Agent developers almost inevitably face this question: how do you chain multiple agents together so they collaborate instead of working independently?
LangGraph is LangChain's answer. But in the Chinese community, its discussion热度 doesn't match its engineering value.
What Problem It Solves
Traditional agent frameworks are mostly DAG-based — A triggers B, B triggers C. Linear, one-way, easy to understand.
But real agent workflows need:
- Loops — Agent A outputs, Agent B evaluates, rejects sends back to A
- State management — Maintaining context across rounds
- Conditional branches — Dynamically choosing the next path based on intermediate results
LangGraph is a stateful graph execution engine. Nodes are agents or tools, edges are flow logic, state passes through the entire graph.
My Take
LangGraph needs more discussion in the Chinese community. If you're building serious agent applications, graph orchestration is inevitable.
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