refactor: simplify LaunchDarkly context and remove redundant code

Extract role and segment logic into reusable _add_role_to_attributes helper function.
This removes the duplicate code between use_user_id_only and full context paths.

- Add _add_role_to_attributes() helper function
- Remove duplicated role/segment logic in is_feature_enabled()
- Cleaner, more maintainable code with single source of truth

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

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

.dockerignore

@@ -15,7 +15,6 @@
 !autogpt_platform/backend/pyproject.toml
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
-!autogpt_platform/backend/.env
 
 # Platform - Market
 !autogpt_platform/market/market/
@@ -28,7 +27,6 @@
 # Platform - Frontend
 !autogpt_platform/frontend/src/
 !autogpt_platform/frontend/public/
-!autogpt_platform/frontend/scripts/
 !autogpt_platform/frontend/package.json
 !autogpt_platform/frontend/pnpm-lock.yaml
 !autogpt_platform/frontend/tsconfig.json
@@ -36,7 +34,6 @@
 ## config
 !autogpt_platform/frontend/*.config.*
 !autogpt_platform/frontend/.env.*
-!autogpt_platform/frontend/.env
 
 # Classic - AutoGPT
 !classic/original_autogpt/autogpt/

.github/PULL_REQUEST_TEMPLATE.md

@@ -24,8 +24,7 @@
 </details>
 
 #### For configuration changes:
-
-- [ ] `.env.default` is updated or already compatible with my changes
+- [ ] `.env.example` is updated or already compatible with my changes
 - [ ] `docker-compose.yml` is updated or already compatible with my changes
 - [ ] I have included a list of my configuration changes in the PR description (under **Changes**)
 

.github/copilot-instructions.md

@@ -1,244 +0,0 @@
-# GitHub Copilot Instructions for AutoGPT
-
-This file provides comprehensive onboarding information for GitHub Copilot coding agent to work efficiently with the AutoGPT repository.
-
-## Repository Overview
-
-**AutoGPT** is a powerful platform for creating, deploying, and managing continuous AI agents that automate complex workflows. This is a large monorepo (~150MB) containing multiple components:
-
-- **AutoGPT Platform** (`autogpt_platform/`) - Main focus: Modern AI agent platform (Polyform Shield License)
-- **Classic AutoGPT** (`classic/`) - Legacy agent system (MIT License)
-- **Documentation** (`docs/`) - MkDocs-based documentation site
-- **Infrastructure** - Docker configurations, CI/CD, and development tools
-
-**Primary Languages & Frameworks:**
-- **Backend**: Python 3.10-3.13, FastAPI, Prisma ORM, PostgreSQL, RabbitMQ
-- **Frontend**: TypeScript, Next.js 15, React, Tailwind CSS, Radix UI
-- **Development**: Docker, Poetry, pnpm, Playwright, Storybook
-
-## Build and Validation Instructions
-
-### Essential Setup Commands
-
-**Always run these commands in the correct directory and in this order:**
-
-1. **Initial Setup** (required once):
-   ```bash
-   # Clone and enter repository
-   git clone <repo> && cd AutoGPT
-   
-   # Start all services (database, redis, rabbitmq, clamav)
-   cd autogpt_platform && docker compose --profile local up deps --build --detach
-   ```
-
-2. **Backend Setup** (always run before backend development):
-   ```bash
-   cd autogpt_platform/backend
-   poetry install                    # Install dependencies
-   poetry run prisma migrate dev     # Run database migrations
-   poetry run prisma generate        # Generate Prisma client
-   ```
-
-3. **Frontend Setup** (always run before frontend development):
-   ```bash
-   cd autogpt_platform/frontend
-   pnpm install                      # Install dependencies
-   ```
-
-### Runtime Requirements
-
-**Critical:** Always ensure Docker services are running before starting development:
-```bash
-cd autogpt_platform && docker compose --profile local up deps --build --detach
-```
-
-**Python Version:** Use Python 3.11 (required; managed by Poetry via pyproject.toml)
-**Node.js Version:** Use Node.js 21+ with pnpm package manager
-
-### Development Commands
-
-**Backend Development:**
-```bash
-cd autogpt_platform/backend
-poetry run serve                     # Start development server (port 8000)
-poetry run test                      # Run all tests (requires ~5 minutes)
-poetry run pytest path/to/test.py    # Run specific test
-poetry run format                    # Format code (Black + isort) - always run first
-poetry run lint                      # Lint code (ruff) - run after format
-```
-
-**Frontend Development:**
-```bash
-cd autogpt_platform/frontend
-pnpm dev                            # Start development server (port 3000) - use for active development
-pnpm build                          # Build for production (only needed for E2E tests or deployment)
-pnpm test                           # Run Playwright E2E tests (requires build first)
-pnpm test-ui                        # Run tests with UI
-pnpm format                         # Format and lint code
-pnpm storybook                      # Start component development server
-```
-
-### Testing Strategy
-
-**Backend Tests:**
-- **Block Tests**: `poetry run pytest backend/blocks/test/test_block.py -xvs` (validates all blocks)
-- **Specific Block**: `poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[BlockName]' -xvs`
-- **Snapshot Tests**: Use `--snapshot-update` when output changes, always review with `git diff`
-
-**Frontend Tests:**
-- **E2E Tests**: Always run `pnpm dev` before `pnpm test` (Playwright requires running instance)
-- **Component Tests**: Use Storybook for isolated component development
-
-### Critical Validation Steps
-
-**Before committing changes:**
-1. Run `poetry run format` (backend) and `pnpm format` (frontend)
-2. Ensure all tests pass in modified areas
-3. Verify Docker services are still running
-4. Check that database migrations apply cleanly
-
-**Common Issues & Workarounds:**
-- **Prisma issues**: Run `poetry run prisma generate` after schema changes
-- **Permission errors**: Ensure Docker has proper permissions
-- **Port conflicts**: Check the `docker-compose.yml` file for the current list of exposed ports. You can list all mapped ports with:
-- **Test timeouts**: Backend tests can take 5+ minutes, use `-x` flag to stop on first failure
-
-## Project Layout & Architecture
-
-### Core Architecture
-
-**AutoGPT Platform** (`autogpt_platform/`):
-- `backend/` - FastAPI server with async support
-  - `backend/backend/` - Core API logic
-  - `backend/blocks/` - Agent execution blocks
-  - `backend/data/` - Database models and schemas
-  - `schema.prisma` - Database schema definition
-- `frontend/` - Next.js application
-  - `src/app/` - App Router pages and layouts
-  - `src/components/` - Reusable React components
-  - `src/lib/` - Utilities and configurations
-- `autogpt_libs/` - Shared Python utilities
-- `docker-compose.yml` - Development stack orchestration
-
-**Key Configuration Files:**
-- `pyproject.toml` - Python dependencies and tooling
-- `package.json` - Node.js dependencies and scripts
-- `schema.prisma` - Database schema and migrations
-- `next.config.mjs` - Next.js configuration
-- `tailwind.config.ts` - Styling configuration
-
-### Security & Middleware
-
-**Cache Protection**: Backend includes middleware preventing sensitive data caching in browsers/proxies
-**Authentication**: JWT-based with Supabase integration
-**User ID Validation**: All data access requires user ID checks - verify this for any `data/*.py` changes
-
-### Development Workflow
-
-**GitHub Actions**: Multiple CI/CD workflows in `.github/workflows/`
-- `platform-backend-ci.yml` - Backend testing and validation
-- `platform-frontend-ci.yml` - Frontend testing and validation
-- `platform-fullstack-ci.yml` - End-to-end integration tests
-
-**Pre-commit Hooks**: Run linting and formatting checks
-**Conventional Commits**: Use format `type(scope): description` (e.g., `feat(backend): add API`)
-
-### Key Source Files
-
-**Backend Entry Points:**
-- `backend/backend/server/server.py` - FastAPI application setup
-- `backend/backend/data/` - Database models and user management
-- `backend/blocks/` - Agent execution blocks and logic
-
-**Frontend Entry Points:**
-- `frontend/src/app/layout.tsx` - Root application layout
-- `frontend/src/app/page.tsx` - Home page
-- `frontend/src/lib/supabase/` - Authentication and database client
-
-**Protected Routes**: Update `frontend/lib/supabase/middleware.ts` when adding protected routes
-
-### Agent Block System
-
-Agents are built using a visual block-based system where each block performs a single action. Blocks are defined in `backend/blocks/` and must include:
-- Block definition with input/output schemas
-- Execution logic with proper error handling
-- Tests validating functionality
-
-### Database & ORM
-
-**Prisma ORM** with PostgreSQL backend including pgvector for embeddings:
-- Schema in `schema.prisma`
-- Migrations in `backend/migrations/`
-- Always run `prisma migrate dev` and `prisma generate` after schema changes
-
-## Environment Configuration
-
-### Configuration Files Priority Order
-1. **Backend**: `/backend/.env.default` → `/backend/.env` (user overrides)
-2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)  
-3. **Platform**: `/.env.default` (Supabase/shared) → `/.env` (user overrides)
-4. Docker Compose `environment:` sections override file-based config
-5. Shell environment variables have highest precedence
-
-### Docker Environment Setup
-- All services use hardcoded defaults (no `${VARIABLE}` substitutions)
-- The `env_file` directive loads variables INTO containers at runtime
-- Backend/Frontend services use YAML anchors for consistent configuration
-- Copy `.env.default` files to `.env` for local development customization
-
-## Advanced Development Patterns
-
-### Adding New Blocks
-1. Create file in `/backend/backend/blocks/`
-2. Inherit from `Block` base class with input/output schemas
-3. Implement `run` method with proper error handling
-4. Generate block UUID using `uuid.uuid4()`
-5. Register in block registry
-6. Write tests alongside block implementation
-7. Consider how inputs/outputs connect with other blocks in graph editor
-
-### API Development
-1. Update routes in `/backend/backend/server/routers/`
-2. Add/update Pydantic models in same directory
-3. Write tests alongside route files
-4. For `data/*.py` changes, validate user ID checks
-5. Run `poetry run test` to verify changes
-
-### Frontend Development
-1. Components in `/frontend/src/components/`
-2. Use existing UI components from `/frontend/src/components/ui/`
-3. Add Storybook stories for component development
-4. Test user-facing features with Playwright E2E tests
-5. Update protected routes in middleware when needed
-
-### Security Guidelines
-**Cache Protection Middleware** (`/backend/backend/server/middleware/security.py`):
-- Default: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
-- Uses allow list approach for cacheable paths (static assets, health checks, public pages)
-- Prevents sensitive data caching in browsers/proxies
-- Add new cacheable endpoints to `CACHEABLE_PATHS`
-
-### CI/CD Alignment
-The repository has comprehensive CI workflows that test:
-- **Backend**: Python 3.11-3.13, services (Redis/RabbitMQ/ClamAV), Prisma migrations, Poetry lock validation
-- **Frontend**: Node.js 21, pnpm, Playwright with Docker Compose stack, API schema validation
-- **Integration**: Full-stack type checking and E2E testing
-
-Match these patterns when developing locally - the copilot setup environment mirrors these CI configurations.
-
-## Collaboration with Other AI Assistants
-
-This repository is actively developed with assistance from Claude (via CLAUDE.md files). When working on this codebase:
-- Check for existing CLAUDE.md files that provide additional context
-- Follow established patterns and conventions already in the codebase
-- Maintain consistency with existing code style and architecture
-- Consider that changes may be reviewed and extended by both human developers and AI assistants
-
-## Trust These Instructions
-
-These instructions are comprehensive and tested. Only perform additional searches if:
-1. Information here is incomplete for your specific task
-2. You encounter errors not covered by the workarounds
-3. You need to understand implementation details not covered above
-
-For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.

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

@@ -1,302 +0,0 @@
-name: "Copilot Setup Steps"
-
-# Automatically run the setup steps when they are changed to allow for easy validation, and
-# allow manual testing through the repository's "Actions" tab
-on:
-  workflow_dispatch:
-  push:
-    paths:
-      - .github/workflows/copilot-setup-steps.yml
-  pull_request:
-    paths:
-      - .github/workflows/copilot-setup-steps.yml
-
-jobs:
-  # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
-  copilot-setup-steps:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
-
-    # Set the permissions to the lowest permissions possible needed for your steps.
-    # Copilot will be given its own token for its operations.
-    permissions:
-      # If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete.
-      contents: read
-
-    # You can define any steps you want, and they will run before the agent starts.
-    # If you do not check out your code, Copilot will do this for you.
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      # 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)"

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

@@ -32,7 +32,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.11"]
     runs-on: ubuntu-latest
 
     services:
@@ -201,7 +201,7 @@ jobs:
           DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
           SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
           SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
-          JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
+          SUPABASE_JWT_SECRET: ${{ steps.supabase.outputs.JWT_SECRET }}
           REDIS_HOST: "localhost"
           REDIS_PORT: "6379"
           REDIS_PASSWORD: "testpassword"

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

@@ -82,6 +82,37 @@ jobs:
       - name: Run lint
         run: pnpm lint
 
+  type-check:
+    runs-on: ubuntu-latest
+    needs: setup
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "21"
+
+      - name: Enable corepack
+        run: corepack enable
+
+      - name: Restore dependencies cache
+        uses: actions/cache@v4
+        with:
+          path: ~/.pnpm-store
+          key: ${{ needs.setup.outputs.cache-key }}
+          restore-keys: |
+            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
+            ${{ runner.os }}-pnpm-
+
+      - name: Install dependencies
+        run: pnpm install --frozen-lockfile
+
+      - name: Run tsc check
+        run: pnpm type-check
+
   chromatic:
     runs-on: ubuntu-latest
     needs: setup
@@ -145,7 +176,11 @@ jobs:
 
       - name: Copy default supabase .env
         run: |
-          cp ../.env.default ../.env
+          cp ../.env.example ../.env
+
+      - name: Copy backend .env
+        run: |
+          cp ../backend/.env.example ../backend/.env
 
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
@@ -217,6 +252,15 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
+      - name: Setup .env
+        run: cp .env.example .env
+
+      - name: Build frontend
+        run: pnpm build --turbo
+        # uses Turbopack, much faster and safe enough for a test pipeline
+        env:
+          NEXT_PUBLIC_PW_TEST: true
+
       - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 

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

@@ -1,132 +0,0 @@
-name: AutoGPT Platform - Frontend CI
-
-on:
-  push:
-    branches: [master, dev]
-    paths:
-      - ".github/workflows/platform-fullstack-ci.yml"
-      - "autogpt_platform/**"
-  pull_request:
-    paths:
-      - ".github/workflows/platform-fullstack-ci.yml"
-      - "autogpt_platform/**"
-  merge_group:
-
-defaults:
-  run:
-    shell: bash
-    working-directory: autogpt_platform/frontend
-
-jobs:
-  setup:
-    runs-on: ubuntu-latest
-    outputs:
-      cache-key: ${{ steps.cache-key.outputs.key }}
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "21"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Generate cache key
-        id: cache-key
-        run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
-
-      - name: Cache dependencies
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ steps.cache-key.outputs.key }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-  types:
-    runs-on: ubuntu-latest
-    needs: setup
-    strategy:
-      fail-fast: false
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          submodules: recursive
-
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "21"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Copy default supabase .env
-        run: |
-          cp ../.env.default ../.env
-
-      - name: Copy backend .env
-        run: |
-          cp ../backend/.env.default ../backend/.env
-
-      - name: Run docker compose
-        run: |
-          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
-
-      - name: Restore dependencies cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ needs.setup.outputs.cache-key }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-
-
-      - name: Install dependencies
-        run: pnpm install --frozen-lockfile
-
-      - name: Setup .env
-        run: cp .env.default .env
-
-      - name: Wait for services to be ready
-        run: |
-          echo "Waiting for rest_server to be ready..."
-          timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
-          echo "Waiting for database to be ready..."
-          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
-
-      - name: Generate API queries
-        run: pnpm generate:api:force
-
-      - name: Check for API schema changes
-        run: |
-          if ! git diff --exit-code src/app/api/openapi.json; then
-            echo "❌ API schema changes detected in src/app/api/openapi.json"
-            echo ""
-            echo "The openapi.json file has been modified after running 'pnpm generate:api-all'."
-            echo "This usually means changes have been made in the BE endpoints without updating the Frontend."
-            echo "The API schema is now out of sync with the Front-end queries."
-            echo ""
-            echo "To fix this:"
-            echo "1. Pull the backend 'docker compose pull && docker compose up -d --build --force-recreate'"
-            echo "2. Run 'pnpm generate:api' locally"
-            echo "3. Run 'pnpm types' locally"
-            echo "4. Fix any TypeScript errors that may have been introduced"
-            echo "5. Commit and push your changes"
-            echo ""
-            exit 1
-          else
-            echo "✅ No API schema changes detected"
-          fi
-
-      - name: Run Typescript checks
-        run: pnpm types

.gitignore

@@ -5,8 +5,6 @@ classic/original_autogpt/*.json
 auto_gpt_workspace/*
 *.mpeg
 .env
-# Root .env files
-/.env
 azure.yaml
 .vscode
 .idea/*
@@ -123,6 +121,7 @@ celerybeat.pid
 
 # Environments
 .direnv/
+.env
 .venv
 env/
 venv*/

.pre-commit-config.yaml

@@ -235,7 +235,7 @@ repos:
     hooks:
       - id: tsc
         name: Typecheck - AutoGPT Platform - Frontend
-        entry: bash -c 'cd autogpt_platform/frontend && pnpm types'
+        entry: bash -c 'cd autogpt_platform/frontend && pnpm type-check'
         files: ^autogpt_platform/frontend/
         types: [file]
         language: system

README.md

@@ -3,16 +3,6 @@
 [![Discord Follow](https://img.shields.io/badge/dynamic/json?url=https%3A%2F%2Fdiscord.com%2Fapi%2Finvites%2Fautogpt%3Fwith_counts%3Dtrue&query=%24.approximate_member_count&label=total%20members&logo=discord&logoColor=white&color=7289da)](https://discord.gg/autogpt)  
 [![Twitter Follow](https://img.shields.io/twitter/follow/Auto_GPT?style=social)](https://twitter.com/Auto_GPT)  
 
-<!-- Keep these links. Translations will automatically update with the README. -->
-[Deutsch](https://zdoc.app/de/Significant-Gravitas/AutoGPT) | 
-[Español](https://zdoc.app/es/Significant-Gravitas/AutoGPT) | 
-[français](https://zdoc.app/fr/Significant-Gravitas/AutoGPT) | 
-[日本語](https://zdoc.app/ja/Significant-Gravitas/AutoGPT) | 
-[한국어](https://zdoc.app/ko/Significant-Gravitas/AutoGPT) | 
-[Português](https://zdoc.app/pt/Significant-Gravitas/AutoGPT) | 
-[Русский](https://zdoc.app/ru/Significant-Gravitas/AutoGPT) | 
-[中文](https://zdoc.app/zh/Significant-Gravitas/AutoGPT)
-
 **AutoGPT** is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows. 
 
 ## Hosting Options 

autogpt_platform/CLAUDE.md

@@ -1,11 +1,9 @@
 # CLAUDE.md
 
 This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
 ## Repository Overview
 
 AutoGPT Platform is a monorepo containing:
-
 - **Backend** (`/backend`): Python FastAPI server with async support
 - **Frontend** (`/frontend`): Next.js React application
 - **Shared Libraries** (`/autogpt_libs`): Common Python utilities
@@ -13,7 +11,6 @@ AutoGPT Platform is a monorepo containing:
 ## Essential Commands
 
 ### Backend Development
-
 ```bash
 # Install dependencies
 cd backend && poetry install
@@ -44,7 +41,6 @@ poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetC
 poetry run format  # Black + isort
 poetry run lint    # ruff
 ```
-
 More details can be found in TESTING.md
 
 #### Creating/Updating Snapshots
@@ -57,8 +53,8 @@ poetry run pytest path/to/test.py --snapshot-update
 
 ⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
 
-### Frontend Development
 
+### Frontend Development
 ```bash
 # Install dependencies
 cd frontend && npm install
@@ -76,13 +72,12 @@ npm run storybook
 npm run build
 
 # Type checking
-npm run types
+npm run type-check
 ```
 
 ## Architecture Overview
 
 ### Backend Architecture
-
 - **API Layer**: FastAPI with REST and WebSocket endpoints
 - **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
 - **Queue System**: RabbitMQ for async task processing
@@ -91,7 +86,6 @@ npm run types
 - **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
 
 ### Frontend Architecture
-
 - **Framework**: Next.js App Router with React Server Components
 - **State Management**: React hooks + Supabase client for real-time updates
 - **Workflow Builder**: Visual graph editor using @xyflow/react
@@ -99,7 +93,6 @@ npm run types
 - **Feature Flags**: LaunchDarkly integration
 
 ### Key Concepts
-
 1. **Agent Graphs**: Workflow definitions stored as JSON, executed by the backend
 2. **Blocks**: Reusable components in `/backend/blocks/` that perform specific tasks
 3. **Integrations**: OAuth and API connections stored per user
@@ -107,16 +100,13 @@ npm run types
 5. **Virus Scanning**: ClamAV integration for file upload security
 
 ### Testing Approach
-
 - Backend uses pytest with snapshot testing for API responses
 - Test files are colocated with source files (`*_test.py`)
 - Frontend uses Playwright for E2E tests
 - Component testing via Storybook
 
 ### Database Schema
-
 Key models (defined in `/backend/schema.prisma`):
-
 - `User`: Authentication and profile data
 - `AgentGraph`: Workflow definitions with version control
 - `AgentGraphExecution`: Execution history and results
@@ -124,59 +114,30 @@ Key models (defined in `/backend/schema.prisma`):
 - `StoreListing`: Marketplace listings for sharing agents
 
 ### Environment Configuration
-
-#### Configuration Files
-
-- **Backend**: `/backend/.env.default` (defaults) → `/backend/.env` (user overrides)
-- **Frontend**: `/frontend/.env.default` (defaults) → `/frontend/.env` (user overrides)
-- **Platform**: `/.env.default` (Supabase/shared defaults) → `/.env` (user overrides)
-
-#### Docker Environment Loading Order
-
-1. `.env.default` files provide base configuration (tracked in git)
-2. `.env` files provide user-specific overrides (gitignored)
-3. Docker Compose `environment:` sections provide service-specific overrides
-4. Shell environment variables have highest precedence
-
-#### Key Points
-
-- All services use hardcoded defaults in docker-compose files (no `${VARIABLE}` substitutions)
-- The `env_file` directive loads variables INTO containers at runtime
-- Backend/Frontend services use YAML anchors for consistent configuration
-- Supabase services (`db/docker/docker-compose.yml`) follow the same pattern
+- Backend: `.env` file in `/backend`
+- Frontend: `.env.local` file in `/frontend`
+- Both require Supabase credentials and API keys for various services
 
 ### Common Development Tasks
 
 **Adding a new block:**
-
-Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-sdk-guide.md) which covers:
-- Provider configuration with `ProviderBuilder`
-- Block schema definition
-- Authentication (API keys, OAuth, webhooks)
-- Testing and validation
-- File organization
-
-Quick steps:
 1. Create new file in `/backend/backend/blocks/`
-2. Configure provider using `ProviderBuilder` in `_config.py`
-3. Inherit from `Block` base class
-4. Define input/output schemas using `BlockSchema`
-5. Implement async `run` method
-6. Generate unique block ID using `uuid.uuid4()`
-7. Test with `poetry run pytest backend/blocks/test/test_block.py`
+2. Inherit from `Block` base class
+3. Define input/output schemas
+4. Implement `run` method
+5. Register in block registry
+6. Generate the block uuid using `uuid.uuid4()`
 
-Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
+Note: when making many new blocks analyze the interfaces for each of these blcoks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
 ex: do the inputs and outputs tie well together?
 
 **Modifying the API:**
-
 1. Update route in `/backend/backend/server/routers/`
 2. Add/update Pydantic models in same directory
 3. Write tests alongside the route file
 4. Run `poetry run test` to verify
 
 **Frontend feature development:**
-
 1. Components go in `/frontend/src/components/`
 2. Use existing UI components from `/frontend/src/components/ui/`
 3. Add Storybook stories for new components
@@ -185,7 +146,6 @@ ex: do the inputs and outputs tie well together?
 ### Security Implementation
 
 **Cache Protection Middleware:**
-
 - Located in `/backend/backend/server/middleware/security.py`
 - Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
 - Uses an allow list approach - only explicitly permitted paths can be cached
@@ -194,20 +154,14 @@ ex: do the inputs and outputs tie well together?
 - To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
 - Applied to both main API server and external API applications
 
-### Creating Pull Requests
 
+### Creating Pull Requests
 - Create the PR aginst the `dev` branch of the repository.
 - Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
 - Use conventional commit messages (see below)/
 - Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
 - Run the github pre-commit hooks to ensure code quality.
 
-### Reviewing/Revising Pull Requests
-
-- When the user runs /pr-comments or tries to fetch them, also run gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews to get the reviews
-- Use gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews/[review_id]/comments to get the review contents
-- Use gh api /repos/Significant-Gravitas/AutoGPT/issues/9924/comments to get the pr specific comments
-
 ### Conventional Commits
 
 Use this format for commit messages and Pull Request titles:

autogpt_platform/README.md

@@ -8,6 +8,7 @@ Welcome to the AutoGPT Platform - a powerful system for creating and running AI
 
 - Docker
 - Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
+- Node.js & NPM (for running the frontend application)
 
 ### Running the System
 
@@ -23,10 +24,10 @@ To run the AutoGPT Platform, follow these steps:
 2. Run the following command:
 
    ```
-   cp .env.default .env
+   cp .env.example .env
    ```
 
-   This command will copy the `.env.default` file to `.env`. You can modify the `.env` file to add your own environment variables.
+   This command will copy the `.env.example` file to `.env`. You can modify the `.env` file to add your own environment variables.
 
 3. Run the following command:
 
@@ -36,7 +37,44 @@ To run the AutoGPT Platform, follow these steps:
 
    This command will start all the necessary backend services defined in the `docker-compose.yml` file in detached mode.
 
-4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
+4. Navigate to `frontend` within the `autogpt_platform` directory:
+
+   ```
+   cd frontend
+   ```
+
+   You will need to run your frontend application separately on your local machine.
+
+5. Run the following command:
+
+   ```
+   cp .env.example .env.local
+   ```
+
+   This command will copy the `.env.example` file to `.env.local` in the `frontend` directory. You can modify the `.env.local` within this folder to add your own environment variables for the frontend application.
+
+6. Run the following command:
+
+   Enable corepack and install dependencies by running:
+
+   ```
+   corepack enable
+   pnpm i
+   ```
+
+   Generate the API client (this step is required before running the frontend):
+
+   ```
+   pnpm generate:api-client
+   ```
+
+   Then start the frontend application in development mode:
+
+   ```
+   pnpm dev
+   ```
+
+7. Open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
 ### Docker Compose Commands
 
@@ -139,21 +177,20 @@ The platform includes scripts for generating and managing the API client:
 
 - `pnpm fetch:openapi`: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)
 - `pnpm generate:api-client`: Generates the TypeScript API client from the OpenAPI specification using Orval
-- `pnpm generate:api`: Runs both fetch and generate commands in sequence
+- `pnpm generate:api-all`: Runs both fetch and generate commands in sequence
 
 #### Manual API Client Updates
 
 If you need to update the API client after making changes to the backend API:
 
