Merge branch 'dev' into redesigning-block-menu

list]

.dockerignore

@@ -9,13 +9,11 @@
 
 # Platform - Backend
 !autogpt_platform/backend/backend/
-!autogpt_platform/backend/test/e2e_test_data.py
 !autogpt_platform/backend/migrations/
 !autogpt_platform/backend/schema.prisma
 !autogpt_platform/backend/pyproject.toml
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
-!autogpt_platform/backend/.env
 
 # Platform - Market
 !autogpt_platform/market/market/
@@ -28,7 +26,6 @@
 # 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 +33,6 @@
 ## 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:
@@ -50,23 +50,6 @@ jobs:
         env:
           RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
           RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
-      clamav:
-        image: clamav/clamav-debian:latest
-        ports:
-          - 3310:3310
-        env:
-          CLAMAV_NO_FRESHCLAMD: false
-          CLAMD_CONF_StreamMaxLength: 50M
-          CLAMD_CONF_MaxFileSize: 100M
-          CLAMD_CONF_MaxScanSize: 100M
-          CLAMD_CONF_MaxThreads: 4
-          CLAMD_CONF_ReadTimeout: 300
-        options: >-
-          --health-cmd "clamdscan --version || exit 1"
-          --health-interval 30s
-          --health-timeout 10s
-          --health-retries 5
-          --health-start-period 180s
 
     steps:
       - name: Checkout repository
@@ -148,35 +131,6 @@ jobs:
         # outputs:
         # DB_URL, API_URL, GRAPHQL_URL, ANON_KEY, SERVICE_ROLE_KEY, JWT_SECRET
 
-      - name: Wait for ClamAV to be ready
-        run: |
-          echo "Waiting for ClamAV daemon to start..."
-          max_attempts=60
-          attempt=0
-          
-          until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
-            echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
-            sleep 5
-            attempt=$((attempt+1))
-          done
-          
-          if [ $attempt -eq $max_attempts ]; then
-            echo "ClamAV failed to start after $((max_attempts*5)) seconds"
-            echo "Checking ClamAV service logs..."
-            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
-            exit 1
-          fi
-          
-          echo "ClamAV is ready!"
-          
-          # Verify ClamAV is responsive
-          echo "Testing ClamAV connection..."
-          timeout 10 bash -c 'echo "PING" | nc localhost 3310' || {
-            echo "ClamAV is not responding to PING"
-            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
-            exit 1
-          }
-
       - name: Run Database Migrations
         run: poetry run prisma migrate dev --name updates
         env:
@@ -190,9 +144,9 @@ jobs:
       - name: Run pytest with coverage
         run: |
           if [[ "${{ runner.debug }}" == "1" ]]; then
-            poetry run pytest -s -vv -o log_cli=true -o log_cli_level=DEBUG
+            poetry run pytest -s -vv -o log_cli=true -o log_cli_level=DEBUG test
           else
-            poetry run pytest -s -vv
+            poetry run pytest -s -vv test
           fi
         if: success() || (failure() && steps.lint.outcome == 'failure')
         env:
@@ -205,7 +159,6 @@ jobs:
           REDIS_HOST: "localhost"
           REDIS_PORT: "6379"
           REDIS_PASSWORD: "testpassword"
-          ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=" # DO NOT USE IN PRODUCTION!!
 
     env:
       CI: true

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

@@ -18,46 +18,11 @@ defaults:
     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
-
   lint:
     runs-on: ubuntu-latest
-    needs: setup
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
+      - uses: actions/checkout@v4
 
       - name: Set up Node.js
         uses: actions/setup-node@v4
@@ -67,32 +32,17 @@ jobs:
       - 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 lint
         run: pnpm lint
 
-  chromatic:
+  type-check:
     runs-on: ubuntu-latest
-    needs: setup
-    # Only run on dev branch pushes or PRs targeting dev
-    if: github.ref == 'refs/heads/dev' || github.base_ref == 'dev'
 
     steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
+      - uses: actions/checkout@v4
 
       - name: Set up Node.js
         uses: actions/setup-node@v4
@@ -102,32 +52,18 @@ jobs:
       - 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 Chromatic
-        uses: chromaui/action@latest
-        with:
-          projectToken: chpt_9e7c1a76478c9c8
-          onlyChanged: true
-          workingDir: autogpt_platform/frontend
-          token: ${{ secrets.GITHUB_TOKEN }}
-          exitOnceUploaded: true
+      - name: Run tsc check
+        run: pnpm type-check
 
   test:
-    runs-on: big-boi
-    needs: setup
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
+      matrix:
+        browser: [chromium, webkit]
 
     steps:
       - name: Checkout repository
@@ -143,93 +79,47 @@ jobs:
       - name: Enable corepack
         run: corepack enable
 
+      - name: Free Disk Space (Ubuntu)
+        uses: jlumbroso/free-disk-space@main
+        with:
+          large-packages: false # slow
+          docker-images: false # limited benefit
+
       - name: Copy default supabase .env
         run: |
-          cp ../.env.default ../.env
+          cp ../.env.example ../.env
 
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Cache Docker layers
-        uses: actions/cache@v4
-        with:
-          path: /tmp/.buildx-cache
-          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
-          restore-keys: |
-            ${{ runner.os }}-buildx-frontend-test-
+      - name: Copy backend .env
+        run: |
+          cp ../backend/.env.example ../backend/.env
 
       - name: Run docker compose
         run: |
           docker compose -f ../docker-compose.yml up -d
-        env:
-          DOCKER_BUILDKIT: 1
-          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
-          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
-
-      - name: Move cache
-        run: |
-          rm -rf /tmp/.buildx-cache
-          if [ -d "/tmp/.buildx-cache-new" ]; then
-            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
-          fi
-
-      - 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: Create E2E test data
-        run: |
-          echo "Creating E2E test data..."
-          # First try to run the script from inside the container
-          if docker compose -f ../docker-compose.yml exec -T rest_server test -f /app/autogpt_platform/backend/test/e2e_test_data.py; then
-            echo "✅ Found e2e_test_data.py in container, running it..."
-            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python backend/test/e2e_test_data.py" || {
-              echo "❌ E2E test data creation failed!"
-              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
-              exit 1
-            }
-          else
-            echo "⚠️ e2e_test_data.py not found in container, copying and running..."
-            # Copy the script into the container and run it
-            docker cp ../backend/test/e2e_test_data.py $(docker compose -f ../docker-compose.yml ps -q rest_server):/tmp/e2e_test_data.py || {
-              echo "❌ Failed to copy script to container"
-              exit 1
-            }
-            docker compose -f ../docker-compose.yml exec -T rest_server sh -c "cd /app/autogpt_platform && python /tmp/e2e_test_data.py" || {
-              echo "❌ E2E test data creation failed!"
-              docker compose -f ../docker-compose.yml logs --tail=50 rest_server
-              exit 1
-            }
-          fi
-
-      - 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: Install Browser 'chromium'
-        run: pnpm playwright install --with-deps chromium
+      - 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
+
+      - name: Install Browser '${{ matrix.browser }}'
+        run: pnpm playwright install --with-deps ${{ matrix.browser }}
 
       - name: Run Playwright tests
-        run: pnpm test:no-build
-
-      - name: Upload Playwright artifacts
-        if: failure()
-        uses: actions/upload-artifact@v4
-        with:
-          name: playwright-report
-          path: playwright-report
+        run: pnpm test:no-build --project=${{ matrix.browser }}
 
       - name: Print Final Docker Compose logs
         if: always()
         run: docker compose -f ../docker-compose.yml logs
+
+      - uses: actions/upload-artifact@v4
+        if: ${{ !cancelled() }}
+        with:
+          name: playwright-report-${{ matrix.browser }}
+          path: playwright-report/
+          retention-days: 30

.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*/
@@ -166,7 +165,7 @@ package-lock.json
 
 # Allow for locally private items
 # private
-pri*
+pri* 
 # ignore
 ig*
 .github_access_token

.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

.vscode/launch.json

@@ -6,7 +6,7 @@
       "type": "node-terminal",
       "request": "launch",
       "cwd": "${workspaceFolder}/autogpt_platform/frontend",
-      "command": "pnpm dev"
+      "command": "yarn dev"
     },
     {
       "name": "Frontend: Client Side",
@@ -19,12 +19,12 @@
       "type": "node-terminal",
 
       "request": "launch",
-      "command": "pnpm dev",
+      "command": "yarn dev",
       "cwd": "${workspaceFolder}/autogpt_platform/frontend",
       "serverReadyAction": {
         "pattern": "- Local:.+(https?://.+)",
         "uriFormat": "%s",
-        "action": "debugWithChrome"
+        "action": "debugWithEdge"
       }
     },
     {

LICENSE

@@ -1,197 +1,6 @@
-All portions of this repository are under one of two licenses. 
+All portions of this repository are under one of two licenses. The majority of the AutoGPT repository is under the MIT License below. The autogpt_platform folder is under the
+Polyform Shield License. 
 
-- Everything inside the autogpt_platform folder is under the Polyform Shield License.
-- Everything outside the autogpt_platform folder is under the MIT License. 
-
-More info:
-
-**Polyform Shield License:**
-Code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.
-Read more about this effort here: https://agpt.co/blog/introducing-the-autogpt-platform
-
-**MIT License:**
-All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes:
-- The Original, stand-alone AutoGPT Agent
-- Forge: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge
-- AG Benchmark: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark
-- AutoGPT Classic GUI: https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend.
-
-We also publish additional work under the MIT Licence in other repositories, such as GravitasML (https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform, and our [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
-
-Both licences are available to read below:
-
-=====================================================
------------------------------------------------------
-=====================================================
-
-# PolyForm Shield License 1.0.0
-
-<https://polyformproject.org/licenses/shield/1.0.0>
-
-## Acceptance
-
-In order to get any license under these terms, you must agree
-to them as both strict obligations and conditions to all
-your licenses.
-
-## Copyright License
-
-The licensor grants you a copyright license for the
-software to do everything you might do with the software
-that would otherwise infringe the licensor's copyright
-in it for any permitted purpose.  However, you may
-only distribute the software according to [Distribution
-License](#distribution-license) and make changes or new works
-based on the software according to [Changes and New Works
-License](#changes-and-new-works-license).
-
-## Distribution License
-
-The licensor grants you an additional copyright license
-to distribute copies of the software.  Your license
-to distribute covers distributing the software with
-changes and new works permitted by [Changes and New Works
-License](#changes-and-new-works-license).
-
-## Notices
-
-You must ensure that anyone who gets a copy of any part of
-the software from you also gets a copy of these terms or the
-URL for them above, as well as copies of any plain-text lines
-beginning with `Required Notice:` that the licensor provided
-with the software.  For example:
-
-> Required Notice: Copyright Yoyodyne, Inc. (http://example.com)
-
-## Changes and New Works License
-
-The licensor grants you an additional copyright license to
-make changes and new works based on the software for any
-permitted purpose.
-
-## Patent License
-
-The licensor grants you a patent license for the software that
-covers patent claims the licensor can license, or becomes able
-to license, that you would infringe by using the software.
-
-## Noncompete
-
-Any purpose is a permitted purpose, except for providing any
-product that competes with the software or any product the
-licensor or any of its affiliates provides using the software.
-
-## Competition
-
-Goods and services compete even when they provide functionality
-through different kinds of interfaces or for different technical
-platforms.  Applications can compete with services, libraries
-with plugins, frameworks with development tools, and so on,
-even if they're written in different programming languages
-or for different computer architectures.  Goods and services
-compete even when provided free of charge.  If you market a
-product as a practical substitute for the software or another
-product, it definitely competes.
-
-## New Products
-
-If you are using the software to provide a product that does
-not compete, but the licensor or any of its affiliates brings
-your product into competition by providing a new version of
-the software or another product using the software, you may
-continue using versions of the software available under these
-terms beforehand to provide your competing product, but not
-any later versions.
-
-## Discontinued Products
-
-You may begin using the software to compete with a product
-or service that the licensor or any of its affiliates has
-stopped providing, unless the licensor includes a plain-text
-line beginning with `Licensor Line of Business:` with the
-software that mentions that line of business.  For example:
-
-> Licensor Line of Business: YoyodyneCMS Content Management
-System (http://example.com/cms)
-
-## Sales of Business
-
-If the licensor or any of its affiliates sells a line of
-business developing the software or using the software
-to provide a product, the buyer can also enforce
-[Noncompete](#noncompete) for that product.
-
-## Fair Use
-
-You may have "fair use" rights for the software under the
-law. These terms do not limit them.
-
-## No Other Rights
-
-These terms do not allow you to sublicense or transfer any of
-your licenses to anyone else, or prevent the licensor from
-granting licenses to anyone else.  These terms do not imply
-any other licenses.
-
-## Patent Defense
-
-If you make any written claim that the software infringes or
-contributes to infringement of any patent, your patent license
-for the software granted under these terms ends immediately. If
-your company makes such a claim, your patent license ends
-immediately for work on behalf of your company.
-
-## Violations
-
-The first time you are notified in writing that you have
-violated any of these terms, or done anything with the software
-not covered by your licenses, your licenses can nonetheless
-continue if you come into full compliance with these terms,
-and take practical steps to correct past violations, within
-32 days of receiving notice.  Otherwise, all your licenses
-end immediately.
-
-## No Liability
-
-***As far as the law allows, the software comes as is, without
-any warranty or condition, and the licensor will not be liable
-to you for any damages arising out of these terms or the use
-or nature of the software, under any kind of legal claim.***
-
-## Definitions
-
-The **licensor** is the individual or entity offering these
-terms, and the **software** is the software the licensor makes
-available under these terms.
-
-A **product** can be a good or service, or a combination
-of them.
-
-**You** refers to the individual or entity agreeing to these
-terms.
-
-**Your company** is any legal entity, sole proprietorship,
-or other kind of organization that you work for, plus all
-its affiliates.
-
-**Affiliates** means the other organizations than an
-organization has control over, is under the control of, or is
-under common control with.
-
-**Control** means ownership of substantially all the assets of
-an entity, or the power to direct its management and policies
-by vote, contract, or otherwise.  Control can be direct or
-indirect.
-
-**Your licenses** are all the licenses granted to you for the
-software under these terms.
-
-**Use** means anything you do with the software requiring one
-of your licenses.
-
-=====================================================
------------------------------------------------------
-=====================================================
 
 MIT License
 

README.md

@@ -1,25 +1,16 @@
 # AutoGPT: Build, Deploy, and Run AI Agents
 
-[![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)  
+[![Discord Follow](https://dcbadge.vercel.app/api/server/autogpt?style=flat)](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)
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 **AutoGPT** is a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows. 
 
 ## Hosting Options 
-   - Download to self-host (Free!)
-   - [Join the Waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta (Closed Beta - Public release Coming Soon!)
+   - Download to self-host
+   - [Join the Waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta  
 
-## How to Self-Host the AutoGPT Platform
+## How to Setup for Self-Hosting
 > [!NOTE]
 > Setting up and hosting the AutoGPT Platform yourself is a technical process. 
 > If you'd rather something that just works, we recommend [joining the waitlist](https://bit.ly/3ZDijAI) for the cloud-hosted beta.
@@ -59,24 +50,6 @@ We've moved to a fully maintained and regularly updated documentation site.
 
 This tutorial assumes you have Docker, VSCode, git and npm installed.
 
----
-
-#### ⚡ Quick Setup with One-Line Script (Recommended for Local Hosting)
-
-Skip the manual steps and get started in minutes using our automatic setup script.
-
-For macOS/Linux:
-```
-curl -fsSL https://setup.agpt.co/install.sh -o install.sh && bash install.sh
-```
-
-For Windows (PowerShell):
-```
-powershell -c "iwr https://setup.agpt.co/install.bat -o install.bat; ./install.bat"
-```
-
-This will install dependencies, configure Docker, and launch your local instance — all in one go.
-
 ### 🧱 AutoGPT Frontend
 
 The AutoGPT frontend is where users interact with our powerful AI automation platform. It offers multiple ways to engage with and leverage our AI agents. This is the interface where you'll bring your AI automation ideas to life:
@@ -123,17 +96,7 @@ Here are two examples of what you can do with AutoGPT:
 These examples show just a glimpse of what you can achieve with AutoGPT! You can create customized workflows to build agents for any use case.
 
 ---
-
-### **License Overview:**
-
-🛡️ **Polyform Shield License:**
-All code and content within the `autogpt_platform` folder is licensed under the Polyform Shield License. This new project is our in-developlemt platform for building, deploying and managing agents.</br>_[Read more about this effort](https://agpt.co/blog/introducing-the-autogpt-platform)_
-
-🦉 **MIT License:**
-All other portions of the AutoGPT repository (i.e., everything outside the `autogpt_platform` folder) are licensed under the MIT License. This includes the original stand-alone AutoGPT Agent, along with projects such as [Forge](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/forge), [agbenchmark](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/benchmark) and the [AutoGPT Classic GUI](https://github.com/Significant-Gravitas/AutoGPT/tree/master/classic/frontend).</br>We also publish additional work under the MIT Licence in other repositories, such as [GravitasML](https://github.com/Significant-Gravitas/gravitasml) which is developed for and used in the AutoGPT Platform. See also our MIT Licenced [Code Ability](https://github.com/Significant-Gravitas/AutoGPT-Code-Ability) project.
-
----
-### Mission
+### Mission and Licencing
 Our mission is to provide the tools, so that you can focus on what matters:
 
 - 🏗️ **Building** - Lay the foundation for something amazing.
@@ -146,6 +109,14 @@ Be part of the revolution! **AutoGPT** is here to stay, at the forefront of AI i
 &ensp;|&ensp;
 **🚀 [Contributing](CONTRIBUTING.md)**
 
+**Licensing:**
+
+MIT License: The majority of the AutoGPT repository is under the MIT License.
+
+Polyform Shield License: This license applies to the autogpt_platform folder. 
+
+For more information, see https://agpt.co/blog/introducing-the-autogpt-platform
+
 ---
 ## 🤖 AutoGPT Classic
 > Below is information about the classic version of AutoGPT.

autogpt_platform/CLAUDE.md

@@ -5,7 +5,6 @@ This file provides guidance to Claude Code (claude.ai/code) when working with co
 ## 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 +12,6 @@ AutoGPT Platform is a monorepo containing:
 ## Essential Commands
 
 ### Backend Development
-
 ```bash
 # Install dependencies
 cd backend && poetry install
@@ -21,7 +19,7 @@ cd backend && poetry install
 # Run database migrations
 poetry run prisma migrate dev
 
-# Start all services (database, redis, rabbitmq, clamav)
+# Start all services (database, redis, rabbitmq)
 docker compose up -d
 
 # Run the backend server
@@ -33,18 +31,10 @@ poetry run test
 # Run specific test
 poetry run pytest path/to/test_file.py::test_function_name
 
-# Run block tests (tests that validate all blocks work correctly)
-poetry run pytest backend/blocks/test/test_block.py -xvs
-
-# Run tests for a specific block (e.g., GetCurrentTimeBlock)
-poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetCurrentTimeBlock]' -xvs
-
 # Lint and format
-# prefer format if you want to just "fix" it and only get the errors that can't be autofixed
 poetry run format  # Black + isort
 poetry run lint    # ruff
 ```
-
 More details can be found in TESTING.md
 
 #### Creating/Updating Snapshots
@@ -57,8 +47,8 @@ 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
@@ -76,22 +66,19 @@ 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
 - **Execution Engine**: Separate executor service processes agent workflows
 - **Authentication**: JWT-based with Supabase integration
-- **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
@@ -99,24 +86,19 @@ 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
 4. **Store**: Marketplace for sharing agent templates
-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
@@ -124,107 +106,27 @@ 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:**
-
 1. Create new file in `/backend/backend/blocks/`
 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?
-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
-4. Test with Playwright if user-facing
-
-### 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
-- Cacheable paths include: static assets (`/static/*`, `/_next/static/*`), health checks, public store pages, documentation
-- Prevents sensitive data (auth tokens, API keys, user data) from being cached by browsers/proxies
-- 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
-
-- 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:
-
-**Conventional Commit Types:**
-
-- `feat`: Introduces a new feature to the codebase
-- `fix`: Patches a bug in the codebase
-- `refactor`: Code change that neither fixes a bug nor adds a feature; also applies to removing features
-- `ci`: Changes to CI configuration
-- `docs`: Documentation-only changes
-- `dx`: Improvements to the developer experience
-
-**Recommended Base Scopes:**
-
-- `platform`: Changes affecting both frontend and backend
-- `frontend`
-- `backend`
-- `infra`
-- `blocks`: Modifications/additions of individual blocks
-
-**Subscope Examples:**
-
-- `backend/executor`
-- `backend/db`
-- `frontend/builder` (includes changes to the block UI component)
-- `infra/prod`
-
-Use these scopes and subscopes for clarity and consistency in commit messages.
+4. Test with Playwright if user-facing

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 +37,38 @@ 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
+   ```
+
+   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
 
@@ -132,28 +164,3 @@ To persist data for PostgreSQL and Redis, you can modify the `docker-compose.yml
 3. Save the file and run `docker compose up -d` to apply the changes.
 
 This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
-
-### API Client Generation
-
-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
-
-#### 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
-   ```
-
-This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.

autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py

@@ -7,5 +7,9 @@ class Settings:
         self.ENABLE_AUTH: bool = os.getenv("ENABLE_AUTH", "false").lower() == "true"
         self.JWT_ALGORITHM: str = "HS256"
 
+    @property
+    def is_configured(self) -> bool:
+        return bool(self.JWT_SECRET_KEY)
+
 
 settings = Settings()

autogpt_platform/autogpt_libs/autogpt_libs/auth/middleware.py

@@ -10,8 +10,8 @@ from starlette.status import HTTP_401_UNAUTHORIZED
 from .config import settings
 from .jwt_utils import parse_jwt_token
 
+security = HTTPBearer()
 logger = logging.getLogger(__name__)
-bearer_auth = HTTPBearer(auto_error=False)
 
 
 async def auth_middleware(request: Request):
@@ -20,10 +20,11 @@ async def auth_middleware(request: Request):
         logger.warning("Auth disabled")
         return {}
 
-    credentials = await bearer_auth(request)
+    security = HTTPBearer()
+    credentials = await security(request)
 
     if not credentials:
-        raise HTTPException(status_code=401, detail="Not authenticated")
+        raise HTTPException(status_code=401, detail="Authorization header is missing")
 
     try:
         payload = parse_jwt_token(credentials.credentials)

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client.py

@@ -0,0 +1,166 @@
+import asyncio
+import contextlib
+import logging
+from functools import wraps
+from typing import Any, Awaitable, Callable, Dict, Optional, TypeVar, Union, cast
+
+import ldclient
+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")
+
+
+def get_client() -> LDClient:
+    """Get the LaunchDarkly client singleton."""
+    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():
+        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."""
+    builder = Context.builder(str(user_id)).kind("user")
+    if additional_attributes:
+        for key, value in additional_attributes.items():
+            builder.set(key, value)
+    return builder.build()
+
+
+def feature_flag(
+    flag_key: str,
+    default: bool = False,
+) -> Callable[
+    [Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
+]:
+    """
+    Decorator for feature flag protected endpoints.
+    """
+
+    def decorator(
+        func: Callable[P, Union[T, Awaitable[T]]],
+    ) -> Callable[P, Union[T, 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:
+                    context = create_context(str(user_id))
+                    is_enabled = get_client().variation(flag_key, context, default)
+
+                if not is_enabled:
+                    raise HTTPException(status_code=404, detail="Feature not available")
+
+                result = func(*args, **kwargs)
+                if asyncio.iscoroutine(result):
+                    return await result
+                return cast(T, result)
+            except Exception as e:
+                logger.error(f"Error evaluating feature flag {flag_key}: {e}")
+                raise
+
+        @wraps(func)
+        def sync_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:
+                    context = create_context(str(user_id))
+                    is_enabled = get_client().variation(flag_key, context, default)
+
+                if not is_enabled:
+                    raise HTTPException(status_code=404, detail="Feature not available")
+
+                return cast(T, func(*args, **kwargs))
+            except Exception as e:
+                logger.error(f"Error evaluating feature flag {flag_key}: {e}")
+                raise
+
+        return cast(
+            Callable[P, Union[T, Awaitable[T]]],
+            async_wrapper if asyncio.iscoroutinefunction(func) else sync_wrapper,
+        )
+
+    return decorator
+
+
+def percentage_rollout(
+    flag_key: str,
+    default: bool = False,
+) -> Callable[
+    [Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
+]:
+    """Decorator for percentage-based rollouts."""
+    return feature_flag(flag_key, default)
+
+
+def beta_feature(
+    flag_key: Optional[str] = None,
+    unauthorized_response: Any = {"message": "Not available in beta"},
+) -> Callable[
+    [Callable[P, Union[T, Awaitable[T]]]], Callable[P, Union[T, Awaitable[T]]]
+]:
+    """Decorator for beta features."""
+    actual_key = f"beta-{flag_key}" if flag_key else "beta"
+    return feature_flag(actual_key, False)
+
+
+@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 +1,45 @@
+import pytest
+from ldclient import LDClient
+
+from autogpt_libs.feature_flag.client import feature_flag, 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)

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

@@ -1,34 +1,17 @@
 import inspect
-import logging
 import threading
-import time
-from functools import wraps
-from typing import (
-    Awaitable,
-    Callable,
-    ParamSpec,
-    Protocol,
-    Tuple,
-    TypeVar,
-    cast,
-    overload,
-    runtime_checkable,
-)
+from typing import Awaitable, Callable, ParamSpec, TypeVar, cast, overload
 
 P = ParamSpec("P")
 R = TypeVar("R")
 
-logger = logging.getLogger(__name__)
+
+@overload
+def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
 
 
 @overload
-def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]:
-    pass
-
-
-@overload
-def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
-    pass
+def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...
 
 
 def thread_cached(
@@ -74,193 +57,3 @@ def thread_cached(
 def clear_thread_cache(func: Callable) -> None:
     if clear := getattr(func, "clear_cache", None):
         clear()
-
-
-FuncT = TypeVar("FuncT")
-
-
-R_co = TypeVar("R_co", covariant=True)
-
-
-@runtime_checkable
-class AsyncCachedFunction(Protocol[P, R_co]):
-    """Protocol for async functions with cache management methods."""
-
-    def cache_clear(self) -> None:
-        """Clear all cached entries."""
-        return None
-
-    def cache_info(self) -> dict[str, int | None]:
-        """Get cache statistics."""
-        return {}
-
-    async def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
-        """Call the cached function."""
-        return None  # type: ignore
-
-
-def async_ttl_cache(
-    maxsize: int = 128, ttl_seconds: int | None = None
-) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
-    """
-    TTL (Time To Live) cache decorator for async functions.
-
-    Similar to functools.lru_cache but works with async functions and includes optional TTL.
-
-    Args:
-        maxsize: Maximum number of cached entries
-        ttl_seconds: Time to live in seconds. If None, entries never expire (like lru_cache)
-
-    Returns:
-        Decorator function
-
-    Example:
-        # With TTL
-        @async_ttl_cache(maxsize=1000, ttl_seconds=300)
-        async def api_call(param: str) -> dict:
-            return {"result": param}
-
-        # Without TTL (permanent cache like lru_cache)
-        @async_ttl_cache(maxsize=1000)
-        async def expensive_computation(param: str) -> dict:
-            return {"result": param}
-    """
-
-    def decorator(
-        async_func: Callable[P, Awaitable[R]],
-    ) -> AsyncCachedFunction[P, R]:
-        # Cache storage - use union type to handle both cases
-        cache_storage: dict[tuple, R | Tuple[R, float]] = {}
-
-        @wraps(async_func)
-        async def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
-            # Create cache key from arguments
-            key = (args, tuple(sorted(kwargs.items())))
-            current_time = time.time()
-
-            # Check if we have a valid cached entry
-            if key in cache_storage:
-                if ttl_seconds is None:
-                    # No TTL - return cached result directly
-                    logger.debug(
-                        f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
-                    )
-                    return cast(R, cache_storage[key])
-                else:
-                    # With TTL - check expiration
-                    cached_data = cache_storage[key]
-                    if isinstance(cached_data, tuple):
-                        result, timestamp = cached_data
-                        if current_time - timestamp < ttl_seconds:
-                            logger.debug(
-                                f"Cache hit for {async_func.__name__} with key: {str(key)[:50]}"
-                            )
-                            return cast(R, result)
-                        else:
-                            # Expired entry
-                            del cache_storage[key]
-                            logger.debug(
-                                f"Cache entry expired for {async_func.__name__}"
-                            )
-
-            # Cache miss or expired - fetch fresh data
-            logger.debug(
-                f"Cache miss for {async_func.__name__} with key: {str(key)[:50]}"
-            )
-            result = await async_func(*args, **kwargs)
-
-            # Store in cache
-            if ttl_seconds is None:
-                cache_storage[key] = result
-            else:
-                cache_storage[key] = (result, current_time)
-
-            # Simple cleanup when cache gets too large
-            if len(cache_storage) > maxsize:
-                # Remove oldest entries (simple FIFO cleanup)
-                cutoff = maxsize // 2
-                oldest_keys = list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
-                for old_key in oldest_keys:
-                    cache_storage.pop(old_key, None)
-                logger.debug(
-                    f"Cache cleanup: removed {len(oldest_keys)} entries for {async_func.__name__}"
-                )
-
-            return result
-
-        # Add cache management methods (similar to functools.lru_cache)
-        def cache_clear() -> None:
-            cache_storage.clear()
-
-        def cache_info() -> dict[str, int | None]:
-            return {
-                "size": len(cache_storage),
-                "maxsize": maxsize,
-                "ttl_seconds": ttl_seconds,
-            }
-
-        # Attach methods to wrapper
-        setattr(wrapper, "cache_clear", cache_clear)
-        setattr(wrapper, "cache_info", cache_info)
-
-        return cast(AsyncCachedFunction[P, R], wrapper)
-
-    return decorator
-
-
-@overload
-def async_cache(
-    func: Callable[P, Awaitable[R]],
-) -> AsyncCachedFunction[P, R]:
-    pass
-
-
-@overload
-def async_cache(
-    func: None = None,
-    *,
-    maxsize: int = 128,
-) -> Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]:
-    pass
-
-
-def async_cache(
-    func: Callable[P, Awaitable[R]] | None = None,
-    *,
-    maxsize: int = 128,
-) -> (
-    AsyncCachedFunction[P, R]
-    | Callable[[Callable[P, Awaitable[R]]], AsyncCachedFunction[P, R]]
-):
-    """
-    Process-level cache decorator for async functions (no TTL).
-
-    Similar to functools.lru_cache but works with async functions.
-    This is a convenience wrapper around async_ttl_cache with ttl_seconds=None.
-
-    Args:
-        func: The async function to cache (when used without parentheses)
-        maxsize: Maximum number of cached entries
-
-    Returns:
-        Decorated function or decorator
-
-    Example:
-        # Without parentheses (uses default maxsize=128)
-        @async_cache
-        async def get_data(param: str) -> dict:
-            return {"result": param}
-
-        # With parentheses and custom maxsize
-        @async_cache(maxsize=1000)
-        async def expensive_computation(param: str) -> dict:
-            # Expensive computation here
-            return {"result": param}
-    """
-    if func is None:
-        # Called with parentheses @async_cache() or @async_cache(maxsize=...)
-        return async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
-    else:
-        # Called without parentheses @async_cache
-        decorator = async_ttl_cache(maxsize=maxsize, ttl_seconds=None)
-        return decorator(func)

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

@@ -1,705 +0,0 @@
-"""Tests for the @thread_cached decorator.
-
-This module tests the thread-local caching functionality including:
-- Basic caching for sync and async functions
-- Thread isolation (each thread has its own cache)
-- Cache clearing functionality
-- Exception handling (exceptions are not cached)
-- Argument handling (positional vs keyword arguments)
-"""
-
-import asyncio
-import threading
-import time
-from concurrent.futures import ThreadPoolExecutor
-from unittest.mock import Mock
-
-import pytest
-
-from autogpt_libs.utils.cache import (
-    async_cache,
-    async_ttl_cache,
-    clear_thread_cache,
-    thread_cached,
-)
-
-
-class TestThreadCached:
-    def test_sync_function_caching(self):
-        call_count = 0
-
-        @thread_cached
-        def expensive_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        assert expensive_function(1, 2) == 3
-        assert call_count == 1
-
-        assert expensive_function(1, 2) == 3
-        assert call_count == 1
-
-        assert expensive_function(1, y=2) == 3
-        assert call_count == 2
-
-        assert expensive_function(2, 3) == 5
-        assert call_count == 3
-
-        assert expensive_function(1) == 1
-        assert call_count == 4
-
-    @pytest.mark.asyncio
-    async def test_async_function_caching(self):
-        call_count = 0
-
-        @thread_cached
-        async def expensive_async_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x + y
-
-        assert await expensive_async_function(1, 2) == 3
-        assert call_count == 1
-
-        assert await expensive_async_function(1, 2) == 3
-        assert call_count == 1
-
-        assert await expensive_async_function(1, y=2) == 3
-        assert call_count == 2
-
-        assert await expensive_async_function(2, 3) == 5
-        assert call_count == 3
-
-    def test_thread_isolation(self):
-        call_count = 0
-        results = {}
-
-        @thread_cached
-        def thread_specific_function(x: int) -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"{threading.current_thread().name}-{x}"
-
-        def worker(thread_id: int):
-            result1 = thread_specific_function(1)
-            result2 = thread_specific_function(1)
-            result3 = thread_specific_function(2)
-            results[thread_id] = (result1, result2, result3)
-
-        with ThreadPoolExecutor(max_workers=3) as executor:
-            futures = [executor.submit(worker, i) for i in range(3)]
-            for future in futures:
-                future.result()
-
-        assert call_count >= 2
-
-        for thread_id, (r1, r2, r3) in results.items():
-            assert r1 == r2
-            assert r1 != r3
-
-    @pytest.mark.asyncio
-    async def test_async_thread_isolation(self):
-        call_count = 0
-        results = {}
-
-        @thread_cached
-        async def async_thread_specific_function(x: int) -> str:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return f"{threading.current_thread().name}-{x}"
-
-        async def async_worker(worker_id: int):
-            result1 = await async_thread_specific_function(1)
-            result2 = await async_thread_specific_function(1)
-            result3 = await async_thread_specific_function(2)
-            results[worker_id] = (result1, result2, result3)
-
-        tasks = [async_worker(i) for i in range(3)]
-        await asyncio.gather(*tasks)
-
-        for worker_id, (r1, r2, r3) in results.items():
-            assert r1 == r2
-            assert r1 != r3
-
-    def test_clear_cache_sync(self):
-        call_count = 0
-
-        @thread_cached
-        def clearable_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 2
-
-        assert clearable_function(5) == 10
-        assert call_count == 1
-
-        assert clearable_function(5) == 10
-        assert call_count == 1
-
-        clear_thread_cache(clearable_function)
-
-        assert clearable_function(5) == 10
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_clear_cache_async(self):
-        call_count = 0
-
-        @thread_cached
-        async def clearable_async_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x * 2
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 1
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 1
-
-        clear_thread_cache(clearable_async_function)
-
-        assert await clearable_async_function(5) == 10
-        assert call_count == 2
-
-    def test_simple_arguments(self):
-        call_count = 0
-
-        @thread_cached
-        def simple_function(a: str, b: int, c: str = "default") -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"{a}-{b}-{c}"
-
-        # First call with all positional args
-        result1 = simple_function("test", 42, "custom")
-        assert call_count == 1
-
-        # Same args, all positional - should hit cache
-        result2 = simple_function("test", 42, "custom")
-        assert call_count == 1
-        assert result1 == result2
-
-        # Same values but last arg as keyword - creates different cache key
-        result3 = simple_function("test", 42, c="custom")
-        assert call_count == 2
-        assert result1 == result3  # Same result, different cache entry
-
-        # Different value - new cache entry
-        result4 = simple_function("test", 43, "custom")
-        assert call_count == 3
-        assert result1 != result4
-
-    def test_positional_vs_keyword_args(self):
-        """Test that positional and keyword arguments create different cache entries."""
-        call_count = 0
-
-        @thread_cached
-        def func(a: int, b: int = 10) -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"result-{a}-{b}"
-
-        # All positional
-        result1 = func(1, 2)
-        assert call_count == 1
-        assert result1 == "result-1-2"
-
-        # Same values, but second arg as keyword
-        result2 = func(1, b=2)
-        assert call_count == 2  # Different cache key!
-        assert result2 == "result-1-2"  # Same result
-
-        # Verify both are cached separately
-        func(1, 2)  # Uses first cache entry
-        assert call_count == 2
-
-        func(1, b=2)  # Uses second cache entry
-        assert call_count == 2
-
-    def test_exception_handling(self):
-        call_count = 0
-
-        @thread_cached
-        def failing_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            if x < 0:
-                raise ValueError("Negative value")
-            return x * 2
-
-        assert failing_function(5) == 10
-        assert call_count == 1
-
-        with pytest.raises(ValueError):
-            failing_function(-1)
-        assert call_count == 2
-
-        with pytest.raises(ValueError):
-            failing_function(-1)
-        assert call_count == 3
-
-        assert failing_function(5) == 10
-        assert call_count == 3
-
-    @pytest.mark.asyncio
-    async def test_async_exception_handling(self):
-        call_count = 0
-
-        @thread_cached
-        async def async_failing_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            if x < 0:
-                raise ValueError("Negative value")
-            return x * 2
-
-        assert await async_failing_function(5) == 10
-        assert call_count == 1
-
-        with pytest.raises(ValueError):
-            await async_failing_function(-1)
-        assert call_count == 2
-
-        with pytest.raises(ValueError):
-            await async_failing_function(-1)
-        assert call_count == 3
-
-    def test_sync_caching_performance(self):
-        @thread_cached
-        def slow_function(x: int) -> int:
-            print(f"slow_function called with x={x}")
-            time.sleep(0.1)
-            return x * 2
-
-        start = time.time()
-        result1 = slow_function(5)
-        first_call_time = time.time() - start
-        print(f"First call took {first_call_time:.4f} seconds")
-
-        start = time.time()
-        result2 = slow_function(5)
-        second_call_time = time.time() - start
-        print(f"Second call took {second_call_time:.4f} seconds")
-
-        assert result1 == result2 == 10
-        assert first_call_time > 0.09
-        assert second_call_time < 0.01
-
-    @pytest.mark.asyncio
-    async def test_async_caching_performance(self):
-        @thread_cached
-        async def slow_async_function(x: int) -> int:
-            print(f"slow_async_function called with x={x}")
-            await asyncio.sleep(0.1)
-            return x * 2
-
-        start = time.time()
-        result1 = await slow_async_function(5)
-        first_call_time = time.time() - start
-        print(f"First async call took {first_call_time:.4f} seconds")
-
-        start = time.time()
-        result2 = await slow_async_function(5)
-        second_call_time = time.time() - start
-        print(f"Second async call took {second_call_time:.4f} seconds")
-
-        assert result1 == result2 == 10
-        assert first_call_time > 0.09
-        assert second_call_time < 0.01
-
-    def test_with_mock_objects(self):
-        mock = Mock(return_value=42)
-
-        @thread_cached
-        def function_using_mock(x: int) -> int:
-            return mock(x)
-
-        assert function_using_mock(1) == 42
-        assert mock.call_count == 1
-
-        assert function_using_mock(1) == 42
-        assert mock.call_count == 1
-
-        assert function_using_mock(2) == 42
-        assert mock.call_count == 2
-
-
-class TestAsyncTTLCache:
-    """Tests for the @async_ttl_cache decorator."""
-
-    @pytest.mark.asyncio
-    async def test_basic_caching(self):
-        """Test basic caching functionality."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def cached_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)  # Simulate async work
-            return x + y
-
-        # First call
-        result1 = await cached_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = await cached_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1  # No additional call
-
-        # Different args - should call function again
-        result3 = await cached_function(2, 3)
-        assert result3 == 5
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_ttl_expiration(self):
-        """Test that cache entries expire after TTL."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
-        async def short_lived_cache(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 2
-
-        # First call
-        result1 = await short_lived_cache(5)
-        assert result1 == 10
-        assert call_count == 1
-
-        # Second call immediately - should use cache
-        result2 = await short_lived_cache(5)
-        assert result2 == 10
-        assert call_count == 1
-
-        # Wait for TTL to expire
-        await asyncio.sleep(1.1)
-
-        # Third call after expiration - should call function again
-        result3 = await short_lived_cache(5)
-        assert result3 == 10
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_cache_info(self):
-        """Test cache info functionality."""
-
-        @async_ttl_cache(maxsize=5, ttl_seconds=300)
-        async def info_test_function(x: int) -> int:
-            return x * 3
-
-        # Check initial cache info
-        info = info_test_function.cache_info()
-        assert info["size"] == 0
-        assert info["maxsize"] == 5
-        assert info["ttl_seconds"] == 300
-
-        # Add an entry
-        await info_test_function(1)
-        info = info_test_function.cache_info()
-        assert info["size"] == 1
-
-    @pytest.mark.asyncio
-    async def test_cache_clear(self):
-        """Test cache clearing functionality."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def clearable_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 4
-
-        # First call
-        result1 = await clearable_function(2)
-        assert result1 == 8
-        assert call_count == 1
-
-        # Second call - should use cache
-        result2 = await clearable_function(2)
-        assert result2 == 8
-        assert call_count == 1
-
-        # Clear cache
-        clearable_function.cache_clear()
-
-        # Third call after clear - should call function again
-        result3 = await clearable_function(2)
-        assert result3 == 8
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_maxsize_cleanup(self):
-        """Test that cache cleans up when maxsize is exceeded."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=3, ttl_seconds=60)
-        async def size_limited_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x**2
-
-        # Fill cache to maxsize
-        await size_limited_function(1)  # call_count: 1
-        await size_limited_function(2)  # call_count: 2
-        await size_limited_function(3)  # call_count: 3
-
-        info = size_limited_function.cache_info()
-        assert info["size"] == 3
-
-        # Add one more entry - should trigger cleanup
-        await size_limited_function(4)  # call_count: 4
-
-        # Cache size should be reduced (cleanup removes oldest entries)
-        info = size_limited_function.cache_info()
-        assert info["size"] is not None and info["size"] <= 3  # Should be cleaned up
-
-    @pytest.mark.asyncio
-    async def test_argument_variations(self):
-        """Test caching with different argument patterns."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def arg_test_function(a: int, b: str = "default", *, c: int = 100) -> str:
-            nonlocal call_count
-            call_count += 1
-            return f"{a}-{b}-{c}"
-
-        # Different ways to call with same logical arguments
-        result1 = await arg_test_function(1, "test", c=200)
-        assert call_count == 1
-
-        # Same arguments, same order - should use cache
-        result2 = await arg_test_function(1, "test", c=200)
-        assert call_count == 1
-        assert result1 == result2
-
-        # Different arguments - should call function
-        result3 = await arg_test_function(2, "test", c=200)
-        assert call_count == 2
-        assert result1 != result3
-
-    @pytest.mark.asyncio
-    async def test_exception_handling(self):
-        """Test that exceptions are not cached."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def exception_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            if x < 0:
-                raise ValueError("Negative value not allowed")
-            return x * 2
-
-        # Successful call - should be cached
-        result1 = await exception_function(5)
-        assert result1 == 10
-        assert call_count == 1
-
-        # Same successful call - should use cache
-        result2 = await exception_function(5)
-        assert result2 == 10
-        assert call_count == 1
-
-        # Exception call - should not be cached
-        with pytest.raises(ValueError):
-            await exception_function(-1)
-        assert call_count == 2
-
-        # Same exception call - should call again (not cached)
-        with pytest.raises(ValueError):
-            await exception_function(-1)
-        assert call_count == 3
-
-    @pytest.mark.asyncio
-    async def test_concurrent_calls(self):
-        """Test caching behavior with concurrent calls."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=60)
-        async def concurrent_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.05)  # Simulate work
-            return x * x
-
-        # Launch concurrent calls with same arguments
-        tasks = [concurrent_function(3) for _ in range(5)]
-        results = await asyncio.gather(*tasks)
-
-        # All results should be the same
-        assert all(result == 9 for result in results)
-
-        # Note: Due to race conditions, call_count might be up to 5 for concurrent calls
-        # This tests that the cache doesn't break under concurrent access
-        assert 1 <= call_count <= 5
-
-
-class TestAsyncCache:
-    """Tests for the @async_cache decorator (no TTL)."""
-
-    @pytest.mark.asyncio
-    async def test_basic_caching_no_ttl(self):
-        """Test basic caching functionality without TTL."""
-        call_count = 0
-
-        @async_cache(maxsize=10)
-        async def cached_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)  # Simulate async work
-            return x + y
-
-        # First call
-        result1 = await cached_function(1, 2)
-        assert result1 == 3
-        assert call_count == 1
-
-        # Second call with same args - should use cache
-        result2 = await cached_function(1, 2)
-        assert result2 == 3
-        assert call_count == 1  # No additional call
-
-        # Third call after some time - should still use cache (no TTL)
-        await asyncio.sleep(0.05)
-        result3 = await cached_function(1, 2)
-        assert result3 == 3
-        assert call_count == 1  # Still no additional call
-
-        # Different args - should call function again
-        result4 = await cached_function(2, 3)
-        assert result4 == 5
-        assert call_count == 2
-
-    @pytest.mark.asyncio
-    async def test_no_ttl_vs_ttl_behavior(self):
-        """Test the difference between TTL and no-TTL caching."""
-        ttl_call_count = 0
-        no_ttl_call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # Short TTL
-        async def ttl_function(x: int) -> int:
-            nonlocal ttl_call_count
-            ttl_call_count += 1
-            return x * 2
-
-        @async_cache(maxsize=10)  # No TTL
-        async def no_ttl_function(x: int) -> int:
-            nonlocal no_ttl_call_count
-            no_ttl_call_count += 1
-            return x * 2
-
-        # First calls
-        await ttl_function(5)
-        await no_ttl_function(5)
-        assert ttl_call_count == 1
-        assert no_ttl_call_count == 1
-
-        # Wait for TTL to expire
-        await asyncio.sleep(1.1)
-
-        # Second calls after TTL expiry
-        await ttl_function(5)  # Should call function again (TTL expired)
-        await no_ttl_function(5)  # Should use cache (no TTL)
-        assert ttl_call_count == 2  # TTL function called again
-        assert no_ttl_call_count == 1  # No-TTL function still cached
-
-    @pytest.mark.asyncio
-    async def test_async_cache_info(self):
-        """Test cache info for no-TTL cache."""
-
-        @async_cache(maxsize=5)
-        async def info_test_function(x: int) -> int:
-            return x * 3
-
-        # Check initial cache info
-        info = info_test_function.cache_info()
-        assert info["size"] == 0
-        assert info["maxsize"] == 5
-        assert info["ttl_seconds"] is None  # No TTL
-
-        # Add an entry
-        await info_test_function(1)
-        info = info_test_function.cache_info()
-        assert info["size"] == 1
-
-
-class TestTTLOptional:
-    """Tests for optional TTL functionality."""
-
-    @pytest.mark.asyncio
-    async def test_ttl_none_behavior(self):
-        """Test that ttl_seconds=None works like no TTL."""
-        call_count = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=None)
-        async def no_ttl_via_none(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x**2
-
-        # First call
-        result1 = await no_ttl_via_none(3)
-        assert result1 == 9
-        assert call_count == 1
-
-        # Wait (would expire if there was TTL)
-        await asyncio.sleep(0.1)
-
-        # Second call - should still use cache
-        result2 = await no_ttl_via_none(3)
-        assert result2 == 9
-        assert call_count == 1  # No additional call
-
-        # Check cache info
-        info = no_ttl_via_none.cache_info()
-        assert info["ttl_seconds"] is None
-
-    @pytest.mark.asyncio
-    async def test_cache_options_comparison(self):
-        """Test different cache options work as expected."""
-        ttl_calls = 0
-        no_ttl_calls = 0
-
-        @async_ttl_cache(maxsize=10, ttl_seconds=1)  # With TTL
-        async def ttl_function(x: int) -> int:
-            nonlocal ttl_calls
-            ttl_calls += 1
-            return x * 10
-
-        @async_cache(maxsize=10)  # Process-level cache (no TTL)
-        async def process_function(x: int) -> int:
-            nonlocal no_ttl_calls
-            no_ttl_calls += 1
-            return x * 10
-
-        # Both should cache initially
-        await ttl_function(3)
-        await process_function(3)
-        assert ttl_calls == 1
-        assert no_ttl_calls == 1
-
-        # Immediate second calls - both should use cache
-        await ttl_function(3)
-        await process_function(3)
-        assert ttl_calls == 1
-        assert no_ttl_calls == 1
-
-        # Wait for TTL to expire
-        await asyncio.sleep(1.1)
-
-        # After TTL expiry
-        await ttl_function(3)  # Should call function again
-        await process_function(3)  # Should still use cache
-        assert ttl_calls == 2  # TTL cache expired, called again
-        assert no_ttl_calls == 1  # Process cache never expires

