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langchain

Build LLM chains, agents, and RAG systems with LangChain and LangGraph.

Updated Feb 2, 2026
Search Tags:#langchain#llm#rag#ai applications#agents#langgraph
One-click install
npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill langchain-jnzader-vault
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/JNZader-Vault/project-starter-framework/tree/main/.ai-config/skills/data-ai/langchain
Command: npx skills add https://github.com/JNZader-Vault/project-starter-framework --skill langchain-jnzader-vault

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain-openai, langchain-community, langgraph, langsmith, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill simplifies the development of complex Large Language Model (LLM) applications by providing pre-built components and patterns for chains, agents, and Retrieval Augmented Generation (RAG).

Core Features & Use Cases

  • Chains: Create sequential or complex data processing pipelines with LLMs.
  • Agents: Enable LLMs to use tools and make decisions to accomplish tasks.
  • RAG: Integrate external knowledge bases for more informed LLM responses.
  • Use Case: Develop a customer support chatbot that can access a knowledge base, understand user queries, and take actions like updating a ticket status.

Quick Start

Use the langchain skill to create a basic chain that analyzes input data and returns a structured output.

Frequently Asked Questions about langchain

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

FAQPage Schema
How do I build LLM applications with agents and RAG using LangChain?▼

LangChain lets you build LLM applications by providing pre-built components and patterns to construct chains, agents with tools, and RAG systems. This simplifies complex AI-driven workflows without needing to write everything from scratch.

What is the best way to create an AI chatbot that can access a knowledge base and update tickets?▼

The best way to create such an AI chatbot is using LangChain and LangGraph frameworks to develop an agent with RAG capabilities. This allows the LLM to access external knowledge, understand queries, and use tools to take actions like updating ticket statuses.

Do I need LangSmith to trace and evaluate my LLM application workflows?▼

Yes, LangSmith is required for tracing and feedback in LLM applications built with this Skill. It works alongside LangChain, LangChain-OpenAI, LangChain-Community, and LangGraph to support complex AI-driven workflows and decision-making processes.

Can I use LangGraph to build sequential data processing pipelines with LLMs?▼

Yes, you can create sequential or complex data processing pipelines with LLMs using chains. The Skill facilitates building these pipelines alongside agents and RAG systems, using LangGraph to manage the complex AI-driven workflows.

How do agents with tools make decisions in LangChain?▼

Agents in LangChain enable LLMs to use external tools and make decisions to accomplish specific tasks. This mechanism allows the LLM to process information, interact with external systems, and execute actions autonomously within complex AI workflows.

When do I need Retrieval Augmented Generation for my LLM application?▼

You need Retrieval Augmented Generation (RAG) when your LLM application requires integration with external knowledge bases for more informed responses. RAG allows the model to access and reference specific data outside its original training corpus.

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