 1. Ensure the backend services are running:
-
    ```
    docker compose up -d
    ```
 
 2. Generate the updated API client:
    ```
-   pnpm generate:api
+   pnpm generate:api-all
    ```
 
 This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,13 +1,13 @@
-from .config import verify_settings
-from .dependencies import get_user_id, requires_admin_user, requires_user
-from .helpers import add_auth_responses_to_openapi
+from .depends import requires_admin_user, requires_user
+from .jwt_utils import parse_jwt_token
+from .middleware import APIKeyValidator, auth_middleware
 from .models import User
 
 __all__ = [
-    "verify_settings",
-    "get_user_id",
-    "requires_admin_user",
+    "parse_jwt_token",
     "requires_user",
-    "add_auth_responses_to_openapi",
+    "requires_admin_user",
+    "APIKeyValidator",
+    "auth_middleware",
     "User",
 ]

autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py

@@ -1,90 +1,11 @@
-import logging
 import os
 
-from jwt.algorithms import get_default_algorithms, has_crypto
-
-logger = logging.getLogger(__name__)
-
-
-class AuthConfigError(ValueError):
-    """Raised when authentication configuration is invalid."""
-
-    pass
-
-
-ALGO_RECOMMENDATION = (
-    "We highly recommend using an asymmetric algorithm such as ES256, "
-    "because when leaked, a shared secret would allow anyone to "
-    "forge valid tokens and impersonate users. "
-    "More info: https://supabase.com/docs/guides/auth/signing-keys#choosing-the-right-signing-algorithm"  # noqa
-)
-
 
 class Settings:
     def __init__(self):
-        self.JWT_VERIFY_KEY: str = os.getenv(
-            "JWT_VERIFY_KEY", os.getenv("SUPABASE_JWT_SECRET", "")
-        ).strip()
-        self.JWT_ALGORITHM: str = os.getenv("JWT_SIGN_ALGORITHM", "HS256").strip()
-
-        self.validate()
-
-    def validate(self):
-        if not self.JWT_VERIFY_KEY:
-            raise AuthConfigError(
-                "JWT_VERIFY_KEY must be set. "
-                "An empty JWT secret would allow anyone to forge valid tokens."
-            )
-
-        if len(self.JWT_VERIFY_KEY) < 32:
-            logger.warning(
-                "⚠️ JWT_VERIFY_KEY appears weak (less than 32 characters). "
-                "Consider using a longer, cryptographically secure secret."
-            )
-
-        supported_algorithms = get_default_algorithms().keys()
-
-        if not has_crypto:
-            logger.warning(
-                "⚠️ Asymmetric JWT verification is not available "
-                "because the 'cryptography' package is not installed. "
-                + ALGO_RECOMMENDATION
-            )
-
-        if (
-            self.JWT_ALGORITHM not in supported_algorithms
-            or self.JWT_ALGORITHM == "none"
-        ):
-            raise AuthConfigError(
-                f"Invalid JWT_SIGN_ALGORITHM: '{self.JWT_ALGORITHM}'. "
-                "Supported algorithms are listed on "
-                "https://pyjwt.readthedocs.io/en/stable/algorithms.html"
-            )
-
-        if self.JWT_ALGORITHM.startswith("HS"):
-            logger.warning(
-                f"⚠️ JWT_SIGN_ALGORITHM is set to '{self.JWT_ALGORITHM}', "
-                "a symmetric shared-key signature algorithm. " + ALGO_RECOMMENDATION
-            )
+        self.JWT_SECRET_KEY: str = os.getenv("SUPABASE_JWT_SECRET", "")
+        self.ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
+        self.JWT_ALGORITHM: str = "HS256"
 
 
-_settings: Settings = None  # type: ignore
-
-
-def get_settings() -> Settings:
-    global _settings
-
-    if not _settings:
-        _settings = Settings()
-
-    return _settings
-
-
-def verify_settings() -> None:
-    global _settings
-
-    if not _settings:
-        _settings = Settings()  # calls validation indirectly
-        return
-
-    _settings.validate()
+settings = Settings()

autogpt_platform/autogpt_libs/autogpt_libs/auth/config_test.py

@@ -1,306 +0,0 @@
-"""
-Comprehensive tests for auth configuration to ensure 100% line and branch coverage.
-These tests verify critical security checks preventing JWT token forgery.
-"""
-
-import logging
-import os
-
-import pytest
-from pytest_mock import MockerFixture
-
-from autogpt_libs.auth.config import AuthConfigError, Settings
-
-
-def test_environment_variable_precedence(mocker: MockerFixture):
-    """Test that environment variables take precedence over defaults."""
-    secret = "environment-secret-key-with-proper-length-123456"
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == secret
-
-
-def test_environment_variable_backwards_compatible(mocker: MockerFixture):
-    """Test that SUPABASE_JWT_SECRET is read if JWT_VERIFY_KEY is not set."""
-    secret = "environment-secret-key-with-proper-length-123456"
-    mocker.patch.dict(os.environ, {"SUPABASE_JWT_SECRET": secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == secret
-
-
-def test_auth_config_error_inheritance():
-    """Test that AuthConfigError is properly defined as an Exception."""
-    assert issubclass(AuthConfigError, Exception)
-    error = AuthConfigError("test message")
-    assert str(error) == "test message"
-
-
-def test_settings_static_after_creation(mocker: MockerFixture):
-    """Test that settings maintain their values after creation."""
-    secret = "immutable-secret-key-with-proper-length-12345"
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
-
-    settings = Settings()
-    original_secret = settings.JWT_VERIFY_KEY
-
-    # Changing environment after creation shouldn't affect settings
-    os.environ["JWT_VERIFY_KEY"] = "different-secret"
-
-    assert settings.JWT_VERIFY_KEY == original_secret
-
-
-def test_settings_load_with_valid_secret(mocker: MockerFixture):
-    """Test auth enabled with a valid JWT secret."""
-    valid_secret = "a" * 32  # 32 character secret
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": valid_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == valid_secret
-
-
-def test_settings_load_with_strong_secret(mocker: MockerFixture):
-    """Test auth enabled with a cryptographically strong secret."""
-    strong_secret = "super-secret-jwt-token-with-at-least-32-characters-long"
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": strong_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == strong_secret
-    assert len(settings.JWT_VERIFY_KEY) >= 32
-
-
-def test_secret_empty_raises_error(mocker: MockerFixture):
-    """Test that auth enabled with empty secret raises AuthConfigError."""
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": ""}, clear=True)
-
-    with pytest.raises(Exception) as exc_info:
-        Settings()
-    assert "JWT_VERIFY_KEY" in str(exc_info.value)
-
-
-def test_secret_missing_raises_error(mocker: MockerFixture):
-    """Test that auth enabled without secret env var raises AuthConfigError."""
-    mocker.patch.dict(os.environ, {}, clear=True)
-
-    with pytest.raises(Exception) as exc_info:
-        Settings()
-    assert "JWT_VERIFY_KEY" in str(exc_info.value)
-
-
-@pytest.mark.parametrize("secret", [" ", "  ", "\t", "\n", " \t\n "])
-def test_secret_only_whitespace_raises_error(mocker: MockerFixture, secret: str):
-    """Test that auth enabled with whitespace-only secret raises error."""
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
-
-    with pytest.raises(ValueError):
-        Settings()
-
-
-def test_secret_weak_logs_warning(
-    mocker: MockerFixture, caplog: pytest.LogCaptureFixture
-):
-    """Test that weak JWT secret triggers warning log."""
-    weak_secret = "short"  # Less than 32 characters
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": weak_secret}, clear=True)
-
-    with caplog.at_level(logging.WARNING):
-        settings = Settings()
-        assert settings.JWT_VERIFY_KEY == weak_secret
-        assert "key appears weak" in caplog.text.lower()
-        assert "less than 32 characters" in caplog.text
-
-
-def test_secret_31_char_logs_warning(
-    mocker: MockerFixture, caplog: pytest.LogCaptureFixture
-):
-    """Test that 31-character secret triggers warning (boundary test)."""
-    secret_31 = "a" * 31  # Exactly 31 characters
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_31}, clear=True)
-
-    with caplog.at_level(logging.WARNING):
-        settings = Settings()
-        assert len(settings.JWT_VERIFY_KEY) == 31
-        assert "key appears weak" in caplog.text.lower()
-
-
-def test_secret_32_char_no_warning(
-    mocker: MockerFixture, caplog: pytest.LogCaptureFixture
-):
-    """Test that 32-character secret does not trigger warning (boundary test)."""
-    secret_32 = "a" * 32  # Exactly 32 characters
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret_32}, clear=True)
-
-    with caplog.at_level(logging.WARNING):
-        settings = Settings()
-        assert len(settings.JWT_VERIFY_KEY) == 32
-        assert "JWT secret appears weak" not in caplog.text
-
-
-def test_secret_whitespace_stripped(mocker: MockerFixture):
-    """Test that JWT secret whitespace is stripped."""
-    secret = "a" * 32
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": f"  {secret}  "}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == secret
-
-
-def test_secret_with_special_characters(mocker: MockerFixture):
-    """Test JWT secret with special characters."""
-    special_secret = "!@#$%^&*()_+-=[]{}|;:,.<>?`~" + "a" * 10  # 40 chars total
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": special_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == special_secret
-
-
-def test_secret_with_unicode(mocker: MockerFixture):
-    """Test JWT secret with unicode characters."""
-    unicode_secret = "秘密🔐キー" + "a" * 25  # Ensure >32 bytes
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": unicode_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == unicode_secret
-
-
-def test_secret_very_long(mocker: MockerFixture):
-    """Test JWT secret with excessive length."""
-    long_secret = "a" * 1000  # 1000 character secret
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": long_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == long_secret
-    assert len(settings.JWT_VERIFY_KEY) == 1000
-
-
-def test_secret_with_newline(mocker: MockerFixture):
-    """Test JWT secret containing newlines."""
-    multiline_secret = "secret\nwith\nnewlines" + "a" * 20
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": multiline_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == multiline_secret
-
-
-def test_secret_base64_encoded(mocker: MockerFixture):
-    """Test JWT secret that looks like base64."""
-    base64_secret = "dGhpc19pc19hX3NlY3JldF9rZXlfd2l0aF9wcm9wZXJfbGVuZ3Ro"
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": base64_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == base64_secret
-
-
-def test_secret_numeric_only(mocker: MockerFixture):
-    """Test JWT secret with only numbers."""
-    numeric_secret = "1234567890" * 4  # 40 character numeric secret
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": numeric_secret}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_VERIFY_KEY == numeric_secret
-
-
-def test_algorithm_default_hs256(mocker: MockerFixture):
-    """Test that JWT algorithm defaults to HS256."""
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": "a" * 32}, clear=True)
-
-    settings = Settings()
-    assert settings.JWT_ALGORITHM == "HS256"
-
-
-def test_algorithm_whitespace_stripped(mocker: MockerFixture):
-    """Test that JWT algorithm whitespace is stripped."""
-    secret = "a" * 32
-    mocker.patch.dict(
-        os.environ,
-        {"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "  HS256  "},
-        clear=True,
-    )
-
-    settings = Settings()
-    assert settings.JWT_ALGORITHM == "HS256"
-
-
-def test_no_crypto_warning(mocker: MockerFixture, caplog: pytest.LogCaptureFixture):
-    """Test warning when crypto package is not available."""
-    secret = "a" * 32
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": secret}, clear=True)
-
-    # Mock has_crypto to return False
-    mocker.patch("autogpt_libs.auth.config.has_crypto", False)
-
-    with caplog.at_level(logging.WARNING):
-        Settings()
-        assert "Asymmetric JWT verification is not available" in caplog.text
-        assert "cryptography" in caplog.text
-
-
-def test_algorithm_invalid_raises_error(mocker: MockerFixture):
-    """Test that invalid JWT algorithm raises AuthConfigError."""
-    secret = "a" * 32
-    mocker.patch.dict(
-        os.environ,
-        {"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "INVALID_ALG"},
-        clear=True,
-    )
-
-    with pytest.raises(AuthConfigError) as exc_info:
-        Settings()
-    assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
-    assert "INVALID_ALG" in str(exc_info.value)
-
-
-def test_algorithm_none_raises_error(mocker: MockerFixture):
-    """Test that 'none' algorithm raises AuthConfigError."""
-    secret = "a" * 32
-    mocker.patch.dict(
-        os.environ,
-        {"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": "none"},
-        clear=True,
-    )
-
-    with pytest.raises(AuthConfigError) as exc_info:
-        Settings()
-    assert "Invalid JWT_SIGN_ALGORITHM" in str(exc_info.value)
-
-
-@pytest.mark.parametrize("algorithm", ["HS256", "HS384", "HS512"])
-def test_algorithm_symmetric_warning(
-    mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
-):
-    """Test warning for symmetric algorithms (HS256, HS384, HS512)."""
-    secret = "a" * 32
-    mocker.patch.dict(
-        os.environ,
-        {"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
-        clear=True,
-    )
-
-    with caplog.at_level(logging.WARNING):
-        settings = Settings()
-        assert algorithm in caplog.text
-        assert "symmetric shared-key signature algorithm" in caplog.text
-        assert settings.JWT_ALGORITHM == algorithm
-
-
-@pytest.mark.parametrize(
-    "algorithm",
-    ["ES256", "ES384", "ES512", "RS256", "RS384", "RS512", "PS256", "PS384", "PS512"],
-)
-def test_algorithm_asymmetric_no_warning(
-    mocker: MockerFixture, caplog: pytest.LogCaptureFixture, algorithm: str
-):
-    """Test that asymmetric algorithms do not trigger warning."""
-    secret = "a" * 32
-    mocker.patch.dict(
-        os.environ,
-        {"JWT_VERIFY_KEY": secret, "JWT_SIGN_ALGORITHM": algorithm},
-        clear=True,
-    )
-
-    with caplog.at_level(logging.WARNING):
-        settings = Settings()
-        # Should not contain the symmetric algorithm warning
-        assert "symmetric shared-key signature algorithm" not in caplog.text
-        assert settings.JWT_ALGORITHM == algorithm

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -1,45 +0,0 @@
-"""
-FastAPI dependency functions for JWT-based authentication and authorization.
-
-These are the high-level dependency functions used in route definitions.
-"""
-
-import fastapi
-
-from .jwt_utils import get_jwt_payload, verify_user
-from .models import User
-
-
-def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
-    """
-    FastAPI dependency that requires a valid authenticated user.
-
-    Raises:
-        HTTPException: 401 for authentication failures
-    """
-    return verify_user(jwt_payload, admin_only=False)
-
-
-def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
-    """
-    FastAPI dependency that requires a valid admin user.
-
-    Raises:
-        HTTPException: 401 for authentication failures, 403 for insufficient permissions
-    """
-    return verify_user(jwt_payload, admin_only=True)
-
-
-def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
-    """
-    FastAPI dependency that returns the ID of the authenticated user.
-
-    Raises:
-        HTTPException: 401 for authentication failures or missing user ID
-    """
-    user_id = jwt_payload.get("sub")
-    if not user_id:
-        raise fastapi.HTTPException(
-            status_code=401, detail="User ID not found in token"
-        )
-    return user_id

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -1,335 +0,0 @@
-"""
-Comprehensive integration tests for authentication dependencies.
-Tests the full authentication flow from HTTP requests to user validation.
-"""
-
-import os
-
-import pytest
-from fastapi import FastAPI, HTTPException, Security
-from fastapi.testclient import TestClient
-from pytest_mock import MockerFixture
-
-from autogpt_libs.auth.dependencies import (
-    get_user_id,
-    requires_admin_user,
-    requires_user,
-)
-from autogpt_libs.auth.models import User
-
-
-class TestAuthDependencies:
-    """Test suite for authentication dependency functions."""
-
-    @pytest.fixture
-    def app(self):
-        """Create a test FastAPI application."""
-        app = FastAPI()
-
-        @app.get("/user")
-        def get_user_endpoint(user: User = Security(requires_user)):
-            return {"user_id": user.user_id, "role": user.role}
-
-        @app.get("/admin")
-        def get_admin_endpoint(user: User = Security(requires_admin_user)):
-            return {"user_id": user.user_id, "role": user.role}
-
-        @app.get("/user-id")
-        def get_user_id_endpoint(user_id: str = Security(get_user_id)):
-            return {"user_id": user_id}
-
-        return app
-
-    @pytest.fixture
-    def client(self, app):
-        """Create a test client."""
-        return TestClient(app)
-
-    def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
-        """Test requires_user with valid JWT payload."""
-        jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
-
-        # Mock get_jwt_payload to return our test payload
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-        user = requires_user(jwt_payload)
-        assert isinstance(user, User)
-        assert user.user_id == "user-123"
-        assert user.role == "user"
-
-    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
-        """Test requires_user accepts admin users."""
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-        user = requires_user(jwt_payload)
-        assert user.user_id == "admin-456"
-        assert user.role == "admin"
-
-    def test_requires_user_missing_sub(self):
-        """Test requires_user with missing user ID."""
-        jwt_payload = {"role": "user", "email": "user@example.com"}
-
-        with pytest.raises(HTTPException) as exc_info:
-            requires_user(jwt_payload)
-        assert exc_info.value.status_code == 401
-        assert "User ID not found" in exc_info.value.detail
-
-    def test_requires_user_empty_sub(self):
-        """Test requires_user with empty user ID."""
-        jwt_payload = {"sub": "", "role": "user"}
-
-        with pytest.raises(HTTPException) as exc_info:
-            requires_user(jwt_payload)
-        assert exc_info.value.status_code == 401
-
-    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
-        """Test requires_admin_user with admin role."""
-        jwt_payload = {
-            "sub": "admin-789",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-        user = requires_admin_user(jwt_payload)
-        assert user.user_id == "admin-789"
-        assert user.role == "admin"
-
-    def test_requires_admin_user_with_regular_user(self):
-        """Test requires_admin_user rejects regular users."""
-        jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
-
-        with pytest.raises(HTTPException) as exc_info:
-            requires_admin_user(jwt_payload)
-        assert exc_info.value.status_code == 403
-        assert "Admin access required" in exc_info.value.detail
-
-    def test_requires_admin_user_missing_role(self):
-        """Test requires_admin_user with missing role."""
-        jwt_payload = {"sub": "user-123", "email": "user@example.com"}
-
-        with pytest.raises(KeyError):
-            requires_admin_user(jwt_payload)
-
-    def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
-        """Test get_user_id extracts user ID correctly."""
-        jwt_payload = {"sub": "user-id-xyz", "role": "user"}
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-        user_id = get_user_id(jwt_payload)
-        assert user_id == "user-id-xyz"
-
-    def test_get_user_id_missing_sub(self):
-        """Test get_user_id with missing user ID."""
-        jwt_payload = {"role": "user"}
-
-        with pytest.raises(HTTPException) as exc_info:
-            get_user_id(jwt_payload)
-        assert exc_info.value.status_code == 401
-        assert "User ID not found" in exc_info.value.detail
-
-    def test_get_user_id_none_sub(self):
-        """Test get_user_id with None user ID."""
-        jwt_payload = {"sub": None, "role": "user"}
-
-        with pytest.raises(HTTPException) as exc_info:
-            get_user_id(jwt_payload)
-        assert exc_info.value.status_code == 401
-
-
-class TestAuthDependenciesIntegration:
-    """Integration tests for auth dependencies with FastAPI."""
-
-    acceptable_jwt_secret = "test-secret-with-proper-length-123456"
-
-    @pytest.fixture
-    def create_token(self, mocker: MockerFixture):
-        """Helper to create JWT tokens."""
-        import jwt
-
-        mocker.patch.dict(
-            os.environ,
-            {"JWT_VERIFY_KEY": self.acceptable_jwt_secret},
-            clear=True,
-        )
-
-        def _create_token(payload, secret=self.acceptable_jwt_secret):
-            return jwt.encode(payload, secret, algorithm="HS256")
-
-        return _create_token
-
-    def test_endpoint_auth_enabled_no_token(self):
-        """Test endpoints require token when auth is enabled."""
-        app = FastAPI()
-
-        @app.get("/test")
-        def test_endpoint(user: User = Security(requires_user)):
-            return {"user_id": user.user_id}
-
-        client = TestClient(app)
-
-        # Should fail without auth header
-        response = client.get("/test")
-        assert response.status_code == 401
-
-    def test_endpoint_with_valid_token(self, create_token):
-        """Test endpoint with valid JWT token."""
-        app = FastAPI()
-
-        @app.get("/test")
-        def test_endpoint(user: User = Security(requires_user)):
-            return {"user_id": user.user_id, "role": user.role}
-
-        client = TestClient(app)
-
-        token = create_token(
-            {"sub": "test-user", "role": "user", "aud": "authenticated"},
-            secret=self.acceptable_jwt_secret,
-        )
-
-        response = client.get("/test", headers={"Authorization": f"Bearer {token}"})
-        assert response.status_code == 200
-        assert response.json()["user_id"] == "test-user"
-
-    def test_admin_endpoint_requires_admin_role(self, create_token):
-        """Test admin endpoint rejects non-admin users."""
-        app = FastAPI()
-
-        @app.get("/admin")
-        def admin_endpoint(user: User = Security(requires_admin_user)):
-            return {"user_id": user.user_id}
-
-        client = TestClient(app)
-
-        # Regular user token
-        user_token = create_token(
-            {"sub": "regular-user", "role": "user", "aud": "authenticated"},
-            secret=self.acceptable_jwt_secret,
-        )
-
-        response = client.get(
-            "/admin", headers={"Authorization": f"Bearer {user_token}"}
-        )
-        assert response.status_code == 403
-
-        # Admin token
-        admin_token = create_token(
-            {"sub": "admin-user", "role": "admin", "aud": "authenticated"},
-            secret=self.acceptable_jwt_secret,
-        )
-
-        response = client.get(
-            "/admin", headers={"Authorization": f"Bearer {admin_token}"}
-        )
-        assert response.status_code == 200
-        assert response.json()["user_id"] == "admin-user"
-
-
-class TestAuthDependenciesEdgeCases:
-    """Edge case tests for authentication dependencies."""
-
-    def test_dependency_with_complex_payload(self):
-        """Test dependencies handle complex JWT payloads."""
-        complex_payload = {
-            "sub": "user-123",
-            "role": "admin",
-            "email": "test@example.com",
-            "app_metadata": {"provider": "email", "providers": ["email"]},
-            "user_metadata": {
-                "full_name": "Test User",
-                "avatar_url": "https://example.com/avatar.jpg",
-            },
-            "aud": "authenticated",
-            "iat": 1234567890,
-            "exp": 9999999999,
-        }
-
-        user = requires_user(complex_payload)
-        assert user.user_id == "user-123"
-        assert user.email == "test@example.com"
-
-        admin = requires_admin_user(complex_payload)
-        assert admin.role == "admin"
-
-    def test_dependency_with_unicode_in_payload(self):
-        """Test dependencies handle unicode in JWT payloads."""
-        unicode_payload = {
-            "sub": "user-😀-123",
-            "role": "user",
-            "email": "测试@example.com",
-            "name": "日本語",
-        }
-
-        user = requires_user(unicode_payload)
-        assert "😀" in user.user_id
-        assert user.email == "测试@example.com"
-
-    def test_dependency_with_null_values(self):
-        """Test dependencies handle null values in payload."""
-        null_payload = {
-            "sub": "user-123",
-            "role": "user",
-            "email": None,
-            "phone": None,
-            "metadata": None,
-        }
-
-        user = requires_user(null_payload)
-        assert user.user_id == "user-123"
-        assert user.email is None
-
-    def test_concurrent_requests_isolation(self):
-        """Test that concurrent requests don't interfere with each other."""
-        payload1 = {"sub": "user-1", "role": "user"}
-        payload2 = {"sub": "user-2", "role": "admin"}
-
-        # Simulate concurrent processing
-        user1 = requires_user(payload1)
-        user2 = requires_admin_user(payload2)
-
-        assert user1.user_id == "user-1"
-        assert user2.user_id == "user-2"
-        assert user1.role == "user"
-        assert user2.role == "admin"
-
-    @pytest.mark.parametrize(
-        "payload,expected_error,admin_only",
-        [
-            (None, "Authorization header is missing", False),
-            ({}, "User ID not found", False),
-            ({"sub": ""}, "User ID not found", False),
-            ({"role": "user"}, "User ID not found", False),
-            ({"sub": "user", "role": "user"}, "Admin access required", True),
-        ],
-    )
-    def test_dependency_error_cases(
-        self, payload, expected_error: str, admin_only: bool
-    ):
-        """Test that errors propagate correctly through dependencies."""
-        # Import verify_user to test it directly since dependencies use FastAPI Security
-        from autogpt_libs.auth.jwt_utils import verify_user
-
-        with pytest.raises(HTTPException) as exc_info:
-            verify_user(payload, admin_only=admin_only)
-        assert expected_error in exc_info.value.detail
-
-    def test_dependency_valid_user(self):
-        """Test valid user case for dependency."""
-        # Import verify_user to test it directly since dependencies use FastAPI Security
-        from autogpt_libs.auth.jwt_utils import verify_user
-
-        # Valid case
-        user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
-        assert user.user_id == "user"

autogpt_platform/autogpt_libs/autogpt_libs/auth/depends.py

@@ -0,0 +1,46 @@
+import fastapi
+
+from .config import settings
+from .middleware import auth_middleware
+from .models import DEFAULT_USER_ID, User
+
+
+def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
+    return verify_user(payload, admin_only=False)
+
+
+def requires_admin_user(
+    payload: dict = fastapi.Depends(auth_middleware),
+) -> User:
+    return verify_user(payload, admin_only=True)
+
+
+def verify_user(payload: dict | None, admin_only: bool) -> User:
+    if not payload:
+        if settings.ENABLE_AUTH:
+            raise fastapi.HTTPException(
+                status_code=401, detail="Authorization header is missing"
+            )
+        # This handles the case when authentication is disabled
+        payload = {"sub": DEFAULT_USER_ID, "role": "admin"}
+
+    user_id = payload.get("sub")
+
+    if not user_id:
+        raise fastapi.HTTPException(
+            status_code=401, detail="User ID not found in token"
+        )
+
+    if admin_only and payload["role"] != "admin":
+        raise fastapi.HTTPException(status_code=403, detail="Admin access required")
+
+    return User.from_payload(payload)
+
+
+def get_user_id(payload: dict = fastapi.Depends(auth_middleware)) -> str:
+    user_id = payload.get("sub")
+    if not user_id:
+        raise fastapi.HTTPException(
+            status_code=401, detail="User ID not found in token"
+        )
+    return user_id

autogpt_platform/autogpt_libs/autogpt_libs/auth/depends_tests.py

@@ -0,0 +1,68 @@
+import pytest
+
+from .depends import requires_admin_user, requires_user, verify_user
+
+
+def test_verify_user_no_payload():
+    user = verify_user(None, admin_only=False)
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_verify_user_no_user_id():
+    with pytest.raises(Exception):
+        verify_user({"role": "admin"}, admin_only=False)
+
+
+def test_verify_user_not_admin():
+    with pytest.raises(Exception):
+        verify_user(
+            {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
+            admin_only=True,
+        )
+
+
+def test_verify_user_with_admin_role():
+    user = verify_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"},
+        admin_only=True,
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_verify_user_with_user_role():
+    user = verify_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"},
+        admin_only=False,
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "user"
+
+
+def test_requires_user():
+    user = requires_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "user"
+
+
+def test_requires_user_no_user_id():
+    with pytest.raises(Exception):
+        requires_user({"role": "user"})
+
+
+def test_requires_admin_user():
+    user = requires_admin_user(
+        {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "admin"}
+    )
+    assert user.user_id == "3e53486c-cf57-477e-ba2a-cb02dc828e1a"
+    assert user.role == "admin"
+
+
+def test_requires_admin_user_not_admin():
+    with pytest.raises(Exception):
+        requires_admin_user(
+            {"sub": "3e53486c-cf57-477e-ba2a-cb02dc828e1a", "role": "user"}
+        )

autogpt_platform/autogpt_libs/autogpt_libs/auth/helpers.py

@@ -1,68 +0,0 @@
-from fastapi import FastAPI
-from fastapi.openapi.utils import get_openapi
-
-from .jwt_utils import bearer_jwt_auth
-
-
-def add_auth_responses_to_openapi(app: FastAPI) -> None:
-    """
-    Set up custom OpenAPI schema generation that adds 401 responses
-    to all authenticated endpoints.
-
-    This is needed when using HTTPBearer with auto_error=False to get proper
-    401 responses instead of 403, but FastAPI only automatically adds security
-    responses when auto_error=True.
-    """
-
-    def custom_openapi():
-        if app.openapi_schema:
-            return app.openapi_schema
-
-        openapi_schema = get_openapi(
-            title=app.title,
-            version=app.version,
-            description=app.description,
-            routes=app.routes,
-        )
-
-        # Add 401 response to all endpoints that have security requirements
-        for path, methods in openapi_schema["paths"].items():
-            for method, details in methods.items():
-                security_schemas = [
-                    schema
-                    for auth_option in details.get("security", [])
-                    for schema in auth_option.keys()
-                ]
-                if bearer_jwt_auth.scheme_name not in security_schemas:
-                    continue
-
-                if "responses" not in details:
-                    details["responses"] = {}
-
-                details["responses"]["401"] = {
-                    "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
-                }
-
-        # Ensure #/components/responses exists
-        if "components" not in openapi_schema:
-            openapi_schema["components"] = {}
-        if "responses" not in openapi_schema["components"]:
-            openapi_schema["components"]["responses"] = {}
-
-        # Define 401 response
-        openapi_schema["components"]["responses"]["HTTP401NotAuthenticatedError"] = {
-            "description": "Authentication required",
-            "content": {
-                "application/json": {
-                    "schema": {
-                        "type": "object",
-                        "properties": {"detail": {"type": "string"}},
-                    }
-                }
-            },
-        }
-
-        app.openapi_schema = openapi_schema
-        return app.openapi_schema
-
-    app.openapi = custom_openapi

autogpt_platform/autogpt_libs/autogpt_libs/auth/helpers_test.py

@@ -1,435 +0,0 @@
-"""
-Comprehensive tests for auth helpers module to achieve 100% coverage.
-Tests OpenAPI schema generation and authentication response handling.
-"""
-
-from unittest import mock
-
-from fastapi import FastAPI
-from fastapi.openapi.utils import get_openapi
-
-from autogpt_libs.auth.helpers import add_auth_responses_to_openapi
-from autogpt_libs.auth.jwt_utils import bearer_jwt_auth
-
-
-def test_add_auth_responses_to_openapi_basic():
-    """Test adding 401 responses to OpenAPI schema."""
-    app = FastAPI(title="Test App", version="1.0.0")
-
-    # Add some test endpoints with authentication
-    from fastapi import Depends
-
-    from autogpt_libs.auth.dependencies import requires_user
-
-    @app.get("/protected", dependencies=[Depends(requires_user)])
-    def protected_endpoint():
-        return {"message": "Protected"}
-
-    @app.get("/public")
-    def public_endpoint():
-        return {"message": "Public"}
-
-    # Apply the OpenAPI customization
-    add_auth_responses_to_openapi(app)
-
-    # Get the OpenAPI schema
-    schema = app.openapi()
-
-    # Verify basic schema properties
-    assert schema["info"]["title"] == "Test App"
-    assert schema["info"]["version"] == "1.0.0"
-
-    # Verify 401 response component is added
-    assert "components" in schema
-    assert "responses" in schema["components"]
-    assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-    # Verify 401 response structure
-    error_response = schema["components"]["responses"]["HTTP401NotAuthenticatedError"]
-    assert error_response["description"] == "Authentication required"
-    assert "application/json" in error_response["content"]
-    assert "schema" in error_response["content"]["application/json"]
-
-    # Verify schema properties
-    response_schema = error_response["content"]["application/json"]["schema"]
-    assert response_schema["type"] == "object"
-    assert "detail" in response_schema["properties"]
-    assert response_schema["properties"]["detail"]["type"] == "string"
-
-
-def test_add_auth_responses_to_openapi_with_security():
-    """Test that 401 responses are added only to secured endpoints."""
-    app = FastAPI()
-
-    # Mock endpoint with security
-    from fastapi import Security
-
-    from autogpt_libs.auth.dependencies import get_user_id
-
-    @app.get("/secured")
-    def secured_endpoint(user_id: str = Security(get_user_id)):
-        return {"user_id": user_id}
-
-    @app.post("/also-secured")
-    def another_secured(user_id: str = Security(get_user_id)):
-        return {"status": "ok"}
-
-    @app.get("/unsecured")
-    def unsecured_endpoint():
-        return {"public": True}
-
-    # Apply OpenAPI customization
-    add_auth_responses_to_openapi(app)
-
-    # Get schema
-    schema = app.openapi()
-
-    # Check that secured endpoints have 401 responses
-    if "/secured" in schema["paths"]:
-        if "get" in schema["paths"]["/secured"]:
-            secured_get = schema["paths"]["/secured"]["get"]
-            if "responses" in secured_get:
-                assert "401" in secured_get["responses"]
-                assert (
-                    secured_get["responses"]["401"]["$ref"]
-                    == "#/components/responses/HTTP401NotAuthenticatedError"
-                )
-
-    if "/also-secured" in schema["paths"]:
-        if "post" in schema["paths"]["/also-secured"]:
-            secured_post = schema["paths"]["/also-secured"]["post"]
-            if "responses" in secured_post:
-                assert "401" in secured_post["responses"]
-
-    # Check that unsecured endpoint does not have 401 response
-    if "/unsecured" in schema["paths"]:
-        if "get" in schema["paths"]["/unsecured"]:
-            unsecured_get = schema["paths"]["/unsecured"]["get"]
-            if "responses" in unsecured_get:
-                assert "401" not in unsecured_get.get("responses", {})
-
-
-def test_add_auth_responses_to_openapi_cached_schema():
-    """Test that OpenAPI schema is cached after first generation."""
-    app = FastAPI()
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    # Get schema twice
-    schema1 = app.openapi()
-    schema2 = app.openapi()
-
-    # Should return the same cached object
-    assert schema1 is schema2
-
-
-def test_add_auth_responses_to_openapi_existing_responses():
-    """Test handling endpoints that already have responses defined."""
-    app = FastAPI()
-
-    from fastapi import Security
-
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    @app.get(
-        "/with-responses",
-        responses={
-            200: {"description": "Success"},
-            404: {"description": "Not found"},
-        },
-    )
-    def endpoint_with_responses(jwt: dict = Security(get_jwt_payload)):
-        return {"data": "test"}
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    schema = app.openapi()
-
-    # Check that existing responses are preserved and 401 is added
-    if "/with-responses" in schema["paths"]:
-        if "get" in schema["paths"]["/with-responses"]:
-            responses = schema["paths"]["/with-responses"]["get"].get("responses", {})
-            # Original responses should be preserved
-            if "200" in responses:
-                assert responses["200"]["description"] == "Success"
-            if "404" in responses:
-                assert responses["404"]["description"] == "Not found"
-            # 401 should be added
-            if "401" in responses:
-                assert (
-                    responses["401"]["$ref"]
-                    == "#/components/responses/HTTP401NotAuthenticatedError"
-                )
-
-
-def test_add_auth_responses_to_openapi_no_security_endpoints():
-    """Test with app that has no secured endpoints."""
-    app = FastAPI()
-
-    @app.get("/public1")
-    def public1():
-        return {"message": "public1"}
-
-    @app.post("/public2")
-    def public2():
-        return {"message": "public2"}
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    schema = app.openapi()
-
-    # Component should still be added for consistency
-    assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-    # But no endpoints should have 401 responses
-    for path in schema["paths"].values():
-        for method in path.values():
-            if isinstance(method, dict) and "responses" in method:
-                assert "401" not in method["responses"]
-
-
-def test_add_auth_responses_to_openapi_multiple_security_schemes():
-    """Test endpoints with multiple security requirements."""
-    app = FastAPI()
-
-    from fastapi import Security
-
-    from autogpt_libs.auth.dependencies import requires_admin_user, requires_user
-    from autogpt_libs.auth.models import User
-
-    @app.get("/multi-auth")
-    def multi_auth(
-        user: User = Security(requires_user),
-        admin: User = Security(requires_admin_user),
-    ):
-        return {"status": "super secure"}
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    schema = app.openapi()
-
-    # Should have 401 response
-    if "/multi-auth" in schema["paths"]:
-        if "get" in schema["paths"]["/multi-auth"]:
-            responses = schema["paths"]["/multi-auth"]["get"].get("responses", {})
-            if "401" in responses:
-                assert (
-                    responses["401"]["$ref"]
-                    == "#/components/responses/HTTP401NotAuthenticatedError"
-                )
-
-
-def test_add_auth_responses_to_openapi_empty_components():
-    """Test when OpenAPI schema has no components section initially."""
-    app = FastAPI()
-
-    # Mock get_openapi to return schema without components
-    original_get_openapi = get_openapi
-
-    def mock_get_openapi(*args, **kwargs):
-        schema = original_get_openapi(*args, **kwargs)
-        # Remove components if it exists
-        if "components" in schema:
-            del schema["components"]
-        return schema
-
-    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
-        # Apply customization
-        add_auth_responses_to_openapi(app)
-
-        schema = app.openapi()
-
-        # Components should be created
-        assert "components" in schema
-        assert "responses" in schema["components"]
-        assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-
-def test_add_auth_responses_to_openapi_all_http_methods():
-    """Test that all HTTP methods are handled correctly."""
-    app = FastAPI()
-
-    from fastapi import Security
-
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    @app.get("/resource")
-    def get_resource(jwt: dict = Security(get_jwt_payload)):
-        return {"method": "GET"}
-
-    @app.post("/resource")
-    def post_resource(jwt: dict = Security(get_jwt_payload)):
-        return {"method": "POST"}
-
-    @app.put("/resource")
-    def put_resource(jwt: dict = Security(get_jwt_payload)):
-        return {"method": "PUT"}
-
-    @app.patch("/resource")
-    def patch_resource(jwt: dict = Security(get_jwt_payload)):
-        return {"method": "PATCH"}
-
-    @app.delete("/resource")
-    def delete_resource(jwt: dict = Security(get_jwt_payload)):
-        return {"method": "DELETE"}
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    schema = app.openapi()
-
-    # All methods should have 401 response
-    if "/resource" in schema["paths"]:
-        for method in ["get", "post", "put", "patch", "delete"]:
-            if method in schema["paths"]["/resource"]:
-                method_spec = schema["paths"]["/resource"][method]
-                if "responses" in method_spec:
-                    assert "401" in method_spec["responses"]
-
-
-def test_bearer_jwt_auth_scheme_config():
-    """Test that bearer_jwt_auth is configured correctly."""
-    assert bearer_jwt_auth.scheme_name == "HTTPBearerJWT"
-    assert bearer_jwt_auth.auto_error is False
-
-
-def test_add_auth_responses_with_no_routes():
-    """Test OpenAPI generation with app that has no routes."""
-    app = FastAPI(title="Empty App")
-
-    # Apply customization to empty app
-    add_auth_responses_to_openapi(app)
-
-    schema = app.openapi()
-
-    # Should still have basic structure
-    assert schema["info"]["title"] == "Empty App"
-    assert "components" in schema
-    assert "responses" in schema["components"]
-    assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-
-def test_custom_openapi_function_replacement():
-    """Test that the custom openapi function properly replaces the default."""
-    app = FastAPI()
-
-    # Store original function
-    original_openapi = app.openapi
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    # Function should be replaced
-    assert app.openapi != original_openapi
-    assert callable(app.openapi)
-
-
-def test_endpoint_without_responses_section():
-    """Test endpoint that has security but no responses section initially."""
-    app = FastAPI()
-
-    from fastapi import Security
-    from fastapi.openapi.utils import get_openapi as original_get_openapi
-
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    # Create endpoint
-    @app.get("/no-responses")
-    def endpoint_without_responses(jwt: dict = Security(get_jwt_payload)):
-        return {"data": "test"}
-
-    # Mock get_openapi to remove responses from the endpoint
-    def mock_get_openapi(*args, **kwargs):
-        schema = original_get_openapi(*args, **kwargs)
-        # Remove responses from our endpoint to trigger line 40
-        if "/no-responses" in schema.get("paths", {}):
-            if "get" in schema["paths"]["/no-responses"]:
-                # Delete responses to force the code to create it
-                if "responses" in schema["paths"]["/no-responses"]["get"]:
-                    del schema["paths"]["/no-responses"]["get"]["responses"]
-        return schema
-
-    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
-        # Apply customization
-        add_auth_responses_to_openapi(app)
-
-        # Get schema and verify 401 was added
-        schema = app.openapi()
-
-        # The endpoint should now have 401 response
-        if "/no-responses" in schema["paths"]:
-            if "get" in schema["paths"]["/no-responses"]:
-                responses = schema["paths"]["/no-responses"]["get"].get("responses", {})
-                assert "401" in responses
-                assert (
-                    responses["401"]["$ref"]
-                    == "#/components/responses/HTTP401NotAuthenticatedError"
-                )
-
-
-def test_components_with_existing_responses():
-    """Test when components already has a responses section."""
-    app = FastAPI()
-
-    # Mock get_openapi to return schema with existing components/responses
-    from fastapi.openapi.utils import get_openapi as original_get_openapi
-
-    def mock_get_openapi(*args, **kwargs):
-        schema = original_get_openapi(*args, **kwargs)
-        # Add existing components/responses
-        if "components" not in schema:
-            schema["components"] = {}
-        schema["components"]["responses"] = {
-            "ExistingResponse": {"description": "An existing response"}
-        }
-        return schema
-
-    with mock.patch("autogpt_libs.auth.helpers.get_openapi", mock_get_openapi):
-        # Apply customization
-        add_auth_responses_to_openapi(app)
-
-        schema = app.openapi()
-
-        # Both responses should exist
-        assert "ExistingResponse" in schema["components"]["responses"]
-        assert "HTTP401NotAuthenticatedError" in schema["components"]["responses"]
-
-        # Verify our 401 response structure
-        error_response = schema["components"]["responses"][
-            "HTTP401NotAuthenticatedError"
-        ]
-        assert error_response["description"] == "Authentication required"
-
-
-def test_openapi_schema_persistence():
-    """Test that modifications to OpenAPI schema persist correctly."""
-    app = FastAPI()
-
-    from fastapi import Security
-
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    @app.get("/test")
-    def test_endpoint(jwt: dict = Security(get_jwt_payload)):
-        return {"test": True}
-
-    # Apply customization
-    add_auth_responses_to_openapi(app)
-
-    # Get schema multiple times
-    schema1 = app.openapi()
-
-    # Modify the cached schema (shouldn't affect future calls)
-    schema1["info"]["title"] = "Modified Title"
-
-    # Clear cache and get again
-    app.openapi_schema = None
-    schema2 = app.openapi()
-
-    # Should regenerate with original title
-    assert schema2["info"]["title"] == app.title
-    assert schema2["info"]["title"] != "Modified Title"

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -1,48 +1,11 @@
-import logging
-from typing import Any
+from typing import Any, Dict
 
 import jwt
-from fastapi import HTTPException, Security
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 
-from .config import get_settings
-from .models import User
-
-logger = logging.getLogger(__name__)
-
-# Bearer token authentication scheme
-bearer_jwt_auth = HTTPBearer(
-    bearerFormat="jwt", scheme_name="HTTPBearerJWT", auto_error=False
-)
+from .config import settings
 
 
-def get_jwt_payload(
-    credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
-) -> dict[str, Any]:
-    """
-    Extract and validate JWT payload from HTTP Authorization header.
-
-    This is the core authentication function that handles:
-    - Reading the `Authorization` header to obtain the JWT token
-    - Verifying the JWT token's signature
-    - Decoding the JWT token's payload
-
-    :param credentials: HTTP Authorization credentials from bearer token
-    :return: JWT payload dictionary
-    :raises HTTPException: 401 if authentication fails
-    """
-    if not credentials:
-        raise HTTPException(status_code=401, detail="Authorization header is missing")
-
-    try:
-        payload = parse_jwt_token(credentials.credentials)
-        logger.debug("Token decoded successfully")
-        return payload
-    except ValueError as e:
-        raise HTTPException(status_code=401, detail=str(e))
-
-
-def parse_jwt_token(token: str) -> dict[str, Any]:
+def parse_jwt_token(token: str) -> Dict[str, Any]:
     """
     Parse and validate a JWT token.
 
@@ -50,11 +13,10 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
     :return: The decoded payload
     :raises ValueError: If the token is invalid or expired
     """
-    settings = get_settings()
     try:
         payload = jwt.decode(
             token,
-            settings.JWT_VERIFY_KEY,
+            settings.JWT_SECRET_KEY,
             algorithms=[settings.JWT_ALGORITHM],
             audience="authenticated",
         )
@@ -63,18 +25,3 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
         raise ValueError("Token has expired")
     except jwt.InvalidTokenError as e:
         raise ValueError(f"Invalid token: {str(e)}")
-
-
-def verify_user(jwt_payload: dict | None, admin_only: bool) -> User:
-    if jwt_payload is None:
-        raise HTTPException(status_code=401, detail="Authorization header is missing")
-
-    user_id = jwt_payload.get("sub")
-
-    if not user_id:
-        raise HTTPException(status_code=401, detail="User ID not found in token")
-
-    if admin_only and jwt_payload["role"] != "admin":
-        raise HTTPException(status_code=403, detail="Admin access required")
-
-    return User.from_payload(jwt_payload)

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -1,308 +0,0 @@
-"""
-Comprehensive tests for JWT token parsing and validation.
-Ensures 100% line and branch coverage for JWT security functions.
-"""
-
-import os
-from datetime import datetime, timedelta, timezone
-
-import jwt
-import pytest
-from fastapi import HTTPException
-from fastapi.security import HTTPAuthorizationCredentials
-from pytest_mock import MockerFixture
-
-from autogpt_libs.auth import config, jwt_utils
-from autogpt_libs.auth.config import Settings
-from autogpt_libs.auth.models import User
-
-MOCK_JWT_SECRET = "test-secret-key-with-at-least-32-characters"
-TEST_USER_PAYLOAD = {
-    "sub": "test-user-id",
-    "role": "user",
-    "aud": "authenticated",
-    "email": "test@example.com",
-}
-TEST_ADMIN_PAYLOAD = {
-    "sub": "admin-user-id",
-    "role": "admin",
-    "aud": "authenticated",
-    "email": "admin@example.com",
-}
-
-
-@pytest.fixture(autouse=True)
-def mock_config(mocker: MockerFixture):
-    mocker.patch.dict(os.environ, {"JWT_VERIFY_KEY": MOCK_JWT_SECRET}, clear=True)
-    mocker.patch.object(config, "_settings", Settings())
-    yield
-
-
-def create_token(payload, secret=None, algorithm="HS256"):
-    """Helper to create JWT tokens."""
-    if secret is None:
-        secret = MOCK_JWT_SECRET
-    return jwt.encode(payload, secret, algorithm=algorithm)
-
-
-def test_parse_jwt_token_valid():
-    """Test parsing a valid JWT token."""
-    token = create_token(TEST_USER_PAYLOAD)
-    result = jwt_utils.parse_jwt_token(token)
-
-    assert result["sub"] == "test-user-id"
-    assert result["role"] == "user"
-    assert result["aud"] == "authenticated"
-
-
-def test_parse_jwt_token_expired():
-    """Test parsing an expired JWT token."""
-    expired_payload = {
-        **TEST_USER_PAYLOAD,
-        "exp": datetime.now(timezone.utc) - timedelta(hours=1),
-    }
-    token = create_token(expired_payload)
-
-    with pytest.raises(ValueError) as exc_info:
-        jwt_utils.parse_jwt_token(token)
-    assert "Token has expired" in str(exc_info.value)
-
-
-def test_parse_jwt_token_invalid_signature():
-    """Test parsing a token with invalid signature."""
-    # Create token with different secret
-    token = create_token(TEST_USER_PAYLOAD, secret="wrong-secret")
-
-    with pytest.raises(ValueError) as exc_info:
-        jwt_utils.parse_jwt_token(token)
-    assert "Invalid token" in str(exc_info.value)
-
-
-def test_parse_jwt_token_malformed():
-    """Test parsing a malformed token."""
-    malformed_tokens = [
-        "not.a.token",
-        "invalid",
-        "",
-        # Header only
-        "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9",
-        # No signature
-        "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ0ZXN0In0",
-    ]
-
-    for token in malformed_tokens:
-        with pytest.raises(ValueError) as exc_info:
-            jwt_utils.parse_jwt_token(token)
-        assert "Invalid token" in str(exc_info.value)
-
-
-def test_parse_jwt_token_wrong_audience():
-    """Test parsing a token with wrong audience."""
-    wrong_aud_payload = {**TEST_USER_PAYLOAD, "aud": "wrong-audience"}
-    token = create_token(wrong_aud_payload)
-
-    with pytest.raises(ValueError) as exc_info:
-        jwt_utils.parse_jwt_token(token)
-    assert "Invalid token" in str(exc_info.value)
-
-
-def test_parse_jwt_token_missing_audience():
-    """Test parsing a token without audience claim."""
-    no_aud_payload = {k: v for k, v in TEST_USER_PAYLOAD.items() if k != "aud"}
-    token = create_token(no_aud_payload)
-
-    with pytest.raises(ValueError) as exc_info:
-        jwt_utils.parse_jwt_token(token)
-    assert "Invalid token" in str(exc_info.value)
-
-
-def test_get_jwt_payload_with_valid_token():
-    """Test extracting JWT payload with valid bearer token."""
-    token = create_token(TEST_USER_PAYLOAD)
-    credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
-
-    result = jwt_utils.get_jwt_payload(credentials)
-    assert result["sub"] == "test-user-id"
-    assert result["role"] == "user"
-
-
-def test_get_jwt_payload_no_credentials():
-    """Test JWT payload when no credentials provided."""
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.get_jwt_payload(None)
-    assert exc_info.value.status_code == 401
-    assert "Authorization header is missing" in exc_info.value.detail
-
-
-def test_get_jwt_payload_invalid_token():
-    """Test JWT payload extraction with invalid token."""
-    credentials = HTTPAuthorizationCredentials(
-        scheme="Bearer", credentials="invalid.token.here"
-    )
-
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.get_jwt_payload(credentials)
-    assert exc_info.value.status_code == 401
-    assert "Invalid token" in exc_info.value.detail
-
-
-def test_verify_user_with_valid_user():
-    """Test verifying a valid user."""
-    user = jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=False)
-    assert isinstance(user, User)
