Agent Maestro

🎼 Multi-agent task orchestration — delegate work from an orchestrator to specialized sub-agents via a file-based queue.

Architecture

Orchestrator (Antigravity) → maestro delegate → .agent_bridge/pending/ → watcher → Copilot CLI → .agent_bridge/completed/
                                                                                      ↓
                                                                            --agent <name>
                                                                                      ↓
                                                                        .github/agents/*.agent.md

Custom Agents

Agent Maestro leverages VS Code Copilot's custom agents for specialized roles:

Agent definitions live in .github/agents/ and are routed via --agent <name> when the watcher invokes Copilot CLI.

Installation

Option 1: From PyPI (Recommended)

pip install agent-maestro

Option 2: From GitHub (Latest)

pip install git+https://github.com/migoueel/multi_agents.git

Option 3: For Development

git clone https://github.com/migoueel/multi_agents.git
cd multi_agents
pip install -e ".[dev]"

Quick Start

# 1. Scaffold config + custom agents (in your project)
maestro init

# 2. (Optional) Edit config.yaml and .github/agents/*.agent.md

# 3. Start the watcher daemon
maestro start

# 4. Delegate a task
maestro delegate "Add error handling to auth module" \
  --files src/auth.py \
  --agent-type implementer

# 5. Check status
maestro status <task-id>
maestro list
maestro stats

CLI Reference

Delegate Options

maestro delegate "..." [OPTIONS]

  --files, -f      Target files for the agent
  --action, -a     Task type: implement, test, refactor, fix, review
  --agent-type, -t Custom agent: orchestrator, implementer, tester, reviewer
  --context, -c    Extra context string
  --priority       Priority level (0=normal, higher=urgent)

Configuration

Edit config.yaml in your project root:

bridge:
  poll_interval_seconds: 3
  max_concurrent_tasks: 1
  task_timeout_seconds: 300
  agent_bridge_dir: ".agent_bridge"

runners:
  default: "copilot-cli"
  copilot_cli:
    command: "copilot"       # path to copilot.bat
    model: "gpt-5-mini"
    allow_all_tools: true

Package Structure

agent_maestro/
├── __init__.py           # Public API: delegate_task, check_status, list_tasks
├── __main__.py           # python -m agent_maestro
├── cli.py                # maestro CLI commands
├── protocol.py           # Task, TaskStatus, RunResult schemas
├── queue.py              # File-based task queue
├── config.py             # YAML config loader
├── watcher.py            # Background watcher daemon
├── runners/
│   ├── base.py           # Abstract BaseRunner
│   └── copilot_runner.py # Copilot CLI runner with --agent support
└── scaffold/
    ├── config.yaml       # Default config template
    └── agents/           # Custom agent templates
        ├── orchestrator.agent.md
        ├── implementer.agent.md
        ├── tester.agent.md
        └── reviewer.agent.md

How It Works

1. Task Creation: maestro delegate (or delegate_task() from Python) writes a JSON file to .agent_bridge/pending/
2. Watcher Pickup: The background watcher polls for pending tasks, claims the highest-priority one
3. Runner Dispatch: The CopilotRunner invokes copilot -p <prompt> --agent <agent_type>
4. Custom Agent: Copilot CLI loads the matching .github/agents/<name>.agent.md for specialized behavior
5. Result Recording: Output moves to .agent_bridge/completed/ (or failed/)

Testing

pytest tests/ -v

License

MIT