refactor: simplify LaunchDarkly context and remove redundant code

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

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

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

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

.dockerignore

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

.github/PULL_REQUEST_TEMPLATE.md

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

.github/copilot-instructions.md

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

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

@@ -1,302 +0,0 @@
-name: "Copilot Setup Steps"
-
-# Automatically run the setup steps when they are changed to allow for easy validation, and
-# allow manual testing through the repository's "Actions" tab
-on:
-  workflow_dispatch:
-  push:
-    paths:
-      - .github/workflows/copilot-setup-steps.yml
-  pull_request:
-    paths:
-      - .github/workflows/copilot-setup-steps.yml
-
-jobs:
-  # The job MUST be called `copilot-setup-steps` or it will not be picked up by Copilot.
-  copilot-setup-steps:
-    runs-on: ubuntu-latest
-    timeout-minutes: 45
-
-    # Set the permissions to the lowest permissions possible needed for your steps.
-    # Copilot will be given its own token for its operations.
-    permissions:
-      # If you want to clone the repository as part of your setup steps, for example to install dependencies, you'll need the `contents: read` permission. If you don't clone the repository in your setup steps, Copilot will do this for you automatically after the steps complete.
-      contents: read
-
-    # You can define any steps you want, and they will run before the agent starts.
-    # If you do not check out your code, Copilot will do this for you.
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"  # Use standard version matching CI
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          # Extract Poetry version from backend/poetry.lock (matches CI)
-          cd autogpt_platform/backend
-          HEAD_POETRY_VERSION=$(python3 ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Found Poetry version ${HEAD_POETRY_VERSION} in backend/poetry.lock"
-          
-          # Install Poetry
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-          
-          # Add Poetry to PATH
-          echo "$HOME/.local/bin" >> $GITHUB_PATH
-
-      - name: Check poetry.lock
-        working-directory: autogpt_platform/backend
-        run: |
-          poetry lock
-          if ! git diff --quiet --ignore-matching-lines="^# " poetry.lock; then
-            echo "Warning: poetry.lock not up to date, but continuing for setup"
-            git checkout poetry.lock  # Reset for clean setup
-          fi
-
-      - name: Install Python dependencies
-        working-directory: autogpt_platform/backend
-        run: poetry install
-
-      - name: Generate Prisma Client
-        working-directory: autogpt_platform/backend
-        run: poetry run prisma generate
-
-      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
-      - name: Set up Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "21"
-
-      - name: Enable corepack
-        run: corepack enable
-
-      - name: Set pnpm store directory
-        run: |
-          pnpm config set store-dir ~/.pnpm-store
-          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
-
-      - name: Cache frontend dependencies
-        uses: actions/cache@v4
-        with:
-          path: ~/.pnpm-store
-          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
-          restore-keys: |
-            ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml') }}
-            ${{ runner.os }}-pnpm-
-
-      - name: Install JavaScript dependencies
-        working-directory: autogpt_platform/frontend
-        run: pnpm install --frozen-lockfile
-
-      # Install Playwright browsers for frontend testing
-      # NOTE: Disabled to save ~1 minute of setup time. Re-enable if Copilot needs browser automation (e.g., for MCP)
-      # - name: Install Playwright browsers
-      #   working-directory: autogpt_platform/frontend
-      #   run: pnpm playwright install --with-deps chromium
-
-      # Docker setup for development environment
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Copy default environment files
-        working-directory: autogpt_platform
-        run: |
-          # Copy default environment files for development
-          cp .env.default .env
-          cp backend/.env.default backend/.env
-          cp frontend/.env.default frontend/.env
-
-      # Phase 1: Cache and load Docker images for faster setup
-      - name: Set up Docker image cache
-        id: docker-cache
-        uses: actions/cache@v4
-        with:
-          path: ~/docker-cache
-          # Use a versioned key for cache invalidation when image list changes
-          key: docker-images-v2-${{ runner.os }}-${{ hashFiles('.github/workflows/copilot-setup-steps.yml') }}
-          restore-keys: |
-            docker-images-v2-${{ runner.os }}-
-            docker-images-v1-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-          
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "clamav/clamav-debian:latest"
-            "busybox:latest"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-            "supabase/postgres-meta:v0.86.1"
-            "supabase/studio:20250224-d10db0f"
-          )
-          
-          # Check if any cached tar files exist (more reliable than cache-hit)
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              # Convert image name to filename (replace : and / with -)
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            # Pull all images in parallel
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-            
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                # Convert image name to filename (replace : and / with -)
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-          
-          echo "Docker images ready for use"
-
-      # Phase 2: Build migrate service with GitHub Actions cache
-      - name: Build migrate Docker image with cache
-        working-directory: autogpt_platform
-        run: |
-          # Build the migrate image with buildx for GHA caching
-          docker buildx build \
-            --cache-from type=gha \
-            --cache-to type=gha,mode=max \
-            --target migrate \
-            --tag autogpt_platform-migrate:latest \
-            --load \
-            -f backend/Dockerfile \
-            ..
-
-      # Start services using pre-built images
-      - name: Start Docker services for development
-        working-directory: autogpt_platform
-        run: |
-          # Start essential services (migrate image already built with correct tag)
-          docker compose --profile local up deps --no-build --detach
-          echo "Waiting for services to be ready..."
-          
-          # Wait for database to be ready
-          echo "Checking database readiness..."
-          timeout 30 sh -c 'until docker compose exec -T db pg_isready -U postgres 2>/dev/null; do 
-            echo "  Waiting for database..."
-            sleep 2
-          done' && echo "✅ Database is ready" || echo "⚠️ Database ready check timeout after 30s, continuing..."
-          
-          # Check migrate service status
-          echo "Checking migration status..."
-          docker compose ps migrate || echo "  Migrate service not visible in ps output"
-          
-          # Wait for migrate service to complete
-          echo "Waiting for migrations to complete..."
-          timeout 30 bash -c '
-            ATTEMPTS=0
-            while [ $ATTEMPTS -lt 15 ]; do
-              ATTEMPTS=$((ATTEMPTS + 1))
-              
-              # Check using docker directly (more reliable than docker compose ps)
-              CONTAINER_STATUS=$(docker ps -a --filter "label=com.docker.compose.service=migrate" --format "{{.Status}}" | head -1)
-              
-              if [ -z "$CONTAINER_STATUS" ]; then
-                echo "  Attempt $ATTEMPTS: Migrate container not found yet..."
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited (0)"; then
-                echo "✅ Migrations completed successfully"
-                docker compose logs migrate --tail=5 2>/dev/null || true
-                exit 0
-              elif echo "$CONTAINER_STATUS" | grep -q "Exited ([1-9]"; then
-                EXIT_CODE=$(echo "$CONTAINER_STATUS" | grep -oE "Exited \([0-9]+\)" | grep -oE "[0-9]+")
-                echo "❌ Migrations failed with exit code: $EXIT_CODE"
-                echo "Migration logs:"
-                docker compose logs migrate --tail=20 2>/dev/null || true
-                exit 1
-              elif echo "$CONTAINER_STATUS" | grep -q "Up"; then
-                echo "  Attempt $ATTEMPTS: Migrate container is running... ($CONTAINER_STATUS)"
-              else
-                echo "  Attempt $ATTEMPTS: Migrate container status: $CONTAINER_STATUS"
-              fi
-              
-              sleep 2
-            done
-            
-            echo "⚠️ Timeout: Could not determine migration status after 30 seconds"
-            echo "Final container check:"
-            docker ps -a --filter "label=com.docker.compose.service=migrate" || true
-            echo "Migration logs (if available):"
-            docker compose logs migrate --tail=10 2>/dev/null || echo "  No logs available"
-          ' || echo "⚠️ Migration check completed with warnings, continuing..."
-          
-          # Brief wait for other services to stabilize
-          echo "Waiting 5 seconds for other services to stabilize..."
-          sleep 5
-
-      # Verify installations and provide environment info
-      - name: Verify setup and show environment info
-        run: |
-          echo "=== Python Setup ==="
-          python --version
-          poetry --version
-          
-          echo "=== Node.js Setup ==="
-          node --version
-          pnpm --version
-          
-          echo "=== Additional Tools ==="
-          docker --version
-          docker compose version
-          gh --version || true
-          
-          echo "=== Services Status ==="
-          cd autogpt_platform
-          docker compose ps || true
-          
-          echo "=== Backend Dependencies ==="
-          cd backend
-          poetry show | head -10 || true
-          
-          echo "=== Frontend Dependencies ==="
-          cd ../frontend
-          pnpm list --depth=0 | head -10 || true
-          
-          echo "=== Environment Files ==="
-          ls -la ../.env* || true
-          ls -la .env* || true
-          ls -la ../backend/.env* || true
-          
-          echo "✅ AutoGPT Platform development environment setup complete!"
-          echo "🚀 Ready for development with Docker services running"
-          echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"

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

@@ -32,7 +32,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.11"]
     runs-on: ubuntu-latest
 
     services:

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

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

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

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

.gitignore

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

.pre-commit-config.yaml

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

README.md

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

autogpt_platform/CLAUDE.md

@@ -1,11 +1,9 @@
 # CLAUDE.md
 
 This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
-
 ## Repository Overview
 
 AutoGPT Platform is a monorepo containing:
-
 - **Backend** (`/backend`): Python FastAPI server with async support
 - **Frontend** (`/frontend`): Next.js React application
 - **Shared Libraries** (`/autogpt_libs`): Common Python utilities
@@ -13,7 +11,6 @@ AutoGPT Platform is a monorepo containing:
 ## Essential Commands
 
 ### Backend Development
-
 ```bash
 # Install dependencies
 cd backend && poetry install
@@ -44,7 +41,6 @@ poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[GetC
 poetry run format  # Black + isort
 poetry run lint    # ruff
 ```
-
 More details can be found in TESTING.md
 
 #### Creating/Updating Snapshots
@@ -57,8 +53,8 @@ poetry run pytest path/to/test.py --snapshot-update
 
 ⚠️ **Important**: Always review snapshot changes before committing! Use `git diff` to verify the changes are expected.
 
-### Frontend Development
 
+### Frontend Development
 ```bash
 # Install dependencies
 cd frontend && npm install
@@ -76,13 +72,12 @@ npm run storybook
 npm run build
 
 # Type checking
-npm run types
+npm run type-check
 ```
 
 ## Architecture Overview
 
 ### Backend Architecture
-
 - **API Layer**: FastAPI with REST and WebSocket endpoints
 - **Database**: PostgreSQL with Prisma ORM, includes pgvector for embeddings
 - **Queue System**: RabbitMQ for async task processing
@@ -91,7 +86,6 @@ npm run types
 - **Security**: Cache protection middleware prevents sensitive data caching in browsers/proxies
 
 ### Frontend Architecture
-
 - **Framework**: Next.js App Router with React Server Components
 - **State Management**: React hooks + Supabase client for real-time updates
 - **Workflow Builder**: Visual graph editor using @xyflow/react
@@ -99,7 +93,6 @@ npm run types
 - **Feature Flags**: LaunchDarkly integration
 
 ### Key Concepts
-
 1. **Agent Graphs**: Workflow definitions stored as JSON, executed by the backend
 2. **Blocks**: Reusable components in `/backend/blocks/` that perform specific tasks
 3. **Integrations**: OAuth and API connections stored per user
@@ -107,16 +100,13 @@ npm run types
 5. **Virus Scanning**: ClamAV integration for file upload security
 
 ### Testing Approach
-
 - Backend uses pytest with snapshot testing for API responses
 - Test files are colocated with source files (`*_test.py`)
 - Frontend uses Playwright for E2E tests
 - Component testing via Storybook
 
 ### Database Schema
-
 Key models (defined in `/backend/schema.prisma`):
-
 - `User`: Authentication and profile data
 - `AgentGraph`: Workflow definitions with version control
 - `AgentGraphExecution`: Execution history and results
@@ -124,31 +114,13 @@ 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
@@ -156,18 +128,16 @@ Key models (defined in `/backend/schema.prisma`):
 5. Register in block registry
 6. Generate the block uuid using `uuid.uuid4()`
 
-Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
+Note: when making many new blocks analyze the interfaces for each of these blcoks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
 ex: do the inputs and outputs tie well together?
 
 **Modifying the API:**
-
 1. Update route in `/backend/backend/server/routers/`
 2. Add/update Pydantic models in same directory
 3. Write tests alongside the route file
 4. Run `poetry run test` to verify
 
 **Frontend feature development:**
-
 1. Components go in `/frontend/src/components/`
 2. Use existing UI components from `/frontend/src/components/ui/`
 3. Add Storybook stories for new components
@@ -176,7 +146,6 @@ ex: do the inputs and outputs tie well together?
 ### Security Implementation
 
 **Cache Protection Middleware:**
-
 - Located in `/backend/backend/server/middleware/security.py`
 - Default behavior: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
 - Uses an allow list approach - only explicitly permitted paths can be cached
@@ -185,20 +154,14 @@ ex: do the inputs and outputs tie well together?
 - To allow caching for a new endpoint, add it to `CACHEABLE_PATHS` in the middleware
 - Applied to both main API server and external API applications
 
-### Creating Pull Requests
 
+### Creating Pull Requests
 - Create the PR aginst the `dev` branch of the repository.
 - Ensure the branch name is descriptive (e.g., `feature/add-new-block`)/
 - Use conventional commit messages (see below)/
 - Fill out the .github/PULL_REQUEST_TEMPLATE.md template as the PR description/
 - Run the github pre-commit hooks to ensure code quality.
 
-### Reviewing/Revising Pull Requests
-
-- When the user runs /pr-comments or tries to fetch them, also run gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews to get the reviews
-- Use gh api /repos/Significant-Gravitas/AutoGPT/pulls/[issuenum]/reviews/[review_id]/comments to get the review contents
-- Use gh api /repos/Significant-Gravitas/AutoGPT/issues/9924/comments to get the pr specific comments
-
 ### Conventional Commits
 
 Use this format for commit messages and Pull Request titles:

autogpt_platform/README.md

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

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

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/client_test.py

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

autogpt_platform/autogpt_libs/autogpt_libs/feature_flag/config.py

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

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

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

autogpt_platform/autogpt_libs/autogpt_libs/logging/utils.py

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

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

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

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py

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

autogpt_platform/autogpt_libs/poetry.lock

@@ -1253,31 +1253,30 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.12.9"
+version = "0.12.3"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
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+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_i686.whl", hash = "sha256:6b16647cbb470eaf4750d27dddc6ebf7758b918887b56d39e9c22cce2049082b"},
+    {file = "ruff-0.12.3-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:e1417051edb436230023575b149e8ff843a324557fe0a265863b7602df86722f"},
+    {file = "ruff-0.12.3-py3-none-win32.whl", hash = "sha256:dfd45e6e926deb6409d0616078a666ebce93e55e07f0fb0228d4b2608b2c248d"},
+    {file = "ruff-0.12.3-py3-none-win_amd64.whl", hash = "sha256:a946cf1e7ba3209bdef039eb97647f1c77f6f540e5845ec9c114d3af8df873e7"},
+    {file = "ruff-0.12.3-py3-none-win_arm64.whl", hash = "sha256:5f9c7c9c8f84c2d7f27e93674d27136fbf489720251544c4da7fb3d742e011b1"},
+    {file = "ruff-0.12.3.tar.gz", hash = "sha256:f1b5a4b6668fd7b7ea3697d8d98857390b40c1320a63a178eee6be0899ea2d77"},
 ]
 
 [[package]]
@@ -1615,4 +1614,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "4cc687aabe5865665fb8c4ccc0ea7e0af80b41e401ca37919f57efa6e0b5be00"
+content-hash = "f67db13e6f68b1d67a55eee908c1c560bfa44da8509f98f842889a7570a9830f"

autogpt_platform/autogpt_libs/pyproject.toml

@@ -23,7 +23,7 @@ supabase = "^2.16.0"
 uvicorn = "^0.35.0"
 
 [tool.poetry.group.dev.dependencies]
-ruff = "^0.12.9"
+ruff = "^0.12.3"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/.dockerignore

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

autogpt_platform/backend/.env.example

@@ -1,9 +1,3 @@
-# Backend Configuration
-# This file contains environment variables that MUST be set for the AutoGPT platform
-# Variables with working defaults in settings.py are not included here
-
-## ===== REQUIRED DATABASE CONFIGURATION ===== ##
-# PostgreSQL Database Connection
 DB_USER=postgres
 DB_PASS=your-super-secret-and-long-postgres-password
 DB_NAME=postgres
@@ -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 +121,104 @@ LINEAR_CLIENT_SECRET=
 TODOIST_CLIENT_ID=
 TODOIST_CLIENT_SECRET=
 
-NOTION_CLIENT_ID=
-NOTION_CLIENT_SECRET=
+## ===== OPTIONAL API KEYS ===== ##
 
-# Discord OAuth App credentials
-# 1. Go to https://discord.com/developers/applications
-# 2. Create a new application
-# 3. Go to OAuth2 section and add redirect URI: http://localhost:3000/auth/integrations/oauth_callback
-# 4. Copy Client ID and Client Secret below
-DISCORD_CLIENT_ID=
-DISCORD_CLIENT_SECRET=
+# LLM
+OPENAI_API_KEY=
+ANTHROPIC_API_KEY=
+AIML_API_KEY=
+GROQ_API_KEY=
+OPEN_ROUTER_API_KEY=
+LLAMA_API_KEY=
 
