Our current multi-agent setup in LangChain is getting too bloated and slow. Is the approach to agent delegation and "hand-offs" actually faster or more memory-efficient? I'm looking for a way to streamline how our researcher agent passes structured data to our writer agent without losing context.
3 answers
Efficiency in PydanticAI comes from its "Agent as a Tool" philosophy. You can wrap an entire agent as a tool and pass it to another agent. Because both use Pydantic models for inputs and outputs, the "hand-off" is a strictly typed exchange. There’s no messy re-parsing of strings or hidden prompt injection risks during the delegation. In LangChain, you often have to write custom output parsers to ensure the second agent understands the first. PydanticAI eliminates this boilerplate, which reduces token overhead and minimizes the chance of the sequence breaking mid-way through a complex task.
That sounds great for speed, but does it support the "Human-in-the-loop" pattern during these agent hand-offs as easily as LangGraph does?
The dependency injection is the real winner here. Passing database connections or API keys securely across agents is a breeze.
I agree with Brian. The way it handles dependencies makes the code much cleaner and more secure, which is a major concern when scaling these agents in a cloud environment.
It actually does! It uses an "ApprovalRequired" exception pattern. When an agent wants to call a sensitive tool (or another agent), it can trigger a pause that waits for external validation. It’s very lightweight and doesn't require a massive graph architecture to implement. You just catch the exception, get human feedback, and resume the run. It’s a much more direct way to manage control flow without the complexity of persistent state checkpointers if you don't need them.