langchain

Build LLM applications with chains, agents, memory, and RAG.

6|2|Updated Jan 22, 2026

Search Tags:#langchain #llm #rag #agents #orchestration #conversational ai

Ready to run

npx skills add https://github.com/fgarofalo56/Suppercharge_Microsoft_Fabric --skill langchain-fgarofalo56

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/fgarofalo56/Suppercharge_Microsoft_Fabric/tree/main/.github/skills/langchain
Command: npx skills add https://github.com/fgarofalo56/Suppercharge_Microsoft_Fabric --skill langchain-fgarofalo56

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

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

Core Features & Use Cases

LLM Orchestration: Create sophisticated LLM workflows using composable chains and agents.
Conversational AI: Implement memory systems for stateful interactions.
Tool Integration: Connect LLMs to external APIs and data sources via agents.
RAG Implementation: Build Retrieval-Augmented Generation systems for knowledge-based Q&A.

Quick Start

Install the core LangChain libraries and then use the ChatOpenAI class to interact with GPT-4o.

Frequently Asked Questions about langchain

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

FAQPage Schema

How do I build LLM apps with LangChain for conversational AI?▼

Build LLM apps with LangChain by using its framework to chain components, implement memory systems for stateful interactions, and create sophisticated conversational AI workflows.

What's the best way to implement Retrieval-Augmented Generation for document Q&A?▼

Implement Retrieval-Augmented Generation by using LangChain's framework to build RAG systems, enabling knowledge-based Q&A through document retrieval and LLM orchestration.

Do I need to install specific LLM provider packages to use LangChain?▼

Yes, using LangChain requires installing langchain, langchain-core, langchain-community, and specific LLM provider packages like langchain-openai, langchain-anthropic, or langchain-google-genai.

Can I connect LLMs to external APIs and data sources using agents?▼

Connect LLMs to external APIs and data sources by using LangChain agents, which facilitate tool integration for retrieving external information during LLM orchestration.

Does LangChain support vector stores like Chroma and Pinecone for RAG?▼

LangChain supports vector stores for RAG implementations through dependencies including langchain-chroma and langchain-pinecone, enabling document storage and retrieval.

How does LLM orchestration work with composable chains?▼

LLM orchestration with composable chains allows developers to create sophisticated LLM workflows by linking components and agents together for complex application logic.