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GPTSwarm

GPTSwarm AI Agent
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Overview

A research framework that represents language agents as graphs and optimizes their prompts and connections.

GPTSwarm represents operations as nodes and information flow as edges. Researchers can combine agents into larger graphs, then optimize node prompts or connections between agents against a chosen task and evaluation objective. The repository includes inference examples and experimental workflows from the Language Agents as Optimizable Graphs paper.

Use it to study agent composition and optimization rather than as a ready-made business service. You choose the task, graph components, model backends, evaluation data and run budget. Results from a particular experiment do not guarantee improvements on another task. The short video shows a minimal inference example.

Product overview

Capabilities, pricing and fit

A research framework that represents language agents as graphs and optimizes their prompts and connections.

Information as of October 13, 2026

What it can do

  • Graph composition

    Represent operations and information flow as connected nodes.[1], [2]

  • Optimization

    Adjust node prompts and inter-agent connections against an objective.[1], [2]

  • Model backends

    Use configured model access, including documented local serving examples.[1], [2]

Where it fits best

  • Researching multi-agent graph design[1], [2]
  • Optimizing prompts against a defined evaluation[1], [2]
  • Reproducing agent-composition experiments[1], [2]

Pricing and availability

The repository uses the MIT license. Model API calls, local inference, search services and experiment compute are separate costs; repeated optimization runs can multiply usage.[1], [2]

Access: Developer-managed application or research environment.[1], [2]

Sources (2)
  1. github.com — ProductOfficial site · checked 2026-10-13
  2. arxiv.org — Documentation and product detailsOfficial site · checked 2026-10-13

Autonomy level

74/100

Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.

Reasoning: GPTSwarm can execute agent graphs and optimize prompts or inter-agent connections within a researcher-defined experiment. People supply the task, components, model access, evaluation objective and budget, and decide whether the results are useful. This is bounded research automation, not independent operation of arbitrary business workflows.

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Custom Comparisons

Some of the use cases of GPTSwarm:

  • Researching multi-agent graph design
  • Optimizing prompts against a defined evaluation
  • Reproducing agent-composition experiments

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Popularity level: 61%

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