by PathOnAIOrg
The Library for LLM-based multi-agent applications
# Add to your Claude Code skills
git clone https://github.com/PathOnAIOrg/LiteMultiAgentGuides for using ai agents skills like LiteMultiAgent.
Last scanned: 5/30/2026
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"scannedAt": "2026-05-30T17:02:03.763Z",
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}LiteMultiAgent is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by PathOnAIOrg. The Library for LLM-based multi-agent applications. It has 102 GitHub stars.
Yes. LiteMultiAgent passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/PathOnAIOrg/LiteMultiAgent" and add it to your Claude Code skills directory (see the Installation section above).
LiteMultiAgent is primarily written in Python. It is open-source under PathOnAIOrg on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh LiteMultiAgent against similar tools.
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In our exploration of building multi-agent systems, we have investigated AutoGen, CrewAI, LangGraph, and MetaGPT, but failed to find a multi-agent system that intuitively makes multi-agent systems more efficient. We have found that most examples provided by these frameworks could be accomplished with just one agent. We reimplemented the multi-agent examples using only one agent with a set of self-defined tools. However, we then realized that this system is not scalable when we have more and more tools. But if we can categorize the agents with different sets of tools into categories, then we are building a hierarchy of agents, where we could accomplish more types of tasks. At the same time, because of this design, the execution of sub-agents is naturally parallelized by parallel function calling, since agents use sub-agents as tools.
Disclaimer: Please note that LiteMultiAgent is not affiliated with any for-profit company. This is a collaborative project from PathOnAI.org, an open-source AI research community, where Danqing Zhang (danqing.zhang.personal@gmail.com) is the main contributor. If anyone claims LiteMultiAgent is affiliated with any for-profit company, please contact Danqing Zhang (danqing.zhang.personal@gmail.com) for verification.
From PyPI: https://pypi.org/project/litemultiagent/
pip install litemultiagent
Set up locally First set up virtual environment, and allow your code to be able to see 'litemultiagent'
python3 -m venv venv
. venv/bin/activate
pip install -e .
Then please create a .env file, and update your API keys:
cp .env.example .env
If you want to save log to Supabase. Set up a Supabase project and retrieve the database URL from: https://supabase.com/dashboard/project/[PROJECT_NAME]/settings/database.
The DATABASE_URL follows: postgresql://:@:/. You can get this from Project Settings -> Database -> Connection string.
Save this URL in the .env file as SUPABASE_DATABASE_URL. Then, run:
python supabase_db_setup.py
This will create the multiagent table in your database
After setting up your API keys, you can explore the examples in the examples folder:
python examples/atomic/IOAgent.py
python examples/composite/MasterAgent.py
python examples/new_tool/add_llm_generated_function_example.py
For how to contribute, see CONTRIBUTE. If you would like to contribute to the codebase, issues or pull requests are always welcome!
@misc{zhang2024litemultiagent,
title={LiteMultiAgent: The Library for LLM-based multi-agent applications},
author={Zhang, Danqing and Rama, Balaji and He, Shiying and Ni, Jingyi},
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.15500241},
url = {https://doi.org/10.5281/zenodo.15500241}
}