langchain

Develop LLM applications with LangChain for chaining calls, prompts, agents, and retrieval.

3|1|Updated Feb 2, 2026

Search Tags:#python #ai development #langchain #llm #rag #agents

Ready to run

npx skills add https://github.com/HouseGarofalo/claude-code-base --skill langchain-housegarofalo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/HouseGarofalo/claude-code-base/tree/main/.claude/skills/langchain
Command: npx skills add https://github.com/HouseGarofalo/claude-code-base --skill langchain-housegarofalo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain-core, langchain-community, langchain-openai, langchain-anthropic, langchain-google-genai, langchain-chroma, langchain-pinecone, and includes references (resource) components.

What problem does it solve?

This Skill simplifies the development of complex Large Language Model (LLM) applications by providing a structured framework for chaining components, managing conversational memory, and integrating external tools.

Core Features & Use Cases

LLM Orchestration: Build sophisticated LLM workflows using LangChain Expression Language (LCEL).
Agent Development: Create intelligent agents capable of reasoning and tool use.
RAG Implementation: Easily implement Retrieval-Augmented Generation for context-aware responses.
Use Case: Develop a customer support chatbot that can access a knowledge base, understand conversation history, and provide accurate, context-aware answers.

Quick Start

Install the necessary LangChain libraries using pip.

Frequently Asked Questions about langchain

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

FAQPage Schema

How do I implement Retrieval-Augmented Generation for context-aware LLM responses?▼

To implement Retrieval-Augmented Generation (RAG) for context-aware responses, use LangChain to integrate document retrieval mechanisms like Chroma or Pinecone with your LLM workflow. This provides context-aware answers by chaining LLM calls with retrieved knowledge base content.

How do I chain multiple LLM calls and manage prompts for conversational AI?▼

You can chain multiple LLM calls and manage prompts using LangChain Expression Language (LCEL). This orchestration framework structures complex LLM workflows, manages conversational memory, and integrates external tools for sophisticated conversational AI applications.

Can I create autonomous agents that use tools with LangChain?▼

Yes, you can create intelligent agents capable of reasoning and tool use with LangChain. The framework provides specific components for agent development, allowing LLMs to autonomously interact with external tools and execute complex multi-step reasoning workflows.

Does LangChain support integrating models from OpenAI, Anthropic, and Google?▼

Yes, LangChain supports integrating models from OpenAI, Anthropic, and Google through dedicated provider packages. You install the specific integration libraries required, such as langchain-openai or langchain-google-genai, to connect your LLM application to these providers.

What Python packages do I need to install to start developing LLM apps with LangChain?▼

To start developing LLM apps with LangChain, you need to install the core langchain and langchain-core Python packages via pip. Depending on your use case, you also install specific provider and vector store integration packages like langchain-chroma.