Files
AutoGPT/autogpt_platform
Zamil Majdy 3bea584659 feat(chat/sdk): route SDK through OpenRouter with observability (#12084)
## Summary
- Routes Claude Agent SDK API calls through OpenRouter via
`ANTHROPIC_BASE_URL` / `ANTHROPIC_AUTH_TOKEN` env vars, enabling
per-call token and cost tracking on the OpenRouter dashboard
- Adds `sdk_model` and `sdk_max_budget_usd` config fields for
SDK-specific model selection and budget control
- Emits `StreamUsage` from SDK `ResultMessage` so the frontend receives
token counts, and persists usage to `session.usage`
- Fixes Langfuse tracing to use the configured model name instead of a
hardcoded default
- Updates Anthropic fallback to use `config.api_key` / `config.base_url`
(OpenRouter routing) instead of raw `ANTHROPIC_API_KEY` env var

## Test plan
- [ ] Deploy and send a CoPilot message — verify the API call appears on
the OpenRouter dashboard
- [ ] Check Langfuse trace shows correct model name (e.g.
`claude-opus-4.6` not hardcoded `claude-sonnet-4-20250514`)
- [ ] Verify frontend receives `StreamUsage` with `promptTokens` /
`completionTokens` values
- [ ] Set `CHAT_SDK_MAX_BUDGET_USD` and verify budget is respected
- [ ] Test fallback path (without `claude-agent-sdk` installed) still
works via OpenRouter

<!-- greptile_comment -->

<h2>Greptile Overview</h2>

<details><summary><h3>Greptile Summary</h3></summary>

Routes Claude Agent SDK API calls through OpenRouter for enhanced
observability and cost tracking. The PR enables per-call token tracking
on the OpenRouter dashboard by configuring the SDK to use
`ANTHROPIC_BASE_URL` and `ANTHROPIC_AUTH_TOKEN` environment variables
derived from the chat configuration.

Key changes:
- Added `sdk_model` and `sdk_max_budget_usd` configuration fields for
SDK-specific control
- Implemented automatic model name resolution that strips OpenRouter
provider prefixes
- Updated SDK client initialization to route through OpenRouter with
proper environment variables
- Emits `StreamUsage` events from SDK `ResultMessage` for frontend token
visibility
- Persists usage data to `session.usage` for historical tracking
- Fixed Langfuse tracing to use the configured model name instead of
hardcoded defaults
- Updated fallback path to use OpenRouter routing instead of direct
Anthropic API
</details>


<details><summary><h3>Confidence Score: 4/5</h3></summary>

- Safe to merge with minor observations - the implementation is solid
and the changes are well-structured
- The code quality is high with proper error handling, clear separation
of concerns, and good defensive coding practices. The changes integrate
cleanly with existing patterns. Minor observations include missing
validation for sdk_max_budget_usd and a potential edge case in model
name resolution, but these don't block merging
- No files require special attention - all changes follow existing
patterns and maintain consistency
</details>


<details><summary><h3>Sequence Diagram</h3></summary>

```mermaid
sequenceDiagram
    participant Frontend
    participant Backend
    participant SDK as Claude Agent SDK
    participant OpenRouter
    participant Anthropic
    participant Langfuse

    Frontend->>Backend: POST /chat/completions
    Backend->>Backend: Load config (api_key, base_url)
    Backend->>Backend: Resolve SDK model (strip OpenRouter prefix)
    Backend->>Backend: Build SDK env vars (ANTHROPIC_BASE_URL, ANTHROPIC_AUTH_TOKEN)
    
    Backend->>Langfuse: Initialize TracedSession with model name
    Backend->>SDK: ClaudeSDKClient(model, env, max_budget_usd)
    
    SDK->>SDK: Use ANTHROPIC_BASE_URL from env
    SDK->>OpenRouter: POST /messages (via configured base_url)
    OpenRouter->>Anthropic: Forward request with routing
    Anthropic-->>OpenRouter: Stream response chunks
    OpenRouter-->>SDK: Stream response with usage data
    
    loop For each SDK message
        SDK-->>Backend: AssistantMessage/UserMessage/ResultMessage
        Backend->>Langfuse: log_sdk_message()
        Backend->>Backend: SDKResponseAdapter.convert_message()
        Backend->>Backend: Extract usage from ResultMessage
        Backend->>Backend: Persist Usage to session.usage
        Backend-->>Frontend: StreamUsage(promptTokens, completionTokens)
        Backend-->>Frontend: StreamTextDelta/StreamToolInput/etc
    end
    
    Backend->>Langfuse: Log final generation with model name
    Backend->>Backend: Save session with usage data
    Backend-->>Frontend: StreamFinish
```
</details>


<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->
2026-02-12 21:47:39 +07:00
..
2026-02-12 08:26:26 +04:00

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.