GPTSwarm
Build and self-organize language-agent swarms as optimizable graphs.
What does this agent do, and when should you use it?
GPTSwarm is a Python framework for building LLM-based agent graphs and agent swarms. It places domain operations, agents, tools, and tasks in swarm.environment, while swarm.graph creates and executes agent graphs and composite swarm graphs. swarm.llm selects LLM backends and calculates operational costs, swarm.memory provides index-based memory, and swarm.optimizer is intended to improve agent performance and swarm efficiency. Examples instantiate the Swarm class with agent types and an environment, then execute tasks through run or arun and receive a result. It runs as a local Python library, with documented OpenAI and search API configuration plus a local-model route through LM Studio.
A caller imports Swarm from swarm.graph.swarm and creates a predefined swarm such as Swarm(["IO", "IO", "IO"], "gaia"), then passes a task in {"task": task} and calls await swarm.arun(inputs). The framework executes the swarm through its graph and returns the call result. For file analysis, the documented example creates Swarm(["IO", "TOT"], "gaia"), passes image paths in {"task": task, "files": files}, and calls swarm.run(inputs). When BING_API_KEY, SEARCHAPI_API_KEY, or GOOGLE_API_KEY is configured, it selects Bing API, SearchAPI, or Google API in that order; swarm.llm also handles backend selection and operational-cost calculation.
- A Python developer who needs to arrange predefined agents into a graph and answer a knowledge question through the gaia environment.
- A researcher studying inter-agent edge optimization, including probability changes toward edge pruning or creation.
- An application developer who needs to submit local image paths to a file-analysis task and request a three-sentence summary.
- A multi-agent research team experimenting with reinforcement-learning and prompting optimization for swarm performance.
- A developer with an LM Studio server who wants to run a local language model using model_name='lmstudio'.
What are this agent's strengths and limitations?
- Uses graphs as the core representation for agents and composite swarms, with explicit graph creation and execution components.
- Separates environments, graphs, LLM backends, index-based memory, and optimization algorithms into distinct modules for experimentation.
- Documents inter-agent edge optimization and an optimizer aimed at agent performance and swarm efficiency.
- Documents a search-provider selection order for Bing, SearchAPI, and Google, plus a local-inference path through LM Studio.
- The installation path specifies Python 3.10, Conda, and Poetry, which may require runtime changes in an existing project.
- The documented online LLM and search paths depend on API keys; no pricing, quota, or offline alternative is described.
- The README shows only limited calls using the gaia environment and IO/TOT configurations, without a complete contract for custom environments or agents.
- Only the LM Studio desktop route for Mac or Windows is explicitly documented for local inference; support for other local services is not evidenced.
How do you install or deploy this agent?
Clone and install:
git clone https://github.com/metauto-ai/GPTSwarm.git
cd GPTSwarm/
conda create -n swarm python=3.10
conda activate swarm
pip install poetry
poetry installRename .env.template to .env and provide OPENAI_API_KEY for the OpenAI backend. For search, add BING_API_KEY, SEARCHAPI_API_KEY, or GOOGLE_API_KEY. The README does not document a complete credential configuration for using none of these services.
How do you use this agent?
Minimal invocation:
from swarm.graph.swarm import Swarm
swarm = Swarm(["IO", "IO", "IO"], "gaia")
inputs = {"task": "What is the capital of Jordan?"}
answer = await swarm.arun(inputs)For a local model, choose a model and start a server in LM Studio, then use model_name='lmstudio' in GPTSwarm code.
How does this agent compare with similar options?
The README names OpenAI Swarm as a related alternative and positions GPTSwarm around “Swarm Intelligence”; it does not provide a verifiable feature matrix or benchmark comparison.