mao-agents 0.1.0

Multi Agent Orchestration - A modern framework for orchestrating AI agents

MAO - Multi Agent Orchestration

MAO is a modern framework for orchestrating AI agents. It combines the power of vector databases, LLMs, and the Model Context Protocol (MCP) to enable robust and scalable agent workflows.

Features

• 🤖 Agent Orchestration - Manage complex multi-agent workflows
• 🧠 Vector-based Memory - Store and retrieve context information
• 🔄 MCP Integration - Seamless communication between agents and tools
• 🛠️ Extensible Tools - Easy integration of new capabilities
• 📊 DuckDB Analytics - Powerful data analysis and processing
• 🔍 Semantic Search - Find relevant information across agent memories
• 🤝 Team Management - Organize agents into collaborative teams
• 🔒 Secure Configuration - Centralized management of API keys and settings
• 📤 Import/Export - Backup and restore system configurations
• 🔄 Supervisor Agents - Coordinate team workflows with supervisor agents
• 📚 Knowledge & Experience Trees - Structured storage for agent knowledge
• 🌐 Multi-LLM Support - Works with OpenAI, Anthropic, and Ollama models

API Endpoints

The MAO API provides the following main endpoints:

• /agents - Agent creation, management, and interaction
• /teams - Team creation and management
• /teams/supervisors - Supervisor management for agent teams
• /mcp - MCP server and tool management
• /config - Global configuration settings
• /export, /import - Configuration import/export utilities
• /health - API health check endpoint

API documentation is available at:

• Swagger UI: /docs
• ReDoc: /redoc

Requirements

• Python 3.11+
• Qdrant vector database (accessible via HTTP)
• DuckDB for configuration storage
• LLM provider API keys (OpenAI, Anthropic, or local Ollama instance)

Installation

# With uv (recommended)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Review the install script or use your package manager if you prefer.
uv sync

Quick Start

from mao.agents import create_agent
from mao.storage import KnowledgeTree, ExperienceTree

# Initialize storage
knowledge_tree = await KnowledgeTree.create(collection_name="agent-memory")
experience_tree = await ExperienceTree.create(collection_name="agent-experience")

# Create an agent
agent_app = await create_agent(
    provider="anthropic",
    model_name="claude-3-opus-20240229",
    agent_name="assistant",
    knowledge_tree=knowledge_tree,
    experience_tree=experience_tree,
)

# Execute a query
response = await agent_app.ainvoke(
    {"messages": [{"role": "user", "content": "Analyze the latest economic data"}]}
)
if hasattr(response, "content"):
    print(response.content)
elif isinstance(response, dict) and response.get("messages"):
    print(response["messages"][-1].content)
else:
    print(response)

Environment Variables

The following environment variables are supported:

# LLM API Keys
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-...

# Vector Database
QDRANT_URL=http://localhost:6333
QDRANT_API_KEY=your-qdrant-api-key
EMBEDDING_MODEL=text-embedding-3-small

# DuckDB Configuration
MCP_DB_PATH=/path/to/mcp_config.duckdb

# MCP Configuration
MCP_CONFIG_PATH=/path/to/mcp.json
OLLAMA_HOST=http://localhost:11434

# MCP Server API Keys
CONTEXT7_API_KEY=your-context7-api-key  # For up-to-date code documentation

# Server
PORT=8000

MCP Servers

MAO includes several MCP (Model Context Protocol) servers configured by default:

• context7: Provides up-to-date code documentation and examples
  • Requires: CONTEXT7_API_KEY environment variable
  • Get your API key from context7.com
  • Enables AI agents to access current library documentation
• dockerailabs: Docker-based MCP server via socat
• perplexity-ask: AI-powered search via Perplexity API

To configure MCP servers, edit mcp.json in the project root.

