Files
AutoGPT/autogpt_platform
Zamil Majdy bf92e7dbc8 hotfix(backend/executor): Fix RabbitMQ channel retry logic in executor (#10661)
## Summary
**HOTFIX for production** - Fixes executor being stuck in infinite retry
loop when RabbitMQ channels are closed
- Ensures proper reconnection by checking channel state before
attempting to consume messages
- Prevents accumulation of thousands of retry attempts (was seeing 7000+
retries)

## Changes
The executor was stuck repeatedly failing with "Channel is closed"
errors because the `continuous_retry` decorator was attempting to reuse
closed channels instead of creating new ones.

Added channel state checks (`is_ready`) before connecting in both:
- `_consume_execution_run()` 
- `_consume_execution_cancel()`

When a channel is not ready (closed), the code now:
1. Disconnects the client (safe operation, checks if already
disconnected)
2. Establishes a fresh connection with new channel
3. Proceeds with message consumption

## Test plan
- [x] Verified the disconnect() method is safe to call on already
disconnected clients
- [x] Confirmed is_ready property checks both connection and channel
state
- [ ] Deploy to environment and verify executors reconnect properly
after channel failures
- [ ] Monitor logs to ensure no more "Channel is closed" retry loops

## Related Issues
Fixes critical production issue where:
- Executor pods show repeated "Channel is closed" errors
- 757 messages stuck in `graph_execution_queue`
- 102,286 messages in `failed_notifications` queue
- RabbitMQ logs show connections being closed due to missed heartbeats

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

Co-authored-by: Claude <noreply@anthropic.com>
2025-08-16 17:06:06 -05:00
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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)
  • Node.js & NPM (for running the frontend application)

Running the System

To run the AutoGPT Platform, follow these steps:

  1. Clone this repository to your local machine and navigate to the autogpt_platform directory within the repository:

    git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git>
    cd AutoGPT/autogpt_platform
    
  2. Run the following command:

    cp .env.example .env
    

    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:

    docker compose up -d
    

    This command will start all the necessary backend services defined in the docker-compose.yml file in detached mode.

  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

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:

  1. Updating and restarting a specific service:

    docker compose build api_srv
    docker compose up -d --no-deps api_srv
    

    This rebuilds the api_srv service and restarts it without affecting other services.

  2. Viewing logs for troubleshooting:

    docker compose logs -f api_srv ws_srv
    

    This shows and follows the logs for both api_srv and ws_srv services.

  3. Scaling a service for increased load:

    docker compose up -d --scale executor=3
    

    This scales the executor service to 3 instances to handle increased load.

  4. Stopping the entire system for maintenance:

    docker compose stop
    docker compose rm -f
    docker compose pull
    docker compose up -d
    

    This stops all services, removes containers, pulls the latest images, and restarts the system.

  5. Developing with live updates:

    docker compose watch
    

    This watches for changes in your code and automatically updates the relevant services.

  6. Checking the status of services:

    docker compose ps
    

    This 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:

  1. Open the docker-compose.yml file in a text editor.

  2. 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:
    
  3. Save the file and run docker compose up -d to apply the changes.

This configuration will create named volumes for PostgreSQL and Redis, ensuring that your data persists across container restarts.

API Client Generation

The platform includes scripts for generating and managing the API client:

  • pnpm fetch:openapi: Fetches the OpenAPI specification from the backend service (requires backend to be running on port 8006)
  • pnpm generate:api-client: Generates the TypeScript API client from the OpenAPI specification using Orval
  • pnpm generate:api-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-all
    

This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.