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langchain-fundamentals

Develop and configure LangChain agents with tools, middleware, and structured output.

Updated Mar 20, 2026
Search Tags:#middleware#agent development#agent creation#LangChain#structured output
One-click install
npx skills add https://github.com/orezek/paradegolf --skill langchain-fundamentals-orezek
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/orezek/paradegolf/tree/main/.agents/skills/langchain-fundamentals
Command: npx skills add https://github.com/orezek/paradegolf --skill langchain-fundamentals-orezek

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain_core, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of creating sophisticated LangChain agents that can handle complex tasks with human-in-the-loop capabilities and error handling.

Core Features & Use Cases

  • Agent Creation: Use create_agent() to build agents with tool execution and state management.
  • Tool Definition: Create tools with clear descriptions and schemas for predictable agent behavior.
  • Middleware: Implement middleware for human approval, error handling, and additional processing hooks.
  • Structured Output: Generate typed, validated responses with structured output for better data handling.
  • Model Configuration: Configure agents with different models and settings for tailored performance.
  • Use Case: Develop a LangChain agent capable of processing legal documents, extracting key information, and providing summary reports with human-in-the-loop validation.

Quick Start

Build an agent that summarizes legal documents using LangChain with the following command: langchain-fundamentals create_agent --model gpt-4.1 --tools [legal_document_summary] --system_prompt "Summarize the key points of the provided legal document."

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema
How do I build a LangChain agent with custom tools and system prompts?▼

Build LangChain agents with custom tools and system prompts using `create_agent()`, specifying model configurations, tool schemas, and system instructions for predictable agent behavior and execution.

How does middleware work in LangChain agent development?▼

Middleware in LangChain agent development implements processing hooks for human approval workflows, error handling, and additional logic injection between agent execution steps and tool calls.

What's the best way to get structured output from a LangChain agent?▼

Generate typed, validated structured output from LangChain agents by configuring response schemas, enabling agents to return predictable data formats for complex information processing tasks.

Do I need Python and LangChain Core to create advanced agents?▼

Creating advanced LangChain agents requires Python along with the `langchain` and `langchain_core` libraries to handle state management, tool execution, and agent configuration.

Can I implement human-in-the-loop validation for processing legal documents with LangChain?▼

Implement human-in-the-loop validation for processing legal documents with LangChain by configuring middleware that pauses agent execution for human approval before finalizing summary reports.

Why does my LangChain agent fail to execute tools predictably?▼

LangChain agent tool execution fails predictably when tools lack clear descriptions and defined schemas, making it essential to configure structured tool definitions for reliable agent behavior.

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