Metadata-Version: 2.1
Name: lagent
Version: 0.1.2
Summary: A lightweight framework for building LLM-based agents
Home-page: https://github.com/InternLM/lagent
License: Apache 2.0
Keywords: artificial general intelligence,agent,agi,llm
Platform: UNKNOWN
Description-Content-Type: text/markdown
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Requires-Dist: lmdeploy ; extra == 'all'
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Requires-Dist: lmdeploy ; extra == 'optional'
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Requires-Dist: torch ; extra == 'optional'
Requires-Dist: transformers ; extra == 'optional'

<div align="center">
  <img src="docs/imgs/lagent_logo.png" width="450"/>

[![docs](https://img.shields.io/badge/docs-latest-blue)](https://lagent.readthedocs.io/en/latest/)
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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)

</div>

<p align="center">
    👋 join us on <a href="https://twitter.com/intern_lm" target="_blank">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>
</p>

## Introduction

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:

![image](https://github.com/InternLM/lagent/assets/24351120/cefc4145-2ad8-4f80-b88b-97c05d1b9d3e)

### Major Features

**0.1.2** was released in 24/10/2023:

- **Support efficient inference engine.** Lagent now supports efficient inference engine [lmdeploy turbomind](https://github.com/InternLM/lmdeploy/tree/main).

- **Support multiple kinds of agents out of box.** Lagent now supports [ReAct](https://arxiv.org/abs/2210.03629), [AutoGPT](https://github.com/Significant-Gravitas/Auto-GPT) and [ReWOO](https://arxiv.org/abs/2305.18323), which can drive the large language models(LLMs) for multiple trials of reasoning and function calling.

- **Extremely simple and easy to extend.** The framework is quite simple with a clear structure. With only 20 lines of code, you are able to construct your own agent. It also supports three typical tools: Python interpreter, API call, and google search.

- **Support various large language models.** We support different LLMs, including API-based (GPT-3.5/4) and open-source (LLaMA 2, InternLM) models.

## 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
```

Optionally, you could also build Lagent from source in case you want to modify the code:

```bash
git clone https://github.com/InternLM/lagent.git
cd lagent
pip install -e .
```

### Run ReAct Web Demo

```bash
# You need to install streamlit first
# pip install streamlit
streamlit run examples/react_web_demo.py
```

Then you can chat through the UI shown as below
![image](https://github.com/InternLM/lagent/assets/24622904/3aebb8b4-07d1-42a2-9da3-46080c556f68)

### Run a ReWOO agent with GPT-3.5

Below is an example for running ReWOO with GPT-3.5

```python
from lagent.agents import ReWOO
from lagent.actions import ActionExecutor, GoogleSearch, LLMQA
from lagent.llms import GPTAPI

llm = GPTAPI(model_type='gpt-3.5-turbo', key=['Your OPENAI_API_KEY'])
search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
llmqa_tool = LLMQA(llm)

chatbot = ReWOO(
    llm=llm,
    action_executor=ActionExecutor(
        actions=[search_tool, llmqa_tool]),
)

response = chatbot.chat('What profession does Nicholas Ray and Elia Kazan have in common')
print(response.response)
>>> Film director.
```

### Run a ReAct agent with InternLM

NOTE: If you want to run a HuggingFace model, please run `pip install -e .[all]` first.

```python
from lagent.agents import ReAct
from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.llms import HFTransformer

llm = HFTransformer('internlm/internlm-chat-7b-v1_1')
search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
python_interpreter = PythonInterpreter()

chatbot = ReAct(
    llm=llm,
    action_executor=ActionExecutor(
        actions=[search_tool, python_interpreter]),
)

response = chatbot.chat('若$z=-1+\sqrt{3}i$,则$\frac{z}{{z\overline{z}-1}}=\left(\ \ \right)$')
print(response.response)
>>> $-\\frac{1}{3}+\\frac{{\\sqrt{3}}}{3}i$
```

## License

This project is released under the [Apache 2.0 license](LICENSE).


