Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/metauto-ai/gptswarm

🐝 GPTSwarm: LLM agents as (Optimizable) Graphs
https://github.com/metauto-ai/gptswarm

agent ai gpt multi-agent python reinforcement-learning self-improvement society-of-mind swarm-intelligence

Last synced: about 2 months ago
JSON representation

🐝 GPTSwarm: LLM agents as (Optimizable) Graphs

Host: GitHub
URL: https://github.com/metauto-ai/gptswarm
Owner: metauto-ai
License: mit
Created: 2024-02-26T20:29:59.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-07-26T15:53:46.000Z (about 2 months ago)
Last Synced: 2024-08-01T15:22:30.996Z (about 2 months ago)
Topics: agent, ai, gpt, multi-agent, python, reinforcement-learning, self-improvement, society-of-mind, swarm-intelligence
Language: Python
Homepage: https://gptswarm.org
Size: 18.8 MB
Stars: 476
Watchers: 6
Forks: 23
Open Issues: 2
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-ChatGPT-repositories - GPTSwarm - 🐝 GPTSwarm: LLM agents as (Optimizable) Graphs (NLP)

README

        [![Page](https://img.shields.io/badge/Project-Page-lightgreen.svg)](https://gptswarm.org)

[![arXiv](https://img.shields.io/badge/arXiv-Paper-gold.svg)](https://arxiv.org/abs/2402.16823)

[![License](https://img.shields.io/badge/License-MIT-orange.svg)](https://github.com/metauto-ai/GPTSwarm/blob/main/LICENSE)

[![Issues](https://img.shields.io/github/issues/metauto-ai/GPTSwarm?color=00afaa)](https://github.com/metauto-ai/gptswarm/issues)

[![Twitter Follow](https://img.shields.io/twitter/follow/AI_KAUST?style=social)](https://twitter.com/AI_KAUST)

[![Wechat](https://img.shields.io/badge/Wechat-7BB32E?logo=wechat&logoColor=white)](https://metauto.ai/images/wechat.jpeg)

[![Coverage Status](https://coveralls.io/repos/github/metauto-ai/GPTSwarm/badge.svg?branch=main)](https://coveralls.io/github/metauto-ai/GPTSwarm?branch=main)







🐝 **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.

## About GPTSwarm



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

| Module | Description |

| ---- | --- |

| [**swarm.environment**](swarm/environment) | Domain-specific operations, agents, tools, and tasks |

| [**swarm.graph**](swarm/graph) | Graph-related functions for creating and executing agent graphs and swarm composite graphs |

| [**swarm.llm**](swarm/llm) | Interface for selecting LLM backends and calculating their operational costs |

| [**swarm.memory**](swarm/memory) | Index-based memory |

| [**swarm.optimizer**](swarm/optimizer) | Optimization algorithms designed to enhance agent performance and overall swarm efficiency |

## Visualizations of the graphs



## News

* 🔥 **[07/25]** Louis and Francesco gave the oral presentation in ICML 2024.

* 🔥 **[07/05]** Mingchen gave an invited talk to WAIC.

* 🔥 **[06/21]** Mingchen gave an invited talk to IA-CAS.

* 🔥 **[06/20]** Dmitrii gave an invited talk to Sberbank.

* 🔥 **[06/03]** [SDAIA](https://sdaia.gov.sa/en) invited GPTSwarm team (Wenyi, Francesco, Dmitrii) for a 5 hour seminar.

* 🔥 **[06/02]** GPTSwarm has been selected as **Oral Presentation** (top 1.5%, 144 in 9,473) by [ICML2024](https://icml.cc)! Congratulation!

* 🔥 **[05/01]** GPTSwarm has been accepted by [ICML2024](https://icml.cc).

* 🔥 **[04/18]** Mingchen gave the invited talk to ByteDance.

* 🔥 **[03/20]** Mingchen gave the invited talk to HUAWEI.

* 🔥 **[03/13]** [MITTR China Exclusive Interview](https://mp.weixin.qq.com/s/Nsn3rk07mXC_3JaRk4qrTQ) with Mingchen.

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

* 🔥 **[02/27]** Our academic paper: [Language Agents as Optimizable Graphs](https://arxiv.org/abs/2402.16823) 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).



## Quickstart

**Clone the repo**

```bash

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`**

```python

OPENAI_API_KEY="" # for OpenAI LLM backend

SEARCHAPI_API_KEY="" # for Web Search

```

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

```python

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**

```python

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: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metauto-ai/GPTSwarm/blob/main/notebooks/demo_swarm.ipynb).

See how to create a custom Agent and run a Swarm with it here: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/metauto-ai/GPTSwarm/blob/main/notebooks/demo_custom_agent.ipynb).

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

[](https://www.youtube.com/watch?v=QOLQse5ZBV8&t=8s&ab_channel=GPTSwarm "Running swarm inference")

**🔥🔥🔥 See [our experiments](https://github.com/metauto-ai/GPTSwarm/tree/main/experiments) for more advanced use of our framework.**

## Class diagram



## Example of the Swarm



## Running with a local LLM

We support local LM inference via [LM Studio](https://lmstudio.ai). 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](http://metauto.ai) (PhD Student@KAUST; Project Initiator)

* [Wenyi Wang](https://scholar.google.com/citations?user=79ODhuQAAAAJ&hl=en&oi=ao) (PhD Student @ KAUST; Initial Participant)

* [Dmitrii Khizbullin](http://www.khizbullin.tech) (Research Engineer Lead @ KAUST; Project Engineer Lead)

* [Louis Kirsch](http://louiskirsch.com) (PhD Student @ IDSIA)

* [Francesco Faccio](https://scholar.google.com/citations?user=0z3DkrkAAAAJ&hl=en&oi=ao) (PostDoc@IDSIA; Visiting Researcher@KAUST)

* [Jürgen Schmidhuber](http://www.idsia.ch/~juergen/) (Director @ KAUST AI Initiative; Scientific Director @ IDSIA)

* [William FH](https://github.com/hinthornw) (Engineering @ LangChain)

* [Lukas Vierling](https://www.linkedin.com/in/lukas-vierling/) (BS Student @ RWTH & HKUST)

Please read our [developer document](DEVELOPMENT.md) 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}

}

```