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 -->
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:
-
Clone this repository to your local machine and navigate to the
autogpt_platformdirectory within the repository:git clone <https://github.com/Significant-Gravitas/AutoGPT.git | git@github.com:Significant-Gravitas/AutoGPT.git> cd AutoGPT/autogpt_platform -
Run the following command:
cp .env.default .envThis command will copy the
.env.defaultfile to.env. You can modify the.envfile to add your own environment variables. -
Run the following command:
docker compose up -dThis command will start all the necessary backend services defined in the
docker-compose.ymlfile in detached mode. -
After all the services are in ready state, open your browser and navigate to
http://localhost:3000to 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:
-
Updating and restarting a specific service:
docker compose build api_srv docker compose up -d --no-deps api_srvThis rebuilds the
api_srvservice and restarts it without affecting other services. -
Viewing logs for troubleshooting:
docker compose logs -f api_srv ws_srvThis shows and follows the logs for both
api_srvandws_srvservices. -
Scaling a service for increased load:
docker compose up -d --scale executor=3This scales the
executorservice to 3 instances to handle increased load. -
Stopping the entire system for maintenance:
docker compose stop docker compose rm -f docker compose pull docker compose up -dThis stops all services, removes containers, pulls the latest images, and restarts the system.
-
Developing with live updates:
docker compose watchThis watches for changes in your code and automatically updates the relevant services.
-
Checking the status of services:
docker compose psThis 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:
-
Open the
docker-compose.ymlfile in a text editor. -
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: -
Save the file and run
docker compose up -dto 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 Orvalpnpm 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:
-
Ensure the backend services are running:
docker compose up -d -
Generate the updated API client:
pnpm generate:api
This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.