GPTSwarm is a graph-based framework for building and orchestrating LLM-powered agents, enabling self-organization and self-improvement within agent swarms. It is designed for researchers and developers working with complex multi-agent systems who need a flexible and optimizable architecture for LLM interactions.

How It Works

GPTSwarm structures agent interactions as graphs, allowing for dynamic composition and execution. It incorporates Reinforcement Learning (RL) and prompt optimization techniques to enhance agent performance and swarm efficiency. Key components include modules for defining environments, managing graph operations, interfacing with LLMs, implementing memory, and optimizing agent behavior.

Quick Start & Requirements

• Install: pip install gptswarm
• Prerequisites: Python 3.10, Poetry, API keys for LLM backends (OpenAI, Bing, Google, SearchAPI). Local LLM support via LM Studio is available.
• Setup: Requires cloning the repository and installing dependencies via Poetry. API key configuration is necessary.
• Resources: Official Colab notebook and demo notebooks are available.

Highlighted Details

• Graph-based agent construction and swarm orchestration.
• Built-in RL and prompt optimization for self-improvement.
• Supports multiple LLM backends and web search tools.
• Academic paper accepted at ICML2024 with oral presentation (top 1.5%).

Maintenance & Community

The project is initiated by Mingchen Zhuge, with contributions from researchers at KAUST and IDSIA, including Jürgen Schmidhuber. Developer documentation is available for contributions.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The README does not specify a license, which may impact commercial adoption. Detailed performance benchmarks or comparisons beyond the ICML acceptance are not immediately apparent.