Files
AutoGPT/autogpt_platform
Zamil Majdy 27d94e395c feat(backend/sdk): enable WebSearch, block WebFetch, consolidate tool constants (#12108)
## Summary
- Enable Claude Agent SDK built-in **WebSearch** tool (Brave Search via
Anthropic API) for the CoPilot SDK agent
- Explicitly **block WebFetch** via `SDK_DISALLOWED_TOOLS`. The agent
uses the SSRF-protected `mcp__copilot__web_fetch` MCP tool instead
- **Consolidate** all tool security constants (`BLOCKED_TOOLS`,
`WORKSPACE_SCOPED_TOOLS`, `DANGEROUS_PATTERNS`, `SDK_DISALLOWED_TOOLS`)
into `tool_adapter.py` as a single source of truth — previously
scattered across `tool_adapter.py`, `security_hooks.py`, and inline in
`service.py`

## Changes
- `tool_adapter.py`: Add `WebSearch` to `_SDK_BUILTIN_TOOLS`, add
`SDK_DISALLOWED_TOOLS`, move security constants here
- `security_hooks.py`: Import constants from `tool_adapter.py` instead
of defining locally
- `service.py`: Use `SDK_DISALLOWED_TOOLS` instead of inline `["Bash"]`

## Test plan
- [x] All 21 security hooks tests pass
- [x] Ruff lint clean
- [x] All pre-commit hooks pass
- [ ] Verify WebSearch works in CoPilot chat (manual test)

<!-- greptile_comment -->

<h2>Greptile Overview</h2>

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

Consolidates tool security constants into `tool_adapter.py` as single
source of truth, enables WebSearch (Brave via Anthropic API), and
explicitly blocks WebFetch to prevent SSRF attacks. The change improves
security by ensuring the agent uses the SSRF-protected
`mcp__copilot__web_fetch` tool instead of the built-in WebFetch which
can access internal networks like `localhost:8006`.
</details>


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

- This PR is safe to merge with minimal risk
- The changes improve security by blocking WebFetch (SSRF risk) while
enabling safe WebSearch. The consolidation of constants into a single
source of truth improves maintainability. All existing tests pass (21
security hooks tests), and the refactoring is straightforward with no
behavioral changes to existing security logic. The only suggestions are
minor improvements: adding a test for WebFetch blocking and considering
a lowercase alias for consistency.
- No files require special attention
</details>


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

```mermaid
sequenceDiagram
    participant Agent as SDK Agent
    participant Hooks as Security Hooks
    participant TA as tool_adapter.py
    participant MCP as MCP Tools
    
    Note over TA: SDK_DISALLOWED_TOOLS = ["Bash", "WebFetch"]
    Note over TA: _SDK_BUILTIN_TOOLS includes WebSearch
    
    Agent->>Hooks: Request WebSearch (Brave API)
    Hooks->>TA: Check BLOCKED_TOOLS
    TA-->>Hooks: Not blocked
    Hooks-->>Agent: Allowed ✓
    Agent->>Agent: Execute via Anthropic API
    
    Agent->>Hooks: Request WebFetch (SSRF risk)
    Hooks->>TA: Check BLOCKED_TOOLS
    Note over TA: WebFetch in SDK_DISALLOWED_TOOLS
    TA-->>Hooks: Blocked
    Hooks-->>Agent: Denied ✗
    Note over Agent: Use mcp__copilot__web_fetch instead
    
    Agent->>Hooks: Request mcp__copilot__web_fetch
    Hooks->>MCP: Validate (MCP tool, not SDK builtin)
    MCP-->>Hooks: Has SSRF protection
    Hooks-->>Agent: Allowed ✓
    Agent->>MCP: Execute with SSRF checks
```
</details>


<sub>Last reviewed commit: 2d9975f</sub>

<!-- greptile_other_comments_section -->

<!-- /greptile_comment -->
2026-02-15 06:51:25 +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.

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.