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Towards Automated General Intelligence
LionAGI is a cutting-edge intelligent agent framework. It integrates data manipulation with advanced machine learning tools, such as Large Language Models (i.e. OpenAI's GPT).
- Designed for data-centric, production-level projects,
- dramatically lowers the barrier in creating intelligent, automated systems
- that can understand and interact meaningfully with large volumes of data.
Install LionAGI with pip:
pip install lionagiDownload the .env_template file, input your appropriate API_KEY, save the file, rename as .env and put in your project's root directory.
by default we use OPENAI_API_KEY.
*we now also supports all models in OpenRouter (not released yet, you can have a preview in main)
* https://lionagi.readthedocs.io/en/v0.0.111/ is the the latest stable, please pip install lionagi==0.0.111 if you find 0.0.112 buggy
- Robust performance
- Efficient data operations for reading 8000 , chunking, binning, writing, storing and managing data.
- Fast interaction with LLM services like OpenAI with configurable rate limiting concurrent API calls for maximum throughput.
- Create a production ready LLM application in hours. Intuitive workflow management to streamline the process from idea to market.
- (Work In Progress): verstile intergration with most API and local LLM services.
LionAGI is designed to be async only, please check python official documentation on how async work: here
Notice:
- calling API with maximum throughput over large set of data with advanced models i.e. gpt-4 can get EXPENSIVE IN JUST SECONDS,
- please know what you are doing, and check the usage on OpenAI regularly
- default rate limits are set to be tier 1 of OpenAI model
gpt-4-1104-preview, please check the OpenAI usage limit documentation you can modify token rate parameters to fit different use cases. - if you would like to build from source, please download the latest release, main is under development and will be changed without notice
The following example shows how to use LionAGI's Session object to interact with gpt-4 model:
import lionagi as li
# define system messages, context and user instruction
system = "You are a helpful assistant designed to perform calculations."
instruction = {"Addition":"Add the two numbers together i.e. x+y"}
context = {"x": 10, "y": 5}# in interactive environment (.ipynb for example)
calculator = li.Session(system=system)
result = await calculator.initiate(instruction=instruction,
context=context,
model="gpt-4-1106-preview")
print(f"Calculation Result: {result}")# or otherwise, you can use
import asyncio
async def main():
calculator = li.Session(system=system)
result = await calculator.initiate(instruction=instruction,
context=context,
model="gpt-4-1106-preview")
print(f"Calculation Result: {result}")
if __name__ == "__main__":
asyncio.run(main())Visit our notebooks for our examples.
We encourage contributions to LionAGI and invite you to enrich its features and capabilities. Engage with us and other community members Join Our Discord
When referencing LionAGI in your projects or research, please cite:
@software{Li_LionAGI_2023,
author = {Haiyang Li},
month = {12},
year = {2023},
title = {LionAGI: Towards Automated General Intelligence},
url = {https://github.com/lion-agi/lionagi},
}Python 3.9 or higher.