Aura Platform

Aura is a distributed microservices platform for autonomous economic negotiations between AI agents and service providers. It provides a scalable architecture with separate API Gateway and Core Service components, using Protocol Buffers for efficient communication and gRPC for internal service-to-service communication.

🏗️ Architecture

🚀 Quick Start

Prerequisites

• Python 3.8+

• uv (Python package manager) - pip install uv

• buf (Protocol Buffer toolkit) - Installation Guide

• Docker and Docker Compose

• Mistral AI API key (for LLM-based negotiation)

• Redis and PostgreSQL (provided via Docker)

Setup

1. Clone the repository:

   git clone https://github.com/zaebee/aura.git
   cd aura

2. Install Python dependencies:

   uv sync

3. Set up environment variables:

   cp .env.example .env
   # Edit .env and add your API keys using the new nested structure:
   # AURA_LLM__API_KEY="your_mistral_key"
   # AURA_DATABASE__URL="postgresql://user:password@localhost:5432/aura_db"

4. Train the Brain (DSPy):

   make train

5. Generate Protocol Buffer code:

   buf generate

🏗️ Running the Platform

Using Docker Compose (Recommended)

# Start all services (PostgreSQL, Redis, Core, Gateway, Telegram Bot, Prometheus)
docker-compose up --build

This will start:

• PostgreSQL with pgvector extension (port 5432)

• Redis for caching and rate limiting (port 6379)

• Prometheus for metrics (port 9090)

• Core Service (gRPC on port 50051)

• API Gateway (HTTP on port 8000)

• Telegram Bot (Telegram interface)

• Jaeger for distributed tracing (UI on port 16686)

• Frontend Agent Console (port 3000)

Running Services Individually

Core Service:

cd core-service
uv run python -m src.main

API Gateway:

cd api-gateway
uv run python -m src.main

🧪 Testing and Simulation

Run Tests

# Run all tests
make test

# Run tests with coverage
make test-cov

Run Simulators

Agent Negotiation Simulator:

python -m tools.simulators.agent_sim

Autonomous Buyer (Continuous Loop):

python -m tools.simulators.autonomous_buyer

📂 Project Structure

aura/
├── proto/                 # Protocol Buffer definitions
├── api-gateway/          # API Gateway service (FastAPI)
├── core-service/         # Core business logic service (DSPy Engine)
├── adapters/             # External interface adapters
│   ├── telegram-bot/     # Telegram Bot interface
│   └── mcp-server/       # Model Context Protocol (MCP) server
├── docs/                 # Detailed documentation
├── frontend/             # Next.js Agent Console
├── compose.yml           # Docker Compose configuration
├── pyproject.toml        # Python dependencies
└── Makefile              # Common development tasks

Services & Adapters

• Core Service: The brain of the platform. Uses DSPy for ML-optimized negotiation strategies and PostgreSQL/pgvector for semantic search.

• API Gateway: Secure entry point for autonomous agents. Handles signature verification and rate limiting.

• Telegram Bot: User-friendly interface for manual search and negotiation via Telegram.

• MCP Server: Allows AI agents (Claude/Cursor) to natively use Aura tools.

🔧 Development Workflow

Code Generation

After modifying .proto files:

buf generate

Linting and Formatting

# Lint code
make lint

# Format code
make format

Database Migrations

# Run migrations
docker-compose exec core-service alembic upgrade head

# Create new migration
docker-compose exec core-service alembic revision --autogenerate -m "description"

📖 API Endpoints

Negotiation Endpoint

POST /v1/negotiate

Request:

{
  "item_id": "hotel_alpha",
  "bid_amount": 850.0,
  "currency": "USD",
  "agent_did": "did:agent:007"
}

Response:

{
  "session_token": "sess_...",
  "status": "accepted",
  "valid_until": 1234567890,
  "data": {
    "final_price": 850.0,
    "reservation_code": "MISTRAL-1234567890"
  }
}

Search Endpoint

POST /v1/search

Request:

{
  "query": "Luxury stay with spa and ocean view",
  "limit": 3
}

Response:

{
  "results": [
    {
      "id": "hotel_alpha",
      "name": "Luxury Beach Resort",
      "price": 1000.0,
      "score": 0.95,
      "details": "5-star resort with private beach"
    }
  ]
}

🔒 Security

The platform now includes cryptographic signature verification using Ed25519:

Security Features

• Signed Headers: Agents must sign requests with X-Agent-ID, X-Timestamp, and X-Signature

• Replay Protection: Timestamps validated within ±60 seconds

• Request Integrity: Body hash included in signature to prevent tampering

• Agent Authentication: DID-based identity verification using Ed25519

• Rate Limiting: Prevents abuse through Redis-backed rate limiting

• Hidden Knowledge: Floor prices are never exposed to agents

Running with Security

1. Generate agent keys:

   from tools.simulators.agent_identity import AgentWallet
   wallet = AgentWallet()
   print(f"DID: {wallet.did}")
   print(f"Private Key: {wallet.private_key_hex}")

2. Run the secure gateway:

   cd api-gateway
   uv run python -m src.main

3. Run secure simulators:

   python -m tools.simulators.agent_sim
   python -m tools.simulators.autonomous_buyer

4. Test security:

   python tools/test_security.py

Security Implementation Details

• Algorithm: Ed25519 (PyNaCl library)

• Signature Format: METHOD + PATH + TIMESTAMP + BodyHash

• DID Format: did:key:public_key_hex

• Timestamp Validation: ±60 seconds tolerance for clock skew

See docs/SECURITY.md for comprehensive security documentation.

📊 Observability

• Distributed Tracing: Jaeger integration for end-to-end request tracing

• Structured Logging: JSON logging with request IDs

• Metrics: OpenTelemetry for performance monitoring

🤝 Contributing

1. Follow the existing code style

2. Update Protocol Buffers as needed

3. Regenerate code after proto changes (buf generate)

4. Add tests for new functionality

5. Update documentation

📄 License

This project is licensed under the MIT License.