LangGraph, CrewAI, or raw A2A - this is what I learned actually running multi-agent orchestration in production and not in a notebook
Summary
The author shares practical lessons learned from deploying multi-agent orchestration frameworks (LangGraph, CrewAI, and A2A) in production, contrasting with simple notebook experiments.
Similar Articles
Which framework feels most production-ready today: LangGraph, CrewAI, AutoGen, or OpenAI Agents?
A community discussion asking practitioners which AI agent orchestration framework—LangGraph, CrewAI, AutoGen, or OpenAI Agents—is most production-ready and scales well in real deployments.
Learning LangGraph : A Journey Through Agents, Blackboards, and Bottlenecks
An educational article exploring LangGraph, covering agent architectures, the blackboard pattern, and common bottlenecks in building agent systems.
@hwchase17: https://x.com/hwchase17/status/2053157547985834227
The article outlines a systematic 'Agent Development Lifecycle' (Build, Test, Deploy, Monitor) for creating and managing AI agents effectively, highlighting key frameworks like LangChain, LangGraph, and CrewAI.
"At what point does adding another agent actually hurt your system? Asking because my 6-agent pipeline is slower and less reliable than my old 2-agent one
A developer shares real-world experiences with AI orchestration frameworks (LangGraph, CrewAI, AutoGen), noting trade-offs between ease of prototyping and production reliability, and asks the community about handling failures, human-in-the-loop, and token costs.
Simple Multi-Agent Architecture Running Across Our Entire Org. Keeping everything in Loop.
This article describes a multi-agent architecture running at scale, using LangGraph, CrewAI, and Harbor to handle goal agents, task coordination, and secure access with tracing.