+# Reddit
+# Go to https://www.reddit.com/prefs/apps and create a new app
+# Choose "script" for the type
+# Fill in the redirect uri as <your_frontend_url>/auth/integrations/oauth_callback, e.g. http://localhost:3000/auth/integrations/oauth_callback
 REDDIT_CLIENT_ID=
 REDDIT_CLIENT_SECRET=
+REDDIT_USER_AGENT="AutoGPT:1.0 (by /u/autogpt)"
 
-# Payment Processing
-STRIPE_API_KEY=
-STRIPE_WEBHOOK_SECRET=
-
-# Email Service (for sending notifications and confirmations)
-POSTMARK_SERVER_API_TOKEN=
-POSTMARK_SENDER_EMAIL=invalid@invalid.com
-POSTMARK_WEBHOOK_TOKEN=
-
-# Error Tracking
-SENTRY_DSN=
-
-# Cloudflare Turnstile (CAPTCHA) Configuration
-# Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
-# This is the backend secret key
-TURNSTILE_SECRET_KEY=
-# This is the verify URL
-TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
-
-# Feature Flags
-LAUNCH_DARKLY_SDK_KEY=
-
-# Content Generation & Media
-DID_API_KEY=
-FAL_API_KEY=
-IDEOGRAM_API_KEY=
-REPLICATE_API_KEY=
-REVID_API_KEY=
-SCREENSHOTONE_API_KEY=
-UNREAL_SPEECH_API_KEY=
-
-# Data & Search Services
-E2B_API_KEY=
-EXA_API_KEY=
-JINA_API_KEY=
-MEM0_API_KEY=
-OPENWEATHERMAP_API_KEY=
-GOOGLE_MAPS_API_KEY=
-
-# Communication Services
+# Discord
 DISCORD_BOT_TOKEN=
-MEDIUM_API_KEY=
-MEDIUM_AUTHOR_ID=
+
+# SMTP/Email
 SMTP_SERVER=
 SMTP_PORT=
 SMTP_USERNAME=
 SMTP_PASSWORD=
 
-# Business & Marketing Tools
+# D-ID
+DID_API_KEY=
+
+# Open Weather Map
+OPENWEATHERMAP_API_KEY=
+
+# SMTP
+SMTP_SERVER=
+SMTP_PORT=
+SMTP_USERNAME=
+SMTP_PASSWORD=
+
+# Medium
+MEDIUM_API_KEY=
+MEDIUM_AUTHOR_ID=
+
+# Google Maps
+GOOGLE_MAPS_API_KEY=
+
+# Replicate
+REPLICATE_API_KEY=
+
+# Ideogram
+IDEOGRAM_API_KEY=
+
+# Fal
+FAL_API_KEY=
+
+# Exa
+EXA_API_KEY=
+
+# E2B
+E2B_API_KEY=
+
+# Mem0
+MEM0_API_KEY=
+
+# Nvidia
+NVIDIA_API_KEY=
+
+# Apollo
 APOLLO_API_KEY=
-ENRICHLAYER_API_KEY=
-AYRSHARE_API_KEY=
-AYRSHARE_JWT_KEY=
+
+# SmartLead
 SMARTLEAD_API_KEY=
+
+# ZeroBounce
 ZEROBOUNCE_API_KEY=
 
-# Other Services
-AUTOMOD_API_KEY=
+# Ayrshare
+AYRSHARE_API_KEY=
+AYRSHARE_JWT_KEY=
+
+## ===== OPTIONAL API KEYS END ===== ##
+
+# Block Error Rate Monitoring
+BLOCK_ERROR_RATE_THRESHOLD=0.5
+BLOCK_ERROR_RATE_CHECK_INTERVAL_SECS=86400
+
+# Logging Configuration
+LOG_LEVEL=INFO
+ENABLE_CLOUD_LOGGING=false
+ENABLE_FILE_LOGGING=false
+# Use to manually set the log directory
+# LOG_DIR=./logs
+
+# Example Blocks Configuration
+# Set to true to enable example blocks in development
+# These blocks are disabled by default in production
+ENABLE_EXAMPLE_BLOCKS=false
+
+# Cloud Storage Configuration
+# Cleanup interval for expired files (hours between cleanup runs, 1-24 hours)
+CLOUD_STORAGE_CLEANUP_INTERVAL_HOURS=6

autogpt_platform/backend/.gitignore

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

autogpt_platform/backend/Dockerfile

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

autogpt_platform/backend/backend/blocks/agent.py

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

autogpt_platform/backend/backend/blocks/ai_music_generator.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/discord.py

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/llm.py

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

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

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

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

@@ -1 +0,0 @@
-

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

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

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

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

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

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

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

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

autogpt_platform/backend/backend/blocks/time_blocks.py

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

autogpt_platform/backend/backend/data/block_cost_config.py

@@ -5,12 +5,6 @@ from backend.blocks.ai_shortform_video_block import AIShortformVideoCreatorBlock
 from backend.blocks.apollo.organization import SearchOrganizationsBlock
 from backend.blocks.apollo.people import SearchPeopleBlock
 from backend.blocks.apollo.person import GetPersonDetailBlock
-from backend.blocks.enrichlayer.linkedin import (
-    GetLinkedinProfileBlock,
-    GetLinkedinProfilePictureBlock,
-    LinkedinPersonLookupBlock,
-    LinkedinRoleLookupBlock,
-)
 from backend.blocks.flux_kontext import AIImageEditorBlock, FluxKontextModelName
 from backend.blocks.ideogram import IdeogramModelBlock
 from backend.blocks.jina.embeddings import JinaEmbeddingBlock
@@ -36,7 +30,6 @@ from backend.integrations.credentials_store import (
     anthropic_credentials,
     apollo_credentials,
     did_credentials,
-    enrichlayer_credentials,
     groq_credentials,
     ideogram_credentials,
     jina_credentials,
@@ -46,7 +39,6 @@ from backend.integrations.credentials_store import (
     replicate_credentials,
     revid_credentials,
     unreal_credentials,
-    v0_credentials,
 )
 
 # =============== Configure the cost for each LLM Model call =============== #
@@ -123,10 +115,6 @@ MODEL_COST: dict[LlmModel, int] = {
     LlmModel.GEMINI_2_5_FLASH_LITE_PREVIEW: 1,
     LlmModel.GEMINI_2_0_FLASH_LITE: 1,
     LlmModel.DEEPSEEK_R1_0528: 1,
-    # v0 by Vercel models
-    LlmModel.V0_1_5_MD: 1,
-    LlmModel.V0_1_5_LG: 2,
-    LlmModel.V0_1_0_MD: 1,
 }
 
 for model in LlmModel:
@@ -216,23 +204,6 @@ LLM_COST = (
         for model, cost in MODEL_COST.items()
         if MODEL_METADATA[model].provider == "llama_api"
     ]
-    # v0 by Vercel Models
-    + [
-        BlockCost(
-            cost_type=BlockCostType.RUN,
-            cost_filter={
-                "model": model,
-                "credentials": {
-                    "id": v0_credentials.id,
-                    "provider": v0_credentials.provider,
-                    "type": v0_credentials.type,
-                },
-            },
-            cost_amount=cost,
-        )
-        for model, cost in MODEL_COST.items()
-        if MODEL_METADATA[model].provider == "v0"
-    ]
     # AI/ML Api Models
     + [
         BlockCost(
@@ -405,54 +376,6 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
             },
         )
     ],
-    GetLinkedinProfileBlock: [
-        BlockCost(
-            cost_amount=1,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    LinkedinPersonLookupBlock: [
-        BlockCost(
-            cost_amount=2,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    LinkedinRoleLookupBlock: [
-        BlockCost(
-            cost_amount=3,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
-    GetLinkedinProfilePictureBlock: [
-        BlockCost(
-            cost_amount=3,
-            cost_filter={
-                "credentials": {
-                    "id": enrichlayer_credentials.id,
-                    "provider": enrichlayer_credentials.provider,
-                    "type": enrichlayer_credentials.type,
-                }
-            },
-        )
-    ],
     SmartDecisionMakerBlock: LLM_COST,
     SearchOrganizationsBlock: [
         BlockCost(

autogpt_platform/backend/backend/data/credit.py

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

autogpt_platform/backend/backend/data/credit_test.py

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

autogpt_platform/backend/backend/data/execution.py

@@ -33,13 +33,12 @@ from prisma.types import (
     AgentNodeExecutionUpdateInput,
     AgentNodeExecutionWhereInput,
 )
-from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
+from pydantic import BaseModel, ConfigDict, JsonValue
 from pydantic.fields import Field
 
 from backend.server.v2.store.exceptions import DatabaseError
 from backend.util import type as type_utils
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 from backend.util.retry import func_retry
 from backend.util.settings import Config
 from backend.util.truncate import truncate
@@ -60,7 +59,7 @@ from .includes import (
     GRAPH_EXECUTION_INCLUDE_WITH_NODES,
     graph_execution_include,
 )
-from .model import GraphExecutionStats, NodeExecutionStats
+from .model import GraphExecutionStats
 
 T = TypeVar("T")
 
@@ -90,7 +89,6 @@ ExecutionStatus = AgentExecutionStatus
 
 
 class GraphExecutionMeta(BaseDbModel):
-    id: str  # type: ignore # Override base class to make this required
     user_id: str
     graph_id: str
     graph_version: int
@@ -292,14 +290,13 @@ class GraphExecutionWithNodes(GraphExecution):
             node_executions=node_executions,
         )
 
-    def to_graph_execution_entry(self, user_context: "UserContext"):
+    def to_graph_execution_entry(self):
         return GraphExecutionEntry(
             user_id=self.user_id,
             graph_id=self.graph_id,
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
             nodes_input_masks={},  # FIXME: store credentials on AgentGraphExecution
-            user_context=user_context,
         )
 
 
@@ -321,30 +318,18 @@ class NodeExecutionResult(BaseModel):
 
     @staticmethod
     def from_db(_node_exec: AgentNodeExecution, user_id: Optional[str] = None):
-        try:
-            stats = NodeExecutionStats.model_validate(_node_exec.stats or {})
-        except (ValueError, ValidationError):
-            stats = NodeExecutionStats()
-
-        if stats.cleared_inputs:
-            input_data: BlockInput = defaultdict()
-            for name, messages in stats.cleared_inputs.items():
-                input_data[name] = messages[-1] if messages else ""
-        elif _node_exec.executionData:
+        if _node_exec.executionData:
+            # Execution that has been queued for execution will persist its data.
             input_data = type_utils.convert(_node_exec.executionData, dict[str, Any])
         else:
+            # For incomplete execution, executionData will not be yet available.
             input_data: BlockInput = defaultdict()
             for data in _node_exec.Input or []:
                 input_data[data.name] = type_utils.convert(data.data, type[Any])
 
         output_data: CompletedBlockOutput = defaultdict(list)
-
-        if stats.cleared_outputs:
-            for name, messages in stats.cleared_outputs.items():
-                output_data[name].extend(messages)
-        else:
-            for data in _node_exec.Output or []:
-                output_data[data.name].append(type_utils.convert(data.data, type[Any]))
+        for data in _node_exec.Output or []:
+            output_data[data.name].append(type_utils.convert(data.data, type[Any]))
 
         graph_execution: AgentGraphExecution | None = _node_exec.GraphExecution
         if graph_execution:
@@ -371,9 +356,7 @@ class NodeExecutionResult(BaseModel):
             end_time=_node_exec.endedTime,
         )
 
-    def to_node_execution_entry(
-        self, user_context: "UserContext"
-    ) -> "NodeExecutionEntry":
+    def to_node_execution_entry(self) -> "NodeExecutionEntry":
         return NodeExecutionEntry(
             user_id=self.user_id,
             graph_exec_id=self.graph_exec_id,
@@ -382,7 +365,6 @@ class NodeExecutionResult(BaseModel):
             node_id=self.node_id,
             block_id=self.block_id,
             inputs=self.input_data,
-            user_context=user_context,
         )
 
 
@@ -390,13 +372,13 @@ class NodeExecutionResult(BaseModel):
 
 
 async def get_graph_executions(
-    graph_exec_id: Optional[str] = None,
-    graph_id: Optional[str] = None,
-    user_id: Optional[str] = None,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-    limit: Optional[int] = None,
+    graph_exec_id: str | None = None,
+    graph_id: str | None = None,
+    user_id: str | None = None,
+    statuses: list[ExecutionStatus] | None = None,
+    created_time_gte: datetime | None = None,
+    created_time_lte: datetime | None = None,
+    limit: int | None = None,
 ) -> list[GraphExecutionMeta]:
     """⚠️ **Optional `user_id` check**: MUST USE check in user-facing endpoints."""
     where_filter: AgentGraphExecutionWhereInput = {
@@ -424,60 +406,6 @@ async def get_graph_executions(
     return [GraphExecutionMeta.from_db(execution) for execution in executions]
 
 
-class GraphExecutionsPaginated(BaseModel):
-    """Response schema for paginated graph executions."""
-
-    executions: list[GraphExecutionMeta]
-    pagination: Pagination
-
-
-async def get_graph_executions_paginated(
-    user_id: str,
-    graph_id: Optional[str] = None,
-    page: int = 1,
-    page_size: int = 25,
-    statuses: Optional[list[ExecutionStatus]] = None,
-    created_time_gte: Optional[datetime] = None,
-    created_time_lte: Optional[datetime] = None,
-) -> GraphExecutionsPaginated:
-    """Get paginated graph executions for a specific graph."""
-    where_filter: AgentGraphExecutionWhereInput = {
-        "isDeleted": False,
-        "userId": user_id,
-    }
-
-    if graph_id:
-        where_filter["agentGraphId"] = graph_id
-    if created_time_gte or created_time_lte:
-        where_filter["createdAt"] = {
-            "gte": created_time_gte or datetime.min.replace(tzinfo=timezone.utc),
-            "lte": created_time_lte or datetime.max.replace(tzinfo=timezone.utc),
-        }
-    if statuses:
-        where_filter["OR"] = [{"executionStatus": status} for status in statuses]
-
-    total_count = await AgentGraphExecution.prisma().count(where=where_filter)
-    total_pages = (total_count + page_size - 1) // page_size
-
-    offset = (page - 1) * page_size
-    executions = await AgentGraphExecution.prisma().find_many(
-        where=where_filter,
-        order={"createdAt": "desc"},
-        take=page_size,
-        skip=offset,
-    )
-
-    return GraphExecutionsPaginated(
-        executions=[GraphExecutionMeta.from_db(execution) for execution in executions],
-        pagination=Pagination(
-            total_items=total_count,
-            total_pages=total_pages,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
 async def get_graph_execution_meta(
     user_id: str, execution_id: str
 ) -> GraphExecutionMeta | None:
@@ -877,19 +805,12 @@ async def get_latest_node_execution(
 # ----------------- Execution Infrastructure ----------------- #
 
 
-class UserContext(BaseModel):
-    """Generic user context for graph execution containing user-specific settings."""
-
-    timezone: str
-
-
 class GraphExecutionEntry(BaseModel):
     user_id: str
     graph_exec_id: str
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]] = None
-    user_context: UserContext
 
 
 class NodeExecutionEntry(BaseModel):
@@ -900,7 +821,6 @@ class NodeExecutionEntry(BaseModel):
     node_id: str
     block_id: str
     inputs: BlockInput
-    user_context: UserContext
 
 
 class ExecutionQueue(Generic[T]):

autogpt_platform/backend/backend/data/generate_data.py

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

autogpt_platform/backend/backend/data/model.py

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

autogpt_platform/backend/backend/data/notifications.py

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

autogpt_platform/backend/backend/data/notifications_test.py

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

autogpt_platform/backend/backend/data/user.py

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

autogpt_platform/backend/backend/executor/activity_status_generator.py

@@ -6,17 +6,20 @@ import json
 import logging
 from typing import TYPE_CHECKING, Any, NotRequired, TypedDict
 
+from autogpt_libs.feature_flag.client import is_feature_enabled
 from pydantic import SecretStr
 
 from backend.blocks.llm import LlmModel, llm_call
 from backend.data.block import get_block
 from backend.data.execution import ExecutionStatus, NodeExecutionResult
 from backend.data.model import APIKeyCredentials, GraphExecutionStats
-from backend.util.feature_flag import Flag, is_feature_enabled
 from backend.util.retry import func_retry
 from backend.util.settings import Settings
 from backend.util.truncate import truncate
 
+# LaunchDarkly feature flag key for AI activity status generation
+AI_ACTIVITY_STATUS_FLAG_KEY = "ai-agent-execution-summary"
+
 if TYPE_CHECKING:
     from backend.executor import DatabaseManagerAsyncClient
 
@@ -100,7 +103,9 @@ async def generate_activity_status_for_execution(
         AI-generated activity status string, or None if feature is disabled
     """
     # Check LaunchDarkly feature flag for AI activity status generation with full context support
-    if not await is_feature_enabled(Flag.AI_ACTIVITY_STATUS, user_id):
+    if not await is_feature_enabled(
+        AI_ACTIVITY_STATUS_FLAG_KEY, user_id, default=False
+    ):
         logger.debug("AI activity status generation is disabled via LaunchDarkly")
         return None
 

autogpt_platform/backend/backend/executor/database.py

@@ -20,7 +20,6 @@ from backend.data.execution import (
     upsert_execution_input,
     upsert_execution_output,
 )
-from backend.data.generate_data import get_user_execution_summary_data
 from backend.data.graph import (
     get_connected_output_nodes,
     get_graph,
@@ -36,14 +35,13 @@ from backend.data.notifications import (
 )
 from backend.data.user import (
     get_active_user_ids_in_timerange,
+    get_user_by_id,
     get_user_email_by_id,
     get_user_email_verification,
     get_user_integrations,
     get_user_notification_preference,
     update_user_integrations,
 )
-from backend.server.v2.library.db import add_store_agent_to_library, list_library_agents
-from backend.server.v2.store.db import get_store_agent_details, get_store_agents
 from backend.util.service import (
     AppService,
     AppServiceClient,
@@ -132,6 +130,7 @@ class DatabaseManager(AppService):
 
