12.875rem]" httpError={{status: 500, statusText: "Internal Server Error", message: error?.detail || 'An error occurred'}} />
+      </div>
+  }
+
+  const topLevelMenuItems = [
+    {
+      name: "Suggestion",
+      type: "suggestion",
+    },
+    {
+      name: "All blocks",
+      type: "all_blocks",
+      number: blockCounts?.all_blocks,
+    },
+  ];
+
+  const subMenuItems = [
+    {
+      name: "Input blocks",
+      type: "input_blocks",
+      number: blockCounts?.input_blocks,
+    },
+    {
+      name: "Action blocks",
+      type: "action_blocks",
+      number: blockCounts?.action_blocks,
+    },
+    {
+      name: "Output blocks",
+      type: "output_blocks",
+      number: blockCounts?.output_blocks,
+    },
+  ];
+
+  const bottomMenuItems = [
+    {
+      name: "Integrations",
+      type: "integrations",
+      number: blockCounts?.integrations,
+      onClick: () => {
+        setDefaultState("integrations");
+      },
+    },
+    {
+      name: "Marketplace Agents",
+      type: "marketplace_agents",
+      number: blockCounts?.marketplace_agents,
+    },
+    {
+      name: "My Agents",
+      type: "my_agents",
+      number: blockCounts?.my_agents,
+    },
+  ];
+
+  return (
+    <div className="w-fit space-y-2 px-4 pt-4">
+      {topLevelMenuItems.map((item) => (
+        <MenuItem
+          key={item.type}
+          name={item.name}
+          number={item.number}
+          selected={defaultState === item.type}
+          onClick={() => setDefaultState(item.type as DefaultStateType)}
+        />
+      ))}
+      <div className="ml-[0.5365rem] space-y-2 border-l border-black/10 pl-[0.75rem]">
+        {subMenuItems.map((item) => (
+          <MenuItem
+            key={item.type}
+            name={item.name}
+            number={item.number}
+            className="max-w-[11.5339rem]"
+            selected={defaultState === item.type}
+            onClick={() => setDefaultState(item.type as DefaultStateType)}
+          />
+        ))}
+      </div>
+      {bottomMenuItems.map((item) => (
+        <MenuItem
+          key={item.type}
+          name={item.name}
+          number={item.number}
+          selected={defaultState === item.type}
+          onClick={
+            item.onClick ||
+            (() => setDefaultState(item.type as DefaultStateType))
+          }
+        />
+      ))}
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/BlockMenuSidebar/useBlockMenuSidebar.ts

@@ -0,0 +1,24 @@
+import { useGetV2GetBuilderItemCounts } from "@/app/api/__generated__/endpoints/default/default";
+import { useBlockMenuContext } from "../block-menu-provider";
+import { CountResponse } from "@/app/api/__generated__/models/countResponse";
+
+export const useBlockMenuSidebar = () => {
+  const { defaultState, setDefaultState } = useBlockMenuContext();
+
+  const { data: blockCounts, isLoading, isError, error} = useGetV2GetBuilderItemCounts({
+    query : {
+        select : (x) =>{
+            return x.data as CountResponse
+        }
+    }
+  });
+
+  return {
+    blockCounts,
+    setDefaultState,
+    defaultState,
+    isLoading,
+    isError,
+    error,
+  }
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenu/GraphMenu.tsx

@@ -0,0 +1,74 @@
+import React from "react";
+import {
+  Popover,
+  PopoverContent,
+  PopoverTrigger,
+} from "@/components/ui/popover";
+import { MagnifyingGlassIcon } from "@phosphor-icons/react";
+import { GraphSearchContent } from "../GraphMenuContent/GraphContent";
+import { ControlPanelButton } from "../ControlPanelButton";
+import { CustomNode } from "@/components/CustomNode";
+import { useGraphMenu } from "./useGraphMenu";
+
+interface GraphSearchMenuProps {
+  nodes: CustomNode[];
+  blockMenuSelected: "save" | "block" | "search" | "";
+  setBlockMenuSelected: React.Dispatch<
+    React.SetStateAction<"" | "save" | "block" | "search">
+  >;
+  onNodeSelect: (nodeId: string) => void;
+  onNodeHover?: (nodeId: string | null) => void;
+}
+
+export const GraphSearchMenu: React.FC<GraphSearchMenuProps> = ({
+  nodes,
+  blockMenuSelected,
+  setBlockMenuSelected,
+  onNodeSelect,
+  onNodeHover,
+}) => {
+  const {
+    open,
+    searchQuery,
+    setSearchQuery,
+    filteredNodes,
+    handleNodeSelect,
+    handleOpenChange,
+  } = useGraphMenu({
+    nodes,
+    blockMenuSelected,
+    setBlockMenuSelected,
+    onNodeSelect,
+  });
+
+  return (
+    <Popover open={open} onOpenChange={handleOpenChange}>
+      <PopoverTrigger className="hover:cursor-pointer">
+        <ControlPanelButton
+          data-id="graph-search-control-popover-trigger"
+          data-testid="graph-search-control-button"
+          selected={blockMenuSelected === "search"}
+          className="rounded-none"
+        >
+          <MagnifyingGlassIcon className="h-5 w-6" strokeWidth={2} />
+        </ControlPanelButton>
+      </PopoverTrigger>
+
+      <PopoverContent
+        side="right"
+        align="start"
+        sideOffset={16}
+        className="absolute h-[75vh] w-[46.625rem] overflow-hidden rounded-[1rem] border-none p-0 shadow-[0_2px_6px_0_rgba(0,0,0,0.05)]"
+        data-id="graph-search-popover-content"
+      >
+        <GraphSearchContent
+          searchQuery={searchQuery}
+          onSearchChange={setSearchQuery}
+          filteredNodes={filteredNodes}
+          onNodeSelect={handleNodeSelect}
+          onNodeHover={onNodeHover}
+        />
+      </PopoverContent>
+    </Popover>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenu/useGraphMenu.ts

@@ -0,0 +1,41 @@
+import { useGraphSearch } from "../GraphMenuSearchBar/useGraphMenuSearchBar";
+import { CustomNode } from "@/components/CustomNode";
+
+interface UseGraphMenuProps {
+  nodes: CustomNode[];
+  blockMenuSelected: "save" | "block" | "search" | "";
+  setBlockMenuSelected: React.Dispatch<
+    React.SetStateAction<"" | "save" | "block" | "search">
+  >;
+  onNodeSelect: (nodeId: string) => void;
+}
+
+export const useGraphMenu = ({
+  nodes,
+  setBlockMenuSelected,
+  onNodeSelect,
+}: UseGraphMenuProps) => {
+  const { open, setOpen, searchQuery, setSearchQuery, filteredNodes } = 
+    useGraphSearch(nodes);
+
+  const handleNodeSelect = (nodeId: string) => {
+    onNodeSelect(nodeId);
+    setOpen(false);
+    setSearchQuery("");
+    setBlockMenuSelected("");
+  };
+
+  const handleOpenChange = (newOpen: boolean) => {
+    setOpen(newOpen);
+    setBlockMenuSelected(newOpen ? "search" : "");
+  };
+
+  return {
+    open,
+    searchQuery,
+    setSearchQuery,
+    filteredNodes,
+    handleNodeSelect,
+    handleOpenChange,
+  };
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenuContent/GraphContent.tsx

@@ -0,0 +1,131 @@
+import React from "react";
+import { Separator } from "@/components/ui/separator";
+import { ScrollArea } from "@/components/ui/scroll-area";
+import { beautifyString, getPrimaryCategoryColor } from "@/lib/utils";
+import { SearchableNode } from "../GraphMenuSearchBar/useGraphMenuSearchBar";
+import { TextRenderer } from "@/components/ui/render";
+import {
+  Tooltip,
+  TooltipContent,
+  TooltipTrigger,
+  TooltipProvider,
+} from "@/components/ui/tooltip";
+import { GraphMenuSearchBar } from "../GraphMenuSearchBar/GraphMenuSearchBar";
+import { useGraphContent } from "./useGraphContent";
+
+interface GraphSearchContentProps {
+  searchQuery: string;
+  onSearchChange: (query: string) => void;
+  filteredNodes: SearchableNode[];
+  onNodeSelect: (nodeId: string) => void;
+  onNodeHover?: (nodeId: string | null) => void;
+}
+
+export const GraphSearchContent: React.FC<GraphSearchContentProps> = ({
+  searchQuery,
+  onSearchChange,
+  filteredNodes,
+  onNodeSelect,
+  onNodeHover,
+}) => {
+  const {
+    selectedIndex,
+    setSelectedIndex,
+    handleKeyDown,
+    getNodeInputOutputSummary,
+  } = useGraphContent({
+    searchQuery,
+    filteredNodes,
+    onNodeSelect,
+  });
+
+  return (
+    <div className="flex h-full w-full flex-col">
+      {/* Search Bar */}
+      <GraphMenuSearchBar 
+        searchQuery={searchQuery}
+        onSearchChange={onSearchChange}
+        onKeyDown={handleKeyDown}
+      />
+      
+      <Separator className="h-[1px] w-full text-zinc-300" />
+      
+      {/* Search Results */}
+      <div className="flex-1 overflow-hidden">
+        {searchQuery && (
+          <div className="px-4 py-2 text-xs text-gray-500">
+            Found {filteredNodes.length} node{filteredNodes.length !== 1 ? "s" : ""}
+          </div>
+        )}
+        <ScrollArea className="h-full w-full">
+          {filteredNodes.length === 0 ? (
+            <div className="flex h-32 items-center justify-center text-sm text-gray-500 dark:text-gray-400">
+              {searchQuery ? "No nodes found matching your search" : "Start typing to search nodes"}
+            </div>
+          ) : (
+            filteredNodes.map((node, index) => {
+              // Safety check for node data
+              if (!node || !node.data) {
+                return null;
+              }
+              
+              const nodeTitle = node.data?.metadata?.customized_name || 
+                               beautifyString(node.data?.blockType || "").replace(/ Block$/, "");
+              const nodeType = beautifyString(node.data?.blockType || "").replace(/ Block$/, "");
+              
+              return (
+                <TooltipProvider key={node.id}>
+                  <Tooltip delayDuration={300}>
+                    <TooltipTrigger asChild>
+                      <div
+                        className={`mx-4 my-2 flex h-20 cursor-pointer rounded-lg border border-zinc-200 bg-white ${
+                          index === selectedIndex 
+                            ? "border-zinc-400 shadow-md" 
+                            : "hover:border-zinc-300 hover:shadow-sm"
+                        }`}
+                        onClick={() => onNodeSelect(node.id)}
+                        onMouseEnter={() => {
+                          setSelectedIndex(index);
+                          onNodeHover?.(node.id);
+                        }}
+                        onMouseLeave={() => onNodeHover?.(null)}
+                      >
+                        <div
+                          className={`h-full w-3 rounded-l-[7px] ${getPrimaryCategoryColor(node.data?.categories)}`}
+                        />
+                        <div className="mx-3 flex flex-1 items-center justify-between">
+                          <div className="mr-2 min-w-0">
+                            <span className="block truncate pb-1 text-sm font-semibold text-zinc-800">
+                              <TextRenderer
+                                value={nodeTitle}
+                                truncateLengthLimit={45}
+                              />
+                            </span>
+                            <span className="block break-all text-xs font-normal text-zinc-500">
+                              <TextRenderer
+                                value={getNodeInputOutputSummary(node) || node.data?.description || ""}
+                                truncateLengthLimit={165}
+                              />
+                            </span>
+                          </div>
+                        </div>
+                      </div>
+                    </TooltipTrigger>
+                    <TooltipContent side="right" className="max-w-xs">
+                      <div className="space-y-1">
+                        <div className="font-semibold">Node Type: {nodeType}</div>
+                        {node.data?.metadata?.customized_name && (
+                          <div className="text-xs text-gray-500">Custom Name: {node.data.metadata.customized_name}</div>
+                        )}
+                      </div>
+                    </TooltipContent>
+                  </Tooltip>
+                </TooltipProvider>
+              );
+            })
+          )}
+        </ScrollArea>
+      </div>
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenuContent/useGraphContent.ts

