Files
AutoGPT/autogpt_platform
Zamil Majdy 193866232c hotfix(backend): fix rate-limited messages blocking queue by republishing to back (#11326)
## Summary
Fix critical queue blocking issue where rate-limited user messages
prevent other users' executions from being processed, causing the 135
late executions reported in production.

## Root Cause Analysis
When a user exceeds `max_concurrent_graph_executions_per_user` (25), the
executor uses `basic_nack(requeue=True)` which sends the message to the
**FRONT** of the RabbitMQ queue. This creates an infinite blocking loop
where:
1. Rate-limited message goes to front of queue
2. Gets processed, hits rate limit again  
3. Goes back to front of queue
4. Blocks all other users' messages indefinitely

## Solution Implementation

### 🔧 Core Changes
- **New setting**: `requeue_by_republishing` (default: `True`) in
`backend/util/settings.py`
- **Smart `_ack_message`**: Automatically uses republishing when
`requeue=True` and setting enabled
- **Efficient implementation**: Uses existing `self.run_client`
connection instead of creating new ones
- **Integration test**: Real RabbitMQ test validates queue ordering
behavior

### 🔄 Technical Implementation
**Before (blocking):**
```python
basic_nack(delivery_tag, requeue=True)  # Goes to FRONT of queue 
```

**After (non-blocking):**
```python
if requeue and self.config.requeue_by_republishing:
    # First: Republish to BACK of queue
    self.run_client.publish_message(...)
    # Then: Reject without requeue
    basic_nack(delivery_tag, requeue=False)
```

### 📊 Impact
-  **Other users' executions no longer blocked** by rate-limited users
-  **Fair queue processing** - FIFO behavior maintained for all users
-  **Rate limiting still works** - just doesn't block others
-  **Configurable** - can revert to old behavior with
`requeue_by_republishing=False`
-  **Zero performance impact** - uses existing connections

## Test Plan
- **Integration test**: `test_requeue_integration.py` validates real
RabbitMQ queue ordering
- **Scenario testing**: Confirms rate-limited messages go to back of
queue
- **Cross-user validation**: Verifies other users' messages process
correctly
- **Setting test**: Confirms configuration loads with correct defaults

## Deployment Strategy
This is a **hotfix** that can be deployed immediately:
- **Backward compatible**: Old behavior available via config
- **Safe default**: New behavior is safer than current state
- **No breaking changes**: All existing functionality preserved
- **Immediate relief**: Resolves production queue blocking

## Files Modified
- `backend/executor/manager.py`: Enhanced `_ack_message` logic and
`_requeue_message_to_back` method
- `backend/util/settings.py`: Added `requeue_by_republishing`
configuration field
- `test_requeue_integration.py`: Integration test for queue ordering
validation

## Related Issues
Fixes the 135 late executions issue where messages were stuck in QUEUED
state despite available executor capacity (583m/600m utilization).

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

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

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-11-05 16:24:07 +00:00
..

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:

  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.default .env
    

    This command will copy the .env.default 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. After all the services are in ready state, open your browser and navigate to http://localhost:3000 to 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:

  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: Runs both fetch and generate commands in sequence

Manual API Client Updates

If you need to update the API client after making changes to the backend API:

  1. Ensure the backend services are running:

    docker compose up -d
    
  2. Generate the updated API client:

    pnpm generate:api
    

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