pydantic-ai

Enforce typed, schema-constrained outputs from LLMs using Pydantic models.

40|9|Updated Jan 4, 2026

Search Tags:#pydantic #validation #schema #llm #deterministic #retries #structured-output

Ready to run

npx skills add https://github.com/akillness/jeo-skills --skill pydantic-ai-akillness

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: pydantic-ai
Source: https://github.com/akillness/jeo-skills/tree/main/.agent-skills/pydantic-ai
Command: npx skills add https://github.com/akillness/jeo-skills --skill pydantic-ai-akillness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LLMs often produce free-form text that is hard to parse and unreliable for downstream automation. PydanticAI provides a schema-first approach that enforces typed outputs, integrates tools, and adds validation, retries, and deterministic handoffs to ensure reliable data exchange.

Core Features & Use Cases

Schema-first design with Pydantic models and field constraints to enforce data shape
Unify tool input/output with Pydantic types and define retry rules for missing fields or type mismatches
Validation and repair loop that categorizes errors (validated, repaired, failed) and prevents silent failures
Operational deployment with versioned schemas, server-side validation, and observability for long-running workflows

Quick Start

Define a Pydantic schema for your expected outputs and integrate it with your Agent to validate and repair results automatically.

Frequently Asked Questions about pydantic-ai

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I enforce structured outputs from LLMs using Pydantic models?▼

Pydantic enforces structured outputs from LLMs by applying schema-first design with typed models and field constraints. It validates responses, maps errors, and retries automatically to guarantee reliable data handoffs.

How do I validate and repair missing fields in LLM generated data?▼

Pydantic validates and repairs missing fields in LLM generated data through an automated loop that categorizes errors as validated, repaired, or failed. This prevents silent failures and ensures type correctness for your downstream pipelines.

Can I use Pydantic to coordinate tool interactions and define retry rules for type mismatches?▼

Yes, Pydantic unifies tool input and output with typed models to coordinate tool interactions and define retry rules for type mismatches. It applies validation loops to ensure deterministic handoffs.

Does schema-based LLM output validation support versioned schemas for long-running workflows?▼

Schema-based LLM output validation supports versioned schemas for long-running workflows. It provides server-side validation and observability to maintain operational deployment stability across evolving data pipelines.

Do I need a Python environment with Pydantic to ensure deterministic downstream handoffs in data pipelines?▼

Yes, you need a Python environment with Pydantic to ensure deterministic downstream handoffs in data pipelines. This environment enables automated retries, error mapping, and structured validation for API calls and database storage.