Files
AutoGPT/autogpt_platform
Swifty 05aaf7a85e fix(backend): Rename LINEAR_API_KEY to COPILOT_LINEAR_API_KEY to prevent global access (#12143)
The `LINEAR_API_KEY` environment variable name is too generic — it
matches the key name used by integrations/blocks, meaning that if set
globally, it could inadvertently grant all users access to Linear
through the blocks system rather than restricting it to the copilot
feature-request tool.

This renames the setting to `COPILOT_LINEAR_API_KEY` to make it clear
this key is scoped exclusively to the copilot's feature-request
functionality, preventing it from being picked up as a general-purpose
Linear credential.

### Changes 🏗️

- Renamed `linear_api_key` → `copilot_linear_api_key` in `Secrets`
settings model (`backend/util/settings.py`)
- Updated all references in the copilot feature-request tool
(`backend/api/features/chat/tools/feature_requests.py`)

### 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 the rename is consistent across all references (settings
+ feature_requests tool)
  - [x] No other files reference the old `linear_api_key` setting name

#### 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**)

> **Note:** The env var changes from `LINEAR_API_KEY` to
`COPILOT_LINEAR_API_KEY`. Any deployment using the old name will need to
update accordingly.

<!-- greptile_comment -->

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

Renamed `LINEAR_API_KEY` to `COPILOT_LINEAR_API_KEY` in settings and the
copilot feature-request tool to prevent unintended access through Linear
blocks.

**Key changes:**
- Updated `Secrets.linear_api_key` → `Secrets.copilot_linear_api_key` in
`backend/util/settings.py`
- Updated all references in
`backend/api/features/chat/tools/feature_requests.py`
- The rename prevents the copilot Linear key from being picked up by the
Linear blocks integration (which uses `LINEAR_API_KEY` via
`ProviderBuilder` in `backend/blocks/linear/_config.py`)

**Issues found:**
- `.env.default` still references `LINEAR_API_KEY` instead of
`COPILOT_LINEAR_API_KEY`
- Frontend styleguide has a hardcoded error message with the old
variable name
</details>


<details><summary><h3>Confidence Score: 3/5</h3></summary>

- Generally safe but requires fixing `.env.default` before deployment
- The code changes are correct and achieve the intended security
improvement by preventing scope leakage. However, the PR is incomplete -
`.env.default` wasn't updated (critical for deployment) and a frontend
error message reference was missed. These issues will cause
configuration problems for anyone deploying with the new variable name.
- Check `autogpt_platform/backend/.env.default` and
`autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx`
- both need updates to match the renamed variable
</details>


<details><summary><h3>Flowchart</h3></summary>

```mermaid
flowchart TD
    A[".env file<br/>COPILOT_LINEAR_API_KEY"] --> B["Secrets model<br/>copilot_linear_api_key"]
    B --> C["feature_requests.py<br/>_get_linear_config()"]
    C --> D["Creates APIKeyCredentials<br/>for copilot feature requests"]
    
    E[".env file<br/>LINEAR_API_KEY"] --> F["ProviderBuilder<br/>in blocks/linear/_config.py"]
    F --> G["Linear blocks integration<br/>for user workflows"]
    
    style A fill:#90EE90
    style B fill:#90EE90
    style C fill:#90EE90
    style D fill:#90EE90
    style E fill:#FFD700
    style F fill:#FFD700
    style G fill:#FFD700
```
</details>


<sub>Last reviewed commit: 86dc57a</sub>

<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->
2026-02-17 11:16:43 +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.