OpenAI Swarm — AI Agent Framework: Live Stats & TrendScore

Live GitHub stats, community sentiment, and trend data for OpenAI Swarm. TrendingBots tracks star velocity, fork activity, and what developers are saying — updated from real data sources.

GitHub data synced: Apr 15, 2026 • Sentiment updated: Apr 19, 2026

GitHub Statistics

Community Sentiment

Community Buzz: I turned Claude Code into a multi-agent swarm and it actually changed how I work

Pros & Cons

What People Love

Multi-Agent Orchestration, Improved productivity with Claude Code

Common Complaints

Limited Windows Support, Potential security risks

Biggest Positive: Multi-Agent Orchestration

Biggest Negative: Limited Windows Support

Why OpenAI Swarm Stands Out

Swarm stands out from alternatives like the Assistants API due to its focus on lightweight, highly controllable, and easily testable agent orchestration. This approach enables the creation of complex, scalable systems that can handle a large number of independent capabilities and instructions. By using Swarm, developers can avoid the steep learning curve associated with more complex systems and focus on building custom, real-world solutions. Additionally, Swarm's use of the Chat Completions API allows for stateless conversations between agents, making it well-suited for large-scale, real-world applications.

Built With

Build a personal shopping agent that can help with making sales and refunding orders — Swarm enables the creation of multi-agent setups for handling different customer service requests in a lightweight and highly controllable way, Build a real-world solution for scalable customer service — Swarm's lightweight, highly controllable, and easily testable agent orchestration enables the creation of complex, scalable systems, Build a triage system that routes users to the right agents — Swarm's handoff mechanism allows agents to pass control to other agents based on specific conditions, Build a customer service bot that includes a user interface agent and a help center agent with several tools — Swarm's agent primitives and handoff mechanism enable the creation of complex, customizable systems, Build a large-scale, real-world solution for multi-agent orchestration — Swarm's lightweight, highly controllable, and easily testable agent orchestration enables the creation of complex, scalable systems

Getting Started

  1. pip install git+ssh://git@github.com/openai/swarm.git
  2. pip install git+https://github.com/openai/swarm.git
  3. from swarm import Swarm
  4. client = Swarm()
  5. try client.run() to verify it works
  6. try modifying the agent and instructions to see how it affects the output

About

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

Category & Tags

Category: multi-agent

Market Context

Competitive AI market with increasing demand for multi-agent systems