Files
AutoGPT/autogpt_platform
Bently 3718b948ea feat(blocks): Add Ideogram V3 model (#10752)
Adds support for Ideogram V3 model while maintaining backward
compatibility with existing models (V1,
V1_TURBO, V2, V2_TURBO). Updates default model to V3 and implements
smart API routing to handle
Ideogram's new V3 endpoint requirements.

Changes Made

- Added V3 model support: Added V_3 to IdeogramModelName enum and set as
default
- Dual API endpoint handling:
- V3 models route to new /v1/ideogram-v3/generate endpoint with updated
payload format
- Legacy models (V1, V2, Turbo variants) continue using /generate
endpoint
- Model-specific feature filtering:
- V1 models: Basic parameters only (no style_type or color_palette
support)
- V2/V2_TURBO: Full legacy feature support including style_type and
color_palette
- V3: New endpoint with aspect ratio mapping and updated parameter
structure
- Aspect ratio compatibility: Added mapping between internal enum values
and V3's expected format
(ASPECT_1_1 → 1x1)
- Updated pricing: V3 model costs 18 credits (vs 16 for other models)
- Updated default usage: Store image generation now uses V3 by default

Technical Details

Ideogram updated their API with a separate V3 endpoint that has
different requirements:
- Different URL path (/v1/ideogram-v3/generate)
- Different aspect ratio format (e.g., 1x1 instead of ASPECT_1_1)
- Model-specific feature support (V1 models don't support style_type,
etc.)

The implementation intelligently routes requests to the appropriate
endpoint based on the selected model
while maintaining a single unified interface.

I tested all the models and they are working here
<img width="1804" height="887" alt="image"
src="https://github.com/user-attachments/assets/9f2e44ca-50a4-487f-987c-3230dd72fb5e"
/>


### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  <!-- Put your test plan here: -->
  - [x] Test the Ideogram model block and watch as they all work!
2025-08-27 08:33:47 +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.