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GPTSwarm

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Basic Information

GPTSwarm is a graph-based open-source framework for building, running, and optimizing LLM-based agent swarms. It is designed for researchers and developers who want to represent language agents and their interactions as graphs, compose multi-agent systems, and enable automatic self-organization and self-improvement of swarms. The library exposes modules for domain operations and tools, graph creation and execution, LLM backend selection and cost calculation, index-based memory, and optimization routines. The README provides installation and quickstart instructions, example notebooks and demos, visualizations of agent graphs and edge optimization, and support for running with local LLM servers. The project is research-oriented with an accompanying academic paper and experiments for advanced use.

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App URL
https://github.com/metauto-ai/GPTSwarm
Github Repository
https://github.com/metauto-ai/GPTSwarm/blob/main/README.md

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App Details

Features
Key features include graph-based agent construction and execution, swarm composite graphs, and inter-agent edge optimization that prunes or creates connections to improve benchmark scores. Modular components are provided: swarm.environment for domain tools and tasks, swarm.graph for graph operations, swarm.llm for backend selection and cost accounting, swarm.memory for index-based memory, and swarm.optimizer for algorithms that adjust swarm structure and parameters. The repo includes visualizations, class diagrams, demo notebooks, example swarms and a file-analyzer tool. It supports remote LLM backends and local model inference via LM Studio. Installation via pip and project scripts are documented. The README also documents search engine selection priorities and examples for running predefined swarms and custom agents.
Use Cases
GPTSwarm helps developers and researchers prototype, evaluate, and iterate on multi-agent language systems by making agent structure explicit and optimizable. The graph abstraction simplifies composing agents and tools while the optimizer and edge optimization allow automated improvement of swarm topology and performance. Built-in memory indexing and LLM backend selection let users compare costs and run experiments with cloud or local models. The quickstart, notebooks, demos, and experiments lower the barrier to reproduce results and test new agent designs. Support for multiple web search backends and a file-analyzer tool extend practical use cases. The framework is intended for experimental research, benchmarking, and extending agent architectures rather than as an end-user chatbot product.

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