-    assert user.user_id == "test-user-id"
-    assert user.role == "user"
-    assert user.email == "test@example.com"
-
-
-def test_verify_user_with_admin():
-    """Test verifying an admin user."""
-    user = jwt_utils.verify_user(TEST_ADMIN_PAYLOAD, admin_only=True)
-    assert isinstance(user, User)
-    assert user.user_id == "admin-user-id"
-    assert user.role == "admin"
-
-
-def test_verify_user_admin_only_with_regular_user():
-    """Test verifying regular user when admin is required."""
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.verify_user(TEST_USER_PAYLOAD, admin_only=True)
-    assert exc_info.value.status_code == 403
-    assert "Admin access required" in exc_info.value.detail
-
-
-def test_verify_user_no_payload():
-    """Test verifying user with no payload."""
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.verify_user(None, admin_only=False)
-    assert exc_info.value.status_code == 401
-    assert "Authorization header is missing" in exc_info.value.detail
-
-
-def test_verify_user_missing_sub():
-    """Test verifying user with payload missing 'sub' field."""
-    invalid_payload = {"role": "user", "email": "test@example.com"}
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.verify_user(invalid_payload, admin_only=False)
-    assert exc_info.value.status_code == 401
-    assert "User ID not found in token" in exc_info.value.detail
-
-
-def test_verify_user_empty_sub():
-    """Test verifying user with empty 'sub' field."""
-    invalid_payload = {"sub": "", "role": "user"}
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.verify_user(invalid_payload, admin_only=False)
-    assert exc_info.value.status_code == 401
-    assert "User ID not found in token" in exc_info.value.detail
-
-
-def test_verify_user_none_sub():
-    """Test verifying user with None 'sub' field."""
-    invalid_payload = {"sub": None, "role": "user"}
-    with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.verify_user(invalid_payload, admin_only=False)
-    assert exc_info.value.status_code == 401
-    assert "User ID not found in token" in exc_info.value.detail
-
-
-def test_verify_user_missing_role_admin_check():
-    """Test verifying admin when role field is missing."""
-    no_role_payload = {"sub": "user-id"}
-    with pytest.raises(KeyError):
-        # This will raise KeyError when checking payload["role"]
-        jwt_utils.verify_user(no_role_payload, admin_only=True)
-
-
-# ======================== EDGE CASES ======================== #
-
-
-def test_jwt_with_additional_claims():
-    """Test JWT token with additional custom claims."""
-    extra_claims_payload = {
-        "sub": "user-id",
-        "role": "user",
-        "aud": "authenticated",
-        "custom_claim": "custom_value",
-        "permissions": ["read", "write"],
-        "metadata": {"key": "value"},
-    }
-    token = create_token(extra_claims_payload)
-
-    result = jwt_utils.parse_jwt_token(token)
-    assert result["sub"] == "user-id"
-    assert result["custom_claim"] == "custom_value"
-    assert result["permissions"] == ["read", "write"]
-
-
-def test_jwt_with_numeric_sub():
-    """Test JWT token with numeric user ID."""
-    payload = {
-        "sub": 12345,  # Numeric ID
-        "role": "user",
-        "aud": "authenticated",
-    }
-    # Should convert to string internally
-    user = jwt_utils.verify_user(payload, admin_only=False)
-    assert user.user_id == 12345
-
-
-def test_jwt_with_very_long_sub():
-    """Test JWT token with very long user ID."""
-    long_id = "a" * 1000
-    payload = {
-        "sub": long_id,
-        "role": "user",
-        "aud": "authenticated",
-    }
-    user = jwt_utils.verify_user(payload, admin_only=False)
-    assert user.user_id == long_id
-
-
-def test_jwt_with_special_characters_in_claims():
-    """Test JWT token with special characters in claims."""
-    payload = {
-        "sub": "user@example.com/special-chars!@#$%",
-        "role": "admin",
-        "aud": "authenticated",
-        "email": "test+special@example.com",
-    }
-    user = jwt_utils.verify_user(payload, admin_only=True)
-    assert "special-chars!@#$%" in user.user_id
-
-
-def test_jwt_with_future_iat():
-    """Test JWT token with issued-at time in future."""
-    future_payload = {
-        "sub": "user-id",
-        "role": "user",
-        "aud": "authenticated",
-        "iat": datetime.now(timezone.utc) + timedelta(hours=1),
-    }
-    token = create_token(future_payload)
-
-    # PyJWT validates iat claim and should reject future tokens
-    with pytest.raises(ValueError, match="not yet valid"):
-        jwt_utils.parse_jwt_token(token)
-
-
-def test_jwt_with_different_algorithms():
-    """Test that only HS256 algorithm is accepted."""
-    payload = {
-        "sub": "user-id",
-        "role": "user",
-        "aud": "authenticated",
-    }
-
-    # Try different algorithms
-    algorithms = ["HS384", "HS512", "none"]
-    for algo in algorithms:
-        if algo == "none":
-            # Special case for 'none' algorithm (security vulnerability if accepted)
-            token = create_token(payload, "", algorithm="none")
-        else:
-            token = create_token(payload, algorithm=algo)
-
-        with pytest.raises(ValueError) as exc_info:
-            jwt_utils.parse_jwt_token(token)
-        assert "Invalid token" in str(exc_info.value)

autogpt_platform/autogpt_libs/autogpt_libs/auth/middleware.py

@@ -0,0 +1,140 @@
+import inspect
+import logging
+import secrets
+from typing import Any, Callable, Optional
+
+from fastapi import HTTPException, Request, Security
+from fastapi.security import APIKeyHeader, HTTPBearer
+from starlette.status import HTTP_401_UNAUTHORIZED
+
+from .config import settings
+from .jwt_utils import parse_jwt_token
+
+security = HTTPBearer()
+logger = logging.getLogger(__name__)
+
+
+async def auth_middleware(request: Request):
+    if not settings.ENABLE_AUTH:
+        # If authentication is disabled, allow the request to proceed
+        logger.warning("Auth disabled")
+        return {}
+
+    security = HTTPBearer()
+    credentials = await security(request)
+
+    if not credentials:
+        raise HTTPException(status_code=401, detail="Authorization header is missing")
+
+    try:
+        payload = parse_jwt_token(credentials.credentials)
+        request.state.user = payload
+        logger.debug("Token decoded successfully")
+    except ValueError as e:
+        raise HTTPException(status_code=401, detail=str(e))
+    return payload
+
+
+class APIKeyValidator:
+    """
+    Configurable API key validator that supports custom validation functions
+    for FastAPI applications.
+
+    This class provides a flexible way to implement API key authentication with optional
+    custom validation logic. It can be used for simple token matching
+    or more complex validation scenarios like database lookups.
+
+    Examples:
+        Simple token validation:
+        ```python
+        validator = APIKeyValidator(
+            header_name="X-API-Key",
+            expected_token="your-secret-token"
+        )
+
+        @app.get("/protected", dependencies=[Depends(validator.get_dependency())])
+        def protected_endpoint():
+            return {"message": "Access granted"}
+        ```
+
+        Custom validation with database lookup:
+        ```python
+        async def validate_with_db(api_key: str):
+            api_key_obj = await db.get_api_key(api_key)
+            return api_key_obj if api_key_obj and api_key_obj.is_active else None
+
+        validator = APIKeyValidator(
+            header_name="X-API-Key",
+            validate_fn=validate_with_db
+        )
+        ```
+
+    Args:
+        header_name (str): The name of the header containing the API key
+        expected_token (Optional[str]): The expected API key value for simple token matching
+        validate_fn (Optional[Callable]): Custom validation function that takes an API key
+            string and returns a boolean or object. Can be async.
+        error_status (int): HTTP status code to use for validation errors
+        error_message (str): Error message to return when validation fails
+    """
+
+    def __init__(
+        self,
+        header_name: str,
+        expected_token: Optional[str] = None,
+        validate_fn: Optional[Callable[[str], bool]] = None,
+        error_status: int = HTTP_401_UNAUTHORIZED,
+        error_message: str = "Invalid API key",
+    ):
+        # Create the APIKeyHeader as a class property
+        self.security_scheme = APIKeyHeader(name=header_name)
+        self.expected_token = expected_token
+        self.custom_validate_fn = validate_fn
+        self.error_status = error_status
+        self.error_message = error_message
+
+    async def default_validator(self, api_key: str) -> bool:
+        if not self.expected_token:
+            raise ValueError(
+                "Expected Token Required to be set when uisng API Key Validator default validation"
+            )
+        return secrets.compare_digest(api_key, self.expected_token)
+
+    async def __call__(
+        self, request: Request, api_key: str = Security(APIKeyHeader)
+    ) -> Any:
+        if api_key is None:
+            raise HTTPException(status_code=self.error_status, detail="Missing API key")
+
+        # Use custom validation if provided, otherwise use default equality check
+        validator = self.custom_validate_fn or self.default_validator
+        result = (
+            await validator(api_key)
+            if inspect.iscoroutinefunction(validator)
+            else validator(api_key)
+        )
+
+        if not result:
+            raise HTTPException(
+                status_code=self.error_status, detail=self.error_message
+            )
+
+        # Store validation result in request state if it's not just a boolean
+        if result is not True:
+            request.state.api_key = result
+
+        return result
+
+    def get_dependency(self):
+        """
+        Returns a callable dependency that FastAPI will recognize as a security scheme
+        """
+
+        async def validate_api_key(
+            request: Request, api_key: str = Security(self.security_scheme)
+        ) -> Any:
+            return await self(request, api_key)
+
+        # This helps FastAPI recognize it as a security dependency
+        validate_api_key.__name__ = f"validate_{self.security_scheme.model.name}"
+        return validate_api_key

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client.py

@@ -0,0 +1,324 @@
+import contextlib
+import logging
+from functools import wraps
+from json import JSONDecodeError
+from typing import TYPE_CHECKING, Any, Awaitable, Callable, Optional, TypeVar
+
+if TYPE_CHECKING:
+    from backend.data.model import User
+
+import ldclient
+from backend.util.json import loads as json_loads
+from fastapi import HTTPException
+from ldclient import Context, LDClient
+from ldclient.config import Config
+from typing_extensions import ParamSpec
+
+from .config import SETTINGS
+
+logger = logging.getLogger(__name__)
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+_is_initialized = False
+
+
+def get_client() -> LDClient:
+    """Get the LaunchDarkly client singleton."""
+    if not _is_initialized:
+        initialize_launchdarkly()
+    return ldclient.get()
+
+
+def initialize_launchdarkly() -> None:
+    sdk_key = SETTINGS.launch_darkly_sdk_key
+    logger.debug(
+        f"Initializing LaunchDarkly with SDK key: {'present' if sdk_key else 'missing'}"
+    )
+
+    if not sdk_key:
+        logger.warning("LaunchDarkly SDK key not configured")
+        return
+
+    config = Config(sdk_key)
+    ldclient.set_config(config)
+
+    if ldclient.get().is_initialized():
+        global _is_initialized
+        _is_initialized = True
+        logger.info("LaunchDarkly client initialized successfully")
+    else:
+        logger.error("LaunchDarkly client failed to initialize")
+
+
+def shutdown_launchdarkly() -> None:
+    """Shutdown the LaunchDarkly client."""
+    if ldclient.get().is_initialized():
+        ldclient.get().close()
+        logger.info("LaunchDarkly client closed successfully")
+
+
+def create_context(
+    user_id: str, additional_attributes: Optional[dict[str, Any]] = None
+) -> Context:
+    """Create LaunchDarkly context with optional additional attributes."""
+    # Use the key from attributes if provided, otherwise use user_id
+    context_key = user_id
+    if additional_attributes and "key" in additional_attributes:
+        context_key = additional_attributes["key"]
+
+    builder = Context.builder(str(context_key)).kind("user")
+
+    if additional_attributes:
+        for key, value in additional_attributes.items():
+            # Skip kind and key as they're already set
+            if key in ["kind", "key"]:
+                continue
+            elif key == "custom" and isinstance(value, dict):
+                # Handle custom attributes object - these go as individual attributes
+                for custom_key, custom_value in value.items():
+                    builder.set(custom_key, custom_value)
+            else:
+                builder.set(key, value)
+    return builder.build()
+
+
+async def _fetch_user_context_data(user_id: str) -> dict[str, Any]:
+    """
+    Fetch user data and build LaunchDarkly context.
+
+    Args:
+        user_id: The user ID to fetch data for
+
+    Returns:
+        Dictionary with user context data including role
+    """
+    # Use the unified database access approach
+    from backend.util.clients import get_database_manager_async_client
+
+    db_client = get_database_manager_async_client()
+    user = await db_client.get_user_by_id(user_id)
+
+    # Build LaunchDarkly context from user data
+    return _build_launchdarkly_context(user)
+
+
+def _build_launchdarkly_context(user: "User") -> dict[str, Any]:
+    """
+    Build LaunchDarkly context data matching frontend format.
+
+    Returns a context like:
+    {
+        "kind": "user",
+        "key": "user-id",
+        "email": "user@example.com",  # Optional
+        "anonymous": false,
+        "custom": {
+            "role": "admin"  # Optional
+        }
+    }
+
+    Args:
+        user: User object from database
+
+    Returns:
+        Dictionary with user context data
+    """
+    from autogpt_libs.auth.models import DEFAULT_USER_ID
+
+    # Build basic context - always include kind, key, and anonymous
+    context_data: dict[str, Any] = {
+        "kind": "user",
+        "key": user.id,
+        "anonymous": False,
+    }
+
+    # Add email if present
+    if user.email:
+        context_data["email"] = user.email
+
+    # Initialize custom attributes
+    custom: dict[str, Any] = {}
+
+    # Determine user role from metadata
+    role = None
+
+    # Check if user is default/system user
+    if user.id == DEFAULT_USER_ID:
+        role = "admin"  # Default user has admin privileges when auth is disabled
+    elif user.metadata:
+        # Check for role in metadata
+        try:
+            # Handle both string (direct DB) and dict (RPC) formats
+            if isinstance(user.metadata, str):
+                metadata = json_loads(user.metadata)
+            elif isinstance(user.metadata, dict):
+                metadata = user.metadata
+            else:
+                metadata = {}
+
+            # Extract role from metadata if present
+            if metadata.get("role"):
+                role = metadata["role"]
+
+        except (JSONDecodeError, TypeError) as e:
+            logger.debug(f"Failed to parse user metadata for context: {e}")
+
+    # Add role to custom attributes if present
+    if role:
+        custom["role"] = role
+
+    # Only add custom object if it has content
+    if custom:
+        context_data["custom"] = custom
+
+    return context_data
+
+
+async def is_feature_enabled(
+    flag_key: str,
+    user_id: str,
+    default: bool = False,
+    use_user_id_only: bool = False,
+    additional_attributes: Optional[dict[str, Any]] = None,
+    user_role: Optional[str] = None,
+) -> bool:
+    """
+    Check if a feature flag is enabled for a user with full LaunchDarkly context support.
+
+    Args:
+        flag_key: The LaunchDarkly feature flag key
+        user_id: The user ID to evaluate the flag for
+        default: Default value if LaunchDarkly is unavailable or flag evaluation fails
+        use_user_id_only: If True, only use user_id without fetching database context
+        additional_attributes: Additional attributes to include in the context
+        user_role: Optional user role (e.g., "admin", "user") to add to segments
+
+    Returns:
+        True if feature is enabled, False otherwise
+    """
+    try:
+        client = get_client()
+
+        if use_user_id_only:
+            # Simple context with just user ID (for backward compatibility)
+            attrs = additional_attributes or {}
+            if user_role:
+                # Add role to custom attributes for consistency
+                if "custom" not in attrs:
+                    attrs["custom"] = {}
+                if isinstance(attrs["custom"], dict):
+                    attrs["custom"]["role"] = user_role
+            context = create_context(str(user_id), attrs)
+        else:
+            # Full context with user segments and metadata from database
+            try:
+                user_data = await _fetch_user_context_data(user_id)
+            except ImportError as e:
+                # Database modules not available - fallback to simple context
+                logger.debug(f"Database modules not available: {e}")
+                user_data = {}
+            except Exception as e:
+                # Database error - log and fallback to simple context
+                logger.warning(f"Failed to fetch user context for {user_id}: {e}")
+                user_data = {}
+
+            # Merge additional attributes and role
+            attrs = additional_attributes or {}
+
+            # If user_role is provided, add it to custom attributes
+            if user_role:
+                if "custom" not in user_data:
+                    user_data["custom"] = {}
+                user_data["custom"]["role"] = user_role
+
+            # Merge additional attributes with user data
+            # Handle custom attributes specially
+            if "custom" in attrs and isinstance(attrs["custom"], dict):
+                if "custom" not in user_data:
+                    user_data["custom"] = {}
+                user_data["custom"].update(attrs["custom"])
+                # Remove custom from attrs to avoid duplication
+                attrs = {k: v for k, v in attrs.items() if k != "custom"}
+
+            # Merge remaining attributes
+            final_attrs = {**user_data, **attrs}
+
+            context = create_context(str(user_id), final_attrs)
+
+        # Evaluate the flag
+        result = client.variation(flag_key, context, default)
+
+        logger.debug(
+            f"Feature flag {flag_key} for user {user_id}: {result} "
+            f"(use_user_id_only: {use_user_id_only})"
+        )
+
+        return result
+
+    except Exception as e:
+        logger.debug(
+            f"LaunchDarkly flag evaluation failed for {flag_key}: {e}, using default={default}"
+        )
+        return default
+
+
+def feature_flag(
+    flag_key: str,
+    default: bool = False,
+) -> Callable[[Callable[P, Awaitable[T]]], Callable[P, Awaitable[T]]]:
+    """
+    Decorator for async feature flag protected endpoints.
+
+    Args:
+        flag_key: The LaunchDarkly feature flag key
+        default: Default value if flag evaluation fails
+
+    Returns:
+        Decorator that only works with async functions
+    """
+
+    def decorator(func: Callable[P, Awaitable[T]]) -> Callable[P, Awaitable[T]]:
+        @wraps(func)
+        async def async_wrapper(*args: P.args, **kwargs: P.kwargs) -> T:
+            try:
+                user_id = kwargs.get("user_id")
+                if not user_id:
+                    raise ValueError("user_id is required")
+
+                if not get_client().is_initialized():
+                    logger.warning(
+                        f"LaunchDarkly not initialized, using default={default}"
+                    )
+                    is_enabled = default
+                else:
+                    # Use the unified function with full context support
+                    is_enabled = await is_feature_enabled(
+                        flag_key, str(user_id), default, use_user_id_only=False
+                    )
+
+                if not is_enabled:
+                    raise HTTPException(status_code=404, detail="Feature not available")
+
+                return await func(*args, **kwargs)
+            except Exception as e:
+                logger.error(f"Error evaluating feature flag {flag_key}: {e}")
+                raise
+
+        return async_wrapper
+
+    return decorator
+
+
+@contextlib.contextmanager
+def mock_flag_variation(flag_key: str, return_value: Any):
+    """Context manager for testing feature flags."""
+    original_variation = get_client().variation
+    get_client().variation = lambda key, context, default: (
+        return_value if key == flag_key else original_variation(key, context, default)
+    )
+    try:
+        yield
+    finally:
+        get_client().variation = original_variation

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client_test.py

@@ -0,0 +1,84 @@
+import pytest
+from ldclient import LDClient
+
+from autogpt_libs.feature_flag.client import (
+    feature_flag,
+    is_feature_enabled,
+    mock_flag_variation,
+)
+
+
+@pytest.fixture
+def ld_client(mocker):
+    client = mocker.Mock(spec=LDClient)
+    mocker.patch("ldclient.get", return_value=client)
+    client.is_initialized.return_value = True
+    return client
+
+
+@pytest.mark.asyncio
+async def test_feature_flag_enabled(ld_client):
+    ld_client.variation.return_value = True
+
+    @feature_flag("test-flag")
+    async def test_function(user_id: str):
+        return "success"
+
+    result = test_function(user_id="test-user")
+    assert result == "success"
+    ld_client.variation.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_feature_flag_unauthorized_response(ld_client):
+    ld_client.variation.return_value = False
+
+    @feature_flag("test-flag")
+    async def test_function(user_id: str):
+        return "success"
+
+    result = test_function(user_id="test-user")
+    assert result == {"error": "disabled"}
+
+
+def test_mock_flag_variation(ld_client):
+    with mock_flag_variation("test-flag", True):
+        assert ld_client.variation("test-flag", None, False)
+
+    with mock_flag_variation("test-flag", False):
+        assert ld_client.variation("test-flag", None, False)
+
+
+def test_is_feature_enabled(ld_client):
+    """Test the is_feature_enabled helper function."""
+    ld_client.is_initialized.return_value = True
+    ld_client.variation.return_value = True
+
+    result = is_feature_enabled("test-flag", "user123", default=False)
+    assert result is True
+
+    ld_client.variation.assert_called_once()
+    call_args = ld_client.variation.call_args
+    assert call_args[0][0] == "test-flag"  # flag_key
+    assert call_args[0][2] is False  # default value
+
+
+def test_is_feature_enabled_not_initialized(ld_client):
+    """Test is_feature_enabled when LaunchDarkly is not initialized."""
+    ld_client.is_initialized.return_value = False
+
+    result = is_feature_enabled("test-flag", "user123", default=True)
+    assert result is True  # Should return default
+
+    ld_client.variation.assert_not_called()
+
+
+def test_is_feature_enabled_exception(mocker):
+    """Test is_feature_enabled when get_client() raises an exception."""
+    mocker.patch(
+        "autogpt_libs.feature_flag.client.get_client",
+        side_effect=Exception("Client error"),
+    )
+
+    result = is_feature_enabled("test-flag", "user123", default=True)
+    assert result is True  # Should return default

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/config.py

@@ -0,0 +1,15 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+    launch_darkly_sdk_key: str = Field(
+        default="",
+        description="The Launch Darkly SDK key",
+        validation_alias="LAUNCH_DARKLY_SDK_KEY",
+    )
+
+    model_config = SettingsConfigDict(case_sensitive=True, extra="ignore")
+
+
+SETTINGS = Settings()

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -1,8 +1,6 @@
 """Logging module for Auto-GPT."""
 
 import logging
-import os
-import socket
 import sys
 from pathlib import Path
 
@@ -12,15 +10,6 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
 from .filters import BelowLevelFilter
 from .formatters import AGPTFormatter
 
-# Configure global socket timeout and gRPC keepalive to prevent deadlocks
-# This must be done at import time before any gRPC connections are established
-socket.setdefaulttimeout(30)  # 30-second socket timeout
-
-# Enable gRPC keepalive to detect dead connections faster
-os.environ.setdefault("GRPC_KEEPALIVE_TIME_MS", "30000")  # 30 seconds
-os.environ.setdefault("GRPC_KEEPALIVE_TIMEOUT_MS", "5000")  # 5 seconds
-os.environ.setdefault("GRPC_KEEPALIVE_PERMIT_WITHOUT_CALLS", "true")
-
 LOG_DIR = Path(__file__).parent.parent.parent.parent / "logs"
 LOG_FILE = "activity.log"
 DEBUG_LOG_FILE = "debug.log"
@@ -90,6 +79,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
     Note: This function is typically called at the start of the application
     to set up the logging infrastructure.
     """
+
     config = LoggingConfig()
     log_handlers: list[logging.Handler] = []
 
@@ -115,17 +105,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
     if config.enable_cloud_logging or force_cloud_logging:
         import google.cloud.logging
         from google.cloud.logging.handlers import CloudLoggingHandler
-        from google.cloud.logging_v2.handlers.transports import (
-            BackgroundThreadTransport,
-        )
+        from google.cloud.logging_v2.handlers.transports.sync import SyncTransport
 
         client = google.cloud.logging.Client()
-        # Use BackgroundThreadTransport to prevent blocking the main thread
-        # and deadlocks when gRPC calls to Google Cloud Logging hang
         cloud_handler = CloudLoggingHandler(
             client,
             name="autogpt_logs",
-            transport=BackgroundThreadTransport,
+            transport=SyncTransport,
         )
         cloud_handler.setLevel(config.level)
         log_handlers.append(cloud_handler)

autogpt_platform/autogpt_libs/autogpt_libs/logging/utils.py

@@ -1,5 +1,39 @@
+import logging
 import re
+from typing import Any
+
+import uvicorn.config
+from colorama import Fore
 
 
 def remove_color_codes(s: str) -> str:
     return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
+
+
+def fmt_kwargs(kwargs: dict) -> str:
+    return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
+
+
+def print_attribute(
+    title: str, value: Any, title_color: str = Fore.GREEN, value_color: str = ""
+) -> None:
+    logger = logging.getLogger()
+    logger.info(
+        str(value),
+        extra={
+            "title": f"{title.rstrip(':')}:",
+            "title_color": title_color,
+            "color": value_color,
+        },
+    )
+
+
+def generate_uvicorn_config():
+    """
+    Generates a uvicorn logging config that silences uvicorn's default logging and tells it to use the native logging module.
+    """
+    log_config = dict(uvicorn.config.LOGGING_CONFIG)
+    log_config["loggers"]["uvicorn"] = {"handlers": []}
+    log_config["loggers"]["uvicorn.error"] = {"handlers": []}
+    log_config["loggers"]["uvicorn.access"] = {"handlers": []}
+    return log_config

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -4,6 +4,7 @@ import threading
 import time
 from functools import wraps
 from typing import (
+    Any,
     Awaitable,
     Callable,
     ParamSpec,
@@ -22,13 +23,11 @@ logger = logging.getLogger(__name__)
 
 
 @overload
-def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
-    pass
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
 
 
 @overload
-def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
-    pass
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...
 
 
 def thread_cached(
@@ -76,32 +75,26 @@ def clear_thread_cache(func: Callable) -> None:
         clear()
 
 
-FuncT = TypeVar("FuncT")
-
-
-R_co = TypeVar("R_co", covariant=True)
-
-
 @runtime_checkable
-class AsyncCachedFunction(Protocol[P, R_co]):
+class AsyncCachedFunction(Protocol):
     """Protocol for async functions with cache management methods."""
 
     def cache_clear(self) -> None:
         """Clear all cached entries."""
         return None
 
-    def cache_info(self) -> dict[str, int | None]:
+    def cache_info(self) -> dict[str, Any]:
         """Get cache statistics."""
         return {}
 
-    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+    async def __call__(self, *args: Any, **kwargs: Any) -> Any:
         """Call the cached function."""
-        return None  # type: ignore
+        return None
 
 
 def async_ttl_cache(
     maxsize: int = 128, ttl_seconds: int | None = None
-) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
+) -> Callable[[Callable[..., Awaitable[Any]]], AsyncCachedFunction]:
     """
     TTL (Time To Live) cache decorator for async functions.
 
@@ -127,13 +120,13 @@ def async_ttl_cache(
     """
 
     def decorator(
-        async_func: Callable[P, Awaitable[R]],
-    ) -> AsyncCachedFunction[P, R]:
+        async_func: Callable[..., Awaitable[Any]],
+    ) -> AsyncCachedFunction:
         # Cache storage - use union type to handle both cases
-        cache_storage: dict[tuple, R | Tuple[R, float]] = {}
+        cache_storage: dict[Any, Any | Tuple[Any, float]] = {}
 
         @wraps(async_func)
-        async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
+        async def wrapper(*args, **kwargs):
             # Create cache key from arguments
             key = (args, tuple(sorted(kwargs.items())))
             current_time = time.time()
@@ -145,7 +138,7 @@ def async_ttl_cache(
                     logger.debug(
                         f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
                     )
-                    return cast(R, cache_storage[key])
+                    return cache_storage[key]
                 else:
                     # With TTL - check expiration
                     cached_data = cache_storage[key]
@@ -155,7 +148,7 @@ def async_ttl_cache(
                             logger.debug(
                                 f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
                             )
-                            return cast(R, result)
+                            return result
                         else:
                             # Expired entry
                             del cache_storage[key]
@@ -192,7 +185,7 @@ def async_ttl_cache(
         def cache_clear() -> None:
             cache_storage.clear()
 
-        def cache_info() -> dict[str, int | None]:
+        def cache_info() -> dict[str, Any]:
             return {
                 "size": len(cache_storage),
                 "maxsize": maxsize,
@@ -203,35 +196,14 @@ def async_ttl_cache(
         setattr(wrapper, "cache_clear", cache_clear)
         setattr(wrapper, "cache_info", cache_info)
 
-        return cast(AsyncCachedFunction[P, R], wrapper)
+        return cast(AsyncCachedFunction, wrapper)
 
     return decorator
 
 
-@overload
 def async_cache(
-    func: Callable[P, Awaitable[R]],
-) -> AsyncCachedFunction[P, R]:
-    pass
-
-
-@overload
-def async_cache(
-    func: None = None,
-    *,
     maxsize: int = 128,
-) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
-    pass
-
-
-def async_cache(
-    func: Callable[P, Awaitable[R]] | None = None,
-    *,
-    maxsize: int = 128,
-) -> (
-    AsyncCachedFunction[P, R]
-    | Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]
-):
+) -> Callable[[Callable[..., Awaitable[Any]]], AsyncCachedFunction]:
     """
     Process-level cache decorator for async functions (no TTL).
 
@@ -239,28 +211,15 @@ def async_cache(
     This is a convenience wrapper around async_ttl_cache with ttl_seconds=None.
 
     Args:
-        func: The async function to cache (when used without parentheses)
         maxsize: Maximum number of cached entries
 
     Returns:
-        Decorated function or decorator
+        Decorator function
 
     Example:
-        # Without parentheses (uses default maxsize=128)
-        @async_cache
-        async def get_data(param: str) -> dict:
-            return {"result": param}
-
-        # With parentheses and custom maxsize
         @async_cache(maxsize=1000)
         async def expensive_computation(param: str) -> dict:
             # Expensive computation here
             return {"result": param}
     """
-    if func is None:
-        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
-        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
-    else:
-        # Called without parentheses @async_cache
-        decorator = async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
-        return decorator(func)
+    return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -461,7 +461,7 @@ class TestAsyncTTLCache:
 
         # Cache size should be reduced (cleanup removes oldest entries)
         info = size_limited_function.cache_info()
-        assert info["size"] is not None and info["size"] <= 3  # Should be cleaned up
+        assert info["size"] <= 3  # Should be cleaned up
 
     @pytest.mark.asyncio
     async def test_argument_variations(self):

autogpt_platform/autogpt_libs/poetry.lock

@@ -54,7 +54,7 @@ version = "1.2.0"
 description = "Backport of asyncio.Runner, a context manager that controls event loop life cycle."
 optional = false
 python-versions = "<3.11,>=3.8"
-groups = ["dev"]
+groups = ["main"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5"},
@@ -85,87 +85,6 @@ files = [
     {file = "certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995"},
 ]
 
-[[package]]
-name = "cffi"
-version = "1.17.1"
-description = "Foreign Function Interface for Python calling C code."
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-markers = "platform_python_implementation != \"PyPy\""
-files = [
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 python-versions = ">=3.8"
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+groups = ["main"]
 files = [
     {file = "pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b"},
     {file = "pygments-2.19.2.tar.gz", hash = "sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887"},
@@ -1326,9 +1068,6 @@ files = [
     {file = "pyjwt-2.10.1.tar.gz", hash = "sha256:3cc5772eb20009233caf06e9d8a0577824723b44e6648ee0a2aedb6cf9381953"},
 ]
 
-[package.dependencies]
-cryptography = {version = ">=3.4.0", optional = true, markers = "extra == \"crypto\""}
-
 [package.extras]
 crypto = ["cryptography (>=3.4.0)"]
 dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx", "sphinx-rtd-theme", "zope.interface"]
@@ -1353,7 +1092,7 @@ version = "8.4.1"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.9"
-groups = ["dev"]
+groups = ["main"]
 files = [
     {file = "pytest-8.4.1-py3-none-any.whl", hash = "sha256:539c70ba6fcead8e78eebbf1115e8b589e7565830d7d006a8723f19ac8a0afb7"},
     {file = "pytest-8.4.1.tar.gz", hash = "sha256:7c67fd69174877359ed9371ec3af8a3d2b04741818c51e5e99cc1742251fa93c"},
@@ -1377,7 +1116,7 @@ version = "1.1.0"
 description = "Pytest support for asyncio"
 optional = false
 python-versions = ">=3.9"
-groups = ["dev"]
+groups = ["main"]
 files = [
     {file = "pytest_asyncio-1.1.0-py3-none-any.whl", hash = "sha256:5fe2d69607b0bd75c656d1211f969cadba035030156745ee09e7d71740e58ecf"},
     {file = "pytest_asyncio-1.1.0.tar.gz", hash = "sha256:796aa822981e01b68c12e4827b8697108f7205020f24b5793b3c41555dab68ea"},
@@ -1391,33 +1130,13 @@ pytest = ">=8.2,<9"
 docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1)"]
 testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
-[[package]]
-name = "pytest-cov"
-version = "6.2.1"
-description = "Pytest plugin for measuring coverage."
-optional = false
-python-versions = ">=3.9"
-groups = ["dev"]
-files = [
-    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
-    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
-]
-
-[package.dependencies]
-coverage = {version = ">=7.5", extras = ["toml"]}
-pluggy = ">=1.2"
-pytest = ">=6.2.5"
-
-[package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
-
 [[package]]
 name = "pytest-mock"
 version = "3.14.1"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main"]
 files = [
     {file = "pytest_mock-3.14.1-py3-none-any.whl", hash = "sha256:178aefcd11307d874b4cd3100344e7e2d888d9791a6a1d9bfe90fbc1b74fd1d0"},
     {file = "pytest_mock-3.14.1.tar.gz", hash = "sha256:159e9edac4c451ce77a5cdb9fc5d1100708d2dd4ba3c3df572f14097351af80e"},
@@ -1534,31 +1253,30 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.12.9"
+version = "0.12.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
-    {file = "ruff-0.12.9-py3-none-linux_armv6l.whl", hash = "sha256:fcebc6c79fcae3f220d05585229463621f5dbf24d79fdc4936d9302e177cfa3e"},
-    {file = "ruff-0.12.9-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:aed9d15f8c5755c0e74467731a007fcad41f19bcce41cd75f768bbd687f8535f"},
-    {file = "ruff-0.12.9-py3-none-macosx_11_0_arm64.whl", hash = "sha256:5b15ea354c6ff0d7423814ba6d44be2807644d0c05e9ed60caca87e963e93f70"},
-    {file = "ruff-0.12.9-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d596c2d0393c2502eaabfef723bd74ca35348a8dac4267d18a94910087807c53"},
-    {file = "ruff-0.12.9-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:1b15599931a1a7a03c388b9c5df1bfa62be7ede6eb7ef753b272381f39c3d0ff"},
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-    {file = "ruff-0.12.9-py3-none-manylinux_2_31_riscv64.whl", hash = "sha256:881465ed56ba4dd26a691954650de6ad389a2d1fdb130fe51ff18a25639fe4bb"},
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-    {file = "ruff-0.12.9.tar.gz", hash = "sha256:fbd94b2e3c623f659962934e52c2bea6fc6da11f667a427a368adaf3af2c866a"},
+    {file = "ruff-0.12.3-py3-none-linux_armv6l.whl", hash = "sha256:47552138f7206454eaf0c4fe827e546e9ddac62c2a3d2585ca54d29a890137a2"},
+    {file = "ruff-0.12.3-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:0a9153b000c6fe169bb307f5bd1b691221c4286c133407b8827c406a55282041"},
+    {file = "ruff-0.12.3-py3-none-macosx_11_0_arm64.whl", hash = "sha256:fa6b24600cf3b750e48ddb6057e901dd5b9aa426e316addb2a1af185a7509882"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e2506961bf6ead54887ba3562604d69cb430f59b42133d36976421bc8bd45901"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:c4faaff1f90cea9d3033cbbcdf1acf5d7fb11d8180758feb31337391691f3df0"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:40dced4a79d7c264389de1c59467d5d5cefd79e7e06d1dfa2c75497b5269a5a6"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:0262d50ba2767ed0fe212aa7e62112a1dcbfd46b858c5bf7bbd11f326998bafc"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:12371aec33e1a3758597c5c631bae9a5286f3c963bdfb4d17acdd2d395406687"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:560f13b6baa49785665276c963edc363f8ad4b4fc910a883e2625bdb14a83a9e"},
+    {file = "ruff-0.12.3-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:023040a3499f6f974ae9091bcdd0385dd9e9eb4942f231c23c57708147b06311"},
+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:883d844967bffff5ab28bba1a4d246c1a1b2933f48cb9840f3fdc5111c603b07"},
+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:2120d3aa855ff385e0e562fdee14d564c9675edbe41625c87eeab744a7830d12"},
+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6b16647cbb470eaf4750d27dddc6ebf7758b918887b56d39e9c22cce2049082b"},
+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e1417051edb436230023575b149e8ff843a324557fe0a265863b7602df86722f"},
+    {file = "ruff-0.12.3-py3-none-win32.whl", hash = "sha256:dfd45e6e926deb6409d0616078a666ebce93e55e07f0fb0228d4b2608b2c248d"},
+    {file = "ruff-0.12.3-py3-none-win_amd64.whl", hash = "sha256:a946cf1e7ba3209bdef039eb97647f1c77f6f540e5845ec9c114d3af8df873e7"},
+    {file = "ruff-0.12.3-py3-none-win_arm64.whl", hash = "sha256:5f9c7c9c8f84c2d7f27e93674d27136fbf489720251544c4da7fb3d742e011b1"},
+    {file = "ruff-0.12.3.tar.gz", hash = "sha256:f1b5a4b6668fd7b7ea3697d8d98857390b40c1320a63a178eee6be0899ea2d77"},
 ]
 
 [[package]]
@@ -1692,7 +1410,7 @@ version = "2.2.1"
 description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main"]
 markers = "python_version < \"3.11\""
 files = [
     {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
@@ -1735,12 +1453,11 @@ version = "4.14.1"
 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
-groups = ["main", "dev"]
+groups = ["main"]
 files = [
     {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
     {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
-markers = {dev = "python_version < \"3.11\""}
 
 [[package]]
 name = "typing-inspection"
@@ -1897,4 +1614,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "ef7818fba061cea2841c6d7ca4852acde83e4f73b32fca1315e58660002bb0d0"
+content-hash = "f67db13e6f68b1d67a55eee908c1c560bfa44da8509f98f842889a7570a9830f"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -15,17 +15,15 @@ google-cloud-logging = "^3.12.1"
 launchdarkly-server-sdk = "^9.12.0"
 pydantic = "^2.11.7"
 pydantic-settings = "^2.10.1"
-pyjwt = { version = "^2.10.1", extras = ["crypto"] }
+pyjwt = "^2.10.1"
+pytest-asyncio = "^1.1.0"
+pytest-mock = "^3.14.1"
 redis = "^6.2.0"
 supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.12.9"
-pytest = "^8.4.1"
-pytest-asyncio = "^1.1.0"
-pytest-mock = "^3.14.1"
-pytest-cov = "^6.2.1"
+ruff = "^0.12.3"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.dockerignore

@@ -1,52 +0,0 @@
-# Development and testing files
-**/__pycache__
-**/*.pyc
-**/*.pyo
-**/*.pyd
-**/.Python
-**/env/
-**/venv/
-**/.venv/
-**/pip-log.txt
-**/.pytest_cache/
-**/test-results/
-**/snapshots/
-**/test/
-
-# IDE and editor files
-**/.vscode/
-**/.idea/
-**/*.swp
-**/*.swo
-*~
-
-# OS files
-.DS_Store
-Thumbs.db
-
-# Logs
-**/*.log
-**/logs/
-
-# Git
-.git/
-.gitignore
-
-# Documentation
-**/*.md
-!README.md
-
-# Local development files
-.env
-.env.local
-**/.env.test
-
-# Build artifacts
-**/dist/
-**/build/
-**/target/
-
-# Docker files (avoid recursion)
-Dockerfile*
-docker-compose*
-.dockerignore

autogpt_platform/backend/.env.example

@@ -1,9 +1,3 @@
-# Backend Configuration
-# This file contains environment variables that MUST be set for the AutoGPT platform
-# Variables with working defaults in settings.py are not included here
-
-## ===== REQUIRED DATABASE CONFIGURATION ===== ##
-# PostgreSQL Database Connection
 DB_USER=postgres
 DB_PASS=your-super-secret-and-long-postgres-password
 DB_NAME=postgres
@@ -17,48 +11,71 @@ DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME
 DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
 PRISMA_SCHEMA="postgres/schema.prisma"
 
-## ===== REQUIRED SERVICE CREDENTIALS ===== ##
-# Redis Configuration
+# EXECUTOR
+NUM_GRAPH_WORKERS=10
+
+BACKEND_CORS_ALLOW_ORIGINS=["http://localhost:3000"]
+
+# generate using `from cryptography.fernet import Fernet;Fernet.generate_key().decode()`
+ENCRYPTION_KEY='dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw='
+UNSUBSCRIBE_SECRET_KEY = 'HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio='
+
 REDIS_HOST=localhost
 REDIS_PORT=6379
 REDIS_PASSWORD=password
 
-# RabbitMQ Credentials
+ENABLE_CREDIT=false
+STRIPE_API_KEY=
+STRIPE_WEBHOOK_SECRET=
+
+# What environment things should be logged under: local dev or prod
+APP_ENV=local
+# What environment to behave as: "local" or "cloud"
+BEHAVE_AS=local
+PYRO_HOST=localhost
+SENTRY_DSN=
+
+# Email For Postmark so we can send emails
+POSTMARK_SERVER_API_TOKEN=
+POSTMARK_SENDER_EMAIL=invalid@invalid.com
+POSTMARK_WEBHOOK_TOKEN=
+
+## User auth with Supabase is required for any of the 3rd party integrations with auth to work.
+ENABLE_AUTH=true
+SUPABASE_URL=http://localhost:8000
+SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
+SUPABASE_JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
+
+# RabbitMQ credentials -- Used for communication between services
+RABBITMQ_HOST=localhost
+RABBITMQ_PORT=5672
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
 RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
 
-# Supabase Authentication
-SUPABASE_URL=http://localhost:8000
-SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
-JWT_VERIFY_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
-
-## ===== REQUIRED SECURITY KEYS ===== ##
-# Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()
-ENCRYPTION_KEY=dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=
-UNSUBSCRIBE_SECRET_KEY=HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio=
-
-## ===== IMPORTANT OPTIONAL CONFIGURATION ===== ##
-# Platform URLs (set these for webhooks and OAuth to work)
-PLATFORM_BASE_URL=http://localhost:8000
-FRONTEND_BASE_URL=http://localhost:3000
-
-# Media Storage (required for marketplace and library functionality)
+## GCS bucket is required for marketplace and library functionality
 MEDIA_GCS_BUCKET_NAME=
 
-## ===== API KEYS AND OAUTH CREDENTIALS ===== ##
-# All API keys below are optional - only add what you need
+## For local development, you may need to set FRONTEND_BASE_URL for the OAuth flow
+## for integrations to work. Defaults to the value of PLATFORM_BASE_URL if not set.
+# FRONTEND_BASE_URL=http://localhost:3000
 
-# AI/LLM Services
-OPENAI_API_KEY=
-ANTHROPIC_API_KEY=
-GROQ_API_KEY=
-LLAMA_API_KEY=
-AIML_API_KEY=
-V0_API_KEY=
-OPEN_ROUTER_API_KEY=
-NVIDIA_API_KEY=
+## PLATFORM_BASE_URL must be set to a *publicly accessible* URL pointing to your backend
+## to use the platform's webhook-related functionality.
+## If you are developing locally, you can use something like ngrok to get a publc URL
+## and tunnel it to your locally running backend.
+PLATFORM_BASE_URL=http://localhost:3000
+
+## Cloudflare Turnstile (CAPTCHA) Configuration
+## Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
+## This is the backend secret key
+TURNSTILE_SECRET_KEY=
+## This is the verify URL
+TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
+
+## == INTEGRATION CREDENTIALS == ##
+# Each set of server side credentials is required for the corresponding 3rd party
+# integration to work.
 
-# OAuth Credentials
 # For the OAuth callback URL, use <your_frontend_url>/auth/integrations/oauth_callback,
 # e.g. http://localhost:3000/auth/integrations/oauth_callback
 
@@ -68,6 +85,7 @@ GITHUB_CLIENT_SECRET=
 
 # Google OAuth App server credentials - https://console.cloud.google.com/apis/credentials, and enable gmail api and set scopes
 # https://console.cloud.google.com/apis/credentials/consent ?project=<your_project_id>
+
 # You'll need to add/enable the following scopes (minimum):
 # https://console.developers.google.com/apis/api/gmail.googleapis.com/overview ?project=<your_project_id>
 # https://console.cloud.google.com/apis/library/sheets.googleapis.com/ ?project=<your_project_id>
@@ -103,75 +121,104 @@ LINEAR_CLIENT_SECRET=
 TODOIST_CLIENT_ID=
 TODOIST_CLIENT_SECRET=
 
-NOTION_CLIENT_ID=
-NOTION_CLIENT_SECRET=
+## ===== OPTIONAL API KEYS ===== ##
 
-# Discord OAuth App credentials
-# 1. Go to https://discord.com/developers/applications
-# 2. Create a new application
-# 3. Go to OAuth2 section and add redirect URI: http://localhost:3000/auth/integrations/oauth_callback
-# 4. Copy Client ID and Client Secret below
-DISCORD_CLIENT_ID=
-DISCORD_CLIENT_SECRET=
+# LLM
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=
+AIML_API_KEY=
+GROQ_API_KEY=
+OPEN_ROUTER_API_KEY=
+LLAMA_API_KEY=
 
+# Reddit
+# Go to https://www.reddit.com/prefs/apps and create a new app
+# Choose "script" for the type
+# Fill in the redirect uri as <your_frontend_url>/auth/integrations/oauth_callback, e.g. http://localhost:3000/auth/integrations/oauth_callback
 REDDIT_CLIENT_ID=
 REDDIT_CLIENT_SECRET=
+REDDIT_USER_AGENT="AutoGPT:1.0 (by /u/autogpt)"
 
-# Payment Processing
-STRIPE_API_KEY=
-STRIPE_WEBHOOK_SECRET=
-
-# Email Service (for sending notifications and confirmations)
-POSTMARK_SERVER_API_TOKEN=
-POSTMARK_SENDER_EMAIL=invalid@invalid.com
-POSTMARK_WEBHOOK_TOKEN=
-
-# Error Tracking
-SENTRY_DSN=
-
-# Cloudflare Turnstile (CAPTCHA) Configuration
-# Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
-# This is the backend secret key
-TURNSTILE_SECRET_KEY=
-# This is the verify URL
-TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
-
-# Feature Flags
-LAUNCH_DARKLY_SDK_KEY=
-
-# Content Generation & Media
-DID_API_KEY=
-FAL_API_KEY=
-IDEOGRAM_API_KEY=
-REPLICATE_API_KEY=
-REVID_API_KEY=
-SCREENSHOTONE_API_KEY=
-UNREAL_SPEECH_API_KEY=
-
-# Data & Search Services
-E2B_API_KEY=
-EXA_API_KEY=
-JINA_API_KEY=
-MEM0_API_KEY=
-OPENWEATHERMAP_API_KEY=
-GOOGLE_MAPS_API_KEY=
-
-# Communication Services
+# Discord
 DISCORD_BOT_TOKEN=
-MEDIUM_API_KEY=
-MEDIUM_AUTHOR_ID=
+
+# SMTP/Email
 SMTP_SERVER=
 SMTP_PORT=
 SMTP_USERNAME=
 SMTP_PASSWORD=
 
-# Business & Marketing Tools
+# D-ID
+DID_API_KEY=
+
+# Open Weather Map
+OPENWEATHERMAP_API_KEY=
+
+# SMTP
+SMTP_SERVER=
+SMTP_PORT=
+SMTP_USERNAME=
+SMTP_PASSWORD=
+
+# Medium
+MEDIUM_API_KEY=
+MEDIUM_AUTHOR_ID=
+
+# Google Maps
+GOOGLE_MAPS_API_KEY=
+
+# Replicate
+REPLICATE_API_KEY=
+
+# Ideogram
+IDEOGRAM_API_KEY=
+
+# Fal
+FAL_API_KEY=
+
+# Exa
+EXA_API_KEY=
+
+# E2B
+E2B_API_KEY=
+
+# Mem0
+MEM0_API_KEY=
+
+# Nvidia
+NVIDIA_API_KEY=
+
+# Apollo
 APOLLO_API_KEY=
-ENRICHLAYER_API_KEY=
-AYRSHARE_API_KEY=
-AYRSHARE_JWT_KEY=
+
+# SmartLead
 SMARTLEAD_API_KEY=
+
+# ZeroBounce
 ZEROBOUNCE_API_KEY=
 
-# Other Services
-AUTOMOD_API_KEY=
+# Ayrshare
+AYRSHARE_API_KEY=
+AYRSHARE_JWT_KEY=
+
+## ===== OPTIONAL API KEYS END ===== ##
+
+# Block Error Rate Monitoring
+BLOCK_ERROR_RATE_THRESHOLD=0.5
+BLOCK_ERROR_RATE_CHECK_INTERVAL_SECS=86400
+
+# Logging Configuration
+LOG_LEVEL=INFO
+ENABLE_CLOUD_LOGGING=false
+ENABLE_FILE_LOGGING=false
+# Use to manually set the log directory
+# LOG_DIR=./logs
+
+# Example Blocks Configuration
+# Set to true to enable example blocks in development
+# These blocks are disabled by default in production
+ENABLE_EXAMPLE_BLOCKS=false
+
+# Cloud Storage Configuration
+# Cleanup interval for expired files (hours between cleanup runs, 1-24 hours)
+CLOUD_STORAGE_CLEANUP_INTERVAL_HOURS=6

autogpt_platform/backend/.gitignore

@@ -1,4 +1,3 @@
-.env
 database.db
 database.db-journal
 dev.db

autogpt_platform/backend/Dockerfile

@@ -1,34 +1,31 @@
-FROM debian:13-slim AS builder
+FROM python:3.11.10-slim-bookworm AS builder
 
 # Set environment variables
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV PYTHONUNBUFFERED=1
-ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONDONTWRITEBYTECODE 1
+ENV PYTHONUNBUFFERED 1
 
 WORKDIR /app
 
 RUN echo 'Acquire::http::Pipeline-Depth 0;\nAcquire::http::No-Cache true;\nAcquire::BrokenProxy true;\n' > /etc/apt/apt.conf.d/99fixbadproxy
 
-# Update package list and install Python and build dependencies
-RUN apt-get update --allow-releaseinfo-change --fix-missing \
-    && apt-get install -y \
-    python3.13 \
-    python3.13-dev \
-    python3.13-venv \
-    python3-pip \
-    build-essential \
-    libpq5 \
-    libz-dev \
-    libssl-dev \
-    postgresql-client
+RUN apt-get update --allow-releaseinfo-change --fix-missing
+
+# Install build dependencies
+RUN apt-get install -y build-essential 
+RUN apt-get install -y libpq5
+RUN apt-get install -y libz-dev
+RUN apt-get install -y libssl-dev
+RUN apt-get install -y postgresql-client
 
 ENV POETRY_HOME=/opt/poetry
 ENV POETRY_NO_INTERACTION=1
-ENV POETRY_VIRTUALENVS_CREATE=true
-ENV POETRY_VIRTUALENVS_IN_PROJECT=true
+ENV POETRY_VIRTUALENVS_CREATE=false
 ENV PATH=/opt/poetry/bin:$PATH
 
-RUN pip3 install poetry --break-system-packages
+# Upgrade pip and setuptools to fix security vulnerabilities
+RUN pip3 install --upgrade pip setuptools
+
+RUN pip3 install poetry
 
 # Copy and install dependencies
 COPY autogpt_platform/autogpt_libs /app/autogpt_platform/autogpt_libs
@@ -40,30 +37,27 @@ RUN poetry install --no-ansi --no-root
 COPY autogpt_platform/backend/schema.prisma ./
 RUN poetry run prisma generate
 
-FROM debian:13-slim AS server_dependencies
+FROM python:3.11.10-slim-bookworm AS server_dependencies
 
 WORKDIR /app
 
 ENV POETRY_HOME=/opt/poetry \
     POETRY_NO_INTERACTION=1 \
-    POETRY_VIRTUALENVS_CREATE=true \
-    POETRY_VIRTUALENVS_IN_PROJECT=true \
-    DEBIAN_FRONTEND=noninteractive
+    POETRY_VIRTUALENVS_CREATE=false
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python without upgrading system-managed packages
-RUN apt-get update && apt-get install -y \
-    python3.13 \
-    python3-pip
+# Upgrade pip and setuptools to fix security vulnerabilities
+RUN pip3 install --upgrade pip setuptools
 
 # Copy only necessary files from builder
 COPY --from=builder /app /app
-COPY --from=builder /usr/local/lib/python3* /usr/local/lib/python3*
-COPY --from=builder /usr/local/bin/poetry /usr/local/bin/poetry
+COPY --from=builder /usr/local/lib/python3.11 /usr/local/lib/python3.11
+COPY --from=builder /usr/local/bin /usr/local/bin
 # Copy Prisma binaries
 COPY --from=builder /root/.cache/prisma-python/binaries /root/.cache/prisma-python/binaries
 
-ENV PATH="/app/autogpt_platform/backend/.venv/bin:$PATH"
+
+ENV PATH="/app/.venv/bin:$PATH"
 
 RUN mkdir -p /app/autogpt_platform/autogpt_libs
 RUN mkdir -p /app/autogpt_platform/backend
@@ -74,12 +68,6 @@ COPY autogpt_platform/backend/poetry.lock autogpt_platform/backend/pyproject.tom
 
 WORKDIR /app/autogpt_platform/backend
 
-FROM server_dependencies AS migrate
-
-# Migration stage only needs schema and migrations - much lighter than full backend
-COPY autogpt_platform/backend/schema.prisma /app/autogpt_platform/backend/
-COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migrations
-
 FROM server_dependencies AS server
 
 COPY autogpt_platform/backend /app/autogpt_platform/backend

autogpt_platform/backend/TESTING.md

@@ -132,58 +132,17 @@ def test_endpoint_success(snapshot: Snapshot):
 
 ### Testing with Authentication
 
-For the main API routes that use JWT authentication, auth is provided by the `autogpt_libs.auth` module. If the test actually uses the `user_id`, the recommended approach for testing is to mock the `get_jwt_payload` function, which underpins all higher-level auth functions used in the API (`requires_user`, `requires_admin_user`, `get_user_id`).
-
-If the test doesn't need the `user_id` specifically, mocking is not necessary as during tests auth is disabled anyway (see `conftest.py`).
-
-#### Using Global Auth Fixtures
-
-Two global auth fixtures are provided by `backend/server/conftest.py`:
-
-- `mock_jwt_user` - Regular user with `test_user_id` ("test-user-id")
-- `mock_jwt_admin` - Admin user with `admin_user_id` ("admin-user-id")
-
-These provide the easiest way to set up authentication mocking in test modules:
-
 ```python
-import fastapi
-import fastapi.testclient
-import pytest
-from backend.server.v2.myroute import router
+def override_auth_middleware():
+    return {"sub": "test-user-id"}
 