@@ -0,0 +1,60 @@
+import { useEffect, useState } from "react";
+import { SearchableNode } from "../GraphMenuSearchBar/useGraphMenuSearchBar";
+
+interface UseGraphContentProps {
+  searchQuery: string;
+  filteredNodes: SearchableNode[];
+  onNodeSelect: (nodeId: string) => void;
+}
+
+export const useGraphContent = ({
+  searchQuery,
+  filteredNodes,
+  onNodeSelect,
+}: UseGraphContentProps) => {
+  const [selectedIndex, setSelectedIndex] = useState(0);
+
+  useEffect(() => {
+    setSelectedIndex(0);
+  }, [searchQuery]);
+
+  const handleKeyDown = (e: React.KeyboardEvent) => {
+    if (e.key === "ArrowDown") {
+      e.preventDefault();
+      setSelectedIndex((prev) => Math.min(prev + 1, filteredNodes.length - 1));
+    } else if (e.key === "ArrowUp") {
+      e.preventDefault();
+      setSelectedIndex((prev) => Math.max(prev - 1, 0));
+    } else if (e.key === "Enter" && filteredNodes.length > 0) {
+      e.preventDefault();
+      onNodeSelect(filteredNodes[selectedIndex].id);
+    }
+  };
+
+  const getNodeInputOutputSummary = (node: SearchableNode) => {
+    // Safety check for node data
+    if (!node || !node.data) {
+      return "";
+    }
+    
+    const inputs = Object.keys(node.data?.inputSchema?.properties || {});
+    const outputs = Object.keys(node.data?.outputSchema?.properties || {});
+    const parts = [];
+    
+    if (inputs.length > 0) {
+      parts.push(`Inputs: ${inputs.slice(0, 3).join(", ")}${inputs.length > 3 ? "..." : ""}`);
+    }
+    if (outputs.length > 0) {
+      parts.push(`Outputs: ${outputs.slice(0, 3).join(", ")}${outputs.length > 3 ? "..." : ""}`);
+    }
+    
+    return parts.join(" | ");
+  };
+
+  return {
+    selectedIndex,
+    setSelectedIndex,
+    handleKeyDown,
+    getNodeInputOutputSummary,
+  };
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenuSearchBar/GraphMenuSearchBar.tsx

@@ -0,0 +1,60 @@
+import { cn } from "@/lib/utils";
+import React from "react";
+import { Input } from "@/components/ui/input";
+import { Button } from "@/components/ui/button";
+import { MagnifyingGlassIcon, XIcon } from "@phosphor-icons/react";
+import { useGraphMenuSearchBarComponent } from "./useGraphMenuSearchBarComponent";
+
+interface GraphMenuSearchBarProps {
+  className?: string;
+  searchQuery: string;
+  onSearchChange: (query: string) => void;
+  onKeyDown?: (e: React.KeyboardEvent) => void;
+}
+
+export const GraphMenuSearchBar: React.FC<GraphMenuSearchBarProps> = ({
+  className = "",
+  searchQuery,
+  onSearchChange,
+  onKeyDown,
+}) => {
+  const { inputRef, handleClear } = useGraphMenuSearchBarComponent({
+    onSearchChange,
+  });
+
+  return (
+    <div
+      className={cn(
+        "flex min-h-[3.5625rem] items-center gap-2.5 px-4",
+        className,
+      )}
+    >
+      <div className="flex h-6 w-6 items-center justify-center">
+        <MagnifyingGlassIcon className="h-6 w-6 text-zinc-700" strokeWidth={2} />
+      </div>
+      <Input
+        ref={inputRef}
+        type="text"
+        value={searchQuery}
+        onChange={(e) => onSearchChange(e.target.value)}
+        onKeyDown={onKeyDown}
+        placeholder={"Search your graph for nodes, inputs, outputs..."}
+        className={cn(
+          "m-0 border-none p-0 font-sans text-base font-normal text-zinc-800 shadow-none outline-none",
+          "placeholder:text-zinc-400 focus:shadow-none focus:outline-none focus:ring-0",
+        )}
+        autoFocus
+      />
+      {searchQuery.length > 0 && (
+        <Button
+          variant="ghost"
+          size={"sm"}
+          onClick={handleClear}
+          className="p-0 hover:bg-transparent"
+        >
+          <XIcon className="h-6 w-6 text-zinc-700" strokeWidth={2} />
+        </Button>
+      )}
+    </div>
+  );
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenuSearchBar/useGraphMenuSearchBar.tsx

@@ -0,0 +1,146 @@
+import { useState, useMemo, useDeferredValue } from "react";
+import { CustomNode } from "@/components/CustomNode";
+import { beautifyString } from "@/lib/utils";
+import jaro from "jaro-winkler";
+
+export type SearchableNode = CustomNode & {
+  searchScore?: number;
+  matchedFields?: string[];
+};
+
+export const useGraphSearch = (nodes: CustomNode[]) => {
+  const [open, setOpen] = useState(false);
+  const [searchQuery, setSearchQuery] = useState("");
+  const deferredSearchQuery = useDeferredValue(searchQuery);
+
+  const filteredNodes = useMemo(() => {
+    // Filter out invalid nodes
+    const validNodes = (nodes || []).filter(node => node && node.data);
+    
+    if (!deferredSearchQuery.trim()) {
+      return validNodes.map(node => ({ ...node, searchScore: 1, matchedFields: [] }));
+    }
+
+    const query = deferredSearchQuery.toLowerCase().trim();
+    const queryWords = query.split(/\s+/);
+
+    return validNodes
+      .map((node): SearchableNode => {
+        const { score, matchedFields } = calculateNodeScore(node, query, queryWords);
+        return { ...node, searchScore: score, matchedFields };
+      })
+      .filter(node => node.searchScore! > 0)
+      .sort((a, b) => b.searchScore! - a.searchScore!);
+  }, [nodes, deferredSearchQuery]);
+
+  return {
+    open,
+    setOpen,
+    searchQuery,
+    setSearchQuery,
+    filteredNodes,
+  };
+};
+
+function calculateNodeScore(
+  node: CustomNode, 
+  query: string, 
+  queryWords: string[]
+): { score: number; matchedFields: string[] } {
+  const matchedFields: string[] = [];
+  let score = 0;
+
+  // Safety check for node data
+  if (!node || !node.data) {
+    return { score: 0, matchedFields: [] };
+  }
+
+  // Prepare searchable text with defensive checks
+  const nodeTitle = (node.data?.title || "").toLowerCase(); // This includes the ID
+  const nodeId = (node.id || "").toLowerCase();
+  const nodeDescription = (node.data?.description || "").toLowerCase();
+  const blockType = (node.data?.blockType || "").toLowerCase();
+  const beautifiedBlockType = beautifyString(blockType).toLowerCase();
+  const customizedName = (node.data?.metadata?.customized_name || "").toLowerCase();
+  
+  // Get input and output names with defensive checks
+  const inputNames = Object.keys(node.data?.inputSchema?.properties || {})
+    .map(key => key.toLowerCase());
+  const outputNames = Object.keys(node.data?.outputSchema?.properties || {})
+    .map(key => key.toLowerCase());
+  
+  // 1. Check exact match in customized name, title (includes ID), node ID, or block type (highest priority)
+  if (customizedName.includes(query) || nodeTitle.includes(query) || nodeId.includes(query) || blockType.includes(query) || beautifiedBlockType.includes(query)) {
+    score = 4;
+    matchedFields.push("title");
+  }
+  
+  // 2. Check all query words in customized name, title or block type
+  else if (queryWords.every(word => customizedName.includes(word) || nodeTitle.includes(word) || beautifiedBlockType.includes(word))) {
+    score = 3.5;
+    matchedFields.push("title");
+  }
+  
+  // 3. Check exact match in input/output names
+  else if (inputNames.some(name => name.includes(query))) {
+    score = 3;
+    matchedFields.push("inputs");
+  }
+  else if (outputNames.some(name => name.includes(query))) {
+    score = 2.8;
+    matchedFields.push("outputs");
+  }
+  
+  // 4. Check all query words in input/output names
+  else if (inputNames.some(name => queryWords.every(word => name.includes(word)))) {
+    score = 2.5;
+    matchedFields.push("inputs");
+  }
+  else if (outputNames.some(name => queryWords.every(word => name.includes(word)))) {
+    score = 2.3;
+    matchedFields.push("outputs");
+  }
+  
+  // 5. Similarity matching using Jaro-Winkler
+  else {
+    const titleSimilarity = Math.max(
+      jaro(customizedName, query),
+      jaro(nodeTitle, query),
+      jaro(nodeId, query),
+      jaro(beautifiedBlockType, query)
+    );
+    
+    if (titleSimilarity > 0.7) {
+      score = 1.5 + titleSimilarity;
+      matchedFields.push("title");
+    }
+    
+    // Check similarity with input/output names
+    const inputSimilarity = Math.max(...inputNames.map(name => jaro(name, query)), 0);
+    const outputSimilarity = Math.max(...outputNames.map(name => jaro(name, query)), 0);
+    
+    if (inputSimilarity > 0.7 && inputSimilarity > score) {
+      score = 1 + inputSimilarity;
+      matchedFields.push("inputs");
+    }
+    if (outputSimilarity > 0.7 && outputSimilarity > score) {
+      score = 0.8 + outputSimilarity;
+      matchedFields.push("outputs");
+    }
+  }
+  
+  // 6. Check description (lower priority)
+  if (score === 0 && nodeDescription.includes(query)) {
+    score = 0.5;
+    matchedFields.push("description");
+  }
+  
+  // 7. Check if all query words appear in description
+  if (score === 0 && queryWords.every(word => nodeDescription.includes(word))) {
+    score = 0.3;
+    matchedFields.push("description");
+  }
+
+  return { score, matchedFields };
+}
+

