langchain

Build AI applications with agents, chains, and RAG using LangChain.

Updated Apr 3, 2026

Search Tags:#rag #build #agents #tool-calling #chains #retrieval-augmented-generation #ai-apps

Ready to run

npx skills add https://github.com/handsomelong922/my-codex-skills --skill langchain-handsomelong922

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/handsomelong922/my-codex-skills/tree/main/skills/langchain
Command: npx skills add https://github.com/handsomelong922/my-codex-skills --skill langchain-handsomelong922

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

LangChain provides a comprehensive framework for building LLM-powered applications by orchestrating models, tools, memory, and retrieval-augmented workflows into reusable components for agents, chains, and RAG pipelines.

Core Features & Use Cases

Models, Chains, and Agents: Swap providers easily, build agents that reason and act, and create retrieval-augmented generation pipelines.
Memory, Observability, and RAG: Integrate memory management and observability with LangSmith to monitor and refine AI workflows.
Use Case Scenarios: Build chatbots, QA systems, and autonomous automation workflows that orchestrate tools and data.

Quick Start

Install LangChain, create an LLM client, and run a simple agent example to get started.

Frequently Asked Questions about langchain

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

FAQPage Schema

How do I build AI apps with agents, chains, and RAG?▼

Build AI apps with agents, chains, and RAG by orchestrating models, tools, memory, and retrieval-augmented workflows into reusable components. This framework enables rapid prototyping and production deployment of autonomous chatbots and QA systems.

Can I use tool-calling and memory management for autonomous agents?▼

Yes, tool-calling and memory management for autonomous agents are supported. You can build agents that reason and act, integrating external tools and retaining conversational context for automated workflows.

Does LangSmith observability work with RAG pipelines?▼

LangSmith observability integrates with RAG pipelines to monitor and refine AI workflows. It tracks model interactions and retrieval-augmented generation steps to improve application performance.

How do I create a retrieval-augmented generation pipeline for a QA system?▼

Create a retrieval-augmented generation pipeline for a QA system by connecting an LLM client with external data sources. The framework orchestrates data retrieval and model generation to produce accurate answers.

Do I need multiple LLM providers to build chatbots with chains?▼

No, you do not need multiple LLM providers to build chatbots with chains. The framework allows you to easily swap providers, so you can integrate a single LLM or switch between multiple models as needed.

What's the best way to orchestrate tools and data for autonomous automation?▼

The best way to orchestrate tools and data for autonomous automation is using an agent-based framework. Agents reason about tasks, call external tools, and leverage memory to execute complex workflows autonomously.