autogpt_platform/autogpt_libs/pyproject.toml

@@ -1,8 +1,8 @@
 [tool.poetry]
 name = "autogpt-libs"
 version = "0.2.0"
-description = "Shared libraries across AutoGPT Platform"
-authors = ["AutoGPT team <info@agpt.co>"]
+description = "Shared libraries across NextGen AutoGPT"
+authors = ["Aarushi <aarushik93@gmail.com>"]
 readme = "README.md"
 packages = [{ include = "autogpt_libs" }]
 
@@ -10,20 +10,20 @@ packages = [{ include = "autogpt_libs" }]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
 expiringdict = "^1.2.2"
-fastapi = "^0.116.1"
 google-cloud-logging = "^3.12.1"
-launchdarkly-server-sdk = "^9.12.0"
-pydantic = "^2.11.7"
-pydantic-settings = "^2.10.1"
+pydantic = "^2.11.4"
+pydantic-settings = "^2.9.1"
 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"
+pytest-asyncio = "^0.26.0"
+pytest-mock = "^3.14.0"
+supabase = "^2.15.1"
+launchdarkly-server-sdk = "^9.11.1"
+fastapi = "^0.115.12"
+uvicorn = "^0.34.3"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.12.9"
+redis = "^5.2.1"
+ruff = "^0.11.10"
 
 [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
@@ -16,50 +10,72 @@ DB_SCHEMA=platform
 DATABASE_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
 DIRECT_URL="postgresql://${DB_USER}:${DB_PASS}@${DB_HOST}:${DB_PORT}/${DB_NAME}?schema=${DB_SCHEMA}&connect_timeout=${DB_CONNECT_TIMEOUT}"
 PRISMA_SCHEMA="postgres/schema.prisma"
-ENABLE_AUTH=true
 
-## ===== 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
-RABBITMQ_DEFAULT_USER=rabbitmq_user_default
-RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
+ENABLE_CREDIT=false
+STRIPE_API_KEY=
+STRIPE_WEBHOOK_SECRET=
 
-# Supabase Authentication
+# 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
 
-## ===== REQUIRED SECURITY KEYS ===== ##
-# Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()
-ENCRYPTION_KEY=dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw=
-UNSUBSCRIBE_SECRET_KEY=HlP8ivStJjmbf6NKi78m_3FnOogut0t5ckzjsIqeaio=
+# RabbitMQ credentials -- Used for communication between services
+RABBITMQ_HOST=localhost
+RABBITMQ_PORT=5672
+RABBITMQ_DEFAULT_USER=rabbitmq_user_default
+RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7
 
-## ===== 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
 
@@ -69,6 +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>
@@ -104,75 +121,87 @@ 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=
+## ===== OPTIONAL API KEYS END ===== ##
+
+# Logging Configuration
+LOG_LEVEL=INFO
+ENABLE_CLOUD_LOGGING=false
+ENABLE_FILE_LOGGING=false
+# Use to manually set the log directory
+# LOG_DIR=./logs

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/backend/TEST_DATA_README.md

@@ -1,150 +0,0 @@
-# Test Data Scripts
-
-This directory contains scripts for creating and updating test data in the AutoGPT Platform database, specifically designed to test the materialized views for the store functionality.
-
-## Scripts
-
-### test_data_creator.py
-Creates a comprehensive set of test data including:
-- Users with profiles
-- Agent graphs, nodes, and executions
-- Store listings with multiple versions
-- Reviews and ratings
-- Library agents
-- Integration webhooks
-- Onboarding data
-- Credit transactions
-
-**Image/Video Domains Used:**
-- Images: `picsum.photos` (for all image URLs)
-- Videos: `youtube.com` (for store listing videos)
-
-### test_data_updater.py
-Updates existing test data to simulate real-world changes:
-- Adds new agent graph executions
-- Creates new store listing reviews
-- Updates store listing versions
-- Adds credit transactions
-- Refreshes materialized views
-
-### check_db.py
-Tests and verifies materialized views functionality:
-- Checks pg_cron job status (for automatic refresh)
-- Displays current materialized view counts
-- Adds test data (executions and reviews)
-- Creates store listings if none exist
-- Manually refreshes materialized views
-- Compares before/after counts to verify updates
-- Provides a summary of test results
-
-## Materialized Views
-
-The scripts test three key database views:
-
-1. **mv_agent_run_counts**: Tracks execution counts by agent
-2. **mv_review_stats**: Tracks review statistics (count, average rating) by store listing
-3. **StoreAgent**: A view that combines store listing data with execution counts and ratings for display
-
-The materialized views (mv_agent_run_counts and mv_review_stats) are automatically refreshed every 15 minutes via pg_cron, or can be manually refreshed using the `refresh_store_materialized_views()` function.
-
-## Usage
-
-### Prerequisites
-
-1. Ensure the database is running:
-```bash
-docker compose up -d
-# or for test database:
-docker compose -f docker-compose.test.yaml --env-file ../.env up -d
-```
-
-2. Run database migrations:
-```bash
-poetry run prisma migrate deploy
-```
-
-### Running the Scripts
-
-#### Option 1: Use the helper script (from backend directory)
-```bash
-poetry run python run_test_data.py
-```
-
-#### Option 2: Run individually
-```bash
-# From backend/test directory:
-# Create initial test data
-poetry run python test_data_creator.py
-
-# Update data to test materialized view changes
-poetry run python test_data_updater.py
-
-# From backend directory:
-# Test materialized views functionality
-poetry run python check_db.py
-
-# Check store data status
-poetry run python check_store_data.py
-```
-
-#### Option 3: Use the shell script (from backend directory)
-```bash
-./run_test_data_scripts.sh
-```
-
-### Manual Materialized View Refresh
-
-To manually refresh the materialized views:
-```sql
-SELECT refresh_store_materialized_views();
-```
-
-## Configuration
-
-The scripts use the database configuration from your `.env` file:
-- `DATABASE_URL`: PostgreSQL connection string
-- Database should have the platform schema
-
-## Data Generation Limits
-
-Configured in `test_data_creator.py`:
-- 100 users
-- 100 agent blocks
-- 1-5 graphs per user
-- 2-5 nodes per graph
-- 1-5 presets per user
-- 1-10 library agents per user
-- 1-20 executions per graph
-- 1-5 reviews per store listing version
-
-## Notes
-
-- All image URLs use `picsum.photos` for consistency with Next.js image configuration
-- The scripts create realistic relationships between entities
-- Materialized views are refreshed at the end of each script
-- Data is designed to test both happy paths and edge cases
-
-## Troubleshooting
-
-### Reviews and StoreAgent view showing 0
-
-If `check_db.py` shows that reviews remain at 0 and StoreAgent view shows 0 store agents:
-
-1. **No store listings exist**: The script will automatically create test store listings if none exist
-2. **No approved versions**: Store listings need approved versions to appear in the StoreAgent view
-3. **Check with `check_store_data.py`**: This script provides detailed information about:
-   - Total store listings
-   - Store listing versions by status
-   - Existing reviews
-   - StoreAgent view contents
-   - Agent graph executions
-
-### pg_cron not installed
-
-The warning "pg_cron extension is not installed" is normal in local development environments. The materialized views can still be refreshed manually using the `refresh_store_materialized_views()` function, which all scripts do automatically.
-
-### Common Issues
-
-- **Type errors with None values**: Fixed in the latest version of check_db.py by using `or 0` for nullable numeric fields
-- **Missing relations**: Ensure you're using the correct field names (e.g., `StoreListing` not `storeListing` in includes)
-- **Column name mismatches**: The database uses camelCase for column names (e.g., `agentGraphId` not `agent_graph_id`)

autogpt_platform/backend/backend/app.py

@@ -1,10 +1,6 @@
 import logging
 from typing import TYPE_CHECKING
 
-from dotenv import load_dotenv
-
-load_dotenv()
-
 if TYPE_CHECKING:
     from backend.util.process import AppProcess
 
@@ -42,12 +38,12 @@ def main(**kwargs):
     from backend.server.ws_api import WebsocketServer
 
     run_processes(
-        DatabaseManager().set_log_level("warning"),
+        DatabaseManager(),
+        ExecutionManager(),
         Scheduler(),
         NotificationManager(),
         WebsocketServer(),
         AgentServer(),
-        ExecutionManager(),
         **kwargs,
     )
 

autogpt_platform/backend/backend/blocks/__init__.py

@@ -1,14 +1,10 @@
 import functools
 import importlib
-import logging
 import os
 import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar
 
-logger = logging.getLogger(__name__)
-
-
 if TYPE_CHECKING:
     from backend.data.block import Block
 
@@ -18,27 +14,14 @@ T = TypeVar("T")
 @functools.cache
 def load_all_blocks() -> dict[str, type["Block"]]:
     from backend.data.block import Block
-    from backend.util.settings import Config
-
-    # Check if example blocks should be loaded from settings
-    config = Config()
-    load_examples = config.enable_example_blocks
 
     # Dynamically load all modules under backend.blocks
     current_dir = Path(__file__).parent
-    modules = []
-    for f in current_dir.rglob("*.py"):
-        if not f.is_file() or f.name == "__init__.py" or f.name.startswith("test_"):
-            continue
-
-        # Skip examples directory if not enabled
-        relative_path = f.relative_to(current_dir)
-        if not load_examples and relative_path.parts[0] == "examples":
-            continue
-
-        module_path = str(relative_path)[:-3].replace(os.path.sep, ".")
-        modules.append(module_path)
-
+    modules = [
+        str(f.relative_to(current_dir))[:-3].replace(os.path.sep, ".")
+        for f in current_dir.rglob("*.py")
+        if f.is_file() and f.name != "__init__.py"
+    ]
     for module in modules:
         if not re.match("^[a-z0-9_.]+$", module):
             raise ValueError(
@@ -103,15 +86,7 @@ def load_all_blocks() -> dict[str, type["Block"]]:
 
         available_blocks[block.id] = block_cls
 
-    # Filter out blocks with incomplete auth configs, e.g. missing OAuth server secrets
-    from backend.data.block import is_block_auth_configured
-
-    filtered_blocks = {}
-    for block_id, block_cls in available_blocks.items():
-        if is_block_auth_configured(block_cls):
-            filtered_blocks[block_id] = block_cls
-
-    return filtered_blocks
+    return available_blocks
 
 
 __all__ = ["load_all_blocks"]

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,8 +1,7 @@
+import asyncio
 import logging
 from typing import Any, Optional
 
-from pydantic import JsonValue
-
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -13,11 +12,10 @@ from backend.data.block import (
     get_block,
 )
 from backend.data.execution import ExecutionStatus
-from backend.data.model import NodeExecutionStats, SchemaField
-from backend.util.json import validate_with_jsonschema
-from backend.util.retry import func_retry
+from backend.data.model import CredentialsMetaInput, SchemaField
+from backend.util import json
 
-_logger = logging.getLogger(__name__)
+logger = logging.getLogger(__name__)
 
 
 class AgentExecutorBlock(Block):
@@ -33,9 +31,9 @@ class AgentExecutorBlock(Block):
         input_schema: dict = SchemaField(description="Input schema for the graph")
         output_schema: dict = SchemaField(description="Output schema for the graph")
 
-        nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = SchemaField(
-            default=None, hidden=True
-        )
+        node_credentials_input_map: Optional[
+            dict[str, dict[str, CredentialsMetaInput]]
+        ] = SchemaField(default=None, hidden=True)
 
         @classmethod
         def get_input_schema(cls, data: BlockInput) -> dict[str, Any]:
@@ -52,7 +50,7 @@ class AgentExecutorBlock(Block):
 
         @classmethod
         def get_mismatch_error(cls, data: BlockInput) -> str | None:
-            return validate_with_jsonschema(cls.get_input_schema(data), data)
+            return json.validate_with_jsonschema(cls.get_input_schema(data), data)
 
     class Output(BlockSchema):
         pass
@@ -76,17 +74,8 @@ class AgentExecutorBlock(Block):
             graph_version=input_data.graph_version,
             user_id=input_data.user_id,
             inputs=input_data.inputs,
-            nodes_input_masks=input_data.nodes_input_masks,
-        )
-
-        logger = execution_utils.LogMetadata(
-            logger=_logger,
-            user_id=input_data.user_id,
-            graph_eid=graph_exec.id,
-            graph_id=input_data.graph_id,
-            node_eid="*",
-            node_id="*",
-            block_name=self.name,
+            node_credentials_input_map=input_data.node_credentials_input_map,
+            use_db_query=False,
         )
 
         try:
@@ -95,17 +84,21 @@ class AgentExecutorBlock(Block):
                 graph_version=input_data.graph_version,
                 graph_exec_id=graph_exec.id,
                 user_id=input_data.user_id,
-                logger=logger,
             ):
                 yield name, data
-        except BaseException as e:
-            await self._stop(
-                graph_exec_id=graph_exec.id,
-                user_id=input_data.user_id,
-                logger=logger,
-            )
+        except asyncio.CancelledError:
             logger.warning(
-                f"Execution of graph {input_data.graph_id}v{input_data.graph_version} failed: {e.__class__.__name__} {str(e)}; execution is stopped."
+                f"Execution of graph {input_data.graph_id} version {input_data.graph_version} was cancelled."
+            )
+            await execution_utils.stop_graph_execution(
+                graph_exec.id, use_db_query=False
+            )
+        except Exception as e:
+            logger.error(
+                f"Execution of graph {input_data.graph_id} version {input_data.graph_version} failed: {e}, stopping execution."
+            )
+            await execution_utils.stop_graph_execution(
+                graph_exec.id, use_db_query=False
             )
             raise
 
@@ -115,7 +108,6 @@ class AgentExecutorBlock(Block):
         graph_version: int,
         graph_exec_id: str,
         user_id: str,
-        logger,
     ) -> BlockOutput:
 
         from backend.data.execution import ExecutionEventType
@@ -125,7 +117,6 @@ class AgentExecutorBlock(Block):
 
         log_id = f"Graph #{graph_id}-V{graph_version}, exec-id: {graph_exec_id}"
         logger.info(f"Starting execution of {log_id}")
-        yielded_node_exec_ids = set()
 
         async for event in event_bus.listen(
             user_id=user_id,
@@ -145,26 +136,12 @@ class AgentExecutorBlock(Block):
             if event.event_type == ExecutionEventType.GRAPH_EXEC_UPDATE:
                 # If the graph execution is COMPLETED, TERMINATED, or FAILED,
                 # we can stop listening for further events.
-                self.merge_stats(
-                    NodeExecutionStats(
-                        extra_cost=event.stats.cost if event.stats else 0,
-                        extra_steps=event.stats.node_exec_count if event.stats else 0,
-                    )
-                )
                 break
 
             logger.debug(
                 f"Execution {log_id} produced input {event.input_data} output {event.output_data}"
             )
 
-            if event.node_exec_id in yielded_node_exec_ids:
-                logger.warning(
-                    f"{log_id} received duplicate event for node execution {event.node_exec_id}"
-                )
-                continue
-            else:
-                yielded_node_exec_ids.add(event.node_exec_id)
-
             if not event.block_id:
                 logger.warning(f"{log_id} received event without block_id {event}")
                 continue
@@ -183,25 +160,3 @@ class AgentExecutorBlock(Block):
                     f"Execution {log_id} produced {output_name}: {output_data}"
                 )
                 yield output_name, output_data
-
-    @func_retry
-    async def _stop(
-        self,
-        graph_exec_id: str,
-        user_id: str,
-        logger,
-    ) -> None:
-        from backend.executor import utils as execution_utils
-
-        log_id = f"Graph exec-id: {graph_exec_id}"
-        logger.info(f"Stopping execution of {log_id}")
-
-        try:
-            await execution_utils.stop_graph_execution(
-                graph_exec_id=graph_exec_id,
-                user_id=user_id,
-                wait_timeout=3600,
-            )
-            logger.info(f"Execution {log_id} stopped successfully.")
-        except TimeoutError as e:
-            logger.error(f"Execution {log_id} stop timed out: {e}")

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/ai_shortform_video_block.py

@@ -53,7 +53,6 @@ class AudioTrack(str, Enum):
     REFRESHER = ("Refresher",)
     TOURIST = ("Tourist",)
     TWIN_TYCHES = ("Twin Tyches",)
-    DONT_STOP_ME_ABSTRACT_FUTURE_BASS = ("Dont Stop Me Abstract Future Bass",)
 
     @property
     def audio_url(self):
@@ -79,7 +78,6 @@ class AudioTrack(str, Enum):
             AudioTrack.REFRESHER: "https://cdn.tfrv.xyz/audio/refresher.mp3",
             AudioTrack.TOURIST: "https://cdn.tfrv.xyz/audio/tourist.mp3",
             AudioTrack.TWIN_TYCHES: "https://cdn.tfrv.xyz/audio/twin-tynches.mp3",
-            AudioTrack.DONT_STOP_ME_ABSTRACT_FUTURE_BASS: "https://cdn.revid.ai/audio/_dont-stop-me-abstract-future-bass.mp3",
         }
         return audio_urls[self]
 
@@ -107,7 +105,6 @@ class GenerationPreset(str, Enum):
     MOVIE = ("Movie",)
     STYLIZED_ILLUSTRATION = ("Stylized Illustration",)
     MANGA = ("Manga",)
-    DEFAULT = ("DEFAULT",)
 
 
 class Voice(str, Enum):
@@ -117,7 +114,6 @@ class Voice(str, Enum):
     JESSICA = "Jessica"
     CHARLOTTE = "Charlotte"
     CALLUM = "Callum"
-    EVA = "Eva"
 
     @property
     def voice_id(self):
@@ -128,7 +124,6 @@ class Voice(str, Enum):
             Voice.JESSICA: "cgSgspJ2msm6clMCkdW9",
             Voice.CHARLOTTE: "XB0fDUnXU5powFXDhCwa",
             Voice.CALLUM: "N2lVS1w4EtoT3dr4eOWO",
-            Voice.EVA: "FGY2WhTYpPnrIDTdsKH5",
         }
         return voice_id_map[self]
 
@@ -146,8 +141,6 @@ logger = logging.getLogger(__name__)
 
 
 class AIShortformVideoCreatorBlock(Block):
-    """Creates a short‑form text‑to‑video clip using stock or AI imagery."""
-
     class Input(BlockSchema):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REVID], Literal["api_key"]
@@ -191,58 +184,6 @@ class AIShortformVideoCreatorBlock(Block):
         video_url: str = SchemaField(description="The URL of the created video")
         error: str = SchemaField(description="Error message if the request failed")
 
-    async def create_webhook(self) -> tuple[str, str]:
-        """Create a new webhook URL for receiving notifications."""
-        url = "https://webhook.site/token"
-        headers = {"Accept": "application/json", "Content-Type": "application/json"}
-        response = await Requests().post(url, headers=headers)
-        webhook_data = response.json()
-        return webhook_data["uuid"], f"https://webhook.site/{webhook_data['uuid']}"
-
-    async def create_video(self, api_key: SecretStr, payload: dict) -> dict:
-        """Create a video using the Revid API."""
-        url = "https://www.revid.ai/api/public/v2/render"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().post(url, json=payload, headers=headers)
-        logger.debug(
-            f"API Response Status Code: {response.status}, Content: {response.text}"
-        )
-        return response.json()
-
-    async def check_video_status(self, api_key: SecretStr, pid: str) -> dict:
-        """Check the status of a video creation job."""
-        url = f"https://www.revid.ai/api/public/v2/status?pid={pid}"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().get(url, headers=headers)
-        return response.json()
-
-    async def wait_for_video(
-        self,
-        api_key: SecretStr,
-        pid: str,
-        max_wait_time: int = 1000,
-    ) -> str:
-        """Wait for video creation to complete and return the video URL."""
-        start_time = time.time()
-        while time.time() - start_time < max_wait_time:
-            status = await self.check_video_status(api_key, pid)
-            logger.debug(f"Video status: {status}")
-
-            if status.get("status") == "ready" and "videoUrl" in status:
-                return status["videoUrl"]
-            elif status.get("status") == "error":
-                error_message = status.get("error", "Unknown error occurred")
-                logger.error(f"Video creation failed: {error_message}")
-                raise ValueError(f"Video creation failed: {error_message}")
-            elif status.get("status") in ["FAILED", "CANCELED"]:
-                logger.error(f"Video creation failed: {status.get('message')}")
-                raise ValueError(f"Video creation failed: {status.get('message')}")
-
-            await asyncio.sleep(10)
-
-        logger.error("Video creation timed out")
-        raise TimeoutError("Video creation timed out")
-
     def __init__(self):
         super().__init__(
             id="361697fb-0c4f-4feb-aed3-8320c88c771b",
@@ -261,22 +202,70 @@ class AIShortformVideoCreatorBlock(Block):
                 "voice": Voice.LILY,
                 "video_style": VisualMediaType.STOCK_VIDEOS,
             },
-            test_output=("video_url", "https://example.com/video.mp4"),
+            test_output=(
+                "video_url",
+                "https://example.com/video.mp4",
+            ),
             test_mock={
-                "create_webhook": lambda *args, **kwargs: (
+                "create_webhook": lambda: (
                     "test_uuid",
                     "https://webhook.site/test_uuid",
                 ),
-                "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
-                "check_video_status": lambda *args, **kwargs: {
-                    "status": "ready",
-                    "videoUrl": "https://example.com/video.mp4",
-                },
-                "wait_for_video": lambda *args, **kwargs: "https://example.com/video.mp4",
+                "create_video": lambda api_key, payload: {"pid": "test_pid"},
+                "wait_for_video": lambda api_key, pid, webhook_token, max_wait_time=1000: "https://example.com/video.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
+    async def create_webhook(self):
+        url = "https://webhook.site/token"
+        headers = {"Accept": "application/json", "Content-Type": "application/json"}
+        response = await Requests().post(url, headers=headers)
+        webhook_data = response.json()
+        return webhook_data["uuid"], f"https://webhook.site/{webhook_data['uuid']}"
+
+    async def create_video(self, api_key: SecretStr, payload: dict) -> dict:
+        url = "https://www.revid.ai/api/public/v2/render"
+        headers = {"key": api_key.get_secret_value()}
+        response = await Requests().post(url, json=payload, headers=headers)
+        logger.debug(
+            f"API Response Status Code: {response.status}, Content: {response.text}"
+        )
+        return response.json()
+
+    async def check_video_status(self, api_key: SecretStr, pid: str) -> dict:
+        url = f"https://www.revid.ai/api/public/v2/status?pid={pid}"
+        headers = {"key": api_key.get_secret_value()}
+        response = await Requests().get(url, headers=headers)
+        return response.json()
+
+    async def wait_for_video(
+        self,
+        api_key: SecretStr,
+        pid: str,
+        webhook_token: str,
+        max_wait_time: int = 1000,
+    ) -> str:
+        start_time = time.time()
+        while time.time() - start_time < max_wait_time:
+            status = await self.check_video_status(api_key, pid)
+            logger.debug(f"Video status: {status}")
+
+            if status.get("status") == "ready" and "videoUrl" in status:
+                return status["videoUrl"]
+            elif status.get("status") == "error":
+                error_message = status.get("error", "Unknown error occurred")
+                logger.error(f"Video creation failed: {error_message}")
+                raise ValueError(f"Video creation failed: {error_message}")
+            elif status.get("status") in ["FAILED", "CANCELED"]:
+                logger.error(f"Video creation failed: {status.get('message')}")
+                raise ValueError(f"Video creation failed: {status.get('message')}")
+
+            await asyncio.sleep(10)
+
+        logger.error("Video creation timed out")
+        raise TimeoutError("Video creation timed out")
+
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
@@ -284,18 +273,20 @@ class AIShortformVideoCreatorBlock(Block):
         webhook_token, webhook_url = await self.create_webhook()
         logger.debug(f"Webhook URL: {webhook_url}")
 
+        audio_url = input_data.background_music.audio_url
+
         payload = {
             "frameRate": input_data.frame_rate,
             "resolution": input_data.resolution,
             "frameDurationMultiplier": 18,
-            "webhook": None,
+            "webhook": webhook_url,
             "creationParams": {
                 "mediaType": input_data.video_style,
                 "captionPresetName": "Wrap 1",
                 "selectedVoice": input_data.voice.voice_id,
                 "hasEnhancedGeneration": True,
                 "generationPreset": input_data.generation_preset.name,
-                "selectedAudio": input_data.background_music.value,
+                "selectedAudio": input_data.background_music,
                 "origin": "/create",
                 "inputText": input_data.script,
                 "flowType": "text-to-video",
@@ -311,7 +302,7 @@ class AIShortformVideoCreatorBlock(Block):
                 "selectedStoryStyle": {"value": "custom", "label": "Custom"},
                 "hasToGenerateVideos": input_data.video_style
                 != VisualMediaType.STOCK_VIDEOS,
-                "audioUrl": input_data.background_music.audio_url,
+                "audioUrl": audio_url,
             },
         }
 
@@ -328,370 +319,8 @@ class AIShortformVideoCreatorBlock(Block):
             logger.debug(
                 f"Video created with project ID: {pid}. Waiting for completion..."
             )
-            video_url = await self.wait_for_video(credentials.api_key, pid)
+            video_url = await self.wait_for_video(
+                credentials.api_key, pid, webhook_token
+            )
             logger.debug(f"Video ready: {video_url}")
             yield "video_url", video_url
-
-
-class AIAdMakerVideoCreatorBlock(Block):
-    """Generates a 30‑second vertical AI advert using optional user‑supplied imagery."""
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput[
-            Literal[ProviderName.REVID], Literal["api_key"]
-        ] = CredentialsField(
-            description="Credentials for Revid.ai API access.",
-        )
-        script: str = SchemaField(
-            description="Short advertising copy. Line breaks create new scenes.",
-            placeholder="Introducing Foobar – [show product photo] the gadget that does it all.",
-        )
-        ratio: str = SchemaField(description="Aspect ratio", default="9 / 16")
-        target_duration: int = SchemaField(
-            description="Desired length of the ad in seconds.", default=30
-        )
-        voice: Voice = SchemaField(
-            description="Narration voice", default=Voice.EVA, placeholder=Voice.EVA
-        )
-        background_music: AudioTrack = SchemaField(
-            description="Background track",
-            default=AudioTrack.DONT_STOP_ME_ABSTRACT_FUTURE_BASS,
-        )
-        input_media_urls: list[str] = SchemaField(
-            description="List of image URLs to feature in the advert.", default=[]
-        )
-        use_only_provided_media: bool = SchemaField(
-            description="Restrict visuals to supplied images only.", default=True
-        )
-
-    class Output(BlockSchema):
-        video_url: str = SchemaField(description="URL of the finished advert")
-        error: str = SchemaField(description="Error message on failure")
-
-    async def create_webhook(self) -> tuple[str, str]:
-        """Create a new webhook URL for receiving notifications."""
-        url = "https://webhook.site/token"
-        headers = {"Accept": "application/json", "Content-Type": "application/json"}
-        response = await Requests().post(url, headers=headers)
-        webhook_data = response.json()
-        return webhook_data["uuid"], f"https://webhook.site/{webhook_data['uuid']}"
-
-    async def create_video(self, api_key: SecretStr, payload: dict) -> dict:
-        """Create a video using the Revid API."""
-        url = "https://www.revid.ai/api/public/v2/render"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().post(url, json=payload, headers=headers)
-        logger.debug(
-            f"API Response Status Code: {response.status}, Content: {response.text}"
-        )
-        return response.json()
-
-    async def check_video_status(self, api_key: SecretStr, pid: str) -> dict:
-        """Check the status of a video creation job."""
-        url = f"https://www.revid.ai/api/public/v2/status?pid={pid}"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().get(url, headers=headers)
-        return response.json()
-
-    async def wait_for_video(
-        self,
-        api_key: SecretStr,
-        pid: str,
-        max_wait_time: int = 1000,
-    ) -> str:
-        """Wait for video creation to complete and return the video URL."""
-        start_time = time.time()
-        while time.time() - start_time < max_wait_time:
-            status = await self.check_video_status(api_key, pid)
-            logger.debug(f"Video status: {status}")
-
-            if status.get("status") == "ready" and "videoUrl" in status:
-                return status["videoUrl"]
-            elif status.get("status") == "error":
-                error_message = status.get("error", "Unknown error occurred")
-                logger.error(f"Video creation failed: {error_message}")
-                raise ValueError(f"Video creation failed: {error_message}")
-            elif status.get("status") in ["FAILED", "CANCELED"]:
-                logger.error(f"Video creation failed: {status.get('message')}")
-                raise ValueError(f"Video creation failed: {status.get('message')}")
-
-            await asyncio.sleep(10)
-
-        logger.error("Video creation timed out")
-        raise TimeoutError("Video creation timed out")
-
-    def __init__(self):
-        super().__init__(
-            id="58bd2a19-115d-4fd1-8ca4-13b9e37fa6a0",
-            description="Creates an AI‑generated 30‑second advert (text + images)",
-            categories={BlockCategory.MARKETING, BlockCategory.AI},
-            input_schema=AIAdMakerVideoCreatorBlock.Input,
-            output_schema=AIAdMakerVideoCreatorBlock.Output,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "script": "Test product launch!",
-                "input_media_urls": [
-                    "https://cdn.revid.ai/uploads/1747076315114-image.png",
-                ],
-            },
-            test_output=("video_url", "https://example.com/ad.mp4"),
-            test_mock={
-                "create_webhook": lambda *args, **kwargs: (
-                    "test_uuid",
-                    "https://webhook.site/test_uuid",
-                ),
-                "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
-                "check_video_status": lambda *args, **kwargs: {
-                    "status": "ready",
-                    "videoUrl": "https://example.com/ad.mp4",
-                },
-                "wait_for_video": lambda *args, **kwargs: "https://example.com/ad.mp4",
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
-        webhook_token, webhook_url = await self.create_webhook()
-
-        payload = {
-            "webhook": webhook_url,
-            "creationParams": {
-                "targetDuration": input_data.target_duration,
-                "ratio": input_data.ratio,
-                "mediaType": "aiVideo",
-                "inputText": input_data.script,
-                "flowType": "text-to-video",
-                "slug": "ai-ad-generator",
-                "slugNew": "",
-                "isCopiedFrom": False,
-                "hasToGenerateVoice": True,
-                "hasToTranscript": False,
-                "hasToSearchMedia": True,
-                "hasAvatar": False,
-                "hasWebsiteRecorder": False,
-                "hasTextSmallAtBottom": False,
-                "selectedAudio": input_data.background_music.value,
-                "selectedVoice": input_data.voice.voice_id,
-                "selectedAvatar": "https://cdn.revid.ai/avatars/young-woman.mp4",
-                "selectedAvatarType": "video/mp4",
-                "websiteToRecord": "",
-                "hasToGenerateCover": True,
-                "nbGenerations": 1,
-                "disableCaptions": False,
-                "mediaMultiplier": "medium",
-                "characters": [],
-                "captionPresetName": "Revid",
-                "sourceType": "contentScraping",
-                "selectedStoryStyle": {"value": "custom", "label": "General"},
-                "generationPreset": "DEFAULT",
-                "hasToGenerateMusic": False,
-                "isOptimizedForChinese": False,
-                "generationUserPrompt": "",
-                "enableNsfwFilter": False,
-                "addStickers": False,
-                "typeMovingImageAnim": "dynamic",
-                "hasToGenerateSoundEffects": False,
-                "forceModelType": "gpt-image-1",
-                "selectedCharacters": [],
-                "lang": "",
-                "voiceSpeed": 1,
-                "disableAudio": False,
-                "disableVoice": False,
-                "useOnlyProvidedMedia": input_data.use_only_provided_media,
-                "imageGenerationModel": "ultra",
-                "videoGenerationModel": "pro",
-                "hasEnhancedGeneration": True,
-                "hasEnhancedGenerationPro": True,
-                "inputMedias": [
-                    {"url": url, "title": "", "type": "image"}
-                    for url in input_data.input_media_urls
-                ],
-                "hasToGenerateVideos": True,
-                "audioUrl": input_data.background_music.audio_url,
-                "watermark": None,
-            },
-        }
-
-        response = await self.create_video(credentials.api_key, payload)
-        pid = response.get("pid")
-        if not pid:
-            raise RuntimeError("Failed to create video: No project ID returned")
-
-        video_url = await self.wait_for_video(credentials.api_key, pid)
-        yield "video_url", video_url
-
-
-class AIScreenshotToVideoAdBlock(Block):
-    """Creates an advert where the supplied screenshot is narrated by an AI avatar."""
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput[
-            Literal[ProviderName.REVID], Literal["api_key"]
-        ] = CredentialsField(description="Revid.ai API key")
-        script: str = SchemaField(
-            description="Narration that will accompany the screenshot.",
-            placeholder="Check out these amazing stats!",
-        )
-        screenshot_url: str = SchemaField(
-            description="Screenshot or image URL to showcase."
-        )
-        ratio: str = SchemaField(default="9 / 16")
-        target_duration: int = SchemaField(default=30)
-        voice: Voice = SchemaField(default=Voice.EVA)
-        background_music: AudioTrack = SchemaField(
-            default=AudioTrack.DONT_STOP_ME_ABSTRACT_FUTURE_BASS
-        )
-
-    class Output(BlockSchema):
-        video_url: str = SchemaField(description="Rendered video URL")
-        error: str = SchemaField(description="Error, if encountered")
-
-    async def create_webhook(self) -> tuple[str, str]:
-        """Create a new webhook URL for receiving notifications."""
-        url = "https://webhook.site/token"
-        headers = {"Accept": "application/json", "Content-Type": "application/json"}
-        response = await Requests().post(url, headers=headers)
-        webhook_data = response.json()
-        return webhook_data["uuid"], f"https://webhook.site/{webhook_data['uuid']}"
-
-    async def create_video(self, api_key: SecretStr, payload: dict) -> dict:
-        """Create a video using the Revid API."""
-        url = "https://www.revid.ai/api/public/v2/render"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().post(url, json=payload, headers=headers)
-        logger.debug(
-            f"API Response Status Code: {response.status}, Content: {response.text}"
-        )
-        return response.json()
-
-    async def check_video_status(self, api_key: SecretStr, pid: str) -> dict:
-        """Check the status of a video creation job."""
-        url = f"https://www.revid.ai/api/public/v2/status?pid={pid}"
-        headers = {"key": api_key.get_secret_value()}
-        response = await Requests().get(url, headers=headers)
-        return response.json()
-
-    async def wait_for_video(
-        self,
-        api_key: SecretStr,
-        pid: str,
-        max_wait_time: int = 1000,
-    ) -> str:
-        """Wait for video creation to complete and return the video URL."""
-        start_time = time.time()
-        while time.time() - start_time < max_wait_time:
-            status = await self.check_video_status(api_key, pid)
-            logger.debug(f"Video status: {status}")
-
-            if status.get("status") == "ready" and "videoUrl" in status:
-                return status["videoUrl"]
-            elif status.get("status") == "error":
-                error_message = status.get("error", "Unknown error occurred")
-                logger.error(f"Video creation failed: {error_message}")
-                raise ValueError(f"Video creation failed: {error_message}")
-            elif status.get("status") in ["FAILED", "CANCELED"]:
-                logger.error(f"Video creation failed: {status.get('message')}")
-                raise ValueError(f"Video creation failed: {status.get('message')}")
-
-            await asyncio.sleep(10)
-
-        logger.error("Video creation timed out")
-        raise TimeoutError("Video creation timed out")
-
-    def __init__(self):
-        super().__init__(
-            id="0f3e4635-e810-43d9-9e81-49e6f4e83b7c",
-            description="Turns a screenshot into an engaging, avatar‑narrated video advert.",
-            categories={BlockCategory.AI, BlockCategory.MARKETING},
-            input_schema=AIScreenshotToVideoAdBlock.Input,
-            output_schema=AIScreenshotToVideoAdBlock.Output,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "script": "Amazing numbers!",
-                "screenshot_url": "https://cdn.revid.ai/uploads/1747080376028-image.png",
-            },
-            test_output=("video_url", "https://example.com/screenshot.mp4"),
-            test_mock={
-                "create_webhook": lambda *args, **kwargs: (
-                    "test_uuid",
-                    "https://webhook.site/test_uuid",
-                ),
-                "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
-                "check_video_status": lambda *args, **kwargs: {
-                    "status": "ready",
-                    "videoUrl": "https://example.com/screenshot.mp4",
-                },
-                "wait_for_video": lambda *args, **kwargs: "https://example.com/screenshot.mp4",
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
-        webhook_token, webhook_url = await self.create_webhook()
-
-        payload = {
-            "webhook": webhook_url,
-            "creationParams": {
-                "targetDuration": input_data.target_duration,
-                "ratio": input_data.ratio,
-                "mediaType": "aiVideo",
-                "hasAvatar": True,
-                "removeAvatarBackground": True,
-                "inputText": input_data.script,
-                "flowType": "text-to-video",
-                "slug": "ai-ad-generator",
-                "slugNew": "screenshot-to-video-ad",
-                "isCopiedFrom": "ai-ad-generator",
-                "hasToGenerateVoice": True,
-                "hasToTranscript": False,
-                "hasToSearchMedia": True,
-                "hasWebsiteRecorder": False,
-                "hasTextSmallAtBottom": False,
-                "selectedAudio": input_data.background_music.value,
-                "selectedVoice": input_data.voice.voice_id,
-                "selectedAvatar": "https://cdn.revid.ai/avatars/young-woman.mp4",
-                "selectedAvatarType": "video/mp4",
-                "websiteToRecord": "",
-                "hasToGenerateCover": True,
-                "nbGenerations": 1,
-                "disableCaptions": False,
-                "mediaMultiplier": "medium",
-                "characters": [],
-                "captionPresetName": "Revid",
-                "sourceType": "contentScraping",
-                "selectedStoryStyle": {"value": "custom", "label": "General"},
-                "generationPreset": "DEFAULT",
-                "hasToGenerateMusic": False,
-                "isOptimizedForChinese": False,
-                "generationUserPrompt": "",
-                "enableNsfwFilter": False,
-                "addStickers": False,
-                "typeMovingImageAnim": "dynamic",
-                "hasToGenerateSoundEffects": False,
-                "forceModelType": "gpt-image-1",
-                "selectedCharacters": [],
-                "lang": "",
-                "voiceSpeed": 1,
-                "disableAudio": False,
-                "disableVoice": False,
-                "useOnlyProvidedMedia": True,
-                "imageGenerationModel": "ultra",
-                "videoGenerationModel": "ultra",
-                "hasEnhancedGeneration": True,
-                "hasEnhancedGenerationPro": True,
-                "inputMedias": [
-                    {"url": input_data.screenshot_url, "title": "", "type": "image"}
-                ],
-                "hasToGenerateVideos": True,
-                "audioUrl": input_data.background_music.audio_url,
-                "watermark": None,
-            },
-        }
-
-        response = await self.create_video(credentials.api_key, payload)
-        pid = response.get("pid")
-        if not pid:
-            raise RuntimeError("Failed to create video: No project ID returned")
-
-        video_url = await self.wait_for_video(credentials.api_key, pid)
-        yield "video_url", video_url

