Files
AutoGPT/autogpt_platform
Zamil Majdy 89eb5d1189 feat(feature-flag): add LaunchDarkly user context and metadata support (#10595)
## Summary

Enable LaunchDarkly feature flags to use rich user context and metadata
for advanced targeting, including user segments, account age, email
domains, and custom attributes. This unlocks LaunchDarkly's powerful
targeting capabilities beyond simple user ID checks.

## Problem

LaunchDarkly feature flags were only receiving basic user IDs,
preventing the use of:
- **Segment-based targeting** (e.g., "employees", "beta users", "new
accounts")
- **Contextual rules** (e.g., account age, email domain, custom
metadata)
- **Advanced LaunchDarkly features** like percentage rollouts by user
attributes

This limited feature flag flexibility and required manual user ID
management for targeting.

## Solution

### 🎯 **LaunchDarkly Context Enhancement**
- **Rich user context**: Send user metadata, segments, account age,
email domain to LaunchDarkly
- **Automatic segmentation**: Users automatically categorized as
"employee", "new_user", "established_user" etc.
- **Custom metadata support**: Any user metadata becomes available for
LaunchDarkly targeting
- **24-hour caching**: Efficient user context retrieval with TTL cache
to reduce database calls

### 📊 **User Context Data**
```python
# Before: Only user ID
context = Context.builder("user-123").build()

# After: Full context with targeting data
context = {
    "email": "user@agpt.co",
    "created_at": "2023-01-15T10:00:00Z",
    "segments": ["employee", "established_user"],
    "email_domain": "agpt.co", 
    "account_age_days": 365,
    "custom_role": "admin"
}
```

### 🏗️ **Required Infrastructure Changes**

To support proper LaunchDarkly serialization, we needed to implement
clean application models:

#### **Application-Layer User Model**
- Created snake_case User model (`created_at`, `email_verified`) for
proper JSON serialization
- LaunchDarkly expects consistent field naming - camelCase Prisma
objects caused validation errors
- Added `User.from_db()` converter to safely transform database objects

#### **HTTP Client Reliability**  
- Fixed HTTP 4xx retry issue that was causing unnecessary load
- Added layer validation to prevent database objects leaking to external
services

#### **Type Safety**
- Eliminated `Any` types and defensive coding patterns
- Proper typing enables better IDE support and catches errors early

## Technical Implementation

### **Core LaunchDarkly Enhancement**
```python
# autogpt_libs/feature_flag/client.py
@async_ttl_cache(maxsize=1000, ttl_seconds=86400)  # 24h cache
async def _fetch_user_context_data(user_id: str) -> dict[str, Any]:
    user = await get_user_by_id(user_id)
    return _build_launchdarkly_context(user)

def _build_launchdarkly_context(user: User) -> dict[str, Any]:
    return {
        "email": user.email,
        "created_at": user.created_at.isoformat(),  # snake_case for serialization
        "segments": determine_user_segments(user),
        "account_age_days": calculate_account_age(user),
        # ... more context data
    }
```

### **User Segmentation Logic**
- **Role-based**: `admin`, `user`, `system` segments
- **Domain-based**: `employee` for @agpt.co emails  
- **Account age**: `new_user` (<7 days), `recent_user` (7-30 days),
`established_user` (>30 days)
- **Custom metadata**: Any user metadata becomes available for targeting

### **Infrastructure Updates**
- `backend/data/model.py`: Application User model with proper
serialization
- `backend/util/service.py`: HTTP client improvements and layer
validation
- Multiple files: Migration to use application models for consistency

## LaunchDarkly Usage Examples

With this enhancement, you can now create LaunchDarkly rules like:

```yaml
# Target employees only
- variation: true
  targets:
    - values: ["employee"]
      contextKind: "user"
      attribute: "segments"

# Target new users for gradual rollout  
- variation: true
  rollout:
    variations:
      - variation: true
        weight: 25000  # 25% of new users
    contextKind: "user" 
    bucketBy: "segments"
    filters:
      - attribute: "segments"
        op: "contains"
        values: ["new_user"]
```

## Performance & Caching

- **24-hour TTL cache**: Dramatically reduces database calls for user
context
- **Graceful fallbacks**: Simple user ID context if database unavailable
- **Efficient caching**: 1000 entry LRU cache with automatic TTL
expiration

## Testing

- [x] LaunchDarkly context includes all expected user attributes
- [x] Segmentation logic correctly categorizes users
- [x] 24-hour cache reduces database load
- [x] Fallback to simple context works when database unavailable
- [x] All existing feature flag functionality preserved
- [x] HTTP retry improvements work correctly

## Breaking Changes

 **No external API changes** - all existing feature flag usage
continues to work

⚠️ **Internal changes only**:
- `get_user_by_id()` returns application User model instead of Prisma
model
- Test utilities need to import User from `backend.data.model`

## Impact

🎯 **Product Impact**:
- **Advanced targeting**: Product teams can now use sophisticated
LaunchDarkly rules
- **Better user experience**: Gradual rollouts, A/B testing, and
segment-based features
- **Operational efficiency**: Reduced need for manual user ID management

🚀 **Performance Impact**:
- **Reduced database load**: 24-hour caching minimizes repeated user
context queries
- **Improved reliability**: Fixed HTTP retry inefficiencies
- **Better monitoring**: Cleaner logs without 4xx retry noise

---

**Primary goal**: Enable rich LaunchDarkly targeting with user context
and segments
**Infrastructure changes**: Required for proper serialization and
reliability

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

---------

Co-authored-by: Claude <noreply@anthropic.com>
2025-08-12 05:25:56 +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)
  • Node.js & NPM (for running the frontend application)

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.example .env
    

    This command will copy the .env.example 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. Navigate to frontend within the autogpt_platform directory:

    cd frontend
    

    You will need to run your frontend application separately on your local machine.

  5. Run the following command:

    cp .env.example .env.local
    

    This command will copy the .env.example file to .env.local in the frontend directory. You can modify the .env.local within this folder to add your own environment variables for the frontend application.

  6. Run the following command:

    Enable corepack and install dependencies by running:

    corepack enable
    pnpm i
    

    Generate the API client (this step is required before running the frontend):

    pnpm generate:api-client
    

    Then start the frontend application in development mode:

    pnpm dev
    
  7. Open your browser and navigate to http://localhost:3000 to access the AutoGPT Platform 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-all: 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-all
    

This will fetch the latest OpenAPI specification and regenerate the TypeScript client code.