LangChain.js

LangChain.js is a comprehensive TypeScript framework for building applications powered by large language models. It provides standardized interfaces for connecting LLMs with diverse data sources, tools, and external systems through a modular architecture.

When to Use

Building AI agents with tool-calling capabilities
Creating chatbots with conversation memory
Implementing Retrieval Augmented Generation (RAG) systems
Connecting LLMs to external data sources and APIs
Building chains of LLM operations
Switching between AI providers without code changes
Streaming LLM responses in real-time
Implementing structured output from LLMs
Creating document Q&A systems
Building semantic search applications

Core Concepts

Agents

Autonomous entities that use LLMs to decide which actions to take. Agents can call tools, access memory, and orchestrate complex workflows.

Chains

Sequences of operations that process inputs through multiple steps. Chains can combine prompts, LLM calls, and post-processing.

Tools

Functions that agents can call to interact with external systems (APIs, databases, web search, etc.).

Memory

Short-term and long-term context management for maintaining conversation state and persistent information.

Retrieval

Integration with vector stores and retrievers for finding relevant documents and context.

Messages

Structured communication format for chat-based interactions (system, human, AI, tool messages).

Structured Output

Constraining LLM responses to specific formats and schemas using Zod or JSON Schema.

Installation

Core Packages

bash

# Install core packages
npm install langchain @langchain/core

# Or with other package managers
pnpm install langchain @langchain/core
yarn add langchain @langchain/core
bun add langchain @langchain/core

Requirement: Node.js 20+

Provider Packages

Install provider-specific packages as needed:

bash

# OpenAI
npm install @langchain/openai

# Anthropic
npm install @langchain/anthropic

# Google
npm install @langchain/google-genai

# AWS Bedrock
npm install @langchain/aws

# Azure OpenAI
npm install @langchain/azure-openai

# Mistral
npm install @langchain/mistralai

# Cohere
npm install @langchain/cohere

# Ollama (local models)
npm install @langchain/ollama

Package Structure

LangChain.js is organized as a monorepo with specialized packages:

Package	Purpose
`langchain`	Main entry point, high-level abstractions
`@langchain/core`	Base interfaces and foundational abstractions
`@langchain/community`	Community-contributed integrations
`@langchain/textsplitters`	Text chunking utilities
`@langchain/openai`	OpenAI integration
`@langchain/anthropic`	Anthropic Claude integration
`@langchain/google-genai`	Google AI integration
`@langchain/mcp-adapters`	Model Context Protocol adapters

Supported Environments

Node.js (ESM/CommonJS) - versions 20.x, 22.x, 24.x
Cloudflare Workers
Vercel/Next.js (all execution contexts)
Supabase Edge Functions
Modern browsers
Deno
Bun

Basic Usage

Chat Models

typescript

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

// Initialize chat model
const model = new ChatOpenAI({
  modelName: "gpt-4",
  temperature: 0.7,
});

// Simple invocation
const response = await model.invoke("What is the capital of France?");
console.log(response.content);

// With message array
import { HumanMessage, SystemMessage } from "@langchain/core/messages";

const messages = [
  new SystemMessage("You are a helpful assistant."),
  new HumanMessage("What is the capital of France?"),
];

const result = await model.invoke(messages);

Using Anthropic

typescript

import { ChatAnthropic } from "@langchain/anthropic";

const model = new ChatAnthropic({
  modelName: "claude-sonnet-4-20250514",
  temperature: 0,
});

const response = await model.invoke("Explain quantum computing in simple terms.");

Streaming Responses

typescript

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

const model = new ChatOpenAI({
  modelName: "gpt-4",
  streaming: true,
});

// Stream tokens
const stream = await model.stream("Write a poem about coding.");

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}

Prompt Templates

typescript

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

// Create template
const prompt = ChatPromptTemplate.fromMessages([
  ["system", "You are a {role} expert."],
  ["human", "{question}"],
]);

