langchain

Name: langchain
Availability: InStock
Author: Alex

Build tool-calling agents and RAG pipelines with LangChain.

Updated May 2, 2026

Search Tags:#llm #rag #tool calling #agents #memory #langsmith #vector stores

Ready to run

npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langchain-qcmuu

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/qcmuu/AI-Research-Skills/tree/main/14-agents/langchain
Command: npx skills add https://github.com/qcmuu/AI-Research-Skills --skill langchain-qcmuu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, langchain-core, langchain-openai, langchain-anthropic, and includes references (resource) components.

What problem does it solve?

LangChain helps you design and ship LLM-powered applications that can reliably call tools and answer questions over your own documents, instead of producing responses without context.

Core Features & Use Cases

Agent orchestration with tool calling: Create ReAct-style and tool-calling agents that can plan, invoke tools, and synthesize results.
RAG pipelines for knowledge-grounded answers: Load documents, chunk them, embed them, retrieve relevant passages, and generate answers with source context.
Provider-agnostic LLM integration: Swap model providers (e.g., OpenAI/Anthropic/Google) while keeping your app logic stable.
Memory, streaming, and observability: Support multi-turn conversations, stream outputs for better UX, and use LangSmith tracing to debug and improve reliability.

Quick Start

Ask the AI to build a tool-calling LangChain agent that performs retrieval-augmented question answering over a provided document set.

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 for grounded question answering over my own documents?▼

To build a RAG pipeline, you load documents, chunk them with splitters, embed the chunks, store them in vector stores, and use retriever chains to generate answers with source context. This grounds responses in your specific data.

Can I create a tool-calling agent that plans and executes structured workflows?▼

Yes, you can create ReAct-style and tool-calling agents that plan, invoke tools, and synthesize results. This enables agents to execute structured workflows and reliably answer complex user queries.

Does LangChain support swapping model providers like OpenAI and Anthropic without changing app logic?▼

Yes, LangChain provides provider-agnostic LLM integration. You can swap model providers like OpenAI, Anthropic, or Google while keeping your application logic stable across different model implementations.

How do I debug and trace LLM application execution for reliability issues?▼

You can debug and trace LLM application execution using LangSmith tracing. This provides observability for your application, helping you monitor streaming execution, memory support, and improve overall reliability.

What is the best way to maintain context in multi-turn conversations with LLM apps?▼

The best way to maintain context in multi-turn conversations is by using built-in memory support. This allows your LLM application to retain conversation history and stream outputs for a better user experience.