autogpt_platform/backend/backend/blocks/airtable/_api_test.py

@@ -1,323 +0,0 @@
-from os import getenv
-from uuid import uuid4
-
-import pytest
-
-from backend.sdk import APIKeyCredentials, SecretStr
-
-from ._api import (
-    TableFieldType,
-    WebhookFilters,
-    WebhookSpecification,
-    create_base,
-    create_field,
-    create_record,
-    create_table,
-    create_webhook,
-    delete_multiple_records,
-    delete_record,
-    delete_webhook,
-    get_record,
-    list_bases,
-    list_records,
-    list_webhook_payloads,
-    update_field,
-    update_multiple_records,
-    update_record,
-    update_table,
-)
-
-
-@pytest.mark.asyncio
-async def test_create_update_table():
-
-    key = getenv("AIRTABLE_API_KEY")
-    if not key:
-        return pytest.skip("AIRTABLE_API_KEY is not set")
-
-    credentials = APIKeyCredentials(
-        provider="airtable",
-        api_key=SecretStr(key),
-    )
-    postfix = uuid4().hex[:4]
-    workspace_id = "wsphuHmfllg7V3Brd"
-    response = await create_base(credentials, workspace_id, "API Testing Base")
-    assert response is not None, f"Checking create base response: {response}"
-    assert (
-        response.get("id") is not None
-    ), f"Checking create base response id: {response}"
-    base_id = response.get("id")
-    assert base_id is not None, f"Checking create base response id: {base_id}"
-
-    response = await list_bases(credentials)
-    assert response is not None, f"Checking list bases response: {response}"
-    assert "API Testing Base" in [
-        base.get("name") for base in response.get("bases", [])
-    ], f"Checking list bases response bases: {response}"
-
-    table_name = f"test_table_{postfix}"
-    table_fields = [{"name": "test_field", "type": "singleLineText"}]
-    table = await create_table(credentials, base_id, table_name, table_fields)
-    assert table.get("name") == table_name
-
-    table_id = table.get("id")
-
-    assert table_id is not None
-
-    table_name = f"test_table_updated_{postfix}"
-    table_description = "test_description_updated"
-    table = await update_table(
-        credentials,
-        base_id,
-        table_id,
-        table_name=table_name,
-        table_description=table_description,
-    )
-    assert table.get("name") == table_name
-    assert table.get("description") == table_description
-
-
-@pytest.mark.asyncio
-async def test_invalid_field_type():
-
-    key = getenv("AIRTABLE_API_KEY")
-    if not key:
-        return pytest.skip("AIRTABLE_API_KEY is not set")
-
-    credentials = APIKeyCredentials(
-        provider="airtable",
-        api_key=SecretStr(key),
-    )
-    postfix = uuid4().hex[:4]
-    base_id = "appZPxegHEU3kDc1S"
-    table_name = f"test_table_{postfix}"
-    table_fields = [{"name": "test_field", "type": "notValid"}]
-    with pytest.raises(AssertionError):
-        await create_table(credentials, base_id, table_name, table_fields)
-
-
-@pytest.mark.asyncio
-async def test_create_and_update_field():
-    key = getenv("AIRTABLE_API_KEY")
-    if not key:
-        return pytest.skip("AIRTABLE_API_KEY is not set")
-
-    credentials = APIKeyCredentials(
-        provider="airtable",
-        api_key=SecretStr(key),
-    )
-    postfix = uuid4().hex[:4]
-    base_id = "appZPxegHEU3kDc1S"
-    table_name = f"test_table_{postfix}"
-    table_fields = [{"name": "test_field", "type": "singleLineText"}]
-    table = await create_table(credentials, base_id, table_name, table_fields)
-    assert table.get("name") == table_name
-
-    table_id = table.get("id")
-
-    assert table_id is not None
-
-    field_name = f"test_field_{postfix}"
-    field_type = TableFieldType.SINGLE_LINE_TEXT
-    field = await create_field(credentials, base_id, table_id, field_type, field_name)
-    assert field.get("name") == field_name
-
-    field_id = field.get("id")
-
-    assert field_id is not None
-    assert isinstance(field_id, str)
-
-    field_name = f"test_field_updated_{postfix}"
-    field = await update_field(credentials, base_id, table_id, field_id, field_name)
-    assert field.get("name") == field_name
-
-    field_description = "test_description_updated"
-    field = await update_field(
-        credentials, base_id, table_id, field_id, description=field_description
-    )
-    assert field.get("description") == field_description
-
-
-@pytest.mark.asyncio
-async def test_record_management():
-    key = getenv("AIRTABLE_API_KEY")
-    if not key:
-        return pytest.skip("AIRTABLE_API_KEY is not set")
-
-    credentials = APIKeyCredentials(
-        provider="airtable",
-        api_key=SecretStr(key),
-    )
-    postfix = uuid4().hex[:4]
-    base_id = "appZPxegHEU3kDc1S"
-    table_name = f"test_table_{postfix}"
-    table_fields = [{"name": "test_field", "type": "singleLineText"}]
-    table = await create_table(credentials, base_id, table_name, table_fields)
-    assert table.get("name") == table_name
-
-    table_id = table.get("id")
-    assert table_id is not None
-
-    # Create a record
-    record_fields = {"test_field": "test_value"}
-    record = await create_record(credentials, base_id, table_id, fields=record_fields)
-    fields = record.get("fields")
-    assert fields is not None
-    assert isinstance(fields, dict)
-    assert fields.get("test_field") == "test_value"
-
-    record_id = record.get("id")
-
-    assert record_id is not None
-    assert isinstance(record_id, str)
-
-    # Get a record
-    record = await get_record(credentials, base_id, table_id, record_id)
-    fields = record.get("fields")
-    assert fields is not None
-    assert isinstance(fields, dict)
-    assert fields.get("test_field") == "test_value"
-
-    # Updata a record
-    record_fields = {"test_field": "test_value_updated"}
-    record = await update_record(
-        credentials, base_id, table_id, record_id, fields=record_fields
-    )
-    fields = record.get("fields")
-    assert fields is not None
-    assert isinstance(fields, dict)
-    assert fields.get("test_field") == "test_value_updated"
-
-    # Delete a record
-    record = await delete_record(credentials, base_id, table_id, record_id)
-    assert record is not None
-    assert record.get("id") == record_id
-    assert record.get("deleted")
-
-    # Create 2 records
-    records = [
-        {"fields": {"test_field": "test_value_1"}},
-        {"fields": {"test_field": "test_value_2"}},
-    ]
-    response = await create_record(credentials, base_id, table_id, records=records)
-    created_records = response.get("records")
-    assert created_records is not None
-    assert isinstance(created_records, list)
-    assert len(created_records) == 2, f"Created records: {created_records}"
-    first_record = created_records[0]  # type: ignore
-    second_record = created_records[1]  # type: ignore
-    first_record_id = first_record.get("id")
-    second_record_id = second_record.get("id")
-    assert first_record_id is not None
-    assert second_record_id is not None
-    assert first_record_id != second_record_id
-    first_fields = first_record.get("fields")
-    second_fields = second_record.get("fields")
-    assert first_fields is not None
-    assert second_fields is not None
-    assert first_fields.get("test_field") == "test_value_1"  # type: ignore
-    assert second_fields.get("test_field") == "test_value_2"  # type: ignore
-
-    # List records
-    response = await list_records(credentials, base_id, table_id)
-    records = response.get("records")
-    assert records is not None
-    assert len(records) == 2, f"Records: {records}"
-    assert isinstance(records, list), f"Type of records: {type(records)}"
-
-    # Update multiple records
-    records = [
-        {"id": first_record_id, "fields": {"test_field": "test_value_1_updated"}},
-        {"id": second_record_id, "fields": {"test_field": "test_value_2_updated"}},
-    ]
-    response = await update_multiple_records(
-        credentials, base_id, table_id, records=records
-    )
-    updated_records = response.get("records")
-    assert updated_records is not None
-    assert len(updated_records) == 2, f"Updated records: {updated_records}"
-    assert isinstance(
-        updated_records, list
-    ), f"Type of updated records: {type(updated_records)}"
-    first_updated = updated_records[0]  # type: ignore
-    second_updated = updated_records[1]  # type: ignore
-    first_updated_fields = first_updated.get("fields")
-    second_updated_fields = second_updated.get("fields")
-    assert first_updated_fields is not None
-    assert second_updated_fields is not None
-    assert first_updated_fields.get("test_field") == "test_value_1_updated"  # type: ignore
-    assert second_updated_fields.get("test_field") == "test_value_2_updated"  # type: ignore
-
-    # Delete multiple records
-    assert isinstance(first_record_id, str)
-    assert isinstance(second_record_id, str)
-    response = await delete_multiple_records(
-        credentials, base_id, table_id, records=[first_record_id, second_record_id]
-    )
-    deleted_records = response.get("records")
-    assert deleted_records is not None
-    assert len(deleted_records) == 2, f"Deleted records: {deleted_records}"
-    assert isinstance(
-        deleted_records, list
-    ), f"Type of deleted records: {type(deleted_records)}"
-    first_deleted = deleted_records[0]  # type: ignore
-    second_deleted = deleted_records[1]  # type: ignore
-    assert first_deleted.get("deleted")
-    assert second_deleted.get("deleted")
-
-
-@pytest.mark.asyncio
-async def test_webhook_management():
-    key = getenv("AIRTABLE_API_KEY")
-    if not key:
-        return pytest.skip("AIRTABLE_API_KEY is not set")
-
-    credentials = APIKeyCredentials(
-        provider="airtable",
-        api_key=SecretStr(key),
-    )
-    postfix = uuid4().hex[:4]
-    base_id = "appZPxegHEU3kDc1S"
-    table_name = f"test_table_{postfix}"
-    table_fields = [{"name": "test_field", "type": "singleLineText"}]
-    table = await create_table(credentials, base_id, table_name, table_fields)
-    assert table.get("name") == table_name
-
-    table_id = table.get("id")
-    assert table_id is not None
-    webhook_specification = WebhookSpecification(
-        filters=WebhookFilters(
-            dataTypes=["tableData", "tableFields", "tableMetadata"],
-            changeTypes=["add", "update", "remove"],
-        )
-    )
-    response = await create_webhook(credentials, base_id, webhook_specification)
-    assert response is not None, f"Checking create webhook response: {response}"
-    assert (
-        response.get("id") is not None
-    ), f"Checking create webhook response id: {response}"
-    assert (
-        response.get("macSecretBase64") is not None
-    ), f"Checking create webhook response macSecretBase64: {response}"
-
-    webhook_id = response.get("id")
-    assert webhook_id is not None, f"Webhook ID: {webhook_id}"
-    assert isinstance(webhook_id, str)
-
-    response = await create_record(
-        credentials, base_id, table_id, fields={"test_field": "test_value"}
-    )
-    assert response is not None, f"Checking create record response: {response}"
-    assert (
-        response.get("id") is not None
-    ), f"Checking create record response id: {response}"
-    fields = response.get("fields")
-    assert fields is not None, f"Checking create record response fields: {response}"
-    assert (
-        fields.get("test_field") == "test_value"
-    ), f"Checking create record response fields test_field: {response}"
-
-    response = await list_webhook_payloads(credentials, base_id, webhook_id)
-    assert response is not None, f"Checking list webhook payloads response: {response}"
-
-    response = await delete_webhook(credentials, base_id, webhook_id)

autogpt_platform/backend/backend/blocks/airtable/_config.py

@@ -1,32 +0,0 @@
-"""
-Shared configuration for all Airtable blocks using the SDK pattern.
-"""
-
-from backend.sdk import BlockCostType, ProviderBuilder
-
-from ._oauth import AirtableOAuthHandler, AirtableScope
-from ._webhook import AirtableWebhookManager
-
-# Configure the Airtable provider with API key authentication
-airtable = (
-    ProviderBuilder("airtable")
-    .with_api_key("AIRTABLE_API_KEY", "Airtable Personal Access Token")
-    .with_webhook_manager(AirtableWebhookManager)
-    .with_base_cost(1, BlockCostType.RUN)
-    .with_oauth(
-        AirtableOAuthHandler,
-        scopes=[
-            v.value
-            for v in [
-                AirtableScope.DATA_RECORDS_READ,
-                AirtableScope.DATA_RECORDS_WRITE,
-                AirtableScope.SCHEMA_BASES_READ,
-                AirtableScope.SCHEMA_BASES_WRITE,
-                AirtableScope.WEBHOOK_MANAGE,
-            ]
-        ],
-        client_id_env_var="AIRTABLE_CLIENT_ID",
-        client_secret_env_var="AIRTABLE_CLIENT_SECRET",
-    )
-    .build()
-)

autogpt_platform/backend/backend/blocks/airtable/_oauth.py

@@ -1,185 +0,0 @@
-"""
-Airtable OAuth handler implementation.
-"""
-
-import time
-from enum import Enum
-from logging import getLogger
-from typing import Optional
-
-from backend.sdk import BaseOAuthHandler, OAuth2Credentials, ProviderName, SecretStr
-
-from ._api import (
-    OAuthTokenResponse,
-    make_oauth_authorize_url,
-    oauth_exchange_code_for_tokens,
-    oauth_refresh_tokens,
-)
-
-logger = getLogger(__name__)
-
-
-class AirtableScope(str, Enum):
-    # Basic scopes
-    DATA_RECORDS_READ = "data.records:read"
-    DATA_RECORDS_WRITE = "data.records:write"
-    DATA_RECORD_COMMENTS_READ = "data.recordComments:read"
-    DATA_RECORD_COMMENTS_WRITE = "data.recordComments:write"
-    SCHEMA_BASES_READ = "schema.bases:read"
-    SCHEMA_BASES_WRITE = "schema.bases:write"
-    WEBHOOK_MANAGE = "webhook:manage"
-    BLOCK_MANAGE = "block:manage"
-    USER_EMAIL_READ = "user.email:read"
-
-    # Enterprise member scopes
-    ENTERPRISE_GROUPS_READ = "enterprise.groups:read"
-    WORKSPACES_AND_BASES_READ = "workspacesAndBases:read"
-    WORKSPACES_AND_BASES_WRITE = "workspacesAndBases:write"
-    WORKSPACES_AND_BASES_SHARES_MANAGE = "workspacesAndBases.shares:manage"
-
-    # Enterprise admin scopes
-    ENTERPRISE_SCIM_USERS_AND_GROUPS_MANAGE = "enterprise.scim.usersAndGroups:manage"
-    ENTERPRISE_AUDIT_LOGS_READ = "enterprise.auditLogs:read"
-    ENTERPRISE_CHANGE_EVENTS_READ = "enterprise.changeEvents:read"
-    ENTERPRISE_EXPORTS_MANAGE = "enterprise.exports:manage"
-    ENTERPRISE_ACCOUNT_READ = "enterprise.account:read"
-    ENTERPRISE_ACCOUNT_WRITE = "enterprise.account:write"
-    ENTERPRISE_USER_READ = "enterprise.user:read"
-    ENTERPRISE_USER_WRITE = "enterprise.user:write"
-    ENTERPRISE_GROUPS_MANAGE = "enterprise.groups:manage"
-    WORKSPACES_AND_BASES_MANAGE = "workspacesAndBases:manage"
-    HYPERDB_RECORDS_READ = "hyperDB.records:read"
-    HYPERDB_RECORDS_WRITE = "hyperDB.records:write"
-
-
-class AirtableOAuthHandler(BaseOAuthHandler):
-    """
-    OAuth2 handler for Airtable with PKCE support.
-    """
-
-    PROVIDER_NAME = ProviderName("airtable")
-    DEFAULT_SCOPES = [
-        v.value
-        for v in [
-            AirtableScope.DATA_RECORDS_READ,
-            AirtableScope.DATA_RECORDS_WRITE,
-            AirtableScope.SCHEMA_BASES_READ,
-            AirtableScope.SCHEMA_BASES_WRITE,
-            AirtableScope.WEBHOOK_MANAGE,
-        ]
-    ]
-
-    def __init__(self, client_id: str, client_secret: Optional[str], redirect_uri: str):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.redirect_uri = redirect_uri
-        self.scopes = self.DEFAULT_SCOPES
-        self.auth_base_url = "https://airtable.com/oauth2/v1/authorize"
-        self.token_url = "https://airtable.com/oauth2/v1/token"
-
-    def get_login_url(
-        self, scopes: list[str], state: str, code_challenge: Optional[str]
-    ) -> str:
-        logger.debug("Generating Airtable OAuth login URL")
-        # Generate code_challenge if not provided (PKCE is required)
-        if not scopes:
-            logger.debug("No scopes provided, using default scopes")
-            scopes = self.scopes
-
-        logger.debug(f"Using scopes: {scopes}")
-        logger.debug(f"State: {state}")
-        logger.debug(f"Code challenge: {code_challenge}")
-        if not code_challenge:
-            logger.error("Code challenge is required but none was provided")
-            raise ValueError("No code challenge provided")
-
-        try:
-            url = make_oauth_authorize_url(
-                self.client_id, self.redirect_uri, scopes, state, code_challenge
-            )
-            logger.debug(f"Generated OAuth URL: {url}")
-            return url
-        except Exception as e:
-            logger.error(f"Failed to generate OAuth URL: {str(e)}")
-            raise
-
-    async def exchange_code_for_tokens(
-        self, code: str, scopes: list[str], code_verifier: Optional[str]
-    ) -> OAuth2Credentials:
-        logger.debug("Exchanging authorization code for tokens")
-        logger.debug(f"Code: {code[:4]}...")  # Log first 4 chars only for security
-        logger.debug(f"Scopes: {scopes}")
-        if not code_verifier:
-            logger.error("Code verifier is required but none was provided")
-            raise ValueError("No code verifier provided")
-
-        try:
-            response: OAuthTokenResponse = await oauth_exchange_code_for_tokens(
-                client_id=self.client_id,
-                code=code,
-                code_verifier=code_verifier.encode("utf-8"),
-                redirect_uri=self.redirect_uri,
-                client_secret=self.client_secret,
-            )
-            logger.info("Successfully exchanged code for tokens")
-
-            credentials = OAuth2Credentials(
-                access_token=SecretStr(response.access_token),
-                refresh_token=SecretStr(response.refresh_token),
-                access_token_expires_at=int(time.time()) + response.expires_in,
-                refresh_token_expires_at=int(time.time()) + response.refresh_expires_in,
-                provider=self.PROVIDER_NAME,
-                scopes=scopes,
-            )
-            logger.debug(f"Access token expires in {response.expires_in} seconds")
-            logger.debug(
-                f"Refresh token expires in {response.refresh_expires_in} seconds"
-            )
-            return credentials
-
-        except Exception as e:
-            logger.error(f"Failed to exchange code for tokens: {str(e)}")
-            raise
-
-    async def _refresh_tokens(
-        self, credentials: OAuth2Credentials
-    ) -> OAuth2Credentials:
-        logger.debug("Attempting to refresh OAuth tokens")
-
-        if credentials.refresh_token is None:
-            logger.error("Cannot refresh tokens - no refresh token available")
-            raise ValueError("No refresh token available")
-
-        try:
-            response: OAuthTokenResponse = await oauth_refresh_tokens(
-                client_id=self.client_id,
-                refresh_token=credentials.refresh_token.get_secret_value(),
-                client_secret=self.client_secret,
-            )
-            logger.info("Successfully refreshed tokens")
-
-            new_credentials = OAuth2Credentials(
-                id=credentials.id,
-                access_token=SecretStr(response.access_token),
-                refresh_token=SecretStr(response.refresh_token),
-                access_token_expires_at=int(time.time()) + response.expires_in,
-                refresh_token_expires_at=int(time.time()) + response.refresh_expires_in,
-                provider=self.PROVIDER_NAME,
-                scopes=self.scopes,
-            )
-            logger.debug(f"New access token expires in {response.expires_in} seconds")
-            logger.debug(
-                f"New refresh token expires in {response.refresh_expires_in} seconds"
-            )
-            return new_credentials
-
-        except Exception as e:
-            logger.error(f"Failed to refresh tokens: {str(e)}")
-            raise
-
-    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
-        logger.debug("Token revocation requested")
-        logger.info(
-            "Airtable doesn't provide a token revocation endpoint - tokens will expire naturally after 60 minutes"
-        )
-        return False

autogpt_platform/backend/backend/blocks/airtable/_webhook.py

@@ -1,154 +0,0 @@
-"""
-Webhook management for Airtable blocks.
-"""
-
-import hashlib
-import hmac
-import logging
-from enum import Enum
-
-from backend.sdk import (
-    BaseWebhooksManager,
-    Credentials,
-    ProviderName,
-    Webhook,
-    update_webhook,
-)
-
-from ._api import (
-    WebhookFilters,
-    WebhookSpecification,
-    create_webhook,
-    delete_webhook,
-    list_webhook_payloads,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class AirtableWebhookEvent(str, Enum):
-    TABLE_DATA = "tableData"
-    TABLE_FIELDS = "tableFields"
-    TABLE_METADATA = "tableMetadata"
-
-
-class AirtableWebhookManager(BaseWebhooksManager):
-    """Webhook manager for Airtable API."""
-
-    PROVIDER_NAME = ProviderName("airtable")
-
-    @classmethod
-    async def validate_payload(
-        cls, webhook: Webhook, request, credentials: Credentials | None
-    ) -> tuple[dict, str]:
-        """Validate incoming webhook payload and signature."""
-
-        if not credentials:
-            raise ValueError("Missing credentials in webhook metadata")
-
-        payload = await request.json()
-
-        # Verify webhook signature using HMAC-SHA256
-        if webhook.secret:
-            mac_secret = webhook.config.get("mac_secret")
-            if mac_secret:
-                # Get the raw body for signature verification
-                body = await request.body()
-
-                # Calculate expected signature
-                mac_secret_decoded = mac_secret.encode()
-                hmac_obj = hmac.new(mac_secret_decoded, body, hashlib.sha256)
-                expected_mac = f"hmac-sha256={hmac_obj.hexdigest()}"
-
-                # Get signature from headers
-                signature = request.headers.get("X-Airtable-Content-MAC")
-
-                if signature and not hmac.compare_digest(signature, expected_mac):
-                    raise ValueError("Invalid webhook signature")
-
-        # Validate payload structure
-        required_fields = ["base", "webhook", "timestamp"]
-        if not all(field in payload for field in required_fields):
-            raise ValueError("Invalid webhook payload structure")
-
-        if "id" not in payload["base"] or "id" not in payload["webhook"]:
-            raise ValueError("Missing required IDs in webhook payload")
-        base_id = payload["base"]["id"]
-        webhook_id = payload["webhook"]["id"]
-
-        # get payload request parameters
-        cursor = webhook.config.get("cursor", 1)
-
-        response = await list_webhook_payloads(credentials, base_id, webhook_id, cursor)
-
-        # update webhook config
-        await update_webhook(
-            webhook.id,
-            config={"base_id": base_id, "cursor": response.cursor},
-        )
-
-        event_type = "notification"
-        return response.model_dump(), event_type
-
-    async def _register_webhook(
-        self,
-        credentials: Credentials,
-        webhook_type: str,
-        resource: str,
-        events: list[str],
-        ingress_url: str,
-        secret: str,
-    ) -> tuple[str, dict]:
-        """Register webhook with Airtable API."""
-
-        # Parse resource to get base_id and table_id/name
-        # Resource format: "{base_id}/{table_id_or_name}"
-        parts = resource.split("/", 1)
-        if len(parts) != 2:
-            raise ValueError("Resource must be in format: {base_id}/{table_id_or_name}")
-
-        base_id, table_id_or_name = parts
-
-        # Prepare webhook specification
-        webhook_specification = WebhookSpecification(
-            filters=WebhookFilters(
-                dataTypes=events,
-            )
-        )
-
-        # Create webhook
-        webhook_data = await create_webhook(
-            credentials=credentials,
-            base_id=base_id,
-            webhook_specification=webhook_specification,
-            notification_url=ingress_url,
-        )
-
-        webhook_id = webhook_data["id"]
-        mac_secret = webhook_data.get("macSecretBase64")
-
-        return webhook_id, {
-            "webhook_id": webhook_id,
-            "base_id": base_id,
-            "table_id_or_name": table_id_or_name,
-            "events": events,
-            "mac_secret": mac_secret,
-            "cursor": 1,
-            "expiration_time": webhook_data.get("expirationTime"),
-        }
-
-    async def _deregister_webhook(
-        self, webhook: Webhook, credentials: Credentials
-    ) -> None:
-        """Deregister webhook from Airtable API."""
-
-        base_id = webhook.config.get("base_id")
-        webhook_id = webhook.config.get("webhook_id")
-
-        if not base_id:
-            raise ValueError("Missing base_id in webhook metadata")
-
-        if not webhook_id:
-            raise ValueError("Missing webhook_id in webhook metadata")
-
-        await delete_webhook(credentials, base_id, webhook_id)

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

@@ -1,122 +0,0 @@
-"""
-Airtable base operation blocks.
-"""
-
-from typing import Optional
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._api import create_base, list_bases
-from ._config import airtable
-
-
-class AirtableCreateBaseBlock(Block):
-    """
-    Creates a new base in an Airtable workspace.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        workspace_id: str = SchemaField(
-            description="The workspace ID where the base will be created"
-        )
-        name: str = SchemaField(description="The name of the new base")
-        tables: list[dict] = SchemaField(
-            description="At least one table and field must be specified. Array of table objects to create in the base. Each table should have 'name' and 'fields' properties",
-            default=[
-                {
-                    "description": "Default table",
-                    "name": "Default table",
-                    "fields": [
-                        {
-                            "name": "ID",
-                            "type": "number",
-                            "description": "Auto-incrementing ID field",
-                            "options": {"precision": 0},
-                        }
-                    ],
-                }
-            ],
-        )
-
-    class Output(BlockSchema):
-        base_id: str = SchemaField(description="The ID of the created base")
-        tables: list[dict] = SchemaField(description="Array of table objects")
-        table: dict = SchemaField(description="A single table object")
-
-    def __init__(self):
-        super().__init__(
-            id="f59b88a8-54ce-4676-a508-fd614b4e8dce",
-            description="Create a new base in Airtable",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        data = await create_base(
-            credentials,
-            input_data.workspace_id,
-            input_data.name,
-            input_data.tables,
-        )
-
-        yield "base_id", data.get("id", None)
-        yield "tables", data.get("tables", [])
-        for table in data.get("tables", []):
-            yield "table", table
-
-
-class AirtableListBasesBlock(Block):
-    """
-    Lists all bases in an Airtable workspace that the user has access to.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        trigger: str = SchemaField(
-            description="Trigger the block to run - value is ignored", default="manual"
-        )
-        offset: str = SchemaField(
-            description="Pagination offset from previous request", default=""
-        )
-
-    class Output(BlockSchema):
-        bases: list[dict] = SchemaField(description="Array of base objects")
-        offset: Optional[str] = SchemaField(
-            description="Offset for next page (null if no more bases)", default=None
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="4bd8d466-ed5d-4e44-8083-97f25a8044e7",
-            description="List all bases in Airtable",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        data = await list_bases(
-            credentials,
-            offset=input_data.offset if input_data.offset else None,
-        )
-
-        yield "bases", data.get("bases", [])
-        yield "offset", data.get("offset", None)

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

@@ -1,283 +0,0 @@
-"""
-Airtable record operation blocks.
-"""
-
-from typing import Optional
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    SchemaField,
-)
-
-from ._api import (
-    create_record,
-    delete_multiple_records,
-    get_record,
-    list_records,
-    update_multiple_records,
-)
-from ._config import airtable
-
-
-class AirtableListRecordsBlock(Block):
-    """
-    Lists records from an Airtable table with optional filtering, sorting, and pagination.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id_or_name: str = SchemaField(description="Table ID or name")
-        filter_formula: str = SchemaField(
-            description="Airtable formula to filter records", default=""
-        )
-        view: str = SchemaField(description="View ID or name to use", default="")
-        sort: list[dict] = SchemaField(
-            description="Sort configuration (array of {field, direction})", default=[]
-        )
-        max_records: int = SchemaField(
-            description="Maximum number of records to return", default=100
-        )
-        page_size: int = SchemaField(
-            description="Number of records per page (max 100)", default=100
-        )
-        offset: str = SchemaField(
-            description="Pagination offset from previous request", default=""
-        )
-        return_fields: list[str] = SchemaField(
-            description="Specific fields to return (comma-separated)", default=[]
-        )
-
-    class Output(BlockSchema):
-        records: list[dict] = SchemaField(description="Array of record objects")
-        offset: Optional[str] = SchemaField(
-            description="Offset for next page (null if no more records)", default=None
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="588a9fde-5733-4da7-b03c-35f5671e960f",
-            description="List records from an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        data = await list_records(
-            credentials,
-            input_data.base_id,
-            input_data.table_id_or_name,
-            filter_by_formula=(
-                input_data.filter_formula if input_data.filter_formula else None
-            ),
-            view=input_data.view if input_data.view else None,
-            sort=input_data.sort if input_data.sort else None,
-            max_records=input_data.max_records if input_data.max_records else None,
-            page_size=min(input_data.page_size, 100) if input_data.page_size else None,
-            offset=input_data.offset if input_data.offset else None,
-            fields=input_data.return_fields if input_data.return_fields else None,
-        )
-
-        yield "records", data.get("records", [])
-        yield "offset", data.get("offset", None)
-
-
-class AirtableGetRecordBlock(Block):
-    """
-    Retrieves a single record from an Airtable table by its ID.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id_or_name: str = SchemaField(description="Table ID or name")
-        record_id: str = SchemaField(description="The record ID to retrieve")
-
-    class Output(BlockSchema):
-        id: str = SchemaField(description="The record ID")
-        fields: dict = SchemaField(description="The record fields")
-        created_time: str = SchemaField(description="The record created time")
-
-    def __init__(self):
-        super().__init__(
-            id="c29c5cbf-0aff-40f9-bbb5-f26061792d2b",
-            description="Get a single record from Airtable",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        record = await get_record(
-            credentials,
-            input_data.base_id,
-            input_data.table_id_or_name,
-            input_data.record_id,
-        )
-
-        yield "id", record.get("id", None)
-        yield "fields", record.get("fields", None)
-        yield "created_time", record.get("createdTime", None)
-
-
-class AirtableCreateRecordsBlock(Block):
-    """
-    Creates one or more records in an Airtable table.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id_or_name: str = SchemaField(description="Table ID or name")
-        records: list[dict] = SchemaField(
-            description="Array of records to create (each with 'fields' object)"
-        )
-        typecast: bool = SchemaField(
-            description="Automatically convert string values to appropriate types",
-            default=False,
-        )
-        return_fields_by_field_id: bool | None = SchemaField(
-            description="Return fields by field ID",
-            default=None,
-        )
-
-    class Output(BlockSchema):
-        records: list[dict] = SchemaField(description="Array of created record objects")
-        details: dict = SchemaField(description="Details of the created records")
-
-    def __init__(self):
-        super().__init__(
-            id="42527e98-47b6-44ce-ac0e-86b4883721d3",
-            description="Create records in an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # The create_record API expects records in a specific format
-        data = await create_record(
-            credentials,
-            input_data.base_id,
-            input_data.table_id_or_name,
-            records=[{"fields": record} for record in input_data.records],
-            typecast=input_data.typecast if input_data.typecast else None,
-            return_fields_by_field_id=input_data.return_fields_by_field_id,
-        )
-
-        yield "records", data.get("records", [])
-        details = data.get("details", None)
-        if details:
-            yield "details", details
-
-
-class AirtableUpdateRecordsBlock(Block):
-    """
-    Updates one or more existing records in an Airtable table.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id_or_name: str = SchemaField(
-            description="Table ID or name - It's better to use the table ID instead of the name"
-        )
-        records: list[dict] = SchemaField(
-            description="Array of records to update (each with 'id' and 'fields')"
-        )
-        typecast: bool | None = SchemaField(
-            description="Automatically convert string values to appropriate types",
-            default=None,
-        )
-
-    class Output(BlockSchema):
-        records: list[dict] = SchemaField(description="Array of updated record objects")
-
-    def __init__(self):
-        super().__init__(
-            id="6e7d2590-ac2b-4b5d-b08c-fc039cd77e1f",
-            description="Update records in an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        # The update_multiple_records API expects records with id and fields
-        data = await update_multiple_records(
-            credentials,
-            input_data.base_id,
-            input_data.table_id_or_name,
-            records=input_data.records,
-            typecast=input_data.typecast if input_data.typecast else None,
-            return_fields_by_field_id=False,  # Use field names, not IDs
-        )
-
-        yield "records", data.get("records", [])
-
-
-class AirtableDeleteRecordsBlock(Block):
-    """
-    Deletes one or more records from an Airtable table.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id_or_name: str = SchemaField(
-            description="Table ID or name - It's better to use the table ID instead of the name"
-        )
-        record_ids: list[str] = SchemaField(
-            description="Array of upto 10 record IDs to delete"
-        )
-
-    class Output(BlockSchema):
-        records: list[dict] = SchemaField(description="Array of deletion results")
-
-    def __init__(self):
-        super().__init__(
-            id="93e22b8b-3642-4477-aefb-1c0929a4a3a6",
-            description="Delete records from an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        if len(input_data.record_ids) > 10:
-            yield "error", "Only upto 10 record IDs can be deleted at a time"
-        else:
-            data = await delete_multiple_records(
-                credentials,
-                input_data.base_id,
-                input_data.table_id_or_name,
-                input_data.record_ids,
-            )
-
-            yield "records", data.get("records", [])

autogpt_platform/backend/backend/blocks/airtable/schema.py

@@ -1,252 +0,0 @@
-"""
-Airtable schema and table management blocks.
-"""
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._api import TableFieldType, create_field, create_table, update_field, update_table
-from ._config import airtable
-
-
-class AirtableListSchemaBlock(Block):
-    """
-    Retrieves the complete schema of an Airtable base, including all tables,
-    fields, and views.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-
-    class Output(BlockSchema):
-        base_schema: dict = SchemaField(
-            description="Complete base schema with tables, fields, and views"
-        )
-        tables: list[dict] = SchemaField(description="Array of table objects")
-
-    def __init__(self):
-        super().__init__(
-            id="64291d3c-99b5-47b7-a976-6d94293cdb2d",
-            description="Get the complete schema of an Airtable base",
-            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()
-
-        # Get base schema
-        response = await Requests().get(
-            f"https://api.airtable.com/v0/meta/bases/{input_data.base_id}/tables",
-            headers={"Authorization": f"Bearer {api_key}"},
-        )
-
-        data = response.json()
-
-        yield "base_schema", data
-        yield "tables", data.get("tables", [])
-
-
-class AirtableCreateTableBlock(Block):
-    """
-    Creates a new table in an Airtable base with specified fields and views.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_name: str = SchemaField(description="The name of the table to create")
-        table_fields: list[dict] = SchemaField(
-            description="Table fields with name, type, and options",
-            default=[{"name": "Name", "type": "singleLineText"}],
-        )
-
-    class Output(BlockSchema):
-        table: dict = SchemaField(description="Created table object")
-        table_id: str = SchemaField(description="ID of the created table")
-
-    def __init__(self):
-        super().__init__(
-            id="fcc20ced-d817-42ea-9b40-c35e7bf34b4f",
-            description="Create a new table in an Airtable base",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        table_data = await create_table(
-            credentials,
-            input_data.base_id,
-            input_data.table_name,
-            input_data.table_fields,
-        )
-
-        yield "table", table_data
-        yield "table_id", table_data.get("id", "")
-
-
-class AirtableUpdateTableBlock(Block):
-    """
-    Updates an existing table's properties such as name or description.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id: str = SchemaField(description="The table ID to update")
-        table_name: str | None = SchemaField(
-            description="The name of the table to update", default=None
-        )
-        table_description: str | None = SchemaField(
-            description="The description of the table to update", default=None
-        )
-        date_dependency: dict | None = SchemaField(
-            description="The date dependency of the table to update", default=None
-        )
-
-    class Output(BlockSchema):
-        table: dict = SchemaField(description="Updated table object")
-
-    def __init__(self):
-        super().__init__(
-            id="34077c5f-f962-49f2-9ec6-97c67077013a",
-            description="Update table properties",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        table_data = await update_table(
-            credentials,
-            input_data.base_id,
-            input_data.table_id,
-            input_data.table_name,
-            input_data.table_description,
-            input_data.date_dependency,
-        )
-
-        yield "table", table_data
-
-
-class AirtableCreateFieldBlock(Block):
-    """
-    Adds a new field (column) to an existing Airtable table.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id: str = SchemaField(description="The table ID to add field to")
-        field_type: TableFieldType = SchemaField(
-            description="The type of the field to create",
-            default=TableFieldType.SINGLE_LINE_TEXT,
-            advanced=False,
-        )
-        name: str = SchemaField(description="The name of the field to create")
-        description: str | None = SchemaField(
-            description="The description of the field to create", default=None
-        )
-        options: dict[str, str] | None = SchemaField(
-            description="The options of the field to create", default=None
-        )
-
-    class Output(BlockSchema):
-        field: dict = SchemaField(description="Created field object")
-        field_id: str = SchemaField(description="ID of the created field")
-
-    def __init__(self):
-        super().__init__(
-            id="6c98a32f-dbf9-45d8-a2a8-5e97e8326351",
-            description="Add a new field to an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        field_data = await create_field(
-            credentials,
-            input_data.base_id,
-            input_data.table_id,
-            input_data.field_type,
-            input_data.name,
-        )
-
-        yield "field", field_data
-        yield "field_id", field_data.get("id", "")
-
-
-class AirtableUpdateFieldBlock(Block):
-    """
-    Updates an existing field's properties in an Airtable table.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="The Airtable base ID")
-        table_id: str = SchemaField(description="The table ID containing the field")
-        field_id: str = SchemaField(description="The field ID to update")
-        name: str | None = SchemaField(
-            description="The name of the field to update", default=None, advanced=False
-        )
-        description: str | None = SchemaField(
-            description="The description of the field to update",
-            default=None,
-            advanced=False,
-        )
-
-    class Output(BlockSchema):
-        field: dict = SchemaField(description="Updated field object")
-
-    def __init__(self):
-        super().__init__(
-            id="f46ac716-3b18-4da1-92e4-34ca9a464d48",
-            description="Update field properties in an Airtable table",
-            categories={BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        field_data = await update_field(
-            credentials,
-            input_data.base_id,
-            input_data.table_id,
-            input_data.field_id,
-            input_data.name,
-            input_data.description,
-        )
-
-        yield "field", field_data

