I’m trying to build a system where one agent writes code and another agent reviews it. In standard LangChain, this was messy to coordinate. How does LangGraph simplify the "hand-off" between these two specialized agents? I want to ensure they can pass the state back and forth until the code passes all tests without a manual trigger.
3 answers
The "Supervisor" pattern is the most effective here. In LangGraph, you create a top-level node (the Supervisor) that decides which worker agent to call next based on the current state. When the "Coder Agent" finishes, it returns its output to the state; the Supervisor then sees the task isn't "complete" and routes the edge to the "Reviewer Agent." If the reviewer finds a bug, the edge simply points back to the coder. This "cycle" is a first-class citizen in the graph. It makes multi-agent collaboration look like a clean, visual flowchart rather than a confusing web of nested function calls, making it much easier for your team to scale.
Does this pattern increase the latency of the response significantly due to the supervisor overhead?
I prefer the "Peer-to-Peer" pattern where agents just signal each other. LangGraph supports both, which is why it's so flexible.
Exactly, the flexibility is key. Whether it's a hierarchy or a flat structure, the graph keeps the communication protocol standardized.
There is a slight overhead for the extra LLM call to the Supervisor, but you can optimize this by using a smaller, faster model (like GPT-3.5 or Haiku) for the routing logic while keeping the "heavy lifting" for the Coder agent. The reliability gains far outweigh the extra 500ms of latency in most production scenarios.