     # User Comms - async
     get_active_user_ids_in_timerange = _(get_active_user_ids_in_timerange)
+    get_user_by_id = _(get_user_by_id)
     get_user_email_by_id = _(get_user_email_by_id)
     get_user_email_verification = _(get_user_email_verification)
     get_user_notification_preference = _(get_user_notification_preference)
@@ -147,17 +146,6 @@ class DatabaseManager(AppService):
         get_user_notification_oldest_message_in_batch
     )
 
-    # Library
-    list_library_agents = _(list_library_agents)
-    add_store_agent_to_library = _(add_store_agent_to_library)
-
-    # Store
-    get_store_agents = _(get_store_agents)
-    get_store_agent_details = _(get_store_agent_details)
-
-    # Summary data - async
-    get_user_execution_summary_data = _(get_user_execution_summary_data)
-
 
 class DatabaseManagerClient(AppServiceClient):
     d = DatabaseManager
@@ -186,17 +174,6 @@ class DatabaseManagerClient(AppServiceClient):
     # Block error monitoring
     get_block_error_stats = _(d.get_block_error_stats)
 
-    # User Emails
-    get_user_email_by_id = _(d.get_user_email_by_id)
-
-    # Library
-    list_library_agents = _(d.list_library_agents)
-    add_store_agent_to_library = _(d.add_store_agent_to_library)
-
-    # Store
-    get_store_agents = _(d.get_store_agents)
-    get_store_agent_details = _(d.get_store_agent_details)
-
 
 class DatabaseManagerAsyncClient(AppServiceClient):
     d = DatabaseManager
@@ -226,6 +203,7 @@ class DatabaseManagerAsyncClient(AppServiceClient):
 
     # User Comms
     get_active_user_ids_in_timerange = d.get_active_user_ids_in_timerange
+    get_user_by_id = d.get_user_by_id
     get_user_email_by_id = d.get_user_email_by_id
     get_user_email_verification = d.get_user_email_verification
     get_user_notification_preference = d.get_user_notification_preference
@@ -240,14 +218,3 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_user_notification_oldest_message_in_batch = (
         d.get_user_notification_oldest_message_in_batch
     )
-
-    # Library
-    list_library_agents = d.list_library_agents
-    add_store_agent_to_library = d.add_store_agent_to_library
-
-    # Store
-    get_store_agents = d.get_store_agents
-    get_store_agent_details = d.get_store_agent_details
-
-    # Summary data
-    get_user_execution_summary_data = d.get_user_execution_summary_data

autogpt_platform/backend/backend/executor/manager.py

@@ -20,7 +20,6 @@ from backend.data.notifications import (
     LowBalanceData,
     NotificationEventModel,
     NotificationType,
-    ZeroBalanceData,
 )
 from backend.data.rabbitmq import SyncRabbitMQ
 from backend.executor.activity_status_generator import (
@@ -28,7 +27,7 @@ from backend.executor.activity_status_generator import (
 )
 from backend.executor.utils import LogMetadata
 from backend.notifications.notifications import queue_notification
-from backend.util.exceptions import InsufficientBalanceError, ModerationError
+from backend.util.exceptions import InsufficientBalanceError
 
 if TYPE_CHECKING:
     from backend.executor import DatabaseManagerClient, DatabaseManagerAsyncClient
@@ -52,7 +51,6 @@ from backend.data.execution import (
     GraphExecutionEntry,
     NodeExecutionEntry,
     NodeExecutionResult,
-    UserContext,
 )
 from backend.data.graph import Link, Node
 from backend.executor.utils import (
@@ -69,14 +67,12 @@ from backend.executor.utils import (
     validate_exec,
 )
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.server.v2.AutoMod.manager import automod_manager
 from backend.util import json
 from backend.util.clients import (
     get_async_execution_event_bus,
     get_database_manager_async_client,
     get_database_manager_client,
     get_execution_event_bus,
-    get_notification_manager_client,
 )
 from backend.util.decorator import (
     async_error_logged,
@@ -86,7 +82,6 @@ from backend.util.decorator import (
 )
 from backend.util.file import clean_exec_files
 from backend.util.logging import TruncatedLogger, configure_logging
-from backend.util.metrics import DiscordChannel
 from backend.util.process import AppProcess, set_service_name
 from backend.util.retry import continuous_retry, func_retry
 from backend.util.settings import Settings
@@ -194,9 +189,6 @@ async def execute_node(
         "user_id": user_id,
     }
 
-    # Add user context from NodeExecutionEntry
-    extra_exec_kwargs["user_context"] = data.user_context
-
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
     # changes during execution. ⚠️ This means a set of credentials can only be used by
     # one (running) block at a time; simultaneous execution of blocks using same
@@ -243,7 +235,6 @@ async def _enqueue_next_nodes(
     graph_id: str,
     log_metadata: LogMetadata,
     nodes_input_masks: Optional[dict[str, dict[str, JsonValue]]],
-    user_context: UserContext,
 ) -> list[NodeExecutionEntry]:
     async def add_enqueued_execution(
         node_exec_id: str, node_id: str, block_id: str, data: BlockInput
@@ -262,7 +253,6 @@ async def _enqueue_next_nodes(
             node_id=node_id,
             block_id=block_id,
             inputs=data,
-            user_context=user_context,
         )
 
     async def register_next_executions(node_link: Link) -> list[NodeExecutionEntry]:
@@ -687,20 +677,19 @@ class ExecutionProcessor:
         self,
         node_exec: NodeExecutionEntry,
         execution_count: int,
-    ) -> tuple[int, int]:
+    ) -> int:
         total_cost = 0
-        remaining_balance = 0
         db_client = get_db_client()
         block = get_block(node_exec.block_id)
         if not block:
             logger.error(f"Block {node_exec.block_id} not found.")
-            return total_cost, 0
+            return total_cost
 
         cost, matching_filter = block_usage_cost(
             block=block, input_data=node_exec.inputs
         )
         if cost > 0:
-            remaining_balance = db_client.spend_credits(
+            db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -718,7 +707,7 @@ class ExecutionProcessor:
 
         cost, usage_count = execution_usage_cost(execution_count)
         if cost > 0:
-            remaining_balance = db_client.spend_credits(
+            db_client.spend_credits(
                 user_id=node_exec.user_id,
                 cost=cost,
                 metadata=UsageTransactionMetadata(
@@ -733,7 +722,7 @@ class ExecutionProcessor:
             )
             total_cost += cost
 
-        return total_cost, remaining_balance
+        return total_cost
 
     @time_measured
     def _on_graph_execution(
@@ -770,22 +759,6 @@ class ExecutionProcessor:
                     amount=1,
                 )
 
-            # Input moderation
-            try:
-                if moderation_error := asyncio.run_coroutine_threadsafe(
-                    automod_manager.moderate_graph_execution_inputs(
-                        db_client=get_db_async_client(),
-                        graph_exec=graph_exec,
-                    ),
-                    self.node_evaluation_loop,
-                ).result(timeout=30.0):
-                    raise moderation_error
-            except asyncio.TimeoutError:
-                log_metadata.warning(
-                    f"Input moderation timed out for graph execution {graph_exec.graph_exec_id}, bypassing moderation and continuing execution"
-                )
-                # Continue execution without moderation
-
             # ------------------------------------------------------------
             # Pre‑populate queue ---------------------------------------
             # ------------------------------------------------------------
@@ -797,8 +770,7 @@ class ExecutionProcessor:
                     ExecutionStatus.TERMINATED,
                 ],
             ):
-                node_entry = node_exec.to_node_execution_entry(graph_exec.user_context)
-                execution_queue.add(node_entry)
+                execution_queue.add(node_exec.to_node_execution_entry())
 
             # ------------------------------------------------------------
             # Main dispatch / polling loop -----------------------------
@@ -816,19 +788,12 @@ class ExecutionProcessor:
 
                 # Charge usage (may raise) ------------------------------
                 try:
-                    cost, remaining_balance = self._charge_usage(
+                    cost = self._charge_usage(
                         node_exec=queued_node_exec,
                         execution_count=increment_execution_count(graph_exec.user_id),
                     )
                     with execution_stats_lock:
                         execution_stats.cost += cost
-                    # Check if we crossed the low balance threshold
-                    self._handle_low_balance(
-                        db_client=db_client,
-                        user_id=graph_exec.user_id,
-                        current_balance=remaining_balance,
-                        transaction_cost=cost,
-                    )
                 except InsufficientBalanceError as balance_error:
                     error = balance_error  # Set error to trigger FAILED status
                     node_exec_id = queued_node_exec.node_exec_id
@@ -843,10 +808,11 @@ class ExecutionProcessor:
                         status=ExecutionStatus.FAILED,
                     )
 
-                    self._handle_insufficient_funds_notif(
+                    self._handle_low_balance_notif(
                         db_client,
                         graph_exec.user_id,
                         graph_exec.graph_id,
+                        execution_stats,
                         error,
                     )
                     # Gracefully stop the execution loop
@@ -931,25 +897,6 @@ class ExecutionProcessor:
                         time.sleep(0.1)
 
             # loop done --------------------------------------------------
-
-            # Output moderation
-            try:
-                if moderation_error := asyncio.run_coroutine_threadsafe(
-                    automod_manager.moderate_graph_execution_outputs(
-                        db_client=get_db_async_client(),
-                        graph_exec_id=graph_exec.graph_exec_id,
-                        user_id=graph_exec.user_id,
-                        graph_id=graph_exec.graph_id,
-                    ),
-                    self.node_evaluation_loop,
-                ).result(timeout=30.0):
-                    raise moderation_error
-            except asyncio.TimeoutError:
-                log_metadata.warning(
-                    f"Output moderation timed out for graph execution {graph_exec.graph_exec_id}, bypassing moderation and continuing execution"
-                )
-                # Continue execution without moderation
-
             # Determine final execution status based on whether there was an error or termination
             if cancel.is_set():
                 execution_status = ExecutionStatus.TERMINATED
@@ -970,12 +917,11 @@ class ExecutionProcessor:
                 else Exception(f"{e.__class__.__name__}: {e}")
             )
 
-            known_errors = (InsufficientBalanceError, ModerationError)
+            known_errors = (InsufficientBalanceError,)
             if isinstance(error, known_errors):
                 execution_stats.error = str(error)
                 return ExecutionStatus.FAILED
 
-            execution_status = ExecutionStatus.FAILED
             log_metadata.exception(
                 f"Failed graph execution {graph_exec.graph_exec_id}: {error}"
             )
@@ -1069,7 +1015,6 @@ class ExecutionProcessor:
         db_client = get_db_async_client()
 
         log_metadata.debug(f"Enqueue nodes for {node_id}: {output}")
-
         for next_execution in await _enqueue_next_nodes(
             db_client=db_client,
             node=output.node,
@@ -1079,7 +1024,6 @@ class ExecutionProcessor:
             graph_id=graph_exec.graph_id,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
-            user_context=graph_exec.user_context,
         ):
             execution_queue.add(next_execution)
 
@@ -1120,25 +1064,25 @@ class ExecutionProcessor:
             )
         )
 
-    def _handle_insufficient_funds_notif(
+    def _handle_low_balance_notif(
         self,
         db_client: "DatabaseManagerClient",
         user_id: str,
         graph_id: str,
+        exec_stats: GraphExecutionStats,
         e: InsufficientBalanceError,
     ):
-        shortfall = abs(e.amount) - e.balance
+        shortfall = e.balance - e.amount
         metadata = db_client.get_graph_metadata(graph_id)
         base_url = (
             settings.config.frontend_base_url or settings.config.platform_base_url
         )
-
         queue_notification(
             NotificationEventModel(
                 user_id=user_id,
-                type=NotificationType.ZERO_BALANCE,
-                data=ZeroBalanceData(
-                    current_balance=e.balance,
+                type=NotificationType.LOW_BALANCE,
+                data=LowBalanceData(
+                    current_balance=exec_stats.cost,
                     billing_page_link=f"{base_url}/profile/credits",
                     shortfall=shortfall,
                     agent_name=metadata.name if metadata else "Unknown Agent",
@@ -1146,73 +1090,6 @@ class ExecutionProcessor:
             )
         )
 
-        try:
-            user_email = db_client.get_user_email_by_id(user_id)
-
-            alert_message = (
-                f"❌ **Insufficient Funds Alert**\n"
-                f"User: {user_email or user_id}\n"
-                f"Agent: {metadata.name if metadata else 'Unknown Agent'}\n"
-                f"Current balance: ${e.balance/100:.2f}\n"
-                f"Attempted cost: ${abs(e.amount)/100:.2f}\n"
-                f"Shortfall: ${abs(shortfall)/100:.2f}\n"
-                f"[View User Details]({base_url}/admin/spending?search={user_email})"
-            )
-
-            get_notification_manager_client().discord_system_alert(
-                alert_message, DiscordChannel.PRODUCT
-            )
-        except Exception as alert_error:
-            logger.error(
-                f"Failed to send insufficient funds Discord alert: {alert_error}"
-            )
-
-    def _handle_low_balance(
-        self,
-        db_client: "DatabaseManagerClient",
-        user_id: str,
-        current_balance: int,
-        transaction_cost: int,
-    ):
-        """Check and handle low balance scenarios after a transaction"""
-        LOW_BALANCE_THRESHOLD = settings.config.low_balance_threshold
-
-        balance_before = current_balance + transaction_cost
-
-        if (
-            current_balance < LOW_BALANCE_THRESHOLD
-            and balance_before >= LOW_BALANCE_THRESHOLD
-        ):
-            base_url = (
-                settings.config.frontend_base_url or settings.config.platform_base_url
-            )
-            queue_notification(
-                NotificationEventModel(
-                    user_id=user_id,
-                    type=NotificationType.LOW_BALANCE,
-                    data=LowBalanceData(
-                        current_balance=current_balance,
-                        billing_page_link=f"{base_url}/profile/credits",
-                    ),
-                )
-            )
-
-            try:
-                user_email = db_client.get_user_email_by_id(user_id)
-                alert_message = (
-                    f"⚠️ **Low Balance Alert**\n"
-                    f"User: {user_email or user_id}\n"
-                    f"Balance dropped below ${LOW_BALANCE_THRESHOLD/100:.2f}\n"
-                    f"Current balance: ${current_balance/100:.2f}\n"
-                    f"Transaction cost: ${transaction_cost/100:.2f}\n"
-                    f"[View User Details]({base_url}/admin/spending?search={user_email})"
-                )
-                get_notification_manager_client().discord_system_alert(
-                    alert_message, DiscordChannel.PRODUCT
-                )
-            except Exception as e:
-                logger.error(f"Failed to send low balance Discord alert: {e}")
-
 
 class ExecutionManager(AppProcess):
     def __init__(self):
@@ -1294,9 +1171,6 @@ class ExecutionManager(AppProcess):
             )
             return
 
-        # Check if channel is closed and force reconnection if needed
-        if not self.cancel_client.is_ready:
-            self.cancel_client.disconnect()
         self.cancel_client.connect()
         cancel_channel = self.cancel_client.get_channel()
         cancel_channel.basic_consume(
@@ -1326,9 +1200,6 @@ class ExecutionManager(AppProcess):
             )
             return
 