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/GraphMenuSearchBar/useGraphMenuSearchBarComponent.ts

@@ -0,0 +1,21 @@
+import { useRef } from "react";
+
+interface UseGraphMenuSearchBarComponentProps {
+  onSearchChange: (query: string) => void;
+}
+
+export const useGraphMenuSearchBarComponent = ({
+  onSearchChange,
+}: UseGraphMenuSearchBarComponentProps) => {
+  const inputRef = useRef<HTMLInputElement>(null);
+
+  const handleClear = () => {
+    onSearchChange("");
+    inputRef.current?.focus();
+  };
+
+  return {
+    inputRef,
+    handleClear,
+  };
+};

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/NewControlPanel/NewControlPanel.tsx

@@ -8,6 +8,9 @@ import { GraphExecutionID } from "@/lib/autogpt-server-api";
 import { history } from "@/components/history";
 import { ControlPanelButton } from "../ControlPanelButton";
 import { ArrowUUpLeftIcon, ArrowUUpRightIcon } from "@phosphor-icons/react";
+import { GraphSearchMenu } from "../GraphMenu/GraphMenu";
+import { CustomNode } from "@/components/CustomNode";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 
 export type Control = {
   icon: React.ReactNode;
@@ -22,6 +25,9 @@ interface ControlPanelProps {
   visualizeBeads: "no" | "static" | "animate";
   pinSavePopover: boolean;
   pinBlocksPopover: boolean;
+  nodes: CustomNode[];
+  onNodeSelect: (nodeId: string) => void;
+  onNodeHover?: (nodeId: string | null) => void;
 }
 
 export const NewControlPanel = ({
@@ -29,8 +35,13 @@ export const NewControlPanel = ({
   visualizeBeads,
   pinSavePopover,
   pinBlocksPopover,
+  nodes,
+  onNodeSelect,
+  onNodeHover,
   className,
 }: ControlPanelProps) => {
+  const isGraphSearchEnabled = useGetFlag(Flag.GRAPH_SEARCH);
+  
   const {
     blockMenuSelected,
     setBlockMenuSelected,
@@ -77,6 +88,18 @@ export const NewControlPanel = ({
           setBlockMenuSelected={setBlockMenuSelected}
         />
         <Separator className="text-[#E1E1E1]" />
+        {isGraphSearchEnabled && (
+          <>
+            <GraphSearchMenu
+              nodes={nodes}
+              blockMenuSelected={blockMenuSelected}
+              setBlockMenuSelected={setBlockMenuSelected}
+              onNodeSelect={onNodeSelect}
+              onNodeHover={onNodeHover}
+            />
+            <Separator className="text-[#E1E1E1]" />
+          </>
+        )}
         {controls.map((control, index) => (
           <ControlPanelButton
             key={index}

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/NewControlPanel/useNewControlPanel.ts

@@ -10,7 +10,7 @@ export interface NewControlPanelProps {
 
 export const useNewControlPanel = ({flowExecutionID, visualizeBeads}: NewControlPanelProps) => {
   const [blockMenuSelected, setBlockMenuSelected] = useState<
-    "save" | "block" | ""
+    "save" | "block" | "search" | ""
   >("");
   const query = useSearchParams();
   const _graphVersion = query.get("flowVersion");

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/SaveControl/NewSaveControl.tsx

@@ -23,9 +23,9 @@ interface SaveControlProps {
   onDescriptionChange: (description: string) => void;
   pinSavePopover: boolean;
 
-  blockMenuSelected: "save" | "block" | "";
+  blockMenuSelected: "save" | "block" | "search" | "";
   setBlockMenuSelected: React.Dispatch<
-    React.SetStateAction<"" | "save" | "block">
+    React.SetStateAction<"" | "save" | "block" | "search">
   >;
 }
 

autogpt_platform/frontend/src/app/(platform)/build/components/NewBlockMenu/block-menu-provider.tsx

@@ -0,0 +1,64 @@
+"use client";
+
+import { createContext, ReactNode, useContext, useState } from "react";
+
+export type DefaultStateType =
+  | "suggestion"
+  | "all_blocks"
+  | "input_blocks"
+  | "action_blocks"
+  | "output_blocks"
+  | "integrations"
+  | "marketplace_agents"
+  | "my_agents";
+
+
+interface BlockMenuContextType {
+  searchQuery: string;
+  setSearchQuery: React.Dispatch<React.SetStateAction<string>>;
+  searchId: string | undefined;
+  setSearchId: React.Dispatch<React.SetStateAction<string | undefined>>;
+  defaultState: DefaultStateType;
+  setDefaultState: React.Dispatch<React.SetStateAction<DefaultStateType>>;
+}   
+
+export const BlockMenuContext = createContext<BlockMenuContextType>(
+  {} as BlockMenuContextType,
+);
+
+interface BlockMenuStateProviderProps {
+  children: ReactNode;
+}
+
+export function BlockMenuStateProvider({
+  children,
+}: BlockMenuStateProviderProps) {
+  const [searchQuery, setSearchQuery] = useState("");
+  const [searchId, setSearchId] = useState<string | undefined>(undefined);
+  const [defaultState, setDefaultState] = useState<DefaultStateType>("suggestion");
+
+  return (
+    <BlockMenuContext.Provider
+      value={{
+        searchQuery,
+        setSearchQuery,
+        searchId,
+        setSearchId,
+        defaultState,
+        setDefaultState,
+      }}
+    >
+      {children}
+    </BlockMenuContext.Provider>
+  );
+}
+
+export function useBlockMenuContext(): BlockMenuContextType {
+  const context = useContext(BlockMenuContext);
+  if (!context) {
+    throw new Error(
+      "useBlockMenuContext must be used within a BlockMenuStateProvider",
+    );
+  }
+  return context;
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/APIKeyCredentialsModal/APIKeyCredentialsModal.tsx

@@ -0,0 +1,125 @@
+import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "@/components/atoms/Button/Button";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { Form, FormDescription, FormField } from "@/components/ui/form";
+import {
+  BlockIOCredentialsSubSchema,
+  CredentialsMetaInput,
+} from "@/lib/autogpt-server-api/types";
+import { useAPIKeyCredentialsModal } from "./useAPIKeyCredentialsModal";
+
+type Props = {
+  schema: BlockIOCredentialsSubSchema;
+  open: boolean;
+  onClose: () => void;
+  onCredentialsCreate: (creds: CredentialsMetaInput) => void;
+  siblingInputs?: Record<string, any>;
+};
+
+export function APIKeyCredentialsModal({
+  schema,
+  open,
+  onClose,
+  onCredentialsCreate,
+  siblingInputs,
+}: Props) {
+  const {
+    form,
+    isLoading,
+    supportsApiKey,
+    providerName,
+    schemaDescription,
+    onSubmit,
+  } = useAPIKeyCredentialsModal({ schema, siblingInputs, onCredentialsCreate });
+
+  if (isLoading || !supportsApiKey) {
+    return null;
+  }
+
+  return (
+    <Dialog
+      title={`Add new API key for ${providerName ?? ""}`}
+      controlled={{
+        isOpen: open,
+        set: (isOpen) => {
+          if (!isOpen) onClose();
+        },
+      }}
+      onClose={onClose}
+      styling={{
+        maxWidth: "25rem",
+      }}
+    >
+      <Dialog.Content>
+        {schemaDescription && (
+          <p className="mb-4 text-sm text-zinc-600">{schemaDescription}</p>
+        )}
+
+        <Form {...form}>
+          <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-2">
+            <FormField
+              control={form.control}
+              name="apiKey"
+              render={({ field }) => (
+                <>
+                  <Input
+                    id="apiKey"
+                    label="API Key"
+                    type="password"
+                    placeholder="Enter API key..."
+                    size="small"
+                    hint={
+                      schema.credentials_scopes ? (
+                        <FormDescription>
+                          Required scope(s) for this block:{" "}
+                          {schema.credentials_scopes?.map((s, i, a) => (
+                            <span key={i}>
+                              <code className="text-xs font-bold">{s}</code>
+                              {i < a.length - 1 && ", "}
+                            </span>
+                          ))}
+                        </FormDescription>
+                      ) : null
+                    }
+                    {...field}
+                  />
+                </>
+              )}
+            />
+            <FormField
+              control={form.control}
+              name="title"
+              render={({ field }) => (
+                <Input
+                  id="title"
+                  label="Name"
+                  type="text"
+                  placeholder="Enter a name for this API key..."
+                  size="small"
+                  {...field}
+                />
+              )}
+            />
+            <FormField
+              control={form.control}
+              name="expiresAt"
+              render={({ field }) => (
+                <Input
+                  id="expiresAt"
+                  label="Expiration Date"
+                  type="datetime-local"
+                  placeholder="Select expiration date..."
+                  size="small"
+                  {...field}
+                />
+              )}
+            />
+            <Button type="submit" size="small" className="min-w-68">
+              Save & use this API key
+            </Button>
+          </form>
+        </Form>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/APIKeyCredentialsModal/useAPIKeyCredentialsModal.ts

