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<img src="docs/imgs/lagent_logo.png" width="450"/>
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[](https://github.com/InternLM/lagent/tree/main/LICENSE)
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English | [简体中文](README_zh-CN.md) | [日本語](README_ja_JP.md) | [हिंदी](README_in_HIN.md) | [বাংলা](README_in_beng.md) | [한국어](README_KR_Kr.md)
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👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">𝕏 (Twitter)</a>, <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://r.vansin.top/?r=internwx" target="_blank">WeChat</a>
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https://github.com/InternLM/lagent/assets/24622904/3242f9bf-32d2-4907-8815-e16a75a4ac0e
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## Getting Started
Please see the [overview](docs/en/get_started/overview.md) for the general introduction of Lagent. Meanwhile, we provide extremely simple code for quick start. You may refer to [examples](examples/) for more details.
### Installation
Install with pip (Recommended).
```bash
pip install lagent
```
### Run a Web Demo
You need to install Streamlit first.
```bash
# pip install streamlit
streamlit run examples/internlm2_agent_web_demo.py
```
## What's Lagent?
Lagent is a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents. It also provides some typical tools to augment LLM. The overview of our framework is shown below:
## Major Features
- Stream Output: Provides the `stream_chat` interface for streaming output, allowing cool streaming demos right at your local setup.
- Interfacing is unified, with a comprehensive design upgrade for enhanced extensibility, including:
- Model: Whether it's the OpenAI API, Transformers, or LMDeploy inference acceleration framework, you can seamlessly switch between models.
- Action: Simple inheritance and decoration allow you to create your own personal toolkit, adaptable to both InternLM and GPT.
- Agent: Consistent with the Model's input interface, the transformation from model to intelligent agent only takes one step, facilitating the exploration and implementation of various agents.
- Documentation has been thoroughly upgraded with full API documentation coverage.
## 💻Tech Stack
<p>
<a href="">
<img src="https://img.shields.io/badge/Python-007ACC?style=for-the-badge&logo=python&logoColor=yellow" alt="python" />
</a>
### All Thanks To Our Contributors:
<a href="https://github.com/InternLM/lagent/graphs/contributors">
<img src="https://contrib.rocks/image?repo=InternLM/lagent" />
</a>
## Citation
If you find this project useful in your research, please consider cite:
```latex
@misc{lagent2023,
title={{Lagent: InternLM} a lightweight open-source framework that allows users to efficiently build large language model(LLM)-based agents},
author={Lagent Developer Team},
howpublished = {\url{https://github.com/InternLM/lagent}},
year={2023}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
<p align="right"><a href="#top">🔼 Back to top</a></p>
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