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langchain-structured-output

Enforce structured outputs for LangChain agents using Pydantic, TypedDict, or JSON Schema.

3|1|Updated Jan 31, 2026
Search Tags:#pydantic#python#validation#langchain#structured-output#json-schema
One-click install
npx skills add https://github.com/christian-bromann/langchain-skills --skill langchain-structured-output
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-structured-output
Source: https://github.com/christian-bromann/langchain-skills/tree/main/skills/langchain-structured-output/python
Command: npx skills add https://github.com/christian-bromann/langchain-skills --skill langchain-structured-output

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Structured outputs enable LangChain agents to return data in defined formats, improving reliability and downstream processing.

Core Features & Use Cases

  • Define schemas with Pydantic, TypedDict, or JSON Schema for outputs.
  • Validate and parse results, reducing errors and post-processing.
  • Use structured output with tools and providers to streamline integration in real-world automations.

Quick Start

Configure a LangChain agent to return a validated structured output using a Pydantic model.

Frequently Asked Questions about langchain-structured-output

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

FAQPage Schema
How do I enforce structured output from LangChain agents?▼

You can enforce structured output from LangChain agents by defining schemas using Pydantic, TypedDict, or JSON Schema, which validates the results and ensures reliable data for downstream processing.

Why should I use Pydantic for LangChain structured outputs?▼

Using Pydantic for LangChain structured outputs provides automatic validation and parsing of agent results, reducing errors and eliminating the need for manual post-processing of the returned data.

Can I validate LangChain tool results with JSON Schema?▼

Yes, you can validate LangChain tool results by defining a JSON Schema to enforce structured outputs, ensuring compatibility with common Python workflows and real-world automations.

What is the best way to parse LangChain agent responses into defined formats?▼

The most reliable way to parse LangChain agent responses is to apply explicit structured output schemas, allowing the agent to return data in defined formats that streamline integration with various providers.

Does this structured output approach work with TypedDict in Python?▼

Yes, this approach supports TypedDict in Python, allowing you to define structured output schemas for LangChain agents alongside Pydantic and JSON Schema to validate and parse results accurately.

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