@@ -0,0 +1,82 @@
+import { z } from "zod";
+import { useForm, type UseFormReturn } from "react-hook-form";
+import { zodResolver } from "@hookform/resolvers/zod";
+import useCredentials from "@/hooks/useCredentials";
+import {
+  BlockIOCredentialsSubSchema,
+  CredentialsMetaInput,
+} from "@/lib/autogpt-server-api/types";
+
+export type APIKeyFormValues = {
+  apiKey: string;
+  title: string;
+  expiresAt?: string;
+};
+
+type Args = {
+  schema: BlockIOCredentialsSubSchema;
+  siblingInputs?: Record<string, any>;
+  onCredentialsCreate: (creds: CredentialsMetaInput) => void;
+};
+
+export function useAPIKeyCredentialsModal({
+  schema,
+  siblingInputs,
+  onCredentialsCreate,
+}: Args): {
+  form: UseFormReturn<APIKeyFormValues>;
+  isLoading: boolean;
+  supportsApiKey: boolean;
+  provider?: string;
+  providerName?: string;
+  schemaDescription?: string;
+  onSubmit: (values: APIKeyFormValues) => Promise<void>;
+} {
+  const credentials = useCredentials(schema, siblingInputs);
+
+  const formSchema = z.object({
+    apiKey: z.string().min(1, "API Key is required"),
+    title: z.string().min(1, "Name is required"),
+    expiresAt: z.string().optional(),
+  });
+
+  const form = useForm<APIKeyFormValues>({
+    resolver: zodResolver(formSchema),
+    defaultValues: {
+      apiKey: "",
+      title: "",
+      expiresAt: "",
+    },
+  });
+
+  async function onSubmit(values: APIKeyFormValues) {
+    if (!credentials || credentials.isLoading) return;
+    const expiresAt = values.expiresAt
+      ? new Date(values.expiresAt).getTime() / 1000
+      : undefined;
+    const newCredentials = await credentials.createAPIKeyCredentials({
+      api_key: values.apiKey,
+      title: values.title,
+      expires_at: expiresAt,
+    });
+    onCredentialsCreate({
+      provider: credentials.provider,
+      id: newCredentials.id,
+      type: "api_key",
+      title: newCredentials.title,
+    });
+  }
+
+  return {
+    form,
+    isLoading: !credentials || credentials.isLoading,
+    supportsApiKey: !!credentials?.supportsApiKey,
+    provider: credentials?.provider,
+    providerName:
+      !credentials || credentials.isLoading
+        ? undefined
+        : credentials.providerName,
+    schemaDescription: schema.description,
+    onSubmit,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/CredentialsInputs/CredentialsInputs.tsx

@@ -1,5 +1,5 @@
 import SchemaTooltip from "@/components/SchemaTooltip";
-import { Button } from "@/components/ui/button";
+import { Button } from "@/components/atoms/Button/Button";
 import { IconKey, IconKeyPlus, IconUserPlus } from "@/components/ui/icons";
 import {
   Select,
@@ -28,10 +28,10 @@ import {
   FaMedium,
   FaTwitter,
 } from "react-icons/fa";
-import { APIKeyCredentialsModal } from "./api-key-credentials-modal";
-import { HostScopedCredentialsModal } from "./host-scoped-credentials-modal";
-import { OAuth2FlowWaitingModal } from "./oauth2-flow-waiting-modal";
-import { UserPasswordCredentialsModal } from "./user-password-credentials-modal";
+import { APIKeyCredentialsModal } from "../APIKeyCredentialsModal/APIKeyCredentialsModal";
+import { HostScopedCredentialsModal } from "../HotScopedCredentialsModal/HotScopedCredentialsModal";
+import { OAuthFlowWaitingModal } from "../OAuthWaitingModal/OAuthWaitingModal";
+import { PasswordCredentialsModal } from "../PasswordCredentialsModal/PasswordCredentialsModal";
 
 const fallbackIcon = FaKey;
 
@@ -290,14 +290,14 @@ export const CredentialsInput: FC<{
         />
       )}
       {supportsOAuth2 && (
-        <OAuth2FlowWaitingModal
+        <OAuthFlowWaitingModal
           open={isOAuth2FlowInProgress}
           onClose={() => oAuthPopupController?.abort("canceled")}
           providerName={providerName}
         />
       )}
       {supportsUserPassword && (
-        <UserPasswordCredentialsModal
+        <PasswordCredentialsModal
           schema={schema}
           open={isUserPasswordCredentialsModalOpen}
           onClose={() => setUserPasswordCredentialsModalOpen(false)}
@@ -335,30 +335,39 @@ export const CredentialsInput: FC<{
   // Show credentials creation UI when no relevant credentials exist
   if (!hasRelevantCredentials) {
     return (
-      <div>
+      <div className="mb-4">
         {fieldHeader}
 
         <div className={cn("flex flex-row space-x-2", className)}>
           {supportsOAuth2 && (
-            <Button onClick={handleOAuthLogin}>
+            <Button onClick={handleOAuthLogin} size="small">
               <ProviderIcon className="mr-2 h-4 w-4" />
               {"Sign in with " + providerName}
             </Button>
           )}
           {supportsApiKey && (
-            <Button onClick={() => setAPICredentialsModalOpen(true)}>
+            <Button
+              onClick={() => setAPICredentialsModalOpen(true)}
+              size="small"
+            >
               <ProviderIcon className="mr-2 h-4 w-4" />
               Enter API key
             </Button>
           )}
           {supportsUserPassword && (
-            <Button onClick={() => setUserPasswordCredentialsModalOpen(true)}>
+            <Button
+              onClick={() => setUserPasswordCredentialsModalOpen(true)}
+              size="small"
+            >
               <ProviderIcon className="mr-2 h-4 w-4" />
               Enter username and password
             </Button>
           )}
           {supportsHostScoped && credentials.discriminatorValue && (
-            <Button onClick={() => setHostScopedCredentialsModalOpen(true)}>
+            <Button
+              onClick={() => setHostScopedCredentialsModalOpen(true)}
+              size="small"
+            >
               <ProviderIcon className="mr-2 h-4 w-4" />
               {`Enter sensitive headers for ${getHostFromUrl(credentials.discriminatorValue)}`}
             </Button>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/HotScopedCredentialsModal/HotScopedCredentialsModal.tsx

@@ -1,24 +1,15 @@
-import { FC, useEffect, useState } from "react";
+import { useEffect, useState } from "react";
 import { z } from "zod";
 import { useForm } from "react-hook-form";
 import { zodResolver } from "@hookform/resolvers/zod";
-import { Input } from "@/components/ui/input";
-import { Button } from "@/components/ui/button";
-import {
-  Dialog,
-  DialogContent,
-  DialogDescription,
-  DialogHeader,
-  DialogTitle,
-} from "@/components/ui/dialog";
+import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "@/components/atoms/Button/Button";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import {
   Form,
-  FormControl,
   FormDescription,
   FormField,
-  FormItem,
   FormLabel,
-  FormMessage,
 } from "@/components/ui/form";
 import useCredentials from "@/hooks/useCredentials";
 import {
@@ -26,14 +17,23 @@ import {
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 import { getHostFromUrl } from "@/lib/utils/url";
+import { PlusIcon, TrashIcon } from "@phosphor-icons/react";
 
-export const HostScopedCredentialsModal: FC<{
+type Props = {
   schema: BlockIOCredentialsSubSchema;
   open: boolean;
   onClose: () => void;
   onCredentialsCreate: (creds: CredentialsMetaInput) => void;
   siblingInputs?: Record<string, any>;
-}> = ({ schema, open, onClose, onCredentialsCreate, siblingInputs }) => {
+};
+
+export function HostScopedCredentialsModal({
+  schema,
+  open,
+  onClose,
+  onCredentialsCreate,
+  siblingInputs,
+}: Props) {
   const credentials = useCredentials(schema, siblingInputs);
 
   // Get current host from siblingInputs or discriminator_values
@@ -129,86 +129,92 @@ export const HostScopedCredentialsModal: FC<{
 
   return (
     <Dialog
-      open={open}
-      onOpenChange={(open) => {
-        if (!open) onClose();
+      title={`Add sensitive headers for ${providerName}`}
+      controlled={{
+        isOpen: open,
+        set: (isOpen) => {
+          if (!isOpen) onClose();
+        },
+      }}
+      onClose={onClose}
+      styling={{
+        maxWidth: "25rem",
       }}
     >
-      <DialogContent className="max-h-[90vh] max-w-2xl overflow-y-auto">
-        <DialogHeader>
-          <DialogTitle>Add sensitive headers for {providerName}</DialogTitle>
-          {schema.description && (
-            <DialogDescription>{schema.description}</DialogDescription>
-          )}
-        </DialogHeader>
+      <Dialog.Content>
+        {schema.description && (
+          <p className="mb-4 text-sm text-zinc-600">{schema.description}</p>
+        )}
 
