langchain-fundamentals

Name: langchain-fundamentals
Availability: InStock
Author: Abdullah

Create and manage LangChain agents with create_agent, tools, and middleware.

Updated Apr 7, 2026

Search Tags:#middleware #langchain #agent development #structured output #tool definition

Ready to run

npx skills add https://github.com/abdullahhqaiser/langgraph_diligence --skill langchain-fundamentals-abdullahhqaiser

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain-fundamentals
Source: https://github.com/abdullahhqaiser/langgraph_diligence/tree/main/.agents/skills/langchain-fundamentals
Command: npx skills add https://github.com/abdullahhqaiser/langgraph_diligence --skill langchain-fundamentals-abdullahhqaiser

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill unit helps developers and AI professionals build complex LangChain agents efficiently, reducing the complexity and time involved in agent development.

Core Features & Use Cases

Agent Creation: Utilizes the create_agent() function to build agents with integrated tool execution and state management.
Middleware Support: Offers middleware for human-in-the-loop and error handling to ensure robust agent operation.
Tool Definition: Guides on defining tools using the @tool decorator and the tool() function.
Structured Output: Shows how to obtain typed, validated responses from agents for better data management.
Model Configuration: Covers how to configure different models and integrate custom settings.

Quick Start

To start building a LangChain agent, use the following command: 'create_agent(model="anthropic:claude-sonnet-4-5", tools=[get_weather], system_prompt="You are a helpful assistant.")'

Frequently Asked Questions about langchain-fundamentals

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

FAQPage Schema

How do I build a LangChain agent with tool execution and state management?▼

To build a LangChain agent with tool execution and state management, use the standardized `create_agent()` function, which integrates tools and manages state automatically. You specify the model, tools array, and system prompt to initialize the agent.

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

The best way to define custom tools for LangChain agents is by using the `@tool` decorator or the `tool()` function. These methods allow you to specify tool behavior and arguments cleanly before passing them into your agent configuration.

How does middleware work for human-in-the-loop and error handling in LangChain?▼

Middleware works in LangChain by intercepting the agent pipeline to insert human-in-the-loop approvals and manage error handling. This ensures robust operation by allowing external validation and graceful failure recovery during execution.

Can I get structured and validated output from a LangChain agent?▼

Yes, you can get structured and validated output from a LangChain agent. This functionality provides patterns for obtaining typed responses, ensuring better data management and allowing downstream applications to reliably parse results.

How do I configure different models for LangChain agent development?▼

You configure different models for LangChain agent development by passing the desired model identifier, such as `anthropic:claude-sonnet-4-5`, into the `create_agent()` function. This allows integration of custom settings and various model providers.

Do I need prior agent development experience to use this LangChain framework?▼

You do not need extensive prior agent development experience, but familiarity with Python and basic LangChain concepts is helpful. The framework reduces complexity through standardized functions, middleware, and tool definitions for efficient agent creation.