Files
AutoGPT/autogpt_platform
Zamil Majdy 6590fcb76f fix(backend): fix broken update_agent_version_in_library and reduce the method code duplication (#11514)
## Summary
Fix broken `update_agent_version_in_library` functionality by eagerly
loading `AgentGraph` while loading the library, also consolidating
duplicate code that updates agent version in library and configures HITL
safe mode settings.

## Problem
The `update_agent_version_in_library` is currently failed with this
error:
```
  File "/Users/abhi/Documents/AutoGPT/autogpt_platform/backend/backend/server/v2/library/model.py", line 110, in from_db
    raise ValueError("Associated Agent record is required.")
ValueError: Associated Agent record is required.
```

also logic was duplicated across two router endpoints with identical
implementations, creating maintenance burden and potential for
inconsistencies.

## Changes Made

### Created Helper Method
- Add `_update_library_agent_version_and_settings()` helper function  
- Fixes broken `update_agent_version_in_library` by centralizing the
logic
- Uses proper error handling and settings merging with `model_copy()`

### Replaced Duplicate Code  
- **In `update_graph` function** (v1.py:863) - replaced 13 lines with
single helper call
- **In `set_graph_active_version` function** (v1.py:920) - replaced 13
lines with single helper call

### Benefits
- **Fixes broken functionality**: Centralizes
`update_agent_version_in_library` logic
- **DRY Principle**: Eliminates code duplication across two router
endpoints
- **Maintainability**: Single place to modify the library agent update
logic
- **Consistency**: Ensures both endpoints use identical logic for HITL
safe mode configuration
- **Readability**: Cleaner, more focused endpoint implementations

## Technical Details
The helper method fixes broken `update_agent_version_in_library` by
handling:
1. Updating agent version in library via
`update_agent_version_in_library()`
2. Conditionally setting `human_in_the_loop_safe_mode: true` if graph
has HITL blocks and setting is not already configured
3. Proper settings merging to preserve existing configuration

## Testing
- [x] Code compiles and passes type checking
- [x] Pre-commit hooks pass (linting, formatting, type checking)
- [x] Both affected endpoints maintain same functionality with cleaner
implementation

Fixes broken duplicate code identified in v1.py router endpoints for
`update_agent_version_in_library`.

Co-authored-by: Claude <noreply@anthropic.com>
2025-12-02 14:31:36 +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.