         <Form {...form}>
-          <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-4">
+          <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-2">
             <FormField
               control={form.control}
               name="host"
               render={({ field }) => (
-                <FormItem>
-                  <FormLabel>Host Pattern</FormLabel>
-                  <FormDescription>
-                    {currentHost
+                <Input
+                  id="host"
+                  label="Host Pattern"
+                  type="text"
+                  size="small"
+                  readOnly={!!currentHost}
+                  hint={
+                    currentHost
                       ? "Auto-populated from the URL field. Headers will be applied to requests to this host."
-                      : "Enter the host/domain to match against request URLs (e.g., api.example.com)."}
-                  </FormDescription>
-                  <FormControl>
-                    <Input
-                      type="text"
-                      readOnly={!!currentHost}
-                      placeholder={
-                        currentHost
-                          ? undefined
-                          : "Enter host (e.g., api.example.com)"
-                      }
-                      {...field}
-                    />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
+                      : "Enter the host/domain to match against request URLs (e.g., api.example.com)."
+                  }
+                  placeholder={
+                    currentHost
+                      ? undefined
+                      : "Enter host (e.g., api.example.com)"
+                  }
+                  {...field}
+                />
               )}
             />
 
             <div className="space-y-2">
               <FormLabel>Headers</FormLabel>
-              <FormDescription>
+              <FormDescription className="max-w-md">
                 Add sensitive headers (like Authorization, X-API-Key) that
                 should be automatically included in requests to the specified
                 host.
               </FormDescription>
 
               {headerPairs.map((pair, index) => (
-                <div key={index} className="flex items-end gap-2">
-                  <div className="flex-1">
-                    <Input
-                      placeholder="Header name (e.g., Authorization)"
-                      value={pair.key}
-                      onChange={(e) =>
-                        updateHeaderPair(index, "key", e.target.value)
-                      }
-                    />
-                  </div>
-                  <div className="flex-1">
-                    <Input
-                      type="password"
-                      placeholder="Header value (e.g., Bearer token123)"
-                      value={pair.value}
-                      onChange={(e) =>
-                        updateHeaderPair(index, "value", e.target.value)
-                      }
-                    />
-                  </div>
+                <div key={index} className="flex w-full items-center gap-4">
+                  <Input
+                    id={`header-${index}-key`}
+                    label="Header Name"
+                    placeholder="Header name (e.g., Authorization)"
+                    size="small"
+                    value={pair.key}
+                    className="flex-1"
+                    onChange={(e) =>
+                      updateHeaderPair(index, "key", e.target.value)
+                    }
+                  />
+
+                  <Input
+                    id={`header-${index}-value`}
+                    label="Header Value"
+                    size="small"
+                    type="password"
+                    className="flex-2"
+                    placeholder="Header value (e.g., Bearer token123)"
+                    value={pair.value}
+                    onChange={(e) =>
+                      updateHeaderPair(index, "value", e.target.value)
+                    }
+                  />
                   <Button
                     type="button"
-                    variant="outline"
-                    size="sm"
+                    variant="secondary"
+                    size="small"
                     onClick={() => removeHeaderPair(index)}
                     disabled={headerPairs.length === 1}
                   >
-                    Remove
+                    <TrashIcon className="size-4" /> Remove
                   </Button>
                 </div>
               ))}
@@ -216,20 +222,21 @@ export const HostScopedCredentialsModal: FC<{
               <Button
                 type="button"
                 variant="outline"
-                size="sm"
+                size="small"
                 onClick={addHeaderPair}
-                className="w-full"
               >
-                Add Another Header
+                <PlusIcon className="size-4" /> Add Another Header
               </Button>
             </div>
 
-            <Button type="submit" className="w-full">
-              Save & use these credentials
-            </Button>
+            <div className="pt-8">
+              <Button type="submit" className="w-full" size="small">
+                Save & use these credentials
+              </Button>
+            </div>
           </form>
         </Form>
-      </DialogContent>
+      </Dialog.Content>
     </Dialog>
   );
-};
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/OAuthWaitingModal/OAuthWaitingModal.tsx

@@ -0,0 +1,30 @@
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+
+type Props = {
+  open: boolean;
+  onClose: () => void;
+  providerName: string;
+};
+
+export function OAuthFlowWaitingModal({ open, onClose, providerName }: Props) {
+  return (
+    <Dialog
+      title={`Waiting on ${providerName} sign-in process...`}
+      controlled={{
+        isOpen: open,
+        set: (isOpen) => {
+          if (!isOpen) onClose();
+        },
+      }}
+      onClose={onClose}
+    >
+      <Dialog.Content>
+        <p className="text-sm text-zinc-600">
+          Complete the sign-in process in the pop-up window.
+          <br />
+          Closing this dialog will cancel the sign-in process.
+        </p>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/PasswordCredentialsModal/PasswordCredentialsModal.tsx

@@ -1,36 +1,31 @@
-import { FC } from "react";
 import { z } from "zod";
 import { useForm } from "react-hook-form";
 import { zodResolver } from "@hookform/resolvers/zod";
-import { Input } from "@/components/ui/input";
-import { Button } from "@/components/ui/button";
-import {
-  Dialog,
-  DialogContent,
-  DialogHeader,
-  DialogTitle,
-} from "@/components/ui/dialog";
-import {
-  Form,
-  FormControl,
-  FormField,
-  FormItem,
-  FormLabel,
-  FormMessage,
-} from "@/components/ui/form";
+import { Input } from "@/components/atoms/Input/Input";
+import { Button } from "@/components/atoms/Button/Button";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { Form, FormField } from "@/components/ui/form";
 import useCredentials from "@/hooks/useCredentials";
 import {
   BlockIOCredentialsSubSchema,
   CredentialsMetaInput,
 } from "@/lib/autogpt-server-api/types";
 
-export const UserPasswordCredentialsModal: FC<{
+type Props = {
   schema: BlockIOCredentialsSubSchema;
   open: boolean;
   onClose: () => void;
   onCredentialsCreate: (creds: CredentialsMetaInput) => void;
   siblingInputs?: Record<string, any>;
-}> = ({ schema, open, onClose, onCredentialsCreate, siblingInputs }) => {
+};
+
+export function PasswordCredentialsModal({
+  schema,
+  open,
+  onClose,
+  onCredentialsCreate,
+  siblingInputs,
+}: Props) {
   const credentials = useCredentials(schema, siblingInputs);
 
   const formSchema = z.object({
@@ -74,76 +69,72 @@ export const UserPasswordCredentialsModal: FC<{
 
   return (
     <Dialog
-      open={open}
-      onOpenChange={(open) => {
-        if (!open) onClose();
+      title={`Add new username & password for ${providerName}`}
+      controlled={{
+        isOpen: open,
+        set: (isOpen) => {
+          if (!isOpen) onClose();
+        },
+      }}
+      onClose={onClose}
+      styling={{
+        maxWidth: "25rem",
       }}
     >
-      <DialogContent>
-        <DialogHeader>
-          <DialogTitle>
-            Add new username & password for {providerName}
-          </DialogTitle>
-        </DialogHeader>
+      <Dialog.Content>
         <Form {...form}>
-          <form onSubmit={form.handleSubmit(onSubmit)} className="space-y-4">
+          <form
+            onSubmit={form.handleSubmit(onSubmit)}
+            className="space-y-2 pt-4"
+          >
             <FormField
               control={form.control}
               name="username"
               render={({ field }) => (
-                <FormItem>
-                  <FormLabel>Username</FormLabel>
-                  <FormControl>
-                    <Input
-                      type="text"
-                      placeholder="Enter username..."
-                      {...field}
-                    />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
+                <Input
+                  id="username"
+                  label="Username"
+                  type="text"
+                  placeholder="Enter username..."
+                  size="small"
+                  {...field}
+                />
               )}
             />
             <FormField
               control={form.control}
               name="password"
               render={({ field }) => (
-                <FormItem>
-                  <FormLabel>Password</FormLabel>
-                  <FormControl>
-                    <Input
-                      type="password"
-                      placeholder="Enter password..."
-                      {...field}
-                    />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
+                <Input
+                  id="password"
+                  label="Password"
+                  type="password"
+                  placeholder="Enter password..."
+                  size="small"
+                  {...field}
+                />
               )}
             />
             <FormField
               control={form.control}
               name="title"
               render={({ field }) => (
-                <FormItem>
-                  <FormLabel>Name</FormLabel>
-                  <FormControl>
-                    <Input
-                      type="text"
-                      placeholder="Enter a name for this user login..."
-                      {...field}
-                    />
-                  </FormControl>
-                  <FormMessage />
-                </FormItem>
+                <Input
+                  id="title"
+                  label="Name"
+                  type="text"
+                  placeholder="Enter a name for this user login..."
+                  size="small"
+                  {...field}
+                />
               )}
             />
-            <Button type="submit" className="w-full">
+            <Button type="submit" size="small" className="min-w-68">
               Save & use this user login
             </Button>
           </form>
         </Form>
-      </DialogContent>
+      </Dialog.Content>
     </Dialog>
   );
-};
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentInputs/RunAgentInputs.tsx

