I’m looking at LangGraph vs Pydantic AI for a multi-agent setup where one agent researches and another writes. LangGraph seems powerful but the learning curve is steep. Is Pydantic AI capable of handling these hand-offs efficiently without getting too messy? I’m looking for a way to keep my code maintainable as we scale to more complex agentic behaviors.
3 answers
The approach in Pydantic AI is very different from the graph-based logic you see elsewhere. It treats agents more like standard Python classes or functions. You can have one agent call another just like any other dependency. This makes the logic "flat" and very easy to follow in a standard IDE. We currently run a three-agent system for legal document review using this framework. The "hand-off" is essentially just passing a validated Pydantic object from the output of Agent A to the input of Agent B. It’s less "visual" than a graph, but much easier to track in a large codebase.
If you use Pydantic AI for this, how do you handle state management if one of the agents in the middle of the chain fails?
I’ve found it much easier to debug. You just set a breakpoint in your Python code and see exactly what’s happening.
Exactly, Lisa! No more opaque "chain" objects. Pydantic AI allows you to see the raw data at every step, which is a lifesaver for enterprise-level deployments.
State management is handled through the dependency injection system. You can pass a state object that persists across the run. If one step fails, you have the full validated state of the previous steps to retry from.