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Harmeet10000
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langchain-fundamentals

Build LangChain agents with create_agent(), middleware, and tool definitions.

7|1|Updated Mar 15, 2026
Search Tags:#middleware#langchain#agents#tools#human-in-the-loop#create_agent
One-click install
npx skills add https://github.com/Harmeet10000/skills --skill langchain-fundamentals-harmeet10000
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/Harmeet10000/skills/tree/main/skills/ai-ml/langchain-fundamentals
Command: npx skills add https://github.com/Harmeet10000/skills --skill langchain-fundamentals-harmeet10000

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps developers rapidly assemble production-ready LangChain agents using create_agent(), define tools, and apply middleware for human-in-the-loop and error handling.

Core Features & Use Cases

  • Agent creation: Use create_agent() to manage the agent loop, tool execution, and state.
  • Tool definitions: Define tools with the @tool decorator (Python) or tool() (TypeScript) and expose clear descriptions for proper tool use.
  • Middleware patterns: Implement human-in-the-loop approvals, error handling, and custom hooks to control agent behavior.

Quick Start

Create an agent using create_agent() with a simple tool and a middleware setup to begin testing.

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema
How do I build LangChain agents with middleware for production workflows?▼

Build LangChain agents by using create_agent() to manage the agent loop and state, then apply middleware patterns for human-in-the-loop approvals and error handling to control automated workflows.

How does middleware work with LangChain agents for human-in-the-loop approvals?▼

Middleware intercepts the LangChain agent loop to enable human-in-the-loop approvals, error handling, and custom hooks, allowing developers to control tool execution and decision support flows before proceeding.

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

Define tools using the @tool decorator in Python or tool() in TypeScript, ensuring you provide clear descriptions for proper tool use by the agent during data retrieval and decision support tasks.

Can I use create_agent() for orchestrated data retrieval and automated workflows?▼

Yes, create_agent() manages the agent loop, tool execution, and state, making it suitable for teams needing orchestrated agents for data retrieval, decision support, and automated workflows.

Do I need external dependencies to implement error handling in LangChain agents?▼

No external dependencies are required; you implement error handling and custom hooks directly through middleware integration within the LangChain agent loop to control agent behavior.

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