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JosephRobles23
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langchain-fundamentals

Create LangChain agents with tools, middleware, and checkpointers.

Updated Aug 23, 2026
Search Tags:#middleware#langchain#ai#llm#agent#tool
One-click install
npx skills add https://github.com/JosephRobles23/Vora.IA --skill langchain-fundamentals-josephrobles23
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/JosephRobles23/Vora.IA/tree/main/backend/.windsurf/skills/langchain-fundamentals
Command: npx skills add https://github.com/JosephRobles23/Vora.IA --skill langchain-fundamentals-josephrobles23

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides the foundational knowledge and patterns for building robust AI agents using LangChain, focusing on modern best practices for agent creation, tool definition, and state management.

Core Features & Use Cases

  • Agent Creation: Learn to use create_agent() for building agent loops.
  • Tool Definition: Define custom tools using @tool (Python) or tool() (TypeScript).
  • State Persistence: Implement conversation memory with checkpointers.
  • Middleware: Integrate custom logic for human-in-the-loop and error handling.
  • Use Case: Develop a customer support agent that can access a knowledge base, perform web searches, and maintain conversation history for personalized interactions.

Quick Start

Use the langchain-fundamentals skill to create a basic agent with a search tool and a system prompt.

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-ready LangChain agents with custom tools?▼

Build production-ready LangChain agents using the `create_agent()` function to establish agent loops, and define custom tools using the `@tool` decorator in Python or `tool()` in TypeScript for advanced control flows.

How does middleware work for LangChain agent control flows?▼

Middleware integrates custom logic into LangChain agents to enable advanced control flows, allowing developers to implement human-in-the-loop interactions and robust error handling within the agent execution loop.

Can I persist conversation state and memory for LangChain agents?▼

Persist conversation state and memory for LangChain agents by implementing checkpointers, which maintain conversation history and enable personalized interactions across multiple user sessions.

What is the best way to generate structured output from an LLM agent?▼

Generate structured output from an LLM agent by configuring the agent creation patterns to enforce specific response formats, ensuring reliable data extraction and integration with downstream systems.

Does LangChain support human-in-the-loop agent execution?▼

LangChain supports human-in-the-loop agent execution through middleware integration, allowing developers to inject custom logic that pauses or modifies the agent loop for human review and intervention.

How do I create a customer support agent with web search and knowledge base access?▼

Create a customer support agent using `create_agent()`, defining web search and knowledge base tools with `@tool`, and implementing checkpointers to maintain conversation history for personalized interactions.

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