autogpt_platform/backend/backend/blocks/airtable/triggers.py

@@ -1,113 +0,0 @@
-from backend.sdk import (
-    BaseModel,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    BlockWebhookConfig,
-    CredentialsMetaInput,
-    ProviderName,
-    SchemaField,
-)
-
-from ._api import WebhookPayload
-from ._config import airtable
-
-
-class AirtableEventSelector(BaseModel):
-    """
-    Selects the Airtable webhook event to trigger on.
-    """
-
-    tableData: bool = True
-    tableFields: bool = True
-    tableMetadata: bool = True
-
-
-class AirtableWebhookTriggerBlock(Block):
-    """
-    Starts a flow whenever Airtable emits a webhook event.
-
-    Thin wrapper just forwards the payloads one at a time to the next block.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = airtable.credentials_field(
-            description="Airtable API credentials"
-        )
-        base_id: str = SchemaField(description="Airtable base ID")
-        table_id_or_name: str = SchemaField(description="Airtable table ID or name")
-        payload: dict = SchemaField(hidden=True, default_factory=dict)
-        events: AirtableEventSelector = SchemaField(
-            description="Airtable webhook event filter"
-        )
-
-    class Output(BlockSchema):
-        payload: WebhookPayload = SchemaField(description="Airtable webhook payload")
-
-    def __init__(self):
-        example_payload = {
-            "payloads": [
-                {
-                    "timestamp": "2022-02-01T21:25:05.663Z",
-                    "baseTransactionNumber": 4,
-                    "actionMetadata": {
-                        "source": "client",
-                        "sourceMetadata": {
-                            "user": {
-                                "id": "usr00000000000000",
-                                "email": "foo@bar.com",
-                                "permissionLevel": "create",
-                            }
-                        },
-                    },
-                    "payloadFormat": "v0",
-                }
-            ],
-            "cursor": 5,
-            "mightHaveMore": False,
-        }
-
-        super().__init__(
-            # NOTE: This is disabled whilst the webhook system is finalised.
-            disabled=False,
-            id="d0180ce6-ccb9-48c7-8256-b39e93e62801",
-            description="Starts a flow whenever Airtable emits a webhook event",
-            categories={BlockCategory.INPUT, BlockCategory.DATA},
-            input_schema=self.Input,
-            output_schema=self.Output,
-            block_type=BlockType.WEBHOOK,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName("airtable"),
-                webhook_type="not-used",
-                event_filter_input="events",
-                event_format="{event}",
-                resource_format="{base_id}/{table_id_or_name}",
-            ),
-            test_input={
-                "credentials": airtable.get_test_credentials().model_dump(),
-                "base_id": "app1234567890",
-                "table_id_or_name": "table1234567890",
-                "events": AirtableEventSelector(
-                    tableData=True,
-                    tableFields=True,
-                    tableMetadata=False,
-                ).model_dump(),
-                "payload": example_payload,
-            },
-            test_credentials=airtable.get_test_credentials(),
-            test_output=[
-                (
-                    "payload",
-                    WebhookPayload.model_validate(example_payload["payloads"][0]),
-                ),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        if len(input_data.payload["payloads"]) > 0:
-            for item in input_data.payload["payloads"]:
-                yield "payload", WebhookPayload.model_validate(item)
-        else:
-            yield "error", "No valid payloads found in webhook payload"

autogpt_platform/backend/backend/blocks/apollo/_api.py

@@ -4,7 +4,6 @@ from typing import List
 from backend.blocks.apollo._auth import ApolloCredentials
 from backend.blocks.apollo.models import (
     Contact,
-    EnrichPersonRequest,
     Organization,
     SearchOrganizationsRequest,
     SearchOrganizationsResponse,
@@ -30,10 +29,10 @@ class ApolloClient:
 
     async def search_people(self, query: SearchPeopleRequest) -> List[Contact]:
         """Search for people in Apollo"""
-        response = await self.requests.post(
+        response = await self.requests.get(
             f"{self.API_URL}/mixed_people/search",
             headers=self._get_headers(),
-            json=query.model_dump(exclude={"max_results"}),
+            params=query.model_dump(exclude={"credentials", "max_results"}),
         )
         data = response.json()
         parsed_response = SearchPeopleResponse(**data)
@@ -54,10 +53,10 @@ class ApolloClient:
                 and len(parsed_response.people) > 0
             ):
                 query.page += 1
-                response = await self.requests.post(
+                response = await self.requests.get(
                     f"{self.API_URL}/mixed_people/search",
                     headers=self._get_headers(),
-                    json=query.model_dump(exclude={"max_results"}),
+                    params=query.model_dump(exclude={"credentials", "max_results"}),
                 )
                 data = response.json()
                 parsed_response = SearchPeopleResponse(**data)
@@ -70,10 +69,10 @@ class ApolloClient:
         self, query: SearchOrganizationsRequest
     ) -> List[Organization]:
         """Search for organizations in Apollo"""
-        response = await self.requests.post(
+        response = await self.requests.get(
             f"{self.API_URL}/mixed_companies/search",
             headers=self._get_headers(),
-            json=query.model_dump(exclude={"max_results"}),
+            params=query.model_dump(exclude={"credentials", "max_results"}),
         )
         data = response.json()
         parsed_response = SearchOrganizationsResponse(**data)
@@ -94,10 +93,10 @@ class ApolloClient:
                 and len(parsed_response.organizations) > 0
             ):
                 query.page += 1
-                response = await self.requests.post(
+                response = await self.requests.get(
                     f"{self.API_URL}/mixed_companies/search",
                     headers=self._get_headers(),
-                    json=query.model_dump(exclude={"max_results"}),
+                    params=query.model_dump(exclude={"credentials", "max_results"}),
                 )
                 data = response.json()
                 parsed_response = SearchOrganizationsResponse(**data)
@@ -111,21 +110,3 @@ class ApolloClient:
         return (
             organizations[: query.max_results] if query.max_results else organizations
         )
-
-    async def enrich_person(self, query: EnrichPersonRequest) -> Contact:
-        """Enrich a person's data including email & phone reveal"""
-        response = await self.requests.post(
-            f"{self.API_URL}/people/match",
-            headers=self._get_headers(),
-            json=query.model_dump(),
-            params={
-                "reveal_personal_emails": "true",
-            },
-        )
-        data = response.json()
-        if "person" not in data:
-            raise ValueError(f"Person not found or enrichment failed: {data}")
-
-        contact = Contact(**data["person"])
-        contact.email = contact.email or "-"
-        return contact

autogpt_platform/backend/backend/blocks/apollo/models.py

@@ -1,31 +1,17 @@
 from enum import Enum
 from typing import Any, Optional
 
-from pydantic import BaseModel as OriginalBaseModel
-from pydantic import ConfigDict
+from pydantic import BaseModel, ConfigDict
 
 from backend.data.model import SchemaField
 
 
-class BaseModel(OriginalBaseModel):
-    def model_dump(self, *args, exclude: set[str] | None = None, **kwargs):
-        if exclude is None:
-            exclude = set("credentials")
-        else:
-            exclude.add("credentials")
-
-        kwargs.setdefault("exclude_none", True)
-        kwargs.setdefault("exclude_unset", True)
-        kwargs.setdefault("exclude_defaults", True)
-        return super().model_dump(*args, exclude=exclude, **kwargs)
-
-
 class PrimaryPhone(BaseModel):
     """A primary phone in Apollo"""
 
-    number: Optional[str] = ""
-    source: Optional[str] = ""
-    sanitized_number: Optional[str] = ""
+    number: str
+    source: str
+    sanitized_number: str
 
 
 class SenorityLevels(str, Enum):
@@ -56,102 +42,102 @@ class ContactEmailStatuses(str, Enum):
 class RuleConfigStatus(BaseModel):
     """A rule config status in Apollo"""
 
-    _id: Optional[str] = ""
-    created_at: Optional[str] = ""
-    rule_action_config_id: Optional[str] = ""
-    rule_config_id: Optional[str] = ""
-    status_cd: Optional[str] = ""
-    updated_at: Optional[str] = ""
-    id: Optional[str] = ""
-    key: Optional[str] = ""
+    _id: str
+    created_at: str
+    rule_action_config_id: str
+    rule_config_id: str
+    status_cd: str
+    updated_at: str
+    id: str
+    key: str
 
 
 class ContactCampaignStatus(BaseModel):
     """A contact campaign status in Apollo"""
 
-    id: Optional[str] = ""
-    emailer_campaign_id: Optional[str] = ""
-    send_email_from_user_id: Optional[str] = ""
-    inactive_reason: Optional[str] = ""
-    status: Optional[str] = ""
-    added_at: Optional[str] = ""
-    added_by_user_id: Optional[str] = ""
-    finished_at: Optional[str] = ""
-    paused_at: Optional[str] = ""
-    auto_unpause_at: Optional[str] = ""
-    send_email_from_email_address: Optional[str] = ""
-    send_email_from_email_account_id: Optional[str] = ""
-    manually_set_unpause: Optional[str] = ""
-    failure_reason: Optional[str] = ""
-    current_step_id: Optional[str] = ""
-    in_response_to_emailer_message_id: Optional[str] = ""
-    cc_emails: Optional[str] = ""
-    bcc_emails: Optional[str] = ""
-    to_emails: Optional[str] = ""
+    id: str
+    emailer_campaign_id: str
+    send_email_from_user_id: str
+    inactive_reason: str
+    status: str
+    added_at: str
+    added_by_user_id: str
+    finished_at: str
+    paused_at: str
+    auto_unpause_at: str
+    send_email_from_email_address: str
+    send_email_from_email_account_id: str
+    manually_set_unpause: str
+    failure_reason: str
+    current_step_id: str
+    in_response_to_emailer_message_id: str
+    cc_emails: str
+    bcc_emails: str
+    to_emails: str
 
 
 class Account(BaseModel):
     """An account in Apollo"""
 
-    id: Optional[str] = ""
-    name: Optional[str] = ""
-    website_url: Optional[str] = ""
-    blog_url: Optional[str] = ""
-    angellist_url: Optional[str] = ""
-    linkedin_url: Optional[str] = ""
-    twitter_url: Optional[str] = ""
-    facebook_url: Optional[str] = ""
-    primary_phone: Optional[PrimaryPhone] = PrimaryPhone()
-    languages: Optional[list[str]] = []
-    alexa_ranking: Optional[int] = 0
-    phone: Optional[str] = ""
-    linkedin_uid: Optional[str] = ""
-    founded_year: Optional[int] = 0
-    publicly_traded_symbol: Optional[str] = ""
-    publicly_traded_exchange: Optional[str] = ""
-    logo_url: Optional[str] = ""
-    chrunchbase_url: Optional[str] = ""
-    primary_domain: Optional[str] = ""
-    domain: Optional[str] = ""
-    team_id: Optional[str] = ""
-    organization_id: Optional[str] = ""
-    account_stage_id: Optional[str] = ""
-    source: Optional[str] = ""
-    original_source: Optional[str] = ""
-    creator_id: Optional[str] = ""
-    owner_id: Optional[str] = ""
-    created_at: Optional[str] = ""
-    phone_status: Optional[str] = ""
-    hubspot_id: Optional[str] = ""
-    salesforce_id: Optional[str] = ""
-    crm_owner_id: Optional[str] = ""
-    parent_account_id: Optional[str] = ""
-    sanitized_phone: Optional[str] = ""
+    id: str
+    name: str
+    website_url: str
+    blog_url: str
+    angellist_url: str
+    linkedin_url: str
+    twitter_url: str
+    facebook_url: str
+    primary_phone: PrimaryPhone
+    languages: list[str]
+    alexa_ranking: int
+    phone: str
+    linkedin_uid: str
+    founded_year: int
+    publicly_traded_symbol: str
+    publicly_traded_exchange: str
+    logo_url: str
+    chrunchbase_url: str
+    primary_domain: str
+    domain: str
+    team_id: str
+    organization_id: str
+    account_stage_id: str
+    source: str
+    original_source: str
+    creator_id: str
+    owner_id: str
+    created_at: str
+    phone_status: str
+    hubspot_id: str
+    salesforce_id: str
+    crm_owner_id: str
+    parent_account_id: str
+    sanitized_phone: str
     # no listed type on the API docs
-    account_playbook_statues: Optional[list[Any]] = []
-    account_rule_config_statuses: Optional[list[RuleConfigStatus]] = []
-    existence_level: Optional[str] = ""
-    label_ids: Optional[list[str]] = []
-    typed_custom_fields: Optional[Any] = {}
-    custom_field_errors: Optional[Any] = {}
-    modality: Optional[str] = ""
-    source_display_name: Optional[str] = ""
-    salesforce_record_id: Optional[str] = ""
-    crm_record_url: Optional[str] = ""
+    account_playbook_statues: list[Any]
+    account_rule_config_statuses: list[RuleConfigStatus]
+    existence_level: str
+    label_ids: list[str]
+    typed_custom_fields: Any
+    custom_field_errors: Any
+    modality: str
+    source_display_name: str
+    salesforce_record_id: str
+    crm_record_url: str
 
 
 class ContactEmail(BaseModel):
     """A contact email in Apollo"""
 
-    email: Optional[str] = ""
-    email_md5: Optional[str] = ""
-    email_sha256: Optional[str] = ""
-    email_status: Optional[str] = ""
-    email_source: Optional[str] = ""
-    extrapolated_email_confidence: Optional[str] = ""
-    position: Optional[int] = 0
-    email_from_customer: Optional[str] = ""
-    free_domain: Optional[bool] = True
+    email: str = ""
+    email_md5: str = ""
+    email_sha256: str = ""
+    email_status: str = ""
+    email_source: str = ""
+    extrapolated_email_confidence: str = ""
+    position: int = 0
+    email_from_customer: str = ""
+    free_domain: bool = True
 
 
 class EmploymentHistory(BaseModel):
@@ -164,40 +150,40 @@ class EmploymentHistory(BaseModel):
         populate_by_name=True,
     )
 
-    _id: Optional[str] = ""
-    created_at: Optional[str] = ""
-    current: Optional[bool] = False
-    degree: Optional[str] = ""
-    description: Optional[str] = ""
-    emails: Optional[str] = ""
-    end_date: Optional[str] = ""
-    grade_level: Optional[str] = ""
-    kind: Optional[str] = ""
-    major: Optional[str] = ""
-    organization_id: Optional[str] = ""
-    organization_name: Optional[str] = ""
-    raw_address: Optional[str] = ""
-    start_date: Optional[str] = ""
-    title: Optional[str] = ""
-    updated_at: Optional[str] = ""
-    id: Optional[str] = ""
-    key: Optional[str] = ""
+    _id: Optional[str] = None
+    created_at: Optional[str] = None
+    current: Optional[bool] = None
+    degree: Optional[str] = None
+    description: Optional[str] = None
+    emails: Optional[str] = None
+    end_date: Optional[str] = None
+    grade_level: Optional[str] = None
+    kind: Optional[str] = None
+    major: Optional[str] = None
+    organization_id: Optional[str] = None
+    organization_name: Optional[str] = None
+    raw_address: Optional[str] = None
+    start_date: Optional[str] = None
+    title: Optional[str] = None
+    updated_at: Optional[str] = None
+    id: Optional[str] = None
+    key: Optional[str] = None
 
 
 class Breadcrumb(BaseModel):
     """A breadcrumb in Apollo"""
 
-    label: Optional[str] = ""
-    signal_field_name: Optional[str] = ""
-    value: str | list | None = ""
-    display_name: Optional[str] = ""
+    label: Optional[str] = "N/A"
+    signal_field_name: Optional[str] = "N/A"
+    value: str | list | None = "N/A"
+    display_name: Optional[str] = "N/A"
 
 
 class TypedCustomField(BaseModel):
     """A typed custom field in Apollo"""
 
-    id: Optional[str] = ""
-    value: Optional[str] = ""
+    id: Optional[str] = "N/A"
+    value: Optional[str] = "N/A"
 
 
 class Pagination(BaseModel):
@@ -219,23 +205,23 @@ class Pagination(BaseModel):
 class DialerFlags(BaseModel):
     """A dialer flags in Apollo"""
 
-    country_name: Optional[str] = ""
-    country_enabled: Optional[bool] = True
-    high_risk_calling_enabled: Optional[bool] = True
-    potential_high_risk_number: Optional[bool] = True
+    country_name: str
+    country_enabled: bool
+    high_risk_calling_enabled: bool
+    potential_high_risk_number: bool
 
 
 class PhoneNumber(BaseModel):
     """A phone number in Apollo"""
 
-    raw_number: Optional[str] = ""
-    sanitized_number: Optional[str] = ""
-    type: Optional[str] = ""
-    position: Optional[int] = 0
-    status: Optional[str] = ""
-    dnc_status: Optional[str] = ""
-    dnc_other_info: Optional[str] = ""
-    dailer_flags: Optional[DialerFlags] = DialerFlags(
+    raw_number: str = ""
+    sanitized_number: str = ""
+    type: str = ""
+    position: int = 0
+    status: str = ""
+    dnc_status: str = ""
+    dnc_other_info: str = ""
+    dailer_flags: DialerFlags = DialerFlags(
         country_name="",
         country_enabled=True,
         high_risk_calling_enabled=True,
@@ -253,31 +239,33 @@ class Organization(BaseModel):
         populate_by_name=True,
     )
 
-    id: Optional[str] = ""
-    name: Optional[str] = ""
-    website_url: Optional[str] = ""
-    blog_url: Optional[str] = ""
-    angellist_url: Optional[str] = ""
-    linkedin_url: Optional[str] = ""
-    twitter_url: Optional[str] = ""
-    facebook_url: Optional[str] = ""
-    primary_phone: Optional[PrimaryPhone] = PrimaryPhone()
-    languages: Optional[list[str]] = []
+    id: Optional[str] = "N/A"
+    name: Optional[str] = "N/A"
+    website_url: Optional[str] = "N/A"
+    blog_url: Optional[str] = "N/A"
+    angellist_url: Optional[str] = "N/A"
+    linkedin_url: Optional[str] = "N/A"
+    twitter_url: Optional[str] = "N/A"
+    facebook_url: Optional[str] = "N/A"
+    primary_phone: Optional[PrimaryPhone] = PrimaryPhone(
+        number="N/A", source="N/A", sanitized_number="N/A"
+    )
+    languages: list[str] = []
     alexa_ranking: Optional[int] = 0
-    phone: Optional[str] = ""
-    linkedin_uid: Optional[str] = ""
+    phone: Optional[str] = "N/A"
+    linkedin_uid: Optional[str] = "N/A"
     founded_year: Optional[int] = 0
-    publicly_traded_symbol: Optional[str] = ""
-    publicly_traded_exchange: Optional[str] = ""
-    logo_url: Optional[str] = ""
-    chrunchbase_url: Optional[str] = ""
-    primary_domain: Optional[str] = ""
-    sanitized_phone: Optional[str] = ""
-    owned_by_organization_id: Optional[str] = ""
-    intent_strength: Optional[str] = ""
-    show_intent: Optional[bool] = True
+    publicly_traded_symbol: Optional[str] = "N/A"
+    publicly_traded_exchange: Optional[str] = "N/A"
+    logo_url: Optional[str] = "N/A"
+    chrunchbase_url: Optional[str] = "N/A"
+    primary_domain: Optional[str] = "N/A"
+    sanitized_phone: Optional[str] = "N/A"
+    owned_by_organization_id: Optional[str] = "N/A"
+    intent_strength: Optional[str] = "N/A"
+    show_intent: bool = True
     has_intent_signal_account: Optional[bool] = True
-    intent_signal_account: Optional[str] = ""
+    intent_signal_account: Optional[str] = "N/A"
 
 
 class Contact(BaseModel):
@@ -290,95 +278,95 @@ class Contact(BaseModel):
         populate_by_name=True,
     )
 
-    contact_roles: Optional[list[Any]] = []
-    id: Optional[str] = ""
-    first_name: Optional[str] = ""
-    last_name: Optional[str] = ""
-    name: Optional[str] = ""
-    linkedin_url: Optional[str] = ""
-    title: Optional[str] = ""
-    contact_stage_id: Optional[str] = ""
-    owner_id: Optional[str] = ""
-    creator_id: Optional[str] = ""
-    person_id: Optional[str] = ""
-    email_needs_tickling: Optional[bool] = True
-    organization_name: Optional[str] = ""
-    source: Optional[str] = ""
-    original_source: Optional[str] = ""
-    organization_id: Optional[str] = ""
-    headline: Optional[str] = ""
-    photo_url: Optional[str] = ""
-    present_raw_address: Optional[str] = ""
-    linkededin_uid: Optional[str] = ""
-    extrapolated_email_confidence: Optional[float] = 0.0
-    salesforce_id: Optional[str] = ""
-    salesforce_lead_id: Optional[str] = ""
-    salesforce_contact_id: Optional[str] = ""
-    saleforce_account_id: Optional[str] = ""
-    crm_owner_id: Optional[str] = ""
-    created_at: Optional[str] = ""
-    emailer_campaign_ids: Optional[list[str]] = []
-    direct_dial_status: Optional[str] = ""
-    direct_dial_enrichment_failed_at: Optional[str] = ""
-    email_status: Optional[str] = ""
-    email_source: Optional[str] = ""
-    account_id: Optional[str] = ""
-    last_activity_date: Optional[str] = ""
-    hubspot_vid: Optional[str] = ""
-    hubspot_company_id: Optional[str] = ""
-    crm_id: Optional[str] = ""
-    sanitized_phone: Optional[str] = ""
-    merged_crm_ids: Optional[str] = ""
-    updated_at: Optional[str] = ""
-    queued_for_crm_push: Optional[bool] = True
-    suggested_from_rule_engine_config_id: Optional[str] = ""
-    email_unsubscribed: Optional[str] = ""
-    label_ids: Optional[list[Any]] = []
-    has_pending_email_arcgate_request: Optional[bool] = True
-    has_email_arcgate_request: Optional[bool] = True
-    existence_level: Optional[str] = ""
-    email: Optional[str] = ""
-    email_from_customer: Optional[str] = ""
-    typed_custom_fields: Optional[list[TypedCustomField]] = []
-    custom_field_errors: Optional[Any] = {}
-    salesforce_record_id: Optional[str] = ""
-    crm_record_url: Optional[str] = ""
-    email_status_unavailable_reason: Optional[str] = ""
-    email_true_status: Optional[str] = ""
-    updated_email_true_status: Optional[bool] = True
-    contact_rule_config_statuses: Optional[list[RuleConfigStatus]] = []
-    source_display_name: Optional[str] = ""
-    twitter_url: Optional[str] = ""
-    contact_campaign_statuses: Optional[list[ContactCampaignStatus]] = []
-    state: Optional[str] = ""
-    city: Optional[str] = ""
-    country: Optional[str] = ""
-    account: Optional[Account] = Account()
-    contact_emails: Optional[list[ContactEmail]] = []
-    organization: Optional[Organization] = Organization()
-    employment_history: Optional[list[EmploymentHistory]] = []
-    time_zone: Optional[str] = ""
-    intent_strength: Optional[str] = ""
-    show_intent: Optional[bool] = True
-    phone_numbers: Optional[list[PhoneNumber]] = []
-    account_phone_note: Optional[str] = ""
-    free_domain: Optional[bool] = True
-    is_likely_to_engage: Optional[bool] = True
-    email_domain_catchall: Optional[bool] = True
-    contact_job_change_event: Optional[str] = ""
+    contact_roles: list[Any] = []
+    id: Optional[str] = None
+    first_name: Optional[str] = None
+    last_name: Optional[str] = None
+    name: Optional[str] = None
+    linkedin_url: Optional[str] = None
+    title: Optional[str] = None
+    contact_stage_id: Optional[str] = None
+    owner_id: Optional[str] = None
+    creator_id: Optional[str] = None
+    person_id: Optional[str] = None
+    email_needs_tickling: bool = True
+    organization_name: Optional[str] = None
+    source: Optional[str] = None
+    original_source: Optional[str] = None
+    organization_id: Optional[str] = None
+    headline: Optional[str] = None
+    photo_url: Optional[str] = None
+    present_raw_address: Optional[str] = None
+    linkededin_uid: Optional[str] = None
+    extrapolated_email_confidence: Optional[float] = None
+    salesforce_id: Optional[str] = None
+    salesforce_lead_id: Optional[str] = None
+    salesforce_contact_id: Optional[str] = None
+    saleforce_account_id: Optional[str] = None
+    crm_owner_id: Optional[str] = None
+    created_at: Optional[str] = None
+    emailer_campaign_ids: list[str] = []
+    direct_dial_status: Optional[str] = None
+    direct_dial_enrichment_failed_at: Optional[str] = None
+    email_status: Optional[str] = None
+    email_source: Optional[str] = None
+    account_id: Optional[str] = None
+    last_activity_date: Optional[str] = None
+    hubspot_vid: Optional[str] = None
+    hubspot_company_id: Optional[str] = None
+    crm_id: Optional[str] = None
+    sanitized_phone: Optional[str] = None
+    merged_crm_ids: Optional[str] = None
+    updated_at: Optional[str] = None
+    queued_for_crm_push: bool = True
+    suggested_from_rule_engine_config_id: Optional[str] = None
+    email_unsubscribed: Optional[str] = None
+    label_ids: list[Any] = []
+    has_pending_email_arcgate_request: bool = True
+    has_email_arcgate_request: bool = True
+    existence_level: Optional[str] = None
+    email: Optional[str] = None
+    email_from_customer: Optional[str] = None
+    typed_custom_fields: list[TypedCustomField] = []
+    custom_field_errors: Any = None
+    salesforce_record_id: Optional[str] = None
+    crm_record_url: Optional[str] = None
+    email_status_unavailable_reason: Optional[str] = None
+    email_true_status: Optional[str] = None
+    updated_email_true_status: bool = True
+    contact_rule_config_statuses: list[RuleConfigStatus] = []
+    source_display_name: Optional[str] = None
+    twitter_url: Optional[str] = None
+    contact_campaign_statuses: list[ContactCampaignStatus] = []
+    state: Optional[str] = None
+    city: Optional[str] = None
+    country: Optional[str] = None
+    account: Optional[Account] = None
+    contact_emails: list[ContactEmail] = []
+    organization: Optional[Organization] = None
+    employment_history: list[EmploymentHistory] = []
+    time_zone: Optional[str] = None
+    intent_strength: Optional[str] = None
+    show_intent: bool = True
+    phone_numbers: list[PhoneNumber] = []
+    account_phone_note: Optional[str] = None
+    free_domain: bool = True
+    is_likely_to_engage: bool = True
+    email_domain_catchall: bool = True
+    contact_job_change_event: Optional[str] = None
 
 
 class SearchOrganizationsRequest(BaseModel):
     """Request for Apollo's search organizations API"""
 
-    organization_num_employees_range: Optional[list[int]] = SchemaField(
+    organization_num_empoloyees_range: list[int] = SchemaField(
         description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
 Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
         default=[0, 1000000],
     )
 
-    organization_locations: Optional[list[str]] = SchemaField(
+    organization_locations: list[str] = SchemaField(
         description="""The location of the company headquarters. You can search across cities, US states, and countries.
 
 If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, any Boston-based companies will not appearch in your search results, even if they match other parameters.
@@ -387,30 +375,28 @@ To exclude companies based on location, use the organization_not_locations param
 """,
         default_factory=list,
     )
-    organizations_not_locations: Optional[list[str]] = SchemaField(
+    organizations_not_locations: list[str] = SchemaField(
         description="""Exclude companies from search results based on the location of the company headquarters. You can use cities, US states, and countries as locations to exclude.
 
 This parameter is useful for ensuring you do not prospect in an undesirable territory. For example, if you use ireland as a value, no Ireland-based companies will appear in your search results.
 """,
         default_factory=list,
     )
-    q_organization_keyword_tags: Optional[list[str]] = SchemaField(
-        description="""Filter search results based on keywords associated with companies. For example, you can enter mining as a value to return only companies that have an association with the mining industry.""",
-        default_factory=list,
+    q_organization_keyword_tags: list[str] = SchemaField(
+        description="""Filter search results based on keywords associated with companies. For example, you can enter mining as a value to return only companies that have an association with the mining industry."""
     )
-    q_organization_name: Optional[str] = SchemaField(
+    q_organization_name: str = SchemaField(
         description="""Filter search results to include a specific company name.
 
-If the value you enter for this parameter does not match with a company's name, the company will not appear in search results, even if it matches other parameters. Partial matches are accepted. For example, if you filter by the value marketing, a company called NY Marketing Unlimited would still be eligible as a search result, but NY Market Analysis would not be eligible.""",
-        default="",
+If the value you enter for this parameter does not match with a company's name, the company will not appear in search results, even if it matches other parameters. Partial matches are accepted. For example, if you filter by the value marketing, a company called NY Marketing Unlimited would still be eligible as a search result, but NY Market Analysis would not be eligible."""
     )
-    organization_ids: Optional[list[str]] = SchemaField(
+    organization_ids: list[str] = SchemaField(
         description="""The Apollo IDs for the companies you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
 To find IDs, identify the values for organization_id when you call this endpoint.""",
         default_factory=list,
     )
-    max_results: Optional[int] = SchemaField(
+    max_results: int = SchemaField(
         description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
         default=100,
         ge=1,
@@ -435,11 +421,11 @@ Use the page parameter to search the different pages of data.""",
 class SearchOrganizationsResponse(BaseModel):
     """Response from Apollo's search organizations API"""
 
-    breadcrumbs: Optional[list[Breadcrumb]] = []
-    partial_results_only: Optional[bool] = True
-    has_join: Optional[bool] = True
-    disable_eu_prospecting: Optional[bool] = True
-    partial_results_limit: Optional[int] = 0
+    breadcrumbs: list[Breadcrumb] = []
+    partial_results_only: bool = True
+    has_join: bool = True
+    disable_eu_prospecting: bool = True
+    partial_results_limit: int = 0
     pagination: Pagination = Pagination(
         page=0, per_page=0, total_entries=0, total_pages=0
     )
@@ -447,14 +433,14 @@ class SearchOrganizationsResponse(BaseModel):
     accounts: list[Any] = []
     organizations: list[Organization] = []
     models_ids: list[str] = []
-    num_fetch_result: Optional[str] = ""
-    derived_params: Optional[str] = ""
+    num_fetch_result: Optional[str] = "N/A"
+    derived_params: Optional[str] = "N/A"
 
 
 class SearchPeopleRequest(BaseModel):
     """Request for Apollo's search people API"""
 
-    person_titles: Optional[list[str]] = SchemaField(
+    person_titles: list[str] = SchemaField(
         description="""Job titles held by the people you want to find. For a person to be included in search results, they only need to match 1 of the job titles you add. Adding more job titles expands your search results.
 
 Results also include job titles with the same terms, even if they are not exact matches. For example, searching for marketing manager might return people with the job title content marketing manager.
@@ -464,13 +450,13 @@ Use this parameter in combination with the person_seniorities[] parameter to fin
         default_factory=list,
         placeholder="marketing manager",
     )
-    person_locations: Optional[list[str]] = SchemaField(
+    person_locations: list[str] = SchemaField(
         description="""The location where people live. You can search across cities, US states, and countries.
 
 To find people based on the headquarters locations of their current employer, use the organization_locations parameter.""",
         default_factory=list,
     )
-    person_seniorities: Optional[list[SenorityLevels]] = SchemaField(
+    person_seniorities: list[SenorityLevels] = SchemaField(
         description="""The job seniority that people hold within their current employer. This enables you to find people that currently hold positions at certain reporting levels, such as Director level or senior IC level.
 
 For a person to be included in search results, they only need to match 1 of the seniorities you add. Adding more seniorities expands your search results.
@@ -480,7 +466,7 @@ Searches only return results based on their current job title, so searching for
 Use this parameter in combination with the person_titles[] parameter to find people based on specific job functions and seniority levels.""",
         default_factory=list,
     )
-    organization_locations: Optional[list[str]] = SchemaField(
+    organization_locations: list[str] = SchemaField(
         description="""The location of the company headquarters for a person's current employer. You can search across cities, US states, and countries.
 
 If a company has several office locations, results are still based on the headquarters location. For example, if you search chicago but a company's HQ location is in boston, people that work for the Boston-based company will not appear in your results, even if they match other parameters.
@@ -488,7 +474,7 @@ If a company has several office locations, results are still based on the headqu
 To find people based on their personal location, use the person_locations parameter.""",
         default_factory=list,
     )
-    q_organization_domains: Optional[list[str]] = SchemaField(
+    q_organization_domains: list[str] = SchemaField(
         description="""The domain name for the person's employer. This can be the current employer or a previous employer. Do not include www., the @ symbol, or similar.
 
 You can add multiple domains to search across companies.
@@ -496,23 +482,23 @@ You can add multiple domains to search across companies.
   Examples: apollo.io and microsoft.com""",
         default_factory=list,
     )
-    contact_email_statuses: Optional[list[ContactEmailStatuses]] = SchemaField(
+    contact_email_statuses: list[ContactEmailStatuses] = SchemaField(
         description="""The email statuses for the people you want to find. You can add multiple statuses to expand your search.""",
         default_factory=list,
     )
-    organization_ids: Optional[list[str]] = SchemaField(
+    organization_ids: list[str] = SchemaField(
         description="""The Apollo IDs for the companies (employers) you want to include in your search results. Each company in the Apollo database is assigned a unique ID.
 
 To find IDs, call the Organization Search endpoint and identify the values for organization_id.""",
         default_factory=list,
     )
-    organization_num_employees_range: Optional[list[int]] = SchemaField(
+    organization_num_empoloyees_range: list[int] = SchemaField(
         description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
 Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
         default_factory=list,
     )
-    q_keywords: Optional[str] = SchemaField(
+    q_keywords: str = SchemaField(
         description="""A string of words over which we want to filter the results""",
         default="",
     )
@@ -528,7 +514,7 @@ Use this parameter in combination with the per_page parameter to make search res
 Use the page parameter to search the different pages of data.""",
         default=100,
     )
-    max_results: Optional[int] = SchemaField(
+    max_results: int = SchemaField(
         description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
         default=100,
         ge=1,
@@ -547,61 +533,16 @@ class SearchPeopleResponse(BaseModel):
         populate_by_name=True,
     )
 
-    breadcrumbs: Optional[list[Breadcrumb]] = []
-    partial_results_only: Optional[bool] = True
-    has_join: Optional[bool] = True
-    disable_eu_prospecting: Optional[bool] = True
-    partial_results_limit: Optional[int] = 0
+    breadcrumbs: list[Breadcrumb] = []
+    partial_results_only: bool = True
+    has_join: bool = True
+    disable_eu_prospecting: bool = True
+    partial_results_limit: int = 0
     pagination: Pagination = Pagination(
         page=0, per_page=0, total_entries=0, total_pages=0
     )
     contacts: list[Contact] = []
     people: list[Contact] = []
     model_ids: list[str] = []
-    num_fetch_result: Optional[str] = ""
-    derived_params: Optional[str] = ""
-
-
-class EnrichPersonRequest(BaseModel):
-    """Request for Apollo's person enrichment API"""
-
-    person_id: Optional[str] = SchemaField(
-        description="Apollo person ID to enrich (most accurate method)",
-        default="",
-    )
-    first_name: Optional[str] = SchemaField(
-        description="First name of the person to enrich",
-        default="",
-    )
-    last_name: Optional[str] = SchemaField(
-        description="Last name of the person to enrich",
-        default="",
-    )
-    name: Optional[str] = SchemaField(
-        description="Full name of the person to enrich",
-        default="",
-    )
-    email: Optional[str] = SchemaField(
-        description="Email address of the person to enrich",
-        default="",
-    )
-    domain: Optional[str] = SchemaField(
-        description="Company domain of the person to enrich",
-        default="",
-    )
-    company: Optional[str] = SchemaField(
-        description="Company name of the person to enrich",
-        default="",
-    )
-    linkedin_url: Optional[str] = SchemaField(
-        description="LinkedIn URL of the person to enrich",
-        default="",
-    )
-    organization_id: Optional[str] = SchemaField(
-        description="Apollo organization ID of the person's company",
-        default="",
-    )
-    title: Optional[str] = SchemaField(
-        description="Job title of the person to enrich",
-        default="",
-    )
+    num_fetch_result: Optional[str] = "N/A"
+    derived_params: Optional[str] = "N/A"

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

@@ -11,14 +11,14 @@ from backend.blocks.apollo.models import (
     SearchOrganizationsRequest,
 )
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import CredentialsField, SchemaField
+from backend.data.model import SchemaField
 
 
 class SearchOrganizationsBlock(Block):
     """Search for organizations in Apollo"""
 
     class Input(BlockSchema):
-        organization_num_employees_range: list[int] = SchemaField(
+        organization_num_empoloyees_range: list[int] = SchemaField(
             description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
 Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
@@ -65,7 +65,7 @@ To find IDs, identify the values for organization_id when you call this endpoint
             le=50000,
             advanced=True,
         )
-        credentials: ApolloCredentialsInput = CredentialsField(
+        credentials: ApolloCredentialsInput = SchemaField(
             description="Apollo credentials",
         )
 
@@ -210,7 +210,9 @@ To find IDs, identify the values for organization_id when you call this endpoint
     async def run(
         self, input_data: Input, *, credentials: ApolloCredentials, **kwargs
     ) -> BlockOutput:
-        query = SearchOrganizationsRequest(**input_data.model_dump())
+        query = SearchOrganizationsRequest(
+            **input_data.model_dump(exclude={"credentials"})
+        )
         organizations = await self.search_organizations(query, credentials)
         for organization in organizations:
             yield "organization", organization

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

@@ -1,5 +1,3 @@
-import asyncio
-
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -10,12 +8,11 @@ from backend.blocks.apollo._auth import (
 from backend.blocks.apollo.models import (
     Contact,
     ContactEmailStatuses,
-    EnrichPersonRequest,
     SearchPeopleRequest,
     SenorityLevels,
 )
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import CredentialsField, SchemaField
+from backend.data.model import SchemaField
 
 
 class SearchPeopleBlock(Block):
@@ -80,7 +77,7 @@ class SearchPeopleBlock(Block):
             default_factory=list,
             advanced=False,
         )
-        organization_num_employees_range: list[int] = SchemaField(
+        organization_num_empoloyees_range: list[int] = SchemaField(
             description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.
 
