Files
AutoGPT/autogpt_platform
Swifty 5035b69c79 feat(platform): add feature request tools for CoPilot chat (#12102)
Users can now search for existing feature requests and submit new ones
directly through the CoPilot chat interface. Requests are tracked in
Linear with customer need attribution.

### Changes 🏗️

**Backend:**
- Added `SearchFeatureRequestsTool` and `CreateFeatureRequestTool` to
the CoPilot chat tools registry
- Integrated with Linear GraphQL API for searching issues in the feature
requests project, creating new issues, upserting customers, and
attaching customer needs
- Added `linear_api_key` secret to settings for system-level Linear API
access
- Added response models (`FeatureRequestSearchResponse`,
`FeatureRequestCreatedResponse`, `FeatureRequestInfo`) to the tools
models

**Frontend:**
- Added `SearchFeatureRequestsTool` and `CreateFeatureRequestTool` UI
components with full streaming state handling (input-streaming,
input-available, output-available, output-error)
- Added helper utilities for output parsing, type guards, animation
text, and icon rendering
- Wired tools into `ChatMessagesContainer` for rendering in the chat
- Added styleguide examples covering all tool states

### 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:
  - [x] Verified search returns matching feature requests from Linear
- [x] Verified creating a new feature request creates an issue and
customer need in Linear
- [x] Verified adding a need to an existing issue works via
`existing_issue_id`
  - [x] Verified error states render correctly in the UI
  - [x] Verified styleguide page renders all tool states

#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)

New secret: `LINEAR_API_KEY` — required for system-level Linear API
operations (defaults to empty string).

<!-- greptile_comment -->

<h2>Greptile Overview</h2>

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

Adds feature request search and creation tools to CoPilot chat,
integrating with Linear's GraphQL API to track user feedback. Users can
now search existing feature requests and submit new ones (or add their
need to existing issues) directly through conversation.

**Key changes:**
- Backend: `SearchFeatureRequestsTool` and `CreateFeatureRequestTool`
with Linear API integration via system-level `LINEAR_API_KEY`
- Frontend: React components with streaming state handling and accordion
UI for search results and creation confirmations
- Models: Added `FeatureRequestSearchResponse` and
`FeatureRequestCreatedResponse` to response types
- Customer need tracking: Upserts customers in Linear and attaches needs
to issues for better feedback attribution

**Issues found:**
- Missing `LINEAR_API_KEY` entry in `.env.default` (required per PR
description checklist)
- Hardcoded project/team IDs reduce maintainability
- Global singleton pattern could cause issues in async contexts
- Using `user_id` as customer name reduces readability in Linear
</details>


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

- Safe to merge with minor configuration fix required
- The implementation is well-structured with proper error handling, type
safety, and follows existing patterns in the codebase. The missing
`.env.default` entry is a straightforward configuration issue that must
be fixed before deployment but doesn't affect code quality. The other
findings are style improvements that don't impact functionality.
- Verify that `LINEAR_API_KEY` is added to `.env.default` before merging
</details>


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

```mermaid
sequenceDiagram
    participant User
    participant CoPilot UI
    participant LLM
    participant FeatureRequestTool
    participant LinearClient
    participant Linear API

    User->>CoPilot UI: Request feature via chat
    CoPilot UI->>LLM: Send user message
    
    LLM->>FeatureRequestTool: search_feature_requests(query)
    FeatureRequestTool->>LinearClient: query(SEARCH_ISSUES_QUERY)
    LinearClient->>Linear API: POST /graphql (search)
    Linear API-->>LinearClient: searchIssues.nodes[]
    LinearClient-->>FeatureRequestTool: Feature request data
    FeatureRequestTool-->>LLM: FeatureRequestSearchResponse
    
    alt No existing requests found
        LLM->>FeatureRequestTool: create_feature_request(title, description)
        FeatureRequestTool->>LinearClient: mutate(CUSTOMER_UPSERT_MUTATION)
        LinearClient->>Linear API: POST /graphql (upsert customer)
        Linear API-->>LinearClient: customer {id, name}
        LinearClient-->>FeatureRequestTool: Customer data
        
        FeatureRequestTool->>LinearClient: mutate(ISSUE_CREATE_MUTATION)
        LinearClient->>Linear API: POST /graphql (create issue)
        Linear API-->>LinearClient: issue {id, identifier, url}
        LinearClient-->>FeatureRequestTool: Issue data
        
        FeatureRequestTool->>LinearClient: mutate(CUSTOMER_NEED_CREATE_MUTATION)
        LinearClient->>Linear API: POST /graphql (attach need)
        Linear API-->>LinearClient: need {id, issue}
        LinearClient-->>FeatureRequestTool: Need data
        FeatureRequestTool-->>LLM: FeatureRequestCreatedResponse
    else Existing request found
        LLM->>FeatureRequestTool: create_feature_request(title, description, existing_issue_id)
        FeatureRequestTool->>LinearClient: mutate(CUSTOMER_UPSERT_MUTATION)
        LinearClient->>Linear API: POST /graphql (upsert customer)
        Linear API-->>LinearClient: customer {id}
        LinearClient-->>FeatureRequestTool: Customer data
        
        FeatureRequestTool->>LinearClient: mutate(CUSTOMER_NEED_CREATE_MUTATION)
        LinearClient->>Linear API: POST /graphql (attach need to existing)
        Linear API-->>LinearClient: need {id, issue}
        LinearClient-->>FeatureRequestTool: Need data
        FeatureRequestTool-->>LLM: FeatureRequestCreatedResponse
    end
    
    LLM-->>CoPilot UI: Tool response + continuation
    CoPilot UI-->>User: Display result with accordion UI
```
</details>


<sub>Last reviewed commit: af2e093</sub>

<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->
2026-02-13 15:27:00 +01:00
..

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.