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Implementation of the ReAct (Reason & Action) framework for Large Language Model (LLM) agents. Mainly targeting OpenAI's GPT-4.
Easily create tools from simple python functions or classes with the @tool
decorator. A tools list can then be passed to the ReActAgent
which will automagically generate a prompt for the LLM containing usage instructions for each tool, as well as manage the ReAct decision loop while the LLM performs its task.
Tools can be anything from internet searches to custom interpreters for your domain. Archytas provides a few built-in demo tools e.g. datetime, fibonacci numbers, and a simple calculator.
Short demo of using the PythonTool
to download a COVID-19 dataset, and perform some basic processing/visualization/analysis/etc.
# make sure poetry is installed
pip install poetry
# clone and install
git clone git@github.com:jataware/archytas.git
cd archytas
poetry install
# make sure OPENAI_API_KEY var is set
# or pass it in as an argument to the agent
export OPENAI_API_KEY="sk-..."
# run demo
poetry run chat-repl
Import pre-made tools from the tools module
from archytas.react import ReActAgent, FailedTaskError
from archytas.tools import PythonTool
from easyrepl import REPL
# create the agent with the tools list
some_tools = [PythonTool, ..., etc.]
agent = ReActAgent(tools=some_tools, verbose=True)
# REPL to interact with agent
for query in REPL():
try:
answer = agent.react(query)
print(answer)
except FailedTaskError as e:
print(f"Error: {e}")
See the wiki docs for details.
FAQs
A library for pairing LLM agents with tools so they perform open ended tasks
We found that archytas demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 3 open source maintainers collaborating on the project.
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