         Each range you add needs to be a string, with the upper and lower numbers of the range separated only by a comma.""",
@@ -93,19 +90,14 @@ class SearchPeopleBlock(Block):
             advanced=False,
         )
         max_results: int = SchemaField(
-            description="""The maximum number of results to return. If you don't specify this parameter, the default is 25. Limited to 500 to prevent overspending.""",
-            default=25,
+            description="""The maximum number of results to return. If you don't specify this parameter, the default is 100.""",
+            default=100,
             ge=1,
-            le=500,
-            advanced=True,
-        )
-        enrich_info: bool = SchemaField(
-            description="""Whether to enrich contacts with detailed information including real email addresses. This will double the search cost.""",
-            default=False,
+            le=50000,
             advanced=True,
         )
 
-        credentials: ApolloCredentialsInput = CredentialsField(
+        credentials: ApolloCredentialsInput = SchemaField(
             description="Apollo credentials",
         )
 
@@ -114,6 +106,9 @@ class SearchPeopleBlock(Block):
             description="List of people found",
             default_factory=list,
         )
+        person: Contact = SchemaField(
+            description="Each found person, one at a time",
+        )
         error: str = SchemaField(
             description="Error message if the search failed",
             default="",
@@ -129,6 +124,87 @@ class SearchPeopleBlock(Block):
             test_credentials=TEST_CREDENTIALS,
             test_input={"credentials": TEST_CREDENTIALS_INPUT},
             test_output=[
+                (
+                    "person",
+                    Contact(
+                        contact_roles=[],
+                        id="1",
+                        name="John Doe",
+                        first_name="John",
+                        last_name="Doe",
+                        linkedin_url="https://www.linkedin.com/in/johndoe",
+                        title="Software Engineer",
+                        organization_name="Google",
+                        organization_id="123456",
+                        contact_stage_id="1",
+                        owner_id="1",
+                        creator_id="1",
+                        person_id="1",
+                        email_needs_tickling=True,
+                        source="apollo",
+                        original_source="apollo",
+                        headline="Software Engineer",
+                        photo_url="https://www.linkedin.com/in/johndoe",
+                        present_raw_address="123 Main St, Anytown, USA",
+                        linkededin_uid="123456",
+                        extrapolated_email_confidence=0.8,
+                        salesforce_id="123456",
+                        salesforce_lead_id="123456",
+                        salesforce_contact_id="123456",
+                        saleforce_account_id="123456",
+                        crm_owner_id="123456",
+                        created_at="2021-01-01",
+                        emailer_campaign_ids=[],
+                        direct_dial_status="active",
+                        direct_dial_enrichment_failed_at="2021-01-01",
+                        email_status="active",
+                        email_source="apollo",
+                        account_id="123456",
+                        last_activity_date="2021-01-01",
+                        hubspot_vid="123456",
+                        hubspot_company_id="123456",
+                        crm_id="123456",
+                        sanitized_phone="123456",
+                        merged_crm_ids="123456",
+                        updated_at="2021-01-01",
+                        queued_for_crm_push=True,
+                        suggested_from_rule_engine_config_id="123456",
+                        email_unsubscribed=None,
+                        label_ids=[],
+                        has_pending_email_arcgate_request=True,
+                        has_email_arcgate_request=True,
+                        existence_level=None,
+                        email=None,
+                        email_from_customer=None,
+                        typed_custom_fields=[],
+                        custom_field_errors=None,
+                        salesforce_record_id=None,
+                        crm_record_url=None,
+                        email_status_unavailable_reason=None,
+                        email_true_status=None,
+                        updated_email_true_status=True,
+                        contact_rule_config_statuses=[],
+                        source_display_name=None,
+                        twitter_url=None,
+                        contact_campaign_statuses=[],
+                        state=None,
+                        city=None,
+                        country=None,
+                        account=None,
+                        contact_emails=[],
+                        organization=None,
+                        employment_history=[],
+                        time_zone=None,
+                        intent_strength=None,
+                        show_intent=True,
+                        phone_numbers=[],
+                        account_phone_note=None,
+                        free_domain=True,
+                        is_likely_to_engage=True,
+                        email_domain_catchall=True,
+                        contact_job_change_event=None,
+                    ),
+                ),
                 (
                     "people",
                     [
@@ -303,34 +379,6 @@ class SearchPeopleBlock(Block):
         client = ApolloClient(credentials)
         return await client.search_people(query)
 
-    @staticmethod
-    async def enrich_person(
-        query: EnrichPersonRequest, credentials: ApolloCredentials
-    ) -> Contact:
-        client = ApolloClient(credentials)
-        return await client.enrich_person(query)
-
-    @staticmethod
-    def merge_contact_data(original: Contact, enriched: Contact) -> Contact:
-        """
-        Merge contact data from original search with enriched data.
-        Enriched data complements original data, only filling in missing values.
-        """
-        merged_data = original.model_dump()
-        enriched_data = enriched.model_dump()
-
-        # Only update fields that are None, empty string, empty list, or default values in original
-        for key, enriched_value in enriched_data.items():
-            # Skip if enriched value is None, empty string, or empty list
-            if enriched_value is None or enriched_value == "" or enriched_value == []:
-                continue
-
-            # Update if original value is None, empty string, empty list, or zero
-            if enriched_value:
-                merged_data[key] = enriched_value
-
-        return Contact(**merged_data)
-
     async def run(
         self,
         input_data: Input,
@@ -339,25 +387,8 @@ class SearchPeopleBlock(Block):
         **kwargs,
     ) -> BlockOutput:
 
-        query = SearchPeopleRequest(**input_data.model_dump())
+        query = SearchPeopleRequest(**input_data.model_dump(exclude={"credentials"}))
         people = await self.search_people(query, credentials)
-
-        # Enrich with detailed info if requested
-        if input_data.enrich_info:
-
-            async def enrich_or_fallback(person: Contact):
-                try:
-                    enrich_query = EnrichPersonRequest(person_id=person.id)
-                    enriched_person = await self.enrich_person(
-                        enrich_query, credentials
-                    )
-                    # Merge enriched data with original data, complementing instead of replacing
-                    return self.merge_contact_data(person, enriched_person)
-                except Exception:
-                    return person  # If enrichment fails, use original person data
-
-            people = await asyncio.gather(
-                *(enrich_or_fallback(person) for person in people)
-            )
-
+        for person in people:
+            yield "person", person
         yield "people", people

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

@@ -1,138 +0,0 @@
-from backend.blocks.apollo._api import ApolloClient
-from backend.blocks.apollo._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    ApolloCredentials,
-    ApolloCredentialsInput,
-)
-from backend.blocks.apollo.models import Contact, EnrichPersonRequest
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import CredentialsField, SchemaField
-
-
-class GetPersonDetailBlock(Block):
-    """Get detailed person data with Apollo API, including email reveal"""
-
-    class Input(BlockSchema):
-        person_id: str = SchemaField(
-            description="Apollo person ID to enrich (most accurate method)",
-            default="",
-            advanced=False,
-        )
-        first_name: str = SchemaField(
-            description="First name of the person to enrich",
-            default="",
-            advanced=False,
-        )
-        last_name: str = SchemaField(
-            description="Last name of the person to enrich",
-            default="",
-            advanced=False,
-        )
-        name: str = SchemaField(
-            description="Full name of the person to enrich (alternative to first_name + last_name)",
-            default="",
-            advanced=False,
-        )
-        email: str = SchemaField(
-            description="Known email address of the person (helps with matching)",
-            default="",
-            advanced=False,
-        )
-        domain: str = SchemaField(
-            description="Company domain of the person (e.g., 'google.com')",
-            default="",
-            advanced=False,
-        )
-        company: str = SchemaField(
-            description="Company name of the person",
-            default="",
-            advanced=False,
-        )
-        linkedin_url: str = SchemaField(
-            description="LinkedIn URL of the person",
-            default="",
-            advanced=False,
-        )
-        organization_id: str = SchemaField(
-            description="Apollo organization ID of the person's company",
-            default="",
-            advanced=True,
-        )
-        title: str = SchemaField(
-            description="Job title of the person to enrich",
-            default="",
-            advanced=True,
-        )
-        credentials: ApolloCredentialsInput = CredentialsField(
-            description="Apollo credentials",
-        )
-
-    class Output(BlockSchema):
-        contact: Contact = SchemaField(
-            description="Enriched contact information",
-        )
-        error: str = SchemaField(
-            description="Error message if enrichment failed",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="3b18d46c-3db6-42ae-a228-0ba441bdd176",
-            description="Get detailed person data with Apollo API, including email reveal",
-            categories={BlockCategory.SEARCH},
-            input_schema=GetPersonDetailBlock.Input,
-            output_schema=GetPersonDetailBlock.Output,
-            test_credentials=TEST_CREDENTIALS,
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "first_name": "John",
-                "last_name": "Doe",
-                "company": "Google",
-            },
-            test_output=[
-                (
-                    "contact",
-                    Contact(
-                        id="1",
-                        name="John Doe",
-                        first_name="John",
-                        last_name="Doe",
-                        email="john.doe@gmail.com",
-                        title="Software Engineer",
-                        organization_name="Google",
-                        linkedin_url="https://www.linkedin.com/in/johndoe",
-                    ),
-                ),
-            ],
-            test_mock={
-                "enrich_person": lambda query, credentials: Contact(
-                    id="1",
-                    name="John Doe",
-                    first_name="John",
-                    last_name="Doe",
-                    email="john.doe@gmail.com",
-                    title="Software Engineer",
-                    organization_name="Google",
-                    linkedin_url="https://www.linkedin.com/in/johndoe",
-                )
-            },
-        )
-
-    @staticmethod
-    async def enrich_person(
-        query: EnrichPersonRequest, credentials: ApolloCredentials
-    ) -> Contact:
-        client = ApolloClient(credentials)
-        return await client.enrich_person(query)
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: ApolloCredentials,
-        **kwargs,
-    ) -> BlockOutput:
-        query = EnrichPersonRequest(**input_data.model_dump())
-        yield "contact", await self.enrich_person(query, credentials)

autogpt_platform/backend/backend/blocks/ayrshare/__init__.py

@@ -1,15 +0,0 @@
-AYRSHARE_BLOCK_IDS = [
-    "cbd52c2a-06d2-43ed-9560-6576cc163283",  # PostToBlueskyBlock
-    "3352f512-3524-49ed-a08f-003042da2fc1",  # PostToFacebookBlock
-    "9e8f844e-b4a5-4b25-80f2-9e1dd7d67625",  # PostToXBlock
-    "589af4e4-507f-42fd-b9ac-a67ecef25811",  # PostToLinkedInBlock
-    "89b02b96-a7cb-46f4-9900-c48b32fe1552",  # PostToInstagramBlock
-    "0082d712-ff1b-4c3d-8a8d-6c7721883b83",  # PostToYouTubeBlock
-    "c7733580-3c72-483e-8e47-a8d58754d853",  # PostToRedditBlock
-    "47bc74eb-4af2-452c-b933-af377c7287df",  # PostToTelegramBlock
-    "2c38c783-c484-4503-9280-ef5d1d345a7e",  # PostToGMBBlock
-    "3ca46e05-dbaa-4afb-9e95-5a429c4177e6",  # PostToPinterestBlock
-    "7faf4b27-96b0-4f05-bf64-e0de54ae74e1",  # PostToTikTokBlock
-    "f8c3b2e1-9d4a-4e5f-8c7b-6a9e8d2f1c3b",  # PostToThreadsBlock
-    "a9d7f854-2c83-4e96-b3a1-7f2e9c5d4b8e",  # PostToSnapchatBlock
-]

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

@@ -1,152 +0,0 @@
-from datetime import datetime
-from typing import Optional
-
-from pydantic import BaseModel, Field
-
-from backend.data.block import BlockSchema
-from backend.data.model import SchemaField, UserIntegrations
-from backend.integrations.ayrshare import AyrshareClient
-from backend.util.clients import get_database_manager_async_client
-from backend.util.exceptions import MissingConfigError
-
-
-async def get_profile_key(user_id: str):
-    user_integrations: UserIntegrations = (
-        await get_database_manager_async_client().get_user_integrations(user_id)
-    )
-    return user_integrations.managed_credentials.ayrshare_profile_key
-
-
-class BaseAyrshareInput(BlockSchema):
-    """Base input model for Ayrshare social media posts with common fields."""
-
-    post: str = SchemaField(
-        description="The post text to be published", default="", advanced=False
-    )
-    media_urls: list[str] = SchemaField(
-        description="Optional list of media URLs to include. Set is_video in advanced settings to true if you want to upload videos.",
-        default_factory=list,
-        advanced=False,
-    )
-    is_video: bool = SchemaField(
-        description="Whether the media is a video", default=False, advanced=True
-    )
-    schedule_date: Optional[datetime] = SchemaField(
-        description="UTC datetime for scheduling (YYYY-MM-DDThh:mm:ssZ)",
-        default=None,
-        advanced=True,
-    )
-    disable_comments: bool = SchemaField(
-        description="Whether to disable comments", default=False, advanced=True
-    )
-    shorten_links: bool = SchemaField(
-        description="Whether to shorten links", default=False, advanced=True
-    )
-    unsplash: Optional[str] = SchemaField(
-        description="Unsplash image configuration", default=None, advanced=True
-    )
-    requires_approval: bool = SchemaField(
-        description="Whether to enable approval workflow",
-        default=False,
-        advanced=True,
-    )
-    random_post: bool = SchemaField(
-        description="Whether to generate random post text",
-        default=False,
-        advanced=True,
-    )
-    random_media_url: bool = SchemaField(
-        description="Whether to generate random media", default=False, advanced=True
-    )
-    notes: Optional[str] = SchemaField(
-        description="Additional notes for the post", default=None, advanced=True
-    )
-
-
-class CarouselItem(BaseModel):
-    """Model for Facebook carousel items."""
-
-    name: str = Field(..., description="The name of the item")
-    link: str = Field(..., description="The link of the item")
-    picture: str = Field(..., description="The picture URL of the item")
-
-
-class CallToAction(BaseModel):
-    """Model for Google My Business Call to Action."""
-
-    action_type: str = Field(
-        ..., description="Type of action (book, order, shop, learn_more, sign_up, call)"
-    )
-    url: Optional[str] = Field(
-        description="URL for the action (not required for 'call' action)"
-    )
-
-
-class EventDetails(BaseModel):
-    """Model for Google My Business Event details."""
-
-    title: str = Field(..., description="Event title")
-    start_date: str = Field(..., description="Event start date (ISO format)")
-    end_date: str = Field(..., description="Event end date (ISO format)")
-
-
-class OfferDetails(BaseModel):
-    """Model for Google My Business Offer details."""
-
-    title: str = Field(..., description="Offer title")
-    start_date: str = Field(..., description="Offer start date (ISO format)")
-    end_date: str = Field(..., description="Offer end date (ISO format)")
-    coupon_code: str = Field(..., description="Coupon code (max 58 characters)")
-    redeem_online_url: str = Field(..., description="URL to redeem the offer")
-    terms_conditions: str = Field(..., description="Terms and conditions")
-
-
-class InstagramUserTag(BaseModel):
-    """Model for Instagram user tags."""
-
-    username: str = Field(..., description="Instagram username (without @)")
-    x: Optional[float] = Field(description="X coordinate (0.0-1.0) for image posts")
-    y: Optional[float] = Field(description="Y coordinate (0.0-1.0) for image posts")
-
-
-class LinkedInTargeting(BaseModel):
-    """Model for LinkedIn audience targeting."""
-
-    countries: Optional[list[str]] = Field(
-        description="Country codes (e.g., ['US', 'IN', 'DE', 'GB'])"
-    )
-    seniorities: Optional[list[str]] = Field(
-        description="Seniority levels (e.g., ['Senior', 'VP'])"
-    )
-    degrees: Optional[list[str]] = Field(description="Education degrees")
-    fields_of_study: Optional[list[str]] = Field(description="Fields of study")
-    industries: Optional[list[str]] = Field(description="Industry categories")
-    job_functions: Optional[list[str]] = Field(description="Job function categories")
-    staff_count_ranges: Optional[list[str]] = Field(description="Company size ranges")
-
-
-class PinterestCarouselOption(BaseModel):
-    """Model for Pinterest carousel image options."""
-
-    title: Optional[str] = Field(description="Image title")
-    link: Optional[str] = Field(description="External destination link for the image")
-    description: Optional[str] = Field(description="Image description")
-
-
-class YouTubeTargeting(BaseModel):
-    """Model for YouTube country targeting."""
-
-    block: Optional[list[str]] = Field(
-        description="Country codes to block (e.g., ['US', 'CA'])"
-    )
-    allow: Optional[list[str]] = Field(
-        description="Country codes to allow (e.g., ['GB', 'AU'])"
-    )
-
-
-def create_ayrshare_client():
-    """Create an Ayrshare client instance."""
-    try:
-        return AyrshareClient()
-    except MissingConfigError:
-        return None

autogpt_platform/backend/backend/blocks/ayrshare/post_to_bluesky.py

@@ -1,114 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToBlueskyBlock(Block):
-    """Block for posting to Bluesky with Bluesky-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Bluesky posts."""
-
-        # Override post field to include character limit information
-        post: str = SchemaField(
-            description="The post text to be published (max 300 characters for Bluesky)",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Bluesky-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs to include. Bluesky supports up to 4 images or 1 video.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # Bluesky-specific options
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each media item (accessibility)",
-            default_factory=list,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="cbd52c2a-06d2-43ed-9560-6576cc163283",
-            description="Post to Bluesky using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToBlueskyBlock.Input,
-            output_schema=PostToBlueskyBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToBlueskyBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Bluesky with Bluesky-specific options."""
-
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate character limit for Bluesky
-        if len(input_data.post) > 300:
-            yield "error", f"Post text exceeds Bluesky's 300 character limit ({len(input_data.post)} characters)"
-            return
-
-        # Validate media constraints for Bluesky
-        if len(input_data.media_urls) > 4:
-            yield "error", "Bluesky supports a maximum of 4 images or 1 video"
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Bluesky-specific options
-        bluesky_options = {}
-        if input_data.alt_text:
-            bluesky_options["altText"] = input_data.alt_text
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.BLUESKY],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            bluesky_options=bluesky_options if bluesky_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_facebook.py

@@ -1,212 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import (
-    BaseAyrshareInput,
-    CarouselItem,
-    create_ayrshare_client,
-    get_profile_key,
-)
-
-
-class PostToFacebookBlock(Block):
-    """Block for posting to Facebook with Facebook-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Facebook posts."""
-
-        # Facebook-specific options
-        is_carousel: bool = SchemaField(
-            description="Whether to post a carousel", default=False, advanced=True
-        )
-        carousel_link: str = SchemaField(
-            description="The URL for the 'See More At' button in the carousel",
-            default="",
-            advanced=True,
-        )
-        carousel_items: list[CarouselItem] = SchemaField(
-            description="List of carousel items with name, link and picture URLs. Min 2, max 10 items.",
-            default_factory=list,
-            advanced=True,
-        )
-        is_reels: bool = SchemaField(
-            description="Whether to post to Facebook Reels",
-            default=False,
-            advanced=True,
-        )
-        reels_title: str = SchemaField(
-            description="Title for the Reels video (max 255 chars)",
-            default="",
-            advanced=True,
-        )
-        reels_thumbnail: str = SchemaField(
-            description="Thumbnail URL for Reels video (JPEG/PNG, <10MB)",
-            default="",
-            advanced=True,
-        )
-        is_story: bool = SchemaField(
-            description="Whether to post as a Facebook Story",
-            default=False,
-            advanced=True,
-        )
-        media_captions: list[str] = SchemaField(
-            description="Captions for each media item",
-            default_factory=list,
-            advanced=True,
-        )
-        location_id: str = SchemaField(
-            description="Facebook Page ID or name for location tagging",
-            default="",
-            advanced=True,
-        )
-        age_min: int = SchemaField(
-            description="Minimum age for audience targeting (13,15,18,21,25)",
-            default=0,
-            advanced=True,
-        )
-        target_countries: list[str] = SchemaField(
-            description="List of country codes to target (max 25)",
-            default_factory=list,
-            advanced=True,
-        )
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each media item",
-            default_factory=list,
-            advanced=True,
-        )
-        video_title: str = SchemaField(
-            description="Title for video post", default="", advanced=True
-        )
-        video_thumbnail: str = SchemaField(
-            description="Thumbnail URL for video post", default="", advanced=True
-        )
-        is_draft: bool = SchemaField(
-            description="Save as draft in Meta Business Suite",
-            default=False,
-            advanced=True,
-        )
-        scheduled_publish_date: str = SchemaField(
-            description="Schedule publish time in Meta Business Suite (UTC)",
-            default="",
-            advanced=True,
-        )
-        preview_link: str = SchemaField(
-            description="URL for custom link preview", default="", advanced=True
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="3352f512-3524-49ed-a08f-003042da2fc1",
-            description="Post to Facebook using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToFacebookBlock.Input,
-            output_schema=PostToFacebookBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToFacebookBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Facebook with Facebook-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Facebook-specific options
-        facebook_options = {}
-        if input_data.is_carousel:
-            facebook_options["isCarousel"] = True
-            if input_data.carousel_link:
-                facebook_options["carouselLink"] = input_data.carousel_link
-            if input_data.carousel_items:
-                facebook_options["carouselItems"] = [
-                    item.dict() for item in input_data.carousel_items
-                ]
-
-        if input_data.is_reels:
-            facebook_options["isReels"] = True
-            if input_data.reels_title:
-                facebook_options["reelsTitle"] = input_data.reels_title
-            if input_data.reels_thumbnail:
-                facebook_options["reelsThumbnail"] = input_data.reels_thumbnail
-
-        if input_data.is_story:
-            facebook_options["isStory"] = True
-
-        if input_data.media_captions:
-            facebook_options["mediaCaptions"] = input_data.media_captions
-
-        if input_data.location_id:
-            facebook_options["locationId"] = input_data.location_id
-
-        if input_data.age_min > 0:
-            facebook_options["ageMin"] = input_data.age_min
-
-        if input_data.target_countries:
-            facebook_options["targetCountries"] = input_data.target_countries
-
-        if input_data.alt_text:
-            facebook_options["altText"] = input_data.alt_text
-
-        if input_data.video_title:
-            facebook_options["videoTitle"] = input_data.video_title
-
-        if input_data.video_thumbnail:
-            facebook_options["videoThumbnail"] = input_data.video_thumbnail
-
-        if input_data.is_draft:
-            facebook_options["isDraft"] = True
-
-        if input_data.scheduled_publish_date:
-            facebook_options["scheduledPublishDate"] = input_data.scheduled_publish_date
-
-        if input_data.preview_link:
-            facebook_options["previewLink"] = input_data.preview_link
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.FACEBOOK],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            facebook_options=facebook_options if facebook_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_gmb.py

@@ -1,210 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToGMBBlock(Block):
-    """Block for posting to Google My Business with GMB-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Google My Business posts."""
-
-        # Override media_urls to include GMB-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. GMB supports only one image or video per post.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # GMB-specific options
-        is_photo_video: bool = SchemaField(
-            description="Whether this is a photo/video post (appears in Photos section)",
-            default=False,
-            advanced=True,
-        )
-        photo_category: str = SchemaField(
-            description="Category for photo/video: cover, profile, logo, exterior, interior, product, at_work, food_and_drink, menu, common_area, rooms, teams",
-            default="",
-            advanced=True,
-        )
-        # Call to action options (flattened from CallToAction object)
-        call_to_action_type: str = SchemaField(
-            description="Type of action button: 'book', 'order', 'shop', 'learn_more', 'sign_up', or 'call'",
-            default="",
-            advanced=True,
-        )
-        call_to_action_url: str = SchemaField(
-            description="URL for the action button (not required for 'call' action)",
-            default="",
-            advanced=True,
-        )
-        # Event details options (flattened from EventDetails object)
-        event_title: str = SchemaField(
-            description="Event title for event posts",
-            default="",
-            advanced=True,
-        )
-        event_start_date: str = SchemaField(
-            description="Event start date in ISO format (e.g., '2024-03-15T09:00:00Z')",
-            default="",
-            advanced=True,
-        )
-        event_end_date: str = SchemaField(
-            description="Event end date in ISO format (e.g., '2024-03-15T17:00:00Z')",
-            default="",
-            advanced=True,
-        )
-        # Offer details options (flattened from OfferDetails object)
-        offer_title: str = SchemaField(
-            description="Offer title for promotional posts",
-            default="",
-            advanced=True,
-        )
-        offer_start_date: str = SchemaField(
-            description="Offer start date in ISO format (e.g., '2024-03-15T00:00:00Z')",
-            default="",
-            advanced=True,
-        )
-        offer_end_date: str = SchemaField(
-            description="Offer end date in ISO format (e.g., '2024-04-15T23:59:59Z')",
-            default="",
-            advanced=True,
-        )
-        offer_coupon_code: str = SchemaField(
-            description="Coupon code for the offer (max 58 characters)",
-            default="",
-            advanced=True,
-        )
-        offer_redeem_online_url: str = SchemaField(
-            description="URL where customers can redeem the offer online",
-            default="",
-            advanced=True,
-        )
-        offer_terms_conditions: str = SchemaField(
-            description="Terms and conditions for the offer",
-            default="",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="2c38c783-c484-4503-9280-ef5d1d345a7e",
-            description="Post to Google My Business using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToGMBBlock.Input,
-            output_schema=PostToGMBBlock.Output,
-        )
-
-    async def run(
-        self, input_data: "PostToGMBBlock.Input", *, user_id: str, **kwargs
-    ) -> BlockOutput:
-        """Post to Google My Business with GMB-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate GMB constraints
-        if len(input_data.media_urls) > 1:
-            yield "error", "Google My Business supports only one image or video per post"
-            return
-
-        # Validate offer coupon code length
-        if input_data.offer_coupon_code and len(input_data.offer_coupon_code) > 58:
-            yield "error", "GMB offer coupon code cannot exceed 58 characters"
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build GMB-specific options
-        gmb_options = {}
-
-        # Photo/Video post options
-        if input_data.is_photo_video:
-            gmb_options["isPhotoVideo"] = True
-            if input_data.photo_category:
-                gmb_options["category"] = input_data.photo_category
-
-        # Call to Action (from flattened fields)
-        if input_data.call_to_action_type:
-            cta_dict = {"actionType": input_data.call_to_action_type}
-            # URL not required for 'call' action type
-            if (
-                input_data.call_to_action_type != "call"
-                and input_data.call_to_action_url
-            ):
-                cta_dict["url"] = input_data.call_to_action_url
-            gmb_options["callToAction"] = cta_dict
-
-        # Event details (from flattened fields)
-        if (
-            input_data.event_title
-            and input_data.event_start_date
-            and input_data.event_end_date
-        ):
-            gmb_options["event"] = {
-                "title": input_data.event_title,
-                "startDate": input_data.event_start_date,
-                "endDate": input_data.event_end_date,
-            }
-
-        # Offer details (from flattened fields)
-        if (
-            input_data.offer_title
-            and input_data.offer_start_date
-            and input_data.offer_end_date
-            and input_data.offer_coupon_code
-            and input_data.offer_redeem_online_url
-            and input_data.offer_terms_conditions
-        ):
-            gmb_options["offer"] = {
-                "title": input_data.offer_title,
-                "startDate": input_data.offer_start_date,
-                "endDate": input_data.offer_end_date,
-                "couponCode": input_data.offer_coupon_code,
-                "redeemOnlineUrl": input_data.offer_redeem_online_url,
-                "termsConditions": input_data.offer_terms_conditions,
-            }
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.GOOGLE_MY_BUSINESS],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            gmb_options=gmb_options if gmb_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_instagram.py

@@ -1,249 +0,0 @@
-from typing import Any
-
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import (
-    BaseAyrshareInput,
-    InstagramUserTag,
-    create_ayrshare_client,
-    get_profile_key,
-)
-
-
-class PostToInstagramBlock(Block):
-    """Block for posting to Instagram with Instagram-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Instagram posts."""
-
-        # Override post field to include Instagram-specific information
-        post: str = SchemaField(
-            description="The post text (max 2,200 chars, up to 30 hashtags, 3 @mentions)",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Instagram-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. Instagram supports up to 10 images/videos in a carousel.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # Instagram-specific options
-        is_story: bool | None = SchemaField(
-            description="Whether to post as Instagram Story (24-hour expiration)",
-            default=None,
-            advanced=True,
-        )
-
-        # ------- REELS OPTIONS -------
-        share_reels_feed: bool | None = SchemaField(
-            description="Whether Reel should appear in both Feed and Reels tabs",
-            default=None,
-            advanced=True,
-        )
-        audio_name: str | None = SchemaField(
-            description="Audio name for Reels (e.g., 'The Weeknd - Blinding Lights')",
-            default=None,
-            advanced=True,
-        )
-        thumbnail: str | None = SchemaField(
-            description="Thumbnail URL for Reel video", default=None, advanced=True
-        )
-        thumbnail_offset: int | None = SchemaField(
-            description="Thumbnail frame offset in milliseconds (default: 0)",
-            default=0,
-            advanced=True,
-        )
-
-        # ------- POST OPTIONS -------
-
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each media item (up to 1,000 chars each, accessibility feature), each item in the list corresponds to a media item in the media_urls list",
-            default_factory=list,
-            advanced=True,
-        )
-
-        location_id: str | None = SchemaField(
-            description="Facebook Page ID or name for location tagging (e.g., '7640348500' or '@guggenheimmuseum')",
-            default=None,
-            advanced=True,
-        )
-        user_tags: list[dict[str, Any]] = SchemaField(
-            description="List of users to tag with coordinates for images",
-            default_factory=list,
-            advanced=True,
-        )
-        collaborators: list[str] = SchemaField(
-            description="Instagram usernames to invite as collaborators (max 3, public accounts only)",
-            default_factory=list,
-            advanced=True,
-        )
-        auto_resize: bool | None = SchemaField(
-            description="Auto-resize images to 1080x1080px for Instagram",
-            default=None,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            id="89b02b96-a7cb-46f4-9900-c48b32fe1552",
-            description="Post to Instagram using Ayrshare. Requires a Business or Creator Instagram Account connected with a Facebook Page",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToInstagramBlock.Input,
-            output_schema=PostToInstagramBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToInstagramBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Instagram with Instagram-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate Instagram constraints
-        if len(input_data.post) > 2200:
-            yield "error", f"Instagram post text exceeds 2,200 character limit ({len(input_data.post)} characters)"
-            return
-
-        if len(input_data.media_urls) > 10:
-            yield "error", "Instagram supports a maximum of 10 images/videos in a carousel"
-            return
-
-        if len(input_data.collaborators) > 3:
-            yield "error", "Instagram supports a maximum of 3 collaborators"
-            return
-
-        # Validate that if any reel option is set, all required reel options are set
-        reel_options = [
-            input_data.share_reels_feed,
-            input_data.audio_name,
-            input_data.thumbnail,
-        ]
-
-        if any(reel_options) and not all(reel_options):
-            yield "error", "When posting a reel, all reel options must be set: share_reels_feed, audio_name, and either thumbnail or thumbnail_offset"
-            return
-
-        # Count hashtags and mentions
-        hashtag_count = input_data.post.count("#")
-        mention_count = input_data.post.count("@")
-
-        if hashtag_count > 30:
-            yield "error", f"Instagram allows maximum 30 hashtags ({hashtag_count} found)"
-            return
-
-        if mention_count > 3:
-            yield "error", f"Instagram allows maximum 3 @mentions ({mention_count} found)"
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Instagram-specific options
-        instagram_options = {}
-
-        # Stories
-        if input_data.is_story:
-            instagram_options["stories"] = True
-
-        # Reels options
-        if input_data.share_reels_feed is not None:
-            instagram_options["shareReelsFeed"] = input_data.share_reels_feed
-
-        if input_data.audio_name:
-            instagram_options["audioName"] = input_data.audio_name
-
-        if input_data.thumbnail:
-            instagram_options["thumbNail"] = input_data.thumbnail
-        elif input_data.thumbnail_offset and input_data.thumbnail_offset > 0:
-            instagram_options["thumbNailOffset"] = input_data.thumbnail_offset
-
-        # Alt text
-        if input_data.alt_text:
-            # Validate alt text length
-            for i, alt in enumerate(input_data.alt_text):
-                if len(alt) > 1000:
-                    yield "error", f"Alt text {i+1} exceeds 1,000 character limit ({len(alt)} characters)"
-                    return
-            instagram_options["altText"] = input_data.alt_text
-
-        # Location
-        if input_data.location_id:
-            instagram_options["locationId"] = input_data.location_id
-
-        # User tags
-        if input_data.user_tags:
-            user_tags_list = []
-            for tag in input_data.user_tags:
-                try:
-                    tag_obj = InstagramUserTag(**tag)
-                except Exception as e:
-                    yield "error", f"Invalid user tag: {e}, tages need to be a dictionary with a 3 items: username (str), x (float) and y (float)"
-                    return
-                tag_dict: dict[str, float | str] = {"username": tag_obj.username}
-                if tag_obj.x is not None and tag_obj.y is not None:
-                    # Validate coordinates
-                    if not (0.0 <= tag_obj.x <= 1.0) or not (0.0 <= tag_obj.y <= 1.0):
-                        yield "error", f"User tag coordinates must be between 0.0 and 1.0 (user: {tag_obj.username})"
-                        return
-                    tag_dict["x"] = tag_obj.x
-                    tag_dict["y"] = tag_obj.y
-                user_tags_list.append(tag_dict)
-            instagram_options["userTags"] = user_tags_list
-
-        # Collaborators
-        if input_data.collaborators:
-            instagram_options["collaborators"] = input_data.collaborators
-
-        # Auto resize
-        if input_data.auto_resize:
-            instagram_options["autoResize"] = True
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.INSTAGRAM],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            instagram_options=instagram_options if instagram_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_linkedin.py

@@ -1,222 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToLinkedInBlock(Block):
-    """Block for posting to LinkedIn with LinkedIn-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for LinkedIn posts."""
-
-        # Override post field to include LinkedIn-specific information
-        post: str = SchemaField(
-            description="The post text (max 3,000 chars, hashtags supported with #)",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include LinkedIn-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. LinkedIn supports up to 9 images, videos, or documents (PPT, PPTX, DOC, DOCX, PDF <100MB, <300 pages).",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # LinkedIn-specific options
-        visibility: str = SchemaField(
-            description="Post visibility: 'public' (default), 'connections' (personal only), 'loggedin'",
-            default="public",
-            advanced=True,
-        )
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each image (accessibility feature, not supported for videos/documents)",
-            default_factory=list,
-            advanced=True,
-        )
-        titles: list[str] = SchemaField(
-            description="Title/caption for each image or video",
-            default_factory=list,
-            advanced=True,
-        )
-        document_title: str = SchemaField(
-            description="Title for document posts (max 400 chars, uses filename if not specified)",
-            default="",
-            advanced=True,
-        )
-        thumbnail: str = SchemaField(
-            description="Thumbnail URL for video (PNG/JPG, same dimensions as video, <10MB)",
-            default="",
-            advanced=True,
-        )
-        # LinkedIn targeting options (flattened from LinkedInTargeting object)
-        targeting_countries: list[str] | None = SchemaField(
-            description="Country codes for targeting (e.g., ['US', 'IN', 'DE', 'GB']). Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_seniorities: list[str] | None = SchemaField(
-            description="Seniority levels for targeting (e.g., ['Senior', 'VP']). Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_degrees: list[str] | None = SchemaField(
-            description="Education degrees for targeting. Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_fields_of_study: list[str] | None = SchemaField(
-            description="Fields of study for targeting. Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_industries: list[str] | None = SchemaField(
-            description="Industry categories for targeting. Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_job_functions: list[str] | None = SchemaField(
-            description="Job function categories for targeting. Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-        targeting_staff_count_ranges: list[str] | None = SchemaField(
-            description="Company size ranges for targeting. Requires 300+ followers in target audience.",
-            default=None,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            id="589af4e4-507f-42fd-b9ac-a67ecef25811",
-            description="Post to LinkedIn using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToLinkedInBlock.Input,
-            output_schema=PostToLinkedInBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToLinkedInBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to LinkedIn with LinkedIn-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate LinkedIn constraints
-        if len(input_data.post) > 3000:
-            yield "error", f"LinkedIn post text exceeds 3,000 character limit ({len(input_data.post)} characters)"
-            return
-
-        if len(input_data.media_urls) > 9:
-            yield "error", "LinkedIn supports a maximum of 9 images/videos/documents"
-            return
-
-        if input_data.document_title and len(input_data.document_title) > 400:
-            yield "error", f"LinkedIn document title exceeds 400 character limit ({len(input_data.document_title)} characters)"
-            return
-
-        # Validate visibility option
-        valid_visibility = ["public", "connections", "loggedin"]
-        if input_data.visibility not in valid_visibility:
-            yield "error", f"LinkedIn visibility must be one of: {', '.join(valid_visibility)}"
-            return
-
-        # Check for document extensions
-        document_extensions = [".ppt", ".pptx", ".doc", ".docx", ".pdf"]
-        has_documents = any(
-            any(url.lower().endswith(ext) for ext in document_extensions)
-            for url in input_data.media_urls
-        )
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build LinkedIn-specific options
-        linkedin_options = {}
-
-        # Visibility
-        if input_data.visibility != "public":
-            linkedin_options["visibility"] = input_data.visibility
-
-        # Alt text (not supported for videos or documents)
-        if input_data.alt_text and not has_documents:
-            linkedin_options["altText"] = input_data.alt_text
-
-        # Titles/captions
-        if input_data.titles:
-            linkedin_options["titles"] = input_data.titles
-
-        # Document title
-        if input_data.document_title and has_documents:
-            linkedin_options["title"] = input_data.document_title
-
-        # Video thumbnail
-        if input_data.thumbnail:
-            linkedin_options["thumbNail"] = input_data.thumbnail
-
-        # Audience targeting (from flattened fields)
-        targeting_dict = {}
-        if input_data.targeting_countries:
-            targeting_dict["countries"] = input_data.targeting_countries
-        if input_data.targeting_seniorities:
-            targeting_dict["seniorities"] = input_data.targeting_seniorities
-        if input_data.targeting_degrees:
-            targeting_dict["degrees"] = input_data.targeting_degrees
-        if input_data.targeting_fields_of_study:
-            targeting_dict["fieldsOfStudy"] = input_data.targeting_fields_of_study
-        if input_data.targeting_industries:
-            targeting_dict["industries"] = input_data.targeting_industries
-        if input_data.targeting_job_functions:
-            targeting_dict["jobFunctions"] = input_data.targeting_job_functions
-        if input_data.targeting_staff_count_ranges:
-            targeting_dict["staffCountRanges"] = input_data.targeting_staff_count_ranges
-
-        if targeting_dict:
-            linkedin_options["targeting"] = targeting_dict
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.LINKEDIN],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            linkedin_options=linkedin_options if linkedin_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_pinterest.py

@@ -1,214 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import (
-    BaseAyrshareInput,
-    PinterestCarouselOption,
-    create_ayrshare_client,
-    get_profile_key,
-)
-
-
-class PostToPinterestBlock(Block):
-    """Block for posting to Pinterest with Pinterest-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Pinterest posts."""
-
-        # Override post field to include Pinterest-specific information
-        post: str = SchemaField(
-            description="Pin description (max 500 chars, links not clickable - use link field instead)",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Pinterest-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Required image/video URLs. Pinterest requires at least one image. Videos need thumbnail. Up to 5 images for carousel.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # Pinterest-specific options
-        pin_title: str = SchemaField(
-            description="Pin title displayed in 'Add your title' section (max 100 chars)",
-            default="",
-            advanced=True,
-        )
-        link: str = SchemaField(
-            description="Clickable destination URL when users click the pin (max 2048 chars)",
-            default="",
-            advanced=True,
-        )
-        board_id: str = SchemaField(
-            description="Pinterest Board ID to post to (from /user/details endpoint, uses default board if not specified)",
-            default="",
-            advanced=True,
-        )
-        note: str = SchemaField(
-            description="Private note for the pin (only visible to you and board collaborators)",
-            default="",
-            advanced=True,
-        )
-        thumbnail: str = SchemaField(
-            description="Required thumbnail URL for video pins (must have valid image Content-Type)",
-            default="",
-            advanced=True,
-        )
-        carousel_options: list[PinterestCarouselOption] = SchemaField(
-            description="Options for each image in carousel (title, link, description per image)",
-            default_factory=list,
-            advanced=True,
-        )
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each image/video (max 500 chars each, accessibility feature)",
-            default_factory=list,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="3ca46e05-dbaa-4afb-9e95-5a429c4177e6",
-            description="Post to Pinterest using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToPinterestBlock.Input,
-            output_schema=PostToPinterestBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToPinterestBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Pinterest with Pinterest-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate Pinterest constraints
-        if len(input_data.post) > 500:
-            yield "error", f"Pinterest pin description exceeds 500 character limit ({len(input_data.post)} characters)"
-            return
-
-        if len(input_data.pin_title) > 100:
-            yield "error", f"Pinterest pin title exceeds 100 character limit ({len(input_data.pin_title)} characters)"
-            return
-
-        if len(input_data.link) > 2048:
-            yield "error", f"Pinterest link URL exceeds 2048 character limit ({len(input_data.link)} characters)"
-            return
-
-        if len(input_data.media_urls) == 0:
-            yield "error", "Pinterest requires at least one image or video"
-            return
-
-        if len(input_data.media_urls) > 5:
-            yield "error", "Pinterest supports a maximum of 5 images in a carousel"
-            return
-
-        # Check if video is included and thumbnail is provided
-        video_extensions = [".mp4", ".mov", ".avi", ".mkv", ".wmv", ".flv", ".webm"]
-        has_video = any(
-            any(url.lower().endswith(ext) for ext in video_extensions)
-            for url in input_data.media_urls
-        )
-
-        if (has_video or input_data.is_video) and not input_data.thumbnail:
-            yield "error", "Pinterest video pins require a thumbnail URL"
-            return
-
-        # Validate alt text length
-        for i, alt in enumerate(input_data.alt_text):
-            if len(alt) > 500:
-                yield "error", f"Pinterest alt text {i+1} exceeds 500 character limit ({len(alt)} characters)"
-                return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Pinterest-specific options
-        pinterest_options = {}
-
-        # Pin title
-        if input_data.pin_title:
-            pinterest_options["title"] = input_data.pin_title
-
-        # Clickable link
-        if input_data.link:
-            pinterest_options["link"] = input_data.link
-
-        # Board ID
-        if input_data.board_id:
-            pinterest_options["boardId"] = input_data.board_id
-
-        # Private note
-        if input_data.note:
-            pinterest_options["note"] = input_data.note
-
-        # Video thumbnail
-        if input_data.thumbnail:
-            pinterest_options["thumbNail"] = input_data.thumbnail
-
-        # Carousel options
-        if input_data.carousel_options:
-            carousel_list = []
-            for option in input_data.carousel_options:
-                carousel_dict = {}
-                if option.title:
-                    carousel_dict["title"] = option.title
-                if option.link:
-                    carousel_dict["link"] = option.link
-                if option.description:
-                    carousel_dict["description"] = option.description
-                if carousel_dict:  # Only add if not empty
-                    carousel_list.append(carousel_dict)
-            if carousel_list:
-                pinterest_options["carouselOptions"] = carousel_list
-
-        # Alt text
-        if input_data.alt_text:
-            pinterest_options["altText"] = input_data.alt_text
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.PINTEREST],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            pinterest_options=pinterest_options if pinterest_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_reddit.py

@@ -1,69 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToRedditBlock(Block):
-    """Block for posting to Reddit."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Reddit posts."""
-
-        pass  # Uses all base fields
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="c7733580-3c72-483e-8e47-a8d58754d853",
-            description="Post to Reddit using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToRedditBlock.Input,
-            output_schema=PostToRedditBlock.Output,
-        )
-
-    async def run(
-        self, input_data: "PostToRedditBlock.Input", *, user_id: str, **kwargs
-    ) -> BlockOutput:
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured."
-            return
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.REDDIT],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_snapchat.py

