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GPTSwarm
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GPTSwarm

Active·★ 1.1k·MIT·Updated 2026-02-05

GPTSwarm is a graph-based framework for LLM agents from the paper "Language Agents as Optimizable Graphs", selected as an oral presentation at ICML 2024. It treats an agent as a graph of operations, and a swarm as a composite graph of agents, so both can be built, executed and then optimised.

The library is organised into modules: swarm.environment for domain-specific operations, agents, tools and tasks; swarm.graph for creating and running agent graphs and swarm graphs; swarm.llm for choosing LLM backends and tracking their cost; swarm.memory for index-based memory; and swarm.optimizer for algorithms that improve agent performance and how the swarm is connected, which is the self-improvement capability the project emphasises.

Install with `pip install gptswarm`. It is best suited to researchers and builders who want to experiment with automatically optimising multi-agent structures rather than to teams looking for a production orchestration product. MIT licensed; most of the README's news dates from 2024, and our data shows the last push on 2026-02-05.

This write-up is based on the project's README and public repository data. We have not hand-tested the tool. Data as of 2026-10-23.

License
MIT
Latest release
No GitHub releases
Last commit
2026-02-05
Contributors
7
Forks
109
Open issues and PRs
7
First published
2024-02-26

Source: GitHub, npm and PyPI public APIs.

#LLM Agents#Graph Framework#Swarm Intelligence#Multi-Agent Systems#AI Optimization#Data Analysis
$ Install
$ pip install gptswarm
↗ Visit site★ GitHub
01

Features

01Graph-based LLM agent construction.
02Customizable and automatic self-organization of agent swarms.
03Self-improvement capabilities for agent swarms.
04Modular library for environment, graph, LLM interface, memory, and optimization.
05Optimization of inter-agent connections for performance improvement.
02

Why choose it

+Backed by a peer-reviewed paper (ICML 2024 oral).
+Agents and swarms are graphs that can be optimised automatically, not just hand-wired.
+Clear module split (environment, graph, llm, memory, optimizer) with LLM cost tracking.
+pip install; MIT licensed.
03

Trade-offs

!Research-oriented; it is not a production orchestration platform.
!Most of the README's news dates from 2024, so check how actively it is developed.
!Swarm optimisation runs many LLM calls, which costs money.
04

Maintenance check

SlowingChecked: 2026-10-23
Last commit
2026-02-05
Commits, last 90 days
0
Releases, last 90 days
0
Latest release
—
Open issues
7
Recent issues with at least one comment
—
Commits per week, last 12 weeks
Commits per week, last 12 weeks: 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0

Oldest week on the left; the latest four weeks are orange. Source: GitHub commit activity.

Status rule: archived on GitHub = Archived; last commit within 90 days = Active; 91 to 365 days = Slowing; older = Stale. All figures come from the GitHub API on the date shown.

05

Compatibility

OpenAI
Supported
Verified via docs
LM Studio
Supported
Verified via docs
Bing API
Supported
Verified via docs
Search API
Supported
Verified via docs
Google API
Supported
Verified via docs
06

Quick start

1
$ pip install gptswarm
07

Use cases

↳Complex question answering using agent swarms.
↳Multi-modal analysis and summarization (e.g., image analysis).
↳Research and experimentation with LLM agent architectures and optimization.
08

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On this page
01Features02Why choose it03Trade-offs04Maintenance check05Compatibility06Quick start07Use cases08Alternatives
metauto-ai /
GPTSwarm
↗

MIT research framework (ICML 2024 oral) that builds LLM agents as graphs and optimises how agent swarms are wired together

1.1k109MIT
Last commit8mo ago
StatusActive
CategoryMulti-Agent
Contributors7
Links
Documentation↗Discussion↗Issues↗Releases↗
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