-app = fastapi.FastAPI()
-app.include_router(router)
-client = fastapi.testclient.TestClient(app)
+def override_get_user_id():
+    return "test-user-id"
 
-@pytest.fixture(autouse=True)
-def setup_app_auth(mock_jwt_user):
-    """Setup auth overrides for all tests in this module"""
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    app.dependency_overrides[get_jwt_payload] = mock_jwt_user['get_jwt_payload']
-    yield
-    app.dependency_overrides.clear()
+app.dependency_overrides[auth_middleware] = override_auth_middleware
+app.dependency_overrides[get_user_id] = override_get_user_id
 ```
 
-For admin-only endpoints, use `mock_jwt_admin` instead:
-
-```python
-@pytest.fixture(autouse=True)
-def setup_app_auth(mock_jwt_admin):
-    """Setup auth overrides for admin tests"""
-    from autogpt_libs.auth.jwt_utils import get_jwt_payload
-
-    app.dependency_overrides[get_jwt_payload] = mock_jwt_admin['get_jwt_payload']
-    yield
-    app.dependency_overrides.clear()
-```
-
-The IDs are also available separately as fixtures:
-
-- `test_user_id`
-- `admin_user_id`
-- `target_user_id` (for admin <-> user operations)
-
 ### Mocking External Services
 
 ```python
@@ -194,10 +153,10 @@ def test_external_api_call(mocker, snapshot):
         "backend.services.external_api.call",
         return_value=mock_response
     )
-
+    
     response = client.post("/api/process")
     assert response.status_code == 200
-
+    
     snapshot.snapshot_dir = "snapshots"
     snapshot.assert_match(
         json.dumps(response.json(), indent=2, sort_keys=True),
@@ -228,17 +187,6 @@ def test_external_api_call(mocker, snapshot):
 - Use `async def` with `@pytest.mark.asyncio` for testing async functions directly
 
 ### 5. Fixtures
-
-#### Global Fixtures (conftest.py)
-
-Authentication fixtures are available globally from `conftest.py`:
-
-- `mock_jwt_user` - Standard user authentication
-- `mock_jwt_admin` - Admin user authentication
-- `configured_snapshot` - Pre-configured snapshot fixture
-
-#### Custom Fixtures
-
 Create reusable fixtures for common test data:
 
 ```python
@@ -254,18 +202,9 @@ def test_create_user(sample_user, snapshot):
     # ... test implementation
 ```
 
-#### Test Isolation
-
-All tests must use fixtures that ensure proper isolation:
-
-- Authentication overrides are automatically cleaned up after each test
-- Database connections are properly managed with cleanup
-- Mock objects are reset between tests
-
 ## CI/CD Integration
 
 The GitHub Actions workflow automatically runs tests on:
-
 - Pull requests
 - Pushes to main branch
 
@@ -277,19 +216,16 @@ Snapshot tests work in CI by:
 ## Troubleshooting
 
 ### Snapshot Mismatches
-
 - Review the diff carefully
 - If changes are expected: `poetry run pytest --snapshot-update`
 - If changes are unexpected: Fix the code causing the difference
 
 ### Async Test Issues
-
 - Ensure async functions use `@pytest.mark.asyncio`
 - Use `AsyncMock` for mocking async functions
 - FastAPI TestClient handles async automatically
 
 ### Import Errors
-
 - Check that all dependencies are in `pyproject.toml`
 - Run `poetry install` to ensure dependencies are installed
 - Verify import paths are correct
@@ -298,4 +234,4 @@ Snapshot tests work in CI by:
 
 Snapshot testing provides a powerful way to ensure API responses remain consistent. Combined with traditional assertions, it creates a robust test suite that catches regressions while remaining maintainable.
 
-Remember: Good tests are as important as good code!
+Remember: Good tests are as important as good code!

autogpt_platform/backend/backend/blocks/agent.py

@@ -25,9 +25,6 @@ class AgentExecutorBlock(Block):
         user_id: str = SchemaField(description="User ID")
         graph_id: str = SchemaField(description="Graph ID")
         graph_version: int = SchemaField(description="Graph Version")
-        agent_name: Optional[str] = SchemaField(
-            default=None, description="Name to display in the Builder UI"
-        )
 
         inputs: BlockInput = SchemaField(description="Input data for the graph")
         input_schema: dict = SchemaField(description="Input schema for the graph")

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -166,7 +166,7 @@ class AIMusicGeneratorBlock(Block):
                     output_format=input_data.output_format,
                     normalization_strategy=input_data.normalization_strategy,
                 )
-                if result and isinstance(result, str) and result.startswith("http"):
+                if result and result != "No output received":
                     yield "result", result
                     return
                 else:

autogpt_platform/backend/backend/blocks/baas/_api.py

@@ -1,205 +0,0 @@
-"""
-Meeting BaaS API client module.
-All API calls centralized for consistency and maintainability.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from backend.sdk import Requests
-
-
-class MeetingBaasAPI:
-    """Client for Meeting BaaS API endpoints."""
-
-    BASE_URL = "https://api.meetingbaas.com"
-
-    def __init__(self, api_key: str):
-        """Initialize API client with authentication key."""
-        self.api_key = api_key
-        self.headers = {"x-meeting-baas-api-key": api_key}
-        self.requests = Requests()
-
-    # Bot Management Endpoints
-
-    async def join_meeting(
-        self,
-        bot_name: str,
-        meeting_url: str,
-        reserved: bool = False,
-        bot_image: Optional[str] = None,
-        entry_message: Optional[str] = None,
-        start_time: Optional[int] = None,
-        speech_to_text: Optional[Dict[str, Any]] = None,
-        webhook_url: Optional[str] = None,
-        automatic_leave: Optional[Dict[str, Any]] = None,
-        extra: Optional[Dict[str, Any]] = None,
-        recording_mode: str = "speaker_view",
-        streaming: Optional[Dict[str, Any]] = None,
-        deduplication_key: Optional[str] = None,
-        zoom_sdk_id: Optional[str] = None,
-        zoom_sdk_pwd: Optional[str] = None,
-    ) -> Dict[str, Any]:
-        """
-        Deploy a bot to join and record a meeting.
-
-        POST /bots
-        """
-        body = {
-            "bot_name": bot_name,
-            "meeting_url": meeting_url,
-            "reserved": reserved,
-            "recording_mode": recording_mode,
-        }
-
-        # Add optional fields if provided
-        if bot_image is not None:
-            body["bot_image"] = bot_image
-        if entry_message is not None:
-            body["entry_message"] = entry_message
-        if start_time is not None:
-            body["start_time"] = start_time
-        if speech_to_text is not None:
-            body["speech_to_text"] = speech_to_text
-        if webhook_url is not None:
-            body["webhook_url"] = webhook_url
-        if automatic_leave is not None:
-            body["automatic_leave"] = automatic_leave
-        if extra is not None:
-            body["extra"] = extra
-        if streaming is not None:
-            body["streaming"] = streaming
-        if deduplication_key is not None:
-            body["deduplication_key"] = deduplication_key
-        if zoom_sdk_id is not None:
-            body["zoom_sdk_id"] = zoom_sdk_id
-        if zoom_sdk_pwd is not None:
-            body["zoom_sdk_pwd"] = zoom_sdk_pwd
-
-        response = await self.requests.post(
-            f"{self.BASE_URL}/bots",
-            headers=self.headers,
-            json=body,
-        )
-        return response.json()
-
-    async def leave_meeting(self, bot_id: str) -> bool:
-        """
-        Remove a bot from an ongoing meeting.
-
-        DELETE /bots/{uuid}
-        """
-        response = await self.requests.delete(
-            f"{self.BASE_URL}/bots/{bot_id}",
-            headers=self.headers,
-        )
-        return response.status in [200, 204]
-
-    async def retranscribe(
-        self,
-        bot_uuid: str,
-        speech_to_text: Optional[Dict[str, Any]] = None,
-        webhook_url: Optional[str] = None,
-    ) -> Dict[str, Any]:
-        """
-        Re-run transcription on a bot's audio.
-
-        POST /bots/retranscribe
-        """
-        body: Dict[str, Any] = {"bot_uuid": bot_uuid}
-
-        if speech_to_text is not None:
-            body["speech_to_text"] = speech_to_text
-        if webhook_url is not None:
-            body["webhook_url"] = webhook_url
-
-        response = await self.requests.post(
-            f"{self.BASE_URL}/bots/retranscribe",
-            headers=self.headers,
-            json=body,
-        )
-
-        if response.status == 202:
-            return {"accepted": True}
-        return response.json()
-
-    # Data Retrieval Endpoints
-
-    async def get_meeting_data(
-        self, bot_id: str, include_transcripts: bool = True
-    ) -> Dict[str, Any]:
-        """
-        Retrieve meeting data including recording and transcripts.
-
-        GET /bots/meeting_data
-        """
-        params = {
-            "bot_id": bot_id,
-            "include_transcripts": str(include_transcripts).lower(),
-        }
-
-        response = await self.requests.get(
-            f"{self.BASE_URL}/bots/meeting_data",
-            headers=self.headers,
-            params=params,
-        )
-        return response.json()
-
-    async def get_screenshots(self, bot_id: str) -> List[Dict[str, Any]]:
-        """
-        Retrieve screenshots captured during a meeting.
-
-        GET /bots/{uuid}/screenshots
-        """
-        response = await self.requests.get(
-            f"{self.BASE_URL}/bots/{bot_id}/screenshots",
-            headers=self.headers,
-        )
-        result = response.json()
-        # Ensure we return a list
-        if isinstance(result, list):
-            return result
-        return []
-
-    async def delete_data(self, bot_id: str) -> bool:
-        """
-        Delete a bot's recorded data.
-
-        POST /bots/{uuid}/delete_data
-        """
-        response = await self.requests.post(
-            f"{self.BASE_URL}/bots/{bot_id}/delete_data",
-            headers=self.headers,
-        )
-        return response.status == 200
-
-    async def list_bots_with_metadata(
-        self,
-        limit: Optional[int] = None,
-        offset: Optional[int] = None,
-        sort_by: Optional[str] = None,
-        sort_order: Optional[str] = None,
-        filter_by: Optional[Dict[str, Any]] = None,
-    ) -> Dict[str, Any]:
-        """
-        List bots with metadata including IDs, names, and meeting details.
-
-        GET /bots/bots_with_metadata
-        """
-        params = {}
-        if limit is not None:
-            params["limit"] = limit
-        if offset is not None:
-            params["offset"] = offset
-        if sort_by is not None:
-            params["sort_by"] = sort_by
-        if sort_order is not None:
-            params["sort_order"] = sort_order
-        if filter_by is not None:
-            params.update(filter_by)
-
-        response = await self.requests.get(
-            f"{self.BASE_URL}/bots/bots_with_metadata",
-            headers=self.headers,
-            params=params,
-        )
-        return response.json()

autogpt_platform/backend/backend/blocks/baas/_config.py

@@ -1,13 +0,0 @@
-"""
-Shared configuration for all Meeting BaaS blocks using the SDK pattern.
-"""
-
-from backend.sdk import BlockCostType, ProviderBuilder
-
-# Configure the Meeting BaaS provider with API key authentication
-baas = (
-    ProviderBuilder("baas")
-    .with_api_key("MEETING_BAAS_API_KEY", "Meeting BaaS API Key")
-    .with_base_cost(5, BlockCostType.RUN)  # Higher cost for meeting recording service
-    .build()
-)

autogpt_platform/backend/backend/blocks/baas/bots.py

@@ -1,217 +0,0 @@
-"""
-Meeting BaaS bot (recording) blocks.
-"""
-
-from typing import Optional
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._api import MeetingBaasAPI
-from ._config import baas
-
-
-class BaasBotJoinMeetingBlock(Block):
-    """
-    Deploy a bot immediately or at a scheduled start_time to join and record a meeting.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = baas.credentials_field(
-            description="Meeting BaaS API credentials"
-        )
-        meeting_url: str = SchemaField(
-            description="The URL of the meeting the bot should join"
-        )
-        bot_name: str = SchemaField(
-            description="Display name for the bot in the meeting"
-        )
-        bot_image: str = SchemaField(
-            description="URL to an image for the bot's avatar (16:9 ratio recommended)",
-            default="",
-        )
-        entry_message: str = SchemaField(
-            description="Chat message the bot will post upon entry", default=""
-        )
-        reserved: bool = SchemaField(
-            description="Use a reserved bot slot (joins 4 min before meeting)",
-            default=False,
-        )
-        start_time: Optional[int] = SchemaField(
-            description="Unix timestamp (ms) when bot should join", default=None
-        )
-        webhook_url: str | None = SchemaField(
-            description="URL to receive webhook events for this bot", default=None
-        )
-        timeouts: dict = SchemaField(
-            description="Automatic leave timeouts configuration", default={}
-        )
-        extra: dict = SchemaField(
-            description="Custom metadata to attach to the bot", default={}
-        )
-
-    class Output(BlockSchema):
-        bot_id: str = SchemaField(description="UUID of the deployed bot")
-        join_response: dict = SchemaField(
-            description="Full response from join operation"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="377d1a6a-a99b-46cf-9af3-1d1b12758e04",
-            description="Deploy a bot to join and record a meeting",
-            categories={BlockCategory.COMMUNICATION},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        api_key = credentials.api_key.get_secret_value()
-        api = MeetingBaasAPI(api_key)
-
-        # Call API with all parameters
-        data = await api.join_meeting(
-            bot_name=input_data.bot_name,
-            meeting_url=input_data.meeting_url,
-            reserved=input_data.reserved,
-            bot_image=input_data.bot_image if input_data.bot_image else None,
-            entry_message=(
-                input_data.entry_message if input_data.entry_message else None
-            ),
-            start_time=input_data.start_time,
-            speech_to_text={"provider": "Default"},
-            webhook_url=input_data.webhook_url if input_data.webhook_url else None,
-            automatic_leave=input_data.timeouts if input_data.timeouts else None,
-            extra=input_data.extra if input_data.extra else None,
-        )
-
-        yield "bot_id", data.get("bot_id", "")
-        yield "join_response", data
-
-
-class BaasBotLeaveMeetingBlock(Block):
-    """
-    Force the bot to exit the call.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = baas.credentials_field(
-            description="Meeting BaaS API credentials"
-        )
-        bot_id: str = SchemaField(description="UUID of the bot to remove from meeting")
-
-    class Output(BlockSchema):
-        left: bool = SchemaField(description="Whether the bot successfully left")
-
-    def __init__(self):
-        super().__init__(
-            id="bf77d128-8b25-4280-b5c7-2d553ba7e482",
-            description="Remove a bot from an ongoing meeting",
-            categories={BlockCategory.COMMUNICATION},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        api_key = credentials.api_key.get_secret_value()
-        api = MeetingBaasAPI(api_key)
-
-        # Leave meeting
-        left = await api.leave_meeting(input_data.bot_id)
-
-        yield "left", left
-
-
-class BaasBotFetchMeetingDataBlock(Block):
-    """
-    Pull MP4 URL, transcript & metadata for a completed meeting.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = baas.credentials_field(
-            description="Meeting BaaS API credentials"
-        )
-        bot_id: str = SchemaField(description="UUID of the bot whose data to fetch")
-        include_transcripts: bool = SchemaField(
-            description="Include transcript data in response", default=True
-        )
-
-    class Output(BlockSchema):
-        mp4_url: str = SchemaField(
-            description="URL to download the meeting recording (time-limited)"
-        )
-        transcript: list = SchemaField(description="Meeting transcript data")
-        metadata: dict = SchemaField(description="Meeting metadata and bot information")
-
-    def __init__(self):
-        super().__init__(
-            id="ea7c1309-303c-4da1-893f-89c0e9d64e78",
-            description="Retrieve recorded meeting data",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        api_key = credentials.api_key.get_secret_value()
-        api = MeetingBaasAPI(api_key)
-
-        # Fetch meeting data
-        data = await api.get_meeting_data(
-            bot_id=input_data.bot_id,
-            include_transcripts=input_data.include_transcripts,
-        )
-
-        yield "mp4_url", data.get("mp4", "")
-        yield "transcript", data.get("bot_data", {}).get("transcripts", [])
-        yield "metadata", data.get("bot_data", {}).get("bot", {})
-
-
-class BaasBotDeleteRecordingBlock(Block):
-    """
-    Purge MP4 + transcript data for privacy or storage management.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = baas.credentials_field(
-            description="Meeting BaaS API credentials"
-        )
-        bot_id: str = SchemaField(description="UUID of the bot whose data to delete")
-
-    class Output(BlockSchema):
-        deleted: bool = SchemaField(
-            description="Whether the data was successfully deleted"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="bf8d1aa6-42d8-4944-b6bd-6bac554c0d3b",
-            description="Permanently delete a meeting's recorded data",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        api_key = credentials.api_key.get_secret_value()
-        api = MeetingBaasAPI(api_key)
-
-        # Delete recording data
-        deleted = await api.delete_data(input_data.bot_id)
-
-        yield "deleted", deleted

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

@@ -1,178 +0,0 @@
-"""
-DataForSEO API client with async support using the SDK patterns.
-"""
-
-import base64
-from typing import Any, Dict, List, Optional
-
-from backend.sdk import Requests, UserPasswordCredentials
-
-
-class DataForSeoClient:
-    """Client for the DataForSEO API using async requests."""
-
-    API_URL = "https://api.dataforseo.com"
-
-    def __init__(self, credentials: UserPasswordCredentials):
-        self.credentials = credentials
-        self.requests = Requests(
-            trusted_origins=["https://api.dataforseo.com"],
-            raise_for_status=False,
-        )
-
-    def _get_headers(self) -> Dict[str, str]:
-        """Generate the authorization header using Basic Auth."""
-        username = self.credentials.username.get_secret_value()
-        password = self.credentials.password.get_secret_value()
-        credentials_str = f"{username}:{password}"
-        encoded = base64.b64encode(credentials_str.encode("ascii")).decode("ascii")
-        return {
-            "Authorization": f"Basic {encoded}",
-            "Content-Type": "application/json",
-        }
-
-    async def keyword_suggestions(
-        self,
-        keyword: str,
-        location_code: Optional[int] = None,
-        language_code: Optional[str] = None,
-        include_seed_keyword: bool = True,
-        include_serp_info: bool = False,
-        include_clickstream_data: bool = False,
-        limit: int = 100,
-    ) -> List[Dict[str, Any]]:
-        """
-        Get keyword suggestions from DataForSEO Labs.
-
-        Args:
-            keyword: Seed keyword
-            location_code: Location code for targeting
-            language_code: Language code (e.g., "en")
-            include_seed_keyword: Include seed keyword in results
-            include_serp_info: Include SERP data
-            include_clickstream_data: Include clickstream metrics
-            limit: Maximum number of results (up to 3000)
-
-        Returns:
-            API response with keyword suggestions
-        """
-        endpoint = f"{self.API_URL}/v3/dataforseo_labs/google/keyword_suggestions/live"
-
-        # Build payload only with non-None values to avoid sending null fields
-        task_data: dict[str, Any] = {
-            "keyword": keyword,
-        }
-
-        if location_code is not None:
-            task_data["location_code"] = location_code
-        if language_code is not None:
-            task_data["language_code"] = language_code
-        if include_seed_keyword is not None:
-            task_data["include_seed_keyword"] = include_seed_keyword
-        if include_serp_info is not None:
-            task_data["include_serp_info"] = include_serp_info
-        if include_clickstream_data is not None:
-            task_data["include_clickstream_data"] = include_clickstream_data
-        if limit is not None:
-            task_data["limit"] = limit
-
-        payload = [task_data]
-
-        response = await self.requests.post(
-            endpoint,
-            headers=self._get_headers(),
-            json=payload,
-        )
-
-        data = response.json()
-
-        # Check for API errors
-        if response.status != 200:
-            error_message = data.get("status_message", "Unknown error")
-            raise Exception(
-                f"DataForSEO API error ({response.status}): {error_message}"
-            )
-
-        # Extract the results from the response
-        if data.get("tasks") and len(data["tasks"]) > 0:
-            task = data["tasks"][0]
-            if task.get("status_code") == 20000:  # Success code
-                return task.get("result", [])
-            else:
-                error_msg = task.get("status_message", "Task failed")
-                raise Exception(f"DataForSEO task error: {error_msg}")
-
-        return []
-
-    async def related_keywords(
-        self,
-        keyword: str,
-        location_code: Optional[int] = None,
-        language_code: Optional[str] = None,
-        include_seed_keyword: bool = True,
-        include_serp_info: bool = False,
-        include_clickstream_data: bool = False,
-        limit: int = 100,
-    ) -> List[Dict[str, Any]]:
-        """
-        Get related keywords from DataForSEO Labs.
-
-        Args:
-            keyword: Seed keyword
-            location_code: Location code for targeting
-            language_code: Language code (e.g., "en")
-            include_seed_keyword: Include seed keyword in results
-            include_serp_info: Include SERP data
-            include_clickstream_data: Include clickstream metrics
-            limit: Maximum number of results (up to 3000)
-
-        Returns:
-            API response with related keywords
-        """
-        endpoint = f"{self.API_URL}/v3/dataforseo_labs/google/related_keywords/live"
-
-        # Build payload only with non-None values to avoid sending null fields
-        task_data: dict[str, Any] = {
-            "keyword": keyword,
-        }
-
-        if location_code is not None:
-            task_data["location_code"] = location_code
-        if language_code is not None:
-            task_data["language_code"] = language_code
-        if include_seed_keyword is not None:
-            task_data["include_seed_keyword"] = include_seed_keyword
-        if include_serp_info is not None:
-            task_data["include_serp_info"] = include_serp_info
-        if include_clickstream_data is not None:
-            task_data["include_clickstream_data"] = include_clickstream_data
-        if limit is not None:
-            task_data["limit"] = limit
-
-        payload = [task_data]
-
-        response = await self.requests.post(
-            endpoint,
-            headers=self._get_headers(),
-            json=payload,
-        )
-
-        data = response.json()
-
-        # Check for API errors
-        if response.status != 200:
-            error_message = data.get("status_message", "Unknown error")
-            raise Exception(
-                f"DataForSEO API error ({response.status}): {error_message}"
-            )
-
-        # Extract the results from the response
-        if data.get("tasks") and len(data["tasks"]) > 0:
-            task = data["tasks"][0]
-            if task.get("status_code") == 20000:  # Success code
-                return task.get("result", [])
-            else:
-                error_msg = task.get("status_message", "Task failed")
-                raise Exception(f"DataForSEO task error: {error_msg}")
-
-        return []

autogpt_platform/backend/backend/blocks/dataforseo/_config.py

@@ -1,17 +0,0 @@
-"""
-Configuration for all DataForSEO blocks using the new SDK pattern.
-"""
-
-from backend.sdk import BlockCostType, ProviderBuilder
-
-# Build the DataForSEO provider with username/password authentication
-dataforseo = (
-    ProviderBuilder("dataforseo")
-    .with_user_password(
-        username_env_var="DATAFORSEO_USERNAME",
-        password_env_var="DATAFORSEO_PASSWORD",
-        title="DataForSEO Credentials",
-    )
-    .with_base_cost(1, BlockCostType.RUN)
-    .build()
-)

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

@@ -1,273 +0,0 @@
-"""
-DataForSEO Google Keyword Suggestions block.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-    UserPasswordCredentials,
-)
-
-from ._api import DataForSeoClient
-from ._config import dataforseo
-
-
-class KeywordSuggestion(BlockSchema):
-    """Schema for a keyword suggestion result."""
-
-    keyword: str = SchemaField(description="The keyword suggestion")
-    search_volume: Optional[int] = SchemaField(
-        description="Monthly search volume", default=None
-    )
-    competition: Optional[float] = SchemaField(
-        description="Competition level (0-1)", default=None
-    )
-    cpc: Optional[float] = SchemaField(
-        description="Cost per click in USD", default=None
-    )
-    keyword_difficulty: Optional[int] = SchemaField(
-        description="Keyword difficulty score", default=None
-    )
-    serp_info: Optional[Dict[str, Any]] = SchemaField(
-        description="data from SERP for each keyword", default=None
-    )
-    clickstream_data: Optional[Dict[str, Any]] = SchemaField(
-        description="Clickstream data metrics", default=None
-    )
-
-
-class DataForSeoKeywordSuggestionsBlock(Block):
-    """Block for getting keyword suggestions from DataForSEO Labs."""
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = dataforseo.credentials_field(
-            description="DataForSEO credentials (username and password)"
-        )
-        keyword: str = SchemaField(description="Seed keyword to get suggestions for")
-        location_code: Optional[int] = SchemaField(
-            description="Location code for targeting (e.g., 2840 for USA)",
-            default=2840,  # USA
-        )
-        language_code: Optional[str] = SchemaField(
-            description="Language code (e.g., 'en' for English)",
-            default="en",
-        )
-        include_seed_keyword: bool = SchemaField(
-            description="Include the seed keyword in results",
-            default=True,
-        )
-        include_serp_info: bool = SchemaField(
-            description="Include SERP information",
-            default=False,
-        )
-        include_clickstream_data: bool = SchemaField(
-            description="Include clickstream metrics",
-            default=False,
-        )
-        limit: int = SchemaField(
-            description="Maximum number of results (up to 3000)",
-            default=100,
-            ge=1,
-            le=3000,
-        )
-
-    class Output(BlockSchema):
-        suggestions: List[KeywordSuggestion] = SchemaField(
-            description="List of keyword suggestions with metrics"
-        )
-        suggestion: KeywordSuggestion = SchemaField(
-            description="A single keyword suggestion with metrics"
-        )
-        total_count: int = SchemaField(
-            description="Total number of suggestions returned"
-        )
-        seed_keyword: str = SchemaField(
-            description="The seed keyword used for the query"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="73c3e7c4-2b3f-4e9f-9e3e-8f7a5c3e2d45",
-            description="Get keyword suggestions from DataForSEO Labs Google API",
-            categories={BlockCategory.SEARCH, BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "credentials": dataforseo.get_test_credentials().model_dump(),
-                "keyword": "digital marketing",
-                "location_code": 2840,
-                "language_code": "en",
-                "limit": 1,
-            },
-            test_credentials=dataforseo.get_test_credentials(),
-            test_output=[
-                (
-                    "suggestion",
-                    lambda x: hasattr(x, "keyword")
-                    and x.keyword == "digital marketing strategy",
-                ),
-                ("suggestions", lambda x: isinstance(x, list) and len(x) == 1),
-                ("total_count", 1),
-                ("seed_keyword", "digital marketing"),
-            ],
-            test_mock={
-                "_fetch_keyword_suggestions": lambda *args, **kwargs: [
-                    {
-                        "items": [
-                            {
-                                "keyword": "digital marketing strategy",
-                                "keyword_info": {
-                                    "search_volume": 10000,
-                                    "competition": 0.5,
-                                    "cpc": 2.5,
-                                },
-                                "keyword_properties": {
-                                    "keyword_difficulty": 50,
-                                },
-                            }
-                        ]
-                    }
-                ]
-            },
-        )
-
-    async def _fetch_keyword_suggestions(
-        self,
-        client: DataForSeoClient,
-        input_data: Input,
-    ) -> Any:
-        """Private method to fetch keyword suggestions - can be mocked for testing."""
-        return await client.keyword_suggestions(
-            keyword=input_data.keyword,
-            location_code=input_data.location_code,
-            language_code=input_data.language_code,
-            include_seed_keyword=input_data.include_seed_keyword,
-            include_serp_info=input_data.include_serp_info,
-            include_clickstream_data=input_data.include_clickstream_data,
-            limit=input_data.limit,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: UserPasswordCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        """Execute the keyword suggestions query."""
-        client = DataForSeoClient(credentials)
-
-        results = await self._fetch_keyword_suggestions(client, input_data)
-
-        # Process and format the results
-        suggestions = []
-        if results and len(results) > 0:
-            # results is a list, get the first element
-            first_result = results[0] if isinstance(results, list) else results
-            items = (
-                first_result.get("items", []) if isinstance(first_result, dict) else []
-            )
-            for item in items:
-                # Create the KeywordSuggestion object
-                suggestion = KeywordSuggestion(
-                    keyword=item.get("keyword", ""),
-                    search_volume=item.get("keyword_info", {}).get("search_volume"),
-                    competition=item.get("keyword_info", {}).get("competition"),
-                    cpc=item.get("keyword_info", {}).get("cpc"),
-                    keyword_difficulty=item.get("keyword_properties", {}).get(
-                        "keyword_difficulty"
-                    ),
-                    serp_info=(
-                        item.get("serp_info") if input_data.include_serp_info else None
-                    ),
-                    clickstream_data=(
-                        item.get("clickstream_keyword_info")
-                        if input_data.include_clickstream_data
-                        else None
-                    ),
-                )
-                yield "suggestion", suggestion
-                suggestions.append(suggestion)
-
-        yield "suggestions", suggestions
-        yield "total_count", len(suggestions)
-        yield "seed_keyword", input_data.keyword
-
-
-class KeywordSuggestionExtractorBlock(Block):
-    """Extracts individual fields from a KeywordSuggestion object."""
-
-    class Input(BlockSchema):
-        suggestion: KeywordSuggestion = SchemaField(
-            description="The keyword suggestion object to extract fields from"
-        )
-
-    class Output(BlockSchema):
-        keyword: str = SchemaField(description="The keyword suggestion")
-        search_volume: Optional[int] = SchemaField(
-            description="Monthly search volume", default=None
-        )
-        competition: Optional[float] = SchemaField(
-            description="Competition level (0-1)", default=None
-        )
-        cpc: Optional[float] = SchemaField(
-            description="Cost per click in USD", default=None
-        )
-        keyword_difficulty: Optional[int] = SchemaField(
-            description="Keyword difficulty score", default=None
-        )
-        serp_info: Optional[Dict[str, Any]] = SchemaField(
-            description="data from SERP for each keyword", default=None
-        )
-        clickstream_data: Optional[Dict[str, Any]] = SchemaField(
-            description="Clickstream data metrics", default=None
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="4193cb94-677c-48b0-9eec-6ac72fffd0f2",
-            description="Extract individual fields from a KeywordSuggestion object",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "suggestion": KeywordSuggestion(
-                    keyword="test keyword",
-                    search_volume=1000,
-                    competition=0.5,
-                    cpc=2.5,
-                    keyword_difficulty=60,
-                ).model_dump()
-            },
-            test_output=[
-                ("keyword", "test keyword"),
-                ("search_volume", 1000),
-                ("competition", 0.5),
-                ("cpc", 2.5),
-                ("keyword_difficulty", 60),
-                ("serp_info", None),
-                ("clickstream_data", None),
-            ],
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        **kwargs,
-    ) -> BlockOutput:
-        """Extract fields from the KeywordSuggestion object."""
-        suggestion = input_data.suggestion
-
-        yield "keyword", suggestion.keyword
-        yield "search_volume", suggestion.search_volume
-        yield "competition", suggestion.competition
-        yield "cpc", suggestion.cpc
-        yield "keyword_difficulty", suggestion.keyword_difficulty
-        yield "serp_info", suggestion.serp_info
-        yield "clickstream_data", suggestion.clickstream_data

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

@@ -1,283 +0,0 @@
-"""
-DataForSEO Google Related Keywords block.
-"""
-
-from typing import Any, Dict, List, Optional
-
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-    UserPasswordCredentials,
-)
-
-from ._api import DataForSeoClient
-from ._config import dataforseo
-
-
-class RelatedKeyword(BlockSchema):
-    """Schema for a related keyword result."""
-
-    keyword: str = SchemaField(description="The related keyword")
-    search_volume: Optional[int] = SchemaField(
-        description="Monthly search volume", default=None
-    )
-    competition: Optional[float] = SchemaField(
-        description="Competition level (0-1)", default=None
-    )
-    cpc: Optional[float] = SchemaField(
-        description="Cost per click in USD", default=None
-    )
-    keyword_difficulty: Optional[int] = SchemaField(
-        description="Keyword difficulty score", default=None
-    )
-    serp_info: Optional[Dict[str, Any]] = SchemaField(
-        description="SERP data for the keyword", default=None
-    )
-    clickstream_data: Optional[Dict[str, Any]] = SchemaField(
-        description="Clickstream data metrics", default=None
-    )
-
-
-class DataForSeoRelatedKeywordsBlock(Block):
-    """Block for getting related keywords from DataForSEO Labs."""
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = dataforseo.credentials_field(
-            description="DataForSEO credentials (username and password)"
-        )
-        keyword: str = SchemaField(
-            description="Seed keyword to find related keywords for"
-        )
-        location_code: Optional[int] = SchemaField(
-            description="Location code for targeting (e.g., 2840 for USA)",
-            default=2840,  # USA
-        )
-        language_code: Optional[str] = SchemaField(
-            description="Language code (e.g., 'en' for English)",
-            default="en",
-        )
-        include_seed_keyword: bool = SchemaField(
-            description="Include the seed keyword in results",
-            default=True,
-        )
-        include_serp_info: bool = SchemaField(
-            description="Include SERP information",
-            default=False,
-        )
-        include_clickstream_data: bool = SchemaField(
-            description="Include clickstream metrics",
-            default=False,
-        )
-        limit: int = SchemaField(
-            description="Maximum number of results (up to 3000)",
-            default=100,
-            ge=1,
-            le=3000,
-        )
-
-    class Output(BlockSchema):
-        related_keywords: List[RelatedKeyword] = SchemaField(
-            description="List of related keywords with metrics"
-        )
-        related_keyword: RelatedKeyword = SchemaField(
-            description="A related keyword with metrics"
-        )
-        total_count: int = SchemaField(
-            description="Total number of related keywords returned"
-        )
-        seed_keyword: str = SchemaField(
-            description="The seed keyword used for the query"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8f2e4d6a-1b3c-4a5e-9d7f-2c8e6a4b3f1d",
-            description="Get related keywords from DataForSEO Labs Google API",
-            categories={BlockCategory.SEARCH, BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "credentials": dataforseo.get_test_credentials().model_dump(),
-                "keyword": "content marketing",
-                "location_code": 2840,
-                "language_code": "en",
-                "limit": 1,
-            },
-            test_credentials=dataforseo.get_test_credentials(),
-            test_output=[
-                (
-                    "related_keyword",
-                    lambda x: hasattr(x, "keyword") and x.keyword == "content strategy",
-                ),
-                ("related_keywords", lambda x: isinstance(x, list) and len(x) == 1),
-                ("total_count", 1),
-                ("seed_keyword", "content marketing"),
-            ],
-            test_mock={
-                "_fetch_related_keywords": lambda *args, **kwargs: [
-                    {
-                        "items": [
-                            {
-                                "keyword_data": {
-                                    "keyword": "content strategy",
-                                    "keyword_info": {
-                                        "search_volume": 8000,
-                                        "competition": 0.4,
-                                        "cpc": 3.0,
-                                    },
-                                    "keyword_properties": {
-                                        "keyword_difficulty": 45,
-                                    },
-                                }
-                            }
-                        ]
-                    }
-                ]
-            },
-        )
-
-    async def _fetch_related_keywords(
-        self,
-        client: DataForSeoClient,
-        input_data: Input,
-    ) -> Any:
-        """Private method to fetch related keywords - can be mocked for testing."""
-        return await client.related_keywords(
-            keyword=input_data.keyword,
-            location_code=input_data.location_code,
-            language_code=input_data.language_code,
-            include_seed_keyword=input_data.include_seed_keyword,
-            include_serp_info=input_data.include_serp_info,
-            include_clickstream_data=input_data.include_clickstream_data,
-            limit=input_data.limit,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: UserPasswordCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        """Execute the related keywords query."""
-        client = DataForSeoClient(credentials)
-
-        results = await self._fetch_related_keywords(client, input_data)
-
-        # Process and format the results
-        related_keywords = []
-        if results and len(results) > 0:
-            # results is a list, get the first element
-            first_result = results[0] if isinstance(results, list) else results
-            items = (
-                first_result.get("items", []) if isinstance(first_result, dict) else []
-            )
-            for item in items:
-                # Extract keyword_data from the item
-                keyword_data = item.get("keyword_data", {})
-
-                # Create the RelatedKeyword object
-                keyword = RelatedKeyword(
-                    keyword=keyword_data.get("keyword", ""),
-                    search_volume=keyword_data.get("keyword_info", {}).get(
-                        "search_volume"
-                    ),
-                    competition=keyword_data.get("keyword_info", {}).get("competition"),
-                    cpc=keyword_data.get("keyword_info", {}).get("cpc"),
-                    keyword_difficulty=keyword_data.get("keyword_properties", {}).get(
-                        "keyword_difficulty"
-                    ),
-                    serp_info=(
-                        keyword_data.get("serp_info")
-                        if input_data.include_serp_info
-                        else None
-                    ),
-                    clickstream_data=(
-                        keyword_data.get("clickstream_keyword_info")
-                        if input_data.include_clickstream_data
-                        else None
-                    ),
-                )
-                yield "related_keyword", keyword
-                related_keywords.append(keyword)
-
-        yield "related_keywords", related_keywords
-        yield "total_count", len(related_keywords)
-        yield "seed_keyword", input_data.keyword
-
-
-class RelatedKeywordExtractorBlock(Block):
-    """Extracts individual fields from a RelatedKeyword object."""
-
-    class Input(BlockSchema):
-        related_keyword: RelatedKeyword = SchemaField(
-            description="The related keyword object to extract fields from"
-        )
-
-    class Output(BlockSchema):
-        keyword: str = SchemaField(description="The related keyword")
-        search_volume: Optional[int] = SchemaField(
-            description="Monthly search volume", default=None
-        )
-        competition: Optional[float] = SchemaField(
-            description="Competition level (0-1)", default=None
-        )
-        cpc: Optional[float] = SchemaField(
-            description="Cost per click in USD", default=None
-        )
-        keyword_difficulty: Optional[int] = SchemaField(
-            description="Keyword difficulty score", default=None
-        )
-        serp_info: Optional[Dict[str, Any]] = SchemaField(
-            description="SERP data for the keyword", default=None
-        )
-        clickstream_data: Optional[Dict[str, Any]] = SchemaField(
-            description="Clickstream data metrics", default=None
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="98342061-09d2-4952-bf77-0761fc8cc9a8",
-            description="Extract individual fields from a RelatedKeyword object",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            test_input={
-                "related_keyword": RelatedKeyword(
-                    keyword="test related keyword",
-                    search_volume=800,
-                    competition=0.4,
-                    cpc=3.0,
-                    keyword_difficulty=55,
-                ).model_dump()
-            },
-            test_output=[
-                ("keyword", "test related keyword"),
-                ("search_volume", 800),
-                ("competition", 0.4),
-                ("cpc", 3.0),
-                ("keyword_difficulty", 55),
-                ("serp_info", None),
-                ("clickstream_data", None),
-            ],
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        **kwargs,
-    ) -> BlockOutput:
-        """Extract fields from the RelatedKeyword object."""
-        related_keyword = input_data.related_keyword
-
-        yield "keyword", related_keyword.keyword
-        yield "search_volume", related_keyword.search_volume
-        yield "competition", related_keyword.competition
-        yield "cpc", related_keyword.cpc
-        yield "keyword_difficulty", related_keyword.keyword_difficulty
-        yield "serp_info", related_keyword.serp_info
-        yield "clickstream_data", related_keyword.clickstream_data

autogpt_platform/backend/backend/blocks/discord.py

@@ -2,29 +2,45 @@ import base64
 import io
 import mimetypes
 from pathlib import Path
-from typing import Any
+from typing import Any, Literal
 
 import aiohttp
 import discord
 from pydantic import SecretStr
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import APIKeyCredentials, SchemaField
+from backend.data.model import (
+    APIKeyCredentials,
+    CredentialsField,
+    CredentialsMetaInput,
+    SchemaField,
+)
+from backend.integrations.providers import ProviderName
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType
 