@@ -1,129 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToSnapchatBlock(Block):
-    """Block for posting to Snapchat with Snapchat-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Snapchat posts."""
-
-        # Override post field to include Snapchat-specific information
-        post: str = SchemaField(
-            description="The post text (optional for video-only content)",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Snapchat-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Required video URL for Snapchat posts. Snapchat only supports video content.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # Snapchat-specific options
-        story_type: str = SchemaField(
-            description="Type of Snapchat content: 'story' (24-hour Stories), 'saved_story' (Saved Stories), or 'spotlight' (Spotlight posts)",
-            default="story",
-            advanced=True,
-        )
-        video_thumbnail: str = SchemaField(
-            description="Thumbnail URL for video content (optional, auto-generated if not provided)",
-            default="",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="a9d7f854-2c83-4e96-b3a1-7f2e9c5d4b8e",
-            description="Post to Snapchat using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToSnapchatBlock.Input,
-            output_schema=PostToSnapchatBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToSnapchatBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Snapchat with Snapchat-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate Snapchat constraints
-        if not input_data.media_urls:
-            yield "error", "Snapchat requires at least one video URL"
-            return
-
-        if len(input_data.media_urls) > 1:
-            yield "error", "Snapchat supports only one video per post"
-            return
-
-        # Validate story type
-        valid_story_types = ["story", "saved_story", "spotlight"]
-        if input_data.story_type not in valid_story_types:
-            yield "error", f"Snapchat story type must be one of: {', '.join(valid_story_types)}"
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Snapchat-specific options
-        snapchat_options = {}
-
-        # Story type
-        if input_data.story_type != "story":
-            snapchat_options["storyType"] = input_data.story_type
-
-        # Video thumbnail
-        if input_data.video_thumbnail:
-            snapchat_options["videoThumbnail"] = input_data.video_thumbnail
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.SNAPCHAT],
-            media_urls=input_data.media_urls,
-            is_video=True,  # Snapchat only supports video
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            snapchat_options=snapchat_options if snapchat_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_telegram.py

@@ -1,116 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToTelegramBlock(Block):
-    """Block for posting to Telegram with Telegram-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Telegram posts."""
-
-        # Override post field to include Telegram-specific information
-        post: str = SchemaField(
-            description="The post text (empty string allowed). Use @handle to mention other Telegram users.",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Telegram-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. For animated GIFs, only one URL is allowed. Telegram will auto-preview links unless image/video is included.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # Override is_video to include GIF-specific information
-        is_video: bool = SchemaField(
-            description="Whether the media is a video. Set to true for animated GIFs that don't end in .gif/.GIF extension.",
-            default=False,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="47bc74eb-4af2-452c-b933-af377c7287df",
-            description="Post to Telegram using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToTelegramBlock.Input,
-            output_schema=PostToTelegramBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToTelegramBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Telegram with Telegram-specific validation."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate Telegram constraints
-        # Check for animated GIFs - only one URL allowed
-        gif_extensions = [".gif", ".GIF"]
-        has_gif = any(
-            any(url.endswith(ext) for ext in gif_extensions)
-            for url in input_data.media_urls
-        )
-
-        if has_gif and len(input_data.media_urls) > 1:
-            yield "error", "Telegram animated GIFs support only one URL per post"
-            return
-
-        # Auto-detect if we need to set is_video for GIFs without proper extension
-        detected_is_video = input_data.is_video
-        if input_data.media_urls and not has_gif and not input_data.is_video:
-            # Check if this might be a GIF without proper extension
-            # This is just informational - user needs to set is_video manually
-            pass
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.TELEGRAM],
-            media_urls=input_data.media_urls,
-            is_video=detected_is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_threads.py

@@ -1,111 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToThreadsBlock(Block):
-    """Block for posting to Threads with Threads-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for Threads posts."""
-
-        # Override post field to include Threads-specific information
-        post: str = SchemaField(
-            description="The post text (max 500 chars, empty string allowed). Only 1 hashtag allowed. Use @handle to mention users.",
-            default="",
-            advanced=False,
-        )
-
-        # Override media_urls to include Threads-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. Supports up to 20 images/videos in a carousel. Auto-preview links unless media is included.",
-            default_factory=list,
-            advanced=False,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="f8c3b2e1-9d4a-4e5f-8c7b-6a9e8d2f1c3b",
-            description="Post to Threads using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToThreadsBlock.Input,
-            output_schema=PostToThreadsBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToThreadsBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to Threads with Threads-specific validation."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate Threads constraints
-        if len(input_data.post) > 500:
-            yield "error", f"Threads post text exceeds 500 character limit ({len(input_data.post)} characters)"
-            return
-
-        if len(input_data.media_urls) > 20:
-            yield "error", "Threads supports a maximum of 20 images/videos in a carousel"
-            return
-
-        # Count hashtags (only 1 allowed)
-        hashtag_count = input_data.post.count("#")
-        if hashtag_count > 1:
-            yield "error", f"Threads allows only 1 hashtag per post ({hashtag_count} found)"
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build Threads-specific options
-        threads_options = {}
-        # Note: Based on the documentation, Threads doesn't seem to have specific options
-        # beyond the standard ones. The main constraints are validation-based.
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.THREADS],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            threads_options=threads_options if threads_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_tiktok.py

@@ -1,243 +0,0 @@
-from enum import Enum
-
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class TikTokVisibility(str, Enum):
-    PUBLIC = "public"
-    PRIVATE = "private"
-    FOLLOWERS = "followers"
-
-
-class PostToTikTokBlock(Block):
-    """Block for posting to TikTok with TikTok-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for TikTok posts."""
-
-        # Override post field to include TikTok-specific information
-        post: str = SchemaField(
-            description="The post text (max 2,200 chars, empty string allowed). Use @handle to mention users. Line breaks will be ignored.",
-            advanced=False,
-        )
-
-        # Override media_urls to include TikTok-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Required media URLs. Either 1 video OR up to 35 images (JPG/JPEG/WEBP only). Cannot mix video and images.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # TikTok-specific options
-        auto_add_music: bool = SchemaField(
-            description="Whether to automatically add recommended music to the post. If you set this field to true, you can change the music later in the TikTok app.",
-            default=False,
-            advanced=True,
-        )
-        disable_comments: bool = SchemaField(
-            description="Disable comments on the published post",
-            default=False,
-            advanced=True,
-        )
-        disable_duet: bool = SchemaField(
-            description="Disable duets on published video (video only)",
-            default=False,
-            advanced=True,
-        )
-        disable_stitch: bool = SchemaField(
-            description="Disable stitch on published video (video only)",
-            default=False,
-            advanced=True,
-        )
-        is_ai_generated: bool = SchemaField(
-            description="If you enable the toggle, your video will be labeled as “Creator labeled as AI-generated” once posted and can’t be changed. The “Creator labeled as AI-generated” label indicates that the content was completely AI-generated or significantly edited with AI.",
-            default=False,
-            advanced=True,
-        )
-        is_branded_content: bool = SchemaField(
-            description="Whether to enable the Branded Content toggle. If this field is set to true, the video will be labeled as Branded Content, indicating you are in a paid partnership with a brand. A “Paid partnership” label will be attached to the video.",
-            default=False,
-            advanced=True,
-        )
-        is_brand_organic: bool = SchemaField(
-            description="Whether to enable the Brand Organic Content toggle. If this field is set to true, the video will be labeled as Brand Organic Content, indicating you are promoting yourself or your own business. A “Promotional content” label will be attached to the video.",
-            default=False,
-            advanced=True,
-        )
-        image_cover_index: int = SchemaField(
-            description="Index of image to use as cover (0-based, image posts only)",
-            default=0,
-            advanced=True,
-        )
-        title: str = SchemaField(
-            description="Title for image posts", default="", advanced=True
-        )
-        thumbnail_offset: int = SchemaField(
-            description="Video thumbnail frame offset in milliseconds (video only)",
-            default=0,
-            advanced=True,
-        )
-        visibility: TikTokVisibility = SchemaField(
-            description="Post visibility: 'public', 'private', 'followers', or 'friends'",
-            default=TikTokVisibility.PUBLIC,
-            advanced=True,
-        )
-        draft: bool = SchemaField(
-            description="Create as draft post (video only)",
-            default=False,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            id="7faf4b27-96b0-4f05-bf64-e0de54ae74e1",
-            description="Post to TikTok using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToTikTokBlock.Input,
-            output_schema=PostToTikTokBlock.Output,
-        )
-
-    async def run(
-        self, input_data: "PostToTikTokBlock.Input", *, user_id: str, **kwargs
-    ) -> BlockOutput:
-        """Post to TikTok with TikTok-specific validation and options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate TikTok constraints
-        if len(input_data.post) > 2200:
-            yield "error", f"TikTok post text exceeds 2,200 character limit ({len(input_data.post)} characters)"
-            return
-
-        if not input_data.media_urls:
-            yield "error", "TikTok requires at least one media URL (either 1 video or up to 35 images)"
-            return
-
-        # Check for video vs image constraints
-        video_extensions = [".mp4", ".mov", ".avi", ".mkv", ".wmv", ".flv", ".webm"]
-        image_extensions = [".jpg", ".jpeg", ".webp"]
-
-        has_video = input_data.is_video or any(
-            any(url.lower().endswith(ext) for ext in video_extensions)
-            for url in input_data.media_urls
-        )
-
-        has_images = any(
-            any(url.lower().endswith(ext) for ext in image_extensions)
-            for url in input_data.media_urls
-        )
-
-        if has_video and has_images:
-            yield "error", "TikTok does not support mixing video and images in the same post"
-            return
-
-        if has_video and len(input_data.media_urls) > 1:
-            yield "error", "TikTok supports only 1 video per post"
-            return
-
-        if has_images and len(input_data.media_urls) > 35:
-            yield "error", "TikTok supports a maximum of 35 images per post"
-            return
-
-        # Validate image cover index
-        if has_images and input_data.image_cover_index >= len(input_data.media_urls):
-            yield "error", f"Image cover index {input_data.image_cover_index} is out of range (max: {len(input_data.media_urls) - 1})"
-            return
-
-        # Check for PNG files (not supported)
-        has_png = any(url.lower().endswith(".png") for url in input_data.media_urls)
-        if has_png:
-            yield "error", "TikTok does not support PNG files. Please use JPG, JPEG, or WEBP for images."
-            return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build TikTok-specific options
-        tiktok_options = {}
-
-        # Common options
-        if input_data.auto_add_music and has_images:
-            tiktok_options["autoAddMusic"] = True
-
-        if input_data.disable_comments:
-            tiktok_options["disableComments"] = True
-
-        if input_data.is_branded_content:
-            tiktok_options["isBrandedContent"] = True
-
-        if input_data.is_brand_organic:
-            tiktok_options["isBrandOrganic"] = True
-
-        # Video-specific options
-        if has_video:
-            if input_data.disable_duet:
-                tiktok_options["disableDuet"] = True
-
-            if input_data.disable_stitch:
-                tiktok_options["disableStitch"] = True
-
-            if input_data.is_ai_generated:
-                tiktok_options["isAIGenerated"] = True
-
-            if input_data.thumbnail_offset > 0:
-                tiktok_options["thumbNailOffset"] = input_data.thumbnail_offset
-
-            if input_data.draft:
-                tiktok_options["draft"] = True
-
-        # Image-specific options
-        if has_images:
-            if input_data.image_cover_index > 0:
-                tiktok_options["imageCoverIndex"] = input_data.image_cover_index
-
-            if input_data.title:
-                tiktok_options["title"] = input_data.title
-
-            if input_data.visibility != TikTokVisibility.PUBLIC:
-                tiktok_options["visibility"] = input_data.visibility.value
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.TIKTOK],
-            media_urls=input_data.media_urls,
-            is_video=has_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            tiktok_options=tiktok_options if tiktok_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_x.py

@@ -1,241 +0,0 @@
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class PostToXBlock(Block):
-    """Block for posting to X / Twitter with Twitter-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for X / Twitter posts."""
-
-        # Override post field to include X-specific information
-        post: str = SchemaField(
-            description="The post text (max 280 chars, up to 25,000 for Premium users). Use @handle to mention users. Use \\n\\n for thread breaks.",
-            advanced=False,
-        )
-
-        # Override media_urls to include X-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Optional list of media URLs. X supports up to 4 images or videos per tweet. Auto-preview links unless media is included.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # X-specific options
-        reply_to_id: str | None = SchemaField(
-            description="ID of the tweet to reply to",
-            default=None,
-            advanced=True,
-        )
-        quote_tweet_id: str | None = SchemaField(
-            description="ID of the tweet to quote (low-level Tweet ID)",
-            default=None,
-            advanced=True,
-        )
-        poll_options: list[str] = SchemaField(
-            description="Poll options (2-4 choices)",
-            default_factory=list,
-            advanced=True,
-        )
-        poll_duration: int = SchemaField(
-            description="Poll duration in minutes (1-10080)",
-            default=1440,
-            advanced=True,
-        )
-        alt_text: list[str] = SchemaField(
-            description="Alt text for each image (max 1,000 chars each, not supported for videos)",
-            default_factory=list,
-            advanced=True,
-        )
-        is_thread: bool = SchemaField(
-            description="Whether to automatically break post into thread based on line breaks",
-            default=False,
-            advanced=True,
-        )
-        thread_number: bool = SchemaField(
-            description="Add thread numbers (1/n format) to each thread post",
-            default=False,
-            advanced=True,
-        )
-        thread_media_urls: list[str] = SchemaField(
-            description="Media URLs for thread posts (one per thread, use 'null' to skip)",
-            default_factory=list,
-            advanced=True,
-        )
-        long_post: bool = SchemaField(
-            description="Force long form post (requires Premium X account)",
-            default=False,
-            advanced=True,
-        )
-        long_video: bool = SchemaField(
-            description="Enable long video upload (requires approval and Business/Enterprise plan)",
-            default=False,
-            advanced=True,
-        )
-        subtitle_url: str = SchemaField(
-            description="URL to SRT subtitle file for videos (must be HTTPS and end in .srt)",
-            default="",
-            advanced=True,
-        )
-        subtitle_language: str = SchemaField(
-            description="Language code for subtitles (default: 'en')",
-            default="en",
-            advanced=True,
-        )
-        subtitle_name: str = SchemaField(
-            description="Name of caption track (max 150 chars, default: 'English')",
-            default="English",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            id="9e8f844e-b4a5-4b25-80f2-9e1dd7d67625",
-            description="Post to X / Twitter using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToXBlock.Input,
-            output_schema=PostToXBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToXBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to X / Twitter with enhanced X-specific options."""
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate X constraints
-        if not input_data.long_post and len(input_data.post) > 280:
-            yield "error", f"X post text exceeds 280 character limit ({len(input_data.post)} characters). Enable 'long_post' for Premium accounts."
-            return
-
-        if input_data.long_post and len(input_data.post) > 25000:
-            yield "error", f"X long post text exceeds 25,000 character limit ({len(input_data.post)} characters)"
-            return
-
-        if len(input_data.media_urls) > 4:
-            yield "error", "X supports a maximum of 4 images or videos per tweet"
-            return
-
-        # Validate poll options
-        if input_data.poll_options:
-            if len(input_data.poll_options) < 2 or len(input_data.poll_options) > 4:
-                yield "error", "X polls require 2-4 options"
-                return
-
-            if input_data.poll_duration < 1 or input_data.poll_duration > 10080:
-                yield "error", "X poll duration must be between 1 and 10,080 minutes (7 days)"
-                return
-
-        # Validate alt text
-        if input_data.alt_text:
-            for i, alt in enumerate(input_data.alt_text):
-                if len(alt) > 1000:
-                    yield "error", f"X alt text {i+1} exceeds 1,000 character limit ({len(alt)} characters)"
-                    return
-
-        # Validate subtitle settings
-        if input_data.subtitle_url:
-            if not input_data.subtitle_url.startswith(
-                "https://"
-            ) or not input_data.subtitle_url.endswith(".srt"):
-                yield "error", "Subtitle URL must start with https:// and end with .srt"
-                return
-
-            if len(input_data.subtitle_name) > 150:
-                yield "error", f"Subtitle name exceeds 150 character limit ({len(input_data.subtitle_name)} characters)"
-                return
-
-        # Convert datetime to ISO format if provided
-        iso_date = (
-            input_data.schedule_date.isoformat() if input_data.schedule_date else None
-        )
-
-        # Build X-specific options
-        twitter_options = {}
-
-        # Basic options
-        if input_data.reply_to_id:
-            twitter_options["replyToId"] = input_data.reply_to_id
-
-        if input_data.quote_tweet_id:
-            twitter_options["quoteTweetId"] = input_data.quote_tweet_id
-
-        if input_data.long_post:
-            twitter_options["longPost"] = True
-
-        if input_data.long_video:
-            twitter_options["longVideo"] = True
-
-        # Poll options
-        if input_data.poll_options:
-            twitter_options["poll"] = {
-                "duration": input_data.poll_duration,
-                "options": input_data.poll_options,
-            }
-
-        # Alt text for images
-        if input_data.alt_text:
-            twitter_options["altText"] = input_data.alt_text
-
-        # Thread options
-        if input_data.is_thread:
-            twitter_options["thread"] = True
-
-            if input_data.thread_number:
-                twitter_options["threadNumber"] = True
-
-            if input_data.thread_media_urls:
-                twitter_options["mediaUrls"] = input_data.thread_media_urls
-
-        # Video subtitle options
-        if input_data.subtitle_url:
-            twitter_options["subTitleUrl"] = input_data.subtitle_url
-            twitter_options["subTitleLanguage"] = input_data.subtitle_language
-            twitter_options["subTitleName"] = input_data.subtitle_name
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.TWITTER],
-            media_urls=input_data.media_urls,
-            is_video=input_data.is_video,
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            twitter_options=twitter_options if twitter_options else None,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

autogpt_platform/backend/backend/blocks/ayrshare/post_to_youtube.py

@@ -1,310 +0,0 @@
-from enum import Enum
-from typing import Any
-
-from backend.integrations.ayrshare import PostIds, PostResponse, SocialPlatform
-from backend.sdk import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    SchemaField,
-)
-
-from ._util import BaseAyrshareInput, create_ayrshare_client, get_profile_key
-
-
-class YouTubeVisibility(str, Enum):
-    PRIVATE = "private"
-    PUBLIC = "public"
-    UNLISTED = "unlisted"
-
-
-class PostToYouTubeBlock(Block):
-    """Block for posting to YouTube with YouTube-specific options."""
-
-    class Input(BaseAyrshareInput):
-        """Input schema for YouTube posts."""
-
-        # Override post field to include YouTube-specific information
-        post: str = SchemaField(
-            description="Video description (max 5,000 chars, empty string allowed). Cannot contain < or > characters.",
-            advanced=False,
-        )
-
-        # Override media_urls to include YouTube-specific constraints
-        media_urls: list[str] = SchemaField(
-            description="Required video URL. YouTube only supports 1 video per post.",
-            default_factory=list,
-            advanced=False,
-        )
-
-        # YouTube-specific required options
-        title: str = SchemaField(
-            description="Video title (max 100 chars, required). Cannot contain < or > characters.",
-            advanced=False,
-        )
-
-        # YouTube-specific optional options
-        visibility: YouTubeVisibility = SchemaField(
-            description="Video visibility: 'private' (default), 'public' , or 'unlisted'",
-            default=YouTubeVisibility.PRIVATE,
-            advanced=False,
-        )
-        thumbnail: str | None = SchemaField(
-            description="Thumbnail URL (JPEG/PNG under 2MB, must end in .png/.jpg/.jpeg). Requires phone verification.",
-            default=None,
-            advanced=True,
-        )
-        playlist_id: str | None = SchemaField(
-            description="Playlist ID to add video (user must own playlist)",
-            default=None,
-            advanced=True,
-        )
-        tags: list[str] | None = SchemaField(
-            description="Video tags (min 2 chars each, max 500 chars total)",
-            default=None,
-            advanced=True,
-        )
-        made_for_kids: bool | None = SchemaField(
-            description="Self-declared kids content", default=None, advanced=True
-        )
-        is_shorts: bool | None = SchemaField(
-            description="Post as YouTube Short (max 3 minutes, adds #shorts)",
-            default=None,
-            advanced=True,
-        )
-        notify_subscribers: bool | None = SchemaField(
-            description="Send notification to subscribers", default=None, advanced=True
-        )
-        category_id: int | None = SchemaField(
-            description="Video category ID (e.g., 24 = Entertainment)",
-            default=None,
-            advanced=True,
-        )
-        contains_synthetic_media: bool | None = SchemaField(
-            description="Disclose realistic AI/synthetic content",
-            default=None,
-            advanced=True,
-        )
-        publish_at: str | None = SchemaField(
-            description="UTC publish time (YouTube controlled, format: 2022-10-08T21:18:36Z)",
-            default=None,
-            advanced=True,
-        )
-        # YouTube targeting options (flattened from YouTubeTargeting object)
-        targeting_block_countries: list[str] | None = SchemaField(
-            description="Country codes to block from viewing (e.g., ['US', 'CA'])",
-            default=None,
-            advanced=True,
-        )
-        targeting_allow_countries: list[str] | None = SchemaField(
-            description="Country codes to allow viewing (e.g., ['GB', 'AU'])",
-            default=None,
-            advanced=True,
-        )
-        subtitle_url: str | None = SchemaField(
-            description="URL to SRT or SBV subtitle file (must be HTTPS and end in .srt/.sbv, under 100MB)",
-            default=None,
-            advanced=True,
-        )
-        subtitle_language: str | None = SchemaField(
-            description="Language code for subtitles (default: 'en')",
-            default=None,
-            advanced=True,
-        )
-        subtitle_name: str | None = SchemaField(
-            description="Name of caption track (max 150 chars, default: 'English')",
-            default=None,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        post_result: PostResponse = SchemaField(description="The result of the post")
-        post: PostIds = SchemaField(description="The result of the post")
-
-    def __init__(self):
-        super().__init__(
-            id="0082d712-ff1b-4c3d-8a8d-6c7721883b83",
-            description="Post to YouTube using Ayrshare",
-            categories={BlockCategory.SOCIAL},
-            block_type=BlockType.AYRSHARE,
-            input_schema=PostToYouTubeBlock.Input,
-            output_schema=PostToYouTubeBlock.Output,
-        )
-
-    async def run(
-        self,
-        input_data: "PostToYouTubeBlock.Input",
-        *,
-        user_id: str,
-        **kwargs,
-    ) -> BlockOutput:
-        """Post to YouTube with YouTube-specific validation and options."""
-
-        profile_key = await get_profile_key(user_id)
-        if not profile_key:
-            yield "error", "Please link a social account via Ayrshare"
-            return
-
-        client = create_ayrshare_client()
-        if not client:
-            yield "error", "Ayrshare integration is not configured. Please set up the AYRSHARE_API_KEY."
-            return
-
-        # Validate YouTube constraints
-        if not input_data.title:
-            yield "error", "YouTube requires a video title"
-            return
-
-        if len(input_data.title) > 100:
-            yield "error", f"YouTube title exceeds 100 character limit ({len(input_data.title)} characters)"
-            return
-
-        if len(input_data.post) > 5000:
-            yield "error", f"YouTube description exceeds 5,000 character limit ({len(input_data.post)} characters)"
-            return
-
-        # Check for forbidden characters
-        forbidden_chars = ["<", ">"]
-        for char in forbidden_chars:
-            if char in input_data.title:
-                yield "error", f"YouTube title cannot contain '{char}' character"
-                return
-            if char in input_data.post:
-                yield "error", f"YouTube description cannot contain '{char}' character"
-                return
-
-        if not input_data.media_urls:
-            yield "error", "YouTube requires exactly one video URL"
-            return
-
-        if len(input_data.media_urls) > 1:
-            yield "error", "YouTube supports only 1 video per post"
-            return
-
-        # Validate visibility option
-        valid_visibility = ["private", "public", "unlisted"]
-        if input_data.visibility not in valid_visibility:
-            yield "error", f"YouTube visibility must be one of: {', '.join(valid_visibility)}"
-            return
-
-        # Validate thumbnail URL format
-        if input_data.thumbnail:
-            valid_extensions = [".png", ".jpg", ".jpeg"]
-            if not any(
-                input_data.thumbnail.lower().endswith(ext) for ext in valid_extensions
-            ):
-                yield "error", "YouTube thumbnail must end in .png, .jpg, or .jpeg"
-                return
-
-        # Validate tags
-        if input_data.tags:
-            total_tag_length = sum(len(tag) for tag in input_data.tags)
-            if total_tag_length > 500:
-                yield "error", f"YouTube tags total length exceeds 500 characters ({total_tag_length} characters)"
-                return
-
-            for tag in input_data.tags:
-                if len(tag) < 2:
-                    yield "error", f"YouTube tag '{tag}' is too short (minimum 2 characters)"
-                    return
-
-        # Validate subtitle URL
-        if input_data.subtitle_url:
-            if not input_data.subtitle_url.startswith("https://"):
-                yield "error", "YouTube subtitle URL must start with https://"
-                return
-
-            valid_subtitle_extensions = [".srt", ".sbv"]
-            if not any(
-                input_data.subtitle_url.lower().endswith(ext)
-                for ext in valid_subtitle_extensions
-            ):
-                yield "error", "YouTube subtitle URL must end in .srt or .sbv"
-                return
-
-        if input_data.subtitle_name and len(input_data.subtitle_name) > 150:
-            yield "error", f"YouTube subtitle name exceeds 150 character limit ({len(input_data.subtitle_name)} characters)"
-            return
-
-        # Validate publish_at format if provided
-        if input_data.publish_at and input_data.schedule_date:
-            yield "error", "Cannot use both 'publish_at' and 'schedule_date'. Use 'publish_at' for YouTube-controlled publishing."
-            return
-
-        # Convert datetime to ISO format if provided (only if not using publish_at)
-        iso_date = None
-        if not input_data.publish_at and input_data.schedule_date:
-            iso_date = input_data.schedule_date.isoformat()
-
-        # Build YouTube-specific options
-        youtube_options: dict[str, Any] = {"title": input_data.title}
-
-        # Basic options
-        if input_data.visibility != "private":
-            youtube_options["visibility"] = input_data.visibility
-
-        if input_data.thumbnail:
-            youtube_options["thumbNail"] = input_data.thumbnail
-
-        if input_data.playlist_id:
-            youtube_options["playListId"] = input_data.playlist_id
-
-        if input_data.tags:
-            youtube_options["tags"] = input_data.tags
-
-        if input_data.made_for_kids:
-            youtube_options["madeForKids"] = True
-
-        if input_data.is_shorts:
-            youtube_options["shorts"] = True
-
-        if not input_data.notify_subscribers:
-            youtube_options["notifySubscribers"] = False
-
-        if input_data.category_id and input_data.category_id > 0:
-            youtube_options["categoryId"] = input_data.category_id
-
-        if input_data.contains_synthetic_media:
-            youtube_options["containsSyntheticMedia"] = True
-
-        if input_data.publish_at:
-            youtube_options["publishAt"] = input_data.publish_at
-
-        # Country targeting (from flattened fields)
-        targeting_dict = {}
-        if input_data.targeting_block_countries:
-            targeting_dict["block"] = input_data.targeting_block_countries
-        if input_data.targeting_allow_countries:
-            targeting_dict["allow"] = input_data.targeting_allow_countries
-
-        if targeting_dict:
-            youtube_options["targeting"] = targeting_dict
-
-        # Subtitle options
-        if input_data.subtitle_url:
-            youtube_options["subTitleUrl"] = input_data.subtitle_url
-            youtube_options["subTitleLanguage"] = input_data.subtitle_language
-            youtube_options["subTitleName"] = input_data.subtitle_name
-
-        response = await client.create_post(
-            post=input_data.post,
-            platforms=[SocialPlatform.YOUTUBE],
-            media_urls=input_data.media_urls,
-            is_video=True,  # YouTube only supports videos
-            schedule_date=iso_date,
-            disable_comments=input_data.disable_comments,
-            shorten_links=input_data.shorten_links,
-            unsplash=input_data.unsplash,
-            requires_approval=input_data.requires_approval,
-            random_post=input_data.random_post,
-            random_media_url=input_data.random_media_url,
-            notes=input_data.notes,
-            youtube_options=youtube_options,
-            profile_key=profile_key.get_secret_value(),
-        )
-        yield "post_result", response
-        if response.postIds:
-            for p in response.postIds:
-                yield "post", p

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/basic.py

@@ -1,9 +1,11 @@
 import enum
-from typing import Any
+from typing import Any, List
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
 from backend.data.model import SchemaField
+from backend.util import json
 from backend.util.file import store_media_file
+from backend.util.mock import MockObject
 from backend.util.type import MediaFileType, convert
 
 
@@ -12,12 +14,6 @@ class FileStoreBlock(Block):
         file_in: MediaFileType = SchemaField(
             description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
         )
-        base_64: bool = SchemaField(
-            description="Whether produce an output in base64 format (not recommended, you can pass the string path just fine accross blocks).",
-            default=False,
-            advanced=True,
-            title="Produce Base64 Output",
-        )
 
     class Output(BlockSchema):
         file_out: MediaFileType = SchemaField(
@@ -39,15 +35,14 @@ class FileStoreBlock(Block):
         input_data: Input,
         *,
         graph_exec_id: str,
-        user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        yield "file_out", await store_media_file(
+        file_path = await store_media_file(
             graph_exec_id=graph_exec_id,
             file=input_data.file_in,
-            user_id=user_id,
-            return_content=input_data.base_64,
+            return_content=False,
         )
+        yield "file_out", file_path
 
 
 class StoreValueBlock(Block):
@@ -120,6 +115,266 @@ class PrintToConsoleBlock(Block):
         yield "status", "printed"
 
 
+class FindInDictionaryBlock(Block):
+    class Input(BlockSchema):
+        input: Any = SchemaField(description="Dictionary to lookup from")
+        key: str | int = SchemaField(description="Key to lookup in the dictionary")
+
+    class Output(BlockSchema):
+        output: Any = SchemaField(description="Value found for the given key")
+        missing: Any = SchemaField(
+            description="Value of the input that missing the key"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="0e50422c-6dee-4145-83d6-3a5a392f65de",
+            description="Lookup the given key in the input dictionary/object/list and return the value.",
+            input_schema=FindInDictionaryBlock.Input,
+            output_schema=FindInDictionaryBlock.Output,
+            test_input=[
+                {"input": {"apple": 1, "banana": 2, "cherry": 3}, "key": "banana"},
+                {"input": {"x": 10, "y": 20, "z": 30}, "key": "w"},
+                {"input": [1, 2, 3], "key": 1},
+                {"input": [1, 2, 3], "key": 3},
+                {"input": MockObject(value="!!", key="key"), "key": "key"},
+                {"input": [{"k1": "v1"}, {"k2": "v2"}, {"k1": "v3"}], "key": "k1"},
+            ],
+            test_output=[
+                ("output", 2),
+                ("missing", {"x": 10, "y": 20, "z": 30}),
+                ("output", 2),
+                ("missing", [1, 2, 3]),
+                ("output", "key"),
+                ("output", ["v1", "v3"]),
+            ],
+            categories={BlockCategory.BASIC},
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        obj = input_data.input
+        key = input_data.key
+
+        if isinstance(obj, str):
+            obj = json.loads(obj)
+
+        if isinstance(obj, dict) and key in obj:
+            yield "output", obj[key]
+        elif isinstance(obj, list) and isinstance(key, int) and 0 <= key < len(obj):
+            yield "output", obj[key]
+        elif isinstance(obj, list) and isinstance(key, str):
+            if len(obj) == 0:
+                yield "output", []
+            elif isinstance(obj[0], dict) and key in obj[0]:
+                yield "output", [item[key] for item in obj if key in item]
+            else:
+                yield "output", [getattr(val, key) for val in obj if hasattr(val, key)]
+        elif isinstance(obj, object) and isinstance(key, str) and hasattr(obj, key):
+            yield "output", getattr(obj, key)
+        else:
+            yield "missing", input_data.input
+
+
+class AddToDictionaryBlock(Block):
+    class Input(BlockSchema):
+        dictionary: dict[Any, Any] = SchemaField(
+            default_factory=dict,
+            description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
+        )
+        key: str = SchemaField(
+            default="",
+            description="The key for the new entry.",
+            placeholder="new_key",
+            advanced=False,
+        )
+        value: Any = SchemaField(
+            default=None,
+            description="The value for the new entry.",
+            placeholder="new_value",
+            advanced=False,
+        )
+        entries: dict[Any, Any] = SchemaField(
+            default_factory=dict,
+            description="The entries to add to the dictionary. This is the batch version of the `key` and `value` fields.",
+            advanced=True,
+        )
+
+    class Output(BlockSchema):
+        updated_dictionary: dict = SchemaField(
+            description="The dictionary with the new entry added."
+        )
+        error: str = SchemaField(description="Error message if the operation failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="31d1064e-7446-4693-a7d4-65e5ca1180d1",
+            description="Adds a new key-value pair to a dictionary. If no dictionary is provided, a new one is created.",
+            categories={BlockCategory.BASIC},
+            input_schema=AddToDictionaryBlock.Input,
+            output_schema=AddToDictionaryBlock.Output,
+            test_input=[
+                {
+                    "dictionary": {"existing_key": "existing_value"},
+                    "key": "new_key",
+                    "value": "new_value",
+                },
+                {"key": "first_key", "value": "first_value"},
+                {
+                    "dictionary": {"existing_key": "existing_value"},
+                    "entries": {"new_key": "new_value", "first_key": "first_value"},
+                },
+            ],
+            test_output=[
+                (
+                    "updated_dictionary",
+                    {"existing_key": "existing_value", "new_key": "new_value"},
+                ),
+                ("updated_dictionary", {"first_key": "first_value"}),
+                (
+                    "updated_dictionary",
+                    {
+                        "existing_key": "existing_value",
+                        "new_key": "new_value",
+                        "first_key": "first_value",
+                    },
+                ),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        updated_dict = input_data.dictionary.copy()
+
+        if input_data.value is not None and input_data.key:
+            updated_dict[input_data.key] = input_data.value
+
+        for key, value in input_data.entries.items():
+            updated_dict[key] = value
+
+        yield "updated_dictionary", updated_dict
+
+
+class AddToListBlock(Block):
+    class Input(BlockSchema):
+        list: List[Any] = SchemaField(
+            default_factory=list,
+            advanced=False,
+            description="The list to add the entry to. If not provided, a new list will be created.",
+        )
+        entry: Any = SchemaField(
+            description="The entry to add to the list. Can be of any type (string, int, dict, etc.).",
+            advanced=False,
+            default=None,
+        )
+        entries: List[Any] = SchemaField(
+            default_factory=lambda: list(),
+            description="The entries to add to the list. This is the batch version of the `entry` field.",
+            advanced=True,
+        )
+        position: int | None = SchemaField(
+            default=None,
+            description="The position to insert the new entry. If not provided, the entry will be appended to the end of the list.",
+        )
+
+    class Output(BlockSchema):
+        updated_list: List[Any] = SchemaField(
+            description="The list with the new entry added."
+        )
+        error: str = SchemaField(description="Error message if the operation failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="aeb08fc1-2fc1-4141-bc8e-f758f183a822",
+            description="Adds a new entry to a list. The entry can be of any type. If no list is provided, a new one is created.",
+            categories={BlockCategory.BASIC},
+            input_schema=AddToListBlock.Input,
+            output_schema=AddToListBlock.Output,
+            test_input=[
+                {
+                    "list": [1, "string", {"existing_key": "existing_value"}],
+                    "entry": {"new_key": "new_value"},
+                    "position": 1,
+                },
+                {"entry": "first_entry"},
+                {"list": ["a", "b", "c"], "entry": "d"},
+                {
+                    "entry": "e",
+                    "entries": ["f", "g"],
+                    "list": ["a", "b"],
+                    "position": 1,
+                },
+            ],
+            test_output=[
+                (
+                    "updated_list",
+                    [
+                        1,
+                        {"new_key": "new_value"},
+                        "string",
+                        {"existing_key": "existing_value"},
+                    ],
+                ),
+                ("updated_list", ["first_entry"]),
+                ("updated_list", ["a", "b", "c", "d"]),
+                ("updated_list", ["a", "f", "g", "e", "b"]),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        entries_added = input_data.entries.copy()
+        if input_data.entry:
+            entries_added.append(input_data.entry)
+
+        updated_list = input_data.list.copy()
+        if (pos := input_data.position) is not None:
+            updated_list = updated_list[:pos] + entries_added + updated_list[pos:]
+        else:
+            updated_list += entries_added
+
+        yield "updated_list", updated_list
+
+
+class FindInListBlock(Block):
+    class Input(BlockSchema):
+        list: List[Any] = SchemaField(description="The list to search in.")
+        value: Any = SchemaField(description="The value to search for.")
+
+    class Output(BlockSchema):
+        index: int = SchemaField(description="The index of the value in the list.")
+        found: bool = SchemaField(
+            description="Whether the value was found in the list."
+        )
+        not_found_value: Any = SchemaField(
+            description="The value that was not found in the list."
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="5e2c6d0a-1e37-489f-b1d0-8e1812b23333",
+            description="Finds the index of the value in the list.",
+            categories={BlockCategory.BASIC},
+            input_schema=FindInListBlock.Input,
+            output_schema=FindInListBlock.Output,
+            test_input=[
+                {"list": [1, 2, 3, 4, 5], "value": 3},
+                {"list": [1, 2, 3, 4, 5], "value": 6},
+            ],
+            test_output=[
+                ("index", 2),
+                ("found", True),
+                ("found", False),
+                ("not_found_value", 6),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            yield "index", input_data.list.index(input_data.value)
+            yield "found", True
+        except ValueError:
+            yield "found", False
+            yield "not_found_value", input_data.value
+
+
 class NoteBlock(Block):
     class Input(BlockSchema):
         text: str = SchemaField(description="The text to display in the sticky note.")
@@ -145,6 +400,104 @@ class NoteBlock(Block):
         yield "output", input_data.text
 
 
+class CreateDictionaryBlock(Block):
+    class Input(BlockSchema):
+        values: dict[str, Any] = SchemaField(
+            description="Key-value pairs to create the dictionary with",
+            placeholder="e.g., {'name': 'Alice', 'age': 25}",
+        )
+
+    class Output(BlockSchema):
+        dictionary: dict[str, Any] = SchemaField(
+            description="The created dictionary containing the specified key-value pairs"
+        )
+        error: str = SchemaField(
+            description="Error message if dictionary creation failed"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="b924ddf4-de4f-4b56-9a85-358930dcbc91",
+            description="Creates a dictionary with the specified key-value pairs. Use this when you know all the values you want to add upfront.",
+            categories={BlockCategory.DATA},
+            input_schema=CreateDictionaryBlock.Input,
+            output_schema=CreateDictionaryBlock.Output,
+            test_input=[
+                {
+                    "values": {"name": "Alice", "age": 25, "city": "New York"},
+                },
+                {
+                    "values": {"numbers": [1, 2, 3], "active": True, "score": 95.5},
+                },
+            ],
+            test_output=[
+                (
+                    "dictionary",
+                    {"name": "Alice", "age": 25, "city": "New York"},
+                ),
+                (
+                    "dictionary",
+                    {"numbers": [1, 2, 3], "active": True, "score": 95.5},
+                ),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            # The values are already validated by Pydantic schema
+            yield "dictionary", input_data.values
+        except Exception as e:
+            yield "error", f"Failed to create dictionary: {str(e)}"
+
+
+class CreateListBlock(Block):
+    class Input(BlockSchema):
+        values: List[Any] = SchemaField(
+            description="A list of values to be combined into a new list.",
+            placeholder="e.g., ['Alice', 25, True]",
+        )
+
+    class Output(BlockSchema):
+        list: List[Any] = SchemaField(
+            description="The created list containing the specified values."
+        )
+        error: str = SchemaField(description="Error message if list creation failed.")
+
+    def __init__(self):
+        super().__init__(
+            id="a912d5c7-6e00-4542-b2a9-8034136930e4",
+            description="Creates a list with the specified values. Use this when you know all the values you want to add upfront.",
+            categories={BlockCategory.DATA},
+            input_schema=CreateListBlock.Input,
+            output_schema=CreateListBlock.Output,
+            test_input=[
+                {
+                    "values": ["Alice", 25, True],
+                },
+                {
+                    "values": [1, 2, 3, "four", {"key": "value"}],
+                },
+            ],
+            test_output=[
+                (
+                    "list",
+                    ["Alice", 25, True],
+                ),
+                (
+                    "list",
+                    [1, 2, 3, "four", {"key": "value"}],
+                ),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        try:
+            # The values are already validated by Pydantic schema
+            yield "list", input_data.values
+        except Exception as e:
+            yield "error", f"Failed to create list: {str(e)}"
+
+
 class TypeOptions(enum.Enum):
     STRING = "string"
     NUMBER = "number"
@@ -162,7 +515,6 @@ class UniversalTypeConverterBlock(Block):
 
     class Output(BlockSchema):
         value: Any = SchemaField(description="The converted value.")
-        error: str = SchemaField(description="Error message if conversion failed.")
 
