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8 pages in this section.
Learn why structured output is crucial for autonomous AI agents to prevent errors, ensure reliable operations, and validate model responses.
Learn to use Pydantic AI and Instructor to get structured, type-safe LLM output. Install libraries, define Pydantic models, and run agents.
Constrain an agent's response to a typed Pydantic model. Learn to define fields, enums, and validators for structured, validated output.
Extract validated Pydantic objects from diverse LLM providers using Instructor. Learn to define models, configure clients, and handle retries for robust data extraction.
Define type-safe agent tools using Pydantic AI. Learn to register Python functions, validate arguments, and manage dependencies for robust AI models.
Learn ten best practices for schema design and agent wiring with Pydantic AI and Instructor to ensure strict and useful agent output.