-from ._auth import (
-    TEST_BOT_CREDENTIALS,
-    TEST_BOT_CREDENTIALS_INPUT,
-    DiscordBotCredentialsField,
-    DiscordBotCredentialsInput,
-)
+DiscordCredentials = CredentialsMetaInput[
+    Literal[ProviderName.DISCORD], Literal["api_key"]
+]
 
-# Keep backward compatibility alias
-DiscordCredentials = DiscordBotCredentialsInput
-DiscordCredentialsField = DiscordBotCredentialsField
-TEST_CREDENTIALS = TEST_BOT_CREDENTIALS
-TEST_CREDENTIALS_INPUT = TEST_BOT_CREDENTIALS_INPUT
+
+def DiscordCredentialsField() -> DiscordCredentials:
+    return CredentialsField(description="Discord bot token")
+
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="discord",
+    api_key=SecretStr("test_api_key"),
+    title="Mock Discord API key",
+    expires_at=None,
+)
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.type,
+}
 
 
 class ReadDiscordMessagesBlock(Block):

autogpt_platform/backend/backend/blocks/discord/_api.py

@@ -1,117 +0,0 @@
-"""
-Discord API helper functions for making authenticated requests.
-"""
-
-import logging
-from typing import Optional
-
-from pydantic import BaseModel
-
-from backend.data.model import OAuth2Credentials
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-
-class DiscordAPIException(Exception):
-    """Exception raised for Discord API errors."""
-
-    def __init__(self, message: str, status_code: int):
-        super().__init__(message)
-        self.status_code = status_code
-
-
-class DiscordOAuthUser(BaseModel):
-    """Model for Discord OAuth user response."""
-
-    user_id: str
-    username: str
-    avatar_url: str
-    banner: Optional[str] = None
-    accent_color: Optional[int] = None
-
-
-def get_api(credentials: OAuth2Credentials) -> Requests:
-    """
-    Create a Requests instance configured for Discord API calls with OAuth2 credentials.
-
-    Args:
-        credentials: The OAuth2 credentials containing the access token.
-
-    Returns:
-        A configured Requests instance for Discord API calls.
-    """
-    return Requests(
-        trusted_origins=[],
-        extra_headers={
-            "Authorization": f"Bearer {credentials.access_token.get_secret_value()}",
-            "Content-Type": "application/json",
-        },
-        raise_for_status=False,
-    )
-
-
-async def get_current_user(credentials: OAuth2Credentials) -> DiscordOAuthUser:
-    """
-    Fetch the current user's information using Discord OAuth2 API.
-
-    Reference: https://discord.com/developers/docs/resources/user#get-current-user
-
-    Args:
-        credentials: The OAuth2 credentials.
-
-    Returns:
-        A model containing user data with avatar URL.
-
-    Raises:
-        DiscordAPIException: If the API request fails.
-    """
-    api = get_api(credentials)
-    response = await api.get("https://discord.com/api/oauth2/@me")
-
-    if not response.ok:
-        error_text = response.text()
-        raise DiscordAPIException(
-            f"Failed to fetch user info: {response.status} - {error_text}",
-            response.status,
-        )
-
-    data = response.json()
-    logger.info(f"Discord OAuth2 API Response: {data}")
-
-    # The /api/oauth2/@me endpoint returns a user object nested in the response
-    user_info = data.get("user", {})
-    logger.info(f"User info extracted: {user_info}")
-
-    # Build avatar URL
-    user_id = user_info.get("id")
-    avatar_hash = user_info.get("avatar")
-    if avatar_hash:
-        # Custom avatar
-        avatar_ext = "gif" if avatar_hash.startswith("a_") else "png"
-        avatar_url = (
-            f"https://cdn.discordapp.com/avatars/{user_id}/{avatar_hash}.{avatar_ext}"
-        )
-    else:
-        # Default avatar based on discriminator or user ID
-        discriminator = user_info.get("discriminator", "0")
-        if discriminator == "0":
-            # New username system - use user ID for default avatar
-            default_avatar_index = (int(user_id) >> 22) % 6
-        else:
-            # Legacy discriminator system
-            default_avatar_index = int(discriminator) % 5
-        avatar_url = (
-            f"https://cdn.discordapp.com/embed/avatars/{default_avatar_index}.png"
-        )
-
-    result = DiscordOAuthUser(
-        user_id=user_id,
-        username=user_info.get("username", ""),
-        avatar_url=avatar_url,
-        banner=user_info.get("banner"),
-        accent_color=user_info.get("accent_color"),
-    )
-
-    logger.info(f"Returning user data: {result.model_dump()}")
-    return result

autogpt_platform/backend/backend/blocks/discord/_auth.py

@@ -1,74 +0,0 @@
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    OAuth2Credentials,
-)
-from backend.integrations.providers import ProviderName
-from backend.util.settings import Secrets
-
-secrets = Secrets()
-DISCORD_OAUTH_IS_CONFIGURED = bool(
-    secrets.discord_client_id and secrets.discord_client_secret
-)
-
-# Bot token credentials (existing)
-DiscordBotCredentials = APIKeyCredentials
-DiscordBotCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.DISCORD], Literal["api_key"]
-]
-
-# OAuth2 credentials (new)
-DiscordOAuthCredentials = OAuth2Credentials
-DiscordOAuthCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.DISCORD], Literal["oauth2"]
-]
-
-
-def DiscordBotCredentialsField() -> DiscordBotCredentialsInput:
-    """Creates a Discord bot token credentials field."""
-    return CredentialsField(description="Discord bot token")
-
-
-def DiscordOAuthCredentialsField(scopes: list[str]) -> DiscordOAuthCredentialsInput:
-    """Creates a Discord OAuth2 credentials field."""
-    return CredentialsField(
-        description="Discord OAuth2 credentials",
-        required_scopes=set(scopes) | {"identify"},  # Basic user info scope
-    )
-
-
-# Test credentials for bot tokens
-TEST_BOT_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="discord",
-    api_key=SecretStr("test_api_key"),
-    title="Mock Discord API key",
-    expires_at=None,
-)
-TEST_BOT_CREDENTIALS_INPUT = {
-    "provider": TEST_BOT_CREDENTIALS.provider,
-    "id": TEST_BOT_CREDENTIALS.id,
-    "type": TEST_BOT_CREDENTIALS.type,
-    "title": TEST_BOT_CREDENTIALS.type,
-}
-
-# Test credentials for OAuth2
-TEST_OAUTH_CREDENTIALS = OAuth2Credentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="discord",
-    access_token=SecretStr("test_access_token"),
-    title="Mock Discord OAuth",
-    scopes=["identify"],
-    username="testuser",
-)
-TEST_OAUTH_CREDENTIALS_INPUT = {
-    "provider": TEST_OAUTH_CREDENTIALS.provider,
-    "id": TEST_OAUTH_CREDENTIALS.id,
-    "type": TEST_OAUTH_CREDENTIALS.type,
-    "title": TEST_OAUTH_CREDENTIALS.type,
-}

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

@@ -1,99 +0,0 @@
-"""
-Discord OAuth-based blocks.
-"""
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import OAuth2Credentials, SchemaField
-
-from ._api import DiscordOAuthUser, get_current_user
-from ._auth import (
-    DISCORD_OAUTH_IS_CONFIGURED,
-    TEST_OAUTH_CREDENTIALS,
-    TEST_OAUTH_CREDENTIALS_INPUT,
-    DiscordOAuthCredentialsField,
-    DiscordOAuthCredentialsInput,
-)
-
-
-class DiscordGetCurrentUserBlock(Block):
-    """
-    Gets information about the currently authenticated Discord user using OAuth2.
-    This block requires Discord OAuth2 credentials (not bot tokens).
-    """
-
-    class Input(BlockSchema):
-        credentials: DiscordOAuthCredentialsInput = DiscordOAuthCredentialsField(
-            ["identify"]
-        )
-
-    class Output(BlockSchema):
-        user_id: str = SchemaField(description="The authenticated user's Discord ID")
-        username: str = SchemaField(description="The user's username")
-        avatar_url: str = SchemaField(description="URL to the user's avatar image")
-        banner_url: str = SchemaField(
-            description="URL to the user's banner image (if set)", default=""
-        )
-        accent_color: int = SchemaField(
-            description="The user's accent color as an integer", default=0
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="8c7e39b8-4e9d-4f3a-b4e1-2a8c9d5f6e3b",
-            input_schema=DiscordGetCurrentUserBlock.Input,
-            output_schema=DiscordGetCurrentUserBlock.Output,
-            description="Gets information about the currently authenticated Discord user using OAuth2 credentials.",
-            categories={BlockCategory.SOCIAL},
-            disabled=not DISCORD_OAUTH_IS_CONFIGURED,
-            test_input={
-                "credentials": TEST_OAUTH_CREDENTIALS_INPUT,
-            },
-            test_credentials=TEST_OAUTH_CREDENTIALS,
-            test_output=[
-                ("user_id", "123456789012345678"),
-                ("username", "testuser"),
-                (
-                    "avatar_url",
-                    "https://cdn.discordapp.com/avatars/123456789012345678/avatar.png",
-                ),
-                ("banner_url", ""),
-                ("accent_color", 0),
-            ],
-            test_mock={
-                "get_user": lambda _: DiscordOAuthUser(
-                    user_id="123456789012345678",
-                    username="testuser",
-                    avatar_url="https://cdn.discordapp.com/avatars/123456789012345678/avatar.png",
-                    banner=None,
-                    accent_color=0,
-                )
-            },
-        )
-
-    @staticmethod
-    async def get_user(credentials: OAuth2Credentials) -> DiscordOAuthUser:
-        user_info = await get_current_user(credentials)
-        return user_info
-
-    async def run(
-        self, input_data: Input, *, credentials: OAuth2Credentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            result = await self.get_user(credentials)
-
-            # Yield each output field
-            yield "user_id", result.user_id
-            yield "username", result.username
-            yield "avatar_url", result.avatar_url
-
-            # Handle banner URL if banner hash exists
-            if result.banner:
-                banner_url = f"https://cdn.discordapp.com/banners/{result.user_id}/{result.banner}.png"
-                yield "banner_url", banner_url
-            else:
-                yield "banner_url", ""
-
-            yield "accent_color", result.accent_color or 0
-
-        except Exception as e:
-            raise ValueError(f"Failed to get Discord user info: {e}")

autogpt_platform/backend/backend/blocks/enrichlayer/_api.py

@@ -1,408 +0,0 @@
-"""
-API module for Enrichlayer integration.
-
-This module provides a client for interacting with the Enrichlayer API,
-which allows fetching LinkedIn profile data and related information.
-"""
-
-import datetime
-import enum
-import logging
-from json import JSONDecodeError
-from typing import Any, Optional, TypeVar
-
-from pydantic import BaseModel, Field
-
-from backend.data.model import APIKeyCredentials
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-T = TypeVar("T")
-
-
-class EnrichlayerAPIException(Exception):
-    """Exception raised for Enrichlayer API errors."""
-
-    def __init__(self, message: str, status_code: int):
-        super().__init__(message)
-        self.status_code = status_code
-
-
-class FallbackToCache(enum.Enum):
-    ON_ERROR = "on-error"
-    NEVER = "never"
-
-
-class UseCache(enum.Enum):
-    IF_PRESENT = "if-present"
-    NEVER = "never"
-
-
-class SocialMediaProfiles(BaseModel):
-    """Social media profiles model."""
-
-    twitter: Optional[str] = None
-    facebook: Optional[str] = None
-    github: Optional[str] = None
-
-
-class Experience(BaseModel):
-    """Experience model for LinkedIn profiles."""
-
-    company: Optional[str] = None
-    title: Optional[str] = None
-    description: Optional[str] = None
-    location: Optional[str] = None
-    starts_at: Optional[dict[str, int]] = None
-    ends_at: Optional[dict[str, int]] = None
-    company_linkedin_profile_url: Optional[str] = None
-
-
-class Education(BaseModel):
-    """Education model for LinkedIn profiles."""
-
-    school: Optional[str] = None
-    degree_name: Optional[str] = None
-    field_of_study: Optional[str] = None
-    starts_at: Optional[dict[str, int]] = None
-    ends_at: Optional[dict[str, int]] = None
-    school_linkedin_profile_url: Optional[str] = None
-
-
-class PersonProfileResponse(BaseModel):
-    """Response model for LinkedIn person profile.
-
-    This model represents the response from Enrichlayer's LinkedIn profile API.
-    The API returns comprehensive profile data including work experience,
-    education, skills, and contact information (when available).
-
-    Example API Response:
-    {
-        "public_identifier": "johnsmith",
-        "full_name": "John Smith",
-        "occupation": "Software Engineer at Tech Corp",
-        "experiences": [
-            {
-                "company": "Tech Corp",
-                "title": "Software Engineer",
-                "starts_at": {"year": 2020, "month": 1}
-            }
-        ],
-        "education": [...],
-        "skills": ["Python", "JavaScript", ...]
-    }
-    """
-
-    public_identifier: Optional[str] = None
-    profile_pic_url: Optional[str] = None
-    full_name: Optional[str] = None
-    first_name: Optional[str] = None
-    last_name: Optional[str] = None
-    occupation: Optional[str] = None
-    headline: Optional[str] = None
-    summary: Optional[str] = None
-    country: Optional[str] = None
-    country_full_name: Optional[str] = None
-    city: Optional[str] = None
-    state: Optional[str] = None
-    experiences: Optional[list[Experience]] = None
-    education: Optional[list[Education]] = None
-    languages: Optional[list[str]] = None
-    skills: Optional[list[str]] = None
-    inferred_salary: Optional[dict[str, Any]] = None
-    personal_email: Optional[str] = None
-    personal_contact_number: Optional[str] = None
-    social_media_profiles: Optional[SocialMediaProfiles] = None
-    extra: Optional[dict[str, Any]] = None
-
-
-class SimilarProfile(BaseModel):
-    """Similar profile model for LinkedIn person lookup."""
-
-    similarity: float
-    linkedin_profile_url: str
-
-
-class PersonLookupResponse(BaseModel):
-    """Response model for LinkedIn person lookup.
-
-    This model represents the response from Enrichlayer's person lookup API.
-    The API returns a LinkedIn profile URL and similarity scores when
-    searching for a person by name and company.
-
-    Example API Response:
-    {
-        "url": "https://www.linkedin.com/in/johnsmith/",
-        "name_similarity_score": 0.95,
-        "company_similarity_score": 0.88,
-        "title_similarity_score": 0.75,
-        "location_similarity_score": 0.60
-    }
-    """
-
-    url: str | None = None
-    name_similarity_score: float | None
-    company_similarity_score: float | None
-    title_similarity_score: float | None
-    location_similarity_score: float | None
-    last_updated: datetime.datetime | None = None
-    profile: PersonProfileResponse | None = None
-
-
-class RoleLookupResponse(BaseModel):
-    """Response model for LinkedIn role lookup.
-
-    This model represents the response from Enrichlayer's role lookup API.
-    The API returns LinkedIn profile data for a specific role at a company.
-
-    Example API Response:
-    {
-        "linkedin_profile_url": "https://www.linkedin.com/in/johnsmith/",
-        "profile_data": {...}  // Full PersonProfileResponse data when enrich_profile=True
-    }
-    """
-
-    linkedin_profile_url: Optional[str] = None
-    profile_data: Optional[PersonProfileResponse] = None
-
-
-class ProfilePictureResponse(BaseModel):
-    """Response model for LinkedIn profile picture.
-
-    This model represents the response from Enrichlayer's profile picture API.
-    The API returns a URL to the person's LinkedIn profile picture.
-
-    Example API Response:
-    {
-        "tmp_profile_pic_url": "https://media.licdn.com/dms/image/..."
-    }
-    """
-
-    tmp_profile_pic_url: str = Field(
-        ..., description="URL of the profile picture", alias="tmp_profile_pic_url"
-    )
-
-    @property
-    def profile_picture_url(self) -> str:
-        """Backward compatibility property for profile_picture_url."""
-        return self.tmp_profile_pic_url
-
-
-class EnrichlayerClient:
-    """Client for interacting with the Enrichlayer API."""
-
-    API_BASE_URL = "https://enrichlayer.com/api/v2"
-
-    def __init__(
-        self,
-        credentials: Optional[APIKeyCredentials] = None,
-        custom_requests: Optional[Requests] = None,
-    ):
-        """
-        Initialize the Enrichlayer client.
-
-        Args:
-            credentials: The credentials to use for authentication.
-            custom_requests: Custom Requests instance for testing.
-        """
-        if custom_requests:
-            self._requests = custom_requests
-        else:
-            headers: dict[str, str] = {
-                "Content-Type": "application/json",
-            }
-            if credentials:
-                headers["Authorization"] = (
-                    f"Bearer {credentials.api_key.get_secret_value()}"
-                )
-
-            self._requests = Requests(
-                extra_headers=headers,
-                raise_for_status=False,
-            )
-
-    async def _handle_response(self, response) -> Any:
-        """
-        Handle API response and check for errors.
-
-        Args:
-            response: The response object from the request.
-
-        Returns:
-            The response data.
-
-        Raises:
-            EnrichlayerAPIException: If the API request fails.
-        """
-        if not response.ok:
-            try:
-                error_data = response.json()
-                error_message = error_data.get("message", "")
-            except JSONDecodeError:
-                error_message = response.text
-
-            raise EnrichlayerAPIException(
-                f"Enrichlayer API request failed ({response.status_code}): {error_message}",
-                response.status_code,
-            )
-
-        return response.json()
-
-    async def fetch_profile(
-        self,
-        linkedin_url: str,
-        fallback_to_cache: FallbackToCache = FallbackToCache.ON_ERROR,
-        use_cache: UseCache = UseCache.IF_PRESENT,
-        include_skills: bool = False,
-        include_inferred_salary: bool = False,
-        include_personal_email: bool = False,
-        include_personal_contact_number: bool = False,
-        include_social_media: bool = False,
-        include_extra: bool = False,
-    ) -> PersonProfileResponse:
-        """
-        Fetch a LinkedIn profile with optional parameters.
-
-        Args:
-            linkedin_url: The LinkedIn profile URL to fetch.
-            fallback_to_cache: Cache usage if live fetch fails ('on-error' or 'never').
-            use_cache: Cache utilization ('if-present' or 'never').
-            include_skills: Whether to include skills data.
-            include_inferred_salary: Whether to include inferred salary data.
-            include_personal_email: Whether to include personal email.
-            include_personal_contact_number: Whether to include personal contact number.
-            include_social_media: Whether to include social media profiles.
-            include_extra: Whether to include additional data.
-
-        Returns:
-            The LinkedIn profile data.
-
-        Raises:
-            EnrichlayerAPIException: If the API request fails.
-        """
-        params = {
-            "url": linkedin_url,
-            "fallback_to_cache": fallback_to_cache.value.lower(),
-            "use_cache": use_cache.value.lower(),
-        }
-
-        if include_skills:
-            params["skills"] = "include"
-        if include_inferred_salary:
-            params["inferred_salary"] = "include"
-        if include_personal_email:
-            params["personal_email"] = "include"
-        if include_personal_contact_number:
-            params["personal_contact_number"] = "include"
-        if include_social_media:
-            params["twitter_profile_id"] = "include"
-            params["facebook_profile_id"] = "include"
-            params["github_profile_id"] = "include"
-        if include_extra:
-            params["extra"] = "include"
-
-        response = await self._requests.get(
-            f"{self.API_BASE_URL}/profile", params=params
-        )
-        return PersonProfileResponse(**await self._handle_response(response))
-
-    async def lookup_person(
-        self,
-        first_name: str,
-        company_domain: str,
-        last_name: str | None = None,
-        location: Optional[str] = None,
-        title: Optional[str] = None,
-        include_similarity_checks: bool = False,
-        enrich_profile: bool = False,
-    ) -> PersonLookupResponse:
-        """
-        Look up a LinkedIn profile by person's information.
-
-        Args:
-            first_name: The person's first name.
-            last_name: The person's last name.
-            company_domain: The domain of the company they work for.
-            location: The person's location.
-            title: The person's job title.
-            include_similarity_checks: Whether to include similarity checks.
-            enrich_profile: Whether to enrich the profile.
-
-        Returns:
-            The LinkedIn profile lookup result.
-
-        Raises:
-            EnrichlayerAPIException: If the API request fails.
-        """
-        params = {"first_name": first_name, "company_domain": company_domain}
-
-        if last_name:
-            params["last_name"] = last_name
-        if location:
-            params["location"] = location
-        if title:
-            params["title"] = title
-        if include_similarity_checks:
-            params["similarity_checks"] = "include"
-        if enrich_profile:
-            params["enrich_profile"] = "enrich"
-
-        response = await self._requests.get(
-            f"{self.API_BASE_URL}/profile/resolve", params=params
-        )
-        return PersonLookupResponse(**await self._handle_response(response))
-
-    async def lookup_role(
-        self, role: str, company_name: str, enrich_profile: bool = False
-    ) -> RoleLookupResponse:
-        """
-        Look up a LinkedIn profile by role in a company.
-
-        Args:
-            role: The role title (e.g., CEO, CTO).
-            company_name: The name of the company.
-            enrich_profile: Whether to enrich the profile.
-
-        Returns:
-            The LinkedIn profile lookup result.
-
-        Raises:
-            EnrichlayerAPIException: If the API request fails.
-        """
-        params = {
-            "role": role,
-            "company_name": company_name,
-        }
-
-        if enrich_profile:
-            params["enrich_profile"] = "enrich"
-
-        response = await self._requests.get(
-            f"{self.API_BASE_URL}/find/company/role", params=params
-        )
-        return RoleLookupResponse(**await self._handle_response(response))
-
-    async def get_profile_picture(
-        self, linkedin_profile_url: str
-    ) -> ProfilePictureResponse:
-        """
-        Get a LinkedIn profile picture URL.
-
-        Args:
-            linkedin_profile_url: The LinkedIn profile URL.
-
-        Returns:
-            The profile picture URL.
-
-        Raises:
-            EnrichlayerAPIException: If the API request fails.
-        """
-        params = {
-            "linkedin_person_profile_url": linkedin_profile_url,
-        }
-
-        response = await self._requests.get(
-            f"{self.API_BASE_URL}/person/profile-picture", params=params
-        )
-        return ProfilePictureResponse(**await self._handle_response(response))

autogpt_platform/backend/backend/blocks/enrichlayer/_auth.py

@@ -1,34 +0,0 @@
-"""
-Authentication module for Enrichlayer API integration.
-
-This module provides credential types and test credentials for the Enrichlayer API.
-"""
-
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-# Define the type of credentials input expected for Enrichlayer API
-EnrichlayerCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.ENRICHLAYER], Literal["api_key"]
-]
-
-# Mock credentials for testing Enrichlayer API integration
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="1234a567-89bc-4def-ab12-3456cdef7890",
-    provider="enrichlayer",
-    api_key=SecretStr("mock-enrichlayer-api-key"),
-    title="Mock Enrichlayer API key",
-    expires_at=None,
-)
-
-# Dictionary representation of test credentials for input fields
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}

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

@@ -1,527 +0,0 @@
-"""
-Block definitions for Enrichlayer API integration.
-
-This module implements blocks for interacting with the Enrichlayer API,
-which provides access to LinkedIn profile data and related information.
-"""
-
-import logging
-from typing import Optional
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
-from backend.util.type import MediaFileType
-
-from ._api import (
-    EnrichlayerClient,
-    Experience,
-    FallbackToCache,
-    PersonLookupResponse,
-    PersonProfileResponse,
-    RoleLookupResponse,
-    UseCache,
-)
-from ._auth import TEST_CREDENTIALS, TEST_CREDENTIALS_INPUT, EnrichlayerCredentialsInput
-
-logger = logging.getLogger(__name__)
-
-
-class GetLinkedinProfileBlock(Block):
-    """Block to fetch LinkedIn profile data using Enrichlayer API."""
-
-    class Input(BlockSchema):
-        """Input schema for GetLinkedinProfileBlock."""
-
-        linkedin_url: str = SchemaField(
-            description="LinkedIn profile URL to fetch data from",
-            placeholder="https://www.linkedin.com/in/username/",
-        )
-        fallback_to_cache: FallbackToCache = SchemaField(
-            description="Cache usage if live fetch fails",
-            default=FallbackToCache.ON_ERROR,
-            advanced=True,
-        )
-        use_cache: UseCache = SchemaField(
-            description="Cache utilization strategy",
-            default=UseCache.IF_PRESENT,
-            advanced=True,
-        )
-        include_skills: bool = SchemaField(
-            description="Include skills data",
-            default=False,
-            advanced=True,
-        )
-        include_inferred_salary: bool = SchemaField(
-            description="Include inferred salary data",
-            default=False,
-            advanced=True,
-        )
-        include_personal_email: bool = SchemaField(
-            description="Include personal email",
-            default=False,
-            advanced=True,
-        )
-        include_personal_contact_number: bool = SchemaField(
-            description="Include personal contact number",
-            default=False,
-            advanced=True,
-        )
-        include_social_media: bool = SchemaField(
-            description="Include social media profiles",
-            default=False,
-            advanced=True,
-        )
-        include_extra: bool = SchemaField(
-            description="Include additional data",
-            default=False,
-            advanced=True,
-        )
-        credentials: EnrichlayerCredentialsInput = CredentialsField(
-            description="Enrichlayer API credentials"
-        )
-
-    class Output(BlockSchema):
-        """Output schema for GetLinkedinProfileBlock."""
-
-        profile: PersonProfileResponse = SchemaField(
-            description="LinkedIn profile data"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
-
-    def __init__(self):
-        """Initialize GetLinkedinProfileBlock."""
-        super().__init__(
-            id="f6e0ac73-4f1d-4acb-b4b7-b67066c5984e",
-            description="Fetch LinkedIn profile data using Enrichlayer",
-            categories={BlockCategory.SOCIAL},
-            input_schema=GetLinkedinProfileBlock.Input,
-            output_schema=GetLinkedinProfileBlock.Output,
-            test_input={
-                "linkedin_url": "https://www.linkedin.com/in/williamhgates/",
-                "include_skills": True,
-                "include_social_media": True,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "profile",
-                    PersonProfileResponse(
-                        public_identifier="williamhgates",
-                        full_name="Bill Gates",
-                        occupation="Co-chair at Bill & Melinda Gates Foundation",
-                        experiences=[
-                            Experience(
-                                company="Bill & Melinda Gates Foundation",
-                                title="Co-chair",
-                                starts_at={"year": 2000},
-                            )
-                        ],
-                    ),
-                )
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "_fetch_profile": lambda *args, **kwargs: PersonProfileResponse(
-                    public_identifier="williamhgates",
-                    full_name="Bill Gates",
-                    occupation="Co-chair at Bill & Melinda Gates Foundation",
-                    experiences=[
-                        Experience(
-                            company="Bill & Melinda Gates Foundation",
-                            title="Co-chair",
-                            starts_at={"year": 2000},
-                        )
-                    ],
-                ),
-            },
-        )
-
-    @staticmethod
-    async def _fetch_profile(
-        credentials: APIKeyCredentials,
-        linkedin_url: str,
-        fallback_to_cache: FallbackToCache = FallbackToCache.ON_ERROR,
-        use_cache: UseCache = UseCache.IF_PRESENT,
-        include_skills: bool = False,
-        include_inferred_salary: bool = False,
-        include_personal_email: bool = False,
-        include_personal_contact_number: bool = False,
-        include_social_media: bool = False,
-        include_extra: bool = False,
-    ):
-        client = EnrichlayerClient(credentials)
-        profile = await client.fetch_profile(
-            linkedin_url=linkedin_url,
-            fallback_to_cache=fallback_to_cache,
-            use_cache=use_cache,
-            include_skills=include_skills,
-            include_inferred_salary=include_inferred_salary,
-            include_personal_email=include_personal_email,
-            include_personal_contact_number=include_personal_contact_number,
-            include_social_media=include_social_media,
-            include_extra=include_extra,
-        )
-        return profile
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Run the block to fetch LinkedIn profile data.
-
-        Args:
-            input_data: Input parameters for the block
-            credentials: API key credentials for Enrichlayer
-            **kwargs: Additional keyword arguments
-
-        Yields:
-            Tuples of (output_name, output_value)
-        """
-        try:
-            profile = await self._fetch_profile(
-                credentials=credentials,
-                linkedin_url=input_data.linkedin_url,
-                fallback_to_cache=input_data.fallback_to_cache,
-                use_cache=input_data.use_cache,
-                include_skills=input_data.include_skills,
-                include_inferred_salary=input_data.include_inferred_salary,
-                include_personal_email=input_data.include_personal_email,
-                include_personal_contact_number=input_data.include_personal_contact_number,
-                include_social_media=input_data.include_social_media,
-                include_extra=input_data.include_extra,
-            )
-            yield "profile", profile
-        except Exception as e:
-            logger.error(f"Error fetching LinkedIn profile: {str(e)}")
-            yield "error", str(e)
-
-
-class LinkedinPersonLookupBlock(Block):
-    """Block to look up LinkedIn profiles by person's information using Enrichlayer API."""
-
-    class Input(BlockSchema):
-        """Input schema for LinkedinPersonLookupBlock."""
-
-        first_name: str = SchemaField(
-            description="Person's first name",
-            placeholder="John",
-            advanced=False,
-        )
-        last_name: str | None = SchemaField(
-            description="Person's last name",
-            placeholder="Doe",
-            default=None,
-            advanced=False,
-        )
-        company_domain: str = SchemaField(
-            description="Domain of the company they work for (optional)",
-            placeholder="example.com",
-            advanced=False,
-        )
-        location: Optional[str] = SchemaField(
-            description="Person's location (optional)",
-            placeholder="San Francisco",
-            default=None,
-        )
-        title: Optional[str] = SchemaField(
-            description="Person's job title (optional)",
-            placeholder="CEO",
-            default=None,
-        )
-        include_similarity_checks: bool = SchemaField(
-            description="Include similarity checks",
-            default=False,
-            advanced=True,
-        )
-        enrich_profile: bool = SchemaField(
-            description="Enrich the profile with additional data",
-            default=False,
-            advanced=True,
-        )
-        credentials: EnrichlayerCredentialsInput = CredentialsField(
-            description="Enrichlayer API credentials"
-        )
-
-    class Output(BlockSchema):
-        """Output schema for LinkedinPersonLookupBlock."""
-
-        lookup_result: PersonLookupResponse = SchemaField(
-            description="LinkedIn profile lookup result"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
-
-    def __init__(self):
-        """Initialize LinkedinPersonLookupBlock."""
-        super().__init__(
-            id="d237a98a-5c4b-4a1c-b9e3-e6f9a6c81df7",
-            description="Look up LinkedIn profiles by person information using Enrichlayer",
-            categories={BlockCategory.SOCIAL},
-            input_schema=LinkedinPersonLookupBlock.Input,
-            output_schema=LinkedinPersonLookupBlock.Output,
-            test_input={
-                "first_name": "Bill",
-                "last_name": "Gates",
-                "company_domain": "gatesfoundation.org",
-                "include_similarity_checks": True,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "lookup_result",
-                    PersonLookupResponse(
-                        url="https://www.linkedin.com/in/williamhgates/",
-                        name_similarity_score=0.93,
-                        company_similarity_score=0.83,
-                        title_similarity_score=0.3,
-                        location_similarity_score=0.20,
-                    ),
-                )
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "_lookup_person": lambda *args, **kwargs: PersonLookupResponse(
-                    url="https://www.linkedin.com/in/williamhgates/",
-                    name_similarity_score=0.93,
-                    company_similarity_score=0.83,
-                    title_similarity_score=0.3,
-                    location_similarity_score=0.20,
-                )
-            },
-        )
-
-    @staticmethod
-    async def _lookup_person(
-        credentials: APIKeyCredentials,
-        first_name: str,
-        company_domain: str,
-        last_name: str | None = None,
-        location: Optional[str] = None,
-        title: Optional[str] = None,
-        include_similarity_checks: bool = False,
-        enrich_profile: bool = False,
-    ):
-        client = EnrichlayerClient(credentials=credentials)
-        lookup_result = await client.lookup_person(
-            first_name=first_name,
-            last_name=last_name,
-            company_domain=company_domain,
-            location=location,
-            title=title,
-            include_similarity_checks=include_similarity_checks,
-            enrich_profile=enrich_profile,
-        )
-        return lookup_result
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Run the block to look up LinkedIn profiles.
-
-        Args:
-            input_data: Input parameters for the block
-            credentials: API key credentials for Enrichlayer
-            **kwargs: Additional keyword arguments
-
-        Yields:
-            Tuples of (output_name, output_value)
-        """
-        try:
-            lookup_result = await self._lookup_person(
-                credentials=credentials,
-                first_name=input_data.first_name,
-                last_name=input_data.last_name,
-                company_domain=input_data.company_domain,
-                location=input_data.location,
-                title=input_data.title,
-                include_similarity_checks=input_data.include_similarity_checks,
-                enrich_profile=input_data.enrich_profile,
-            )
-            yield "lookup_result", lookup_result
-        except Exception as e:
-            logger.error(f"Error looking up LinkedIn profile: {str(e)}")
-            yield "error", str(e)
-
-
-class LinkedinRoleLookupBlock(Block):
-    """Block to look up LinkedIn profiles by role in a company using Enrichlayer API."""
-
-    class Input(BlockSchema):
-        """Input schema for LinkedinRoleLookupBlock."""
-
-        role: str = SchemaField(
-            description="Role title (e.g., CEO, CTO)",
-            placeholder="CEO",
-        )
-        company_name: str = SchemaField(
-            description="Name of the company",
-            placeholder="Microsoft",
-        )
-        enrich_profile: bool = SchemaField(
-            description="Enrich the profile with additional data",
-            default=False,
-            advanced=True,
-        )
-        credentials: EnrichlayerCredentialsInput = CredentialsField(
-            description="Enrichlayer API credentials"
-        )
-
-    class Output(BlockSchema):
-        """Output schema for LinkedinRoleLookupBlock."""
-
-        role_lookup_result: RoleLookupResponse = SchemaField(
-            description="LinkedIn role lookup result"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
-
-    def __init__(self):
-        """Initialize LinkedinRoleLookupBlock."""
-        super().__init__(
-            id="3b9fc742-06d4-49c7-b5ce-7e302dd7c8a7",
-            description="Look up LinkedIn profiles by role in a company using Enrichlayer",
-            categories={BlockCategory.SOCIAL},
-            input_schema=LinkedinRoleLookupBlock.Input,
-            output_schema=LinkedinRoleLookupBlock.Output,
-            test_input={
-                "role": "Co-chair",
-                "company_name": "Gates Foundation",
-                "enrich_profile": True,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "role_lookup_result",
-                    RoleLookupResponse(
-                        linkedin_profile_url="https://www.linkedin.com/in/williamhgates/",
-                    ),
-                )
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "_lookup_role": lambda *args, **kwargs: RoleLookupResponse(
-                    linkedin_profile_url="https://www.linkedin.com/in/williamhgates/",
-                ),
-            },
-        )
-
-    @staticmethod
-    async def _lookup_role(
-        credentials: APIKeyCredentials,
-        role: str,
-        company_name: str,
-        enrich_profile: bool = False,
-    ):
-        client = EnrichlayerClient(credentials=credentials)
-        role_lookup_result = await client.lookup_role(
-            role=role,
-            company_name=company_name,
-            enrich_profile=enrich_profile,
-        )
-        return role_lookup_result
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Run the block to look up LinkedIn profiles by role.
-
-        Args:
-            input_data: Input parameters for the block
-            credentials: API key credentials for Enrichlayer
-            **kwargs: Additional keyword arguments
-
-        Yields:
-            Tuples of (output_name, output_value)
-        """
-        try:
-            role_lookup_result = await self._lookup_role(
-                credentials=credentials,
-                role=input_data.role,
-                company_name=input_data.company_name,
-                enrich_profile=input_data.enrich_profile,
-            )
-            yield "role_lookup_result", role_lookup_result
-        except Exception as e:
-            logger.error(f"Error looking up role in company: {str(e)}")
-            yield "error", str(e)
-
-
-class GetLinkedinProfilePictureBlock(Block):
-    """Block to get LinkedIn profile pictures using Enrichlayer API."""
-
-    class Input(BlockSchema):
-        """Input schema for GetLinkedinProfilePictureBlock."""
-
-        linkedin_profile_url: str = SchemaField(
-            description="LinkedIn profile URL",
-            placeholder="https://www.linkedin.com/in/username/",
-        )
-        credentials: EnrichlayerCredentialsInput = CredentialsField(
-            description="Enrichlayer API credentials"
-        )
-
-    class Output(BlockSchema):
-        """Output schema for GetLinkedinProfilePictureBlock."""
-
-        profile_picture_url: MediaFileType = SchemaField(
-            description="LinkedIn profile picture URL"
-        )
-        error: str = SchemaField(description="Error message if the request failed")
-
-    def __init__(self):
-        """Initialize GetLinkedinProfilePictureBlock."""
-        super().__init__(
-            id="68d5a942-9b3f-4e9a-b7c1-d96ea4321f0d",
-            description="Get LinkedIn profile pictures using Enrichlayer",
-            categories={BlockCategory.SOCIAL},
-            input_schema=GetLinkedinProfilePictureBlock.Input,
-            output_schema=GetLinkedinProfilePictureBlock.Output,
-            test_input={
-                "linkedin_profile_url": "https://www.linkedin.com/in/williamhgates/",
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                (
-                    "profile_picture_url",
-                    "https://media.licdn.com/dms/image/C4D03AQFj-xjuXrLFSQ/profile-displayphoto-shrink_800_800/0/1576881858598?e=1686787200&v=beta&t=zrQC76QwsfQQIWthfOnrKRBMZ5D-qIAvzLXLmWgYvTk",
-                )
-            ],
-            test_credentials=TEST_CREDENTIALS,
-            test_mock={
-                "_get_profile_picture": lambda *args, **kwargs: "https://media.licdn.com/dms/image/C4D03AQFj-xjuXrLFSQ/profile-displayphoto-shrink_800_800/0/1576881858598?e=1686787200&v=beta&t=zrQC76QwsfQQIWthfOnrKRBMZ5D-qIAvzLXLmWgYvTk",
-            },
-        )
-
-    @staticmethod
-    async def _get_profile_picture(
-        credentials: APIKeyCredentials, linkedin_profile_url: str
-    ):
-        client = EnrichlayerClient(credentials=credentials)
-        profile_picture_response = await client.get_profile_picture(
-            linkedin_profile_url=linkedin_profile_url,
-        )
-        return profile_picture_response.profile_picture_url
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        """
-        Run the block to get LinkedIn profile pictures.
-
-        Args:
-            input_data: Input parameters for the block
-            credentials: API key credentials for Enrichlayer
-            **kwargs: Additional keyword arguments
-
-        Yields:
-            Tuples of (output_name, output_value)
-        """
-        try:
-            profile_picture = await self._get_profile_picture(
-                credentials=credentials,
-                linkedin_profile_url=input_data.linkedin_profile_url,
-            )
-            yield "profile_picture_url", profile_picture
-        except Exception as e:
-            logger.error(f"Error getting profile picture: {str(e)}")
-            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/websets.py

@@ -93,11 +93,11 @@ class Webset(BaseModel):
     """
     Set of key-value pairs you want to associate with this object.
     """
-    created_at: Annotated[datetime | None, Field(alias="createdAt")] = None
+    created_at: Annotated[datetime, Field(alias="createdAt")] | None = None
     """
     The date and time the webset was created
     """
-    updated_at: Annotated[datetime | None, Field(alias="updatedAt")] = None
+    updated_at: Annotated[datetime, Field(alias="updatedAt")] | None = None
     """
     The date and time the webset was last updated
     """

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

@@ -29,8 +29,8 @@ class FirecrawlExtractBlock(Block):
         prompt: str | None = SchemaField(
             description="The prompt to use for the crawl", default=None, advanced=False
         )
-        output_schema: dict | None = SchemaField(
-            description="A Json Schema describing the output structure if more rigid structure is desired.",
+        output_schema: str | None = SchemaField(
+            description="A more rigid structure if you already know the JSON layout.",
             default=None,
         )
         enable_web_search: bool = SchemaField(
@@ -56,6 +56,7 @@ class FirecrawlExtractBlock(Block):
 
         app = FirecrawlApp(api_key=credentials.api_key.get_secret_value())
 
+        # Sync call
         extract_result = app.extract(
             urls=input_data.urls,
             prompt=input_data.prompt,

autogpt_platform/backend/backend/blocks/ideogram.py

@@ -30,7 +30,6 @@ TEST_CREDENTIALS_INPUT = {
 
 
 class IdeogramModelName(str, Enum):
-    V3 = "V_3"
     V2 = "V_2"
     V1 = "V_1"
     V1_TURBO = "V_1_TURBO"
@@ -96,8 +95,8 @@ class IdeogramModelBlock(Block):
             title="Prompt",
         )
         ideogram_model_name: IdeogramModelName = SchemaField(
-            description="The name of the Image Generation Model, e.g., V_3",
-            default=IdeogramModelName.V3,
+            description="The name of the Image Generation Model, e.g., V_2",
+            default=IdeogramModelName.V2,
             title="Image Generation Model",
             advanced=False,
         )
@@ -237,111 +236,6 @@ class IdeogramModelBlock(Block):
         negative_prompt: Optional[str],
         color_palette_name: str,
         custom_colors: Optional[list[str]],
-    ):
-        # Use V3 endpoint for V3 model, legacy endpoint for others
-        if model_name == "V_3":
-            return await self._run_model_v3(
-                api_key,
-                prompt,
-                seed,
-                aspect_ratio,
-                magic_prompt_option,
-                style_type,
-                negative_prompt,
-                color_palette_name,
-                custom_colors,
-            )
-        else:
-            return await self._run_model_legacy(
-                api_key,
-                model_name,
-                prompt,
-                seed,
-                aspect_ratio,
-                magic_prompt_option,
-                style_type,
-                negative_prompt,
-                color_palette_name,
-                custom_colors,
-            )
-
-    async def _run_model_v3(
-        self,
-        api_key: SecretStr,
-        prompt: str,
-        seed: Optional[int],
-        aspect_ratio: str,
-        magic_prompt_option: str,
-        style_type: str,
-        negative_prompt: Optional[str],
-        color_palette_name: str,
-        custom_colors: Optional[list[str]],
-    ):
-        url = "https://api.ideogram.ai/v1/ideogram-v3/generate"
-        headers = {
-            "Api-Key": api_key.get_secret_value(),
-            "Content-Type": "application/json",
-        }
-
-        # Map legacy aspect ratio values to V3 format
-        aspect_ratio_map = {
-            "ASPECT_10_16": "10x16",
-            "ASPECT_16_10": "16x10",
-            "ASPECT_9_16": "9x16",
-            "ASPECT_16_9": "16x9",
-            "ASPECT_3_2": "3x2",
-            "ASPECT_2_3": "2x3",
-            "ASPECT_4_3": "4x3",
-            "ASPECT_3_4": "3x4",
-            "ASPECT_1_1": "1x1",
-            "ASPECT_1_3": "1x3",
-            "ASPECT_3_1": "3x1",
-            # Additional V3 supported ratios
-            "ASPECT_1_2": "1x2",
-            "ASPECT_2_1": "2x1",
-            "ASPECT_4_5": "4x5",
-            "ASPECT_5_4": "5x4",
-        }
-
-        v3_aspect_ratio = aspect_ratio_map.get(
-            aspect_ratio, "1x1"
-        )  # Default to 1x1 if not found
-
-        # Use JSON for V3 endpoint (simpler than multipart/form-data)
-        data: Dict[str, Any] = {
-            "prompt": prompt,
-            "aspect_ratio": v3_aspect_ratio,
-            "magic_prompt": magic_prompt_option,
-            "style_type": style_type,
-        }
-
-        if seed is not None:
-            data["seed"] = seed
-
-        if negative_prompt:
-            data["negative_prompt"] = negative_prompt
-
-        # Note: V3 endpoint may have different color palette support
-        # For now, we'll omit color palettes for V3 to avoid errors
-
-        try:
-            response = await Requests().post(url, headers=headers, json=data)
-            return response.json()["data"][0]["url"]
-        except RequestException as e:
-            raise Exception(f"Failed to fetch image with V3 endpoint: {str(e)}")
-
-    async def _run_model_legacy(
-        self,
-        api_key: SecretStr,
-        model_name: str,
-        prompt: str,
-        seed: Optional[int],
-        aspect_ratio: str,
-        magic_prompt_option: str,
-        style_type: str,
-        negative_prompt: Optional[str],
-        color_palette_name: str,
-        custom_colors: Optional[list[str]],
     ):
         url = "https://api.ideogram.ai/generate"
         headers = {
@@ -355,33 +249,28 @@ class IdeogramModelBlock(Block):
                 "model": model_name,
                 "aspect_ratio": aspect_ratio,
                 "magic_prompt_option": magic_prompt_option,
+                "style_type": style_type,
             }
         }
 