-        # Check if channel is closed and force reconnection if needed
-        if not self.run_client.is_ready:
-            self.run_client.disconnect()
         self.run_client.connect()
         run_channel = self.run_client.get_channel()
         run_channel.basic_qos(prefetch_count=self.pool_size)

autogpt_platform/backend/backend/executor/manager_low_balance_test.py

@@ -1,149 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-from prisma.enums import NotificationType
-
-from backend.data.notifications import LowBalanceData
-from backend.executor.manager import ExecutionProcessor
-from backend.util.test import SpinTestServer
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_low_balance_threshold_crossing(server: SpinTestServer):
-    """Test that _handle_low_balance triggers notification when crossing threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 400  # $4 - below $5 threshold
-    transaction_cost = 600  # $6 transaction
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.low_balance_threshold = 500  # $5 threshold
-        mock_settings.config.frontend_base_url = "https://test.com"
-
-        # Create mock database client
-        mock_db_client = MagicMock()
-        mock_db_client.get_user_email_by_id.return_value = "test@example.com"
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            db_client=mock_db_client,
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # Verify notification was queued
-        mock_queue_notif.assert_called_once()
-        notification_call = mock_queue_notif.call_args[0][0]
-
-        # Verify notification details
-        assert notification_call.type == NotificationType.LOW_BALANCE
-        assert notification_call.user_id == user_id
-        assert isinstance(notification_call.data, LowBalanceData)
-        assert notification_call.data.current_balance == current_balance
-
-        # Verify Discord alert was sent
-        mock_client.discord_system_alert.assert_called_once()
-        discord_message = mock_client.discord_system_alert.call_args[0][0]
-        assert "Low Balance Alert" in discord_message
-        assert "test@example.com" in discord_message
-        assert "$4.00" in discord_message
-        assert "$6.00" in discord_message
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_low_balance_no_notification_when_not_crossing(
-    server: SpinTestServer,
-):
-    """Test that no notification is sent when not crossing the threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 600  # $6 - above $5 threshold
-    transaction_cost = (
-        100  # $1 transaction (balance before was $7, still above threshold)
-    )
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.low_balance_threshold = 500  # $5 threshold
-
-        # Create mock database client
-        mock_db_client = MagicMock()
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            db_client=mock_db_client,
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # Verify no notification was sent
-        mock_queue_notif.assert_not_called()
-        mock_client.discord_system_alert.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_handle_low_balance_no_duplicate_when_already_below(
-    server: SpinTestServer,
-):
-    """Test that no notification is sent when already below threshold."""
-
-    execution_processor = ExecutionProcessor()
-    user_id = "test-user-123"
-    current_balance = 300  # $3 - below $5 threshold
-    transaction_cost = (
-        100  # $1 transaction (balance before was $4, also below threshold)
-    )
-
-    # Mock dependencies
-    with patch(
-        "backend.executor.manager.queue_notification"
-    ) as mock_queue_notif, patch(
-        "backend.executor.manager.get_notification_manager_client"
-    ) as mock_get_client, patch(
-        "backend.executor.manager.settings"
-    ) as mock_settings:
-
-        # Setup mocks
-        mock_client = MagicMock()
-        mock_get_client.return_value = mock_client
-        mock_settings.config.low_balance_threshold = 500  # $5 threshold
-
-        # Create mock database client
-        mock_db_client = MagicMock()
-
-        # Test the low balance handler
-        execution_processor._handle_low_balance(
-            db_client=mock_db_client,
-            user_id=user_id,
-            current_balance=current_balance,
-            transaction_cost=transaction_cost,
-        )
-
-        # Verify no notification was sent (user was already below threshold)
-        mock_queue_notif.assert_not_called()
-        mock_client.discord_system_alert.assert_not_called()

autogpt_platform/backend/backend/executor/scheduler.py

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

autogpt_platform/backend/backend/executor/utils.py

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

autogpt_platform/backend/backend/integrations/credentials_store.py

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

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

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

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

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

autogpt_platform/backend/backend/integrations/providers.py

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

autogpt_platform/backend/backend/notifications/notifications.py

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

autogpt_platform/backend/backend/notifications/templates/agent_approved.html.jinja2

@@ -1,73 +0,0 @@
-{# Agent Approved Notification Email Template #}
-{#
-  Template variables:
-  data.agent_name: the name of the approved agent
-  data.agent_id: the ID of the agent
-  data.agent_version: the version of the agent
-  data.reviewer_name: the name of the reviewer who approved it
-  data.reviewer_email: the email of the reviewer
-  data.comments: comments from the reviewer
-  data.reviewed_at: when the agent was reviewed
-  data.store_url: URL to view the agent in the store
-
-  Subject: 🎉 Your agent '{{ data.agent_name }}' has been approved!
-#}
-
-{% block content %}
-<h1 style="color: #28a745; font-size: 32px; font-weight: 700; margin: 0 0 24px 0; text-align: center;">
-  🎉 Congratulations!
-</h1>
-
-<p style="color: #586069; font-size: 18px; text-align: center; margin: 0 0 24px 0;">
-  Your agent <strong>'{{ data.agent_name }}'</strong> has been approved and is now live in the store!
-</p>
-
-<div style="height: 32px; background: transparent;"></div>
-
-{% if data.comments %}
-<div style="background: #d4edda; border: 1px solid #c3e6cb; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #155724; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    💬 Creator feedback area
-  </h3>
-  <p style="color: #155724; margin: 0; font-size: 16px; line-height: 1.5;">
-    {{ data.comments }}
-  </p>
-</div>
-
-<div style="height: 40px; background: transparent;"></div>
-{% endif %}
-
-<div style="background: #d1ecf1; border: 1px solid #bee5eb; border-radius: 8px; padding: 20px; margin: 0;">
-  <h3 style="color: #0c5460; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    What's Next?
-  </h3>
-  <ul style="color: #0c5460; margin: 0; padding-left: 18px; font-size: 16px; line-height: 1.6;">
-    <li>Your agent is now live and discoverable in the AutoGPT Store</li>
-    <li>Users can find, install, and run your agent</li>
-    <li>You can update your agent anytime by submitting a new version</li>
-  </ul>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="text-align: center; margin: 24px 0;">
-  <a href="{{ data.store_url }}" style="display: inline-block; background: linear-gradient(135deg, #7c3aed 0%, #5b21b6 100%); color: black; text-decoration: none; padding: 14px 28px; border-radius: 6px; font-weight: 600; font-size: 16px;">
-    View Your Agent in Store
-  </a>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #fff3cd; border: 1px solid #ffeaa7; border-radius: 6px; padding: 16px; margin: 24px 0; text-align: center;">
-  <p style="margin: 0; color: #856404; font-size: 14px;">
-    <strong>💡 Pro Tip:</strong> Share your agent with the community! Post about it on social media, forums, or your blog to help more users discover and benefit from your creation.
-  </p>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<p style="color: #6a737d; font-size: 14px; text-align: center; margin: 24px 0;">
-  Thank you for contributing to the AutoGPT ecosystem! 🚀
-</p>
-
-{% endblock %}

autogpt_platform/backend/backend/notifications/templates/agent_rejected.html.jinja2

@@ -1,77 +0,0 @@
-{# Agent Rejected Notification Email Template #}
-{#
-  Template variables:
-  data.agent_name: the name of the rejected agent
-  data.agent_id: the ID of the agent
-  data.agent_version: the version of the agent
-  data.reviewer_name: the name of the reviewer who rejected it
-  data.reviewer_email: the email of the reviewer
-  data.comments: comments from the reviewer explaining the rejection
-  data.reviewed_at: when the agent was reviewed
-  data.resubmit_url: URL to resubmit the agent
-
-  Subject: Your agent '{{ data.agent_name }}' needs some updates
-#}
-
-
-{% block content %}
-<h1 style="color: #d73a49; font-size: 32px; font-weight: 700; margin: 0 0 24px 0; text-align: center;">
-  📝 Review Complete
-</h1>
-
-<p style="color: #586069; font-size: 18px; text-align: center; margin: 0 0 24px 0;">
-  Your agent <strong>'{{ data.agent_name }}'</strong> needs some updates before approval.
-</p>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #f8d7da; border: 1px solid #f5c6cb; border-radius: 8px; padding: 20px; margin: 0 0 24px 0;">
-  <h3 style="color: #721c24; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    💬 Creator feedback area
-  </h3>
-  <p style="color: #721c24; margin: 0; font-size: 16px; line-height: 1.5;">
-    {{ data.comments }}
-  </p>
-</div>
-
-<div style="height: 40px; background: transparent;"></div>
-
-<div style="background: #d4edda; border: 1px solid #c3e6cb; border-radius: 8px; padding: 20px; margin: 0 0 24px 0;">
-  <h3 style="color: #155724; font-size: 16px; font-weight: 600; margin: 0 0 12px 0;">
-    ☑ Steps to Resubmit:
-  </h3>
-  <ul style="color: #155724; margin: 0; padding-left: 18px; font-size: 16px; line-height: 1.6;">
-    <li>Review the feedback provided above carefully</li>
-    <li>Make the necessary updates to your agent</li>
-    <li>Test your agent thoroughly to ensure it works as expected</li>
-    <li>Submit your updated agent for review</li>
-  </ul>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<div style="background: #fff3cd; border: 1px solid #ffeaa7; border-radius: 6px; padding: 12px; margin: 0 0 24px 0; text-align: center;">
-  <p style="margin: 0; color: #856404; font-size: 14px;">
-    <strong>💡 Tip:</strong> Address all the points mentioned in the feedback to increase your chances of approval in the next review.
-  </p>
-</div>
-
-<div style="text-align: center; margin: 32px 0;">
-  <a href="{{ data.resubmit_url }}" style="display: inline-block; background: linear-gradient(135deg, #7c3aed 0%, #5b21b6 100%); color: black; text-decoration: none; padding: 14px 28px; border-radius: 6px; font-weight: 600; font-size: 16px;">
-    Update & Resubmit Agent
-  </a>
-</div>
-
-<div style="background: #d1ecf1; border: 1px solid #bee5eb; border-radius: 6px; padding: 16px; margin: 24px 0;">
-  <p style="margin: 0; color: #0c5460; font-size: 14px; text-align: center;">
-    <strong>🌟 Don't Give Up!</strong> Many successful agents go through multiple iterations before approval. Our review team is here to help you succeed!
-  </p>
-</div>
-
-<div style="height: 32px; background: transparent;"></div>
-
-<p style="color: #6a737d; font-size: 14px; text-align: center; margin: 32px 0 24px 0;">
-  We're excited to see your improved agent submission! 🚀
-</p>
-
-{% endblock %}

autogpt_platform/backend/backend/notifications/templates/low_balance.html.jinja2

@@ -1,7 +1,9 @@
 {# Low Balance Notification Email Template #}
 {# Template variables:
+data.agent_name: the name of the agent
 data.current_balance: the current balance of the user
 data.billing_page_link: the link to the billing page
+data.shortfall: the shortfall amount
 #}
 
 <p style="
@@ -23,7 +25,7 @@ data.billing_page_link: the link to the billing page
     margin-top: 0;
     margin-bottom: 20px;
   ">
-  Your account balance has dropped below the recommended threshold.
+  Your agent "<strong>{{ data.agent_name }}</strong>" has been stopped due to low balance.
 </p>
 
 <div style="
@@ -42,6 +44,15 @@ data.billing_page_link: the link to the billing page
   ">
     <strong>Current Balance:</strong> ${{ "{:.2f}".format((data.current_balance|float)/100) }}
   </p>
+  <p style="
+    font-family: 'Poppins', sans-serif;
+    color: #070629;
+    font-size: 16px;
+    margin-top: 0;
+    margin-bottom: 10px;
+  ">
+    <strong>Shortfall:</strong> ${{ "{:.2f}".format((data.shortfall|float)/100) }}
+  </p>
 </div>
 
 
@@ -68,7 +79,7 @@ data.billing_page_link: the link to the billing page
     margin-top: 0;
     margin-bottom: 5px;
   ">
-    Your account requires additional credits to continue running agents. Please add credits to your account to avoid service interruption.
+    Your agent "<strong>{{ data.agent_name }}</strong>" requires additional credits to continue running. The current operation has been canceled until your balance is replenished.
   </p>
 </div>
 
@@ -99,5 +110,5 @@ data.billing_page_link: the link to the billing page
     margin-bottom: 10px;
     font-style: italic;
   ">
-  This is an automated low balance notification. Consider adding credits soon to avoid service interruption.
+  This is an automated notification. Your agent is stopped and will need manually restarted unless set to trigger automatically.
 </p>

autogpt_platform/backend/backend/notifications/templates/weekly_summary.html.jinja2

@@ -5,64 +5,23 @@ data.start_date: the start date of the summary
 data.end_date: the end date of the summary
 data.total_credits_used: the total credits used during the summary
 data.total_executions: the total number of executions during the summary
-data.most_used_agent: the most used agent's name during the summary
+data.most_used_agent: the most used agent's nameduring the summary
 data.total_execution_time: the total execution time during the summary
 data.successful_runs: the total number of successful runs during the summary
 data.failed_runs: the total number of failed runs during the summary
 data.average_execution_time: the average execution time during the summary
-data.cost_breakdown: the cost breakdown during the summary (dict mapping agent names to credit amounts)
+data.cost_breakdown: the cost breakdown during the summary
 #}
 
-<h1 style="color: #5D23BB; font-size: 32px; font-weight: 600; margin-bottom: 25px; margin-top: 0;">
-  Weekly Summary
-</h1>
+<h1>Weekly Summary</h1>
 
-<h2 style="color: #070629; font-size: 24px; font-weight: 500; margin-bottom: 20px;">
-  Your Agent Activity: {{ data.start_date.strftime('%B %-d') }} – {{ data.end_date.strftime('%B %-d') }}
-</h2>
-
-<div style="background-color: #ffffff; border-radius: 8px; padding: 20px; margin-bottom: 25px;">
-  <ul style="list-style-type: disc; padding-left: 20px; margin: 0;">
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Total Executions:</strong> {{ data.total_executions }}
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Total Credits Used:</strong> {{ data.total_credits_used|format("%.2f") }}
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Total Execution Time:</strong> {{ data.total_execution_time|format("%.1f") }} seconds
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Successful Runs:</strong> {{ data.successful_runs }}
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Failed Runs:</strong> {{ data.failed_runs }}
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Average Execution Time:</strong> {{ data.average_execution_time|format("%.1f") }} seconds
-    </li>
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Most Used Agent:</strong> {{ data.most_used_agent }}
-    </li>
-    {% if data.cost_breakdown %}
-    <li style="font-size: 16px; line-height: 1.8; margin-bottom: 8px;">
-      <strong>Cost Breakdown:</strong>
-      <ul style="list-style-type: disc; padding-left: 40px; margin-top: 8px;">
-        {% for agent_name, credits in data.cost_breakdown.items() %}
-          <li style="font-size: 16px; line-height: 1.8; margin-bottom: 4px;">
-            {{ agent_name }}: {{ credits|format("%.2f") }} credits
-          </li>
-        {% endfor %}
-      </ul>
-    </li>
-    {% endif %}
-  </ul>
-</div>
-
-<p style="font-size: 16px; line-height: 165%; margin-top: 20px; margin-bottom: 10px;">
-  Thank you for being a part of the AutoGPT community! 🎉
-</p>
-
-<p style="font-size: 16px; line-height: 165%; margin-bottom: 0;">
-  Join the conversation on <a href="https://discord.gg/autogpt" style="color: #4285F4; text-decoration: underline;">Discord here</a>.
-</p>
+<p>Start Date: {{ data.start_date }}</p>
+<p>End Date: {{ data.end_date }}</p>
+<p>Total Credits Used: {{ data.total_credits_used }}</p>
+<p>Total Executions: {{ data.total_executions }}</p>
+<p>Most Used Agent: {{ data.most_used_agent }}</p>
+<p>Total Execution Time: {{ data.total_execution_time }}</p>
+<p>Successful Runs: {{ data.successful_runs }}</p>
+<p>Failed Runs: {{ data.failed_runs }}</p>
+<p>Average Execution Time: {{ data.average_execution_time }}</p>
+<p>Cost Breakdown: {{ data.cost_breakdown }}</p>

autogpt_platform/backend/backend/notifications/templates/zero_balance.html.jinja2

@@ -1,114 +0,0 @@
-{# Low Balance Notification Email Template #}
-{# Template variables:
-data.agent_name: the name of the agent
-data.current_balance: the current balance of the user
-data.billing_page_link: the link to the billing page
-data.shortfall: the shortfall amount
-#}
-
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-  <strong>Zero Balance Warning</strong>
-</p>
-
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 165%;
-    margin-top: 0;
-    margin-bottom: 20px;
-  ">
-  Your agent "<strong>{{ data.agent_name }}</strong>" has been stopped due to low balance.
-</p>
-
-<div style="
-    margin-left: 15px;
-    margin-bottom: 20px;
-    padding: 15px;
-    border-left: 4px solid #5D23BB;
-    background-color: #f8f8ff;
-  ">
-  <p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-    <strong>Current Balance:</strong> ${{ "{:.2f}".format((data.current_balance|float)/100) }}
-  </p>
-  <p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-    <strong>Shortfall:</strong> ${{ "{:.2f}".format((data.shortfall|float)/100) }}
-  </p>
-</div>
-
-
-<div style="
-    margin-left: 15px;
-    margin-bottom: 20px;
-    padding: 15px;
-    border-left: 4px solid #FF6B6B;
-    background-color: #FFF0F0;
-  ">
-  <p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    margin-top: 0;
-    margin-bottom: 10px;
-  ">
-    <strong>Low Balance:</strong>
-  </p>
-  <p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    margin-top: 0;
-    margin-bottom: 5px;
-  ">
-    Your agent "<strong>{{ data.agent_name }}</strong>" requires additional credits to continue running. The current operation has been canceled until your balance is replenished.
-  </p>
-</div>
-
-<div style="
-    text-align: center;
-    margin: 30px 0;
-  ">
-  <a href="{{ data.billing_page_link }}" style="
-    font-family: 'Poppins', sans-serif;
-    background-color: #5D23BB;
-    color: white;
-    padding: 12px 24px;
-    text-decoration: none;
-    border-radius: 4px;
-    font-weight: 500;
-    display: inline-block;
-  ">
-    Manage Billing
-  </a>
-</div>
-
-<p style="
-    font-family: 'Poppins', sans-serif;
-    color: #070629;
-    font-size: 16px;
-    line-height: 150%;
-    margin-top: 30px;
-    margin-bottom: 10px;
-    font-style: italic;
-  ">
-  This is an automated notification. Your agent is stopped and will need manually restarted unless set to trigger automatically.
-</p>

autogpt_platform/backend/backend/sdk/provider.py

@@ -14,8 +14,6 @@ from backend.data.model import (
     CredentialsField,
     CredentialsMetaInput,
     CredentialsType,
-    OAuth2Credentials,
-    UserPasswordCredentials,
 )
 from backend.integrations.oauth.base import BaseOAuthHandler
 from backend.integrations.webhooks._base import BaseWebhooksManager
@@ -106,39 +104,14 @@ class Provider:
         )
 
     def get_test_credentials(self) -> Credentials:
-        """Get test credentials for the provider based on supported auth types."""
-        test_id = str(self.test_credentials_uuid)
-
-        # Return credentials based on the first supported auth type
-        if "user_password" in self.supported_auth_types:
-            return UserPasswordCredentials(
-                id=test_id,
-                provider=self.name,
-                username=SecretStr(f"mock-{self.name}-username"),
-                password=SecretStr(f"mock-{self.name}-password"),
-                title=f"Mock {self.name.title()} credentials",
-            )
-        elif "oauth2" in self.supported_auth_types:
-            return OAuth2Credentials(
-                id=test_id,
-                provider=self.name,
-                username=f"mock-{self.name}-username",
-                access_token=SecretStr(f"mock-{self.name}-access-token"),
-                access_token_expires_at=None,
-                refresh_token=SecretStr(f"mock-{self.name}-refresh-token"),
-                refresh_token_expires_at=None,
-                scopes=[f"mock-{self.name}-scope"],
-                title=f"Mock {self.name.title()} OAuth credentials",
-            )
-        else:
-            # Default to API key credentials
-            return APIKeyCredentials(
-                id=test_id,
-                provider=self.name,
-                api_key=SecretStr(f"mock-{self.name}-api-key"),
-                title=f"Mock {self.name.title()} API key",
-                expires_at=None,
-            )
+        """Get test credentials for the provider."""
+        return APIKeyCredentials(
+            id=str(self.test_credentials_uuid),
+            provider=self.name,
+            api_key=SecretStr("mock-api-key"),
+            title=f"Mock {self.name.title()} API key",
+            expires_at=None,
+        )
 
     def get_api(self, credentials: Credentials) -> Any:
         """Get API client instance for the given credentials."""

