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langchain

Build end-to-end LLM applications with LangChain and LangGraph.

262|59|Updated Jan 7, 2026
Search Tags:#langchain#llm#rag#build#agent#orchestration#memory
One-click install
npx skills add https://github.com/hoodini/ai-agents-skills --skill langchain-hoodini
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/hoodini/ai-agents-skills/tree/main/skills/langchain
Command: npx skills add https://github.com/hoodini/ai-agents-skills --skill langchain-hoodini

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

LangChain and LangGraph streamline the development of sophisticated LLM applications by providing modular components for chaining, memory, and agent orchestration.

Core Features & Use Cases

  • End-to-end LLM app development with modular chains, agents, and graphs
  • RAG pipelines, LCEL-based orchestration, and memory integration
  • Use Case: Build a retrieval-augmented chatbot that queries a vector store, composes results with prompts, and maintains context across interactions

Quick Start

  • pip install langchain langchain-openai langchain-anthropic langgraph
  • from langchain_anthropic import ChatAnthropic
  • from langchain_core.prompts import ChatPromptTemplate
  • chain = ChatAnthropic(model="claude-3") | ChatPromptTemplate.from_template("Explain {topic} in simple terms.")

Frequently Asked Questions about langchain

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

FAQPage Schema
How do I build a RAG pipeline with LangChain and LangGraph?▼

You build a RAG pipeline by chaining LangChain components with LangGraph orchestration, using LCEL to compose vector store retrieval, prompt templates, and LLM generation for retrieval-augmented outputs.

What is the best way to maintain memory across LLM chat interactions?▼

The best way to maintain memory in LLM apps is using LangChain's built-in memory integration features, which preserve conversational context across sequential chat interactions within modular chains.

Can I use Claude models with LangChain for agent orchestration?▼

Yes, you can orchestrate LLM agents with Claude models by installing the langchain-anthropic package and instantiating the ChatAnthropic component within your LangGraph agent graphs.

Do I need Python and pip to set up LangChain LLM apps?▼

Yes, you need Python and pip to install the required packages like langchain, langchain-openai, langchain-anthropic, and langgraph to set up and run LangChain LLM applications.

How does LCEL orchestration work for composing LLM chains?▼

LCEL orchestration works by using the pipe operator to sequentially connect LangChain components, routing data outputs from prompts and models directly into downstream chain steps.

What is the difference between LangChain chains and LangGraph agent graphs?▼

LangChain chains execute linear LCEL component sequences, whereas LangGraph structures agent orchestration as interconnected graphs, enabling complex routing and stateful agent workflows.

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