-        # Only add style_type for V2, V2_TURBO, and V3 models (V1 models don't support it)
-        if model_name in ["V_2", "V_2_TURBO", "V_3"]:
-            data["image_request"]["style_type"] = style_type
-
         if seed is not None:
             data["image_request"]["seed"] = seed
 
         if negative_prompt:
             data["image_request"]["negative_prompt"] = negative_prompt
 
-        # Only add color palette for V2 and V2_TURBO models (V1 models don't support it)
-        if model_name in ["V_2", "V_2_TURBO"]:
-            if color_palette_name != "NONE":
-                data["color_palette"] = {"name": color_palette_name}
-            elif custom_colors:
-                data["color_palette"] = {
-                    "members": [{"color_hex": color} for color in custom_colors]
-                }
+        if color_palette_name != "NONE":
+            data["color_palette"] = {"name": color_palette_name}
+        elif custom_colors:
+            data["color_palette"] = {
+                "members": [{"color_hex": color} for color in custom_colors]
+            }
 
         try:
             response = await Requests().post(url, headers=headers, json=data)
             return response.json()["data"][0]["url"]
         except RequestException as e:
-            raise Exception(f"Failed to fetch image with legacy endpoint: {str(e)}")
+            raise Exception(f"Failed to fetch image: {str(e)}")
 
     async def upscale_image(self, api_key: SecretStr, image_url: str):
         url = "https://api.ideogram.ai/upscale"

autogpt_platform/backend/backend/blocks/llm.py

@@ -37,7 +37,6 @@ LLMProviderName = Literal[
     ProviderName.OPENAI,
     ProviderName.OPEN_ROUTER,
     ProviderName.LLAMA_API,
-    ProviderName.V0,
 ]
 AICredentials = CredentialsMetaInput[LLMProviderName, Literal["api_key"]]
 
@@ -156,10 +155,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
     LLAMA_API_LLAMA4_MAVERICK = "Llama-4-Maverick-17B-128E-Instruct-FP8"
     LLAMA_API_LLAMA3_3_8B = "Llama-3.3-8B-Instruct"
     LLAMA_API_LLAMA3_3_70B = "Llama-3.3-70B-Instruct"
-    # v0 by Vercel models
-    V0_1_5_MD = "v0-1.5-md"
-    V0_1_5_LG = "v0-1.5-lg"
-    V0_1_0_MD = "v0-1.0-md"
 
     @property
     def metadata(self) -> ModelMetadata:
@@ -285,10 +280,6 @@ MODEL_METADATA = {
     LlmModel.LLAMA_API_LLAMA4_MAVERICK: ModelMetadata("llama_api", 128000, 4028),
     LlmModel.LLAMA_API_LLAMA3_3_8B: ModelMetadata("llama_api", 128000, 4028),
     LlmModel.LLAMA_API_LLAMA3_3_70B: ModelMetadata("llama_api", 128000, 4028),
-    # v0 by Vercel models
-    LlmModel.V0_1_5_MD: ModelMetadata("v0", 128000, 64000),
-    LlmModel.V0_1_5_LG: ModelMetadata("v0", 512000, 64000),
-    LlmModel.V0_1_0_MD: ModelMetadata("v0", 128000, 64000),
 }
 
 for model in LlmModel:
@@ -685,11 +676,7 @@ async def llm_call(
         client = openai.OpenAI(
             base_url="https://api.aimlapi.com/v2",
             api_key=credentials.api_key.get_secret_value(),
-            default_headers={
-                "X-Project": "AutoGPT",
-                "X-Title": "AutoGPT",
-                "HTTP-Referer": "https://github.com/Significant-Gravitas/AutoGPT",
-            },
+            default_headers={"X-Project": "AutoGPT"},
         )
 
         completion = client.chat.completions.create(
@@ -709,42 +696,6 @@ async def llm_call(
             ),
             reasoning=None,
         )
-    elif provider == "v0":
-        tools_param = tools if tools else openai.NOT_GIVEN
-        client = openai.AsyncOpenAI(
-            base_url="https://api.v0.dev/v1",
-            api_key=credentials.api_key.get_secret_value(),
-        )
-
-        response_format = None
-        if json_format:
-            response_format = {"type": "json_object"}
-
-        parallel_tool_calls_param = get_parallel_tool_calls_param(
-            llm_model, parallel_tool_calls
-        )
-
-        response = await client.chat.completions.create(
-            model=llm_model.value,
-            messages=prompt,  # type: ignore
-            response_format=response_format,  # type: ignore
-            max_tokens=max_tokens,
-            tools=tools_param,  # type: ignore
-            parallel_tool_calls=parallel_tool_calls_param,
-        )
-
-        tool_calls = extract_openai_tool_calls(response)
-        reasoning = extract_openai_reasoning(response)
-
-        return LLMResponse(
-            raw_response=response.choices[0].message,
-            prompt=prompt,
-            response=response.choices[0].message.content or "",
-            tool_calls=tool_calls,
-            prompt_tokens=response.usage.prompt_tokens if response.usage else 0,
-            completion_tokens=response.usage.completion_tokens if response.usage else 0,
-            reasoning=reasoning,
-        )
     else:
         raise ValueError(f"Unsupported LLM provider: {provider}")
 

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -291,32 +291,9 @@ class SmartDecisionMakerBlock(Block):
 
         for link in links:
             sink_name = SmartDecisionMakerBlock.cleanup(link.sink_name)
-
-            # Handle dynamic fields (e.g., values_#_*, items_$_*, etc.)
-            # These are fields that get merged by the executor into their base field
-            if (
-                "_#_" in link.sink_name
-                or "_$_" in link.sink_name
-                or "_@_" in link.sink_name
-            ):
-                # For dynamic fields, provide a generic string schema
-                # The executor will handle merging these into the appropriate structure
-                properties[sink_name] = {
-                    "type": "string",
-                    "description": f"Dynamic value for {link.sink_name}",
-                }
-            else:
-                # For regular fields, use the block's schema
-                try:
-                    properties[sink_name] = sink_block_input_schema.get_field_schema(
-                        link.sink_name
-                    )
-                except (KeyError, AttributeError):
-                    # If the field doesn't exist in the schema, provide a generic schema
-                    properties[sink_name] = {
-                        "type": "string",
-                        "description": f"Value for {link.sink_name}",
-                    }
+            properties[sink_name] = sink_block_input_schema.get_field_schema(
+                link.sink_name
+            )
 
         tool_function["parameters"] = {
             **block.input_schema.jsonschema(),
@@ -501,6 +478,10 @@ class SmartDecisionMakerBlock(Block):
                 }
             )
             prompt.extend(tool_output)
+        if input_data.multiple_tool_calls:
+            input_data.sys_prompt += "\nYou can call a tool (different tools) multiple times in a single response."
+        else:
+            input_data.sys_prompt += "\nOnly provide EXACTLY one function call, multiple tool calls is strictly prohibited."
 
         values = input_data.prompt_values
         if values:

autogpt_platform/backend/backend/blocks/stagehand/_config.py

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

autogpt_platform/backend/backend/blocks/stagehand/blocks.py

@@ -1,393 +0,0 @@
-import logging
-import signal
-import threading
-from contextlib import contextmanager
-from enum import Enum
-
-# Monkey patch Stagehands to prevent signal handling in worker threads
-import stagehand.main
-from stagehand import Stagehand
-
-from backend.blocks.llm import (
-    MODEL_METADATA,
-    AICredentials,
-    AICredentialsField,
-    LlmModel,
-    ModelMetadata,
-)
-from backend.blocks.stagehand._config import stagehand as stagehand_provider
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-# Store the original method
-original_register_signal_handlers = stagehand.main.Stagehand._register_signal_handlers
-
-
-def safe_register_signal_handlers(self):
-    """Only register signal handlers in the main thread"""
-    if threading.current_thread() is threading.main_thread():
-        original_register_signal_handlers(self)
-    else:
-        # Skip signal handling in worker threads
-        pass
-
-
-# Replace the method
-stagehand.main.Stagehand._register_signal_handlers = safe_register_signal_handlers
-
-
-@contextmanager
-def disable_signal_handling():
-    """Context manager to temporarily disable signal handling"""
-    if threading.current_thread() is not threading.main_thread():
-        # In worker threads, temporarily replace signal.signal with a no-op
-        original_signal = signal.signal
-
-        def noop_signal(*args, **kwargs):
-            pass
-
-        signal.signal = noop_signal
-        try:
-            yield
-        finally:
-            signal.signal = original_signal
-    else:
-        # In main thread, don't modify anything
-        yield
-
-
-logger = logging.getLogger(__name__)
-
-
-class StagehandRecommendedLlmModel(str, Enum):
-    """
-    This is subset of LLModel from autogpt_platform/backend/backend/blocks/llm.py
-
-    It contains only the models recommended by Stagehand
-    """
-
-    # OpenAI
-    GPT41 = "gpt-4.1-2025-04-14"
-    GPT41_MINI = "gpt-4.1-mini-2025-04-14"
-
-    # Anthropic
-    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
-
-    @property
-    def provider_name(self) -> str:
-        """
-        Returns the provider name for the model in the required format for Stagehand:
-        provider/model_name
-        """
-        model_metadata = MODEL_METADATA[LlmModel(self.value)]
-        model_name = self.value
-
-        if len(model_name.split("/")) == 1 and not self.value.startswith(
-            model_metadata.provider
-        ):
-            assert (
-                model_metadata.provider != "open_router"
-            ), "Logic failed and open_router provider attempted to be prepended to model name! in stagehand/_config.py"
-            model_name = f"{model_metadata.provider}/{model_name}"
-
-        logger.error(f"Model name: {model_name}")
-        return model_name
-
-    @property
-    def provider(self) -> str:
-        return MODEL_METADATA[LlmModel(self.value)].provider
-
-    @property
-    def metadata(self) -> ModelMetadata:
-        return MODEL_METADATA[LlmModel(self.value)]
-
-    @property
-    def context_window(self) -> int:
-        return MODEL_METADATA[LlmModel(self.value)].context_window
-
-    @property
-    def max_output_tokens(self) -> int | None:
-        return MODEL_METADATA[LlmModel(self.value)].max_output_tokens
-
-
-class StagehandObserveBlock(Block):
-    class Input(BlockSchema):
-        # Browserbase credentials (Stagehand provider) or raw API key
-        stagehand_credentials: CredentialsMetaInput = (
-            stagehand_provider.credentials_field(
-                description="Stagehand/Browserbase API key"
-            )
-        )
-        browserbase_project_id: str = SchemaField(
-            description="Browserbase project ID (required if using Browserbase)",
-        )
-        # Model selection and credentials (provider-discriminated like llm.py)
-        model: StagehandRecommendedLlmModel = SchemaField(
-            title="LLM Model",
-            description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
-            advanced=False,
-        )
-        model_credentials: AICredentials = AICredentialsField()
-        url: str = SchemaField(
-            description="URL to navigate to.",
-        )
-        instruction: str = SchemaField(
-            description="Natural language description of elements or actions to discover.",
-        )
-        iframes: bool = SchemaField(
-            description="Whether to search within iframes. If True, Stagehand will search for actions within iframes.",
-            default=True,
-        )
-        domSettleTimeoutMs: int = SchemaField(
-            description="Timeout in milliseconds for DOM settlement.Wait longer for dynamic content",
-            default=45000,
-        )
-
-    class Output(BlockSchema):
-        selector: str = SchemaField(description="XPath selector to locate element.")
-        description: str = SchemaField(description="Human-readable description")
-        method: str | None = SchemaField(description="Suggested action method")
-        arguments: list[str] | None = SchemaField(
-            description="Additional action parameters"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="d3863944-0eaf-45c4-a0c9-63e0fe1ee8b9",
-            description="Find suggested actions for your workflows",
-            categories={BlockCategory.AI, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=StagehandObserveBlock.Input,
-            output_schema=StagehandObserveBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        stagehand_credentials: APIKeyCredentials,
-        model_credentials: APIKeyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-
-        logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}")
-        logger.info(
-            f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
-
-        with disable_signal_handling():
-            stagehand = Stagehand(
-                api_key=stagehand_credentials.api_key.get_secret_value(),
-                project_id=input_data.browserbase_project_id,
-                model_name=input_data.model.provider_name,
-                model_api_key=model_credentials.api_key.get_secret_value(),
-            )
-
-            await stagehand.init()
-
-        page = stagehand.page
-
-        assert page is not None, "Stagehand page is not initialized"
-
-        await page.goto(input_data.url)
-
-        observe_results = await page.observe(
-            input_data.instruction,
-            iframes=input_data.iframes,
-            domSettleTimeoutMs=input_data.domSettleTimeoutMs,
-        )
-        for result in observe_results:
-            yield "selector", result.selector
-            yield "description", result.description
-            yield "method", result.method
-            yield "arguments", result.arguments
-
-
-class StagehandActBlock(Block):
-    class Input(BlockSchema):
-        # Browserbase credentials (Stagehand provider) or raw API key
-        stagehand_credentials: CredentialsMetaInput = (
-            stagehand_provider.credentials_field(
-                description="Stagehand/Browserbase API key"
-            )
-        )
-        browserbase_project_id: str = SchemaField(
-            description="Browserbase project ID (required if using Browserbase)",
-        )
-        # Model selection and credentials (provider-discriminated like llm.py)
-        model: StagehandRecommendedLlmModel = SchemaField(
-            title="LLM Model",
-            description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
-            advanced=False,
-        )
-        model_credentials: AICredentials = AICredentialsField()
-        url: str = SchemaField(
-            description="URL to navigate to.",
-        )
-        action: list[str] = SchemaField(
-            description="Action to perform. Suggested actions are: click, fill, type, press, scroll, select from dropdown. For multi-step actions, add an entry for each step.",
-        )
-        variables: dict[str, str] = SchemaField(
-            description="Variables to use in the action. Variables contains data you want the action to use.",
-            default_factory=dict,
-        )
-        iframes: bool = SchemaField(
-            description="Whether to search within iframes. If True, Stagehand will search for actions within iframes.",
-            default=True,
-        )
-        domSettleTimeoutMs: int = SchemaField(
-            description="Timeout in milliseconds for DOM settlement.Wait longer for dynamic content",
-            default=45000,
-        )
-        timeoutMs: int = SchemaField(
-            description="Timeout in milliseconds for DOM ready. Extended timeout for slow-loading forms",
-            default=60000,
-        )
-
-    class Output(BlockSchema):
-        success: bool = SchemaField(
-            description="Whether the action was completed successfully"
-        )
-        message: str = SchemaField(description="Details about the action’s execution.")
-        action: str = SchemaField(description="Action performed")
-
-    def __init__(self):
-        super().__init__(
-            id="86eba68b-9549-4c0b-a0db-47d85a56cc27",
-            description="Interact with a web page by performing actions on a web page. Use it to build self-healing and deterministic automations that adapt to website chang.",
-            categories={BlockCategory.AI, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=StagehandActBlock.Input,
-            output_schema=StagehandActBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        stagehand_credentials: APIKeyCredentials,
-        model_credentials: APIKeyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-
-        logger.info(f"ACT: Stagehand credentials: {stagehand_credentials}")
-        logger.info(
-            f"ACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
-
-        with disable_signal_handling():
-            stagehand = Stagehand(
-                api_key=stagehand_credentials.api_key.get_secret_value(),
-                project_id=input_data.browserbase_project_id,
-                model_name=input_data.model.provider_name,
-                model_api_key=model_credentials.api_key.get_secret_value(),
-            )
-
-            await stagehand.init()
-
-        page = stagehand.page
-
-        assert page is not None, "Stagehand page is not initialized"
-
-        await page.goto(input_data.url)
-        for action in input_data.action:
-            action_results = await page.act(
-                action,
-                variables=input_data.variables,
-                iframes=input_data.iframes,
-                domSettleTimeoutMs=input_data.domSettleTimeoutMs,
-                timeoutMs=input_data.timeoutMs,
-            )
-            yield "success", action_results.success
-            yield "message", action_results.message
-            yield "action", action_results.action
-
-
-class StagehandExtractBlock(Block):
-    class Input(BlockSchema):
-        # Browserbase credentials (Stagehand provider) or raw API key
-        stagehand_credentials: CredentialsMetaInput = (
-            stagehand_provider.credentials_field(
-                description="Stagehand/Browserbase API key"
-            )
-        )
-        browserbase_project_id: str = SchemaField(
-            description="Browserbase project ID (required if using Browserbase)",
-        )
-        # Model selection and credentials (provider-discriminated like llm.py)
-        model: StagehandRecommendedLlmModel = SchemaField(
-            title="LLM Model",
-            description="LLM to use for Stagehand (provider is inferred)",
-            default=StagehandRecommendedLlmModel.CLAUDE_3_7_SONNET,
-            advanced=False,
-        )
-        model_credentials: AICredentials = AICredentialsField()
-        url: str = SchemaField(
-            description="URL to navigate to.",
-        )
-        instruction: str = SchemaField(
-            description="Natural language description of elements or actions to discover.",
-        )
-        iframes: bool = SchemaField(
-            description="Whether to search within iframes. If True, Stagehand will search for actions within iframes.",
-            default=True,
-        )
-        domSettleTimeoutMs: int = SchemaField(
-            description="Timeout in milliseconds for DOM settlement.Wait longer for dynamic content",
-            default=45000,
-        )
-
-    class Output(BlockSchema):
-        extraction: str = SchemaField(description="Extracted data from the page.")
-
-    def __init__(self):
-        super().__init__(
-            id="fd3c0b18-2ba6-46ae-9339-fcb40537ad98",
-            description="Extract structured data from a webpage.",
-            categories={BlockCategory.AI, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=StagehandExtractBlock.Input,
-            output_schema=StagehandExtractBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        stagehand_credentials: APIKeyCredentials,
-        model_credentials: APIKeyCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-
-        logger.info(f"EXTRACT: Stagehand credentials: {stagehand_credentials}")
-        logger.info(
-            f"EXTRACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
-        )
-
-        with disable_signal_handling():
-            stagehand = Stagehand(
-                api_key=stagehand_credentials.api_key.get_secret_value(),
-                project_id=input_data.browserbase_project_id,
-                model_name=input_data.model.provider_name,
-                model_api_key=model_credentials.api_key.get_secret_value(),
-            )
-
-            await stagehand.init()
-
-        page = stagehand.page
-
-        assert page is not None, "Stagehand page is not initialized"
-
-        await page.goto(input_data.url)
-        extraction = await page.extract(
-            input_data.instruction,
-            iframes=input_data.iframes,
-            domSettleTimeoutMs=input_data.domSettleTimeoutMs,
-        )
-        yield "extraction", str(extraction.model_dump()["extraction"])

autogpt_platform/backend/backend/blocks/system/__init__.py

@@ -1 +0,0 @@
-

autogpt_platform/backend/backend/blocks/system/library_operations.py

@@ -1,283 +0,0 @@
-import logging
-from typing import Any
-
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-from backend.util.clients import get_database_manager_async_client
-
-logger = logging.getLogger(__name__)
-
-
-# Duplicate pydantic models for store data so we don't accidently change the data shape in the blocks unintentionally when editing the backend
-class LibraryAgent(BaseModel):
-    """Model representing an agent in the user's library."""
-
-    library_agent_id: str = ""
-    agent_id: str = ""
-    agent_version: int = 0
-    agent_name: str = ""
-    description: str = ""
-    creator: str = ""
-    is_archived: bool = False
-    categories: list[str] = []
-
-
-class AddToLibraryFromStoreBlock(Block):
-    """
-    Block that adds an agent from the store to the user's library.
-    This enables users to easily import agents from the marketplace into their personal collection.
-    """
-
-    class Input(BlockSchema):
-        store_listing_version_id: str = SchemaField(
-            description="The ID of the store listing version to add to library"
-        )
-        agent_name: str | None = SchemaField(
-            description="Optional custom name for the agent in your library",
-            default=None,
-        )
-
-    class Output(BlockSchema):
-        success: bool = SchemaField(
-            description="Whether the agent was successfully added to library"
-        )
-        library_agent_id: str = SchemaField(
-            description="The ID of the library agent entry"
-        )
-        agent_id: str = SchemaField(description="The ID of the agent graph")
-        agent_version: int = SchemaField(
-            description="The version number of the agent graph"
-        )
-        agent_name: str = SchemaField(description="The name of the agent")
-        message: str = SchemaField(description="Success or error message")
-
-    def __init__(self):
-        super().__init__(
-            id="2602a7b1-3f4d-4e5f-9c8b-1a2b3c4d5e6f",
-            description="Add an agent from the store to your personal library",
-            categories={BlockCategory.BASIC},
-            input_schema=AddToLibraryFromStoreBlock.Input,
-            output_schema=AddToLibraryFromStoreBlock.Output,
-            test_input={
-                "store_listing_version_id": "test-listing-id",
-                "agent_name": "My Custom Agent",
-            },
-            test_output=[
-                ("success", True),
-                ("library_agent_id", "test-library-id"),
-                ("agent_id", "test-agent-id"),
-                ("agent_version", 1),
-                ("agent_name", "Test Agent"),
-                ("message", "Agent successfully added to library"),
-            ],
-            test_mock={
-                "_add_to_library": lambda *_, **__: LibraryAgent(
-                    library_agent_id="test-library-id",
-                    agent_id="test-agent-id",
-                    agent_version=1,
-                    agent_name="Test Agent",
-                )
-            },
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        library_agent = await self._add_to_library(
-            user_id=user_id,
-            store_listing_version_id=input_data.store_listing_version_id,
-            custom_name=input_data.agent_name,
-        )
-
-        yield "success", True
-        yield "library_agent_id", library_agent.library_agent_id
-        yield "agent_id", library_agent.agent_id
-        yield "agent_version", library_agent.agent_version
-        yield "agent_name", library_agent.agent_name
-        yield "message", "Agent successfully added to library"
-
-    async def _add_to_library(
-        self,
-        user_id: str,
-        store_listing_version_id: str,
-        custom_name: str | None = None,
-    ) -> LibraryAgent:
-        """
-        Add a store agent to the user's library using the existing library database function.
-        """
-        library_agent = (
-            await get_database_manager_async_client().add_store_agent_to_library(
-                store_listing_version_id=store_listing_version_id, user_id=user_id
-            )
-        )
-
-        # If custom name is provided, we could update the library agent name here
-        # For now, we'll just return the agent info
-        agent_name = custom_name if custom_name else library_agent.name
-
-        return LibraryAgent(
-            library_agent_id=library_agent.id,
-            agent_id=library_agent.graph_id,
-            agent_version=library_agent.graph_version,
-            agent_name=agent_name,
-        )
-
-
-class ListLibraryAgentsBlock(Block):
-    """
-    Block that lists all agents in the user's library.
-    """
-
-    class Input(BlockSchema):
-        search_query: str | None = SchemaField(
-            description="Optional search query to filter agents", default=None
-        )
-        limit: int = SchemaField(
-            description="Maximum number of agents to return", default=50, ge=1, le=100
-        )
-        page: int = SchemaField(
-            description="Page number for pagination", default=1, ge=1
-        )
-
-    class Output(BlockSchema):
-        agents: list[LibraryAgent] = SchemaField(
-            description="List of agents in the library",
-            default_factory=list,
-        )
-        agent: LibraryAgent = SchemaField(
-            description="Individual library agent (yielded for each agent)"
-        )
-        total_count: int = SchemaField(
-            description="Total number of agents in library", default=0
-        )
-        page: int = SchemaField(description="Current page number", default=1)
-        total_pages: int = SchemaField(description="Total number of pages", default=1)
-
-    def __init__(self):
-        super().__init__(
-            id="082602d3-a74d-4600-9e9c-15b3af7eae98",
-            description="List all agents in your personal library",
-            categories={BlockCategory.BASIC, BlockCategory.DATA},
-            input_schema=ListLibraryAgentsBlock.Input,
-            output_schema=ListLibraryAgentsBlock.Output,
-            test_input={
-                "search_query": None,
-                "limit": 10,
-                "page": 1,
-            },
-            test_output=[
-                (
-                    "agents",
-                    [
-                        LibraryAgent(
-                            library_agent_id="test-lib-id",
-                            agent_id="test-agent-id",
-                            agent_version=1,
-                            agent_name="Test Library Agent",
-                            description="A test agent in library",
-                            creator="Test User",
-                        ),
-                    ],
-                ),
-                ("total_count", 1),
-                ("page", 1),
-                ("total_pages", 1),
-                (
-                    "agent",
-                    LibraryAgent(
-                        library_agent_id="test-lib-id",
-                        agent_id="test-agent-id",
-                        agent_version=1,
-                        agent_name="Test Library Agent",
-                        description="A test agent in library",
-                        creator="Test User",
-                    ),
-                ),
-            ],
-            test_mock={
-                "_list_library_agents": lambda *_, **__: {
-                    "agents": [
-                        LibraryAgent(
-                            library_agent_id="test-lib-id",
-                            agent_id="test-agent-id",
-                            agent_version=1,
-                            agent_name="Test Library Agent",
-                            description="A test agent in library",
-                            creator="Test User",
-                        )
-                    ],
-                    "total": 1,
-                    "page": 1,
-                    "total_pages": 1,
-                }
-            },
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        result = await self._list_library_agents(
-            user_id=user_id,
-            search_query=input_data.search_query,
-            limit=input_data.limit,
-            page=input_data.page,
-        )
-
-        agents = result["agents"]
-
-        yield "agents", agents
-        yield "total_count", result["total"]
-        yield "page", result["page"]
-        yield "total_pages", result["total_pages"]
-
-        # Yield each agent individually for better graph connectivity
-        for agent in agents:
-            yield "agent", agent
-
-    async def _list_library_agents(
-        self,
-        user_id: str,
-        search_query: str | None = None,
-        limit: int = 50,
-        page: int = 1,
-    ) -> dict[str, Any]:
-        """
-        List agents in the user's library using the database client.
-        """
-        result = await get_database_manager_async_client().list_library_agents(
-            user_id=user_id,
-            search_term=search_query,
-            page=page,
-            page_size=limit,
-        )
-
-        agents = [
-            LibraryAgent(
-                library_agent_id=agent.id,
-                agent_id=agent.graph_id,
-                agent_version=agent.graph_version,
-                agent_name=agent.name,
-                description=getattr(agent, "description", ""),
-                creator=getattr(agent, "creator", ""),
-                is_archived=getattr(agent, "is_archived", False),
-                categories=getattr(agent, "categories", []),
-            )
-            for agent in result.agents
-        ]
-
-        return {
-            "agents": agents,
-            "total": result.pagination.total_items,
-            "page": result.pagination.current_page,
-            "total_pages": result.pagination.total_pages,
-        }

autogpt_platform/backend/backend/blocks/system/store_operations.py

@@ -1,311 +0,0 @@
-import logging
-from typing import Literal
-
-from pydantic import BaseModel
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-from backend.util.clients import get_database_manager_async_client
-
-logger = logging.getLogger(__name__)
-
-
-# Duplicate pydantic models for store data so we don't accidently change the data shape in the blocks unintentionally when editing the backend
-class StoreAgent(BaseModel):
-    """Model representing a store agent."""
-
-    slug: str = ""
-    name: str = ""
-    description: str = ""
-    creator: str = ""
-    rating: float = 0.0
-    runs: int = 0
-    categories: list[str] = []
-
-
-class StoreAgentDict(BaseModel):
-    """Dictionary representation of a store agent."""
-
-    slug: str
-    name: str
-    description: str
-    creator: str
-    rating: float
-    runs: int
-
-
-class SearchAgentsResponse(BaseModel):
-    """Response from searching store agents."""
-
-    agents: list[StoreAgentDict]
-    total_count: int
-
-
-class StoreAgentDetails(BaseModel):
-    """Detailed information about a store agent."""
-
-    found: bool
-    store_listing_version_id: str = ""
-    agent_name: str = ""
-    description: str = ""
-    creator: str = ""
-    categories: list[str] = []
-    runs: int = 0
-    rating: float = 0.0
-
-
-class GetStoreAgentDetailsBlock(Block):
-    """
-    Block that retrieves detailed information about an agent from the store.
-    """
-
-    class Input(BlockSchema):
-        creator: str = SchemaField(description="The username of the agent creator")
-        slug: str = SchemaField(description="The name of the agent")
-
-    class Output(BlockSchema):
-        found: bool = SchemaField(
-            description="Whether the agent was found in the store"
-        )
-        store_listing_version_id: str = SchemaField(
-            description="The store listing version ID"
-        )
-        agent_name: str = SchemaField(description="Name of the agent")
-        description: str = SchemaField(description="Description of the agent")
-        creator: str = SchemaField(description="Creator of the agent")
-        categories: list[str] = SchemaField(
-            description="Categories the agent belongs to", default_factory=list
-        )
-        runs: int = SchemaField(
-            description="Number of times the agent has been run", default=0
-        )
-        rating: float = SchemaField(
-            description="Average rating of the agent", default=0.0
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="b604f0ec-6e0d-40a7-bf55-9fd09997cced",
-            description="Get detailed information about an agent from the store",
-            categories={BlockCategory.BASIC, BlockCategory.DATA},
-            input_schema=GetStoreAgentDetailsBlock.Input,
-            output_schema=GetStoreAgentDetailsBlock.Output,
-            test_input={"creator": "test-creator", "slug": "test-agent-slug"},
-            test_output=[
-                ("found", True),
-                ("store_listing_version_id", "test-listing-id"),
-                ("agent_name", "Test Agent"),
-                ("description", "A test agent"),
-                ("creator", "Test Creator"),
-                ("categories", ["productivity", "automation"]),
-                ("runs", 100),
-                ("rating", 4.5),
-            ],
-            test_mock={
-                "_get_agent_details": lambda *_, **__: StoreAgentDetails(
-                    found=True,
-                    store_listing_version_id="test-listing-id",
-                    agent_name="Test Agent",
-                    description="A test agent",
-                    creator="Test Creator",
-                    categories=["productivity", "automation"],
-                    runs=100,
-                    rating=4.5,
-                )
-            },
-            static_output=True,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        **kwargs,
-    ) -> BlockOutput:
-        details = await self._get_agent_details(
-            creator=input_data.creator, slug=input_data.slug
-        )
-        yield "found", details.found
-        yield "store_listing_version_id", details.store_listing_version_id
-        yield "agent_name", details.agent_name
-        yield "description", details.description
-        yield "creator", details.creator
-        yield "categories", details.categories
-        yield "runs", details.runs
-        yield "rating", details.rating
-
-    async def _get_agent_details(self, creator: str, slug: str) -> StoreAgentDetails:
-        """
-        Retrieve detailed information about a store agent.
-        """
-        # Get by specific version ID
-        agent_details = (
-            await get_database_manager_async_client().get_store_agent_details(
-                username=creator, agent_name=slug
-            )
-        )
-
-        return StoreAgentDetails(
-            found=True,
-            store_listing_version_id=agent_details.store_listing_version_id,
-            agent_name=agent_details.agent_name,
-            description=agent_details.description,
-            creator=agent_details.creator,
-            categories=(
-                agent_details.categories if hasattr(agent_details, "categories") else []
-            ),
-            runs=agent_details.runs,
-            rating=agent_details.rating,
-        )
-
-
-class SearchStoreAgentsBlock(Block):
-    """
-    Block that searches for agents in the store based on various criteria.
-    """
-
-    class Input(BlockSchema):
-        query: str | None = SchemaField(
-            description="Search query to find agents", default=None
-        )
-        category: str | None = SchemaField(
-            description="Filter by category", default=None
-        )
-        sort_by: Literal["rating", "runs", "name", "recent"] = SchemaField(
-            description="How to sort the results", default="rating"
-        )
-        limit: int = SchemaField(
-            description="Maximum number of results to return", default=10, ge=1, le=100
-        )
-
-    class Output(BlockSchema):
-        agents: list[StoreAgent] = SchemaField(
-            description="List of agents matching the search criteria",
-            default_factory=list,
-        )
-        agent: StoreAgent = SchemaField(description="Basic information of the agent")
-        total_count: int = SchemaField(
-            description="Total number of agents found", default=0
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="39524701-026c-4328-87cc-1b88c8e2cb4c",
-            description="Search for agents in the store",
-            categories={BlockCategory.BASIC, BlockCategory.DATA},
-            input_schema=SearchStoreAgentsBlock.Input,
-            output_schema=SearchStoreAgentsBlock.Output,
-            test_input={
-                "query": "productivity",
-                "category": None,
-                "sort_by": "rating",
-                "limit": 10,
-            },
-            test_output=[
-                (
-                    "agents",
-                    [
-                        {
-                            "slug": "test-agent",
-                            "name": "Test Agent",
-                            "description": "A test agent",
-                            "creator": "Test Creator",
-                            "rating": 4.5,
-                            "runs": 100,
-                        }
-                    ],
-                ),
-                ("total_count", 1),
-                (
-                    "agent",
-                    {
-                        "slug": "test-agent",
-                        "name": "Test Agent",
-                        "description": "A test agent",
-                        "creator": "Test Creator",
-                        "rating": 4.5,
-                        "runs": 100,
-                    },
-                ),
-            ],
-            test_mock={
-                "_search_agents": lambda *_, **__: SearchAgentsResponse(
-                    agents=[
-                        StoreAgentDict(
-                            slug="test-agent",
-                            name="Test Agent",
-                            description="A test agent",
-                            creator="Test Creator",
-                            rating=4.5,
-                            runs=100,
-                        )
-                    ],
-                    total_count=1,
-                )
-            },
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        **kwargs,
-    ) -> BlockOutput:
-        result = await self._search_agents(
-            query=input_data.query,
-            category=input_data.category,
-            sort_by=input_data.sort_by,
-            limit=input_data.limit,
-        )
-
-        agents = result.agents
-        total_count = result.total_count
-
-        # Convert to dict for output
-        agents_as_dicts = [agent.model_dump() for agent in agents]
-
-        yield "agents", agents_as_dicts
-        yield "total_count", total_count
-
-        for agent_dict in agents_as_dicts:
-            yield "agent", agent_dict
-
-    async def _search_agents(
-        self,
-        query: str | None = None,
-        category: str | None = None,
-        sort_by: str = "rating",
-        limit: int = 10,
-    ) -> SearchAgentsResponse:
-        """
-        Search for agents in the store using the existing store database function.
-        """
-        # Map our sort_by to the store's sorted_by parameter
-        sorted_by_map = {
-            "rating": "most_popular",
-            "runs": "most_runs",
-            "name": "alphabetical",
-            "recent": "recently_updated",
-        }
-
-        result = await get_database_manager_async_client().get_store_agents(
-            featured=False,
-            creators=None,
-            sorted_by=sorted_by_map.get(sort_by, "most_popular"),
-            search_query=query,
-            category=category,
-            page=1,
-            page_size=limit,
-        )
-
-        agents = [
-            StoreAgentDict(
-                slug=agent.slug,
-                name=agent.agent_name,
-                description=agent.description,
-                creator=agent.creator,
-                rating=agent.rating,
-                runs=agent.runs,
-            )
-            for agent in result.agents
-        ]
-
-        return SearchAgentsResponse(agents=agents, total_count=len(agents))

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

@@ -1,130 +0,0 @@
-from unittest.mock import Mock
-
-import pytest
-
-from backend.blocks.data_manipulation import AddToListBlock, CreateDictionaryBlock
-from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_handles_dynamic_dict_fields():
-    """Test Smart Decision Maker can handle dynamic dictionary fields (_#_) for any block"""
-
-    # Create a mock node for CreateDictionaryBlock
-    mock_node = Mock()
-    mock_node.block = CreateDictionaryBlock()
-    mock_node.block_id = CreateDictionaryBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic dictionary fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_create_dict_~_name",
-            sink_name="values_#_name",  # Dynamic dict field
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_age",
-            sink_name="values_#_age",  # Dynamic dict field
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_create_dict_~_city",
-            sink_name="values_#_city",  # Dynamic dict field
-            sink_id="dict_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, mock_links  # type: ignore
-    )
-
-    # Verify the signature was created successfully
-    assert signature["type"] == "function"
-    assert "parameters" in signature["function"]
-    assert "properties" in signature["function"]["parameters"]
-
-    # Check that dynamic fields are handled
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 3  # Should have all three fields
-
-    # Each dynamic field should have proper schema
-    for prop_value in properties.values():
-        assert "type" in prop_value
-        assert prop_value["type"] == "string"  # Dynamic fields get string type
-        assert "description" in prop_value
-        assert "Dynamic value for" in prop_value["description"]
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_handles_dynamic_list_fields():
-    """Test Smart Decision Maker can handle dynamic list fields (_$_) for any block"""
-
-    # Create a mock node for AddToListBlock
-    mock_node = Mock()
-    mock_node.block = AddToListBlock()
-    mock_node.block_id = AddToListBlock().id
-    mock_node.input_default = {}
-
-    # Create mock links with dynamic list fields
-    mock_links = [
-        Mock(
-            source_name="tools_^_add_to_list_~_0",
-            sink_name="entries_$_0",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-        Mock(
-            source_name="tools_^_add_to_list_~_1",
-            sink_name="entries_$_1",  # Dynamic list field
-            sink_id="list_node_id",
-            source_id="smart_decision_node_id",
-        ),
-    ]
-
-    # Generate function signature
-    signature = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, mock_links  # type: ignore
-    )
-
-    # Verify dynamic list fields are handled properly
-    assert signature["type"] == "function"
-    properties = signature["function"]["parameters"]["properties"]
-    assert len(properties) == 2  # Should have both list items
-
-    # Each dynamic field should have proper schema
-    for prop_value in properties.values():
-        assert prop_value["type"] == "string"
-        assert "Dynamic value for" in prop_value["description"]
-
-
-@pytest.mark.asyncio
-async def test_create_dict_block_with_dynamic_values():
-    """Test CreateDictionaryBlock processes dynamic values correctly"""
-
-    block = CreateDictionaryBlock()
-
-    # Simulate what happens when executor merges dynamic fields
-    # The executor merges values_#_* fields into the values dict
-    input_data = block.input_schema(
-        values={
-            "existing": "value",
-            "name": "Alice",  # This would come from values_#_name
-            "age": 25,  # This would come from values_#_age
-        }
-    )
-
-    # Run the block
-    result = {}
-    async for output_name, output_value in block.run(input_data):
-        result[output_name] = output_value
-
-    # Check the result
-    assert "dictionary" in result
-    assert result["dictionary"]["existing"] == "value"
-    assert result["dictionary"]["name"] == "Alice"
-    assert result["dictionary"]["age"] == 25

autogpt_platform/backend/backend/blocks/test/test_store_operations.py

@@ -1,155 +0,0 @@
-from unittest.mock import MagicMock
-
-import pytest
-
-from backend.blocks.system.library_operations import (
-    AddToLibraryFromStoreBlock,
-    LibraryAgent,
-)
-from backend.blocks.system.store_operations import (
-    GetStoreAgentDetailsBlock,
-    SearchAgentsResponse,
-    SearchStoreAgentsBlock,
-    StoreAgentDetails,
-    StoreAgentDict,
-)
-
-
-@pytest.mark.asyncio
-async def test_add_to_library_from_store_block_success(mocker):
-    """Test successful addition of agent from store to library."""
-    block = AddToLibraryFromStoreBlock()
-
-    # Mock the library agent response
-    mock_library_agent = MagicMock()
-    mock_library_agent.id = "lib-agent-123"
-    mock_library_agent.graph_id = "graph-456"
-    mock_library_agent.graph_version = 1
-    mock_library_agent.name = "Test Agent"
-
-    mocker.patch.object(
-        block,
-        "_add_to_library",
-        return_value=LibraryAgent(
-            library_agent_id="lib-agent-123",
-            agent_id="graph-456",
-            agent_version=1,
-            agent_name="Test Agent",
-        ),
-    )
-
-    input_data = block.Input(
-        store_listing_version_id="store-listing-v1", agent_name="Custom Agent Name"
-    )
-
-    outputs = {}
-    async for name, value in block.run(input_data, user_id="test-user"):
-        outputs[name] = value
-
-    assert outputs["success"] is True
-    assert outputs["library_agent_id"] == "lib-agent-123"
-    assert outputs["agent_id"] == "graph-456"
-    assert outputs["agent_version"] == 1
-    assert outputs["agent_name"] == "Test Agent"
-    assert outputs["message"] == "Agent successfully added to library"
-
-
-@pytest.mark.asyncio
-async def test_get_store_agent_details_block_success(mocker):
-    """Test successful retrieval of store agent details."""
-    block = GetStoreAgentDetailsBlock()
-
-    mocker.patch.object(
-        block,
-        "_get_agent_details",
-        return_value=StoreAgentDetails(
-            found=True,
-            store_listing_version_id="version-123",
-            agent_name="Test Agent",
-            description="A test agent for testing",
-            creator="Test Creator",
-            categories=["productivity", "automation"],
-            runs=100,
-            rating=4.5,
-        ),
-    )
-
-    input_data = block.Input(creator="Test Creator", slug="test-slug")
-    outputs = {}
-    async for name, value in block.run(input_data):
-        outputs[name] = value
-
-    assert outputs["found"] is True
-    assert outputs["store_listing_version_id"] == "version-123"
-    assert outputs["agent_name"] == "Test Agent"
-    assert outputs["description"] == "A test agent for testing"
-    assert outputs["creator"] == "Test Creator"
-    assert outputs["categories"] == ["productivity", "automation"]
-    assert outputs["runs"] == 100
-    assert outputs["rating"] == 4.5
-
-
-@pytest.mark.asyncio
-async def test_search_store_agents_block(mocker):
-    """Test searching for store agents."""
-    block = SearchStoreAgentsBlock()
-
-    mocker.patch.object(
-        block,
-        "_search_agents",
-        return_value=SearchAgentsResponse(
-            agents=[
-                StoreAgentDict(
-                    slug="creator1/agent1",
-                    name="Agent One",
-                    description="First test agent",
-                    creator="Creator 1",
-                    rating=4.8,
-                    runs=500,
-                ),
-                StoreAgentDict(
-                    slug="creator2/agent2",
-                    name="Agent Two",
-                    description="Second test agent",
-                    creator="Creator 2",
-                    rating=4.2,
-                    runs=200,
-                ),
-            ],
-            total_count=2,
-        ),
-    )
-
-    input_data = block.Input(
-        query="test", category="productivity", sort_by="rating", limit=10
-    )
-
-    outputs = {}
-    async for name, value in block.run(input_data):
-        outputs[name] = value
-
-    assert len(outputs["agents"]) == 2
-    assert outputs["total_count"] == 2
-    assert outputs["agents"][0]["name"] == "Agent One"
-    assert outputs["agents"][0]["rating"] == 4.8
-
-
-@pytest.mark.asyncio
-async def test_search_store_agents_block_empty_results(mocker):
-    """Test searching with no results."""
-    block = SearchStoreAgentsBlock()
-
-    mocker.patch.object(
-        block,
-        "_search_agents",
-        return_value=SearchAgentsResponse(agents=[], total_count=0),
-    )
-
-    input_data = block.Input(query="nonexistent", limit=10)
-
-    outputs = {}
-    async for name, value in block.run(input_data):
-        outputs[name] = value
-
-    assert outputs["agents"] == []
-    assert outputs["total_count"] == 0

autogpt_platform/backend/backend/blocks/time_blocks.py

@@ -1,5 +1,4 @@
 import asyncio
-import logging
 import time
 from datetime import datetime, timedelta
 from typing import Any, Literal, Union
@@ -8,7 +7,6 @@ from zoneinfo import ZoneInfo
 from pydantic import BaseModel
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.execution import UserContext
 from backend.data.model import SchemaField
 
