I am building a research assistant where one agent needs to call another to verify facts. Is there a built-in way in Pydantic AI to handle this agent-to-agent delegation without writing a massive amount of boilerplate code or manually chaining prompts together?
3 answers
Pydantic AI handles delegation very elegantly by treating other agents as tools. You can essentially register a second agent as a function that the primary agent can call. When the primary agent needs a fact-check, it "calls" the researcher agent, passes the query, and waits for a validated Pydantic object in return. This modular design means you can test each agent independently. Furthermore, the dependency injection system allows you to share database connections or API keys across both agents seamlessly. It keeps your code clean and follows standard Pythonic patterns rather than inventing a new, proprietary logic for agent communication.
Can we visualize these hand-offs in a graph like you can with LangGraph or is it strictly linear?
The dependency injection feature is really the secret sauce for keeping these complex multi-agent setups manageable.
Agreed! Being able to pass a shared 'RunContext' across different agents makes the whole architecture feel much more robust and professional.
While it started more linear, the new pydantic_graph module allows you to define these transitions as nodes and edges. It gives you that visual control while maintaining the strict type-safety that the framework is famous for.