Docker

# Build and start the services with Docker Compose
docker compose up -d

# Or build the Docker image manually
docker build -t mao-api -f docker/Dockerfile.api .

# Start the container
docker run -p 8000:8000 -v ./data:/data -v ./.env:/app/.env mao-api

For development, you can use the following commands:

# Build with BuildKit enabled for better caching
DOCKER_BUILDKIT=1 docker build -t mao-api -f docker/Dockerfile.api .

# Run with mounted source directory for development
docker run -p 8000:8000 -v ./data:/data -v ./.env:/app/.env mao-api

# Pass environment variables directly
docker run -p 8000:8000 \
  -e OPENAI_API_KEY=sk-... \
  -e ANTHROPIC_API_KEY=sk-... \
  -e QDRANT_URL=http://localhost:6333 \
  mao-api

# Or use the --env-file option
docker run -p 8000:8000 --env-file .env mao-api

Docker Compose with Environment Variables

You can also use Docker Compose to manage environment variables:

services:
  api:
    build:
      context: .
      dockerfile: docker/Dockerfile.api
    ports:
      - "8000:8000"
    volumes:
      - ./data:/data
    env_file:
      - .env

API Example

import httpx

async with httpx.AsyncClient() as client:
    # Create a new agent
    response = await client.post(
        "http://localhost:8000/agents",
        json={
            "name": "research_assistant",
            "provider": "anthropic",
            "model_name": "claude-3-opus-20240229",
            "system_prompt": "You are a research assistant."
        }
    )
    agent_id = response.json()["id"]
    
    # Send a message to the agent
    response = await client.post(
        f"http://localhost:8000/agents/{agent_id}/chat",
        json={"content": "Summarize the latest developments in AI."}
    )
    print(response.json()["response"])

Team Workflow Example

# Create a team with supervisor
team_id = "team_research"
supervisor_id = "supervisor_research_team"

# Add agents to the team
await client.post(
    f"http://localhost:8000/teams/{team_id}/members",
    json={
        "agent_id": "agent_researcher",
        "role": "researcher",
        "order_index": 1
    }
)

await client.post(
    f"http://localhost:8000/teams/{team_id}/members",
    json={
        "agent_id": "agent_writer",
        "role": "writer",
        "order_index": 2
    }
)

# Start the team
await client.post(f"http://localhost:8000/teams/{team_id}/start")

# Send a task to the team
response = await client.post(
    f"http://localhost:8000/teams/{team_id}/chat",
    json={"message": "Research quantum computing advancements and write a report"}
)

AI Agent Capabilities

CI/CD with GitHub Actions

This project uses GitHub Actions for continuous integration and deployment:

Workflows

• Test and Lint - Runs tests, linting, and type checking on every push and pull request.
• Docker Build - Builds and publishes Docker images on pushes to the main branch and tags.
• Docker Multi-Platform Build - Creates Docker images for multiple platforms (amd64, arm64).
• Dependency Updates - Automatically updates project dependencies weekly.
• Package Publishing - Publishes the package to PyPI on new releases.

Environment Variables and Secrets

To use environment variables in GitHub Actions workflows, you need to add them as GitHub Secrets:

1. Go to your GitHub repository
2. Navigate to Settings > Secrets and variables > Actions
3. Click on "New repository secret"
4. Add each environment variable from your .env file:
   • OPENAI_API_KEY
   • ANTHROPIC_API_KEY
   • QDRANT_URL
   • EMBEDDING_MODEL
   • MCP_DB_PATH
   • MCP_CONFIG_PATH
   • OLLAMA_HOST

These secrets are then passed to the Docker build process as build arguments and set as environment variables in the container.

Workflow Execution

# Manually run the dependency update workflow
gh workflow run dependency-update.yml

# Manually publish a version
gh workflow run publish.yml -f version=0.2.0

# Manually run multi-platform Docker build
gh workflow run docker-multi-platform.yml -f platforms=linux/amd64,linux/arm64,linux/arm/v7

License

This project is licensed under the MIT License - see the LICENSE file for details.