autogpt_platform/backend/backend/server/integrations/utils.py

@@ -0,0 +1,11 @@
+from supabase import Client, create_client
+
+from backend.util.settings import Settings
+
+settings = Settings()
+
+
+def get_supabase() -> Client:
+    return create_client(
+        settings.secrets.supabase_url, settings.secrets.supabase_service_role_key
+    )

autogpt_platform/backend/backend/server/model.py

@@ -5,7 +5,6 @@ import pydantic
 
 from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
 from backend.data.graph import Graph
-from backend.util.timezone_name import TimeZoneName
 
 
 class WSMethod(enum.Enum):
@@ -61,6 +60,21 @@ class UpdatePermissionsRequest(pydantic.BaseModel):
     permissions: list[APIKeyPermission]
 
 
+class Pagination(pydantic.BaseModel):
+    total_items: int = pydantic.Field(
+        description="Total number of items.", examples=[42]
+    )
+    total_pages: int = pydantic.Field(
+        description="Total number of pages.", examples=[2]
+    )
+    current_page: int = pydantic.Field(
+        description="Current_page page number.", examples=[1]
+    )
+    page_size: int = pydantic.Field(
+        description="Number of items per page.", examples=[25]
+    )
+
+
 class RequestTopUp(pydantic.BaseModel):
     credit_amount: int
 
@@ -71,12 +85,3 @@ class UploadFileResponse(pydantic.BaseModel):
     size: int
     content_type: str
     expires_in_hours: int
-
-
-class TimezoneResponse(pydantic.BaseModel):
-    # Allow "not-set" as a special value, or any valid IANA timezone
-    timezone: TimeZoneName | str
-
-
-class UpdateTimezoneRequest(pydantic.BaseModel):
-    timezone: TimeZoneName

autogpt_platform/backend/backend/server/rest_api.py

@@ -9,6 +9,11 @@ import fastapi.responses
 import pydantic
 import starlette.middleware.cors
 import uvicorn
+from autogpt_libs.feature_flag.client import (
+    initialize_launchdarkly,
+    shutdown_launchdarkly,
+)
+from autogpt_libs.logging.utils import generate_uvicorn_config
 from fastapi.exceptions import RequestValidationError
 from fastapi.routing import APIRoute
 
@@ -20,8 +25,6 @@ import backend.server.routers.postmark.postmark
 import backend.server.routers.v1
 import backend.server.v2.admin.credit_admin_routes
 import backend.server.v2.admin.store_admin_routes
-import backend.server.v2.builder
-import backend.server.v2.builder.routes
 import backend.server.v2.library.db
 import backend.server.v2.library.model
 import backend.server.v2.library.routes
@@ -38,7 +41,6 @@ from backend.server.external.api import external_app
 from backend.server.middleware.security import SecurityHeadersMiddleware
 from backend.util import json
 from backend.util.cloud_storage import shutdown_cloud_storage_handler
-from backend.util.feature_flag import initialize_launchdarkly, shutdown_launchdarkly
 from backend.util.service import UnhealthyServiceError
 
 settings = backend.util.settings.Settings()
@@ -197,9 +199,6 @@ app.include_router(backend.server.routers.v1.v1_router, tags=["v1"], prefix="/ap
 app.include_router(
     backend.server.v2.store.routes.router, tags=["v2"], prefix="/api/store"
 )
-app.include_router(
-    backend.server.v2.builder.routes.router, tags=["v2"], prefix="/api/builder"
-)
 app.include_router(
     backend.server.v2.admin.store_admin_routes.router,
     tags=["v2", "admin"],
@@ -251,7 +250,7 @@ class AgentServer(backend.util.service.AppProcess):
             server_app,
             host=backend.util.settings.Config().agent_api_host,
             port=backend.util.settings.Config().agent_api_port,
-            log_config=None,
+            log_config=generate_uvicorn_config(),
         )
 
     def cleanup(self):

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

@@ -8,6 +8,7 @@ from typing import Annotated, Any, Sequence
 import pydantic
 import stripe
 from autogpt_libs.auth.middleware import auth_middleware
+from autogpt_libs.feature_flag.client import feature_flag
 from fastapi import (
     APIRouter,
     Body,
@@ -15,7 +16,6 @@ from fastapi import (
     File,
     HTTPException,
     Path,
-    Query,
     Request,
     Response,
     UploadFile,
@@ -61,11 +61,9 @@ from backend.data.onboarding import (
 )
 from backend.data.user import (
     get_or_create_user,
-    get_user_by_id,
     get_user_notification_preference,
     update_user_email,
     update_user_notification_preference,
-    update_user_timezone,
 )
 from backend.executor import scheduler
 from backend.executor import utils as execution_utils
@@ -80,9 +78,7 @@ from backend.server.model import (
     ExecuteGraphResponse,
     RequestTopUp,
     SetGraphActiveVersion,
-    TimezoneResponse,
     UpdatePermissionsRequest,
-    UpdateTimezoneRequest,
     UploadFileResponse,
 )
 from backend.server.utils import get_user_id
@@ -90,11 +86,6 @@ from backend.util.clients import get_scheduler_client
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.exceptions import GraphValidationError, NotFoundError
 from backend.util.settings import Settings
-from backend.util.timezone_utils import (
-    convert_cron_to_utc,
-    convert_utc_time_to_user_timezone,
-    get_user_timezone_or_utc,
-)
 from backend.util.virus_scanner import scan_content_safe
 
 
@@ -158,35 +149,6 @@ async def update_user_email_route(
     return {"email": email}
 
 
-@v1_router.get(
-    "/auth/user/timezone",
-    summary="Get user timezone",
-    tags=["auth"],
-    dependencies=[Depends(auth_middleware)],
-)
-async def get_user_timezone_route(
-    user_data: dict = Depends(auth_middleware),
-) -> TimezoneResponse:
-    """Get user timezone setting."""
-    user = await get_or_create_user(user_data)
-    return TimezoneResponse(timezone=user.timezone)
-
-
-@v1_router.post(
-    "/auth/user/timezone",
-    summary="Update user timezone",
-    tags=["auth"],
-    dependencies=[Depends(auth_middleware)],
-    response_model=TimezoneResponse,
-)
-async def update_user_timezone_route(
-    user_id: Annotated[str, Depends(get_user_id)], request: UpdateTimezoneRequest
-) -> TimezoneResponse:
-    """Update user timezone. The timezone should be a valid IANA timezone identifier."""
-    user = await update_user_timezone(user_id, str(request.timezone))
-    return TimezoneResponse(timezone=user.timezone)
-
-
 @v1_router.get(
     "/auth/user/preferences",
     summary="Get notification preferences",
@@ -496,16 +458,12 @@ async def stripe_webhook(request: Request):
         event = stripe.Webhook.construct_event(
             payload, sig_header, settings.secrets.stripe_webhook_secret
         )
-    except ValueError as e:
+    except ValueError:
         # Invalid payload
-        raise HTTPException(
-            status_code=400, detail=f"Invalid payload: {str(e) or type(e).__name__}"
-        )
-    except stripe.SignatureVerificationError as e:
+        raise HTTPException(status_code=400)
+    except stripe.SignatureVerificationError:
         # Invalid signature
-        raise HTTPException(
-            status_code=400, detail=f"Invalid signature: {str(e) or type(e).__name__}"
-        )
+        raise HTTPException(status_code=400)
 
     if (
         event["type"] == "checkout.session.completed"
@@ -718,15 +676,7 @@ async def update_graph(
             # Handle deactivation of the previously active version
             await on_graph_deactivate(current_active_version, user_id=user_id)
 
-    # Fetch new graph version *with sub-graphs* (needed for credentials input schema)
-    new_graph_version_with_subgraphs = await graph_db.get_graph(
-        graph_id,
-        new_graph_version.version,
-        user_id=user_id,
-        include_subgraphs=True,
-    )
-    assert new_graph_version_with_subgraphs  # make type checker happy
-    return new_graph_version_with_subgraphs
+    return new_graph_version
 
 
 @v1_router.put(
@@ -858,11 +808,11 @@ async def _stop_graph_run(
 
 @v1_router.get(
     path="/executions",
-    summary="List all executions",
+    summary="Get all executions",
     tags=["graphs"],
     dependencies=[Depends(auth_middleware)],
 )
-async def list_graphs_executions(
+async def get_graphs_executions(
     user_id: Annotated[str, Depends(get_user_id)],
 ) -> list[execution_db.GraphExecutionMeta]:
     return await execution_db.get_graph_executions(user_id=user_id)
@@ -870,24 +820,15 @@ async def list_graphs_executions(
 
 @v1_router.get(
     path="/graphs/{graph_id}/executions",
-    summary="List graph executions",
+    summary="Get graph executions",
     tags=["graphs"],
     dependencies=[Depends(auth_middleware)],
 )
-async def list_graph_executions(
+async def get_graph_executions(
     graph_id: str,
     user_id: Annotated[str, Depends(get_user_id)],
-    page: int = Query(1, ge=1, description="Page number (1-indexed)"),
-    page_size: int = Query(
-        25, ge=1, le=100, description="Number of executions per page"
-    ),
-) -> execution_db.GraphExecutionsPaginated:
-    return await execution_db.get_graph_executions_paginated(
-        graph_id=graph_id,
-        user_id=user_id,
-        page=page,
-        page_size=page_size,
-    )
+) -> list[execution_db.GraphExecutionMeta]:
+    return await execution_db.get_graph_executions(graph_id=graph_id, user_id=user_id)
 
 
 @v1_router.get(
@@ -971,36 +912,16 @@ async def create_graph_execution_schedule(
             detail=f"Graph #{graph_id} v{schedule_params.graph_version} not found.",
         )
 
-    user = await get_user_by_id(user_id)
-    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
-
-    # Convert cron expression from user timezone to UTC
-    try:
-        utc_cron = convert_cron_to_utc(schedule_params.cron, user_timezone)
-    except ValueError as e:
-        raise HTTPException(
-            status_code=400,
-            detail=f"Invalid cron expression for timezone {user_timezone}: {e}",
-        )
-
-    result = await get_scheduler_client().add_execution_schedule(
+    return await get_scheduler_client().add_execution_schedule(
         user_id=user_id,
         graph_id=graph_id,
         graph_version=graph.version,
         name=schedule_params.name,
-        cron=utc_cron,  # Send UTC cron to scheduler
+        cron=schedule_params.cron,
         input_data=schedule_params.inputs,
         input_credentials=schedule_params.credentials,
     )
 
-    # Convert the next_run_time back to user timezone for display
-    if result.next_run_time:
-        result.next_run_time = convert_utc_time_to_user_timezone(
-            result.next_run_time, user_timezone
-        )
-
-    return result
-
 
 @v1_router.get(
     path="/graphs/{graph_id}/schedules",
@@ -1012,24 +933,11 @@ async def list_graph_execution_schedules(
     user_id: Annotated[str, Depends(get_user_id)],
     graph_id: str = Path(),
 ) -> list[scheduler.GraphExecutionJobInfo]:
-    schedules = await get_scheduler_client().get_execution_schedules(
+    return await get_scheduler_client().get_execution_schedules(
         user_id=user_id,
         graph_id=graph_id,
     )
 
-    # Get user timezone for conversion
-    user = await get_user_by_id(user_id)
-    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
-
-    # Convert next_run_time to user timezone for display
-    for schedule in schedules:
-        if schedule.next_run_time:
-            schedule.next_run_time = convert_utc_time_to_user_timezone(
-                schedule.next_run_time, user_timezone
-            )
-
-    return schedules
-
 
 @v1_router.get(
     path="/schedules",
@@ -1040,20 +948,7 @@ async def list_graph_execution_schedules(
 async def list_all_graphs_execution_schedules(
     user_id: Annotated[str, Depends(get_user_id)],
 ) -> list[scheduler.GraphExecutionJobInfo]:
-    schedules = await get_scheduler_client().get_execution_schedules(user_id=user_id)
-
-    # Get user timezone for conversion
-    user = await get_user_by_id(user_id)
-    user_timezone = get_user_timezone_or_utc(user.timezone if user else None)
-
-    # Convert UTC next_run_time to user timezone for display
-    for schedule in schedules:
-        if schedule.next_run_time:
-            schedule.next_run_time = convert_utc_time_to_user_timezone(
-                schedule.next_run_time, user_timezone
-            )
-
-    return schedules
+    return await get_scheduler_client().get_execution_schedules(user_id=user_id)
 
 
 @v1_router.delete(
@@ -1164,6 +1059,7 @@ async def get_api_key(
     tags=["api-keys"],
     dependencies=[Depends(auth_middleware)],
 )
+@feature_flag("api-keys-enabled")
 async def delete_api_key(
     key_id: str, user_id: Annotated[str, Depends(get_user_id)]
 ) -> Optional[APIKeyWithoutHash]:
@@ -1192,6 +1088,7 @@ async def delete_api_key(
     tags=["api-keys"],
     dependencies=[Depends(auth_middleware)],
 )
+@feature_flag("api-keys-enabled")
 async def suspend_key(
     key_id: str, user_id: Annotated[str, Depends(get_user_id)]
 ) -> Optional[APIKeyWithoutHash]:
@@ -1217,6 +1114,7 @@ async def suspend_key(
     tags=["api-keys"],
     dependencies=[Depends(auth_middleware)],
 )
+@feature_flag("api-keys-enabled")
 async def update_permissions(
     key_id: str,
     request: UpdatePermissionsRequest,