@@ -0,0 +1,232 @@
+import React from "react";
+import { format } from "date-fns";
+
+import { Input as DSInput } from "@/components/atoms/Input/Input";
+import { Select as DSSelect } from "@/components/atoms/Select/Select";
+import { MultiToggle } from "@/components/molecules/MultiToggle/MultiToggle";
+// Removed shadcn Select usage in favor of DS Select for time picker
+import {
+  BlockIOObjectSubSchema,
+  BlockIOSubSchema,
+  DataType,
+  determineDataType,
+} from "@/lib/autogpt-server-api/types";
+import { TimePicker } from "@/components/molecules/TimePicker/TimePicker";
+import { FileInput } from "@/components/atoms/FileInput/FileInput";
+import { useRunAgentInputs } from "./useRunAgentInputs";
+import { Switch } from "@/components/atoms/Switch/Switch";
+
+/**
+ * A generic prop structure for the TypeBasedInput.
+ *
+ * onChange expects an event-like object with e.target.value so the parent
+ * can do something like setInputValues(e.target.value).
+ */
+interface Props {
+  schema: BlockIOSubSchema;
+  value?: any;
+  placeholder?: string;
+  onChange: (value: any) => void;
+}
+
+/**
+ * A generic, data-type-based input component that uses Shadcn UI.
+ * It inspects the schema via `determineDataType` and renders
+ * the correct UI component.
+ */
+export function RunAgentInputs({
+  schema,
+  value,
+  placeholder,
+  onChange,
+  ...props
+}: Props & React.HTMLAttributes<HTMLElement>) {
+  const { handleUploadFile, uploadProgress } = useRunAgentInputs();
+
+  const dataType = determineDataType(schema);
+  const baseId = String(schema.title ?? "input")
+    .replace(/\s+/g, "-")
+    .toLowerCase();
+
+  let innerInputElement: React.ReactNode = null;
+  switch (dataType) {
+    case DataType.NUMBER:
+      innerInputElement = (
+        <DSInput
+          id={`${baseId}-number`}
+          label={schema.title ?? placeholder ?? "Number"}
+          hideLabel
+          size="small"
+          type="number"
+          value={value ?? ""}
+          placeholder={placeholder || "Enter number"}
+          onChange={(e) =>
+            onChange(Number((e.target as HTMLInputElement).value))
+          }
+          {...props}
+        />
+      );
+      break;
+
+    case DataType.LONG_TEXT:
+      innerInputElement = (
+        <DSInput
+          id={`${baseId}-textarea`}
+          label={schema.title ?? placeholder ?? "Text"}
+          hideLabel
+          size="small"
+          type="textarea"
+          rows={3}
+          value={value ?? ""}
+          placeholder={placeholder || "Enter text"}
+          onChange={(e) => onChange((e.target as HTMLInputElement).value)}
+          {...props}
+        />
+      );
+      break;
+
+    case DataType.BOOLEAN:
+      innerInputElement = (
+        <>
+          <span className="text-sm text-gray-500">
+            {placeholder || (value ? "Enabled" : "Disabled")}
+          </span>
+          <Switch
+            className="ml-auto"
+            checked={!!value}
+            onCheckedChange={(checked: boolean) => onChange(checked)}
+            {...props}
+          />
+        </>
+      );
+      break;
+
+    case DataType.DATE:
+      innerInputElement = (
+        <DSInput
+          id={`${baseId}-date`}
+          label={schema.title ?? placeholder ?? "Date"}
+          hideLabel
+          size="small"
+          type="date"
+          value={value ? format(value as Date, "yyyy-MM-dd") : ""}
+          onChange={(e) => {
+            const v = (e.target as HTMLInputElement).value;
+            if (!v) onChange(undefined);
+            else {
+              const [y, m, d] = v.split("-").map(Number);
+              onChange(new Date(y, m - 1, d));
+            }
+          }}
+          placeholder={placeholder || "Pick a date"}
+          {...props}
+        />
+      );
+      break;
+
+    case DataType.TIME:
+      innerInputElement = (
+        <TimePicker value={value?.toString()} onChange={onChange} />
+      );
+      break;
+
+    case DataType.DATE_TIME:
+      innerInputElement = (
+        <DSInput
+          id={`${baseId}-datetime`}
+          label={schema.title ?? placeholder ?? "Date time"}
+          hideLabel
+          size="small"
+          type="datetime-local"
+          value={value ?? ""}
+          onChange={(e) => onChange((e.target as HTMLInputElement).value)}
+          placeholder={placeholder || "Enter date and time"}
+          {...props}
+        />
+      );
+      break;
+
+    case DataType.FILE:
+      innerInputElement = (
+        <FileInput
+          value={value}
+          placeholder={placeholder}
+          onChange={onChange}
+          onUploadFile={handleUploadFile}
+          uploadProgress={uploadProgress}
+          {...props}
+        />
+      );
+      break;
+
+    case DataType.SELECT:
+      if (
+        "enum" in schema &&
+        Array.isArray(schema.enum) &&
+        schema.enum.length > 0
+      ) {
+        innerInputElement = (
+          <DSSelect
+            id={`${baseId}-select`}
+            label={schema.title ?? placeholder ?? "Select"}
+            hideLabel
+            value={value ?? ""}
+            size="small"
+            onValueChange={(val: string) => onChange(val)}
+            placeholder={placeholder || "Select an option"}
+            options={schema.enum
+              .filter((opt) => opt)
+              .map((opt) => ({ value: opt, label: String(opt) }))}
+          />
+        );
+        break;
+      }
+
+    case DataType.MULTI_SELECT: {
+      const _schema = schema as BlockIOObjectSubSchema;
+      const allKeys = Object.keys(_schema.properties);
+      const selectedValues = Object.entries(value || {})
+        .filter(([_, v]) => v)
+        .map(([k]) => k);
+
+      innerInputElement = (
+        <MultiToggle
+          items={allKeys.map((key) => ({
+            value: key,
+            label: _schema.properties[key]?.title ?? key,
+            size: "small",
+          }))}
+          selectedValues={selectedValues}
+          onChange={(values: string[]) =>
+            onChange(
+              Object.fromEntries(
+                allKeys.map((opt) => [opt, values.includes(opt)]),
+              ),
+            )
+          }
+          className="nodrag"
+          aria-label={schema.title}
+        />
+      );
+      break;
+    }
+
+    case DataType.SHORT_TEXT:
+    default:
+      innerInputElement = (
+        <DSInput
+          id={`${baseId}-text`}
+          label={schema.title ?? placeholder ?? "Text"}
+          hideLabel
+          size="small"
+          type="text"
+          value={value ?? ""}
+          onChange={(e) => onChange((e.target as HTMLInputElement).value)}
+          placeholder={placeholder || "Enter text"}
+          {...props}
+        />
+      );
+  }
+
+  return <div className="no-drag relative flex">{innerInputElement}</div>;
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentInputs/useRunAgentInputs.ts

@@ -0,0 +1,19 @@
+import BackendAPI from "@/lib/autogpt-server-api";
+import { useState } from "react";
+
+export function useRunAgentInputs() {
+  const api = new BackendAPI();
+  const [uploadProgress, setUploadProgress] = useState(0);
+
+  async function handleUploadFile(file: File) {
+    const result = await api.uploadFile(file, "gcs", 24, (progress) =>
+      setUploadProgress(progress),
+    );
+    return result;
+  }
+
+  return {
+    uploadProgress,
+    handleUploadFile,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/RunAgentModal.tsx

@@ -1,6 +1,7 @@
 "use client";
 
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { Button } from "@/components/atoms/Button/Button";
 import { useState } from "react";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { useAgentRunModal } from "./useAgentRunModal";
@@ -8,10 +9,13 @@ import { ModalHeader } from "./components/ModalHeader/ModalHeader";
 import { AgentCostSection } from "./components/AgentCostSection/AgentCostSection";
 import { AgentSectionHeader } from "./components/AgentSectionHeader/AgentSectionHeader";
 import { DefaultRunView } from "./components/DefaultRunView/DefaultRunView";
+import { RunAgentModalContextProvider } from "./context";
 import { ScheduleView } from "./components/ScheduleView/ScheduleView";
 import { AgentDetails } from "./components/AgentDetails/AgentDetails";
 import { RunActions } from "./components/RunActions/RunActions";
 import { ScheduleActions } from "./components/ScheduleActions/ScheduleActions";
+import { Text } from "@/components/atoms/Text/Text";
+import { AlarmIcon, TrashIcon } from "@phosphor-icons/react";
 
 interface Props {
   triggerSlot: React.ReactNode;
@@ -22,20 +26,45 @@ interface Props {
 
 export function RunAgentModal({ triggerSlot, agent }: Props) {
   const {
+    // UI state
     isOpen,
     setIsOpen,
     showScheduleView,
+
+    // Run mode
     defaultRunType,
+
+    // Form: regular inputs
     inputValues,
     setInputValues,
+
+    // Form: credentials
+    inputCredentials,
+    setInputCredentials,
+
+    // Preset/trigger labels
+    presetName,
+    presetDescription,
+    setPresetName,
+    setPresetDescription,
+
+    // Scheduling
     scheduleName,
     cronExpression,
+
+    // Validation/readiness
     allRequiredInputsAreSet,
-    // agentInputFields, // Available if needed for future use
-    hasInputFields,
+
+    // Schemas
+    agentInputFields,
+    agentCredentialsInputFields,
+
+    // Async states
     isExecuting,
     isCreatingSchedule,
     isSettingUpTrigger,
+
+    // Actions
     handleRun,
     handleSchedule,
     handleShowSchedule,
@@ -46,6 +75,10 @@ export function RunAgentModal({ triggerSlot, agent }: Props) {
 
   const [isScheduleFormValid, setIsScheduleFormValid] = useState(true);
 
+  const hasAnySetupFields =
+    Object.keys(agentInputFields || {}).length > 0 ||
+    Object.keys(agentCredentialsInputFields || {}).length > 0;
+
   function handleInputChange(key: string, value: string) {
     setInputValues((prev) => ({
       ...prev,
@@ -53,92 +86,136 @@ export function RunAgentModal({ triggerSlot, agent }: Props) {
     }));
   }
 
+  function handleCredentialsChange(key: string, value: any | undefined) {
+    setInputCredentials((prev) => {
+      const next = { ...prev } as Record<string, any>;
+      if (value === undefined) {
+        delete next[key];
+        return next;
+      }
+      next[key] = value;
+      return next;
+    });
+  }
+
   function handleSetOpen(open: boolean) {
     setIsOpen(open);
     // Always reset to Run view when opening/closing
     if (open || !open) handleGoBack();
   }
 
