The Multi-Agent Framework Wars: What Actually Works in Production

This article discusses the current state of multi-agent frameworks in AI, highlighting the key options and the real-world challenges faced in production environments.

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The article discusses the current state of the multi-agent framework landscape in AI, highlighting the six main options as of March 2026: LangGraph, CrewAI, OpenAI Agents SDK, AutoGen/AG2, Google ADK, and Claude Agent SDK. The author emphasizes that the choice of framework comes down to three key questions: how agents coordinate, how failures are handled, and the ability to switch between different language models. Graph-based, role-based, handoff-based, and conversational approaches are compared, along with their strengths and limitations in production environments. The article then delves into the author's experience running the OpenClaw framework in production, showcasing the benefits of parallel agent spawning, verification hooks, and model tiering, as well as the challenges faced with Twitter automation, root cause analysis, and extension testing. The overall message is that the frameworks optimized for demos often fail to survive contact with production, and that systems that enforce correctness are more valuable than promises to 'be more careful'.