Autobox Mocks API

A serverless mock API deployed on Vercel that provides pre-generated test data for the Autobox multi-agent AI simulation platform.

Overview

autobox-mocks-api serves as a development and testing backend by exposing all autobox-api endpoints with realistic mock data. It uses pre-generated static data to ensure consistent responses across environments, making it ideal for frontend development, testing, and demos.

Features

• 🚀 Serverless deployment on Vercel
• 📊 Pre-generated mock data with LLM-powered content
• 🔒 Type-safe with TypeScript and Zod validation
• ⚡ Fast responses with static data serving
• 🔄 Automated mock generation and synchronization pipeline
• 🧪 Comprehensive test coverage

Getting Started

Prerequisites

• Node.js 18+
• Yarn package manager
• OpenAI API key (for generating new mock data)

Installation

# Clone the repository
git clone https://github.com/yourusername/autobox-mocks-api.git
cd autobox-mocks-api

# Install dependencies
yarn install

Local Development

# Start Vercel dev server on port 9000
yarn start:vercel

Mock Data Management

Generate New Mock Data

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key

# Generate new mock data
yarn generate

# Or build and run the compiled generator
yarn generate:build
yarn generate:prod

Sync Mock Data

The project uses a two-stage data pipeline:

1. JSON mock files in mocks/ (generated)
2. TypeScript files in src/mocks/ (synced from JSON)

# One-time sync from JSON to TypeScript
yarn sync:mocks

# Watch mode for automatic syncing during development
yarn sync:mocks:watch

# Generate and sync in one command
yarn generate:all

# Generate and sync with watch mode
yarn dev:mocks

Project Structure

autobox-mocks-api/
├── api/                       # Vercel serverless functions
│   ├── index.ts              # Main API router
│   └── ping.ts               # Health check endpoint
├── mocks/                    # Generated JSON mock data
├── src/
│   ├── handler/             # Request handlers
│   ├── mocks/               # TypeScript mock data
│   └── utils/               # Utility functions
└── scripts/
    ├── generator/           # Mock data generation
    │   ├── generators/      # Entity generators
    │   ├── openai/         # LLM integration
    │   └── schemas/        # Zod schemas
    └── sync-mocks.ts       # JSON to TS sync

API Endpoints

Organizations

• GET /api/organizations - List all organizations
• GET /api/organizations/[oid] - Get organization by ID

Projects

• GET /api/organizations/[oid]/projects - List projects for organization
• GET /api/organizations/[oid]/projects/[pid] - Get project by ID

Simulations

• GET /api/organizations/[oid]/projects/[pid]/simulations - List simulations
• GET /api/organizations/[oid]/projects/[pid]/simulations/[id] - Get simulation

Runs

• GET /api/organizations/[oid]/projects/[pid]/simulations/[sid]/runs - List runs
• GET /api/organizations/[oid]/projects/[pid]/simulations/[sid]/runs/[id] - Get run

Agents

• GET /api/.../runs/[rid]/agents - List agents for run
• GET /api/.../runs/[rid]/agents/[id] - Get agent by ID

Traces

• GET /api/.../runs/[rid]/traces - Get traces for run
• GET /api/.../runs/[rid]/agents/[aid]/traces - Get agent traces

Metrics

• GET /api/.../runs/[rid]/metrics - Get run metrics
• GET /api/.../simulations/[sid]/metrics/definitions - Get metric definitions

Other

• GET /api/ping - Health check
• POST /api/organizations/[oid]/chat - Chat endpoint

Testing

# Run all tests
yarn test

# Run unit tests only
yarn test:unit

# Run integration tests
yarn test:integration

# Type checking
yarn check:types

Development Workflow

Adding New Endpoints

1. Create a handler in src/handler/:

export function getNewEndpoint(req: VercelRequest, res: VercelResponse) {
  const data = mockData.newEndpoint;
  return res.status(200).json(data);
}

2. Add route to api/index.ts:

const routes = {
  'GET:/api/new-endpoint': getNewEndpoint,
  // ...
};

3. Add mock data to generator or seeds

Modifying Mock Data

1. Edit generator in scripts/generator/generators/
2. Run yarn generate to create new JSON
3. Run yarn sync:mocks to update TypeScript
4. Test with yarn start:vercel

Deployment

The API is automatically deployed to Vercel on push to main branch.

Manual Deployment

# Install Vercel CLI
npm i -g vercel

# Deploy to production
vercel --prod

Data Model

The mock data follows this hierarchy:

Organizations (1) → (N) Projects
Projects (1) → (N) Simulations  
Simulations (1) → (N) Runs
Runs (1) → (N) Agents, Traces, Metrics

Each entity has realistic attributes generated using OpenAI GPT-4:

• Organizations: Tech companies with various focus areas
• Projects: AI/ML initiatives with business goals
• Simulations: Multi-agent scenarios for negotiation, planning, etc.
• Runs: Execution instances with timestamps and status
• Agents: Specialized AI agents with roles and objectives
• Traces: Conversation history and agent interactions
• Metrics: Performance and outcome measurements

Configuration

Environment Variables

# For mock generation
OPENAI_API_KEY=your-openai-api-key
OPENAI_MODEL=gpt-4-turbo-preview  # Optional, defaults to gpt-4

# For local development
PORT=9000  # Vercel dev server port

Vercel Configuration

See vercel.json for deployment settings:

• Function timeout: 5 seconds
• All routes handled by single function
• GitHub integration enabled

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is part of the Autobox platform. See LICENSE for details.

Related Projects

• autobox-api - Backend API with agent orchestration
• autobox-ui - Frontend application
• autobox-mocks - Python mock data generator

Support

For issues, questions, or suggestions, please open an issue on GitHub.