-  return (
-    <Dialog
-      controlled={{ isOpen, set: handleSetOpen }}
-      styling={{ maxWidth: "600px", maxHeight: "90vh" }}
-    >
-      <Dialog.Trigger>{triggerSlot}</Dialog.Trigger>
-      <Dialog.Content>
-        <div className="flex h-full flex-col">
-          {/* Header */}
-          <div className="flex-shrink-0">
-            <ModalHeader agent={agent} />
-            <AgentCostSection flowId={agent.graph_id} />
-          </div>
+  function handleRemoveSchedule() {
+    handleGoBack();
+    handleSetScheduleName("");
+    handleSetCronExpression("");
+  }
 
-          {/* Scrollable content */}
-          <div
-            className="flex-1 overflow-y-auto overflow-x-hidden pr-1"
-            style={{ scrollbarGutter: "stable" }}
-          >
-            {/* Setup Section */}
-            <div className="mt-10">
-              {showScheduleView ? (
-                <>
-                  <AgentSectionHeader title="Schedule Setup" />
-                  <div>
+  return (
+    <>
+      <Dialog
+        controlled={{ isOpen, set: handleSetOpen }}
+        styling={{ maxWidth: "600px", maxHeight: "90vh" }}
+      >
+        <Dialog.Trigger>{triggerSlot}</Dialog.Trigger>
+        <Dialog.Content>
+          <div className="flex h-full flex-col pb-4">
+            {/* Header */}
+            <div className="flex-shrink-0">
+              <ModalHeader agent={agent} />
+              <AgentCostSection flowId={agent.graph_id} />
+            </div>
+
+            {/* Scrollable content */}
+            <div className="flex-1 pr-1" style={{ scrollbarGutter: "stable" }}>
+              {/* Setup Section */}
+              <div className="mt-10">
+                {hasAnySetupFields ? (
+                  <RunAgentModalContextProvider
+                    value={{
+                      agent,
+                      defaultRunType,
+                      presetName,
+                      setPresetName,
+                      presetDescription,
+                      setPresetDescription,
+                      inputValues,
+                      setInputValue: handleInputChange,
+                      agentInputFields,
+                      inputCredentials,
+                      setInputCredentialsValue: handleCredentialsChange,
+                      agentCredentialsInputFields,
+                    }}
+                  >
+                    <>
+                      <AgentSectionHeader
+                        title={
+                          defaultRunType === "automatic-trigger"
+                            ? "Trigger Setup"
+                            : "Agent Setup"
+                        }
+                      />
+                      <div>
+                        <DefaultRunView />
+                      </div>
+                    </>
+                  </RunAgentModalContextProvider>
+                ) : null}
+              </div>
+
+              {/* Schedule Section - always visible */}
+              <div className="mt-8">
+                <AgentSectionHeader title="Schedule Setup" />
+                {showScheduleView ? (
+                  <>
+                    <div className="mb-3 flex justify-start">
+                      <Button
+                        variant="secondary"
+                        size="small"
+                        onClick={handleRemoveSchedule}
+                      >
+                        <TrashIcon size={16} />
+                        Remove schedule
+                      </Button>
+                    </div>
                     <ScheduleView
-                      agent={agent}
                       scheduleName={scheduleName}
                       cronExpression={cronExpression}
-                      inputValues={inputValues}
                       onScheduleNameChange={handleSetScheduleName}
                       onCronExpressionChange={handleSetCronExpression}
-                      onInputChange={handleInputChange}
                       onValidityChange={setIsScheduleFormValid}
                     />
+                  </>
+                ) : (
+                  <div className="flex flex-col items-start gap-2">
+                    <Text variant="body" className="mb-3 !text-zinc-500">
+                      No schedule configured. Create a schedule to run this
+                      agent automatically at a specific time.{" "}
+                    </Text>
+                    <Button
+                      variant="secondary"
+                      size="small"
+                      onClick={handleShowSchedule}
+                    >
+                      <AlarmIcon size={16} />
+                      Create schedule
+                    </Button>
                   </div>
-                </>
-              ) : hasInputFields ? (
-                <>
-                  <AgentSectionHeader
-                    title={
-                      defaultRunType === "automatic-trigger"
-                        ? "Trigger Setup"
-                        : "Agent Setup"
-                    }
-                  />
-                  <div>
-                    <DefaultRunView
-                      agent={agent}
-                      defaultRunType={defaultRunType}
-                      inputValues={inputValues}
-                      onInputChange={handleInputChange}
-                    />
-                  </div>
-                </>
-              ) : null}
-            </div>
+                )}
+              </div>
 
-            {/* Agent Details Section */}
-            <div className="mt-8">
-              <AgentSectionHeader title="Agent Details" />
-              <AgentDetails agent={agent} />
+              {/* Agent Details Section */}
+              <div className="mt-8">
+                <AgentSectionHeader title="Agent Details" />
+                <AgentDetails agent={agent} />
+              </div>
             </div>
           </div>
-
-          {/* Fixed Actions - sticky inside dialog scroll */}
-          <Dialog.Footer className="sticky bottom-0 z-10 bg-white">
-            {!showScheduleView ? (
-              <RunActions
-                hasExternalTrigger={agent.has_external_trigger}
-                defaultRunType={defaultRunType}
-                onShowSchedule={handleShowSchedule}
-                onRun={handleRun}
-                isExecuting={isExecuting}
-                isSettingUpTrigger={isSettingUpTrigger}
-                allRequiredInputsAreSet={allRequiredInputsAreSet}
-              />
-            ) : (
+          <Dialog.Footer
+            className="fixed bottom-1 left-0 z-10 w-full bg-white p-4"
+            style={{ boxShadow: "0px -8px 10px white" }}
+          >
+            {showScheduleView ? (
               <ScheduleActions
-                onGoBack={handleGoBack}
                 onSchedule={handleSchedule}
                 isCreatingSchedule={isCreatingSchedule}
                 allRequiredInputsAreSet={
@@ -147,10 +224,18 @@ export function RunAgentModal({ triggerSlot, agent }: Props) {
                   isScheduleFormValid
                 }
               />
+            ) : (
+              <RunActions
+                defaultRunType={defaultRunType}
+                onRun={handleRun}
+                isExecuting={isExecuting}
+                isSettingUpTrigger={isSettingUpTrigger}
+                allRequiredInputsAreSet={allRequiredInputsAreSet}
+              />
             )}
           </Dialog.Footer>
-        </div>
-      </Dialog.Content>
-    </Dialog>
+        </Dialog.Content>
+      </Dialog>
+    </>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/AgentDetails/AgentDetails.tsx

@@ -1,7 +1,6 @@
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Text } from "@/components/atoms/Text/Text";
 import { Badge } from "@/components/atoms/Badge/Badge";
-import { formatAgentStatus, getStatusColor } from "./helpers";
 import { formatDate } from "@/lib/utils/time";
 
 interface Props {
@@ -11,20 +10,6 @@ interface Props {
 export function AgentDetails({ agent }: Props) {
   return (
     <div className="mt-4 flex flex-col gap-5">
-      <div>
-        <Text variant="body-medium" className="mb-1 !text-black">
-          Current Status
-        </Text>
-        <div className="flex items-center gap-2">
-          <div
-            className={`h-2 w-2 rounded-full ${getStatusColor(agent.status)}`}
-          />
-          <Text variant="body" className="!text-zinc-700">
-            {formatAgentStatus(agent.status)}
-          </Text>
-        </div>
-      </div>
-
       <div>
         <Text variant="body-medium" className="mb-1 !text-black">
           Version

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/AgentDetails/helpers.tsx

@@ -1,23 +0,0 @@
-import { LibraryAgentStatus } from "@/app/api/__generated__/models/libraryAgentStatus";
-
-export function formatAgentStatus(status: LibraryAgentStatus) {
-  const statusMap: Record<string, string> = {
-    COMPLETED: "Ready",
-    HEALTHY: "Running",
-    WAITING: "Run Queued",
-    ERROR: "Failed Run",
-  };
-
-  return statusMap[status];
-}
-
-export function getStatusColor(status: LibraryAgentStatus): string {
-  const colorMap: Record<LibraryAgentStatus, string> = {
-    COMPLETED: "bg-blue-300",
-    HEALTHY: "bg-green-300",
-    WAITING: "bg-amber-300",
-    ERROR: "bg-red-300",
-  };
-
-  return colorMap[status] || "bg-gray-300";
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/AgentInputFields/AgentInputFields.tsx

@@ -1,51 +0,0 @@
-import { Input } from "@/components/atoms/Input/Input";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-
-interface Props {
-  agent: LibraryAgent;
-  inputValues: Record<string, any>;
-  onInputChange: (key: string, value: string) => void;
-  variant?: "default" | "schedule";
-}
-
-export function AgentInputFields({
-  agent,
-  inputValues,
-  onInputChange,
-  variant = "default",
-}: Props) {
-  const hasInputFields =
-    agent.input_schema &&
-    typeof agent.input_schema === "object" &&
-    "properties" in agent.input_schema;
-
-  if (!hasInputFields) {
-    const emptyStateClass =
-      variant === "schedule"
-        ? "rounded-lg bg-neutral-50 p-4 text-sm text-neutral-500"
-        : "p-4 text-sm text-neutral-500";
-
-    return (
-      <div className={emptyStateClass}>
-        No input fields required for this agent
-      </div>
-    );
-  }
-
-  return (
-    <>
-      {Object.entries((agent.input_schema as any).properties || {}).map(
-        ([key, schema]: [string, any]) => (
-          <Input
-            key={key}
-            id={key}
-            label={schema.title || key}
-            value={inputValues[key] || ""}
-            onChange={(e) => onInputChange(key, e.target.value)}
-            placeholder={schema.description}
-          />
-        ),
-      )}
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/DefaultRunView/DefaultRunView.tsx

