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langchain

Build LLM applications with LangChain JS/TS chains, agents, and RAG systems.

6|1|Updated Feb 27, 2026
Search Tags:#typescript#langchain#llm#rag#agents#chains
One-click install
npx skills add https://github.com/RepairYourTech/cfsa-antigravity --skill langchain-repairyourtech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: langchain
Source: https://github.com/RepairYourTech/cfsa-antigravity/tree/main/.agent/skill-library/stack/ai/langchain
Command: npx skills add https://github.com/RepairYourTech/cfsa-antigravity --skill langchain-repairyourtech

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain, @langchain/core, @langchain/openai, @langchain/anthropic, @langchain/community, zod, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a comprehensive toolkit for building sophisticated Large Language Model (LLM) applications, enabling developers to create intelligent agents, RAG systems, and complex conversational interfaces.

Core Features & Use Cases

  • Composable Chains: Build complex LLM workflows using LangChain Expression Language (LCEL).
  • Agents & Tools: Develop autonomous agents that can use external tools to perform tasks.
  • Retrieval-Augmented Generation (RAG): Integrate external data sources for more informed LLM responses.
  • Memory Management: Maintain conversation history for context-aware interactions.
  • Use Case: Create a customer support chatbot that can access a knowledge base, understand conversation history, and use tools to look up user information.

Quick Start

Use the langchain skill to build a simple chain that translates English to French.

Frequently Asked Questions about langchain

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

FAQPage Schema
How do I build RAG applications with TypeScript and LangChain?▼

Build RAG applications with LangChain by integrating external data sources into vector stores to generate informed LLM responses, utilizing TypeScript components for retrieval-augmented generation workflows.

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

Yes, you can create autonomous agents in LangChain JS that execute complex tasks by integrating custom external tools, enabling deterministic and sophisticated task execution within your LLM-powered applications.

Does this LangChain Skill support integrating multiple LLM providers like OpenAI and Anthropic?▼

Yes, this Skill supports integrating multiple LLM providers like OpenAI and Anthropic, enabling you to connect various models for building chains, agents, and conversational memory systems.

What is the best way to manage conversation history for LLM chatbots?▼

Manage conversation history for LLM chatbots using LangChain's memory management features to maintain context-aware interactions, ensuring the model recalls previous conversational inputs for accurate responses.

How do I compose complex LLM workflows using LangChain Expression Language?▼

Compose complex LLM workflows using LangChain Expression Language (LCEL) by building composable chains that connect various components and tools for deterministic, multi-step task execution in TypeScript.

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