     def __init__(self):
         super().__init__(
@@ -188,31 +540,3 @@ class UniversalTypeConverterBlock(Block):
             yield "value", converted_value
         except Exception as e:
             yield "error", f"Failed to convert value: {str(e)}"
-
-
-class ReverseListOrderBlock(Block):
-    """
-    A block which takes in a list and returns it in the opposite order.
-    """
-
-    class Input(BlockSchema):
-        input_list: list[Any] = SchemaField(description="The list to reverse")
-
-    class Output(BlockSchema):
-        reversed_list: list[Any] = SchemaField(description="The list in reversed order")
-
-    def __init__(self):
-        super().__init__(
-            id="422cb708-3109-4277-bfe3-bc2ae5812777",
-            description="Reverses the order of elements in a list",
-            categories={BlockCategory.BASIC},
-            input_schema=ReverseListOrderBlock.Input,
-            output_schema=ReverseListOrderBlock.Output,
-            test_input={"input_list": [1, 2, 3, 4, 5]},
-            test_output=[("reversed_list", [5, 4, 3, 2, 1])],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        reversed_list = list(input_data.input_list)
-        reversed_list.reverse()
-        yield "reversed_list", reversed_list

autogpt_platform/backend/backend/blocks/branching.py

@@ -3,7 +3,6 @@ from typing import Any
 
 from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
-from backend.util.type import convert
 
 
 class ComparisonOperator(Enum):
@@ -164,7 +163,7 @@ class IfInputMatchesBlock(Block):
                 },
                 {
                     "input": 10,
-                    "value": "None",
+                    "value": None,
                     "yes_value": "Yes",
                     "no_value": "No",
                 },
@@ -182,23 +181,7 @@ class IfInputMatchesBlock(Block):
         )
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-
-        # If input_data.value is not matching input_data.input, convert value to type of input
-        if (
-            input_data.input != input_data.value
-            and input_data.input is not input_data.value
-        ):
-            try:
-                # Only attempt conversion if input is not None and value is not None
-                if input_data.input is not None and input_data.value is not None:
-                    input_type = type(input_data.input)
-                    # Avoid converting if input_type is Any or object
-                    if input_type not in (Any, object):
-                        input_data.value = convert(input_data.value, input_type)
-            except Exception:
-                pass  # If conversion fails, just leave value as is
-
-        if input_data.input == input_data.value:
+        if input_data.input == input_data.value or input_data.input is input_data.value:
             yield "result", True
             yield "yes_output", input_data.yes_value
         else:

autogpt_platform/backend/backend/blocks/csv.py

@@ -0,0 +1,109 @@
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import ContributorDetails, SchemaField
+
+
+class ReadCsvBlock(Block):
+    class Input(BlockSchema):
+        contents: str = SchemaField(
+            description="The contents of the CSV file to read",
+            placeholder="a, b, c\n1,2,3\n4,5,6",
+        )
+        delimiter: str = SchemaField(
+            description="The delimiter used in the CSV file",
+            default=",",
+        )
+        quotechar: str = SchemaField(
+            description="The character used to quote fields",
+            default='"',
+        )
+        escapechar: str = SchemaField(
+            description="The character used to escape the delimiter",
+            default="\\",
+        )
+        has_header: bool = SchemaField(
+            description="Whether the CSV file has a header row",
+            default=True,
+        )
+        skip_rows: int = SchemaField(
+            description="The number of rows to skip from the start of the file",
+            default=0,
+        )
+        strip: bool = SchemaField(
+            description="Whether to strip whitespace from the values",
+            default=True,
+        )
+        skip_columns: list[str] = SchemaField(
+            description="The columns to skip from the start of the row",
+            default_factory=list,
+        )
+
+    class Output(BlockSchema):
+        row: dict[str, str] = SchemaField(
+            description="The data produced from each row in the CSV file"
+        )
+        all_data: list[dict[str, str]] = SchemaField(
+            description="All the data in the CSV file as a list of rows"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="acf7625e-d2cb-4941-bfeb-2819fc6fc015",
+            input_schema=ReadCsvBlock.Input,
+            output_schema=ReadCsvBlock.Output,
+            description="Reads a CSV file and outputs the data as a list of dictionaries and individual rows via rows.",
+            contributors=[ContributorDetails(name="Nicholas Tindle")],
+            categories={BlockCategory.TEXT, BlockCategory.DATA},
+            test_input={
+                "contents": "a, b, c\n1,2,3\n4,5,6",
+            },
+            test_output=[
+                ("row", {"a": "1", "b": "2", "c": "3"}),
+                ("row", {"a": "4", "b": "5", "c": "6"}),
+                (
+                    "all_data",
+                    [
+                        {"a": "1", "b": "2", "c": "3"},
+                        {"a": "4", "b": "5", "c": "6"},
+                    ],
+                ),
+            ],
+        )
+
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+        import csv
+        from io import StringIO
+
+        csv_file = StringIO(input_data.contents)
+        reader = csv.reader(
+            csv_file,
+            delimiter=input_data.delimiter,
+            quotechar=input_data.quotechar,
+            escapechar=input_data.escapechar,
+        )
+
+        header = None
+        if input_data.has_header:
+            header = next(reader)
+            if input_data.strip:
+                header = [h.strip() for h in header]
+
+        for _ in range(input_data.skip_rows):
+            next(reader)
+
+        def process_row(row):
+            data = {}
+            for i, value in enumerate(row):
+                if i not in input_data.skip_columns:
+                    if input_data.has_header and header:
+                        data[header[i]] = value.strip() if input_data.strip else value
+                    else:
+                        data[str(i)] = value.strip() if input_data.strip else value
+            return data
+
+        all_data = []
+        for row in reader:
+            processed_row = process_row(row)
+            all_data.append(processed_row)
+            yield "row", processed_row
+
+        yield "all_data", all_data

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -1,683 +0,0 @@
-from typing import Any, List
-
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
-from backend.data.model import SchemaField
-from backend.util.json import loads
-from backend.util.mock import MockObject
-from backend.util.prompt import estimate_token_count_str
-
-# =============================================================================
-# Dictionary Manipulation Blocks
-# =============================================================================
-
-
-class CreateDictionaryBlock(Block):
-    class Input(BlockSchema):
-        values: dict[str, Any] = SchemaField(
-            description="Key-value pairs to create the dictionary with",
-            placeholder="e.g., {'name': 'Alice', 'age': 25}",
-        )
-
-    class Output(BlockSchema):
-        dictionary: dict[str, Any] = SchemaField(
-            description="The created dictionary containing the specified key-value pairs"
-        )
-        error: str = SchemaField(
-            description="Error message if dictionary creation failed"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="b924ddf4-de4f-4b56-9a85-358930dcbc91",
-            description="Creates a dictionary with the specified key-value pairs. Use this when you know all the values you want to add upfront.",
-            categories={BlockCategory.DATA},
-            input_schema=CreateDictionaryBlock.Input,
-            output_schema=CreateDictionaryBlock.Output,
-            test_input=[
-                {
-                    "values": {"name": "Alice", "age": 25, "city": "New York"},
-                },
-                {
-                    "values": {"numbers": [1, 2, 3], "active": True, "score": 95.5},
-                },
-            ],
-            test_output=[
-                (
-                    "dictionary",
-                    {"name": "Alice", "age": 25, "city": "New York"},
-                ),
-                (
-                    "dictionary",
-                    {"numbers": [1, 2, 3], "active": True, "score": 95.5},
-                ),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            # The values are already validated by Pydantic schema
-            yield "dictionary", input_data.values
-        except Exception as e:
-            yield "error", f"Failed to create dictionary: {str(e)}"
-
-
-class AddToDictionaryBlock(Block):
-    class Input(BlockSchema):
-        dictionary: dict[Any, Any] = SchemaField(
-            default_factory=dict,
-            description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
-        )
-        key: str = SchemaField(
-            default="",
-            description="The key for the new entry.",
-            placeholder="new_key",
-            advanced=False,
-        )
-        value: Any = SchemaField(
-            default=None,
-            description="The value for the new entry.",
-            placeholder="new_value",
-            advanced=False,
-        )
-        entries: dict[Any, Any] = SchemaField(
-            default_factory=dict,
-            description="The entries to add to the dictionary. This is the batch version of the `key` and `value` fields.",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        updated_dictionary: dict = SchemaField(
-            description="The dictionary with the new entry added."
-        )
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="31d1064e-7446-4693-a7d4-65e5ca1180d1",
-            description="Adds a new key-value pair to a dictionary. If no dictionary is provided, a new one is created.",
-            categories={BlockCategory.BASIC},
-            input_schema=AddToDictionaryBlock.Input,
-            output_schema=AddToDictionaryBlock.Output,
-            test_input=[
-                {
-                    "dictionary": {"existing_key": "existing_value"},
-                    "key": "new_key",
-                    "value": "new_value",
-                },
-                {"key": "first_key", "value": "first_value"},
-                {
-                    "dictionary": {"existing_key": "existing_value"},
-                    "entries": {"new_key": "new_value", "first_key": "first_value"},
-                },
-            ],
-            test_output=[
-                (
-                    "updated_dictionary",
-                    {"existing_key": "existing_value", "new_key": "new_value"},
-                ),
-                ("updated_dictionary", {"first_key": "first_value"}),
-                (
-                    "updated_dictionary",
-                    {
-                        "existing_key": "existing_value",
-                        "new_key": "new_value",
-                        "first_key": "first_value",
-                    },
-                ),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        updated_dict = input_data.dictionary.copy()
-
-        if input_data.value is not None and input_data.key:
-            updated_dict[input_data.key] = input_data.value
-
-        for key, value in input_data.entries.items():
-            updated_dict[key] = value
-
-        yield "updated_dictionary", updated_dict
-
-
-class FindInDictionaryBlock(Block):
-    class Input(BlockSchema):
-        input: Any = SchemaField(description="Dictionary to lookup from")
-        key: str | int = SchemaField(description="Key to lookup in the dictionary")
-
-    class Output(BlockSchema):
-        output: Any = SchemaField(description="Value found for the given key")
-        missing: Any = SchemaField(
-            description="Value of the input that missing the key"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="0e50422c-6dee-4145-83d6-3a5a392f65de",
-            description="Lookup the given key in the input dictionary/object/list and return the value.",
-            input_schema=FindInDictionaryBlock.Input,
-            output_schema=FindInDictionaryBlock.Output,
-            test_input=[
-                {"input": {"apple": 1, "banana": 2, "cherry": 3}, "key": "banana"},
-                {"input": {"x": 10, "y": 20, "z": 30}, "key": "w"},
-                {"input": [1, 2, 3], "key": 1},
-                {"input": [1, 2, 3], "key": 3},
-                {"input": MockObject(value="!!", key="key"), "key": "key"},
-                {"input": [{"k1": "v1"}, {"k2": "v2"}, {"k1": "v3"}], "key": "k1"},
-            ],
-            test_output=[
-                ("output", 2),
-                ("missing", {"x": 10, "y": 20, "z": 30}),
-                ("output", 2),
-                ("missing", [1, 2, 3]),
-                ("output", "key"),
-                ("output", ["v1", "v3"]),
-            ],
-            categories={BlockCategory.BASIC},
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        obj = input_data.input
-        key = input_data.key
-
-        if isinstance(obj, str):
-            obj = loads(obj)
-
-        if isinstance(obj, dict) and key in obj:
-            yield "output", obj[key]
-        elif isinstance(obj, list) and isinstance(key, int) and 0 <= key < len(obj):
-            yield "output", obj[key]
-        elif isinstance(obj, list) and isinstance(key, str):
-            if len(obj) == 0:
-                yield "output", []
-            elif isinstance(obj[0], dict) and key in obj[0]:
-                yield "output", [item[key] for item in obj if key in item]
-            else:
-                yield "output", [getattr(val, key) for val in obj if hasattr(val, key)]
-        elif isinstance(obj, object) and isinstance(key, str) and hasattr(obj, key):
-            yield "output", getattr(obj, key)
-        else:
-            yield "missing", input_data.input
-
-
-class RemoveFromDictionaryBlock(Block):
-    class Input(BlockSchema):
-        dictionary: dict[Any, Any] = SchemaField(
-            description="The dictionary to modify."
-        )
-        key: str | int = SchemaField(description="Key to remove from the dictionary.")
-        return_value: bool = SchemaField(
-            default=False, description="Whether to return the removed value."
-        )
-
-    class Output(BlockSchema):
-        updated_dictionary: dict[Any, Any] = SchemaField(
-            description="The dictionary after removal."
-        )
-        removed_value: Any = SchemaField(description="The removed value if requested.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="46afe2ea-c613-43f8-95ff-6692c3ef6876",
-            description="Removes a key-value pair from a dictionary.",
-            categories={BlockCategory.BASIC},
-            input_schema=RemoveFromDictionaryBlock.Input,
-            output_schema=RemoveFromDictionaryBlock.Output,
-            test_input=[
-                {
-                    "dictionary": {"a": 1, "b": 2, "c": 3},
-                    "key": "b",
-                    "return_value": True,
-                },
-                {"dictionary": {"x": "hello", "y": "world"}, "key": "x"},
-            ],
-            test_output=[
-                ("updated_dictionary", {"a": 1, "c": 3}),
-                ("removed_value", 2),
-                ("updated_dictionary", {"y": "world"}),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        updated_dict = input_data.dictionary.copy()
-        try:
-            removed_value = updated_dict.pop(input_data.key)
-            yield "updated_dictionary", updated_dict
-            if input_data.return_value:
-                yield "removed_value", removed_value
-        except KeyError:
-            yield "error", f"Key '{input_data.key}' not found in dictionary"
-
-
-class ReplaceDictionaryValueBlock(Block):
-    class Input(BlockSchema):
-        dictionary: dict[Any, Any] = SchemaField(
-            description="The dictionary to modify."
-        )
-        key: str | int = SchemaField(description="Key to replace the value for.")
-        value: Any = SchemaField(description="The new value for the given key.")
-
-    class Output(BlockSchema):
-        updated_dictionary: dict[Any, Any] = SchemaField(
-            description="The dictionary after replacement."
-        )
-        old_value: Any = SchemaField(description="The value that was replaced.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="27e31876-18b6-44f3-ab97-f6226d8b3889",
-            description="Replaces the value for a specified key in a dictionary.",
-            categories={BlockCategory.BASIC},
-            input_schema=ReplaceDictionaryValueBlock.Input,
-            output_schema=ReplaceDictionaryValueBlock.Output,
-            test_input=[
-                {"dictionary": {"a": 1, "b": 2, "c": 3}, "key": "b", "value": 99},
-                {
-                    "dictionary": {"x": "hello", "y": "world"},
-                    "key": "y",
-                    "value": "universe",
-                },
-            ],
-            test_output=[
-                ("updated_dictionary", {"a": 1, "b": 99, "c": 3}),
-                ("old_value", 2),
-                ("updated_dictionary", {"x": "hello", "y": "universe"}),
-                ("old_value", "world"),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        updated_dict = input_data.dictionary.copy()
-        try:
-            old_value = updated_dict[input_data.key]
-            updated_dict[input_data.key] = input_data.value
-            yield "updated_dictionary", updated_dict
-            yield "old_value", old_value
-        except KeyError:
-            yield "error", f"Key '{input_data.key}' not found in dictionary"
-
-
-class DictionaryIsEmptyBlock(Block):
-    class Input(BlockSchema):
-        dictionary: dict[Any, Any] = SchemaField(description="The dictionary to check.")
-
-    class Output(BlockSchema):
-        is_empty: bool = SchemaField(description="True if the dictionary is empty.")
-
-    def __init__(self):
-        super().__init__(
-            id="a3cf3f64-6bb9-4cc6-9900-608a0b3359b0",
-            description="Checks if a dictionary is empty.",
-            categories={BlockCategory.BASIC},
-            input_schema=DictionaryIsEmptyBlock.Input,
-            output_schema=DictionaryIsEmptyBlock.Output,
-            test_input=[{"dictionary": {}}, {"dictionary": {"a": 1}}],
-            test_output=[("is_empty", True), ("is_empty", False)],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        yield "is_empty", len(input_data.dictionary) == 0
-
-
-# =============================================================================
-# List Manipulation Blocks
-# =============================================================================
-
-
-class CreateListBlock(Block):
-    class Input(BlockSchema):
-        values: List[Any] = SchemaField(
-            description="A list of values to be combined into a new list.",
-            placeholder="e.g., ['Alice', 25, True]",
-        )
-        max_size: int | None = SchemaField(
-            default=None,
-            description="Maximum size of the list. If provided, the list will be yielded in chunks of this size.",
-            advanced=True,
-        )
-        max_tokens: int | None = SchemaField(
-            default=None,
-            description="Maximum tokens for the list. If provided, the list will be yielded in chunks that fit within this token limit.",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        list: List[Any] = SchemaField(
-            description="The created list containing the specified values."
-        )
-        error: str = SchemaField(description="Error message if list creation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="a912d5c7-6e00-4542-b2a9-8034136930e4",
-            description="Creates a list with the specified values. Use this when you know all the values you want to add upfront. This block can also yield the list in batches based on a maximum size or token limit.",
-            categories={BlockCategory.DATA},
-            input_schema=CreateListBlock.Input,
-            output_schema=CreateListBlock.Output,
-            test_input=[
-                {
-                    "values": ["Alice", 25, True],
-                },
-                {
-                    "values": [1, 2, 3, "four", {"key": "value"}],
-                },
-            ],
-            test_output=[
-                (
-                    "list",
-                    ["Alice", 25, True],
-                ),
-                (
-                    "list",
-                    [1, 2, 3, "four", {"key": "value"}],
-                ),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        chunk = []
-        cur_tokens, max_tokens = 0, input_data.max_tokens
-        cur_size, max_size = 0, input_data.max_size
-
-        for value in input_data.values:
-            if max_tokens:
-                tokens = estimate_token_count_str(value)
-            else:
-                tokens = 0
-
-            # Check if adding this value would exceed either limit
-            if (max_tokens and (cur_tokens + tokens > max_tokens)) or (
-                max_size and (cur_size + 1 > max_size)
-            ):
-                yield "list", chunk
-                chunk = [value]
-                cur_size, cur_tokens = 1, tokens
-            else:
-                chunk.append(value)
-                cur_size, cur_tokens = cur_size + 1, cur_tokens + tokens
-
-        # Yield final chunk if any
-        if chunk or not input_data.values:
-            yield "list", chunk
-
-
-class AddToListBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(
-            default_factory=list,
-            advanced=False,
-            description="The list to add the entry to. If not provided, a new list will be created.",
-        )
-        entry: Any = SchemaField(
-            description="The entry to add to the list. Can be of any type (string, int, dict, etc.).",
-            advanced=False,
-            default=None,
-        )
-        entries: List[Any] = SchemaField(
-            default_factory=lambda: list(),
-            description="The entries to add to the list. This is the batch version of the `entry` field.",
-            advanced=True,
-        )
-        position: int | None = SchemaField(
-            default=None,
-            description="The position to insert the new entry. If not provided, the entry will be appended to the end of the list.",
-        )
-
-    class Output(BlockSchema):
-        updated_list: List[Any] = SchemaField(
-            description="The list with the new entry added."
-        )
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="aeb08fc1-2fc1-4141-bc8e-f758f183a822",
-            description="Adds a new entry to a list. The entry can be of any type. If no list is provided, a new one is created.",
-            categories={BlockCategory.BASIC},
-            input_schema=AddToListBlock.Input,
-            output_schema=AddToListBlock.Output,
-            test_input=[
-                {
-                    "list": [1, "string", {"existing_key": "existing_value"}],
-                    "entry": {"new_key": "new_value"},
-                    "position": 1,
-                },
-                {"entry": "first_entry"},
-                {"list": ["a", "b", "c"], "entry": "d"},
-                {
-                    "entry": "e",
-                    "entries": ["f", "g"],
-                    "list": ["a", "b"],
-                    "position": 1,
-                },
-            ],
-            test_output=[
-                (
-                    "updated_list",
-                    [
-                        1,
-                        {"new_key": "new_value"},
-                        "string",
-                        {"existing_key": "existing_value"},
-                    ],
-                ),
-                ("updated_list", ["first_entry"]),
-                ("updated_list", ["a", "b", "c", "d"]),
-                ("updated_list", ["a", "f", "g", "e", "b"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        entries_added = input_data.entries.copy()
-        if input_data.entry:
-            entries_added.append(input_data.entry)
-
-        updated_list = input_data.list.copy()
-        if (pos := input_data.position) is not None:
-            updated_list = updated_list[:pos] + entries_added + updated_list[pos:]
-        else:
-            updated_list += entries_added
-
-        yield "updated_list", updated_list
-
-
-class FindInListBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(description="The list to search in.")
-        value: Any = SchemaField(description="The value to search for.")
-
-    class Output(BlockSchema):
-        index: int = SchemaField(description="The index of the value in the list.")
-        found: bool = SchemaField(
-            description="Whether the value was found in the list."
-        )
-        not_found_value: Any = SchemaField(
-            description="The value that was not found in the list."
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="5e2c6d0a-1e37-489f-b1d0-8e1812b23333",
-            description="Finds the index of the value in the list.",
-            categories={BlockCategory.BASIC},
-            input_schema=FindInListBlock.Input,
-            output_schema=FindInListBlock.Output,
-            test_input=[
-                {"list": [1, 2, 3, 4, 5], "value": 3},
-                {"list": [1, 2, 3, 4, 5], "value": 6},
-            ],
-            test_output=[
-                ("index", 2),
-                ("found", True),
-                ("found", False),
-                ("not_found_value", 6),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            yield "index", input_data.list.index(input_data.value)
-            yield "found", True
-        except ValueError:
-            yield "found", False
-            yield "not_found_value", input_data.value
-
-
-class GetListItemBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(description="The list to get the item from.")
-        index: int = SchemaField(
-            description="The 0-based index of the item (supports negative indices)."
-        )
-
-    class Output(BlockSchema):
-        item: Any = SchemaField(description="The item at the specified index.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="262ca24c-1025-43cf-a578-534e23234e97",
-            description="Returns the element at the given index.",
-            categories={BlockCategory.BASIC},
-            input_schema=GetListItemBlock.Input,
-            output_schema=GetListItemBlock.Output,
-            test_input=[
-                {"list": [1, 2, 3], "index": 1},
-                {"list": [1, 2, 3], "index": -1},
-            ],
-            test_output=[
-                ("item", 2),
-                ("item", 3),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        try:
-            yield "item", input_data.list[input_data.index]
-        except IndexError:
-            yield "error", "Index out of range"
-
-
-class RemoveFromListBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(description="The list to modify.")
-        value: Any = SchemaField(
-            default=None, description="Value to remove from the list."
-        )
-        index: int | None = SchemaField(
-            default=None,
-            description="Index of the item to pop (supports negative indices).",
-        )
-        return_item: bool = SchemaField(
-            default=False, description="Whether to return the removed item."
-        )
-
-    class Output(BlockSchema):
-        updated_list: List[Any] = SchemaField(description="The list after removal.")
-        removed_item: Any = SchemaField(description="The removed item if requested.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="d93c5a93-ac7e-41c1-ae5c-ef67e6e9b826",
-            description="Removes an item from a list by value or index.",
-            categories={BlockCategory.BASIC},
-            input_schema=RemoveFromListBlock.Input,
-            output_schema=RemoveFromListBlock.Output,
-            test_input=[
-                {"list": [1, 2, 3], "index": 1, "return_item": True},
-                {"list": ["a", "b", "c"], "value": "b"},
-            ],
-            test_output=[
-                ("updated_list", [1, 3]),
-                ("removed_item", 2),
-                ("updated_list", ["a", "c"]),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        lst = input_data.list.copy()
-        removed = None
-        try:
-            if input_data.index is not None:
-                removed = lst.pop(input_data.index)
-            elif input_data.value is not None:
-                lst.remove(input_data.value)
-                removed = input_data.value
-            else:
-                raise ValueError("No index or value provided for removal")
-        except (IndexError, ValueError):
-            yield "error", "Index or value not found"
-            return
-
-        yield "updated_list", lst
-        if input_data.return_item:
-            yield "removed_item", removed
-
-
-class ReplaceListItemBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(description="The list to modify.")
-        index: int = SchemaField(
-            description="Index of the item to replace (supports negative indices)."
-        )
-        value: Any = SchemaField(description="The new value for the given index.")
-
-    class Output(BlockSchema):
-        updated_list: List[Any] = SchemaField(description="The list after replacement.")
-        old_item: Any = SchemaField(description="The item that was replaced.")
-        error: str = SchemaField(description="Error message if the operation failed.")
-
-    def __init__(self):
-        super().__init__(
-            id="fbf62922-bea1-4a3d-8bac-23587f810b38",
-            description="Replaces an item at the specified index.",
-            categories={BlockCategory.BASIC},
-            input_schema=ReplaceListItemBlock.Input,
-            output_schema=ReplaceListItemBlock.Output,
-            test_input=[
-                {"list": [1, 2, 3], "index": 1, "value": 99},
-                {"list": ["a", "b"], "index": -1, "value": "c"},
-            ],
-            test_output=[
-                ("updated_list", [1, 99, 3]),
-                ("old_item", 2),
-                ("updated_list", ["a", "c"]),
-                ("old_item", "b"),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        lst = input_data.list.copy()
-        try:
-            old = lst[input_data.index]
-            lst[input_data.index] = input_data.value
-        except IndexError:
-            yield "error", "Index out of range"
-            return
-
-        yield "updated_list", lst
-        yield "old_item", old
-
-
-class ListIsEmptyBlock(Block):
-    class Input(BlockSchema):
-        list: List[Any] = SchemaField(description="The list to check.")
-
-    class Output(BlockSchema):
-        is_empty: bool = SchemaField(description="True if the list is empty.")
-
-    def __init__(self):
-        super().__init__(
-            id="896ed73b-27d0-41be-813c-c1c1dc856c03",
-            description="Checks if a list is empty.",
-            categories={BlockCategory.BASIC},
-            input_schema=ListIsEmptyBlock.Input,
-            output_schema=ListIsEmptyBlock.Output,
-            test_input=[{"list": []}, {"list": [1]}],
-            test_output=[("is_empty", True), ("is_empty", False)],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        yield "is_empty", len(input_data.list) == 0

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

@@ -0,0 +1,237 @@
+from typing import Literal
+
+import aiohttp
+import discord
+from pydantic import SecretStr
+
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import (
+    APIKeyCredentials,
+    CredentialsField,
+    CredentialsMetaInput,
+    SchemaField,
+)
+from backend.integrations.providers import ProviderName
+
+DiscordCredentials = CredentialsMetaInput[
+    Literal[ProviderName.DISCORD], Literal["api_key"]
+]
+
+
+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):
+    class Input(BlockSchema):
+        credentials: DiscordCredentials = DiscordCredentialsField()
+
+    class Output(BlockSchema):
+        message_content: str = SchemaField(
+            description="The content of the message received"
+        )
+        channel_name: str = SchemaField(
+            description="The name of the channel the message was received from"
+        )
+        username: str = SchemaField(
+            description="The username of the user who sent the message"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="df06086a-d5ac-4abb-9996-2ad0acb2eff7",
+            input_schema=ReadDiscordMessagesBlock.Input,  # Assign input schema
+            output_schema=ReadDiscordMessagesBlock.Output,  # Assign output schema
+            description="Reads messages from a Discord channel using a bot token.",
+            categories={BlockCategory.SOCIAL},
+            test_input={
+                "continuous_read": False,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                (
+                    "message_content",
+                    "Hello!\n\nFile from user: example.txt\nContent: This is the content of the file.",
+                ),
+                ("channel_name", "general"),
+                ("username", "test_user"),
+            ],
+            test_mock={
+                "run_bot": lambda token: {
+                    "output_data": "Hello!\n\nFile from user: example.txt\nContent: This is the content of the file.",
+                    "channel_name": "general",
+                    "username": "test_user",
+                }
+            },
+        )
+
+    async def run_bot(self, token: SecretStr):
+        intents = discord.Intents.default()
+        intents.message_content = True
+
+        client = discord.Client(intents=intents)
+
+        self.output_data = None
+        self.channel_name = None
+        self.username = None
+
+        @client.event
+        async def on_ready():
+            print(f"Logged in as {client.user}")
+
+        @client.event
+        async def on_message(message):
+            if message.author == client.user:
+                return
+
+            self.output_data = message.content
+            self.channel_name = message.channel.name
+            self.username = message.author.name
+
+            if message.attachments:
+                attachment = message.attachments[0]  # Process the first attachment
+                if attachment.filename.endswith((".txt", ".py")):
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(attachment.url) as response:
+                            file_content = response.text()
+                            self.output_data += f"\n\nFile from user: {attachment.filename}\nContent: {file_content}"
+
+            await client.close()
+
+        await client.start(token.get_secret_value())
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        async for output_name, output_value in self.__run(input_data, credentials):
+            yield output_name, output_value
+
+    async def __run(
+        self, input_data: Input, credentials: APIKeyCredentials
+    ) -> BlockOutput:
+        try:
+            result = await self.run_bot(credentials.api_key)
+
+            # For testing purposes, use the mocked result
+            if isinstance(result, dict):
+                self.output_data = result.get("output_data")
+                self.channel_name = result.get("channel_name")
+                self.username = result.get("username")
+
+            if (
+                self.output_data is None
+                or self.channel_name is None
+                or self.username is None
+            ):
+                raise ValueError("No message, channel name, or username received.")
+
+            yield "message_content", self.output_data
+            yield "channel_name", self.channel_name
+            yield "username", self.username
+
+        except discord.errors.LoginFailure as login_err:
+            raise ValueError(f"Login error occurred: {login_err}")
+        except Exception as e:
+            raise ValueError(f"An error occurred: {e}")
+
+
+class SendDiscordMessageBlock(Block):
+    class Input(BlockSchema):
+        credentials: DiscordCredentials = DiscordCredentialsField()
+        message_content: str = SchemaField(
+            description="The content of the message received"
+        )
+        channel_name: str = SchemaField(
+            description="The name of the channel the message was received from"
+        )
+
+    class Output(BlockSchema):
+        status: str = SchemaField(
+            description="The status of the operation (e.g., 'Message sent', 'Error')"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d0822ab5-9f8a-44a3-8971-531dd0178b6b",
+            input_schema=SendDiscordMessageBlock.Input,  # Assign input schema
+            output_schema=SendDiscordMessageBlock.Output,  # Assign output schema
+            description="Sends a message to a Discord channel using a bot token.",
+            categories={BlockCategory.SOCIAL},
+            test_input={
+                "channel_name": "general",
+                "message_content": "Hello, Discord!",
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_output=[("status", "Message sent")],
+            test_mock={
+                "send_message": lambda token, channel_name, message_content: "Message sent"
+            },
+            test_credentials=TEST_CREDENTIALS,
+        )
+
+    async def send_message(self, token: str, channel_name: str, message_content: str):
+        intents = discord.Intents.default()
+        intents.guilds = True  # Required for fetching guild/channel information
+        client = discord.Client(intents=intents)
+
+        @client.event
+        async def on_ready():
+            print(f"Logged in as {client.user}")
+            for guild in client.guilds:
+                for channel in guild.text_channels:
+                    if channel.name == channel_name:
+                        # Split message into chunks if it exceeds 2000 characters
+                        for chunk in self.chunk_message(message_content):
+                            await channel.send(chunk)
+                        self.output_data = "Message sent"
+                        await client.close()
+                        return
+
+            self.output_data = "Channel not found"
+            await client.close()
+
+        await client.start(token)
+
+    def chunk_message(self, message: str, limit: int = 2000) -> list:
+        """Splits a message into chunks not exceeding the Discord limit."""
+        return [message[i : i + limit] for i in range(0, len(message), limit)]
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        try:
+            result = await self.send_message(
+                credentials.api_key.get_secret_value(),
+                input_data.channel_name,
+                input_data.message_content,
+            )
+
+            # For testing purposes, use the mocked result
+            if isinstance(result, str):
+                self.output_data = result
+
+            if self.output_data is None:
+                raise ValueError("No status message received.")
+
+            yield "status", self.output_data
+
+        except discord.errors.LoginFailure as login_err:
+            raise ValueError(f"Login error occurred: {login_err}")
+        except Exception as e:
+            raise ValueError(f"An error occurred: {e}")

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/_auth.py

@@ -0,0 +1,32 @@
+from typing import Literal
+
+from pydantic import SecretStr
+
+from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
+from backend.integrations.providers import ProviderName
+
+ExaCredentials = APIKeyCredentials
+ExaCredentialsInput = CredentialsMetaInput[
+    Literal[ProviderName.EXA],
+    Literal["api_key"],
+]
+
+TEST_CREDENTIALS = APIKeyCredentials(
+    id="01234567-89ab-cdef-0123-456789abcdef",
+    provider="exa",
+    api_key=SecretStr("mock-exa-api-key"),
+    title="Mock Exa API key",
+    expires_at=None,
+)
+
+TEST_CREDENTIALS_INPUT = {
+    "provider": TEST_CREDENTIALS.provider,
+    "id": TEST_CREDENTIALS.id,
+    "type": TEST_CREDENTIALS.type,
+    "title": TEST_CREDENTIALS.title,
+}
+
+
+def ExaCredentialsField() -> ExaCredentialsInput:
+    """Creates an Exa credentials input on a block."""
+    return CredentialsField(description="The Exa integration requires an API Key.")

autogpt_platform/backend/backend/blocks/exa/_config.py

@@ -1,16 +0,0 @@
-"""
-Shared configuration for all Exa blocks using the new SDK pattern.
-"""
-
-from backend.sdk import BlockCostType, ProviderBuilder
-
-from ._webhook import ExaWebhookManager
-
-# Configure the Exa provider once for all blocks
-exa = (
-    ProviderBuilder("exa")
-    .with_api_key("EXA_API_KEY", "Exa API Key")
-    .with_webhook_manager(ExaWebhookManager)
-    .with_base_cost(1, BlockCostType.RUN)
-    .build()
-)

autogpt_platform/backend/backend/blocks/exa/_webhook.py

@@ -1,136 +0,0 @@
-"""
-Exa Webhook Manager implementation.
-"""
-
-import hashlib
-import hmac
-from enum import Enum
-
-from backend.data.model import Credentials
-from backend.sdk import (
-    APIKeyCredentials,
-    BaseWebhooksManager,
-    ProviderName,
-    Requests,
-    Webhook,
-)
-
-
-class ExaWebhookType(str, Enum):
-    """Available webhook types for Exa."""
-
-    WEBSET = "webset"
-
-
-class ExaEventType(str, Enum):
-    """Available event types for Exa webhooks."""
-
-    WEBSET_CREATED = "webset.created"
-    WEBSET_DELETED = "webset.deleted"
-    WEBSET_PAUSED = "webset.paused"
-    WEBSET_IDLE = "webset.idle"
-    WEBSET_SEARCH_CREATED = "webset.search.created"
-    WEBSET_SEARCH_CANCELED = "webset.search.canceled"
-    WEBSET_SEARCH_COMPLETED = "webset.search.completed"
-    WEBSET_SEARCH_UPDATED = "webset.search.updated"
-    IMPORT_CREATED = "import.created"
-    IMPORT_COMPLETED = "import.completed"
-    IMPORT_PROCESSING = "import.processing"
-    WEBSET_ITEM_CREATED = "webset.item.created"
-    WEBSET_ITEM_ENRICHED = "webset.item.enriched"
-    WEBSET_EXPORT_CREATED = "webset.export.created"
-    WEBSET_EXPORT_COMPLETED = "webset.export.completed"
-
-
-class ExaWebhookManager(BaseWebhooksManager):
-    """Webhook manager for Exa API."""
-
-    PROVIDER_NAME = ProviderName("exa")
-
-    class WebhookType(str, Enum):
-        WEBSET = "webset"
-
-    @classmethod
-    async def validate_payload(
-        cls, webhook: Webhook, request, credentials: Credentials | None
-    ) -> tuple[dict, str]:
-        """Validate incoming webhook payload and signature."""
-        payload = await request.json()
-
-        # Get event type from payload
-        event_type = payload.get("eventType", "unknown")
-
-        # Verify webhook signature if secret is available
-        if webhook.secret:
-            signature = request.headers.get("X-Exa-Signature")
-            if signature:
-                # Compute expected signature
-                body = await request.body()
-                expected_signature = hmac.new(
-                    webhook.secret.encode(), body, hashlib.sha256
-                ).hexdigest()
-
-                # Compare signatures
-                if not hmac.compare_digest(signature, expected_signature):
-                    raise ValueError("Invalid webhook signature")
-
-        return payload, event_type
-
-    async def _register_webhook(
-        self,
-        credentials: Credentials,
-        webhook_type: str,
-        resource: str,
-        events: list[str],
-        ingress_url: str,
-        secret: str,
-    ) -> tuple[str, dict]:
-        """Register webhook with Exa API."""
-        if not isinstance(credentials, APIKeyCredentials):
-            raise ValueError("Exa webhooks require API key credentials")
-        api_key = credentials.api_key.get_secret_value()
-
-        # Create webhook via Exa API
-        response = await Requests().post(
-            "https://api.exa.ai/v0/webhooks",
-            headers={"x-api-key": api_key},
-            json={
-                "url": ingress_url,
-                "events": events,
-                "metadata": {
-                    "resource": resource,
-                    "webhook_type": webhook_type,
-                },
-            },
-        )
-
-        if not response.ok:
-            error_data = response.json()
-            raise Exception(f"Failed to create Exa webhook: {error_data}")
-
-        webhook_data = response.json()
-
-        # Store the secret returned by Exa
-        return webhook_data["id"], {
-            "events": events,
-            "resource": resource,
-            "exa_secret": webhook_data.get("secret"),
-        }
-
-    async def _deregister_webhook(
-        self, webhook: Webhook, credentials: Credentials
-    ) -> None:
-        """Deregister webhook from Exa API."""
-        if not isinstance(credentials, APIKeyCredentials):
-            raise ValueError("Exa webhooks require API key credentials")
-        api_key = credentials.api_key.get_secret_value()
-
-        # Delete webhook via Exa API
-        response = await Requests().delete(
-            f"https://api.exa.ai/v0/webhooks/{webhook.provider_webhook_id}",
-            headers={"x-api-key": api_key},
-        )
-
-        if not response.ok and response.status != 404:
-            error_data = response.json()
-            raise Exception(f"Failed to delete Exa webhook: {error_data}")

autogpt_platform/backend/backend/blocks/exa/answers.py

@@ -1,121 +0,0 @@
-from backend.sdk import (
-    APIKeyCredentials,
-    BaseModel,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-class CostBreakdown(BaseModel):
-    keywordSearch: float
-    neuralSearch: float
-    contentText: float
-    contentHighlight: float
-    contentSummary: float
-
-
-class SearchBreakdown(BaseModel):
-    search: float
-    contents: float
-    breakdown: CostBreakdown
-
-
-class PerRequestPrices(BaseModel):
-    neuralSearch_1_25_results: float
-    neuralSearch_26_100_results: float
-    neuralSearch_100_plus_results: float
-    keywordSearch_1_100_results: float
-    keywordSearch_100_plus_results: float
-
-
-class PerPagePrices(BaseModel):
-    contentText: float
-    contentHighlight: float
-    contentSummary: float
-
-
-class CostDollars(BaseModel):
-    total: float
-    breakDown: list[SearchBreakdown]
-    perRequestPrices: PerRequestPrices
-    perPagePrices: PerPagePrices
-
-
-class ExaAnswerBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        query: str = SchemaField(
-            description="The question or query to answer",
-            placeholder="What is the latest valuation of SpaceX?",
-        )
-        text: bool = SchemaField(
-            default=False,
-            description="If true, the response includes full text content in the search results",
-            advanced=True,
-        )
-        model: str = SchemaField(
-            default="exa",
-            description="The search model to use (exa or exa-pro)",
-            placeholder="exa",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        answer: str = SchemaField(
-            description="The generated answer based on search results"
-        )
-        citations: list[dict] = SchemaField(
-            description="Search results used to generate the answer",
-            default_factory=list,
-        )
-        cost_dollars: CostDollars = SchemaField(
-            description="Cost breakdown of the request"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="b79ca4cc-9d5e-47d1-9d4f-e3a2d7f28df5",
-            description="Get an LLM answer to a question informed by Exa search results",
-            categories={BlockCategory.SEARCH, BlockCategory.AI},
-            input_schema=ExaAnswerBlock.Input,
-            output_schema=ExaAnswerBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/answer"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        # Build the payload
-        payload = {
-            "query": input_data.query,
-            "text": input_data.text,
-            "model": input_data.model,
-        }
-
-        try:
-            response = await Requests().post(url, headers=headers, json=payload)
-            data = response.json()
-
-            yield "answer", data.get("answer", "")
-            yield "citations", data.get("citations", [])
-            yield "cost_dollars", data.get("costDollars", {})
-
-        except Exception as e:
-            yield "error", str(e)

autogpt_platform/backend/backend/blocks/exa/contents.py

@@ -1,39 +1,57 @@
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
+from typing import List
 
-from ._config import exa
-from .helpers import ContentSettings
+from pydantic import BaseModel
+
+from backend.blocks.exa._auth import (
+    ExaCredentials,
+    ExaCredentialsField,
+    ExaCredentialsInput,
+)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+from backend.util.request import Requests
+
+
+class ContentRetrievalSettings(BaseModel):
+    text: dict = SchemaField(
+        description="Text content settings",
+        default={"maxCharacters": 1000, "includeHtmlTags": False},
+        advanced=True,
+    )
+    highlights: dict = SchemaField(
+        description="Highlight settings",
+        default={
+            "numSentences": 3,
+            "highlightsPerUrl": 3,
+            "query": "",
+        },
+        advanced=True,
+    )
+    summary: dict = SchemaField(
+        description="Summary settings",
+        default={"query": ""},
+        advanced=True,
+    )
 
 
 class ExaContentsBlock(Block):
     class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
+        credentials: ExaCredentialsInput = ExaCredentialsField()
+        ids: List[str] = SchemaField(
+            description="Array of document IDs obtained from searches",
         )
-        ids: list[str] = SchemaField(
-            description="Array of document IDs obtained from searches"
-        )
-        contents: ContentSettings = SchemaField(
+        contents: ContentRetrievalSettings = SchemaField(
             description="Content retrieval settings",
-            default=ContentSettings(),
+            default=ContentRetrievalSettings(),
             advanced=True,
         )
 
     class Output(BlockSchema):
         results: list = SchemaField(
-            description="List of document contents", default_factory=list
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
+            description="List of document contents",
+            default_factory=list,
         )
+        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -45,7 +63,7 @@ class ExaContentsBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self, input_data: Input, *, credentials: ExaCredentials, **kwargs
     ) -> BlockOutput:
         url = "https://api.exa.ai/contents"
         headers = {
@@ -53,7 +71,6 @@ class ExaContentsBlock(Block):
             "x-api-key": credentials.api_key.get_secret_value(),
         }
 