@@ -1,30 +1,102 @@
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { RunVariant } from "../../useAgentRunModal";
 import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBanner";
-import { AgentInputFields } from "../AgentInputFields/AgentInputFields";
+import { Input } from "@/components/atoms/Input/Input";
+import SchemaTooltip from "@/components/SchemaTooltip";
+import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/AgentRunsView/components/CredentialsInputs/CredentialsInputs";
+import { useRunAgentModalContext } from "../../context";
+import { RunAgentInputs } from "../../../RunAgentInputs/RunAgentInputs";
 
-interface Props {
-  agent: LibraryAgent;
-  defaultRunType: RunVariant;
-  inputValues: Record<string, any>;
-  onInputChange: (key: string, value: string) => void;
-}
+export function DefaultRunView() {
+  const {
+    agent,
+    defaultRunType,
+    presetName,
+    setPresetName,
+    presetDescription,
+    setPresetDescription,
+    inputValues,
+    setInputValue,
+    agentInputFields,
+    inputCredentials,
+    setInputCredentialsValue,
+    agentCredentialsInputFields,
+  } = useRunAgentModalContext();
 
-export function DefaultRunView({
-  agent,
-  defaultRunType,
-  inputValues,
-  onInputChange,
-}: Props) {
   return (
-    <div className="mt-6">
+    <div className="mb-12 mt-6">
       {defaultRunType === "automatic-trigger" && <WebhookTriggerBanner />}
 
-      <AgentInputFields
-        agent={agent}
-        inputValues={inputValues}
-        onInputChange={onInputChange}
-      />
+      {/* Preset/Trigger fields */}
+      {defaultRunType === "automatic-trigger" && (
+        <div className="flex flex-col gap-4">
+          <div className="flex flex-col space-y-2">
+            <label className="flex items-center gap-1 text-sm font-medium">
+              Trigger Name
+              <SchemaTooltip description="Name of the trigger you are setting up" />
+            </label>
+            <Input
+              id="trigger_name"
+              label="Trigger Name"
+              size="small"
+              hideLabel
+              value={presetName}
+              placeholder="Enter trigger name"
+              onChange={(e) => setPresetName(e.target.value)}
+            />
+          </div>
+          <div className="flex flex-col space-y-2">
+            <label className="flex items-center gap-1 text-sm font-medium">
+              Trigger Description
+              <SchemaTooltip description="Description of the trigger you are setting up" />
+            </label>
+            <Input
+              id="trigger_description"
+              label="Trigger Description"
+              size="small"
+              hideLabel
+              value={presetDescription}
+              placeholder="Enter trigger description"
+              onChange={(e) => setPresetDescription(e.target.value)}
+            />
+          </div>
+        </div>
+      )}
+
+      {/* Credentials inputs */}
+      {Object.entries(agentCredentialsInputFields || {}).map(
+        ([key, inputSubSchema]) => (
+          <CredentialsInput
+            key={key}
+            schema={{ ...inputSubSchema, discriminator: undefined } as any}
+            selectedCredentials={
+              (inputCredentials && inputCredentials[key]) ??
+              inputSubSchema.default
+            }
+            onSelectCredentials={(value) =>
+              setInputCredentialsValue(key, value)
+            }
+            siblingInputs={inputValues}
+            hideIfSingleCredentialAvailable={!agent.has_external_trigger}
+          />
+        ),
+      )}
+
+      {/* Regular inputs */}
+      {Object.entries(agentInputFields || {}).map(([key, inputSubSchema]) => (
+        <div key={key} className="flex flex-col gap-0 space-y-2">
+          <label className="flex items-center gap-1 text-sm font-medium">
+            {inputSubSchema.title || key}
+            <SchemaTooltip description={inputSubSchema.description} />
+          </label>
+
+          <RunAgentInputs
+            schema={inputSubSchema}
+            value={inputValues[key] ?? inputSubSchema.default}
+            placeholder={inputSubSchema.description}
+            onChange={(value) => setInputValue(key, value)}
+            data-testid={`agent-input-${key}`}
+          />
+        </div>
+      ))}
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/ModalHeader/ModalHeader.tsx

@@ -8,6 +8,7 @@ interface ModalHeaderProps {
 }
 
 export function ModalHeader({ agent }: ModalHeaderProps) {
+  const isUnknownCreator = agent.creator_name === "Unknown";
   return (
     <div className="space-y-4">
       <div className="flex items-center gap-3">
@@ -15,9 +16,9 @@ export function ModalHeader({ agent }: ModalHeaderProps) {
       </div>
       <div>
         <Text variant="h3">{agent.name}</Text>
-        <Text variant="body-medium">
-          by {agent.creator_name === "Unknown" ? "–" : agent.creator_name}
-        </Text>
+        {!isUnknownCreator ? (
+          <Text variant="body-medium">by {agent.creator_name}</Text>
+        ) : null}
         <ShowMoreText
           previewLimit={80}
           variant="small"

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/RunActions/RunActions.tsx

@@ -2,9 +2,7 @@ import { Button } from "@/components/atoms/Button/Button";
 import { RunVariant } from "../../useAgentRunModal";
 
 interface Props {
-  hasExternalTrigger: boolean;
   defaultRunType: RunVariant;
-  onShowSchedule: () => void;
   onRun: () => void;
   isExecuting?: boolean;
   isSettingUpTrigger?: boolean;
@@ -12,9 +10,7 @@ interface Props {
 }
 
 export function RunActions({
-  hasExternalTrigger,
   defaultRunType,
-  onShowSchedule,
   onRun,
   isExecuting = false,
   isSettingUpTrigger = false,
@@ -22,11 +18,6 @@ export function RunActions({
 }: Props) {
   return (
     <div className="flex justify-end gap-3">
-      {!hasExternalTrigger && (
-        <Button variant="secondary" onClick={onShowSchedule}>
-          Schedule Run
-        </Button>
-      )}
       <Button
         variant="primary"
         onClick={onRun}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/ScheduleActions/ScheduleActions.tsx

@@ -1,30 +1,25 @@
 import { Button } from "@/components/atoms/Button/Button";
 
 interface Props {
-  onGoBack: () => void;
   onSchedule: () => void;
   isCreatingSchedule?: boolean;
   allRequiredInputsAreSet?: boolean;
 }
 
 export function ScheduleActions({
-  onGoBack,
   onSchedule,
   isCreatingSchedule = false,
   allRequiredInputsAreSet = true,
 }: Props) {
   return (
     <div className="flex justify-end gap-3">
-      <Button variant="ghost" onClick={onGoBack}>
-        Go Back
-      </Button>
       <Button
         variant="primary"
         onClick={onSchedule}
         disabled={!allRequiredInputsAreSet || isCreatingSchedule}
         loading={isCreatingSchedule}
       >
-        Create Schedule
+        Schedule Agent
       </Button>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/components/ScheduleView/ScheduleView.tsx

@@ -1,7 +1,5 @@
 import { Input } from "@/components/atoms/Input/Input";
 import { MultiToggle } from "@/components/molecules/MultiToggle/MultiToggle";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { AgentInputFields } from "../AgentInputFields/AgentInputFields";
 import { Text } from "@/components/atoms/Text/Text";
 import { Select } from "@/components/atoms/Select/Select";
 import { useScheduleView } from "./useScheduleView";
@@ -9,24 +7,18 @@ import { useCallback, useState } from "react";
 import { validateSchedule } from "./helpers";
 
 interface Props {
-  agent: LibraryAgent;
   scheduleName: string;
   cronExpression: string;
-  inputValues: Record<string, any>;
   onScheduleNameChange: (name: string) => void;
   onCronExpressionChange: (expression: string) => void;
-  onInputChange: (key: string, value: string) => void;
   onValidityChange?: (valid: boolean) => void;
 }
 
 export function ScheduleView({
-  agent,
   scheduleName,
   cronExpression: _cronExpression,
-  inputValues,
   onScheduleNameChange,
   onCronExpressionChange,
-  onInputChange,
   onValidityChange,
 }: Props) {
   const {
@@ -139,12 +131,7 @@ export function ScheduleView({
         error={errors.time}
       />
 
-      <AgentInputFields
-        agent={agent}
-        inputValues={inputValues}
-        onInputChange={onInputChange}
-        variant="schedule"
-      />
+      {/** Agent inputs are rendered in the main modal; none here. */}
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/AgentRunsView/components/RunAgentModal/context.tsx

@@ -0,0 +1,49 @@
+"use client";
+
+import React, { createContext, useContext } from "react";
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { RunVariant } from "./useAgentRunModal";
+
+export interface RunAgentModalContextValue {
+  agent: LibraryAgent;
+  defaultRunType: RunVariant;
+  // Preset / Trigger
+  presetName: string;
+  setPresetName: (value: string) => void;
+  presetDescription: string;
+  setPresetDescription: (value: string) => void;
+  // Inputs
+  inputValues: Record<string, any>;
+  setInputValue: (key: string, value: any) => void;
+  agentInputFields: Record<string, any>;
+  // Credentials
+  inputCredentials: Record<string, any>;
+  setInputCredentialsValue: (key: string, value: any | undefined) => void;
+  agentCredentialsInputFields: Record<string, any>;
+}
+
+const RunAgentModalContext = createContext<RunAgentModalContextValue | null>(
+  null,
+);
+
+export function useRunAgentModalContext(): RunAgentModalContextValue {
+  const ctx = useContext(RunAgentModalContext);
+  if (!ctx) throw new Error("RunAgentModalContext missing provider");
+  return ctx;
+}
+
+interface ProviderProps {
+  value: RunAgentModalContextValue;
+  children: React.ReactNode;
+}
+
+export function RunAgentModalContextProvider({
+  value,
+  children,
+}: ProviderProps) {
+  return (
+    <RunAgentModalContext.Provider value={value}>
+      {children}
+    </RunAgentModalContext.Provider>
+  );
+}