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Powerful Intelligent Workflow Automation
LionAGI is an intelligent agent framework. Tailored for big data analysis in conjunction with advanced machine learning tools, designed for data-centric, production-level projects. Lionagi provides a set of robust tools, enabling flexible and rapid design of agentic workflow, for your own data.
Intelligent AI models such as Large Language Model (LLM), introduced new possibilities of human-computer interaction. LLMs is drawing a lot of attention worldwide due to its “one model fits all”, and incredible performance. One way of using LLM is to use as search engine, however, this usage is complicated by the fact that LLMs hallucinate.
What goes inside of a LLM is more akin to a black-box, lacking interpretability, meaning we don’t know how it reaches certain answer or conclusion, thus we cannot fully trust/rely the output from such a system.
Another approach of using LLM is to treat them as intelligent agent, that are equipped with various tools and data sources. A workflow conducted by such an intelligent agent have clear steps, and we can specify, observe, evaluate and optimize the logic for each decision that the agent made to perform actions. This approach, though we still cannot pinpoint how LLM output what it outputs, but the flow itself is explainable.
LionAGI agent can manage and direct other agents, can also use multiple different tools in parallel.
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.
The following example shows how to use LionAGI's Session object to interact with gpt-4-turbo model:
# 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}
model="gpt-4-turbo-preview"# in interactive environment (.ipynb for example)
from lionagi import Session
calculator = Session(system)
result = await calculator.chat(instruction, context=context, model=model)
print(f"Calculation Result: {result}")# or otherwise, you can use
import asyncio
from dotenv import load_dotenv
load_dotenv()
from lionagi import Session
async def main():
calculator = Session(system)
result = await calculator.chat(instruction, context=context, model=model)
print(f"Calculation Result: {result}")
if __name__ == "__main__":
asyncio.run(main())Visit our notebooks for examples.
LionAGI is designed to be asynchronous 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 1,000 requests, 100,000 tokens per miniute, 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,
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 3.10 or higher.