MetaAgent — Build AI agents effortlessly with a simple…

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About this project

MetaAgent is a minimalistic programming language designed for creating AI agents swiftly. With support for MCP and A2A protocols, agents can easily use various tools and communicate with one another. The lightweight interpreter requires no complex setup, making the development of intelligent agents accessible to everyone.

MetaAgent: An Innovative Language for AI Agent Development

MetaAgent is a lightweight programming language designed to simplify the creation of AI agents. With concise syntax, MetaAgent allows users to define the purpose of an agent, the tools it can utilize, and the responses it can generate, all while the interpreter handles the underlying complexities. Supporting the Model Context Protocol (MCP) and Agent to Agent (A2A) communication natively, MetaAgent empowers agents to seamlessly interact and leverage any MCP tool out of the box.

Key Features

Minimalist Syntax: Build AI agents in just a few lines of code without the overhead of traditional programming environments.
Single Binary Interpreter: Developed in Rust, the interpreter requires no additional frameworks or environments, making it highly efficient and easy to use.
Broad Compatibility: Integrates with any MCP server and allows agents to communicate across different processes and machines.
User-Friendly Error Messaging: Designed for beginners, all error messages are clear and comprehensible, enhancing the development experience.

Example of Agent Creation

Here's a simple example illustrating how to create a weather agent:

agent Weather
  goal "Answer questions about the weather"
  tool weather from mcp "npx -y weather-mcp"
  accepts ask city
  on ask
    forecast = weather.forecast city: city
    reply "In {city} it will be {forecast.summary}"

This code defines an agent that can respond to inquiries about the weather, demonstrating the straightforward syntax of MetaAgent.

Demonstration

Watch a demonstration of the MetaAgent in action:

Why Choose MetaAgent?

MetaAgent is particularly advantageous for non-programmers and those seeking quick agent implementation, contrasting with frameworks like LangChain or CrewAI which are heavily oriented towards Python programming. Here are some comparative insights:

Feature	Python Agent Frameworks	MetaAgent
Development Method	Python code with libraries	Declarative agent files
Installation	Requires Python and dependencies	Just a single binary
Tool Integration	Framework-specific	Any MCP server
Inter-Agent Communication	Primarily in-process	A2A across various processes and machines
Safety Protocols	Defined by user code	Per-agent permissions
Error Handling	Stack traces	User-friendly messages

Current Capabilities

Core Language Features: Supports goals, tools, inputs, replies, along with control structures such as if, loops, and results.
Agent Communication: Agents can dynamically link and communicate with each other using A2A protocols.
Security Measures: Each agent can specify which folders, environment variables, and external agents it can access, providing a secure environment for operations.

Future Development Roadmap

Looking ahead, the project plans to include features like long-running tasks, an agent registry, and improved A2A streaming capabilities, ensuring that MetaAgent continues to evolve and expand its functionality.

Contributions and feedback are encouraged, fostering a community around this innovative tool for AI agent development. To explore more about building agents, refer to the comprehensive documentation and sample agents available in the repository.