nanobot logo
nanobot
★ 49.3k
vs
ma-gym logo
ma-gym
★ 636

nanobot vs ma-gym

nanobot: nanobot is an ultra-lightweight, self-hosted personal AI agent framework built in Python, offering a readable core for multi-agent delegation and automation. It supports various interfaces including WebUI, terminal, and chat apps, integrating tools, long-term memory, and OpenAI-compatible APIs.; ma-gym: ma-gym provides a collection of multi-agent environments built upon the OpenAI Gym interface, facilitating research and development in multi-agent reinforcement learning. It includes various game-like and task-based environments, as well as wrappers to convert single-agent Gym environments into multi-agent forms for debugging.

01

TL;DR

nanobot logo
nanobotMulti-Agent · ★ 49.3k
When to choose:
  • You want a lightweight, hackable Python personal AI agent framework with clean code, a WebUI, and built-in MCP client support.
  • You prefer an easy-to-read, self-hosted agent core without the enterprise boilerplate of heavy agent frameworks.
When to skip:
  • You require production-grade distributed state checkpointing, strict Pydantic type validation, or multi-role team SOPs; Pydantic AI or CrewAI offer more mature infrastructure.
  • Your production stack is .NET or Go; Microsoft Agent Framework covers those runtimes.
ma-gym logo
ma-gymMulti-Agent · ★ 636

Research and experimentation in multi-agent reinforcement learning algorithms

02

Side-by-Side Comparison

Field
nanobot logonanobot
ma-gym logoma-gym
Category
Multi-Agent
Multi-Agent
Stars
★ 49.3k
★ 636
License
MIT
Apache-2.0
Install Command
curl -fsSL https://raw.githubusercontent.com/HKUDS/nanobot/main/scripts/install.sh | sh
pip install ma-gym
Updated
1d ago
2y ago
Open Source
Yes
Yes
Tags
AI Agent Framework, Self-hosted, Python
Reinforcement Learning, Multi-Agent Systems, OpenAI Gym
03

Features

nanobot logonanobot
01Persistent workflows: goals, memory, tools, and chat context survive long-running work.
02Chat-native reach: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, email, and Mattermost.
03Model freedom: OpenAI-compatible APIs, local LLMs, image generation, search, and fallbacks.
04Small core: readable internals with MCP, memory, deployment, and automation built in.
05Own your stack: inspect, customize, self-host, and extend without a giant platform.
ma-gym logoma-gym
01Collection of diverse multi-agent environments (e.g., Checkers, Combat, PredatorPrey, TrafficJunction)
02Built on familiar OpenAI Gym API for easy integration
03Includes a 'multi-agent wrapper' for single-agent OpenAI environments for debugging
04Compatible with multi-agent reinforcement learning frameworks like minimal-marl
05Well-documented with Wiki pages for usage and environment details
04

Use Cases

nanobot logonanobot
↳Running long-horizon goals and scheduled automations.
↳Connecting to various chat applications like Telegram, Discord, Slack for AI interaction.
↳Exposing a Python SDK and OpenAI-compatible API for custom integrations and development.
↳Deploying as a long-running local or server-side agent gateway.
ma-gym logoma-gym
↳Research and experimentation in multi-agent reinforcement learning algorithms
↳Development and testing of multi-agent policies and strategies
↳Educational tool for understanding multi-agent interaction and control
05

Best For

nanobot logonanobot
Most PopularEssential
ma-gym logoma-gym
—
FAQ

FAQ

What is the difference between nanobot and ma-gym?
Both nanobot and ma-gym are in the Multi-Agent category. nanobot has 49.3k stars, while ma-gym has 636 stars.
Which is better, nanobot or ma-gym?
The best choice depends on your use case. Choose nanobot if Running long-horizon goals and scheduled automations., and ma-gym if Research and experimentation in multi-agent reinforcement learning algorithms.
Is nanobot free or open source?
Yes, nanobot is open source on GitHub (MIT).
Is ma-gym free or open source?
Yes, ma-gym is open source on GitHub (Apache-2.0).
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