langchain-fundamentals

Creates and configures LangChain agents with tools, middleware, checkpointers, and typed outputs.

3|Updated Jan 20, 2026

Search Tags:#middleware #langchain #llm #agents #orchestration #tools

Ready to run

npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langchain-fundamentals-sameeh07

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/Sameeh07/AGENT-SKILLS/tree/main/skills/langchain-fundamentals
Command: npx skills add https://github.com/Sameeh07/AGENT-SKILLS --skill langchain-fundamentals-sameeh07

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides the essential patterns and tools for building robust, production-ready agents using the LangChain framework, enabling complex task automation and intelligent workflows.

Core Features & Use Cases

Agent Creation: Utilize create_agent() for streamlined agent construction.
Tool Definition: Define custom tools using @tool (Python) or tool() (TypeScript).
Middleware Integration: Implement human-in-the-loop approval and custom logic with middleware.
State Persistence: Ensure conversation memory with checkpointers and thread IDs.
Structured Output: Obtain typed, 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, ensuring both efficiency and safety.

Quick Start

Use the langchain-fundamentals skill to create an agent that can search the web and answer questions.

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 with human-in-the-loop approval?▼

Define LangChain tools using the `@tool` decorator in Python or the `tool()` function in TypeScript. These methods allow you to wrap custom functions, enabling your agent to execute specific automated tasks like web searches or calculations.

How does state persistence work for LangChain agents?▼

State persistence for LangChain agents works by utilizing checkpointers and thread IDs. This mechanism ensures conversation memory is maintained across interactions, allowing the agent to remember previous context and continue multi-turn dialogues accurately.

What is the best way to get typed, validated responses from an LLM agent?▼

The best way to get typed, validated responses from an LLM agent is to use structured output generation. This feature forces the agent to return responses formatted to specific typed schemas, ensuring data consistency for downstream application logic.

Does LangChain middleware support custom error handling for agent workflows?▼

Yes, LangChain middleware supports custom error handling for agent workflows. Middleware allows you to implement advanced control flows, intercepting runtime exceptions and applying alternative logic or fallback mechanisms during tool execution.

Can I integrate web search and calculation tools into a single LangChain agent?▼

Yes, you can integrate web search and calculation tools into a single LangChain agent. By defining multiple custom tools and attaching them to the agent, it can orchestrate complex task automation, search the web, and perform calculations simultaneously.