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LangChain Fundamentals

Create and configure LangChain agents with tools, middleware, and checkpointers.

1.1k|86|Updated Jan 22, 2026
Search Tags:#middleware#langchain#llm#agents#tools#create_agent
One-click install
npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-fundamentals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LangChain Fundamentals
Source: https://github.com/langchain-ai/langchain-skills/tree/main/config/skills/langchain-fundamentals
Command: npx skills add https://github.com/langchain-ai/langchain-skills --skill langchain-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides the foundational knowledge and tools to build robust, production-ready AI agents using LangChain's create_agent function, enabling custom workflows and reliable operation.

Core Features & Use Cases

  • Agent Creation: Learn to use create_agent() for building agents with LLMs, tools, and system prompts.
  • Tool Definition: Define custom functions as tools for agents using @tool (Python) or tool() (TypeScript).
  • Middleware Integration: Implement middleware for human-in-the-loop approvals, error handling, and custom logic.
  • Persistence: Configure checkpointers for maintaining conversation state and memory.
  • Structured Output: Ensure agents return data in a predictable, typed format.
  • Use Case: Develop an agent that can search the web, perform calculations, and ask for user confirmation before executing a critical action, ensuring both efficiency and safety.

Quick Start

Create a basic agent that can get the weather for a given location using the create_agent function and the @tool decorator.

Frequently Asked Questions about LangChain Fundamentals

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

FAQPage Schema
How do I create a LangChain agent with custom tools?▼

Create LangChain agents using the `create_agent` function, defining custom tools with the `@tool` decorator in Python or `tool()` in TypeScript to enable specific workflows and operations.

What is middleware in LangChain and when should I use it?▼

Middleware in LangChain provides advanced control flow for agents, enabling human-in-the-loop approvals, custom logic, and error handling during execution to ensure safe and reliable operation.

How do I add memory and persistence to a LangChain agent?▼

Add persistence to LangChain agents by configuring checkpointers, which maintain conversation state and memory across interactions for continuous, stateful workflows.

Why does my LangChain agent return untyped or incorrect results?▼

LangChain agents return untyped results when structured output is not configured, or when tool descriptions are missing, causing the agent to misinterpret available functions and access results incorrectly.

Can I stop a LangChain agent from running into infinite loops?▼

Yes, this Skill addresses common LangChain agent issues like infinite loops by guiding proper configuration of tools, persistence, and middleware to establish reliable execution boundaries.

How do I generate structured output from a LangChain agent?▼

Generate structured output from LangChain agents by configuring the agent to return data in a predictable, typed format, ensuring downstream applications receive consistent, structured responses.

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