 # Shared timezone literal type for all time/date blocks
@@ -53,80 +51,16 @@ TimezoneLiteral = Literal[
     "Etc/GMT+12",  # UTC-12:00
 ]
 
-logger = logging.getLogger(__name__)
-
-
-def _get_timezone(
-    format_type: Any,  # Any format type with timezone and use_user_timezone attributes
-    user_timezone: str | None,
-) -> ZoneInfo:
-    """
-    Determine which timezone to use based on format settings and user context.
-
-    Args:
-        format_type: The format configuration containing timezone settings
-        user_timezone: The user's timezone from context
-
-    Returns:
-        ZoneInfo object for the determined timezone
-    """
-    if format_type.use_user_timezone and user_timezone:
-        tz = ZoneInfo(user_timezone)
-        logger.debug(f"Using user timezone: {user_timezone}")
-    else:
-        tz = ZoneInfo(format_type.timezone)
-        logger.debug(f"Using specified timezone: {format_type.timezone}")
-    return tz
-
-
-def _format_datetime_iso8601(dt: datetime, include_microseconds: bool = False) -> str:
-    """
-    Format a datetime object to ISO8601 string.
-
-    Args:
-        dt: The datetime object to format
-        include_microseconds: Whether to include microseconds in the output
-
-    Returns:
-        ISO8601 formatted string
-    """
-    if include_microseconds:
-        return dt.isoformat()
-    else:
-        return dt.isoformat(timespec="seconds")
-
-
-# BACKWARDS COMPATIBILITY NOTE:
-# The timezone field is kept at the format level (not block level) for backwards compatibility.
-# Existing graphs have timezone saved within format_type, moving it would break them.
-#
-# The use_user_timezone flag was added to allow using the user's profile timezone.
-# Default is False to maintain backwards compatibility - existing graphs will continue
-# using their specified timezone.
-#
-# KNOWN ISSUE: If a user switches between format types (strftime <-> iso8601),
-# the timezone setting doesn't carry over. This is a UX issue but fixing it would
-# require either:
-#   1. Moving timezone to block level (breaking change, needs migration)
-#   2. Complex state management to sync timezone across format types
-#
-# Future migration path: When we do a major version bump, consider moving timezone
-# to the block Input level for better UX.
-
 
 class TimeStrftimeFormat(BaseModel):
     discriminator: Literal["strftime"]
     format: str = "%H:%M:%S"
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
 
 
 class TimeISO8601Format(BaseModel):
     discriminator: Literal["iso8601"]
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
     include_microseconds: bool = False
 
 
@@ -181,27 +115,25 @@ class GetCurrentTimeBlock(Block):
             ],
         )
 
-    async def run(
-        self, input_data: Input, *, user_context: UserContext, **kwargs
-    ) -> BlockOutput:
-        # Extract timezone from user_context (always present)
-        effective_timezone = user_context.timezone
-
-        # Get the appropriate timezone
-        tz = _get_timezone(input_data.format_type, effective_timezone)
-        dt = datetime.now(tz=tz)
-
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         if isinstance(input_data.format_type, TimeISO8601Format):
+            # ISO 8601 format for time only (extract time portion from full ISO datetime)
+            tz = ZoneInfo(input_data.format_type.timezone)
+            dt = datetime.now(tz=tz)
+
             # Get the full ISO format and extract just the time portion with timezone
-            full_iso = _format_datetime_iso8601(
-                dt, input_data.format_type.include_microseconds
-            )
+            if input_data.format_type.include_microseconds:
+                full_iso = dt.isoformat()
+            else:
+                full_iso = dt.isoformat(timespec="seconds")
+
             # Extract time portion (everything after 'T')
             current_time = full_iso.split("T")[1] if "T" in full_iso else full_iso
             current_time = f"T{current_time}"  # Add T prefix for ISO 8601 time format
         else:  # TimeStrftimeFormat
+            tz = ZoneInfo(input_data.format_type.timezone)
+            dt = datetime.now(tz=tz)
             current_time = dt.strftime(input_data.format_type.format)
-
         yield "time", current_time
 
 
@@ -209,15 +141,11 @@ class DateStrftimeFormat(BaseModel):
     discriminator: Literal["strftime"]
     format: str = "%Y-%m-%d"
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
 
 
 class DateISO8601Format(BaseModel):
     discriminator: Literal["iso8601"]
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
 
 
 class GetCurrentDateBlock(Block):
@@ -289,23 +217,20 @@ class GetCurrentDateBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Extract timezone from user_context (required keyword argument)
-        user_context: UserContext = kwargs["user_context"]
-        effective_timezone = user_context.timezone
-
         try:
             offset = int(input_data.offset)
         except ValueError:
             offset = 0
 
-        # Get the appropriate timezone
-        tz = _get_timezone(input_data.format_type, effective_timezone)
-        current_date = datetime.now(tz=tz) - timedelta(days=offset)
-
         if isinstance(input_data.format_type, DateISO8601Format):
+            # ISO 8601 format for date only (YYYY-MM-DD)
+            tz = ZoneInfo(input_data.format_type.timezone)
+            current_date = datetime.now(tz=tz) - timedelta(days=offset)
             # ISO 8601 date format is YYYY-MM-DD
             date_str = current_date.date().isoformat()
         else:  # DateStrftimeFormat
+            tz = ZoneInfo(input_data.format_type.timezone)
+            current_date = datetime.now(tz=tz) - timedelta(days=offset)
             date_str = current_date.strftime(input_data.format_type.format)
 
         yield "date", date_str
@@ -315,15 +240,11 @@ class StrftimeFormat(BaseModel):
     discriminator: Literal["strftime"]
     format: str = "%Y-%m-%d %H:%M:%S"
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
 
 
 class ISO8601Format(BaseModel):
     discriminator: Literal["iso8601"]
     timezone: TimezoneLiteral = "UTC"
-    # When True, overrides timezone with user's profile timezone
-    use_user_timezone: bool = False
     include_microseconds: bool = False
 
 
@@ -395,22 +316,20 @@ class GetCurrentDateAndTimeBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        # Extract timezone from user_context (required keyword argument)
-        user_context: UserContext = kwargs["user_context"]
-        effective_timezone = user_context.timezone
-
-        # Get the appropriate timezone
-        tz = _get_timezone(input_data.format_type, effective_timezone)
-        dt = datetime.now(tz=tz)
-
         if isinstance(input_data.format_type, ISO8601Format):
             # ISO 8601 format with specified timezone (also RFC3339-compliant)
-            current_date_time = _format_datetime_iso8601(
-                dt, input_data.format_type.include_microseconds
-            )
-        else:  # StrftimeFormat
-            current_date_time = dt.strftime(input_data.format_type.format)
+            tz = ZoneInfo(input_data.format_type.timezone)
+            dt = datetime.now(tz=tz)
 
+            # Format with or without microseconds
+            if input_data.format_type.include_microseconds:
+                current_date_time = dt.isoformat()
+            else:
+                current_date_time = dt.isoformat(timespec="seconds")
+        else:  # StrftimeFormat
+            tz = ZoneInfo(input_data.format_type.timezone)
+            dt = datetime.now(tz=tz)
+            current_date_time = dt.strftime(input_data.format_type.format)
         yield "date_time", current_date_time
 
 

autogpt_platform/backend/backend/blocks/wordpress/_api.py

@@ -4,8 +4,6 @@ from logging import getLogger
 from typing import Any, Dict, List, Union
 from urllib.parse import urlencode
 
-from pydantic import field_serializer
-
 from backend.sdk import BaseModel, Credentials, Requests
 
 logger = getLogger(__name__)
@@ -384,9 +382,8 @@ class CreatePostRequest(BaseModel):
     # Advanced
     metadata: List[Dict[str, Any]] | None = None
 
-    @field_serializer("date")
-    def serialize_date(self, value: datetime | None) -> str | None:
-        return value.isoformat() if value else None
+    class Config:
+        json_encoders = {datetime: lambda v: v.isoformat()}
 
 
 class PostAuthor(BaseModel):

autogpt_platform/backend/backend/conftest.py

@@ -6,6 +6,8 @@ from dotenv import load_dotenv
 
 from backend.util.logging import configure_logging
 
+os.environ["ENABLE_AUTH"] = "false"
+
 load_dotenv()
 
 #  NOTE: You can run tests like with the --log-cli-level=INFO to see the logs

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -5,12 +5,6 @@ from backend.blocks.ai_shortform_video_block import AIShortformVideoCreatorBlock
 from backend.blocks.apollo.organization import SearchOrganizationsBlock
 from backend.blocks.apollo.people import SearchPeopleBlock
 from backend.blocks.apollo.person import GetPersonDetailBlock
-from backend.blocks.enrichlayer.linkedin import (
-    GetLinkedinProfileBlock,
-    GetLinkedinProfilePictureBlock,
-    LinkedinPersonLookupBlock,
-    LinkedinRoleLookupBlock,
-)
 from backend.blocks.flux_kontext import AIImageEditorBlock, FluxKontextModelName
 from backend.blocks.ideogram import IdeogramModelBlock
 from backend.blocks.jina.embeddings import JinaEmbeddingBlock
@@ -36,7 +30,6 @@ from backend.integrations.credentials_store import (
     anthropic_credentials,
     apollo_credentials,
     did_credentials,
-    enrichlayer_credentials,
     groq_credentials,
     ideogram_credentials,
     jina_credentials,
@@ -46,7 +39,6 @@ from backend.integrations.credentials_store import (
     replicate_credentials,
     revid_credentials,
     unreal_credentials,
-    v0_credentials,
 )
 
 # =============== Configure the cost for each LLM Model call =============== #
@@ -123,10 +115,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
     LlmModel.GEMINI_2_0_FLASH_LITE: 1,
     LlmModel.DEEPSEEK_R1_0528: 1,
-    # v0 by Vercel models
-    LlmModel.V0_1_5_MD: 1,
-    LlmModel.V0_1_5_LG: 2,
-    LlmModel.V0_1_0_MD: 1,
 }
 
 for model in LlmModel:
@@ -216,23 +204,6 @@ LLM_COST = (
         for model, cost in MODEL_COST.items()
         if MODEL_METADATA[model].provider == "llama_api"
     ]
-    # v0 by Vercel Models
-    + [
-        BlockCost(
-            cost_type=BlockCostType.RUN,
-            cost_filter={
-                "model": model,
-                "credentials": {
-                    "id": v0_credentials.id,
-                    "provider": v0_credentials.provider,
-                    "type": v0_credentials.type,
-                },
-            },
-            cost_amount=cost,
-        )
-        for model, cost in MODEL_COST.items()
-        if MODEL_METADATA[model].provider == "v0"
-    ]
     # AI/ML Api Models
     + [
         BlockCost(
@@ -307,18 +278,7 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
                     "type": ideogram_credentials.type,
                 }
             },
-        ),
-        BlockCost(
-            cost_amount=18,
-            cost_filter={
-                "ideogram_model_name": "V_3",
-                "credentials": {
-                    "id": ideogram_credentials.id,
-                    "provider": ideogram_credentials.provider,
-                    "type": ideogram_credentials.type,
-                },
-            },
-        ),
+        )
     ],
     AIShortformVideoCreatorBlock: [
         BlockCost(
@@ -416,54 +376,6 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         )
     ],
-    GetLinkedinProfileBlock: [
-        BlockCost(
-            cost_amount=1,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    LinkedinPersonLookupBlock: [
-        BlockCost(
-            cost_amount=2,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    LinkedinRoleLookupBlock: [
-        BlockCost(
-            cost_amount=3,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    GetLinkedinProfilePictureBlock: [
-        BlockCost(
-            cost_amount=3,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
     SmartDecisionMakerBlock: LLM_COST,
     SearchOrganizationsBlock: [
         BlockCost(

autogpt_platform/backend/backend/data/credit.py

@@ -34,10 +34,10 @@ from backend.data.model import (
 from backend.data.notifications import NotificationEventModel, RefundRequestData
 from backend.data.user import get_user_by_id, get_user_email_by_id
 from backend.notifications.notifications import queue_notification_async
+from backend.server.model import Pagination
 from backend.server.v2.admin.model import UserHistoryResponse
 from backend.util.exceptions import InsufficientBalanceError
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 from backend.util.retry import func_retry
 from backend.util.settings import Settings
 
@@ -286,17 +286,11 @@ class UserCreditBase(ABC):
         transaction = await CreditTransaction.prisma().find_first_or_raise(
             where={"transactionKey": transaction_key, "userId": user_id}
         )
+
         if transaction.isActive:
             return
 
         async with db.locked_transaction(f"usr_trx_{user_id}"):
-
-            transaction = await CreditTransaction.prisma().find_first_or_raise(
-                where={"transactionKey": transaction_key, "userId": user_id}
-            )
-            if transaction.isActive:
-                return
-
             user_balance, _ = await self._get_credits(user_id)
             await CreditTransaction.prisma().update(
                 where={

autogpt_platform/backend/backend/data/credit_test.py

@@ -7,7 +7,7 @@ from prisma.models import CreditTransaction
 from backend.blocks.llm import AITextGeneratorBlock
 from backend.data.block import get_block
 from backend.data.credit import BetaUserCredit, UsageTransactionMetadata
-from backend.data.execution import NodeExecutionEntry, UserContext
+from backend.data.execution import NodeExecutionEntry
 from backend.data.user import DEFAULT_USER_ID
 from backend.executor.utils import block_usage_cost
 from backend.integrations.credentials_store import openai_credentials
@@ -75,7 +75,6 @@ async def test_block_credit_usage(server: SpinTestServer):
                     "type": openai_credentials.type,
                 },
             },
-            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_1 > 0
@@ -89,7 +88,6 @@ async def test_block_credit_usage(server: SpinTestServer):
             node_exec_id="test_node_exec",
             block_id=AITextGeneratorBlock().id,
             inputs={"model": "gpt-4-turbo", "api_key": "owned_api_key"},
-            user_context=UserContext(timezone="UTC"),
         ),
     )
     assert spending_amount_2 == 0

autogpt_platform/backend/backend/data/execution.py

@@ -33,13 +33,12 @@ from prisma.types import (
     AgentNodeExecutionUpdateInput,
     AgentNodeExecutionWhereInput,
 )
-from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
+from pydantic import BaseModel, ConfigDict, JsonValue
 from pydantic.fields import Field
 
 from backend.server.v2.store.exceptions import DatabaseError
 from backend.util import type as type_utils
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 from backend.util.retry import func_retry
 from backend.util.settings import Config
 from backend.util.truncate import truncate
@@ -60,7 +59,7 @@ from .includes import (
     GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     graph_execution_include,
 )
-from .model import GraphExecutionStats, NodeExecutionStats
+from .model import GraphExecutionStats
 
 T = TypeVar("T")
 
@@ -90,7 +89,6 @@ ExecutionStatus = AgentExecutionStatus
 
 
 class GraphExecutionMeta(BaseDbModel):
-    id: str  # type: ignore # Override base class to make this required
     user_id: str
     graph_id: str
     graph_version: int
@@ -292,14 +290,13 @@ class GraphExecutionWithNodes(GraphExecution):
             node_executions=node_executions,
         )
 
-    def to_graph_execution_entry(self, user_context: "UserContext"):
+    def to_graph_execution_entry(self):
         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
-            user_context=user_context,
         )
 
 
@@ -315,37 +312,24 @@ 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):
-        try:
-            stats = NodeExecutionStats.model_validate(_node_exec.stats or {})
-        except (ValueError, ValidationError):
-            stats = NodeExecutionStats()
-
-        if stats.cleared_inputs:
-            input_data: BlockInput = defaultdict()
-            for name, messages in stats.cleared_inputs.items():
-                input_data[name] = messages[-1] if messages else ""
-        elif _node_exec.executionData:
+        if _node_exec.executionData:
+            # Execution that has been queued for execution will persist its data.
             input_data = type_utils.convert(_node_exec.executionData, dict[str, Any])
         else:
+            # For incomplete execution, executionData will not be yet available.
             input_data: BlockInput = defaultdict()
             for data in _node_exec.Input or []:
                 input_data[data.name] = type_utils.convert(data.data, type[Any])
 
         output_data: CompletedBlockOutput = defaultdict(list)
-
-        if stats.cleared_outputs:
-            for name, messages in stats.cleared_outputs.items():
-                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]))
+        for data in _node_exec.Output or []:
+            output_data[data.name].append(type_utils.convert(data.data, type[Any]))
 
         graph_execution: AgentGraphExecution | None = _node_exec.GraphExecution
         if graph_execution:
@@ -370,12 +354,9 @@ 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(
-        self, user_context: "UserContext"
-    ) -> "NodeExecutionEntry":
+    def to_node_execution_entry(self) -> "NodeExecutionEntry":
         return NodeExecutionEntry(
             user_id=self.user_id,
             graph_exec_id=self.graph_exec_id,
@@ -384,7 +365,6 @@ class NodeExecutionResult(BaseModel):
             node_id=self.node_id,
             block_id=self.block_id,
             inputs=self.input_data,
-            user_context=user_context,
         )
 
 
@@ -392,13 +372,13 @@ class NodeExecutionResult(BaseModel):
 
 
 async def get_graph_executions(
-    graph_exec_id: Optional[str] = None,
-    graph_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-    limit: Optional[int] = None,
+    graph_exec_id: str | None = None,
+    graph_id: str | None = None,
+    user_id: str | None = None,
+    statuses: list[ExecutionStatus] | None = None,
+    created_time_gte: datetime | None = None,
+    created_time_lte: datetime | None = None,
+    limit: int | None = None,
 ) -> list[GraphExecutionMeta]:
     """⚠️ **Optional `user_id` check**: MUST USE check in user-facing endpoints."""
     where_filter: AgentGraphExecutionWhereInput = {
@@ -426,60 +406,6 @@ async def get_graph_executions(
     return [GraphExecutionMeta.from_db(execution) for execution in executions]
 
 
-class GraphExecutionsPaginated(BaseModel):
-    """Response schema for paginated graph executions."""
-
-    executions: list[GraphExecutionMeta]
-    pagination: Pagination
-
-
-async def get_graph_executions_paginated(
-    user_id: str,
-    graph_id: Optional[str] = None,
-    page: int = 1,
-    page_size: int = 25,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-) -> GraphExecutionsPaginated:
-    """Get paginated graph executions for a specific graph."""
-    where_filter: AgentGraphExecutionWhereInput = {
-        "isDeleted": False,
-        "userId": user_id,
-    }
-
-    if graph_id:
-        where_filter["agentGraphId"] = graph_id
-    if created_time_gte or created_time_lte:
-        where_filter["createdAt"] = {
-            "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),
-            "lte": created_time_lte or datetime.max.replace(tzinfo=timezone.utc),
-        }
-    if statuses:
-        where_filter["OR"] = [{"executionStatus": status} for status in statuses]
-
-    total_count = await AgentGraphExecution.prisma().count(where=where_filter)
-    total_pages = (total_count + page_size - 1) // page_size
-
-    offset = (page - 1) * page_size
-    executions = await AgentGraphExecution.prisma().find_many(
-        where=where_filter,
-        order={"createdAt": "desc"},
-        take=page_size,
-        skip=offset,
-    )
-
-    return GraphExecutionsPaginated(
-        executions=[GraphExecutionMeta.from_db(execution) for execution in executions],
-        pagination=Pagination(
-            total_items=total_count,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
 async def get_graph_execution_meta(
     user_id: str, execution_id: str
 ) -> GraphExecutionMeta | None:
@@ -656,42 +582,6 @@ 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,
@@ -915,19 +805,12 @@ async def get_latest_node_execution(
 # ----------------- Execution Infrastructure ----------------- #
 
 
-class UserContext(BaseModel):
-    """Generic user context for graph execution containing user-specific settings."""
-
-    timezone: str
-
-
 class GraphExecutionEntry(BaseModel):
     user_id: str
     graph_exec_id: str
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None
-    user_context: UserContext
 
 
 class NodeExecutionEntry(BaseModel):
@@ -938,7 +821,6 @@ class NodeExecutionEntry(BaseModel):
     node_id: str
     block_id: str
     inputs: BlockInput
-    user_context: UserContext
 
 
 class ExecutionQueue(Generic[T]):

autogpt_platform/backend/backend/data/generate_data.py

@@ -1,109 +0,0 @@
-import logging
-from collections import defaultdict
-from datetime import datetime
-
-from prisma.enums import AgentExecutionStatus
-
-from backend.data.execution import get_graph_executions
-from backend.data.graph import get_graph_metadata
-from backend.data.model import UserExecutionSummaryStats
-from backend.server.v2.store.exceptions import DatabaseError
-from backend.util.logging import TruncatedLogger
-
-logger = TruncatedLogger(logging.getLogger(__name__), prefix="[SummaryData]")
-
-
-async def get_user_execution_summary_data(
-    user_id: str, start_time: datetime, end_time: datetime
-) -> UserExecutionSummaryStats:
-    """Gather all summary data for a user in a time range.
-
-    This function fetches graph executions once and aggregates all required
-    statistics in a single pass for efficiency.
-    """
-    try:
-        # Fetch graph executions once
-        executions = await get_graph_executions(
-            user_id=user_id,
-            created_time_gte=start_time,
-            created_time_lte=end_time,
-        )
-
-        # Initialize aggregation variables
-        total_credits_used = 0.0
-        total_executions = len(executions)
-        successful_runs = 0
-        failed_runs = 0
-        terminated_runs = 0
-        execution_times = []
-        agent_usage = defaultdict(int)
-        cost_by_graph_id = defaultdict(float)
-
-        # Single pass through executions to aggregate all stats
-        for execution in executions:
-            # Count execution statuses (including TERMINATED as failed)
-            if execution.status == AgentExecutionStatus.COMPLETED:
-                successful_runs += 1
-            elif execution.status == AgentExecutionStatus.FAILED:
-                failed_runs += 1
-            elif execution.status == AgentExecutionStatus.TERMINATED:
-                terminated_runs += 1
-
-            # Aggregate costs from stats
-            if execution.stats and hasattr(execution.stats, "cost"):
-                cost_in_dollars = execution.stats.cost / 100
-                total_credits_used += cost_in_dollars
-                cost_by_graph_id[execution.graph_id] += cost_in_dollars
-
-            # Collect execution times
-            if execution.stats and hasattr(execution.stats, "duration"):
-                execution_times.append(execution.stats.duration)
-
-            # Count agent usage
-            agent_usage[execution.graph_id] += 1
-
-        # Calculate derived stats
-        total_execution_time = sum(execution_times)
-        average_execution_time = (
-            total_execution_time / len(execution_times) if execution_times else 0
-        )
-
-        # Find most used agent
-        most_used_agent = "No agents used"
-        if agent_usage:
-            most_used_agent_id = max(agent_usage, key=lambda k: agent_usage[k])
-            try:
-                graph_meta = await get_graph_metadata(graph_id=most_used_agent_id)
-                most_used_agent = (
-                    graph_meta.name if graph_meta else f"Agent {most_used_agent_id[:8]}"
-                )
-            except Exception:
-                logger.warning(f"Could not get metadata for graph {most_used_agent_id}")
-                most_used_agent = f"Agent {most_used_agent_id[:8]}"
-
-        # Convert graph_ids to agent names for cost breakdown
-        cost_breakdown = {}
-        for graph_id, cost in cost_by_graph_id.items():
-            try:
-                graph_meta = await get_graph_metadata(graph_id=graph_id)
-                agent_name = graph_meta.name if graph_meta else f"Agent {graph_id[:8]}"
-            except Exception:
-                logger.warning(f"Could not get metadata for graph {graph_id}")
-                agent_name = f"Agent {graph_id[:8]}"
-            cost_breakdown[agent_name] = cost
-
-        # Build the summary stats object (include terminated runs as failed)
-        return UserExecutionSummaryStats(
-            total_credits_used=total_credits_used,
-            total_executions=total_executions,
-            successful_runs=successful_runs,
-            failed_runs=failed_runs + terminated_runs,
-            most_used_agent=most_used_agent,
-            total_execution_time=total_execution_time,
-            average_execution_time=average_execution_time,
-            cost_breakdown=cost_breakdown,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to get user summary data: {e}")
-        raise DatabaseError(f"Failed to get user summary data: {e}") from e

autogpt_platform/backend/backend/data/graph_test.py

@@ -2,6 +2,7 @@ import json
 from typing import Any
 from uuid import UUID
 
+import autogpt_libs.auth.models
 import fastapi.exceptions
 import pytest
 from pytest_snapshot.plugin import Snapshot
@@ -316,7 +317,12 @@ async def test_access_store_listing_graph(server: SpinTestServer):
             is_approved=True,
             comments="Test comments",
         ),
-        user_id=admin_user.id,
+        autogpt_libs.auth.models.User(
+            user_id=admin_user.id,
+            role="admin",
+            email=admin_user.email,
+            phone_number="1234567890",
+        ),
     )
 
     # Now we check the graph can be accessed by a user that does not own the graph

autogpt_platform/backend/backend/data/model.py

@@ -96,12 +96,6 @@ class User(BaseModel):
         default=True, description="Notify on monthly summary"
     )
 
-    # User timezone for scheduling and time display
-    timezone: str = Field(
-        default="not-set",
-        description="User timezone (IANA timezone identifier or 'not-set')",
-    )
-
     @classmethod
     def from_db(cls, prisma_user: "PrismaUser") -> "User":
         """Convert a database User object to application User model."""
@@ -155,7 +149,6 @@ class User(BaseModel):
             notify_on_daily_summary=prisma_user.notifyOnDailySummary or True,
             notify_on_weekly_summary=prisma_user.notifyOnWeeklySummary or True,
             notify_on_monthly_summary=prisma_user.notifyOnMonthlySummary or True,
-            timezone=prisma_user.timezone or "not-set",
         )
 
 
@@ -771,9 +764,6 @@ class NodeExecutionStats(BaseModel):
     output_token_count: int = 0
     extra_cost: int = 0
     extra_steps: int = 0
-    # Moderation fields
-    cleared_inputs: Optional[dict[str, list[str]]] = None
-    cleared_outputs: Optional[dict[str, list[str]]] = None
 
     def __iadd__(self, other: "NodeExecutionStats") -> "NodeExecutionStats":
         """Mutate this instance by adding another NodeExecutionStats."""
@@ -828,21 +818,3 @@ class GraphExecutionStats(BaseModel):
     activity_status: Optional[str] = Field(
         default=None, description="AI-generated summary of what the agent did"
     )
-
-
-class UserExecutionSummaryStats(BaseModel):
-    """Summary of user statistics for a specific user."""
-
-    model_config = ConfigDict(
-        extra="allow",
-        arbitrary_types_allowed=True,
-    )
-
-    total_credits_used: float = Field(default=0)
-    total_executions: int = Field(default=0)
-    successful_runs: int = Field(default=0)
-    failed_runs: int = Field(default=0)
-    most_used_agent: str = Field(default="")
-    total_execution_time: float = Field(default=0)
-    average_execution_time: float = Field(default=0)
-    cost_breakdown: dict[str, float] = Field(default_factory=dict)

autogpt_platform/backend/backend/data/notifications.py

@@ -54,6 +54,19 @@ class AgentRunData(BaseNotificationData):
 
 
 class ZeroBalanceData(BaseNotificationData):
+    last_transaction: float
+    last_transaction_time: datetime
+    top_up_link: str
+
+    @field_validator("last_transaction_time")
+    @classmethod
+    def validate_timezone(cls, value: datetime):
+        if value.tzinfo is None:
+            raise ValueError("datetime must have timezone information")
+        return value
+
+
+class LowBalanceData(BaseNotificationData):
     agent_name: str = Field(..., description="Name of the agent")
     current_balance: float = Field(
         ..., description="Current balance in credits (100 = $1)"
@@ -62,13 +75,6 @@ class ZeroBalanceData(BaseNotificationData):
     shortfall: float = Field(..., description="Amount of credits needed to continue")
 
 
-class LowBalanceData(BaseNotificationData):
-    current_balance: float = Field(
-        ..., description="Current balance in credits (100 = $1)"
-    )
-    billing_page_link: str = Field(..., description="Link to billing page")
-
-
 class BlockExecutionFailedData(BaseNotificationData):
     block_name: str
     block_id: str
@@ -175,42 +181,6 @@ class RefundRequestData(BaseNotificationData):
     balance: int
 
 
-class AgentApprovalData(BaseNotificationData):
-    agent_name: str
-    agent_id: str
-    agent_version: int
-    reviewer_name: str
-    reviewer_email: str
-    comments: str
-    reviewed_at: datetime
-    store_url: str
-
-    @field_validator("reviewed_at")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
-class AgentRejectionData(BaseNotificationData):
-    agent_name: str
-    agent_id: str
-    agent_version: int
-    reviewer_name: str
-    reviewer_email: str
-    comments: str
-    reviewed_at: datetime
-    resubmit_url: str
-
-    @field_validator("reviewed_at")
-    @classmethod
-    def validate_timezone(cls, value: datetime):
-        if value.tzinfo is None:
-            raise ValueError("datetime must have timezone information")
-        return value
-
-
 NotificationData = Annotated[
     Union[
         AgentRunData,
@@ -270,8 +240,6 @@ def get_notif_data_type(
         NotificationType.MONTHLY_SUMMARY: MonthlySummaryData,
         NotificationType.REFUND_REQUEST: RefundRequestData,
         NotificationType.REFUND_PROCESSED: RefundRequestData,
-        NotificationType.AGENT_APPROVED: AgentApprovalData,
-        NotificationType.AGENT_REJECTED: AgentRejectionData,
     }[notification_type]
 
 
@@ -306,7 +274,7 @@ class NotificationTypeOverride:
             # These are batched by the notification service
             NotificationType.AGENT_RUN: QueueType.BATCH,
             # These are batched by the notification service, but with a backoff strategy
-            NotificationType.ZERO_BALANCE: QueueType.IMMEDIATE,
+            NotificationType.ZERO_BALANCE: QueueType.BACKOFF,
             NotificationType.LOW_BALANCE: QueueType.IMMEDIATE,
             NotificationType.BLOCK_EXECUTION_FAILED: QueueType.BACKOFF,
             NotificationType.CONTINUOUS_AGENT_ERROR: QueueType.BACKOFF,
@@ -315,8 +283,6 @@ class NotificationTypeOverride:
             NotificationType.MONTHLY_SUMMARY: QueueType.SUMMARY,
             NotificationType.REFUND_REQUEST: QueueType.ADMIN,
             NotificationType.REFUND_PROCESSED: QueueType.ADMIN,
-            NotificationType.AGENT_APPROVED: QueueType.IMMEDIATE,
-            NotificationType.AGENT_REJECTED: QueueType.IMMEDIATE,
         }
         return BATCHING_RULES.get(self.notification_type, QueueType.IMMEDIATE)
 
@@ -334,8 +300,6 @@ class NotificationTypeOverride:
             NotificationType.MONTHLY_SUMMARY: "monthly_summary.html",
             NotificationType.REFUND_REQUEST: "refund_request.html",
             NotificationType.REFUND_PROCESSED: "refund_processed.html",
-            NotificationType.AGENT_APPROVED: "agent_approved.html",
-            NotificationType.AGENT_REJECTED: "agent_rejected.html",
         }[self.notification_type]
 
     @property
@@ -351,8 +315,6 @@ class NotificationTypeOverride:
             NotificationType.MONTHLY_SUMMARY: "We did a lot this month!",
             NotificationType.REFUND_REQUEST: "[ACTION REQUIRED] You got a ${{data.amount / 100}} refund request from {{data.user_name}}",
             NotificationType.REFUND_PROCESSED: "Refund for ${{data.amount / 100}} to {{data.user_name}} has been processed",
-            NotificationType.AGENT_APPROVED: "🎉 Your agent '{{data.agent_name}}' has been approved!",
-            NotificationType.AGENT_REJECTED: "Your agent '{{data.agent_name}}' needs some updates",
         }[self.notification_type]
 
 

autogpt_platform/backend/backend/data/notifications_test.py

@@ -1,151 +0,0 @@
-"""Tests for notification data models."""
-
-from datetime import datetime, timezone
-
-import pytest
-from pydantic import ValidationError
-
-from backend.data.notifications import AgentApprovalData, AgentRejectionData
-
-
-class TestAgentApprovalData:
-    """Test cases for AgentApprovalData model."""
-
-    def test_valid_agent_approval_data(self):
-        """Test creating valid AgentApprovalData."""
-        data = AgentApprovalData(
-            agent_name="Test Agent",
-            agent_id="test-agent-123",
-            agent_version=1,
-            reviewer_name="John Doe",
-            reviewer_email="john@example.com",
-            comments="Great agent, approved!",
-            reviewed_at=datetime.now(timezone.utc),
-            store_url="https://app.autogpt.com/store/test-agent-123",
-        )
-
-        assert data.agent_name == "Test Agent"
-        assert data.agent_id == "test-agent-123"
-        assert data.agent_version == 1
-        assert data.reviewer_name == "John Doe"
-        assert data.reviewer_email == "john@example.com"
-        assert data.comments == "Great agent, approved!"
-        assert data.store_url == "https://app.autogpt.com/store/test-agent-123"
-        assert data.reviewed_at.tzinfo is not None
-
-    def test_agent_approval_data_without_timezone_raises_error(self):
-        """Test that AgentApprovalData raises error without timezone."""
-        with pytest.raises(
-            ValidationError, match="datetime must have timezone information"
-        ):
-            AgentApprovalData(
-                agent_name="Test Agent",
-                agent_id="test-agent-123",
-                agent_version=1,
-                reviewer_name="John Doe",
-                reviewer_email="john@example.com",
-                comments="Great agent, approved!",
-                reviewed_at=datetime.now(),  # No timezone
-                store_url="https://app.autogpt.com/store/test-agent-123",
-            )
-
-    def test_agent_approval_data_with_empty_comments(self):
-        """Test AgentApprovalData with empty comments."""
-        data = AgentApprovalData(
-            agent_name="Test Agent",
-            agent_id="test-agent-123",
-            agent_version=1,
-            reviewer_name="John Doe",
-            reviewer_email="john@example.com",
-            comments="",  # Empty comments
-            reviewed_at=datetime.now(timezone.utc),
-            store_url="https://app.autogpt.com/store/test-agent-123",
-        )
-
-        assert data.comments == ""
-
-
-class TestAgentRejectionData:
-    """Test cases for AgentRejectionData model."""
-
-    def test_valid_agent_rejection_data(self):
-        """Test creating valid AgentRejectionData."""
-        data = AgentRejectionData(
-            agent_name="Test Agent",
-            agent_id="test-agent-123",
-            agent_version=1,
-            reviewer_name="Jane Doe",
-            reviewer_email="jane@example.com",
-            comments="Please fix the security issues before resubmitting.",
-            reviewed_at=datetime.now(timezone.utc),
-            resubmit_url="https://app.autogpt.com/build/test-agent-123",
-        )
-
-        assert data.agent_name == "Test Agent"
-        assert data.agent_id == "test-agent-123"
-        assert data.agent_version == 1
-        assert data.reviewer_name == "Jane Doe"
-        assert data.reviewer_email == "jane@example.com"
-        assert data.comments == "Please fix the security issues before resubmitting."
-        assert data.resubmit_url == "https://app.autogpt.com/build/test-agent-123"
-        assert data.reviewed_at.tzinfo is not None
-
-    def test_agent_rejection_data_without_timezone_raises_error(self):
-        """Test that AgentRejectionData raises error without timezone."""
-        with pytest.raises(
-            ValidationError, match="datetime must have timezone information"
-        ):
-            AgentRejectionData(
-                agent_name="Test Agent",
-                agent_id="test-agent-123",
-                agent_version=1,
-                reviewer_name="Jane Doe",
-                reviewer_email="jane@example.com",
-                comments="Please fix the security issues.",
-                reviewed_at=datetime.now(),  # No timezone
-                resubmit_url="https://app.autogpt.com/build/test-agent-123",
-            )
-
-    def test_agent_rejection_data_with_long_comments(self):
-        """Test AgentRejectionData with long comments."""
-        long_comment = "A" * 1000  # Very long comment
-        data = AgentRejectionData(
-            agent_name="Test Agent",
-            agent_id="test-agent-123",
-            agent_version=1,
-            reviewer_name="Jane Doe",
-            reviewer_email="jane@example.com",
-            comments=long_comment,
-            reviewed_at=datetime.now(timezone.utc),
-            resubmit_url="https://app.autogpt.com/build/test-agent-123",
-        )
-
-        assert data.comments == long_comment
-
-    def test_model_serialization(self):
-        """Test that models can be serialized and deserialized."""
-        original_data = AgentRejectionData(
-            agent_name="Test Agent",
-            agent_id="test-agent-123",
-            agent_version=1,
-            reviewer_name="Jane Doe",
-            reviewer_email="jane@example.com",
-            comments="Please fix the issues.",
-            reviewed_at=datetime.now(timezone.utc),
-            resubmit_url="https://app.autogpt.com/build/test-agent-123",
-        )
-
-        # Serialize to dict
-        data_dict = original_data.model_dump()
-
-        # Deserialize back
-        restored_data = AgentRejectionData.model_validate(data_dict)
-
-        assert restored_data.agent_name == original_data.agent_name
-        assert restored_data.agent_id == original_data.agent_id
-        assert restored_data.agent_version == original_data.agent_version
-        assert restored_data.reviewer_name == original_data.reviewer_name
-        assert restored_data.reviewer_email == original_data.reviewer_email
-        assert restored_data.comments == original_data.comments
-        assert restored_data.reviewed_at == original_data.reviewed_at
-        assert restored_data.resubmit_url == original_data.resubmit_url

autogpt_platform/backend/backend/data/user.py

@@ -208,8 +208,6 @@ async def get_user_notification_preference(user_id: str) -> NotificationPreferen
             NotificationType.DAILY_SUMMARY: user.notifyOnDailySummary or False,
             NotificationType.WEEKLY_SUMMARY: user.notifyOnWeeklySummary or False,
             NotificationType.MONTHLY_SUMMARY: user.notifyOnMonthlySummary or False,
-            NotificationType.AGENT_APPROVED: user.notifyOnAgentApproved or False,
-            NotificationType.AGENT_REJECTED: user.notifyOnAgentRejected or False,
         }
         daily_limit = user.maxEmailsPerDay or 3
         notification_preference = NotificationPreference(
@@ -268,14 +266,6 @@ async def update_user_notification_preference(
             update_data["notifyOnMonthlySummary"] = data.preferences[
                 NotificationType.MONTHLY_SUMMARY
             ]
-        if NotificationType.AGENT_APPROVED in data.preferences:
-            update_data["notifyOnAgentApproved"] = data.preferences[
-                NotificationType.AGENT_APPROVED
-            ]
-        if NotificationType.AGENT_REJECTED in data.preferences:
-            update_data["notifyOnAgentRejected"] = data.preferences[
-                NotificationType.AGENT_REJECTED
-            ]
         if data.daily_limit:
             update_data["maxEmailsPerDay"] = data.daily_limit
 
@@ -296,8 +286,6 @@ async def update_user_notification_preference(
             NotificationType.DAILY_SUMMARY: user.notifyOnDailySummary or True,
             NotificationType.WEEKLY_SUMMARY: user.notifyOnWeeklySummary or True,
             NotificationType.MONTHLY_SUMMARY: user.notifyOnMonthlySummary or True,
-            NotificationType.AGENT_APPROVED: user.notifyOnAgentApproved or True,
-            NotificationType.AGENT_REJECTED: user.notifyOnAgentRejected or True,
         }
         notification_preference = NotificationPreference(
             user_id=user.id,
@@ -396,17 +384,3 @@ async def unsubscribe_user_by_token(token: str) -> None:
         )
     except Exception as e:
         raise DatabaseError(f"Failed to unsubscribe user by token {token}: {e}") from e
-
-
-async def update_user_timezone(user_id: str, timezone: str) -> User:
-    """Update a user's timezone setting."""
-    try:
-        user = await PrismaUser.prisma().update(
-            where={"id": user_id},
-            data={"timezone": timezone},
-        )
-        if not user:
-            raise ValueError(f"User not found with ID: {user_id}")
-        return User.from_db(user)
-    except Exception as e:
-        raise DatabaseError(f"Failed to update timezone for user {user_id}: {e}") from e

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -6,17 +6,20 @@ import json
 import logging
 from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
 
+from autogpt_libs.feature_flag.client import is_feature_enabled
 from pydantic import SecretStr
 
 from backend.blocks.llm import LlmModel, llm_call
 from backend.data.block import get_block
 from backend.data.execution import ExecutionStatus, NodeExecutionResult
 from backend.data.model import APIKeyCredentials, GraphExecutionStats
-from backend.util.feature_flag import Flag, is_feature_enabled
 from backend.util.retry import func_retry
 from backend.util.settings import Settings
 from backend.util.truncate import truncate
 
