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AI Agent Backend (Python Microservice)

This microservice provides REST endpoints for OpenAI Assistants API and Litellm, enabling agent management and LLM calls from your Next.js platform.

Setup

  1. Create and activate a virtual environment (optional but recommended):

    python3 -m venv venv
source venv/bin/activate

  2. Install dependencies:

    pip install -r requirements.txt

  3. Set environment variables:

  4. Run the server:

    uvicorn main:app --host 0.0.0.0 --port 8000 --reload

    The service will be available at http://localhost:8000.

Docker Build & Deployment

Build the Docker Image

docker build -t atlas-agent-backend .

Run the Backend with Docker

docker run --env-file .env -p 8000:8000 atlas-agent-backend
  • The backend will be available at http://localhost:8000
  • Healthcheck endpoint: / (returns status OK)
  • For production, ensure your .env file contains all required secrets (e.g., OPENAI_API_KEY)

Compose/Production (Optional)

If you have a database or other services, consider adding a docker-compose.yml file.

Endpoints

  • POST /openai/assistant — Create an OpenAI Assistant
  • GET /openai/assistant — List OpenAI Assistants
  • POST /openai/thread — Create a thread
  • GET /openai/thread — List threads
  • POST /openai/message — Add message to thread
  • POST /openai/run — Run assistant on thread
  • POST /litellm/chat — Call LLM via Litellm

All endpoints are currently placeholders. Implementations should be added as needed.

A2A Protocol Endpoints

  • POST /api/v1/a2a/register — Register agent (A2A)

  • POST /api/v1/a2a/handshake — Handshake/authenticate (A2A)

  • POST /api/v1/a2a/send — Relay message to agent (A2A)

  • POST /api/v1/a2a/receive — Receive/process message (A2A)

  • GET /api/v1/a2a/status/{agent_id} — Get agent status (A2A)

  • GET /api/v1/a2a/error — Protocol-compliant error (A2A)

  • Implements Google Agent2Agent (A2A) protocol for standardized, secure agent-to-agent communication. See MODEL_CARD_A2A.md for details.

CI/CD & Quality

  • Automated CI pipeline: see .github/workflows/ci.yml (runs Ruff linter, pytest, and uploads coverage)
  • Linting: Ruff enforced via ruff.toml
  • Test suite: Pytest, with smoke and coverage checks

Profiling

  • Profile agent runner performance: python tools/profile_agent.py
  • Edit tools/profile_agent.py to customize profiling scenarios

Model Cards & Ethics

  • See model_card.md for agent limitations, intended use, and ethical guidance
  • All agents/models must have a model card before deployment