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

@@ -1 +0,0 @@
-# AutoMod integration for content moderation

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

@@ -1,364 +0,0 @@
-import asyncio
-import json
-import logging
-from typing import TYPE_CHECKING, Any, Literal
-
-if TYPE_CHECKING:
-    from backend.executor import DatabaseManagerAsyncClient
-
-from pydantic import ValidationError
-
-from backend.data.execution import ExecutionStatus
-from backend.server.v2.AutoMod.models import (
-    AutoModRequest,
-    AutoModResponse,
-    ModerationConfig,
-)
-from backend.util.exceptions import ModerationError
-from backend.util.feature_flag import Flag, is_feature_enabled
-from backend.util.request import Requests
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-
-
-class AutoModManager:
-
-    def __init__(self):
-        self.config = self._load_config()
-
-    def _load_config(self) -> ModerationConfig:
-        """Load AutoMod configuration from settings"""
-        settings = Settings()
-        return ModerationConfig(
-            enabled=settings.config.automod_enabled,
-            api_url=settings.config.automod_api_url,
-            api_key=settings.secrets.automod_api_key,
-            timeout=settings.config.automod_timeout,
-            retry_attempts=settings.config.automod_retry_attempts,
-            retry_delay=settings.config.automod_retry_delay,
-            fail_open=settings.config.automod_fail_open,
-        )
-
-    async def moderate_graph_execution_inputs(
-        self, db_client: "DatabaseManagerAsyncClient", graph_exec, timeout: int = 10
-    ) -> Exception | None:
-        """
-        Complete input moderation flow for graph execution
-        Returns: error_if_failed (None means success)
-        """
-        if not self.config.enabled:
-            return None
-
-        # Check if AutoMod feature is enabled for this user
-        if not await is_feature_enabled(Flag.AUTOMOD, graph_exec.user_id):
-            logger.debug(f"AutoMod feature not enabled for user {graph_exec.user_id}")
-            return None
-
-        # Get graph model and collect all inputs
-        graph_model = await db_client.get_graph(
-            graph_exec.graph_id,
-            user_id=graph_exec.user_id,
-            version=graph_exec.graph_version,
-        )
-
-        if not graph_model or not graph_model.nodes:
-            return None
-
-        all_inputs = []
-        for node in graph_model.nodes:
-            if node.input_default:
-                all_inputs.extend(str(v) for v in node.input_default.values() if v)
-            if (masks := graph_exec.nodes_input_masks) and (mask := masks.get(node.id)):
-                all_inputs.extend(str(v) for v in mask.values() if v)
-
-        if not all_inputs:
-            return None
-
-        # Combine all content and moderate directly
-        content = " ".join(all_inputs)
-
-        # Run moderation
-        logger.warning(
-            f"Moderating inputs for graph execution {graph_exec.graph_exec_id}"
-        )
-        try:
-            moderation_passed, content_id = await self._moderate_content(
-                content,
-                {
-                    "user_id": graph_exec.user_id,
-                    "graph_id": graph_exec.graph_id,
-                    "graph_exec_id": graph_exec.graph_exec_id,
-                    "moderation_type": "execution_input",
-                },
-            )
-
-            if not moderation_passed:
-                logger.warning(
-                    f"Moderation failed for graph execution {graph_exec.graph_exec_id}"
-                )
-                # Update node statuses for frontend display before raising error
-                await self._update_failed_nodes_for_moderation(
-                    db_client, graph_exec.graph_exec_id, "input", content_id
-                )
-
-                return ModerationError(
-                    message="Execution failed due to input content moderation",
-                    user_id=graph_exec.user_id,
-                    graph_exec_id=graph_exec.graph_exec_id,
-                    moderation_type="input",
-                    content_id=content_id,
-                )
-
-            return None
-
-        except asyncio.TimeoutError:
-            logger.warning(
-                f"Input moderation timed out for graph execution {graph_exec.graph_exec_id}, bypassing moderation"
-            )
-            return None  # Bypass moderation on timeout
-        except Exception as e:
-            logger.warning(f"Input moderation execution failed: {e}")
-            return ModerationError(
-                message="Execution failed due to input content moderation error",
-                user_id=graph_exec.user_id,
-                graph_exec_id=graph_exec.graph_exec_id,
-                moderation_type="input",
-            )
-
-    async def moderate_graph_execution_outputs(
-        self,
-        db_client: "DatabaseManagerAsyncClient",
-        graph_exec_id: str,
-        user_id: str,
-        graph_id: str,
-        timeout: int = 10,
-    ) -> Exception | None:
-        """
-        Complete output moderation flow for graph execution
-        Returns: error_if_failed (None means success)
-        """
-        if not self.config.enabled:
-            return None
-
-        # Check if AutoMod feature is enabled for this user
-        if not await is_feature_enabled(Flag.AUTOMOD, user_id):
-            logger.debug(f"AutoMod feature not enabled for user {user_id}")
-            return None
-
-        # Get completed executions and collect outputs
-        completed_executions = await db_client.get_node_executions(
-            graph_exec_id, statuses=[ExecutionStatus.COMPLETED], include_exec_data=True
-        )
-
-        if not completed_executions:
-            return None
-
-        all_outputs = []
-        for exec_entry in completed_executions:
-            if exec_entry.output_data:
-                all_outputs.extend(str(v) for v in exec_entry.output_data.values() if v)
-
-        if not all_outputs:
-            return None
-
-        # Combine all content and moderate directly
-        content = " ".join(all_outputs)
-
-        # Run moderation
-        logger.warning(f"Moderating outputs for graph execution {graph_exec_id}")
-        try:
-            moderation_passed, content_id = await self._moderate_content(
-                content,
-                {
-                    "user_id": user_id,
-                    "graph_id": graph_id,
-                    "graph_exec_id": graph_exec_id,
-                    "moderation_type": "execution_output",
-                },
-            )
-
-            if not moderation_passed:
-                logger.warning(f"Moderation failed for graph execution {graph_exec_id}")
-                # Update node statuses for frontend display before raising error
-                await self._update_failed_nodes_for_moderation(
-                    db_client, graph_exec_id, "output", content_id
-                )
-
-                return ModerationError(
-                    message="Execution failed due to output content moderation",
-                    user_id=user_id,
-                    graph_exec_id=graph_exec_id,
-                    moderation_type="output",
-                    content_id=content_id,
-                )
-
-            return None
-
-        except asyncio.TimeoutError:
-            logger.warning(
-                f"Output moderation timed out for graph execution {graph_exec_id}, bypassing moderation"
-            )
-            return None  # Bypass moderation on timeout
-        except Exception as e:
-            logger.warning(f"Output moderation execution failed: {e}")
-            return ModerationError(
-                message="Execution failed due to output content moderation error",
-                user_id=user_id,
-                graph_exec_id=graph_exec_id,
-                moderation_type="output",
-            )
-
-    async def _update_failed_nodes_for_moderation(
-        self,
-        db_client: "DatabaseManagerAsyncClient",
-        graph_exec_id: str,
-        moderation_type: Literal["input", "output"],
-        content_id: str | None = None,
-    ):
-        """Update node execution statuses for frontend display when moderation fails"""
-        # Import here to avoid circular imports
-        from backend.executor.manager import send_async_execution_update
-
-        if moderation_type == "input":
-            # For input moderation, mark queued/running/incomplete nodes as failed
-            target_statuses = [
-                ExecutionStatus.QUEUED,
-                ExecutionStatus.RUNNING,
-                ExecutionStatus.INCOMPLETE,
-            ]
-        else:
-            # For output moderation, mark completed nodes as failed
-            target_statuses = [ExecutionStatus.COMPLETED]
-
-        # Get the executions that need to be updated
-        executions_to_update = await db_client.get_node_executions(
-            graph_exec_id, statuses=target_statuses, include_exec_data=True
-        )
-
-        if not executions_to_update:
-            return
-
-        # Create error message with content_id if available
-        error_message = "Failed due to content moderation"
-        if content_id:
-            error_message += f" (Moderation ID: {content_id})"
-
-        # Prepare database update tasks
-        exec_updates = []
-        for exec_entry in executions_to_update:
-            # Collect all input and output names to clear
-            cleared_inputs = {}
-            cleared_outputs = {}
-
-            if exec_entry.input_data:
-                for name in exec_entry.input_data.keys():
-                    cleared_inputs[name] = [error_message]
-
-            if exec_entry.output_data:
-                for name in exec_entry.output_data.keys():
-                    cleared_outputs[name] = [error_message]
-
-            # Add update task to list
-            exec_updates.append(
-                db_client.update_node_execution_status(
-                    exec_entry.node_exec_id,
-                    status=ExecutionStatus.FAILED,
-                    stats={
-                        "error": error_message,
-                        "cleared_inputs": cleared_inputs,
-                        "cleared_outputs": cleared_outputs,
-                    },
-                )
-            )
-
-        # Execute all database updates in parallel
-        updated_execs = await asyncio.gather(*exec_updates)
-
-        # Send all websocket updates in parallel
-        await asyncio.gather(
-            *[
-                send_async_execution_update(updated_exec)
-                for updated_exec in updated_execs
-            ]
-        )
-
-    async def _moderate_content(
-        self, content: str, metadata: dict[str, Any]
-    ) -> tuple[bool, str | None]:
-        """Moderate content using AutoMod API
-
-        Returns:
-            Tuple of (approval_status, content_id)
-            - approval_status: True if approved or timeout occurred, False if rejected
-            - content_id: Reference ID from moderation API, or None if not available
-
-        Raises:
-            asyncio.TimeoutError: When moderation times out (should be bypassed)
-        """
-        try:
-            request_data = AutoModRequest(
-                type="text",
-                content=content,
-                metadata=metadata,
-            )
-
-            response = await self._make_request(request_data)
-
-            if response.success and response.status == "approved":
-                logger.debug(
-                    f"Content approved for {metadata.get('graph_exec_id', 'unknown')}"
-                )
-                return True, response.content_id
-            else:
-                reasons = [r.reason for r in response.moderation_results if r.reason]
-                error_msg = f"Content rejected by AutoMod: {'; '.join(reasons)}"
-                logger.warning(f"Content rejected: {error_msg}")
-                return False, response.content_id
-
-        except asyncio.TimeoutError:
-            # Re-raise timeout to be handled by calling methods
-            logger.warning(
-                f"AutoMod API timeout for {metadata.get('graph_exec_id', 'unknown')}"
-            )
-            raise
-        except Exception as e:
-            logger.error(f"AutoMod moderation error: {e}")
-            return self.config.fail_open, None
-
-    async def _make_request(self, request_data: AutoModRequest) -> AutoModResponse:
-        """Make HTTP request to AutoMod API using the standard request utility"""
-        url = f"{self.config.api_url}/moderate"
-        headers = {
-            "Content-Type": "application/json",
-            "X-API-Key": self.config.api_key.strip(),
-        }
-
-        # Create requests instance with timeout and retry configuration
-        requests = Requests(
-            extra_headers=headers,
-            retry_max_wait=float(self.config.timeout),
-        )
-
-        try:
-            response = await requests.post(
-                url, json=request_data.model_dump(), timeout=self.config.timeout
-            )
-
-            response_data = response.json()
-            return AutoModResponse.model_validate(response_data)
-
-        except asyncio.TimeoutError:
-            # Re-raise timeout error to be caught by _moderate_content
-            raise
-        except (json.JSONDecodeError, ValidationError) as e:
-            raise Exception(f"Invalid response from AutoMod API: {e}")
-        except Exception as e:
-            # Check if this is an aiohttp timeout that we should convert
-            if "timeout" in str(e).lower():
-                raise asyncio.TimeoutError(f"AutoMod API request timed out: {e}")
-            raise Exception(f"AutoMod API request failed: {e}")
-
-
-# Global instance
-automod_manager = AutoModManager()

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

@@ -1,60 +0,0 @@
-from typing import Any, Dict, List, Optional
-
-from pydantic import BaseModel, Field
-
-
-class AutoModRequest(BaseModel):
-    """Request model for AutoMod API"""
-
-    type: str = Field(..., description="Content type - 'text', 'image', 'video'")
-    content: str = Field(..., description="The content to moderate")
-    metadata: Optional[Dict[str, Any]] = Field(
-        default=None, description="Additional context about the content"
-    )
-
-
-class ModerationResult(BaseModel):
-    """Individual moderation result"""
-
-    decision: str = Field(
-        ..., description="Moderation decision: 'approved', 'rejected', 'flagged'"
-    )
-    reason: Optional[str] = Field(default=None, description="Reason for the decision")
-
-
-class AutoModResponse(BaseModel):
-    """Response model for AutoMod API"""
-
-    success: bool = Field(..., description="Whether the request was successful")
-    content_id: str = Field(
-        ..., description="Unique reference ID for this moderation request"
-    )
-    status: str = Field(
-        ..., description="Overall status: 'approved', 'rejected', 'flagged', 'pending'"
-    )
-    moderation_results: List[ModerationResult] = Field(
-        default_factory=list, description="List of moderation results"
-    )
-
-
-class ModerationConfig(BaseModel):
-    """Configuration for AutoMod integration"""
-
-    enabled: bool = Field(default=True, description="Whether moderation is enabled")
-    api_url: str = Field(default="", description="AutoMod API base URL")
-    api_key: str = Field(..., description="AutoMod API key")
-    timeout: int = Field(default=30, description="Request timeout in seconds")
-    retry_attempts: int = Field(default=3, description="Number of retry attempts")
-    retry_delay: float = Field(
-        default=1.0, description="Delay between retries in seconds"
-    )
-    fail_open: bool = Field(
-        default=False,
-        description="If True, allow execution to continue if moderation fails",
-    )
-    moderate_inputs: bool = Field(
-        default=True, description="Whether to moderate block inputs"
-    )
-    moderate_outputs: bool = Field(
-        default=True, description="Whether to moderate block outputs"
-    )

autogpt_platform/backend/backend/server/v2/admin/credit_admin_routes_test.py

@@ -14,7 +14,7 @@ import backend.server.v2.admin.credit_admin_routes as credit_admin_routes
 import backend.server.v2.admin.model as admin_model
 from backend.data.model import UserTransaction
 from backend.server.conftest import ADMIN_USER_ID, TARGET_USER_ID
-from backend.util.models import Pagination
+from backend.server.model import Pagination
 
 app = fastapi.FastAPI()
 app.include_router(credit_admin_routes.router)

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

@@ -1,7 +1,7 @@
 from pydantic import BaseModel
 
 from backend.data.model import UserTransaction
-from backend.util.models import Pagination
+from backend.server.model import Pagination
 
 
 class UserHistoryResponse(BaseModel):

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

@@ -1,376 +0,0 @@
-import functools
-import logging
-from datetime import datetime, timedelta, timezone
-
-import prisma
-
-import backend.data.block
-from backend.blocks import load_all_blocks
-from backend.blocks.llm import LlmModel
-from backend.data.block import Block, BlockCategory, BlockSchema
-from backend.data.credit import get_block_costs
-from backend.integrations.providers import ProviderName
-from backend.server.v2.builder.model import (
-    BlockCategoryResponse,
-    BlockData,
-    BlockResponse,
-    BlockType,
-    CountResponse,
-    Provider,
-    ProviderResponse,
-    SearchBlocksResponse,
-)
-from backend.util.models import Pagination
-
-logger = logging.getLogger(__name__)
-llm_models = [name.name.lower().replace("_", " ") for name in LlmModel]
-_static_counts_cache: dict | None = None
-_suggested_blocks: list[BlockData] | None = None
-
-
-def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse]:
-    categories: dict[BlockCategory, BlockCategoryResponse] = {}
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't have categories (all should have at least one)
-        if not block.categories:
-            continue
-
-        # Add block to the categories
-        for category in block.categories:
-            if category not in categories:
-                categories[category] = BlockCategoryResponse(
-                    name=category.name.lower(),
-                    total_blocks=0,
-                    blocks=[],
-                )
-
-            categories[category].total_blocks += 1
-
-            # Append if the category has less than the specified number of blocks
-            if len(categories[category].blocks) < category_blocks:
-                categories[category].blocks.append(block.to_dict())
-
-    # Sort categories by name
-    return sorted(categories.values(), key=lambda x: x.name)
-
-
-def get_blocks(
-    *,
-    category: str | None = None,
-    type: BlockType | None = None,
-    provider: ProviderName | None = None,
-    page: int = 1,
-    page_size: int = 50,
-) -> BlockResponse:
-    """
-    Get blocks based on either category, type or provider.
-    Providing nothing fetches all block types.
-    """
-    # Only one of category, type, or provider can be specified
-    if (category and type) or (category and provider) or (type and provider):
-        raise ValueError("Only one of category, type, or provider can be specified")
-
-    blocks: list[Block[BlockSchema, BlockSchema]] = []
-    skip = (page - 1) * page_size
-    take = page_size
-    total = 0
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't match the category
-        if category and category not in {c.name.lower() for c in block.categories}:
-            continue
-        # Skip blocks that don't match the type
-        if (
-            (type == "input" and block.block_type.value != "Input")
-            or (type == "output" and block.block_type.value != "Output")
-            or (type == "action" and block.block_type.value in ("Input", "Output"))
-        ):
-            continue
-        # Skip blocks that don't match the provider
-        if provider:
-            credentials_info = block.input_schema.get_credentials_fields_info().values()
-            if not any(provider in info.provider for info in credentials_info):
-                continue
-
-        total += 1
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            blocks.append(block)
-
-    costs = get_block_costs()
-
-    return BlockResponse(
-        blocks=[{**b.to_dict(), "costs": costs.get(b.id, [])} for b in blocks],
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-def search_blocks(
-    include_blocks: bool = True,
-    include_integrations: bool = True,
-    query: str = "",
-    page: int = 1,
-    page_size: int = 50,
-) -> SearchBlocksResponse:
-    """
-    Get blocks based on the filter and query.
-    `providers` only applies for `integrations` filter.
-    """
-    blocks: list[Block[BlockSchema, BlockSchema]] = []
-    query = query.lower()
-
-    total = 0
-    skip = (page - 1) * page_size
-    take = page_size
-    block_count = 0
-    integration_count = 0
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        # Skip disabled blocks
-        if block.disabled:
-            continue
-        # Skip blocks that don't match the query
-        if (
-            query not in block.name.lower()
-            and query not in block.description.lower()
-            and not _matches_llm_model(block.input_schema, query)
-        ):
-            continue
-        keep = False
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        if include_integrations and len(credentials) > 0:
-            keep = True
-            integration_count += 1
-        if include_blocks and len(credentials) == 0:
-            keep = True
-            block_count += 1
-
-        if not keep:
-            continue
-
-        total += 1
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            blocks.append(block)
-
-    costs = get_block_costs()
-
-    return SearchBlocksResponse(
-        blocks=BlockResponse(
-            blocks=[{**b.to_dict(), "costs": costs.get(b.id, [])} for b in blocks],
-            pagination=Pagination(
-                total_items=total,
-                total_pages=(total + page_size - 1) // page_size,
-                current_page=page,
-                page_size=page_size,
-            ),
-        ),
-        total_block_count=block_count,
-        total_integration_count=integration_count,
-    )
-
-
-def get_providers(
-    query: str = "",
-    page: int = 1,
-    page_size: int = 50,
-) -> ProviderResponse:
-    providers = []
-    query = query.lower()
-
-    skip = (page - 1) * page_size
-    take = page_size
-
-    all_providers = _get_all_providers()
-
-    for provider in all_providers.values():
-        if (
-            query not in provider.name.value.lower()
-            and query not in provider.description.lower()
-        ):
-            continue
-        if skip > 0:
-            skip -= 1
-            continue
-        if take > 0:
-            take -= 1
-            providers.append(provider)
-
-    total = len(all_providers)
-
-    return ProviderResponse(
-        providers=providers,
-        pagination=Pagination(
-            total_items=total,
-            total_pages=(total + page_size - 1) // page_size,
-            current_page=page,
-            page_size=page_size,
-        ),
-    )
-
-
-async def get_counts(user_id: str) -> CountResponse:
-    my_agents = await prisma.models.LibraryAgent.prisma().count(
-        where={
-            "userId": user_id,
-            "isDeleted": False,
-            "isArchived": False,
-        }
-    )
-    counts = await _get_static_counts()
-    return CountResponse(
-        my_agents=my_agents,
-        **counts,
-    )
-
-
-async def _get_static_counts():
-    """
-    Get counts of blocks, integrations, and marketplace agents.
-    This is cached to avoid unnecessary database queries and calculations.
-    Can't use functools.cache here because the function is async.
-    """
-    global _static_counts_cache
-    if _static_counts_cache is not None:
-        return _static_counts_cache
-
-    all_blocks = 0
-    input_blocks = 0
-    action_blocks = 0
-    output_blocks = 0
-    integrations = 0
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        if block.disabled:
-            continue
-
-        all_blocks += 1
-
-        if block.block_type.value == "Input":
-            input_blocks += 1
-        elif block.block_type.value == "Output":
-            output_blocks += 1
-        else:
-            action_blocks += 1
-
-        credentials = list(block.input_schema.get_credentials_fields().values())
-        if len(credentials) > 0:
-            integrations += 1
-
-    marketplace_agents = await prisma.models.StoreAgent.prisma().count()
-
-    _static_counts_cache = {
-        "all_blocks": all_blocks,
-        "input_blocks": input_blocks,
-        "action_blocks": action_blocks,
-        "output_blocks": output_blocks,
-        "integrations": integrations,
-        "marketplace_agents": marketplace_agents,
-    }
-
-    return _static_counts_cache
-
-
-def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
-    for field in schema_cls.model_fields.values():
-        if field.annotation == LlmModel:
-            # Check if query matches any value in llm_models
-            if any(query in name for name in llm_models):
-                return True
-    return False
-
-
-@functools.cache
-def _get_all_providers() -> dict[ProviderName, Provider]:
-    providers: dict[ProviderName, Provider] = {}
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        if block.disabled:
-            continue
-
-        credentials_info = block.input_schema.get_credentials_fields_info().values()
-        for info in credentials_info:
-            for provider in info.provider:  # provider is a ProviderName enum member
-                if provider in providers:
-                    providers[provider].integration_count += 1
-                else:
-                    providers[provider] = Provider(
-                        name=provider, description="", integration_count=1
-                    )
-    return providers
-
-
-async def get_suggested_blocks(count: int = 5) -> list[BlockData]:
-    global _suggested_blocks
-
-    if _suggested_blocks is not None and len(_suggested_blocks) >= count:
-        return _suggested_blocks[:count]
-
-    _suggested_blocks = []
-    # Sum the number of executions for each block type
-    # Prisma cannot group by nested relations, so we do a raw query
-    # Calculate the cutoff timestamp
-    timestamp_threshold = datetime.now(timezone.utc) - timedelta(days=30)
-
-    results = await prisma.get_client().query_raw(
-        """
-        SELECT
-            agent_node."agentBlockId" AS block_id,
-            COUNT(execution.id) AS execution_count
-        FROM "AgentNodeExecution" execution
-        JOIN "AgentNode" agent_node ON execution."agentNodeId" = agent_node.id
-        WHERE execution."endedTime" >= $1::timestamp
-        GROUP BY agent_node."agentBlockId"
-        ORDER BY execution_count DESC;
-        """,
-        timestamp_threshold,
-    )
-
-    # Get the top blocks based on execution count
-    # But ignore Input and Output blocks
-    blocks: list[tuple[BlockData, int]] = []
-
-    for block_type in load_all_blocks().values():
-        block: Block[BlockSchema, BlockSchema] = block_type()
-        if block.disabled or block.block_type in (
-            backend.data.block.BlockType.INPUT,
-            backend.data.block.BlockType.OUTPUT,
-            backend.data.block.BlockType.AGENT,
-        ):
-            continue
-        # Find the execution count for this block
-        execution_count = next(
-            (row["execution_count"] for row in results if row["block_id"] == block.id),
-            0,
-        )
-        blocks.append((block.to_dict(), execution_count))
-    # Sort blocks by execution count
-    blocks.sort(key=lambda x: x[1], reverse=True)
-
-    _suggested_blocks = [block[0] for block in blocks]
-
-    # Return the top blocks
-    return _suggested_blocks[:count]

