Files
AutoGPT/autogpt_platform
Zamil Majdy f9f984a8f4 fix(db): Remove redundant migration and fix pgvector schema handling (#11822)
### Changes 🏗️

This PR includes two database migration fixes:

#### 1. Remove redundant Supabase extensions migration

Removes the `20260112173500_add_supabase_extensions_to_platform_schema`
migration which was attempting to manage Supabase-provided extensions
and schemas.

**What was removed:**
- Migration that created extensions (pgcrypto, uuid-ossp,
pg_stat_statements, pg_net, pgjwt, pg_graphql, pgsodium, supabase_vault)
- Schema creation for these extensions

**Why it was removed:**
- These extensions and schemas are pre-installed and managed by Supabase
automatically
- The migration was redundant and could cause schema drift warnings
- Attempting to manage Supabase-owned resources in our migrations is an
anti-pattern

#### 2. Fix pgvector extension schema handling

Improves the `20260109181714_add_docs_embedding` migration to handle
cases where pgvector exists in the wrong schema.

**Problem:**
- If pgvector was previously installed in `public` schema, `CREATE
EXTENSION IF NOT EXISTS` would succeed but not actually install it in
the `platform` schema
- This causes `type "vector" does not exist` errors because the type
isn't in the search_path

**Solution:**
- Detect if vector extension exists in a different schema than the
current one
- Drop it with CASCADE and reinstall in the correct schema (platform)
- Use dynamic SQL with `EXECUTE format()` to explicitly specify the
target schema
- Split exception handling: catch errors during removal, but let
installation fail naturally with clear PostgreSQL errors

**Impact:**
- No functional changes - Supabase continues to provide extensions as
before
- pgvector now correctly installs in the platform schema
- Cleaner migration history
- Prevents schema-related errors

### 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 migrations run successfully without the redundant file
  - [x] Confirmed Supabase extensions are still available
  - [x] Tested pgvector migration handles wrong-schema scenario
  - [x] No schema drift warnings

#### For configuration changes:
- [x] .env.default is updated or already compatible with my changes
- [x] docker-compose.yml is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)
  - N/A - No configuration changes required
2026-01-22 12:06:00 +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.