-        # Convert ContentSettings to API format
         payload = {
             "ids": input_data.ids,
             "text": input_data.contents.text,

autogpt_platform/backend/backend/blocks/exa/helpers.py

@@ -1,6 +1,8 @@
 from typing import Optional
 
-from backend.sdk import BaseModel, SchemaField
+from pydantic import BaseModel
+
+from backend.data.model import SchemaField
 
 
 class TextSettings(BaseModel):
@@ -40,90 +42,13 @@ class SummarySettings(BaseModel):
 class ContentSettings(BaseModel):
     text: TextSettings = SchemaField(
         default=TextSettings(),
+        description="Text content settings",
     )
     highlights: HighlightSettings = SchemaField(
         default=HighlightSettings(),
+        description="Highlight settings",
     )
     summary: SummarySettings = SchemaField(
         default=SummarySettings(),
-    )
-
-
-# Websets Models
-class WebsetEntitySettings(BaseModel):
-    type: Optional[str] = SchemaField(
-        default=None,
-        description="Entity type (e.g., 'company', 'person')",
-        placeholder="company",
-    )
-
-
-class WebsetCriterion(BaseModel):
-    description: str = SchemaField(
-        description="Description of the criterion",
-        placeholder="Must be based in the US",
-    )
-    success_rate: Optional[int] = SchemaField(
-        default=None,
-        description="Success rate percentage",
-        ge=0,
-        le=100,
-    )
-
-
-class WebsetSearchConfig(BaseModel):
-    query: str = SchemaField(
-        description="Search query",
-        placeholder="Marketing agencies based in the US",
-    )
-    count: int = SchemaField(
-        default=10,
-        description="Number of results to return",
-        ge=1,
-        le=100,
-    )
-    entity: Optional[WebsetEntitySettings] = SchemaField(
-        default=None,
-        description="Entity settings for the search",
-    )
-    criteria: Optional[list[WebsetCriterion]] = SchemaField(
-        default=None,
-        description="Search criteria",
-    )
-    behavior: Optional[str] = SchemaField(
-        default="override",
-        description="Behavior when updating results ('override' or 'append')",
-        placeholder="override",
-    )
-
-
-class EnrichmentOption(BaseModel):
-    label: str = SchemaField(
-        description="Label for the enrichment option",
-        placeholder="Option 1",
-    )
-
-
-class WebsetEnrichmentConfig(BaseModel):
-    title: str = SchemaField(
-        description="Title of the enrichment",
-        placeholder="Company Details",
-    )
-    description: str = SchemaField(
-        description="Description of what this enrichment does",
-        placeholder="Extract company information",
-    )
-    format: str = SchemaField(
-        default="text",
-        description="Format of the enrichment result",
-        placeholder="text",
-    )
-    instructions: Optional[str] = SchemaField(
-        default=None,
-        description="Instructions for the enrichment",
-        placeholder="Extract key company metrics",
-    )
-    options: Optional[list[EnrichmentOption]] = SchemaField(
-        default=None,
-        description="Options for the enrichment",
+        description="Summary settings",
     )

autogpt_platform/backend/backend/blocks/exa/model.py

@@ -1,247 +0,0 @@
-from datetime import datetime
-from enum import Enum
-from typing import Any, Dict, List, Optional
-
-from pydantic import BaseModel, Field
-
-
-# Enum definitions based on available options
-class WebsetStatus(str, Enum):
-    IDLE = "idle"
-    PENDING = "pending"
-    RUNNING = "running"
-    PAUSED = "paused"
-
-
-class WebsetSearchStatus(str, Enum):
-    CREATED = "created"
-    # Add more if known, based on example it's "created"
-
-
-class ImportStatus(str, Enum):
-    PENDING = "pending"
-    # Add more if known
-
-
-class ImportFormat(str, Enum):
-    CSV = "csv"
-    # Add more if known
-
-
-class EnrichmentStatus(str, Enum):
-    PENDING = "pending"
-    # Add more if known
-
-
-class EnrichmentFormat(str, Enum):
-    TEXT = "text"
-    # Add more if known
-
-
-class MonitorStatus(str, Enum):
-    ENABLED = "enabled"
-    # Add more if known
-
-
-class MonitorBehaviorType(str, Enum):
-    SEARCH = "search"
-    # Add more if known
-
-
-class MonitorRunStatus(str, Enum):
-    CREATED = "created"
-    # Add more if known
-
-
-class CanceledReason(str, Enum):
-    WEBSET_DELETED = "webset_deleted"
-    # Add more if known
-
-
-class FailedReason(str, Enum):
-    INVALID_FORMAT = "invalid_format"
-    # Add more if known
-
-
-class Confidence(str, Enum):
-    HIGH = "high"
-    # Add more if known
-
-
-# Nested models
-
-
-class Entity(BaseModel):
-    type: str
-
-
-class Criterion(BaseModel):
-    description: str
-    successRate: Optional[int] = None
-
-
-class ExcludeItem(BaseModel):
-    source: str = Field(default="import")
-    id: str
-
-
-class Relationship(BaseModel):
-    definition: str
-    limit: Optional[float] = None
-
-
-class ScopeItem(BaseModel):
-    source: str = Field(default="import")
-    id: str
-    relationship: Optional[Relationship] = None
-
-
-class Progress(BaseModel):
-    found: int
-    analyzed: int
-    completion: int
-    timeLeft: int
-
-
-class Bounds(BaseModel):
-    min: int
-    max: int
-
-
-class Expected(BaseModel):
-    total: int
-    confidence: str = Field(default="high")  # Use str or Confidence enum
-    bounds: Bounds
-
-
-class Recall(BaseModel):
-    expected: Expected
-    reasoning: str
-
-
-class WebsetSearch(BaseModel):
-    id: str
-    object: str = Field(default="webset_search")
-    status: str = Field(default="created")  # Or use WebsetSearchStatus
-    websetId: str
-    query: str
-    entity: Entity
-    criteria: List[Criterion]
-    count: int
-    behavior: str = Field(default="override")
-    exclude: List[ExcludeItem]
-    scope: List[ScopeItem]
-    progress: Progress
-    recall: Recall
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-    canceledAt: Optional[datetime] = None
-    canceledReason: Optional[str] = Field(default=None)  # Or use CanceledReason
-    createdAt: datetime
-    updatedAt: datetime
-
-
-class ImportEntity(BaseModel):
-    type: str
-
-
-class Import(BaseModel):
-    id: str
-    object: str = Field(default="import")
-    status: str = Field(default="pending")  # Or use ImportStatus
-    format: str = Field(default="csv")  # Or use ImportFormat
-    entity: ImportEntity
-    title: str
-    count: int
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-    failedReason: Optional[str] = Field(default=None)  # Or use FailedReason
-    failedAt: Optional[datetime] = None
-    failedMessage: Optional[str] = None
-    createdAt: datetime
-    updatedAt: datetime
-
-
-class Option(BaseModel):
-    label: str
-
-
-class WebsetEnrichment(BaseModel):
-    id: str
-    object: str = Field(default="webset_enrichment")
-    status: str = Field(default="pending")  # Or use EnrichmentStatus
-    websetId: str
-    title: str
-    description: str
-    format: str = Field(default="text")  # Or use EnrichmentFormat
-    options: List[Option]
-    instructions: str
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-    createdAt: datetime
-    updatedAt: datetime
-
-
-class Cadence(BaseModel):
-    cron: str
-    timezone: str = Field(default="Etc/UTC")
-
-
-class BehaviorConfig(BaseModel):
-    query: Optional[str] = None
-    criteria: Optional[List[Criterion]] = None
-    entity: Optional[Entity] = None
-    count: Optional[int] = None
-    behavior: Optional[str] = Field(default=None)
-
-
-class Behavior(BaseModel):
-    type: str = Field(default="search")  # Or use MonitorBehaviorType
-    config: BehaviorConfig
-
-
-class MonitorRun(BaseModel):
-    id: str
-    object: str = Field(default="monitor_run")
-    status: str = Field(default="created")  # Or use MonitorRunStatus
-    monitorId: str
-    type: str = Field(default="search")
-    completedAt: Optional[datetime] = None
-    failedAt: Optional[datetime] = None
-    failedReason: Optional[str] = None
-    canceledAt: Optional[datetime] = None
-    createdAt: datetime
-    updatedAt: datetime
-
-
-class Monitor(BaseModel):
-    id: str
-    object: str = Field(default="monitor")
-    status: str = Field(default="enabled")  # Or use MonitorStatus
-    websetId: str
-    cadence: Cadence
-    behavior: Behavior
-    lastRun: Optional[MonitorRun] = None
-    nextRunAt: Optional[datetime] = None
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-    createdAt: datetime
-    updatedAt: datetime
-
-
-class Webset(BaseModel):
-    id: str
-    object: str = Field(default="webset")
-    status: WebsetStatus
-    externalId: Optional[str] = None
-    title: Optional[str] = None
-    searches: List[WebsetSearch]
-    imports: List[Import]
-    enrichments: List[WebsetEnrichment]
-    monitors: List[Monitor]
-    streams: List[Any]
-    createdAt: datetime
-    updatedAt: datetime
-    metadata: Dict[str, Any] = Field(default_factory=dict)
-
-
-class ListWebsets(BaseModel):
-    data: List[Webset]
-    hasMore: bool
-    nextCursor: Optional[str] = None

autogpt_platform/backend/backend/blocks/exa/search.py

@@ -1,61 +1,71 @@
 from datetime import datetime
+from typing import List
 
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
+from backend.blocks.exa._auth import (
+    ExaCredentials,
+    ExaCredentialsField,
+    ExaCredentialsInput,
 )
-
-from ._config import exa
-from .helpers import ContentSettings
+from backend.blocks.exa.helpers import ContentSettings
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+from backend.util.request import Requests
 
 
 class ExaSearchBlock(Block):
     class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
+        credentials: ExaCredentialsInput = ExaCredentialsField()
         query: str = SchemaField(description="The search query")
         use_auto_prompt: bool = SchemaField(
-            description="Whether to use autoprompt", default=True, advanced=True
+            description="Whether to use autoprompt",
+            default=True,
+            advanced=True,
+        )
+        type: str = SchemaField(
+            description="Type of search",
+            default="",
+            advanced=True,
         )
-        type: str = SchemaField(description="Type of search", default="", advanced=True)
         category: str = SchemaField(
-            description="Category to search within", default="", advanced=True
+            description="Category to search within",
+            default="",
+            advanced=True,
         )
         number_of_results: int = SchemaField(
-            description="Number of results to return", default=10, advanced=True
+            description="Number of results to return",
+            default=10,
+            advanced=True,
         )
-        include_domains: list[str] = SchemaField(
-            description="Domains to include in search", default_factory=list
+        include_domains: List[str] = SchemaField(
+            description="Domains to include in search",
+            default_factory=list,
         )
-        exclude_domains: list[str] = SchemaField(
+        exclude_domains: List[str] = SchemaField(
             description="Domains to exclude from search",
             default_factory=list,
             advanced=True,
         )
         start_crawl_date: datetime = SchemaField(
-            description="Start date for crawled content"
+            description="Start date for crawled content",
         )
         end_crawl_date: datetime = SchemaField(
-            description="End date for crawled content"
+            description="End date for crawled content",
         )
         start_published_date: datetime = SchemaField(
-            description="Start date for published content"
+            description="Start date for published content",
         )
         end_published_date: datetime = SchemaField(
-            description="End date for published content"
+            description="End date for published content",
         )
-        include_text: list[str] = SchemaField(
-            description="Text patterns to include", default_factory=list, advanced=True
+        include_text: List[str] = SchemaField(
+            description="Text patterns to include",
+            default_factory=list,
+            advanced=True,
         )
-        exclude_text: list[str] = SchemaField(
-            description="Text patterns to exclude", default_factory=list, advanced=True
+        exclude_text: List[str] = SchemaField(
+            description="Text patterns to exclude",
+            default_factory=list,
+            advanced=True,
         )
         contents: ContentSettings = SchemaField(
             description="Content retrieval settings",
@@ -65,7 +75,8 @@ class ExaSearchBlock(Block):
 
     class Output(BlockSchema):
         results: list = SchemaField(
-            description="List of search results", default_factory=list
+            description="List of search results",
+            default_factory=list,
         )
         error: str = SchemaField(
             description="Error message if the request failed",
@@ -81,7 +92,7 @@ class ExaSearchBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self, input_data: Input, *, credentials: ExaCredentials, **kwargs
     ) -> BlockOutput:
         url = "https://api.exa.ai/search"
         headers = {
@@ -93,7 +104,7 @@ class ExaSearchBlock(Block):
             "query": input_data.query,
             "useAutoprompt": input_data.use_auto_prompt,
             "numResults": input_data.number_of_results,
-            "contents": input_data.contents.model_dump(),
+            "contents": input_data.contents.dict(),
         }
 
         date_field_mapping = {

autogpt_platform/backend/backend/blocks/exa/similar.py

@@ -1,60 +1,57 @@
 from datetime import datetime
-from typing import Any
+from typing import Any, List
 
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
+from backend.blocks.exa._auth import (
+    ExaCredentials,
+    ExaCredentialsField,
+    ExaCredentialsInput,
 )
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.model import SchemaField
+from backend.util.request import Requests
 
-from ._config import exa
 from .helpers import ContentSettings
 
 
 class ExaFindSimilarBlock(Block):
     class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
+        credentials: ExaCredentialsInput = ExaCredentialsField()
         url: str = SchemaField(
             description="The url for which you would like to find similar links"
         )
         number_of_results: int = SchemaField(
-            description="Number of results to return", default=10, advanced=True
+            description="Number of results to return",
+            default=10,
+            advanced=True,
         )
-        include_domains: list[str] = SchemaField(
+        include_domains: List[str] = SchemaField(
             description="Domains to include in search",
             default_factory=list,
             advanced=True,
         )
-        exclude_domains: list[str] = SchemaField(
+        exclude_domains: List[str] = SchemaField(
             description="Domains to exclude from search",
             default_factory=list,
             advanced=True,
         )
         start_crawl_date: datetime = SchemaField(
-            description="Start date for crawled content"
+            description="Start date for crawled content",
         )
         end_crawl_date: datetime = SchemaField(
-            description="End date for crawled content"
+            description="End date for crawled content",
         )
         start_published_date: datetime = SchemaField(
-            description="Start date for published content"
+            description="Start date for published content",
         )
         end_published_date: datetime = SchemaField(
-            description="End date for published content"
+            description="End date for published content",
         )
-        include_text: list[str] = SchemaField(
+        include_text: List[str] = SchemaField(
             description="Text patterns to include (max 1 string, up to 5 words)",
             default_factory=list,
             advanced=True,
         )
-        exclude_text: list[str] = SchemaField(
+        exclude_text: List[str] = SchemaField(
             description="Text patterns to exclude (max 1 string, up to 5 words)",
             default_factory=list,
             advanced=True,
@@ -66,13 +63,11 @@ class ExaFindSimilarBlock(Block):
         )
 
     class Output(BlockSchema):
-        results: list[Any] = SchemaField(
+        results: List[Any] = SchemaField(
             description="List of similar documents with title, URL, published date, author, and score",
             default_factory=list,
         )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
+        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -84,7 +79,7 @@ class ExaFindSimilarBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+        self, input_data: Input, *, credentials: ExaCredentials, **kwargs
     ) -> BlockOutput:
         url = "https://api.exa.ai/findSimilar"
         headers = {
@@ -95,7 +90,7 @@ class ExaFindSimilarBlock(Block):
         payload = {
             "url": input_data.url,
             "numResults": input_data.number_of_results,
-            "contents": input_data.contents.model_dump(),
+            "contents": input_data.contents.dict(),
         }
 
         optional_field_mapping = {

autogpt_platform/backend/backend/blocks/exa/webhook_blocks.py

@@ -1,202 +0,0 @@
-"""
-Exa Webhook Blocks
-
-These blocks handle webhook events from Exa's API for websets and other events.
-"""
-
-from backend.sdk import (
-    BaseModel,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    BlockType,
-    BlockWebhookConfig,
-    CredentialsMetaInput,
-    Field,
-    ProviderName,
-    SchemaField,
-)
-
-from ._config import exa
-from ._webhook import ExaEventType
-
-
-class WebsetEventFilter(BaseModel):
-    """Filter configuration for Exa webset events."""
-
-    webset_created: bool = Field(
-        default=True, description="Receive notifications when websets are created"
-    )
-    webset_deleted: bool = Field(
-        default=False, description="Receive notifications when websets are deleted"
-    )
-    webset_paused: bool = Field(
-        default=False, description="Receive notifications when websets are paused"
-    )
-    webset_idle: bool = Field(
-        default=False, description="Receive notifications when websets become idle"
-    )
-    search_created: bool = Field(
-        default=True,
-        description="Receive notifications when webset searches are created",
-    )
-    search_completed: bool = Field(
-        default=True, description="Receive notifications when webset searches complete"
-    )
-    search_canceled: bool = Field(
-        default=False,
-        description="Receive notifications when webset searches are canceled",
-    )
-    search_updated: bool = Field(
-        default=False,
-        description="Receive notifications when webset searches are updated",
-    )
-    item_created: bool = Field(
-        default=True, description="Receive notifications when webset items are created"
-    )
-    item_enriched: bool = Field(
-        default=True, description="Receive notifications when webset items are enriched"
-    )
-    export_created: bool = Field(
-        default=False,
-        description="Receive notifications when webset exports are created",
-    )
-    export_completed: bool = Field(
-        default=True, description="Receive notifications when webset exports complete"
-    )
-    import_created: bool = Field(
-        default=False, description="Receive notifications when imports are created"
-    )
-    import_completed: bool = Field(
-        default=True, description="Receive notifications when imports complete"
-    )
-    import_processing: bool = Field(
-        default=False, description="Receive notifications when imports are processing"
-    )
-
-
-class ExaWebsetWebhookBlock(Block):
-    """
-    Receives webhook notifications for Exa webset events.
-
-    This block allows you to monitor various events related to Exa websets,
-    including creation, updates, searches, and exports.
-    """
-
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="Exa API credentials for webhook management"
-        )
-        webhook_url: str = SchemaField(
-            description="URL to receive webhooks (auto-generated)",
-            default="",
-            hidden=True,
-        )
-        webset_id: str = SchemaField(
-            description="The webset ID to monitor (optional, monitors all if empty)",
-            default="",
-        )
-        event_filter: WebsetEventFilter = SchemaField(
-            description="Configure which events to receive", default=WebsetEventFilter()
-        )
-        payload: dict = SchemaField(
-            description="Webhook payload data", default={}, hidden=True
-        )
-
-    class Output(BlockSchema):
-        event_type: str = SchemaField(description="Type of event that occurred")
-        event_id: str = SchemaField(description="Unique identifier for this event")
-        webset_id: str = SchemaField(description="ID of the affected webset")
-        data: dict = SchemaField(description="Event-specific data")
-        timestamp: str = SchemaField(description="When the event occurred")
-        metadata: dict = SchemaField(description="Additional event metadata")
-
-    def __init__(self):
-        super().__init__(
-            disabled=True,
-            id="d0204ed8-8b81-408d-8b8d-ed087a546228",
-            description="Receive webhook notifications for Exa webset events",
-            categories={BlockCategory.INPUT},
-            input_schema=ExaWebsetWebhookBlock.Input,
-            output_schema=ExaWebsetWebhookBlock.Output,
-            block_type=BlockType.WEBHOOK,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName("exa"),
-                webhook_type="webset",
-                event_filter_input="event_filter",
-                resource_format="{webset_id}",
-            ),
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        """Process incoming Exa webhook payload."""
-        try:
-            payload = input_data.payload
-
-            # Extract event details
-            event_type = payload.get("eventType", "unknown")
-            event_id = payload.get("eventId", "")
-
-            # Get webset ID from payload or input
-            webset_id = payload.get("websetId", input_data.webset_id)
-
-            # Check if we should process this event based on filter
-            should_process = self._should_process_event(
-                event_type, input_data.event_filter
-            )
-
-            if not should_process:
-                # Skip events that don't match our filter
-                return
-
-            # Extract event data
-            event_data = payload.get("data", {})
-            timestamp = payload.get("occurredAt", payload.get("createdAt", ""))
-            metadata = payload.get("metadata", {})
-
-            yield "event_type", event_type
-            yield "event_id", event_id
-            yield "webset_id", webset_id
-            yield "data", event_data
-            yield "timestamp", timestamp
-            yield "metadata", metadata
-
-        except Exception as e:
-            # Handle errors gracefully
-            yield "event_type", "error"
-            yield "event_id", ""
-            yield "webset_id", input_data.webset_id
-            yield "data", {"error": str(e)}
-            yield "timestamp", ""
-            yield "metadata", {}
-
-    def _should_process_event(
-        self, event_type: str, event_filter: WebsetEventFilter
-    ) -> bool:
-        """Check if an event should be processed based on the filter."""
-        filter_mapping = {
-            ExaEventType.WEBSET_CREATED: event_filter.webset_created,
-            ExaEventType.WEBSET_DELETED: event_filter.webset_deleted,
-            ExaEventType.WEBSET_PAUSED: event_filter.webset_paused,
-            ExaEventType.WEBSET_IDLE: event_filter.webset_idle,
-            ExaEventType.WEBSET_SEARCH_CREATED: event_filter.search_created,
-            ExaEventType.WEBSET_SEARCH_COMPLETED: event_filter.search_completed,
-            ExaEventType.WEBSET_SEARCH_CANCELED: event_filter.search_canceled,
-            ExaEventType.WEBSET_SEARCH_UPDATED: event_filter.search_updated,
-            ExaEventType.WEBSET_ITEM_CREATED: event_filter.item_created,
-            ExaEventType.WEBSET_ITEM_ENRICHED: event_filter.item_enriched,
-            ExaEventType.WEBSET_EXPORT_CREATED: event_filter.export_created,
-            ExaEventType.WEBSET_EXPORT_COMPLETED: event_filter.export_completed,
-            ExaEventType.IMPORT_CREATED: event_filter.import_created,
-            ExaEventType.IMPORT_COMPLETED: event_filter.import_completed,
-            ExaEventType.IMPORT_PROCESSING: event_filter.import_processing,
-        }
-
-        # Try to convert string to ExaEventType enum
-        try:
-            event_type_enum = ExaEventType(event_type)
-            return filter_mapping.get(event_type_enum, True)
-        except ValueError:
-            # If event_type is not a valid enum value, process it by default
-            return True

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

@@ -1,752 +0,0 @@
-from datetime import datetime
-from enum import Enum
-from typing import Annotated, Any, Dict, List, Optional
-
-from exa_py import Exa
-from exa_py.websets.types import (
-    CreateCriterionParameters,
-    CreateEnrichmentParameters,
-    CreateWebsetParameters,
-    CreateWebsetParametersSearch,
-    ExcludeItem,
-    Format,
-    ImportItem,
-    ImportSource,
-    Option,
-    ScopeItem,
-    ScopeRelationship,
-    ScopeSourceType,
-    WebsetArticleEntity,
-    WebsetCompanyEntity,
-    WebsetCustomEntity,
-    WebsetPersonEntity,
-    WebsetResearchPaperEntity,
-    WebsetStatus,
-)
-from pydantic import Field
-
-from backend.sdk import (
-    APIKeyCredentials,
-    BaseModel,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchema,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-class SearchEntityType(str, Enum):
-    COMPANY = "company"
-    PERSON = "person"
-    ARTICLE = "article"
-    RESEARCH_PAPER = "research_paper"
-    CUSTOM = "custom"
-    AUTO = "auto"
-
-
-class SearchType(str, Enum):
-    IMPORT = "import"
-    WEBSET = "webset"
-
-
-class EnrichmentFormat(str, Enum):
-    TEXT = "text"
-    DATE = "date"
-    NUMBER = "number"
-    OPTIONS = "options"
-    EMAIL = "email"
-    PHONE = "phone"
-
-
-class Webset(BaseModel):
-    id: str
-    status: WebsetStatus | None = Field(..., title="WebsetStatus")
-    """
-    The status of the webset
-    """
-    external_id: Annotated[Optional[str], Field(alias="externalId")] = None
-    """
-    The external identifier for the webset
-    NOTE: Returning dict to avoid ui crashing due to nested objects
-    """
-    searches: List[dict[str, Any]] | None = None
-    """
-    The searches that have been performed on the webset.
-    NOTE: Returning dict to avoid ui crashing due to nested objects
-    """
-    enrichments: List[dict[str, Any]] | None = None
-    """
-    The Enrichments to apply to the Webset Items.
-    NOTE: Returning dict to avoid ui crashing due to nested objects
-    """
-    monitors: List[dict[str, Any]] | None = None
-    """
-    The Monitors for the Webset.
-    NOTE: Returning dict to avoid ui crashing due to nested objects
-    """
-    metadata: Optional[Dict[str, Any]] = {}
-    """
-    Set of key-value pairs you want to associate with this object.
-    """
-    created_at: Annotated[datetime, Field(alias="createdAt")] | None = None
-    """
-    The date and time the webset was created
-    """
-    updated_at: Annotated[datetime, Field(alias="updatedAt")] | None = None
-    """
-    The date and time the webset was last updated
-    """
-
-
-class ExaCreateWebsetBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-
-        # Search parameters (flattened)
-        search_query: str = SchemaField(
-            description="Your search query. Use this to describe what you are looking for. Any URL provided will be crawled and used as context for the search.",
-            placeholder="Marketing agencies based in the US, that focus on consumer products",
-        )
-        search_count: Optional[int] = SchemaField(
-            default=10,
-            description="Number of items the search will attempt to find. The actual number of items found may be less than this number depending on the search complexity.",
-            ge=1,
-            le=1000,
-        )
-        search_entity_type: SearchEntityType = SchemaField(
-            default=SearchEntityType.AUTO,
-            description="Entity type: 'company', 'person', 'article', 'research_paper', or 'custom'. If not provided, we automatically detect the entity from the query.",
-            advanced=True,
-        )
-        search_entity_description: Optional[str] = SchemaField(
-            default=None,
-            description="Description for custom entity type (required when search_entity_type is 'custom')",
-            advanced=True,
-        )
-
-        # Search criteria (flattened)
-        search_criteria: list[str] = SchemaField(
-            default_factory=list,
-            description="List of criteria descriptions that every item will be evaluated against. If not provided, we automatically detect the criteria from the query.",
-            advanced=True,
-        )
-
-        # Search exclude sources (flattened)
-        search_exclude_sources: list[str] = SchemaField(
-            default_factory=list,
-            description="List of source IDs (imports or websets) to exclude from search results",
-            advanced=True,
-        )
-        search_exclude_types: list[SearchType] = SchemaField(
-            default_factory=list,
-            description="List of source types corresponding to exclude sources ('import' or 'webset')",
-            advanced=True,
-        )
-
-        # Search scope sources (flattened)
-        search_scope_sources: list[str] = SchemaField(
-            default_factory=list,
-            description="List of source IDs (imports or websets) to limit search scope to",
-            advanced=True,
-        )
-        search_scope_types: list[SearchType] = SchemaField(
-            default_factory=list,
-            description="List of source types corresponding to scope sources ('import' or 'webset')",
-            advanced=True,
-        )
-        search_scope_relationships: list[str] = SchemaField(
-            default_factory=list,
-            description="List of relationship definitions for hop searches (optional, one per scope source)",
-            advanced=True,
-        )
-        search_scope_relationship_limits: list[int] = SchemaField(
-            default_factory=list,
-            description="List of limits on the number of related entities to find (optional, one per scope relationship)",
-            advanced=True,
-        )
-
-        # Import parameters (flattened)
-        import_sources: list[str] = SchemaField(
-            default_factory=list,
-            description="List of source IDs to import from",
-            advanced=True,
-        )
-        import_types: list[SearchType] = SchemaField(
-            default_factory=list,
-            description="List of source types corresponding to import sources ('import' or 'webset')",
-            advanced=True,
-        )
-
-        # Enrichment parameters (flattened)
-        enrichment_descriptions: list[str] = SchemaField(
-            default_factory=list,
-            description="List of enrichment task descriptions to perform on each webset item",
-            advanced=True,
-        )
-        enrichment_formats: list[EnrichmentFormat] = SchemaField(
-            default_factory=list,
-            description="List of formats for enrichment responses ('text', 'date', 'number', 'options', 'email', 'phone'). If not specified, we automatically select the best format.",
-            advanced=True,
-        )
-        enrichment_options: list[list[str]] = SchemaField(
-            default_factory=list,
-            description="List of option lists for enrichments with 'options' format. Each inner list contains the option labels.",
-            advanced=True,
-        )
-        enrichment_metadata: list[dict] = SchemaField(
-            default_factory=list,
-            description="List of metadata dictionaries for enrichments",
-            advanced=True,
-        )
-
-        # Webset metadata
-        external_id: Optional[str] = SchemaField(
-            default=None,
-            description="External identifier for the webset. You can use this to reference the webset by your own internal identifiers.",
-            placeholder="my-webset-123",
-            advanced=True,
-        )
-        metadata: Optional[dict] = SchemaField(
-            default_factory=dict,
-            description="Key-value pairs to associate with this webset",
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        webset: Webset = SchemaField(
-            description="The unique identifier for the created webset"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="0cda29ff-c549-4a19-8805-c982b7d4ec34",
-            description="Create a new Exa Webset for persistent web search collections",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCreateWebsetBlock.Input,
-            output_schema=ExaCreateWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-
-        exa = Exa(credentials.api_key.get_secret_value())
-
-        # ------------------------------------------------------------
-        # Build entity (if explicitly provided)
-        # ------------------------------------------------------------
-        entity = None
-        if input_data.search_entity_type == SearchEntityType.COMPANY:
-            entity = WebsetCompanyEntity(type="company")
-        elif input_data.search_entity_type == SearchEntityType.PERSON:
-            entity = WebsetPersonEntity(type="person")
-        elif input_data.search_entity_type == SearchEntityType.ARTICLE:
-            entity = WebsetArticleEntity(type="article")
-        elif input_data.search_entity_type == SearchEntityType.RESEARCH_PAPER:
-            entity = WebsetResearchPaperEntity(type="research_paper")
-        elif (
-            input_data.search_entity_type == SearchEntityType.CUSTOM
-            and input_data.search_entity_description
-        ):
-            entity = WebsetCustomEntity(
-                type="custom", description=input_data.search_entity_description
-            )
-
-        # ------------------------------------------------------------
-        # Build criteria list
-        # ------------------------------------------------------------
-        criteria = None
-        if input_data.search_criteria:
-            criteria = [
-                CreateCriterionParameters(description=item)
-                for item in input_data.search_criteria
-            ]
-
-        # ------------------------------------------------------------
-        # Build exclude sources list
-        # ------------------------------------------------------------
-        exclude_items = None
-        if input_data.search_exclude_sources:
-            exclude_items = []
-            for idx, src_id in enumerate(input_data.search_exclude_sources):
-                src_type = None
-                if input_data.search_exclude_types and idx < len(
-                    input_data.search_exclude_types
-                ):
-                    src_type = input_data.search_exclude_types[idx]
-                # Default to IMPORT if type missing
-                if src_type == SearchType.WEBSET:
-                    source_enum = ImportSource.webset
-                else:
-                    source_enum = ImportSource.import_
-                exclude_items.append(ExcludeItem(source=source_enum, id=src_id))
-
-        # ------------------------------------------------------------
-        # Build scope list
-        # ------------------------------------------------------------
-        scope_items = None
-        if input_data.search_scope_sources:
-            scope_items = []
-            for idx, src_id in enumerate(input_data.search_scope_sources):
-                src_type = None
-                if input_data.search_scope_types and idx < len(
-                    input_data.search_scope_types
-                ):
-                    src_type = input_data.search_scope_types[idx]
-                relationship = None
-                if input_data.search_scope_relationships and idx < len(
-                    input_data.search_scope_relationships
-                ):
-                    rel_def = input_data.search_scope_relationships[idx]
-                    lim = None
-                    if input_data.search_scope_relationship_limits and idx < len(
-                        input_data.search_scope_relationship_limits
-                    ):
-                        lim = input_data.search_scope_relationship_limits[idx]
-                    relationship = ScopeRelationship(definition=rel_def, limit=lim)
-                if src_type == SearchType.WEBSET:
-                    src_enum = ScopeSourceType.webset
-                else:
-                    src_enum = ScopeSourceType.import_
-                scope_items.append(
-                    ScopeItem(source=src_enum, id=src_id, relationship=relationship)
-                )
-
-        # ------------------------------------------------------------
-        # Assemble search parameters (only if a query is provided)
-        # ------------------------------------------------------------
-        search_params = None
-        if input_data.search_query:
-            search_params = CreateWebsetParametersSearch(
-                query=input_data.search_query,
-                count=input_data.search_count,
-                entity=entity,
-                criteria=criteria,
-                exclude=exclude_items,
-                scope=scope_items,
-            )
-
-        # ------------------------------------------------------------
-        # Build imports list
-        # ------------------------------------------------------------
-        imports_params = None
-        if input_data.import_sources:
-            imports_params = []
-            for idx, src_id in enumerate(input_data.import_sources):
-                src_type = None
-                if input_data.import_types and idx < len(input_data.import_types):
-                    src_type = input_data.import_types[idx]
-                if src_type == SearchType.WEBSET:
-                    source_enum = ImportSource.webset
-                else:
-                    source_enum = ImportSource.import_
-                imports_params.append(ImportItem(source=source_enum, id=src_id))
-
-        # ------------------------------------------------------------
-        # Build enrichment list
-        # ------------------------------------------------------------
-        enrichments_params = None
-        if input_data.enrichment_descriptions:
-            enrichments_params = []
-            for idx, desc in enumerate(input_data.enrichment_descriptions):
-                fmt = None
-                if input_data.enrichment_formats and idx < len(
-                    input_data.enrichment_formats
-                ):
-                    fmt_enum = input_data.enrichment_formats[idx]
-                    if fmt_enum is not None:
-                        fmt = Format(
-                            fmt_enum.value if isinstance(fmt_enum, Enum) else fmt_enum
-                        )
-                options_list = None
-                if input_data.enrichment_options and idx < len(
-                    input_data.enrichment_options
-                ):
-                    raw_opts = input_data.enrichment_options[idx]
-                    if raw_opts:
-                        options_list = [Option(label=o) for o in raw_opts]
-                metadata_obj = None
-                if input_data.enrichment_metadata and idx < len(
-                    input_data.enrichment_metadata
-                ):
-                    metadata_obj = input_data.enrichment_metadata[idx]
-                enrichments_params.append(
-                    CreateEnrichmentParameters(
-                        description=desc,
-                        format=fmt,
-                        options=options_list,
-                        metadata=metadata_obj,
-                    )
-                )
-
-        # ------------------------------------------------------------
-        # Create the webset
-        # ------------------------------------------------------------
-        webset = exa.websets.create(
-            params=CreateWebsetParameters(
-                search=search_params,
-                imports=imports_params,
-                enrichments=enrichments_params,
-                external_id=input_data.external_id,
-                metadata=input_data.metadata,
-            )
-        )
-
-        # Use alias field names returned from Exa SDK so that nested models validate correctly
-        yield "webset", Webset.model_validate(webset.model_dump(by_alias=True))
-
-
-class ExaUpdateWebsetBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to update",
-            placeholder="webset-id-or-external-id",
-        )
-        metadata: Optional[dict] = SchemaField(
-            default=None,
-            description="Key-value pairs to associate with this webset (set to null to clear)",
-        )
-
-    class Output(BlockSchema):
-        webset_id: str = SchemaField(description="The unique identifier for the webset")
-        status: str = SchemaField(description="The status of the webset")
-        external_id: Optional[str] = SchemaField(
-            description="The external identifier for the webset", default=None
-        )
-        metadata: dict = SchemaField(
-            description="Updated metadata for the webset", default_factory=dict
-        )
-        updated_at: str = SchemaField(
-            description="The date and time the webset was updated"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="89ccd99a-3c2b-4fbf-9e25-0ffa398d0314",
-            description="Update metadata for an existing Webset",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaUpdateWebsetBlock.Input,
-            output_schema=ExaUpdateWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        # Build the payload
-        payload = {}
-        if input_data.metadata is not None:
-            payload["metadata"] = input_data.metadata
-
-        try:
-            response = await Requests().post(url, headers=headers, json=payload)
-            data = response.json()
-
-            yield "webset_id", data.get("id", "")
-            yield "status", data.get("status", "")
-            yield "external_id", data.get("externalId")
-            yield "metadata", data.get("metadata", {})
-            yield "updated_at", data.get("updatedAt", "")
-
-        except Exception as e:
-            yield "error", str(e)
-            yield "webset_id", ""
-            yield "status", ""
-            yield "metadata", {}
-            yield "updated_at", ""
-
-
-class ExaListWebsetsBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        trigger: Any | None = SchemaField(
-            default=None,
-            description="Trigger for the webset, value is ignored!",
-            advanced=False,
-        )
-        cursor: Optional[str] = SchemaField(
-            default=None,
-            description="Cursor for pagination through results",
-            advanced=True,
-        )
-        limit: int = SchemaField(
-            default=25,
-            description="Number of websets to return (1-100)",
-            ge=1,
-            le=100,
-            advanced=True,
-        )
-
-    class Output(BlockSchema):
-        websets: list[Webset] = SchemaField(
-            description="List of websets", default_factory=list
-        )
-        has_more: bool = SchemaField(
-            description="Whether there are more results to paginate through",
-            default=False,
-        )
-        next_cursor: Optional[str] = SchemaField(
-            description="Cursor for the next page of results", default=None
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="1dcd8fd6-c13f-4e6f-bd4c-654428fa4757",
-            description="List all Websets with pagination support",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaListWebsetsBlock.Input,
-            output_schema=ExaListWebsetsBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/websets/v0/websets"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        params: dict[str, Any] = {
-            "limit": input_data.limit,
-        }
-        if input_data.cursor:
-            params["cursor"] = input_data.cursor
-
-        try:
-            response = await Requests().get(url, headers=headers, params=params)
-            data = response.json()
-
-            yield "websets", data.get("data", [])
-            yield "has_more", data.get("hasMore", False)
-            yield "next_cursor", data.get("nextCursor")
-
-        except Exception as e:
-            yield "error", str(e)
-            yield "websets", []
-            yield "has_more", False
-
-
-class ExaGetWebsetBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to retrieve",
-            placeholder="webset-id-or-external-id",
-        )
-
-    class Output(BlockSchema):
-        webset_id: str = SchemaField(description="The unique identifier for the webset")
-        status: str = SchemaField(description="The status of the webset")
-        external_id: Optional[str] = SchemaField(
-            description="The external identifier for the webset", default=None
-        )
-        searches: list[dict] = SchemaField(
-            description="The searches performed on the webset", default_factory=list
-        )
-        enrichments: list[dict] = SchemaField(
-            description="The enrichments applied to the webset", default_factory=list
-        )
-        monitors: list[dict] = SchemaField(
-            description="The monitors for the webset", default_factory=list
-        )
-        items: Optional[list[dict]] = SchemaField(
-            description="The items in the webset (if expand_items is true)",
-            default=None,
-        )
-        metadata: dict = SchemaField(
-            description="Key-value pairs associated with the webset",
-            default_factory=dict,
-        )
-        created_at: str = SchemaField(
-            description="The date and time the webset was created"
-        )
-        updated_at: str = SchemaField(
-            description="The date and time the webset was last updated"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="6ab8e12a-132c-41bf-b5f3-d662620fa832",
-            description="Retrieve a Webset by ID or external ID",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaGetWebsetBlock.Input,
-            output_schema=ExaGetWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        try:
-            response = await Requests().get(url, headers=headers)
-            data = response.json()
-
-            yield "webset_id", data.get("id", "")
-            yield "status", data.get("status", "")
-            yield "external_id", data.get("externalId")
-            yield "searches", data.get("searches", [])
-            yield "enrichments", data.get("enrichments", [])
-            yield "monitors", data.get("monitors", [])
-            yield "items", data.get("items")
-            yield "metadata", data.get("metadata", {})
-            yield "created_at", data.get("createdAt", "")
-            yield "updated_at", data.get("updatedAt", "")
-
-        except Exception as e:
-            yield "error", str(e)
-            yield "webset_id", ""
-            yield "status", ""
-            yield "searches", []
-            yield "enrichments", []
-            yield "monitors", []
-            yield "metadata", {}
-            yield "created_at", ""
-            yield "updated_at", ""
-
-
-class ExaDeleteWebsetBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to delete",
-            placeholder="webset-id-or-external-id",
-        )
-
-    class Output(BlockSchema):
-        webset_id: str = SchemaField(
-            description="The unique identifier for the deleted webset"
-        )
-        external_id: Optional[str] = SchemaField(
-            description="The external identifier for the deleted webset", default=None
-        )
-        status: str = SchemaField(description="The status of the deleted webset")
-        success: str = SchemaField(
-            description="Whether the deletion was successful", default="true"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="aa6994a2-e986-421f-8d4c-7671d3be7b7e",
-            description="Delete a Webset and all its items",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaDeleteWebsetBlock.Input,
-            output_schema=ExaDeleteWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        try:
-            response = await Requests().delete(url, headers=headers)
-            data = response.json()
-
-            yield "webset_id", data.get("id", "")
-            yield "external_id", data.get("externalId")
-            yield "status", data.get("status", "")
-            yield "success", "true"
-
-        except Exception as e:
-            yield "error", str(e)
-            yield "webset_id", ""
-            yield "status", ""
-            yield "success", "false"
-
-
-class ExaCancelWebsetBlock(Block):
-    class Input(BlockSchema):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        webset_id: str = SchemaField(
-            description="The ID or external ID of the Webset to cancel",
-            placeholder="webset-id-or-external-id",
-        )
-
-    class Output(BlockSchema):
-        webset_id: str = SchemaField(description="The unique identifier for the webset")
-        status: str = SchemaField(
-            description="The status of the webset after cancellation"
-        )
-        external_id: Optional[str] = SchemaField(
-            description="The external identifier for the webset", default=None
-        )
-        success: str = SchemaField(
-            description="Whether the cancellation was successful", default="true"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="e40a6420-1db8-47bb-b00a-0e6aecd74176",
-            description="Cancel all operations being performed on a Webset",
-            categories={BlockCategory.SEARCH},
-            input_schema=ExaCancelWebsetBlock.Input,
-            output_schema=ExaCancelWebsetBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}/cancel"
-        headers = {
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        try:
-            response = await Requests().post(url, headers=headers)
-            data = response.json()
-
-            yield "webset_id", data.get("id", "")
-            yield "status", data.get("status", "")
-            yield "external_id", data.get("externalId")
-            yield "success", "true"
-
-        except Exception as e:
-            yield "error", str(e)
-            yield "webset_id", ""
-            yield "status", ""
-            yield "success", "false"