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LangChain Fundamentals

Create LangChain agents with create_agent, @tool, and middleware.

11|2|Updated Feb 3, 2026
Search Tags:#development#middleware#langchain#llm#agents#tools
One-click install
npx skills add https://github.com/jackjin1997/ClawForge --skill langchain-fundamentals-jackjin1997
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LangChain Fundamentals
Source: https://github.com/jackjin1997/ClawForge/tree/main/skills/langchain-fundamentals
Command: npx skills add https://github.com/jackjin1997/ClawForge --skill langchain-fundamentals-jackjin1997

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides the foundational knowledge and practical examples for creating robust LangChain agents, enabling developers to build sophisticated AI applications.

Core Features & Use Cases

  • Agent Creation: Learn to use create_agent() for building agent loops, handling state, and integrating tools.
  • Tool Definition: Master the @tool decorator and tool() function for defining agent capabilities.
  • Middleware Integration: Understand how to use middleware for human-in-the-loop workflows, error handling, and custom logic.
  • Structured Output: Implement typed and validated responses from agents.
  • Use Case: Develop an agent that can search the web, perform calculations, and ask for user confirmation before executing a critical action.

Quick Start

Use the LangChain Fundamentals skill to create a basic agent that can answer questions using a provided tool.

Frequently Asked Questions about LangChain Fundamentals

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

FAQPage Schema
How do I build production LangChain agents using create_agent?▼

Build production LangChain agents using create_agent to manage agent loops, handle state, integrate tools, and configure persistence with checkpointers for robust AI applications.

What is the best way to define tools for LangChain agents?▼

Define tools for LangChain agents using the @tool decorator or tool() function to explicitly establish agent capabilities and structure their available actions.

How does middleware work for human-in-the-loop workflows in LangChain?▼

Middleware in LangChain works by intercepting agent flows to implement human-in-the-loop workflows, manage error handling, and execute custom logic for advanced control.

Can I implement structured typed responses with LangChain agents?▼

Implement structured typed responses with LangChain agents to generate validated and formatted outputs, ensuring predictable and structured output generation from agent interactions.

Do I need checkpointers to persist state in LangChain 1.0?▼

Checkpointers are needed to persist state in LangChain 1.0, enabling agents to maintain context across interactions and resume complex agent loops reliably.

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