We are trying to build a system where one agent writes code and another reviews it. Doing this in a single chain feels clunky. Does LangGraph provide a more robust framework for multi-agent handoffs? I need something that can handle the state between these two agents without losing context during the transition.
3 answers
Absolutely. We implemented a "Coder-Reviewer" loop using LangGraph last November. The key advantage is the shared State object. In traditional LangChain, passing a complex history between agents requires a lot of manual parsing. With the graph approach, you define a schema for your state, and each agent (node) just updates the bits it's responsible for. This makes the handoff seamless. We also used the "human-in-the-loop" feature to pause the graph so a senior dev could approve the code before the next node executed. It’s been a game-changer for our internal tools.
Does the shared state approach cause any race conditions if you have multiple nodes running in parallel?
For multi-agent setups, LangGraph is definitely superior. It treats each agent as a node in a controlled network.
I've seen the same results. The visualization tools in LangSmith also make it much easier to see which agent failed during the handoff compared to a nested chain.
Great question, Marcus. LangGraph actually handles this through its "checkpointer" system and controlled execution. While it supports parallel node execution (using Fan-out/Fan-in), the state updates are merged according to the reducers you define. It prevents the typical "last-write-wins" issues you’d see in a custom-built async Python mess.