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

@@ -1,87 +0,0 @@
-from typing import Any, Literal
-
-from pydantic import BaseModel
-
-import backend.server.v2.library.model as library_model
-import backend.server.v2.store.model as store_model
-from backend.integrations.providers import ProviderName
-from backend.util.models import Pagination
-
-FilterType = Literal[
-    "blocks",
-    "integrations",
-    "marketplace_agents",
-    "my_agents",
-]
-
-BlockType = Literal["all", "input", "action", "output"]
-
-BlockData = dict[str, Any]
-
-
-# Suggestions
-class SuggestionsResponse(BaseModel):
-    otto_suggestions: list[str]
-    recent_searches: list[str]
-    providers: list[ProviderName]
-    top_blocks: list[BlockData]
-
-
-# All blocks
-class BlockCategoryResponse(BaseModel):
-    name: str
-    total_blocks: int
-    blocks: list[BlockData]
-
-    model_config = {"use_enum_values": False}  # <== use enum names like "AI"
-
-
-# Input/Action/Output and see all for block categories
-class BlockResponse(BaseModel):
-    blocks: list[BlockData]
-    pagination: Pagination
-
-
-# Providers
-class Provider(BaseModel):
-    name: ProviderName
-    description: str
-    integration_count: int
-
-
-class ProviderResponse(BaseModel):
-    providers: list[Provider]
-    pagination: Pagination
-
-
-# Search
-class SearchRequest(BaseModel):
-    search_query: str | None = None
-    filter: list[FilterType] | None = None
-    by_creator: list[str] | None = None
-    search_id: str | None = None
-    page: int | None = None
-    page_size: int | None = None
-
-
-class SearchBlocksResponse(BaseModel):
-    blocks: BlockResponse
-    total_block_count: int
-    total_integration_count: int
-
-
-class SearchResponse(BaseModel):
-    items: list[BlockData | library_model.LibraryAgent | store_model.StoreAgent]
-    total_items: dict[FilterType, int]
-    page: int
-    more_pages: bool
-
-
-class CountResponse(BaseModel):
-    all_blocks: int
-    input_blocks: int
-    action_blocks: int
-    output_blocks: int
-    integrations: int
-    marketplace_agents: int
-    my_agents: int

autogpt_platform/backend/backend/server/v2/builder/routes.py

@@ -1,239 +0,0 @@
-import logging
-from typing import Annotated, Sequence
-
-import fastapi
-from autogpt_libs.auth.depends import auth_middleware, get_user_id
-
-import backend.server.v2.builder.db as builder_db
-import backend.server.v2.builder.model as builder_model
-import backend.server.v2.library.db as library_db
-import backend.server.v2.library.model as library_model
-import backend.server.v2.store.db as store_db
-import backend.server.v2.store.model as store_model
-from backend.integrations.providers import ProviderName
-from backend.util.models import Pagination
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter()
-
-
-# Taken from backend/server/v2/store/db.py
-def sanitize_query(query: str | None) -> str | None:
-    if query is None:
-        return query
-    query = query.strip()[:100]
-    return (
-        query.replace("\\", "\\\\")
-        .replace("%", "\\%")
-        .replace("_", "\\_")
-        .replace("[", "\\[")
-        .replace("]", "\\]")
-        .replace("'", "\\'")
-        .replace('"', '\\"')
-        .replace(";", "\\;")
-        .replace("--", "\\--")
-        .replace("/*", "\\/*")
-        .replace("*/", "\\*/")
-    )
-
-
-@router.get(
-    "/suggestions",
-    summary="Get Builder suggestions",
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=builder_model.SuggestionsResponse,
-)
-async def get_suggestions(
-    user_id: Annotated[str, fastapi.Depends(get_user_id)],
-) -> builder_model.SuggestionsResponse:
-    """
-    Get all suggestions for the Blocks Menu.
-    """
-    return builder_model.SuggestionsResponse(
-        otto_suggestions=[
-            "What blocks do I need to get started?",
-            "Help me create a list",
-            "Help me feed my data to Google Maps",
-        ],
-        recent_searches=[
-            "image generation",
-            "deepfake",
-            "competitor analysis",
-        ],
-        providers=[
-            ProviderName.TWITTER,
-            ProviderName.GITHUB,
-            ProviderName.NOTION,
-            ProviderName.GOOGLE,
-            ProviderName.DISCORD,
-            ProviderName.GOOGLE_MAPS,
-        ],
-        top_blocks=await builder_db.get_suggested_blocks(),
-    )
-
-
-@router.get(
-    "/categories",
-    summary="Get Builder block categories",
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=Sequence[builder_model.BlockCategoryResponse],
-)
-async def get_block_categories(
-    blocks_per_category: Annotated[int, fastapi.Query()] = 3,
-) -> Sequence[builder_model.BlockCategoryResponse]:
-    """
-    Get all block categories with a specified number of blocks per category.
-    """
-    return builder_db.get_block_categories(blocks_per_category)
-
-
-@router.get(
-    "/blocks",
-    summary="Get Builder blocks",
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=builder_model.BlockResponse,
-)
-async def get_blocks(
-    category: Annotated[str | None, fastapi.Query()] = None,
-    type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
-    provider: Annotated[ProviderName | None, fastapi.Query()] = None,
-    page: Annotated[int, fastapi.Query()] = 1,
-    page_size: Annotated[int, fastapi.Query()] = 50,
-) -> builder_model.BlockResponse:
-    """
-    Get blocks based on either category, type, or provider.
-    """
-    return builder_db.get_blocks(
-        category=category,
-        type=type,
-        provider=provider,
-        page=page,
-        page_size=page_size,
-    )
-
-
-@router.get(
-    "/providers",
-    summary="Get Builder integration providers",
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=builder_model.ProviderResponse,
-)
-async def get_providers(
-    page: Annotated[int, fastapi.Query()] = 1,
-    page_size: Annotated[int, fastapi.Query()] = 50,
-) -> builder_model.ProviderResponse:
-    """
-    Get all integration providers with their block counts.
-    """
-    return builder_db.get_providers(
-        page=page,
-        page_size=page_size,
-    )
-
-
-@router.post(
-    "/search",
-    summary="Builder search",
-    tags=["store", "private"],
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=builder_model.SearchResponse,
-)
-async def search(
-    options: builder_model.SearchRequest,
-    user_id: Annotated[str, fastapi.Depends(get_user_id)],
-) -> builder_model.SearchResponse:
-    """
-    Search for blocks (including integrations), marketplace agents, and user library agents.
-    """
-    # If no filters are provided, then we will return all types
-    if not options.filter:
-        options.filter = [
-            "blocks",
-            "integrations",
-            "marketplace_agents",
-            "my_agents",
-        ]
-    options.search_query = sanitize_query(options.search_query)
-    options.page = options.page or 1
-    options.page_size = options.page_size or 50
-
-    # Blocks&Integrations
-    blocks = builder_model.SearchBlocksResponse(
-        blocks=builder_model.BlockResponse(
-            blocks=[],
-            pagination=Pagination.empty(),
-        ),
-        total_block_count=0,
-        total_integration_count=0,
-    )
-    if "blocks" in options.filter or "integrations" in options.filter:
-        blocks = builder_db.search_blocks(
-            include_blocks="blocks" in options.filter,
-            include_integrations="integrations" in options.filter,
-            query=options.search_query or "",
-            page=options.page,
-            page_size=options.page_size,
-        )
-
-    # Library Agents
-    my_agents = library_model.LibraryAgentResponse(
-        agents=[],
-        pagination=Pagination.empty(),
-    )
-    if "my_agents" in options.filter:
-        my_agents = await library_db.list_library_agents(
-            user_id=user_id,
-            search_term=options.search_query,
-            page=options.page,
-            page_size=options.page_size,
-        )
-
-    # Marketplace Agents
-    marketplace_agents = store_model.StoreAgentsResponse(
-        agents=[],
-        pagination=Pagination.empty(),
-    )
-    if "marketplace_agents" in options.filter:
-        marketplace_agents = await store_db.get_store_agents(
-            creators=options.by_creator,
-            search_query=options.search_query,
-            page=options.page,
-            page_size=options.page_size,
-        )
-
-    more_pages = False
-    if (
-        blocks.blocks.pagination.current_page < blocks.blocks.pagination.total_pages
-        or my_agents.pagination.current_page < my_agents.pagination.total_pages
-        or marketplace_agents.pagination.current_page
-        < marketplace_agents.pagination.total_pages
-    ):
-        more_pages = True
-
-    return builder_model.SearchResponse(
-        items=blocks.blocks.blocks + my_agents.agents + marketplace_agents.agents,
-        total_items={
-            "blocks": blocks.total_block_count,
-            "integrations": blocks.total_integration_count,
-            "marketplace_agents": marketplace_agents.pagination.total_items,
-            "my_agents": my_agents.pagination.total_items,
-        },
-        page=options.page,
-        more_pages=more_pages,
-    )
-
-
-@router.get(
-    "/counts",
-    summary="Get Builder item counts",
-    dependencies=[fastapi.Depends(auth_middleware)],
-    response_model=builder_model.CountResponse,
-)
-async def get_counts(
-    user_id: Annotated[str, fastapi.Depends(get_user_id)],
-) -> builder_model.CountResponse:
-    """
-    Get item counts for the menu categories in the Blocks Menu.
-    """
-    return await builder_db.get_counts(user_id)

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

@@ -9,6 +9,7 @@ import prisma.models
 import prisma.types
 
 import backend.data.graph as graph_db
+import backend.server.model
 import backend.server.v2.library.model as library_model
 import backend.server.v2.store.exceptions as store_exceptions
 import backend.server.v2.store.image_gen as store_image_gen
@@ -22,7 +23,6 @@ from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
 from backend.util.exceptions import NotFoundError
 from backend.util.json import SafeJson
-from backend.util.models import Pagination
 from backend.util.settings import Config
 
 logger = logging.getLogger(__name__)
@@ -131,7 +131,7 @@ async def list_library_agents(
         # Return the response with only valid agents
         return library_model.LibraryAgentResponse(
             agents=valid_library_agents,
-            pagination=Pagination(
+            pagination=backend.server.model.Pagination(
                 total_items=agent_count,
                 total_pages=(agent_count + page_size - 1) // page_size,
                 current_page=page,
@@ -241,11 +241,7 @@ async def get_library_agent_by_graph_id(
         )
         if not agent:
             return None
-
-        assert agent.AgentGraph  # make type checker happy
-        # Include sub-graphs so we can make a full credentials input schema
-        sub_graphs = await graph_db.get_sub_graphs(agent.AgentGraph)
-        return library_model.LibraryAgent.from_db(agent, sub_graphs=sub_graphs)
+        return library_model.LibraryAgent.from_db(agent)
     except prisma.errors.PrismaError as e:
         logger.error(f"Database error fetching library agent by graph ID: {e}")
         raise store_exceptions.DatabaseError("Failed to fetch library agent") from e
@@ -629,7 +625,7 @@ async def list_presets(
 
         return library_model.LibraryAgentPresetResponse(
             presets=presets,
-            pagination=Pagination(
+            pagination=backend.server.model.Pagination(
                 total_items=total_items,
                 total_pages=total_pages,
                 current_page=page,

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

@@ -8,9 +8,9 @@ import pydantic
 
 import backend.data.block as block_model
 import backend.data.graph as graph_model
+import backend.server.model as server_model
 from backend.data.model import CredentialsMetaInput, is_credentials_field_name
 from backend.integrations.providers import ProviderName
-from backend.util.models import Pagination
 
 
 class LibraryAgentStatus(str, Enum):
@@ -51,7 +51,6 @@ class LibraryAgent(pydantic.BaseModel):
     description: str
 
     input_schema: dict[str, Any]  # Should be BlockIOObjectSubSchema in frontend
-    output_schema: dict[str, Any]
     credentials_input_schema: dict[str, Any] | None = pydantic.Field(
         description="Input schema for credentials required by the agent",
     )
@@ -127,7 +126,6 @@ class LibraryAgent(pydantic.BaseModel):
             name=graph.name,
             description=graph.description,
             input_schema=graph.input_schema,
-            output_schema=graph.output_schema,
             credentials_input_schema=(
                 graph.credentials_input_schema if sub_graphs is not None else None
             ),
@@ -215,7 +213,7 @@ class LibraryAgentResponse(pydantic.BaseModel):
     """Response schema for a list of library agents and pagination info."""
 
     agents: list[LibraryAgent]
-    pagination: Pagination
+    pagination: server_model.Pagination
 
 
 class LibraryAgentPresetCreatable(pydantic.BaseModel):
@@ -319,7 +317,7 @@ class LibraryAgentPresetResponse(pydantic.BaseModel):
     """Response schema for a list of agent presets and pagination info."""
 
     presets: list[LibraryAgentPreset]
-    pagination: Pagination
+    pagination: server_model.Pagination
 
 
 class LibraryAgentFilter(str, Enum):

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

@@ -7,9 +7,9 @@ import pytest
 import pytest_mock
 from pytest_snapshot.plugin import Snapshot
 
