## Summary Introduces a comprehensive Human-In-The-Loop (HITL) review system that allows any block to require human approval before execution. This extends the existing HITL infrastructure to support automatic review requests for potentially dangerous operations. ## 🚀 Key Features ### **Automatic HITL for Any Block** - **Simple opt-in**: Set `self.requires_human_review = True` in any block constructor - **Safe mode integration**: Only activates when `execution_context.safe_mode = True` - **Seamless workflow**: Blocks pause execution → Human reviews via existing UI → Execution continues or stops ### **Unified Review Infrastructure** - **Shared HITLReviewHelper**: Clean, reusable helper class for all review operations - **Single API**: `handle_review_decision()` method with structured return type - **Type-safe**: Proper typing with non-nullable `ReviewDecision.review_result` ### **Smart Graph Detection** - **Updated `has_human_in_the_loop`**: Now detects both dedicated HITL blocks and blocks with `requires_human_review = True` - **Frontend awareness**: UI can properly indicate graphs requiring human intervention ## 🏗️ Implementation ### **Block Usage** ```python class MyBlock(Block): def __init__(self): super().__init__(...) self.requires_human_review = True # Enable automatic HITL async def run(self, input_data, **kwargs): # If we reach here, either safe mode is off OR human approved # No additional HITL code needed - handled automatically by base class yield "result", "Operation completed" ``` ### **Review Workflow** 1. **Block execution starts** → Base class checks `requires_human_review` flag 2. **Safe mode enabled** → Creates review entry, pauses execution 3. **Human reviews** → Uses existing review UI to approve/reject 4. **Execution resumes** → Continues if approved, raises error if rejected 5. **Safe mode disabled** → Executes normally without review ## 🔧 Technical Improvements ### **Code Quality Enhancements** - **Better naming**: `risky_block` → `requires_human_review` (clearer intent) - **Type safety**: Non-nullable `ReviewDecision.review_result` (eliminates Optional checks) - **Exhaustive handling**: Proper error handling for unexpected review statuses - **Clean exception handling**: Removed redundant try-catch-log-reraise patterns ### **Architecture Fixes** - **Circular import resolution**: Fixed `ExecutionContext` import issues breaking 444+ block tests - **Early returns**: Cleaner control flow without nested conditionals - **Defensive programming**: Handles edge cases with clear error messages ## 📊 Changes Made ### **Core Files** - **`Block.requires_human_review`**: New flag for marking blocks requiring approval - **`HITLReviewHelper`**: Shared helper class with clean, testable API - **`HumanInTheLoopBlock`**: Refactored to use shared infrastructure - **`Graph.has_human_in_the_loop`**: Updated to include review-requiring blocks ### **Quality Improvements** - **Type hints**: Proper typing throughout with runtime compatibility - **Error handling**: Exhaustive status handling with descriptive errors - **Code reduction**: -16 lines through removal of redundant exception handling - **Test compatibility**: All 444/445 block tests pass ## ✅ Testing & Validation - **All tests pass**: 444/445 block tests passing ✅ - **Type checking**: All pyright/mypy checks pass ✅ - **Formatting**: All linting and formatting checks pass ✅ - **Circular imports**: Resolved import issues that were breaking tests ✅ - **Backward compatibility**: Existing HITL functionality unchanged ✅ ## 🎯 Use Cases This enables automatic human oversight for blocks performing: - **File operations**: Deletion, modification, system access - **External API calls**: Payments, data modifications, destructive operations - **System commands**: Shell execution, configuration changes - **Data processing**: Sensitive data handling, compliance-required operations ## 🔄 Migration Path **Existing code**: No changes required - fully backward compatible **New blocks**: Simply set `self.requires_human_review = True` to enable automatic HITL **Safe mode**: Controls whether review requests are created (production vs development) --- This creates a robust, type-safe foundation for human oversight in automated workflows while maintaining the existing HITL user experience and API compatibility. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit * **New Features** * Human-in-the-loop review support so executions can pause for human review and resume based on decisions. * **Improvements** * Blocks can opt into requiring human review and will use reviewed input when proceeding. * Unified review decision flow with clearer approved/rejected outcomes and messaging. * Graph detection expanded to recognize nodes that require human review. * **Chores** * Test config adjusted to avoid pytest plugin conflicts. <sub>✏️ Tip: You can customize this high-level summary in your review settings.</sub> <!-- end of auto-generated comment: release notes by coderabbit.ai -->
AutoGPT Platform
Welcome to the AutoGPT Platform - a powerful system for creating and running AI agents to solve business problems. This platform enables you to harness the power of artificial intelligence to automate tasks, analyze data, and generate insights for your organization.
Getting Started
Prerequisites
- Docker
- Docker Compose V2 (comes with Docker Desktop, or can be installed separately)
Running the System
To run the AutoGPT Platform, follow these steps:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to access the AutoGPT Platform frontend.
Running Just Core services
You can now run the following to enable just the core services.
# For help
make help
# Run just Supabase + Redis + RabbitMQ
make start-core
# Stop core services
make stop-core
# View logs from core services
make logs-core
# Run formatting and linting for backend and frontend
make format
# Run migrations for backend database
make migrate
# Run backend server
make run-backend
# Run frontend development server
make run-frontend
Docker Compose Commands
Here are some useful Docker Compose commands for managing your AutoGPT Platform:
docker compose up -d: Start the services in detached mode.docker compose stop: Stop the running services without removing them.docker compose rm: Remove stopped service containers.docker compose build: Build or rebuild services.docker compose down: Stop and remove containers, networks, and volumes.docker compose watch: Watch for changes in your services and automatically update them.
Sample Scenarios
Here are some common scenarios where you might use multiple Docker Compose commands:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
-
Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
-
Checking the status of services:
docker compose psThis shows the current status of all services defined in your docker-compose.yml file.
These scenarios demonstrate how to use Docker Compose commands in combination to manage your AutoGPT Platform effectively.
Persisting Data
To persist data for PostgreSQL and Redis, you can modify the docker-compose.yml file to add volumes. Here's how:
-
Open the
docker-compose.ymlfile in a text editor. -
Add volume configurations for PostgreSQL and Redis services:
services: postgres: # ... other configurations ... volumes: - postgres_data:/var/lib/postgresql/data redis: # ... other configurations ... volumes: - redis_data:/data volumes: postgres_data: redis_data: -
Save the file and run
docker compose up -dto apply the changes.
This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.
API Client Generation
The platform includes scripts for generating and managing the API client:
pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orvalpnpm generate:api: Runs both fetch and generate commands in sequence
Manual API Client Updates
If you need to update the API client after making changes to the backend API:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.