GPTSwarm Save

🐝 GPTSwarm is a graph-based framework for LLM-based agents, providing two high-level features:

• It lets you build LLM-based agents from graphs.
• It enables the customized and automatic self-organization of agent swarms with self-improvement capabilities.

News

• 🔥 [05/01] GPTSwarm has been accepted by ICML2024.

• 🔥 [03/01] GPTSwarm can be installed via pip now: pip install gptswarm

• 🔥 [02/27] Our academic paper: Language Agents as Optimizable Graphs is released.

Edge optimization example

Here is the edge optimization process that updates edge probabilities toward improvement of the benchmark score. Notice that within an agent, the edges are fixed, whereas the inter-agent connections are getting optimized towards either edge pruning (value 0, blue) or creation (value 1, red).

About GPTSwarm

At a granular level, GPTSwarm is a library that includes the following components:

Quickstart

Clone the repo

git clone https://github.com/metauto-ai/GPTSwarm.git
cd GPTSwarm/

Install packages

conda create -n swarm python=3.10
conda activate swarm
pip install poetry
poetry install

You should add API keys in .env.template and change its name to .env

OPENAI_API_KEY="" # for OpenAI LLM backend
SEARCHAPI_API_KEY="" # for Web Search

Getting started with GPTSwarm is easy. Quickly run a predefined swarm

from swarm.graph.swarm import Swarm

swarm = Swarm(["IO", "IO", "IO"], "gaia")
task = "What is the capital of Jordan?"
inputs = {"task": task}
answer = await swarm.arun(inputs)

or make use of tools, such as the file analyzer

from swarm.graph.swarm import Swarm
swarm = Swarm(["IO", "TOT"], "gaia")
task = "Tell me more about this image and summarize it in 3 sentences."
files = ["./datasets/demos/js.png"]
inputs = {"task": task, "files": files}
danswer = swarm.run(inputs)

Check out the minimal Swarm example in Colab here: .

See how to create a custom Agent and run a Swarm with it here: .

Here is a Youtube video on how to run the demo notebooks:

🔥🔥🔥 See our experiments for more advanced use of our framework.

Class diagram

Example of the Swarm

More Visualizations

Running with a local LLM

We support local LM inference via LM Studio. Download their desktop app for Mac or Windows, choose a model from the Huggingface repository and start the server. Use model_name='lmstudio' in GPTSwarm code to run with the local LLM.

Contributors

• Mingchen Zhuge (PhD Student@KAUST; Project Initiator)
• Wenyi Wang (PhD Student @ KAUST; Initial Participant)
• Dmitrii Khizbullin (Research Engineer Lead @ KAUST; Project Engineer Lead)
• Louis Kirsch (PhD Student @ IDSIA)
• Francesco Faccio (PostDoc@IDSIA; Visiting Researcher@KAUST)
• Jürgen Schmidhuber (Director @ KAUST AI Initiative; Scientific Director @ IDSIA)
• William FH (Engineering @ LangChain)
• Lukas Vierling (BS Student @ RWTH & HKUST)

Please read our developer document if you are interested in contributing.

Citation

Please cite our paper if you find the library useful or interesting.

@article{zhuge2024language,
  title={Language Agents as Optimizable Graphs},
  author={Zhuge, Mingchen and Wang, Wenyi and Kirsch, Louis and Faccio, Francesco and Khizbullin, Dmitrii and Schmidhuber, Jurgen},
  journal={arXiv preprint arXiv:2402.16823},
  year={2024}
}