Files
AutoGPT/autogpt_platform
Mitansh Jadhav 469b1fccbb fix(blocks): handle invalid or empty response from MusicGen model (#10533)
Handle invalid or empty response from MusicGen model


Fixes: #9145
> ⚠️ Note: This PR does not directly fix issue #9145 (failed run marked
as success), but improves the validation of the URL to reduce the
chances of invalid states entering the system. This is a related
improvement, but not the root cause fix.


### Description
During execution of the meta/musicgen model via Replicate API, the
application failed
with an error indicating the model returned an empty or invalid
response.
Although some API calls succeeded, this error showed the logic was not
checking the
structure and content of the result properly before processing it.

PROBLEM:
CONTEXT:
API: Replicate
MODEL: meta/musicgen:671ac645
STATUS: Failed after 3 attempts
ERROR_MESSAGE: "Unexpected error: Model returned empty or invalid
response"
CAUSE:
- The original logic did not validate result structure.
- It assumed any non-null output was valid, including strings like "No
output received".
- This led to invalid/malformed results being passed to the frontend.


### Changes 🏗️

- Added `AIMusicGeneratorBlock` to support music generation using Meta’s
MusicGen models via Replicate API.
- Supports configurable inputs like prompt, model version, duration,
temperature, top_k/p, and normalization.
- Uses robust retry logic for reliability.
- Output returns audio URL; errors return user-friendly message.

BEFORE_CODE: |
```
if result and result != "No output received":
     yield "result", result
     return
```

AFTER_CODE: |

```
if result and isinstance(result, str) and result.startswith("http"):
      yield "result", result
      return
```

### Checklist 📋

#### For code changes:
- [x] Clearly listed changes in the PR description
- [x] Added test plan and mock outputs
- [x] Tested with various prompts and confirmed working output

### Test Plan

- [x] Ran locally with valid Replicate API key
- [x] Generated audio with different prompts
- [x] Simulated failure to verify retry and error message

---------

Co-authored-by: Abhimanyu Yadav <122007096+Abhi1992002@users.noreply.github.com>
Co-authored-by: Nicholas Tindle <nicholas.tindle@agpt.co>
2025-08-25 16:35:12 +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.

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.