pydantic-ai

To build AI agents with typed outputs and dependency injection, define an Agent with a typed output contract and inject dependencies via RunContext. This unifies model selection and tool orchestration into a single typed framework.

Yes, you can stream both text and structured data from an AI agent workflow. The framework provides streaming support for text and structured outputs, allowing you to observe typed outputs dynamically during agent execution.

Tool registration for context-aware AI agents works by allowing flexible registration of plain and context-aware tools. You can prepare tools per-run, ensuring the agent has the specific functions and dependencies needed for each execution.

You test AI agent workflows without live model calls by using TestModel and FunctionModel. These testing mechanisms simulate model behavior, allowing you to verify tool registration, dependency injection, and streaming outputs safely.

You enforce safety and validation limits during AI agent execution by using ModelRetry and UsageLimits mechanisms. These ensure the agent operates within defined safety boundaries and validates structured outputs against the typed contract.

Yes, a typed framework can unify model configuration and tool orchestration for Python AI agents. It provides a structured approach to model selection, validation, dependency injection, and streaming within a single Python-based workflow.