meta-agent-cli 1.0.1

Generate fully-functional AI agents from natural language specifications

Meta Agent 🤖

Generate fully-functional AI agents from natural language specifications in minutes.

Meta Agent is a Python CLI tool that automatically produces production-ready OpenAI-powered agents complete with code, tests, and guardrails from simple English descriptions.

🚀 Quick Start

Installation

pip install meta-agent

Create Your First Agent

# Initialize a new project
meta-agent init my-calculator --template hello-world

# Generate an agent from specification
meta-agent generate --spec-file agent_spec.yaml

Example Specification

task_description: |
  Create a calculator agent that can perform basic arithmetic operations.
  The agent should handle addition, subtraction, multiplication, and division.
inputs:
  operation: str  # "+", "-", "*", "/"
  num1: float
  num2: float
outputs:
  result: float
constraints:
  - Must validate division by zero
  - Should handle floating point precision

✨ Key Features

• 🎯 Natural Language Input: Describe what you want in plain English
• ⚡ Instant Generation: Get working agents in minutes, not hours
• 🛡️ Built-in Safety: Automatic guardrails and validation
• 🧪 Test Generation: Unit tests created automatically
• 📊 Telemetry: Built-in monitoring and metrics
• 🔧 Extensible: Template system for custom patterns

🎯 Perfect For

• AI Engineers building production agents quickly
• Solutions Architects integrating AI into workflows
• Rapid Prototypers who need demo-ready agents fast
• Hobbyists exploring AI without deep coding expertise

📖 Documentation

Core Commands

# Initialize new project
meta-agent init <project-name> [--template <template-name>]

# Generate agent from spec
meta-agent generate --spec-file <path> [--metric cost,tokens,latency]

# Manage templates
meta-agent templates list
meta-agent templates docs

# View telemetry
meta-agent dashboard
meta-agent export --format json

Input Formats

Meta Agent supports multiple input formats:

YAML File:

meta-agent generate --spec-file my_agent.yaml

JSON File:

meta-agent generate --spec-file my_agent.json

Direct Text:

meta-agent generate --spec-text "Create an agent that summarizes documents"

Project Structure

my-project/
├── .meta-agent/
│   └── config.yaml          # Project configuration
├── agent_spec.yaml          # Agent specification
└── generated/              # Generated agent code
    ├── agent.py
    ├── tests/
    └── guardrails/

🏗️ Architecture

Meta Agent uses a sophisticated orchestration system:

• Planning Engine: Decomposes specifications into tasks
• Sub-Agent Manager: Coordinates specialized agents
• Tool Designer: Generates custom tools and functions
• Guardrail Designer: Creates safety and validation logic
• Template System: Reusable patterns and best practices

🔧 Development

Requirements

• Python 3.11+
• OpenAI API key (for LLM functionality)

Setup

# Clone repository
git clone https://github.com/DannyMac180/meta-agent.git
cd meta-agent

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install development dependencies
pip install -e ".[test]"

# Run tests
pytest

# Run linting
ruff check .
pyright

📊 Examples

Data Processing Agent

task_description: |
  Create an agent that processes CSV files and generates summary reports.
inputs:
  csv_file: str
  columns_to_analyze: list[str]
outputs:
  summary_report: dict
  charts: list[str]

Web Scraping Agent

task_description: |
  Build an agent that scrapes product information from e-commerce websites.
inputs:
  website_url: str
  product_selectors: dict
outputs:
  product_data: list[dict]
constraints:
  - Must respect robots.txt
  - Rate limit to 1 request per second

🔒 Environment Variables

# Required for LLM functionality
export OPENAI_API_KEY="your-api-key-here"

# Optional: Custom OpenAI base URL
export OPENAI_BASE_URL="https://your-proxy.com/v1"

# Optional: Enable debug logging
export META_AGENT_DEBUG=true

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Quick Contribution Guide

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Run the test suite (pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📄 License

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

🙏 Acknowledgments

• Built on the OpenAI SDK
• Inspired by the growing need for rapid AI agent development
• Thanks to the open-source community for foundational tools

📞 Support

• Documentation: Full documentation
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Made with ❤️ by developers, for developers building the AI-powered future.