LangChain.js Integration

Build LLM-powered applications using the LangChain.js framework. This skill helps you integrate LangChain into your projects, enabling you to chain together interoperable components, connect to multiple LLM providers, manage embeddings and vector stores, and build sophisticated AI applications.

What This Skill Does

This skill guides you through setting up and using LangChain.js to:

Installation Instructions

When a user requests LangChain.js integration, follow these steps:

1. **Verify Environment Compatibility**

- Check if the project uses a supported runtime:

- Node.js (20.x, 22.x, 24.x) with ESM or CommonJS

- Cloudflare Workers, Vercel/Next.js, Supabase Edge Functions

- Browser, Deno, or Bun

- For unsupported environments, inform the user of LangChain.js requirements

2. **Install LangChain.js**

- Detect the package manager in use (check for `package-lock.json`, `pnpm-lock.yaml`, `yarn.lock`, or `bun.lockb`)

- Install using the appropriate command:

```bash

npm install -S langchain

# or

pnpm install langchain

# or

yarn add langchain

# or

bun add langchain

```

3. **Set Up Basic Configuration**

- Create or update environment variables for API keys (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`)

- If the user hasn't specified a provider, ask which LLM provider they plan to use

- Install provider-specific packages if needed (e.g., `@langchain/openai`, `@langchain/anthropic`)

4. **Create Initial Implementation**

- Based on the user's requirements, implement one of these common patterns:

- **Simple LLM call**: Basic chat model integration

- **Chain**: Sequential operations with multiple components

- **RAG (Retrieval-Augmented Generation)**: Vector store + retriever + LLM

- **Agent**: Autonomous decision-making with tools

- Follow LangChain.js best practices for the chosen pattern

5. **Add Documentation**

- Add inline comments explaining the LangChain components used

- If creating new files, include brief header comments about the LangChain pattern implemented

- Reference official docs: https://docs.langchain.com/oss/javascript/langchain/overview

Example Usage Patterns

Basic Chat Model

```typescript

import { ChatOpenAI } from "@langchain/openai";

const model = new ChatOpenAI({

modelName: "gpt-4",

temperature: 0.7,

});

const response = await model.invoke("Hello, world!");

```

Simple Chain

```typescript

import { ChatOpenAI } from "@langchain/openai";

import { PromptTemplate } from "@langchain/core/prompts";

const prompt = PromptTemplate.fromTemplate("Tell me a joke about {topic}");

const model = new ChatOpenAI();

const chain = prompt.pipe(model);

const result = await chain.invoke({ topic: "programming" });

```

RAG Implementation

```typescript

import { ChatOpenAI } from "@langchain/openai";

import { OpenAIEmbeddings } from "@langchain/openai";

import { MemoryVectorStore } from "langchain/vectorstores/memory";

import { createRetrievalChain } from "langchain/chains/retrieval";

const embeddings = new OpenAIEmbeddings();

const vectorStore = await MemoryVectorStore.fromTexts(

["LangChain helps build LLM apps", "Vector stores enable semantic search"],

[{}, {}],

embeddings

);

const retriever = vectorStore.asRetriever();

const model = new ChatOpenAI();

// Use retriever with model for RAG

```

Advanced Features

When users need more sophisticated capabilities:

Important Constraints

Related Resources