+# LaunchDarkly feature flag key for AI activity status generation
+AI_ACTIVITY_STATUS_FLAG_KEY = "ai-agent-execution-summary"
+
 if TYPE_CHECKING:
     from backend.executor import DatabaseManagerAsyncClient
 
@@ -100,7 +103,9 @@ async def generate_activity_status_for_execution(
         AI-generated activity status string, or None if feature is disabled
     """
     # Check LaunchDarkly feature flag for AI activity status generation with full context support
-    if not await is_feature_enabled(Flag.AI_ACTIVITY_STATUS, user_id):
+    if not await is_feature_enabled(
+        AI_ACTIVITY_STATUS_FLAG_KEY, user_id, default=False
+    ):
         logger.debug("AI activity status generation is disabled via LaunchDarkly")
         return None
 
@@ -115,7 +120,7 @@ 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,
@@ -18,9 +17,9 @@ 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
 from backend.data.graph import (
     get_connected_output_nodes,
     get_graph,
@@ -36,14 +35,13 @@ from backend.data.notifications import (
 )
 from backend.data.user import (
     get_active_user_ids_in_timerange,
+    get_user_by_id,
     get_user_email_by_id,
     get_user_email_verification,
     get_user_integrations,
     get_user_notification_preference,
     update_user_integrations,
 )
-from backend.server.v2.library.db import add_store_agent_to_library, list_library_agents
-from backend.server.v2.store.db import get_store_agent_details, get_store_agents
 from backend.util.service import (
     AppService,
     AppServiceClient,
@@ -105,13 +103,13 @@ 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)
@@ -132,6 +130,7 @@ class DatabaseManager(AppService):
 
     # User Comms - async
     get_active_user_ids_in_timerange = _(get_active_user_ids_in_timerange)
+    get_user_by_id = _(get_user_by_id)
     get_user_email_by_id = _(get_user_email_by_id)
     get_user_email_verification = _(get_user_email_verification)
     get_user_notification_preference = _(get_user_notification_preference)
@@ -147,17 +146,6 @@ class DatabaseManager(AppService):
         get_user_notification_oldest_message_in_batch
     )
 
-    # Library
-    list_library_agents = _(list_library_agents)
-    add_store_agent_to_library = _(add_store_agent_to_library)
-
-    # Store
-    get_store_agents = _(get_store_agents)
-    get_store_agent_details = _(get_store_agent_details)
-
-    # Summary data - async
-    get_user_execution_summary_data = _(get_user_execution_summary_data)
-
 
 class DatabaseManagerClient(AppServiceClient):
     d = DatabaseManager
@@ -171,12 +159,10 @@ 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)
@@ -188,9 +174,6 @@ class DatabaseManagerClient(AppServiceClient):
     # Block error monitoring
     get_block_error_stats = _(d.get_block_error_stats)
 
-    # User Emails
-    get_user_email_by_id = _(d.get_user_email_by_id)
-
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager
@@ -201,12 +184,16 @@ 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
@@ -216,6 +203,7 @@ class DatabaseManagerAsyncClient(AppServiceClient):
 
     # User Comms
     get_active_user_ids_in_timerange = d.get_active_user_ids_in_timerange
+    get_user_by_id = d.get_user_by_id
     get_user_email_by_id = d.get_user_email_by_id
     get_user_email_verification = d.get_user_email_verification
     get_user_notification_preference = d.get_user_notification_preference
@@ -230,14 +218,3 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_user_notification_oldest_message_in_batch = (
         d.get_user_notification_oldest_message_in_batch
     )
-
-    # 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
-
-    # Summary data
-    get_user_execution_summary_data = d.get_user_execution_summary_data

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

@@ -5,15 +5,37 @@ 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.io import AgentOutputBlock
+from backend.data.model import GraphExecutionStats, NodeExecutionStats
+from backend.data.notifications import (
+    AgentRunData,
+    LowBalanceData,
+    NotificationEventModel,
+    NotificationType,
+)
+from backend.data.rabbitmq import SyncRabbitMQ
+from backend.executor.activity_status_generator import (
+    generate_activity_status_for_execution,
+)
+from backend.executor.utils import LogMetadata
+from backend.notifications.notifications import queue_notification
+from backend.util.exceptions import InsufficientBalanceError
+
+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.blocks.io import AgentOutputBlock
+from backend.data import redis_client as redis
 from backend.data.block import (
     BlockData,
     BlockInput,
@@ -25,28 +47,18 @@ from backend.data.credit import UsageTransactionMetadata
 from backend.data.execution import (
     ExecutionQueue,
     ExecutionStatus,
+    GraphExecution,
     GraphExecutionEntry,
     NodeExecutionEntry,
-    UserContext,
+    NodeExecutionResult,
 )
 from backend.data.graph import Link, Node
-from backend.data.model import GraphExecutionStats, NodeExecutionStats
-from backend.data.notifications import (
-    AgentRunData,
-    LowBalanceData,
-    NotificationEventModel,
-    NotificationType,
-    ZeroBalanceData,
-)
-from backend.data.rabbitmq import SyncRabbitMQ
-from backend.executor.execution_data import ExecutionDataClient
 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,
@@ -55,20 +67,21 @@ 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,
+)
 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
 from backend.util.process import AppProcess, set_service_name
 from backend.util.retry import continuous_retry, func_retry
 from backend.util.settings import Settings
@@ -120,6 +133,7 @@ async def execute_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.
@@ -175,9 +189,6 @@ async def execute_node(
         "user_id": user_id,
     }
 
-    # Add user context from NodeExecutionEntry
-    extra_exec_kwargs["user_context"] = data.user_context
-
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
     # changes during execution. ⚠️ This means a set of credentials can only be used by
     # one (running) block at a time; simultaneous execution of blocks using same
@@ -216,7 +227,7 @@ async def execute_node(
 
 
 async def _enqueue_next_nodes(
-    execution_data_client: ExecutionDataClient,
+    db_client: "DatabaseManagerAsyncClient",
     node: Node,
     output: BlockData,
     user_id: str,
@@ -224,12 +235,12 @@ async def _enqueue_next_nodes(
     graph_id: str,
     log_metadata: LogMetadata,
     nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]],
-    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,
@@ -242,7 +253,6 @@ async def _enqueue_next_nodes(
             node_id=node_id,
             block_id=block_id,
             inputs=data,
-            user_context=user_context,
         )
 
     async def register_next_executions(node_link: Link) -> list[NodeExecutionEntry]:
@@ -262,22 +272,21 @@ 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 +298,8 @@ 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 +338,9 @@ 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
@@ -394,9 +404,6 @@ 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,
@@ -414,7 +421,8 @@ 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 +430,7 @@ 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 +454,15 @@ 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 +475,22 @@ 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,
     ) -> 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 +502,8 @@ 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 +564,6 @@ 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 +583,9 @@ 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 +595,12 @@ 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 +610,9 @@ 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 +643,12 @@ 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,
                 ),
@@ -650,32 +663,33 @@ 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,
         node_exec: NodeExecutionEntry,
         execution_count: int,
-    ) -> tuple[int, 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.")
-            return total_cost, 0
+            return total_cost
 
         cost, matching_filter = block_usage_cost(
             block=block, input_data=node_exec.inputs
         )
         if cost > 0:
-            remaining_balance = self.execution_data.spend_credits(
+            db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -693,7 +707,7 @@ class ExecutionProcessor:
 
         cost, usage_count = execution_usage_cost(execution_count)
         if cost > 0:
-            remaining_balance = self.execution_data.spend_credits(
+            db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -708,7 +722,7 @@ class ExecutionProcessor:
             )
             total_cost += cost
 
-        return total_cost, remaining_balance
+        return total_cost
 
     @time_measured
     def _on_graph_execution(
@@ -726,6 +740,7 @@ 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 +751,7 @@ 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.",
@@ -744,53 +759,18 @@ class ExecutionProcessor:
                     amount=1,
                 )
 
-            # Input moderation
-            try:
-                if moderation_error := asyncio.run_coroutine_threadsafe(
-                    automod_manager.moderate_graph_execution_inputs(
-                        db_client=self.execution_data.db_client_async,
-                        graph_exec=graph_exec,
-                    ),
-                    self.node_evaluation_loop,
-                ).result(timeout=30.0):
-                    raise moderation_error
-            except asyncio.TimeoutError:
-                log_metadata.warning(
-                    f"Input moderation timed out for graph execution {graph_exec.graph_exec_id}, bypassing moderation and continuing execution"
-                )
-                # Continue execution without moderation
-
             # ------------------------------------------------------------
             # 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"
-            )
+            ):
+                execution_queue.add(node_exec.to_node_execution_entry())
 
             # ------------------------------------------------------------
             # Main dispatch / polling loop -----------------------------
@@ -808,36 +788,31 @@ class ExecutionProcessor:
 
                 # Charge usage (may raise) ------------------------------
                 try:
-                    cost, remaining_balance = self._charge_usage(
+                    cost = 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(
-                        user_id=graph_exec.user_id,
-                        current_balance=remaining_balance,
-                        transaction_cost=cost,
-                    )
                 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(
+                    self._handle_low_balance_notif(
+                        db_client,
                         graph_exec.user_id,
                         graph_exec.graph_id,
+                        execution_stats,
                         error,
                     )
                     # Gracefully stop the execution loop
@@ -922,26 +897,6 @@ 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,
-                        graph_exec_id=graph_exec.graph_exec_id,
-                        user_id=graph_exec.user_id,
-                        graph_id=graph_exec.graph_id,
-                    ),
-                    self.node_evaluation_loop,
-                ).result(timeout=30.0):
-                    raise moderation_error
-            except asyncio.TimeoutError:
-                log_metadata.warning(
-                    f"Output moderation timed out for graph execution {graph_exec.graph_exec_id}, bypassing moderation and continuing execution"
-                )
-                # Continue execution without moderation
-
             # Determine final execution status based on whether there was an error or termination
             if cancel.is_set():
                 execution_status = ExecutionStatus.TERMINATED
@@ -962,12 +917,11 @@ class ExecutionProcessor:
                 else Exception(f"{e.__class__.__name__}: {e}")
             )
 
-            known_errors = (InsufficientBalanceError, ModerationError)
+            known_errors = (InsufficientBalanceError,)
             if isinstance(error, known_errors):
                 execution_stats.error = str(error)
                 return ExecutionStatus.FAILED
 
-            execution_status = ExecutionStatus.FAILED
             log_metadata.exception(
                 f"Failed graph execution {graph_exec.graph_exec_id}: {error}"
             )
@@ -982,6 +936,7 @@ 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 +949,7 @@ 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 +983,8 @@ 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,
@@ -1055,10 +1012,11 @@ class ExecutionProcessor:
             nodes_input_masks: Optional map of node input overrides
             execution_queue: Queue to add next executions to
         """
-        log_metadata.debug(f"Enqueue nodes for {node_id}: {output}")
+        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,
@@ -1066,19 +1024,20 @@ class ExecutionProcessor:
             graph_id=graph_exec.graph_id,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
-            user_context=graph_exec.user_context,
         ):
             execution_queue.add(next_execution)
 
     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],
         )
 
@@ -1105,24 +1064,25 @@ class ExecutionProcessor:
             )
         )
 
-    def _handle_insufficient_funds_notif(
+    def _handle_low_balance_notif(
         self,
+        db_client: "DatabaseManagerClient",
         user_id: str,
         graph_id: str,
+        exec_stats: GraphExecutionStats,
         e: InsufficientBalanceError,
     ):
-        shortfall = abs(e.amount) - e.balance
-        metadata = self.execution_data.get_graph_metadata(graph_id)
+        shortfall = e.balance - e.amount
+        metadata = db_client.get_graph_metadata(graph_id)
         base_url = (
             settings.config.frontend_base_url or settings.config.platform_base_url
         )
-
         queue_notification(
             NotificationEventModel(
                 user_id=user_id,
-                type=NotificationType.ZERO_BALANCE,
-                data=ZeroBalanceData(
-                    current_balance=e.balance,
+                type=NotificationType.LOW_BALANCE,
+                data=LowBalanceData(
+                    current_balance=exec_stats.cost,
                     billing_page_link=f"{base_url}/profile/credits",
                     shortfall=shortfall,
                     agent_name=metadata.name if metadata else "Unknown Agent",
@@ -1130,72 +1090,6 @@ class ExecutionProcessor:
             )
         )
 
-        try:
-            user_email = self.execution_data.get_user_email_by_id(user_id)
-
-            alert_message = (
-                f"❌ **Insufficient Funds Alert**\n"
-                f"User: {user_email or user_id}\n"
-                f"Agent: {metadata.name if metadata else 'Unknown Agent'}\n"
-                f"Current balance: ${e.balance/100:.2f}\n"
-                f"Attempted cost: ${abs(e.amount)/100:.2f}\n"
-                f"Shortfall: ${abs(shortfall)/100:.2f}\n"
-                f"[View User Details]({base_url}/admin/spending?search={user_email})"
-            )
-
-            get_notification_manager_client().discord_system_alert(
-                alert_message, DiscordChannel.PRODUCT
-            )
-        except Exception as alert_error:
-            logger.error(
-                f"Failed to send insufficient funds Discord alert: {alert_error}"
-            )
-
-    def _handle_low_balance(
-        self,
-        user_id: str,
-        current_balance: int,
-        transaction_cost: int,
-    ):
-        """Check and handle low balance scenarios after a transaction"""
-        LOW_BALANCE_THRESHOLD = settings.config.low_balance_threshold
-
-        balance_before = current_balance + transaction_cost
-
-        if (
-            current_balance < LOW_BALANCE_THRESHOLD
-            and balance_before >= LOW_BALANCE_THRESHOLD
-        ):
-            base_url = (
-                settings.config.frontend_base_url or settings.config.platform_base_url
-            )
-            queue_notification(
-                NotificationEventModel(
-                    user_id=user_id,
-                    type=NotificationType.LOW_BALANCE,
-                    data=LowBalanceData(
-                        current_balance=current_balance,
-                        billing_page_link=f"{base_url}/profile/credits",
-                    ),
-                )
-            )
-
-            try:
-                user_email = self.execution_data.get_user_email_by_id(user_id)
-                alert_message = (
-                    f"⚠️ **Low Balance Alert**\n"
-                    f"User: {user_email or user_id}\n"
-                    f"Balance dropped below ${LOW_BALANCE_THRESHOLD/100:.2f}\n"
-                    f"Current balance: ${current_balance/100:.2f}\n"
-                    f"Transaction cost: ${transaction_cost/100:.2f}\n"
-                    f"[View User Details]({base_url}/admin/spending?search={user_email})"
-                )
-                get_notification_manager_client().discord_system_alert(
-                    alert_message, DiscordChannel.PRODUCT
-                )
-            except Exception as e:
-                logger.error(f"Failed to send low balance Discord alert: {e}")
-
 
 class ExecutionManager(AppProcess):
     def __init__(self):
@@ -1277,9 +1171,6 @@ class ExecutionManager(AppProcess):
             )
             return
 
-        # Check if channel is closed and force reconnection if needed
-        if not self.cancel_client.is_ready:
-            self.cancel_client.disconnect()
         self.cancel_client.connect()
         cancel_channel = self.cancel_client.get_channel()
         cancel_channel.basic_consume(
@@ -1309,9 +1200,6 @@ class ExecutionManager(AppProcess):
             )
             return
 
-        # Check if channel is closed and force reconnection if needed
-        if not self.run_client.is_ready:
-            self.run_client.disconnect()
         self.run_client.connect()
         run_channel = self.run_client.get_channel()
         run_channel.basic_qos(prefetch_count=self.pool_size)
@@ -1377,14 +1265,14 @@ class ExecutionManager(AppProcess):
         delivery_tag = method.delivery_tag
 
         @func_retry
-        def _ack_message(reject: bool, requeue: bool):
+        def _ack_message(reject: bool = False):
             """Acknowledge or reject the message based on execution status."""
 
             # Connection can be lost, so always get a fresh channel
             channel = self.run_client.get_channel()
             if reject:
                 channel.connection.add_callback_threadsafe(
-                    lambda: channel.basic_nack(delivery_tag, requeue=requeue)
+                    lambda: channel.basic_nack(delivery_tag, requeue=True)
                 )
             else:
                 channel.connection.add_callback_threadsafe(
@@ -1396,13 +1284,13 @@ class ExecutionManager(AppProcess):
             logger.info(
                 f"[{self.service_name}] Rejecting new execution during shutdown"
             )
-            _ack_message(reject=True, requeue=True)
+            _ack_message(reject=True)
             return
 
         # Check if we can accept more runs
         self._cleanup_completed_runs()
         if len(self.active_graph_runs) >= self.pool_size:
-            _ack_message(reject=True, requeue=True)
+            _ack_message(reject=True)
             return
 
         try:
@@ -1411,7 +1299,7 @@ class ExecutionManager(AppProcess):
             logger.error(
                 f"[{self.service_name}] Could not parse run message: {e}, body={body}"
             )
-            _ack_message(reject=True, requeue=False)
+            _ack_message(reject=True)
             return
 
         graph_exec_id = graph_exec_entry.graph_exec_id
@@ -1423,7 +1311,7 @@ class ExecutionManager(AppProcess):
             logger.error(
                 f"[{self.service_name}] Graph {graph_exec_id} already running; rejecting duplicate run."
             )
-            _ack_message(reject=True, requeue=False)
+            _ack_message(reject=True)
             return
 
         cancel_event = threading.Event()
@@ -1439,9 +1327,9 @@ class ExecutionManager(AppProcess):
                     logger.error(
                         f"[{self.service_name}] Execution for {graph_exec_id} failed: {type(exec_error)} {exec_error}"
                     )
-                    _ack_message(reject=True, requeue=True)
+                    _ack_message(reject=True)
                 else:
-                    _ack_message(reject=False, requeue=False)
+                    _ack_message(reject=False)
             except BaseException as e:
                 logger.exception(
                     f"[{self.service_name}] Error in run completion callback: {e}"
@@ -1559,3 +1447,117 @@ 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

@@ -1,202 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-from prisma.enums import NotificationType
-
-from backend.data.notifications import LowBalanceData
-from backend.executor.manager import ExecutionProcessor
-from backend.util.test import SpinTestServer
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_low_balance_threshold_crossing(server: SpinTestServer):
-    """Test that _handle_low_balance triggers notification when crossing threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 400  # $4 - below $5 threshold
-    transaction_cost = 600  # $6 transaction
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        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"
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # Verify notification was queued
-        mock_queue_notif.assert_called_once()
-        notification_call = mock_queue_notif.call_args[0][0]
-
-        # Verify notification details
-        assert notification_call.type == NotificationType.LOW_BALANCE
-        assert notification_call.user_id == user_id
-        assert isinstance(notification_call.data, LowBalanceData)
-        assert notification_call.data.current_balance == current_balance
-
-        # Verify Discord alert was sent
-        mock_client.discord_system_alert.assert_called_once()
-        discord_message = mock_client.discord_system_alert.call_args[0][0]
-        assert "Low Balance Alert" in discord_message
-        assert "test@example.com" in discord_message
-        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(
-    server: SpinTestServer,
-):
-    """Test that no notification is sent when not crossing the threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 600  # $6 - above $5 threshold
-    transaction_cost = (
-        100  # $1 transaction (balance before was $7, still above threshold)
-    )
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        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"
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # Verify no notification was sent
-        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(
-    server: SpinTestServer,
-):
-    """Test that no notification is sent when already below threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 300  # $3 - below $5 threshold
-    transaction_cost = (
-        100  # $1 transaction (balance before was $4, also below threshold)
-    )
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        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"
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # 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

@@ -1,5 +1,6 @@
 import logging
 
+import autogpt_libs.auth.models
 import fastapi.responses
 import pytest
 
@@ -520,7 +521,12 @@ async def test_store_listing_graph(server: SpinTestServer):
             is_approved=True,
             comments="Test comments",
         ),
-        user_id=admin_user.id,
+        autogpt_libs.auth.models.User(
+            user_id=admin_user.id,
+            role="admin",
+            email=admin_user.email,
+            phone_number="1234567890",
+        ),
     )
     alt_test_user = admin_user
 

autogpt_platform/backend/backend/executor/scheduler.py

@@ -17,7 +17,6 @@ from apscheduler.jobstores.memory import MemoryJobStore
 from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore
 from apscheduler.schedulers.background import BackgroundScheduler
 from apscheduler.triggers.cron import CronTrigger
-from apscheduler.util import ZoneInfo
 from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy import MetaData, create_engine
@@ -270,9 +269,7 @@ class Scheduler(AppService):
 
         self.scheduler = BackgroundScheduler(
             executors={
-                "default": ThreadPoolExecutor(
-                    max_workers=self.db_pool_size()
-                ),  # Match DB pool size to prevent resource contention
+                "default": ThreadPoolExecutor(max_workers=10),  # Max 10 concurrent jobs
             },
             job_defaults={
                 "coalesce": True,  # Skip redundant missed jobs - just run the latest
@@ -304,15 +301,13 @@ class Scheduler(AppService):
                 Jobstores.WEEKLY_NOTIFICATIONS.value: MemoryJobStore(),
             },
             logger=apscheduler_logger,
-            timezone=ZoneInfo("UTC"),
         )
 
         if self.register_system_tasks:
             # Notification PROCESS WEEKLY SUMMARY
-            # Runs every Monday at 9 AM UTC
             self.scheduler.add_job(
                 process_weekly_summary,
-                CronTrigger.from_crontab("0 9 * * 1"),
+                CronTrigger.from_crontab("0 * * * *"),
                 id="process_weekly_summary",
                 kwargs={},
                 replace_existing=True,
@@ -408,8 +403,6 @@ class Scheduler(AppService):
             )
         )
 
-        logger.info(f"Scheduling job for user {user_id} in UTC (cron: {cron})")
-
         job_args = GraphExecutionJobArgs(
             user_id=user_id,
             graph_id=graph_id,
@@ -422,12 +415,12 @@ class Scheduler(AppService):
             execute_graph,
             kwargs=job_args.model_dump(),
             name=name,
-            trigger=CronTrigger.from_crontab(cron, timezone="UTC"),
+            trigger=CronTrigger.from_crontab(cron),
             jobstore=Jobstores.EXECUTION.value,
             replace_existing=True,
         )
         logger.info(
-            f"Added job {job.id} with cron schedule '{cron}' in UTC, input data: {input_data}"
+            f"Added job {job.id} with cron schedule '{cron}' input data: {input_data}"
         )
         return GraphExecutionJobInfo.from_db(job_args, job)
 

autogpt_platform/backend/backend/executor/utils.py

@@ -18,12 +18,10 @@ from backend.data.execution import (
     ExecutionStatus,
     GraphExecutionStats,
     GraphExecutionWithNodes,
-    UserContext,
 )
 from backend.data.graph import GraphModel, Node
 from backend.data.model import CredentialsMetaInput
 from backend.data.rabbitmq import Exchange, ExchangeType, Queue, RabbitMQConfig
-from backend.data.user import get_user_by_id
 from backend.util.clients import (
     get_async_execution_event_bus,
     get_async_execution_queue,
@@ -36,27 +34,6 @@ from backend.util.mock import MockObject
 from backend.util.settings import Config
 from backend.util.type import convert
 
-
-async def get_user_context(user_id: str) -> UserContext:
-    """
-    Get UserContext for a user, always returns a valid context with timezone.
-    Defaults to UTC if user has no timezone set.
-    """
-    user_context = UserContext(timezone="UTC")  # Default to UTC
-    try:
-        user = await get_user_by_id(user_id)
-        if user and user.timezone and user.timezone != "not-set":
-            user_context.timezone = user.timezone
-            logger.debug(f"Retrieved user context: timezone={user.timezone}")
-        else:
-            logger.debug("User has no timezone set, using UTC")
-    except Exception as e:
-        logger.warning(f"Could not fetch user timezone: {e}")
-        # Continue with UTC as default
-
-    return user_context
-
-
 config = Config()
 logger = TruncatedLogger(logging.getLogger(__name__), prefix="[GraphExecutorUtil]")
 
@@ -571,7 +548,7 @@ async def validate_graph_with_credentials(
     return node_input_errors
 
 
-async def _construct_starting_node_execution_input(
+async def _construct_node_execution_input(
     graph: GraphModel,
     user_id: str,
     graph_inputs: BlockInput,
@@ -645,7 +622,7 @@ async def validate_and_construct_node_execution_input(
     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]]]:
+) -> tuple[GraphModel, list[tuple[str, BlockInput]]]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -689,14 +666,14 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks or {},
     )
 
-    starting_nodes_input = await _construct_starting_node_execution_input(
+    starting_nodes_input = await _construct_node_execution_input(
         graph=graph,
         user_id=user_id,
         graph_inputs=graph_inputs,
         nodes_input_masks=nodes_input_masks,
     )
 
-    return graph, starting_nodes_input, nodes_input_masks
+    return graph, starting_nodes_input
 
 
 def _merge_nodes_input_masks(
@@ -879,15 +856,13 @@ async def add_graph_execution(
     else:
         edb = get_database_manager_async_client()
 
-    graph, starting_nodes_input, nodes_input_masks = (
-        await validate_and_construct_node_execution_input(
-            graph_id=graph_id,
-            user_id=user_id,
-            graph_inputs=inputs or {},
-            graph_version=graph_version,
-            graph_credentials_inputs=graph_credentials_inputs,
-            nodes_input_masks=nodes_input_masks,
-        )
+    graph, starting_nodes_input = await validate_and_construct_node_execution_input(
+        graph_id=graph_id,
+        user_id=user_id,
+        graph_inputs=inputs or {},
+        graph_version=graph_version,
+        graph_credentials_inputs=graph_credentials_inputs,
+        nodes_input_masks=nodes_input_masks,
     )
     graph_exec = None
 
@@ -900,11 +875,8 @@ async def add_graph_execution(
             preset_id=preset_id,
         )
 
-        # Fetch user context for the graph execution
-        user_context = await get_user_context(user_id)
-
         queue = await get_async_execution_queue()
-        graph_exec_entry = graph_exec.to_graph_execution_entry(user_context)
+        graph_exec_entry = graph_exec.to_graph_execution_entry()
         if nodes_input_masks:
             graph_exec_entry.nodes_input_masks = nodes_input_masks
 

autogpt_platform/backend/backend/integrations/credentials_store.py

@@ -182,15 +182,6 @@ zerobounce_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
-enrichlayer_credentials = APIKeyCredentials(
-    id="d9fce73a-6c1d-4e8b-ba2e-12a456789def",
-    provider="enrichlayer",
-    api_key=SecretStr(settings.secrets.enrichlayer_api_key),
-    title="Use Credits for Enrichlayer",
-    expires_at=None,
-)
-
-
 llama_api_credentials = APIKeyCredentials(
     id="d44045af-1c33-4833-9e19-752313214de2",
     provider="llama_api",
@@ -199,14 +190,6 @@ llama_api_credentials = APIKeyCredentials(
     expires_at=None,
 )
 
-v0_credentials = APIKeyCredentials(
-    id="c4e6d1a0-3b5f-4789-a8e2-9b123456789f",
-    provider="v0",
-    api_key=SecretStr(settings.secrets.v0_api_key),
-    title="Use Credits for v0 by Vercel",
-    expires_at=None,
-)
-
 DEFAULT_CREDENTIALS = [
     ollama_credentials,
     revid_credentials,
@@ -220,7 +203,6 @@ DEFAULT_CREDENTIALS = [
     jina_credentials,
     unreal_credentials,
     open_router_credentials,
-    enrichlayer_credentials,
     fal_credentials,
     exa_credentials,
     e2b_credentials,
@@ -231,8 +213,6 @@ DEFAULT_CREDENTIALS = [
     smartlead_credentials,
     zerobounce_credentials,
     google_maps_credentials,
-    llama_api_credentials,
-    v0_credentials,
 ]
 
 
@@ -299,8 +279,6 @@ class IntegrationCredentialsStore:
             all_credentials.append(unreal_credentials)
         if settings.secrets.open_router_api_key:
             all_credentials.append(open_router_credentials)
-        if settings.secrets.enrichlayer_api_key:
-            all_credentials.append(enrichlayer_credentials)
         if settings.secrets.fal_api_key:
             all_credentials.append(fal_credentials)
         if settings.secrets.exa_api_key:

autogpt_platform/backend/backend/integrations/oauth/__init__.py

@@ -4,7 +4,6 @@ from pydantic import BaseModel
 
 from backend.integrations.oauth.todoist import TodoistOAuthHandler
 
-from .discord import DiscordOAuthHandler
 from .github import GitHubOAuthHandler
 from .google import GoogleOAuthHandler
 from .notion import NotionOAuthHandler
@@ -16,7 +15,6 @@ if TYPE_CHECKING:
 # --8<-- [start:HANDLERS_BY_NAMEExample]
 # Build handlers dict with string keys for compatibility with SDK auto-registration
 _ORIGINAL_HANDLERS = [
-    DiscordOAuthHandler,
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,

autogpt_platform/backend/backend/integrations/oauth/discord.py

@@ -1,175 +0,0 @@
-import time
-from typing import Optional
-from urllib.parse import urlencode
-
-from backend.data.model import OAuth2Credentials
-from backend.integrations.providers import ProviderName
-from backend.util.request import Requests
-
-from .base import BaseOAuthHandler
-
-
-class DiscordOAuthHandler(BaseOAuthHandler):
-    """
-    Discord OAuth2 handler implementation.
-
-    Based on the documentation at:
-    - https://discord.com/developers/docs/topics/oauth2
-
-    Discord OAuth2 tokens expire after 7 days by default and include refresh tokens.
-    """
-
-    PROVIDER_NAME = ProviderName.DISCORD
-    DEFAULT_SCOPES = ["identify"]  # Basic user information
-
-    def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.redirect_uri = redirect_uri
-        self.auth_base_url = "https://discord.com/oauth2/authorize"
-        self.token_url = "https://discord.com/api/oauth2/token"
-        self.revoke_url = "https://discord.com/api/oauth2/token/revoke"
-
-    def get_login_url(
-        self, scopes: list[str], state: str, code_challenge: Optional[str]
-    ) -> str:
-        # Handle default scopes
-        scopes = self.handle_default_scopes(scopes)
-
-        params = {
-            "client_id": self.client_id,
-            "redirect_uri": self.redirect_uri,
-            "response_type": "code",
-            "scope": " ".join(scopes),
-            "state": state,
-        }
-
-        # Discord supports PKCE
-        if code_challenge:
-            params["code_challenge"] = code_challenge
-            params["code_challenge_method"] = "S256"
-
-        return f"{self.auth_base_url}?{urlencode(params)}"
-
-    async def exchange_code_for_tokens(
-        self, code: str, scopes: list[str], code_verifier: Optional[str]
-    ) -> OAuth2Credentials:
-        params = {
-            "code": code,
-            "redirect_uri": self.redirect_uri,
-            "grant_type": "authorization_code",
-        }
-
-        # Include PKCE verifier if provided
-        if code_verifier:
-            params["code_verifier"] = code_verifier
-
-        return await self._request_tokens(params)
-
-    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
-        if not credentials.access_token:
-            raise ValueError("No access token to revoke")
-
-        # Discord requires client authentication for token revocation
-        data = {
-            "token": credentials.access_token.get_secret_value(),
-            "token_type_hint": "access_token",
-        }
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-        }
-
-        response = await Requests().post(
-            url=self.revoke_url,
-            data=data,
-            headers=headers,
-            auth=(self.client_id, self.client_secret),
-        )
-
-        # Discord returns 200 OK for successful revocation
-        return response.status == 200
-
-    async def _refresh_tokens(
-        self, credentials: OAuth2Credentials
-    ) -> OAuth2Credentials:
-        if not credentials.refresh_token:
-            return credentials
-
-        return await self._request_tokens(
-            {
-                "refresh_token": credentials.refresh_token.get_secret_value(),
-                "grant_type": "refresh_token",
-            },
-            current_credentials=credentials,
-        )
-
-    async def _request_tokens(
-        self,
-        params: dict[str, str],
-        current_credentials: Optional[OAuth2Credentials] = None,
-    ) -> OAuth2Credentials:
-        request_body = {
-            "client_id": self.client_id,
-            "client_secret": self.client_secret,
-            **params,
-        }
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-        }
-
-        response = await Requests().post(
-            self.token_url, data=request_body, headers=headers
-        )
-        token_data: dict = response.json()
-
-        # Get username if this is a new token request
-        username = None
-        if "access_token" in token_data:
-            username = await self._request_username(token_data["access_token"])
-
-        now = int(time.time())
-        new_credentials = OAuth2Credentials(
-            provider=self.PROVIDER_NAME,
-            title=current_credentials.title if current_credentials else None,
-            username=username,
-            access_token=token_data["access_token"],
-            scopes=token_data.get("scope", "").split()
-            or (current_credentials.scopes if current_credentials else []),
-            refresh_token=token_data.get("refresh_token"),
-            # Discord tokens expire after expires_in seconds (typically 7 days)
-            access_token_expires_at=(
-                now + expires_in
-                if (expires_in := token_data.get("expires_in", None))
-                else None
-            ),
-            # Discord doesn't provide separate refresh token expiration
-            refresh_token_expires_at=None,
-        )
-
-        if current_credentials:
-            new_credentials.id = current_credentials.id
-
-        return new_credentials
-
-    async def _request_username(self, access_token: str) -> str | None:
-        """
-        Fetch the username using the Discord OAuth2 @me endpoint.
-        """
-        url = "https://discord.com/api/oauth2/@me"
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-        }
-
-        response = await Requests().get(url, headers=headers)
-
-        if not response.ok:
-            return None
-
-        # Get user info from the response
-        data = response.json()
-        user_info = data.get("user", {})
-
-        # Return username (without discriminator)
-        return user_info.get("username")

autogpt_platform/backend/backend/integrations/providers.py

@@ -25,7 +25,6 @@ class ProviderName(str, Enum):
     GROQ = "groq"
     HTTP = "http"
     HUBSPOT = "hubspot"
-    ENRICHLAYER = "enrichlayer"
     IDEOGRAM = "ideogram"
     JINA = "jina"
     LLAMA_API = "llama_api"
@@ -48,7 +47,6 @@ class ProviderName(str, Enum):
     TWITTER = "twitter"
     TODOIST = "todoist"
     UNREAL_SPEECH = "unreal_speech"
-    V0 = "v0"
     ZEROBOUNCE = "zerobounce"
 
     @classmethod

autogpt_platform/backend/backend/notifications/notifications.py

@@ -29,7 +29,7 @@ from backend.data.user import generate_unsubscribe_link
 from backend.notifications.email import EmailSender
 from backend.util.clients import get_database_manager_async_client
 from backend.util.logging import TruncatedLogger
-from backend.util.metrics import DiscordChannel, discord_send_alert
+from backend.util.metrics import discord_send_alert
 from backend.util.retry import continuous_retry
 from backend.util.service import (
     AppService,
@@ -223,14 +223,10 @@ class NotificationManager(AppService):
             processed_count = 0
             current_time = datetime.now(tz=timezone.utc)
             start_time = current_time - timedelta(days=7)
-            logger.info(
-                f"Querying for active users between {start_time} and {current_time}"
-            )
             users = await get_database_manager_async_client().get_active_user_ids_in_timerange(
                 end_time=current_time.isoformat(),
                 start_time=start_time.isoformat(),
             )
-            logger.info(f"Found {len(users)} active users in the last 7 days")
             for user in users:
                 await self._queue_scheduled_notification(
                     SummaryParamsEventModel(
@@ -382,21 +378,16 @@ class NotificationManager(AppService):
             }
 
     @expose
-    async def discord_system_alert(
-        self, content: str, channel: DiscordChannel = DiscordChannel.PLATFORM
-    ):
-        await discord_send_alert(content, channel)
+    async def discord_system_alert(self, content: str):
+        await discord_send_alert(content)
 
     async def _queue_scheduled_notification(self, event: SummaryParamsEventModel):
         """Queue a scheduled notification - exposed method for other services to call"""
         try:
-            logger.info(
-                f"Queueing scheduled notification type={event.type} user_id={event.user_id}"
-            )
+            logger.debug(f"Received Request to queue scheduled notification {event=}")
 
             exchange = "notifications"
             routing_key = get_routing_key(event.type)
-            logger.info(f"Using routing key: {routing_key}")
 
             # Publish to RabbitMQ
             await self.rabbit.publish_message(
@@ -404,7 +395,6 @@ class NotificationManager(AppService):
                 message=event.model_dump_json(),
                 exchange=next(ex for ex in EXCHANGES if ex.name == exchange),
             )
-            logger.info(f"Successfully queued notification for user {event.user_id}")
 
         except Exception as e:
             logger.exception(f"Error queueing notification: {e}")
@@ -426,99 +416,85 @@ class NotificationManager(AppService):
         # only if both are true, should we email this person
         return validated_email and preference
 
-    async def _gather_summary_data(
+    def _gather_summary_data(
         self, user_id: str, event_type: NotificationType, params: BaseSummaryParams
     ) -> BaseSummaryData:
         """Gathers the data to build a summary notification"""
 
         logger.info(
-            f"Gathering summary data for {user_id} and {event_type} with {params=}"
+            f"Gathering summary data for {user_id} and {event_type} wiht {params=}"
         )
 
-        try:
-            # Get summary data from the database
-            summary_data = await get_database_manager_async_client().get_user_execution_summary_data(
-                user_id=user_id,
-                start_time=params.start_date,
-                end_time=params.end_date,
+        # total_credits_used = self.run_and_wait(
+        #     get_total_credits_used(user_id, start_time, end_time)
+        # )
+
+        # total_executions = self.run_and_wait(
+        #     get_total_executions(user_id, start_time, end_time)
+        # )
+
+        # most_used_agent = self.run_and_wait(
+        #     get_most_used_agent(user_id, start_time, end_time)
+        # )
+
+        # execution_times = self.run_and_wait(
+        #     get_execution_time(user_id, start_time, end_time)
+        # )
+
+        # runs = self.run_and_wait(
+        #     get_runs(user_id, start_time, end_time)
+        # )
+        total_credits_used = 3.0
+        total_executions = 2
+        most_used_agent = {"name": "Some"}
+        execution_times = [1, 2, 3]
+        runs = [{"status": "COMPLETED"}, {"status": "FAILED"}]
+
+        successful_runs = len([run for run in runs if run["status"] == "COMPLETED"])
+        failed_runs = len([run for run in runs if run["status"] != "COMPLETED"])
+        average_execution_time = (
+            sum(execution_times) / len(execution_times) if execution_times else 0
+        )
+        # cost_breakdown = self.run_and_wait(
+        #     get_cost_breakdown(user_id, start_time, end_time)
+        # )
+
+        cost_breakdown = {
+            "agent1": 1.0,
+            "agent2": 2.0,
+        }
+
+        if event_type == NotificationType.DAILY_SUMMARY and isinstance(
+            params, DailySummaryParams
+        ):
+            return DailySummaryData(
+                total_credits_used=total_credits_used,
+                total_executions=total_executions,
+                most_used_agent=most_used_agent["name"],
+                total_execution_time=sum(execution_times),
+                successful_runs=successful_runs,
+                failed_runs=failed_runs,
+                average_execution_time=average_execution_time,
+                cost_breakdown=cost_breakdown,
+                date=params.date,
             )
-
-            # Extract data from summary
-            total_credits_used = summary_data.total_credits_used
-            total_executions = summary_data.total_executions
-            most_used_agent = summary_data.most_used_agent
-            successful_runs = summary_data.successful_runs
-            failed_runs = summary_data.failed_runs
-            total_execution_time = summary_data.total_execution_time
-            average_execution_time = summary_data.average_execution_time
-            cost_breakdown = summary_data.cost_breakdown
-
-            if event_type == NotificationType.DAILY_SUMMARY and isinstance(
-                params, DailySummaryParams
-            ):
-                return DailySummaryData(
-                    total_credits_used=total_credits_used,
-                    total_executions=total_executions,
-                    most_used_agent=most_used_agent,
-                    total_execution_time=total_execution_time,
-                    successful_runs=successful_runs,
-                    failed_runs=failed_runs,
-                    average_execution_time=average_execution_time,
-                    cost_breakdown=cost_breakdown,
-                    date=params.date,
-                )
-            elif event_type == NotificationType.WEEKLY_SUMMARY and isinstance(
-                params, WeeklySummaryParams
-            ):
-                return WeeklySummaryData(
-                    total_credits_used=total_credits_used,
-                    total_executions=total_executions,
-                    most_used_agent=most_used_agent,
-                    total_execution_time=total_execution_time,
-                    successful_runs=successful_runs,
-                    failed_runs=failed_runs,
-                    average_execution_time=average_execution_time,
-                    cost_breakdown=cost_breakdown,
-                    start_date=params.start_date,
-                    end_date=params.end_date,
-                )
-            else:
-                raise ValueError("Invalid event type or params")
-
-        except Exception as e:
-            logger.error(f"Failed to gather summary data: {e}")
-            # Return sensible defaults in case of error
-            if event_type == NotificationType.DAILY_SUMMARY and isinstance(
-                params, DailySummaryParams
-            ):
-                return DailySummaryData(
-                    total_credits_used=0.0,
-                    total_executions=0,
-                    most_used_agent="No data available",
-                    total_execution_time=0.0,
-                    successful_runs=0,
-                    failed_runs=0,
-                    average_execution_time=0.0,
-                    cost_breakdown={},
-                    date=params.date,
-                )
-            elif event_type == NotificationType.WEEKLY_SUMMARY and isinstance(
-                params, WeeklySummaryParams
-            ):
-                return WeeklySummaryData(
-                    total_credits_used=0.0,
-                    total_executions=0,
-                    most_used_agent="No data available",
-                    total_execution_time=0.0,
-                    successful_runs=0,
-                    failed_runs=0,
-                    average_execution_time=0.0,
-                    cost_breakdown={},
-                    start_date=params.start_date,
-                    end_date=params.end_date,
-                )
-            else:
-                raise ValueError("Invalid event type or params") from e
+        elif event_type == NotificationType.WEEKLY_SUMMARY and isinstance(
+            params, WeeklySummaryParams
+        ):
+            return WeeklySummaryData(
+                total_credits_used=total_credits_used,
+                total_executions=total_executions,
+                most_used_agent=most_used_agent["name"],
+                total_execution_time=sum(execution_times),
+                successful_runs=successful_runs,
+                failed_runs=failed_runs,
+                average_execution_time=average_execution_time,
+                cost_breakdown=cost_breakdown,
+                start_date=params.start_date,
+                end_date=params.end_date,
+            )
+        else:
+            raise ValueError("Invalid event type or params")
 
     async def _should_batch(
         self, user_id: str, event_type: NotificationType, event: NotificationEventModel
@@ -788,7 +764,7 @@ class NotificationManager(AppService):
                 )
                 return True
 
-            summary_data = await self._gather_summary_data(
+            summary_data = self._gather_summary_data(
                 event.user_id, event.type, model.data
             )