multi-agent

langroid: Harness AI Agents

Name: langroid: Harness AI Agents
Author: langroid

Streamline AI development with langroid, ideal for founders using multi-agent systems and LLMs, backed by 4.1k+ GitHub stars

4,099 stars396 forksPythonHealth Score 8/10Updated 8/23/2026100% free · open source

What it does

Langroid streamlines AI development by harnessing large language models (LLMs) within multi-agent systems, allowing for more efficient and scalable AI development

Install / run

pip install langroid

When to use it

•You're building a complex AI system that requires coordination between multiple agents
•You want to leverage the power of LLMs but need a framework to manage their interactions
•Your AI project involves multiple tasks or domains that can be addressed by different agents

Quick start

1Clone the langroid repository from GitHub using `git clone https://github.com/langroid/langroid.git`
2Navigate into the cloned repository with `cd langroid`
3Explore the example use cases in the `examples` directory, such as `example_llm_agent.py`
4Modify the `config.json` file to specify your LLM and agent configurations
5Run a sample multi-agent system using `python run.py`

Ready-to-paste prompt

python example_llm_agent.py --llm-model bert-base-uncased --agent-count 5

Heads up: Ensure you have the necessary dependencies installed, including `transformers` and `torch`, as specified in the `requirements.txt` file, to avoid import errors when running langroid

Saves to your device