+import backend.server.model as server_model
 import backend.server.v2.library.model as library_model
 from backend.server.v2.library.routes import router as library_router
-from backend.util.models import Pagination
 
 app = fastapi.FastAPI()
 app.include_router(library_router)
@@ -50,7 +50,6 @@ async def test_get_library_agents_success(
                 creator_name="Test Creator",
                 creator_image_url="",
                 input_schema={"type": "object", "properties": {}},
-                output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
@@ -69,7 +68,6 @@ async def test_get_library_agents_success(
                 creator_name="Test Creator",
                 creator_image_url="",
                 input_schema={"type": "object", "properties": {}},
-                output_schema={"type": "object", "properties": {}},
                 credentials_input_schema={"type": "object", "properties": {}},
                 has_external_trigger=False,
                 status=library_model.LibraryAgentStatus.COMPLETED,
@@ -79,7 +77,7 @@ async def test_get_library_agents_success(
                 updated_at=datetime.datetime(2023, 1, 1, 0, 0, 0),
             ),
         ],
-        pagination=Pagination(
+        pagination=server_model.Pagination(
             total_items=2, total_pages=1, current_page=1, page_size=50
         ),
     )
@@ -134,7 +132,6 @@ def test_add_agent_to_library_success(mocker: pytest_mock.MockFixture):
         creator_name="Test Creator",
         creator_image_url="",
         input_schema={"type": "object", "properties": {}},
-        output_schema={"type": "object", "properties": {}},
         credentials_input_schema={"type": "object", "properties": {}},
         has_external_trigger=False,
         status=library_model.LibraryAgentStatus.COMPLETED,

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

@@ -17,21 +17,8 @@ from backend.data.graph import (
     get_sub_graphs,
 )
 from backend.data.includes import AGENT_GRAPH_INCLUDE
-from backend.data.notifications import (
-    AgentApprovalData,
-    AgentRejectionData,
-    NotificationEventModel,
-)
-from backend.notifications.notifications import queue_notification_async
-from backend.util.settings import Settings
 
 logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-# Constants for default admin values
-DEFAULT_ADMIN_NAME = "AutoGPT Admin"
-DEFAULT_ADMIN_EMAIL = "admin@autogpt.co"
 
 
 def sanitize_query(query: str | None) -> str | None:
@@ -55,7 +42,7 @@ def sanitize_query(query: str | None) -> str | None:
 
 async def get_store_agents(
     featured: bool = False,
-    creators: list[str] | None = None,
+    creator: str | None = None,
     sorted_by: str | None = None,
     search_query: str | None = None,
     category: str | None = None,
@@ -66,15 +53,15 @@ async def get_store_agents(
     Get PUBLIC store agents from the StoreAgent view
     """
     logger.debug(
-        f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
+        f"Getting store agents. featured={featured}, creator={creator}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
     sanitized_query = sanitize_query(search_query)
 
     where_clause = {}
     if featured:
         where_clause["featured"] = featured
-    if creators:
-        where_clause["creator_username"] = {"in": creators}
+    if creator:
+        where_clause["creator_username"] = creator
     if category:
         where_clause["categories"] = {"has": category}
 
@@ -479,8 +466,6 @@ async def get_store_submissions(
                 # internal_comments omitted for regular users
                 reviewed_at=sub.reviewed_at,
                 changes_summary=sub.changes_summary,
-                video_url=sub.video_url,
-                categories=sub.categories,
             )
             submission_models.append(submission_model)
 
@@ -561,7 +546,7 @@ async def create_store_submission(
     description: str = "",
     sub_heading: str = "",
     categories: list[str] = [],
-    changes_summary: str | None = "Initial Submission",
+    changes_summary: str = "Initial Submission",
 ) -> backend.server.v2.store.model.StoreSubmission:
     """
     Create the first (and only) store listing and thus submission as a normal user
@@ -700,160 +685,6 @@ async def create_store_submission(
         ) from e
 
 
-async def edit_store_submission(
-    user_id: str,
-    store_listing_version_id: str,
-    name: str,
-    video_url: str | None = None,
-    image_urls: list[str] = [],
-    description: str = "",
-    sub_heading: str = "",
-    categories: list[str] = [],
-    changes_summary: str | None = "Update submission",
-) -> backend.server.v2.store.model.StoreSubmission:
-    """
-    Edit an existing store listing submission.
-
-    Args:
-        user_id: ID of the authenticated user editing the submission
-        store_listing_version_id: ID of the store listing version to edit
-        agent_id: ID of the agent being submitted
-        agent_version: Version of the agent being submitted
-        slug: URL slug for the listing (only changeable for PENDING submissions)
-        name: Name of the agent
-        video_url: Optional URL to video demo
-        image_urls: List of image URLs for the listing
-        description: Description of the agent
-        sub_heading: Optional sub-heading for the agent
-        categories: List of categories for the agent
-        changes_summary: Summary of changes made in this submission
-
-    Returns:
-        StoreSubmission: The updated store submission
-
-    Raises:
-        SubmissionNotFoundError: If the submission is not found
-        UnauthorizedError: If the user doesn't own the submission
-        InvalidOperationError: If trying to edit a submission that can't be edited
-    """
-    try:
-        # Get the current version and verify ownership
-        current_version = await prisma.models.StoreListingVersion.prisma().find_first(
-            where=prisma.types.StoreListingVersionWhereInput(
-                id=store_listing_version_id
-            ),
-            include={
-                "StoreListing": {
-                    "include": {
-                        "Versions": {"order_by": {"version": "desc"}, "take": 1}
-                    }
-                }
-            },
-        )
-
-        if not current_version:
-            raise backend.server.v2.store.exceptions.SubmissionNotFoundError(
-                f"Store listing version not found: {store_listing_version_id}"
-            )
-
-        # Verify the user owns this submission
-        if (
-            not current_version.StoreListing
-            or current_version.StoreListing.owningUserId != user_id
-        ):
-            raise backend.server.v2.store.exceptions.UnauthorizedError(
-                f"User {user_id} does not own submission {store_listing_version_id}"
-            )
-
-        # Currently we are not allowing user to update the agent associated with a submission
-        # If we allow it in future, then we need a check here to verify the agent belongs to this user.
-
-        # Check if we can edit this submission
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
-            raise backend.server.v2.store.exceptions.InvalidOperationError(
-                "Cannot edit a rejected submission"
-            )
-
-        # For APPROVED submissions, we need to create a new version
-        if current_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
-            # Create a new version for the existing listing
-            return await create_store_version(
-                user_id=user_id,
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
-                store_listing_id=current_version.storeListingId,
-                name=name,
-                video_url=video_url,
-                image_urls=image_urls,
-                description=description,
-                sub_heading=sub_heading,
-                categories=categories,
-                changes_summary=changes_summary,
-            )
-
-        # For PENDING submissions, we can update the existing version
-        elif current_version.submissionStatus == prisma.enums.SubmissionStatus.PENDING:
-            # Update the existing version
-            updated_version = await prisma.models.StoreListingVersion.prisma().update(
-                where={"id": store_listing_version_id},
-                data=prisma.types.StoreListingVersionUpdateInput(
-                    name=name,
-                    videoUrl=video_url,
-                    imageUrls=image_urls,
-                    description=description,
-                    categories=categories,
-                    subHeading=sub_heading,
-                    changesSummary=changes_summary,
-                ),
-            )
-
-            logger.debug(
-                f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
-            )
-
-            if not updated_version:
-                raise backend.server.v2.store.exceptions.DatabaseError(
-                    "Failed to update store listing version"
-                )
-            return backend.server.v2.store.model.StoreSubmission(
-                agent_id=current_version.agentGraphId,
-                agent_version=current_version.agentGraphVersion,
-                name=name,
-                sub_heading=sub_heading,
-                slug=current_version.StoreListing.slug,
-                description=description,
-                image_urls=image_urls,
-                date_submitted=updated_version.submittedAt or updated_version.createdAt,
-                status=updated_version.submissionStatus,
-                runs=0,
-                rating=0.0,
-                store_listing_version_id=updated_version.id,
-                changes_summary=changes_summary,
-                video_url=video_url,
-                categories=categories,
-                version=updated_version.version,
-            )
-
-        else:
-            raise backend.server.v2.store.exceptions.InvalidOperationError(
-                f"Cannot edit submission with status: {current_version.submissionStatus}"
-            )
-
-    except (
-        backend.server.v2.store.exceptions.SubmissionNotFoundError,
-        backend.server.v2.store.exceptions.UnauthorizedError,
-        backend.server.v2.store.exceptions.AgentNotFoundError,
-        backend.server.v2.store.exceptions.ListingExistsError,
-        backend.server.v2.store.exceptions.InvalidOperationError,
-    ):
-        raise
-    except prisma.errors.PrismaError as e:
-        logger.error(f"Database error editing store submission: {e}")
-        raise backend.server.v2.store.exceptions.DatabaseError(
-            "Failed to edit store submission"
-        ) from e
-
-
 async def create_store_version(
     user_id: str,
     agent_id: str,
@@ -865,7 +696,7 @@ async def create_store_version(
     description: str = "",
     sub_heading: str = "",
     categories: list[str] = [],
-    changes_summary: str | None = "Initial submission",
+    changes_summary: str = "Update Submission",
 ) -> backend.server.v2.store.model.StoreSubmission:
     """
     Create a new version for an existing store listing
@@ -1254,8 +1085,7 @@ async def review_store_submission(
                 where={"id": store_listing_version_id},
                 include={
                     "StoreListing": True,
-                    "AgentGraph": {"include": {**AGENT_GRAPH_INCLUDE, "User": True}},
-                    "Reviewer": True,
+                    "AgentGraph": {"include": AGENT_GRAPH_INCLUDE},
                 },
             )
         )
@@ -1266,13 +1096,6 @@ async def review_store_submission(
                 detail=f"Store listing version {store_listing_version_id} not found",
             )
 
-        # Check if we're rejecting an already approved agent
-        is_rejecting_approved = (
-            not is_approved
-            and store_listing_version.submissionStatus
-            == prisma.enums.SubmissionStatus.APPROVED
-        )
-
         # If approving, update the listing to indicate it has an approved version
         if is_approved and store_listing_version.AgentGraph:
             heading = f"Sub-graph of {store_listing_version.name}v{store_listing_version.agentGraphVersion}"
@@ -1303,37 +1126,6 @@ async def review_store_submission(
                 },
             )
 
-        # If rejecting an approved agent, update the StoreListing accordingly
-        if is_rejecting_approved:
-            # Check if there are other approved versions
-            other_approved = (
-                await prisma.models.StoreListingVersion.prisma().find_first(
-                    where={
-                        "storeListingId": store_listing_version.StoreListing.id,
-                        "id": {"not": store_listing_version_id},
-                        "submissionStatus": prisma.enums.SubmissionStatus.APPROVED,
-                    }
-                )
-            )
-
-            if not other_approved:
-                # No other approved versions, update hasApprovedVersion to False
-                await prisma.models.StoreListing.prisma().update(
-                    where={"id": store_listing_version.StoreListing.id},
-                    data={
-                        "hasApprovedVersion": False,
-                        "ActiveVersion": {"disconnect": True},
-                    },
-                )
-            else:
-                # Set the most recent other approved version as active
-                await prisma.models.StoreListing.prisma().update(
-                    where={"id": store_listing_version.StoreListing.id},
-                    data={
-                        "ActiveVersion": {"connect": {"id": other_approved.id}},
-                    },
-                )
-
         submission_status = (
             prisma.enums.SubmissionStatus.APPROVED
             if is_approved
@@ -1362,89 +1154,6 @@ async def review_store_submission(
                 f"Failed to update store listing version {store_listing_version_id}"
             )
 
-        # Send email notification to the agent creator
-        if store_listing_version.AgentGraph and store_listing_version.AgentGraph.User:
-            agent_creator = store_listing_version.AgentGraph.User
-            reviewer = (
-                store_listing_version.Reviewer
-                if store_listing_version.Reviewer
-                else None
-            )
-
-            try:
-                base_url = (
-                    settings.config.frontend_base_url
-                    or settings.config.platform_base_url
-                )
-
-                if is_approved:
-                    store_agent = (
-                        await prisma.models.StoreAgent.prisma().find_first_or_raise(
-                            where={"storeListingVersionId": submission.id}
-                        )
-                    )
-
-                    # Send approval notification
-                    notification_data = AgentApprovalData(
-                        agent_name=submission.name,
-                        agent_id=submission.agentGraphId,
-                        agent_version=submission.agentGraphVersion,
-                        reviewer_name=(
-                            reviewer.name
-                            if reviewer and reviewer.name
-                            else DEFAULT_ADMIN_NAME
-                        ),
-                        reviewer_email=(
-                            reviewer.email if reviewer else DEFAULT_ADMIN_EMAIL
-                        ),
-                        comments=external_comments,
-                        reviewed_at=submission.reviewedAt
-                        or datetime.now(tz=timezone.utc),
-                        store_url=f"{base_url}/marketplace/agent/{store_agent.creator_username}/{store_agent.slug}",
-                    )
-
-                    notification_event = NotificationEventModel[AgentApprovalData](
-                        user_id=agent_creator.id,
-                        type=prisma.enums.NotificationType.AGENT_APPROVED,
-                        data=notification_data,
-                    )
-                else:
-                    # Send rejection notification
-                    notification_data = AgentRejectionData(
-                        agent_name=submission.name,
-                        agent_id=submission.agentGraphId,
-                        agent_version=submission.agentGraphVersion,
-                        reviewer_name=(
-                            reviewer.name
-                            if reviewer and reviewer.name
-                            else DEFAULT_ADMIN_NAME
-                        ),
-                        reviewer_email=(
-                            reviewer.email if reviewer else DEFAULT_ADMIN_EMAIL
-                        ),
-                        comments=external_comments,
-                        reviewed_at=submission.reviewedAt
-                        or datetime.now(tz=timezone.utc),
-                        resubmit_url=f"{base_url}/build?flowID={submission.agentGraphId}",
-                    )
-
-                    notification_event = NotificationEventModel[AgentRejectionData](
-                        user_id=agent_creator.id,
-                        type=prisma.enums.NotificationType.AGENT_REJECTED,
-                        data=notification_data,
-                    )
-
-                # Queue the notification for immediate sending
-                await queue_notification_async(notification_event)
-                logger.info(
-                    f"Queued {'approval' if is_approved else 'rejection'} notification for user {agent_creator.id} and agent {submission.name}"
-                )
-
-            except Exception as e:
-                logger.error(f"Failed to send email notification for agent review: {e}")
-                # Don't fail the review process if email sending fails
-                pass
-
         # Convert to Pydantic model for consistency
         return backend.server.v2.store.model.StoreSubmission(
             agent_id=submission.agentGraphId,

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

@@ -94,15 +94,3 @@ class SubmissionNotFoundError(StoreError):
     """Raised when a submission is not found"""
 
     pass
-
-
-class InvalidOperationError(StoreError):
-    """Raised when an operation is not valid for the current state"""
-
-    pass
-
-
-class UnauthorizedError(StoreError):
-    """Raised when a user is not authorized to perform an action"""
-
-    pass

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

@@ -4,7 +4,7 @@ from typing import List
 import prisma.enums
 import pydantic
 
-from backend.util.models import Pagination
+from backend.server.model import Pagination
 
 
 class MyAgent(pydantic.BaseModel):
@@ -115,9 +115,11 @@ class StoreSubmission(pydantic.BaseModel):
     reviewed_at: datetime.datetime | None = None
     changes_summary: str | None = None
 
-    # Additional fields for editing
-    video_url: str | None = None
-    categories: list[str] = []
+    reviewer_id: str | None = None
+    review_comments: str | None = None  # External comments visible to creator
+    internal_comments: str | None = None  # Private notes for admin use only
+    reviewed_at: datetime.datetime | None = None
+    changes_summary: str | None = None
 
 
 class StoreSubmissionsResponse(pydantic.BaseModel):
@@ -159,16 +161,6 @@ class StoreSubmissionRequest(pydantic.BaseModel):
     changes_summary: str | None = None
 
 
-class StoreSubmissionEditRequest(pydantic.BaseModel):
-    name: str
-    sub_heading: str
-    video_url: str | None = None
-    image_urls: list[str] = []
-    description: str = ""
-    categories: list[str] = []
-    changes_summary: str | None = None
-
-
 class ProfileDetails(pydantic.BaseModel):
     name: str
     username: str

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

@@ -162,7 +162,7 @@ async def get_agents(
     try:
         agents = await backend.server.v2.store.db.get_store_agents(
             featured=featured,
-            creators=[creator] if creator else None,
+            creator=creator,
             sorted_by=sorted_by,
             search_query=search_query,
             category=category,
@@ -564,47 +564,6 @@ async def create_submission(
         )
 
 
-@router.put(
-    "/submissions/{store_listing_version_id}",
-    summary="Edit store submission",
-    tags=["store", "private"],
-    dependencies=[fastapi.Depends(autogpt_libs.auth.middleware.auth_middleware)],
-    response_model=backend.server.v2.store.model.StoreSubmission,
-)
-async def edit_submission(
-    store_listing_version_id: str,
-    submission_request: backend.server.v2.store.model.StoreSubmissionEditRequest,
-    user_id: typing.Annotated[
-        str, fastapi.Depends(autogpt_libs.auth.depends.get_user_id)
-    ],
-):
-    """
-    Edit an existing store listing submission.
-
-    Args:
-        store_listing_version_id (str): ID of the store listing version to edit
-        submission_request (StoreSubmissionRequest): The updated submission details
-        user_id (str): ID of the authenticated user editing the listing
-
-    Returns:
-        StoreSubmission: The updated store submission
-
-    Raises:
-        HTTPException: If there is an error editing the submission
-    """
-    return await backend.server.v2.store.db.edit_store_submission(
-        user_id=user_id,
-        store_listing_version_id=store_listing_version_id,
-        name=submission_request.name,
-        video_url=submission_request.video_url,
-        image_urls=submission_request.image_urls,
-        description=submission_request.description,
-        sub_heading=submission_request.sub_heading,
-        categories=submission_request.categories,
-        changes_summary=submission_request.changes_summary,
-    )
-
-
 @router.post(
     "/submissions/media",
     summary="Upload submission media",