iuyup

iuyup / AgentFlow

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Multi-Agent Collaboration Design Patterns Built on LangGraph with 10+ battle-tested patterns, each with complete code, architecture diagrams, and benchmarks.

iuyup.github.ioAgentFlow agentic-ai agentic-workflow ai-agents design-patterns langgraph
23
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100% credibility
Found Apr 08, 2026 at 23 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

AgentFlow provides ready-to-run examples of multi-agent collaboration patterns built with LangGraph for tasks like reflection, debate, and voting.

How It Works

1
📰 Discover AgentFlow

You stumble upon AgentFlow while exploring ways AI helpers can work as a team on tough tasks.

2
📖 Explore the examples

Browse the friendly guides and live demos to see different teamwork styles like debating or voting.

3
✅ Pick your favorite

Choose a collaboration pattern that matches what you want your AI team to do.

4
🔗 Connect your AI

Add a private password to link a smart AI service so your team can think and chat.

5
🚀 Launch the team

Hit run and watch your AI agents spring to life, collaborating just like the demo.

✨ Enjoy smart results

See your agents improve ideas, make decisions together, or solve problems – now you have a powerful AI team ready for your projects!

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AI-Generated Review

What is AgentFlow?

AgentFlow delivers a curated set of 10+ multi-agent collaboration design patterns built on LangGraph in Python, complete with runnable code, architecture diagrams, benchmarks, and use-case breakdowns. It solves the architecture headaches of multi-agent systems—like coordinating agents without chaos, deciding when to loop or fan out, and benchmarking efficiency—by acting as a reference library you can copy-paste from. Clone the repo, sync with uv, add your OpenAI key, and run patterns like reflection or map-reduce in minutes; browse the live MkDocs docs for interactive demos.

Why is it gaining traction?

In a sea of multi-agent frameworks and vague tutorials, AgentFlow stands out with battle-tested patterns (debate, hierarchical, swarm) that ship with performance benchmarks and real-world comparisons, letting you pick the right flow for your agent collaboration needs without reinventing the wheel. Devs searching "agent flow ai" or "multi agent collaboration frameworks" get instant value: self-contained examples that teach LangGraph's Send API and conditional edges through doing, not docs. Dual-language READMEs and a quick-start CLI make it accessible for global teams prototyping agentic workflows.

Who should use this?

LangGraph users building production multi-agent apps, like AI coding assistants needing reflection loops or research tools using map-reduce on sources. Ideal for AI engineers tackling agent flow optimization, backend devs implementing hierarchical task decomposition, or teams exploring debate-style decision-making in agent collaboration networks. Skip if you're after a full framework—grab it for pattern inspiration on complex tasks like RAG agents or guardrails.

Verdict

Solid starter for multi-agent experimentation (23 stars, 1.0% credibility signals early days), with excellent docs, tests, and benchmarks offsetting the low maturity. Use it to accelerate prototypes; fork and contribute patterns as your agentflow ai gmbh workflows evolve. Worth starring if LangGraph's your stack.

(198 words)

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