Author(s): Chew Loong Nian – AI ENGINEER

Originally published on Towards AI.

I Built the Same Agent in LangGraph, CrewAI, and AutoGen — Microsoft Quit the 56K-Star Favorite

I spent a weekend building the exact same two-agent pipeline three times — once in LangGraph, once in CrewAI, and once in AutoGen — and the most uncomfortable finding had nothing to do with code. AutoGen has the most GitHub stars of the three at roughly 56,000, and it is the one Microsoft quietly put into maintenance mode. New features are over. The most popular multi-agent framework on GitHub is, for all practical purposes, abandoned.

The author compares three popular multi-agent frameworks by implementing the same researcher-then-writer agent in LangGraph, CrewAI, and AutoGen, measuring lines of code, token overhead, latency, and cost. CrewAI is fast to prototype with a simple role/task model but has higher coordination overhead; LangGraph requires more graph/node wiring yet offers tight control, typed state, and explicit transitions with low overhead (~5%); AutoGen supports elegant multi-turn conversation but is expensive (often 5–6x cost) and has been placed in maintenance mode by Microsoft, which discourages greenfield adoption. The article then contrasts “vanity” popularity signals (GitHub stars) with production signals like downloads and real deployments, arguing that LangGraph’s adoption in large companies points to better suitability for shipping, while stars track curiosity. The verdict: start with CrewAI for fast prototyping, choose LangGraph for production-grade stateful agents, treat AutoGen cautiously due to maintenance status, and consider avoiding frameworks entirely (or using Pydantic AI / SDK approaches) when the task is small or you want type safety and less abstraction tax.

Read the full blog for free on Medium.

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Published via Towards AI