// Format with variables
const formattedPrompt = await prompt.format({
  role: "Python",
  question: "How do I read a file?",
});

// Or chain with model
const chain = prompt.pipe(model);
const response = await chain.invoke({
  role: "Python",
  question: "How do I read a file?",
});

Template Variables

typescript

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

const template = PromptTemplate.fromTemplate(
  "Translate the following to {language}: {text}"
);

const result = await template.format({
  language: "Spanish",
  text: "Hello, world!",
});

Structured Output

With Zod Schema

typescript

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

const model = new ChatOpenAI({
  modelName: "gpt-4",
});

// Define schema
const Person = z.object({
  name: z.string().describe("The person's name"),
  age: z.number().describe("The person's age"),
  occupation: z.string().describe("The person's job"),
});

// Use withStructuredOutput
const structuredModel = model.withStructuredOutput(Person);

const result = await structuredModel.invoke(
  "Extract info: John is a 30 year old software engineer."
);

console.log(result);
// { name: "John", age: 30, occupation: "software engineer" }

JSON Mode

typescript

const model = new ChatOpenAI({
  modelName: "gpt-4-turbo",
  modelKwargs: { response_format: { type: "json_object" } },
});

Tools and Function Calling

Defining Tools

typescript

import { tool } from "@langchain/core/tools";
import { z } from "zod";

// Define a tool with Zod schema
const weatherTool = tool(
  async ({ location }) => {
    // Actual API call would go here
    return `The weather in ${location} is sunny, 72°F`;
  },
  {
    name: "get_weather",
    description: "Get the current weather for a location",
    schema: z.object({
      location: z.string().describe("The city and state, e.g. San Francisco, CA"),
    }),
  }
);

// Bind tools to model
const modelWithTools = model.bindTools([weatherTool]);

Using Tools with Agents

typescript

import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const model = new ChatOpenAI({ modelName: "gpt-4" });

const tools = [weatherTool, searchTool, calculatorTool];

// Create ReAct agent
const agent = createReactAgent({
  llm: model,
  tools: tools,
});

// Run agent
const result = await agent.invoke({
  messages: [{ role: "user", content: "What's the weather in NYC?" }],
});

Building Agents

Simple Agent (Under 10 Lines)

typescript

import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";

const model = new ChatOpenAI({ modelName: "gpt-4" });
const tools = [new TavilySearchResults()];

const agent = createReactAgent({ llm: model, tools });

const response = await agent.invoke({
  messages: [{ role: "user", content: "Search for LangChain news" }],
});

Agent with Memory

typescript

import { MemorySaver } from "@langchain/langgraph";

const memory = new MemorySaver();

const agent = createReactAgent({
  llm: model,
  tools: tools,
  checkpointSaver: memory,
});

// First conversation
const config = { configurable: { thread_id: "conversation-1" } };

await agent.invoke(
  { messages: [{ role: "user", content: "My name is Alice" }] },
  config
);

// Agent remembers context
await agent.invoke(
  { messages: [{ role: "user", content: "What's my name?" }] },
  config
);
// Response: "Your name is Alice"

Chains

Simple Chain

typescript

import { ChatOpenAI } from "@langchain/openai";
import { ChatPromptTemplate } from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

const model = new ChatOpenAI();
const prompt = ChatPromptTemplate.fromTemplate("Tell me a joke about {topic}");
const outputParser = new StringOutputParser();

// Create chain using pipe
const chain = prompt.pipe(model).pipe(outputParser);

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

Runnable Sequence

typescript

import { RunnableSequence } from "@langchain/core/runnables";

const chain = RunnableSequence.from([
  {
    topic: (input) => input.topic,
    language: (input) => input.language,
  },
  prompt,
  model,
  outputParser,
]);

const result = await chain.invoke({
  topic: "cats",
  language: "French",
});

Parallel Execution

typescript

import { RunnableParallel } from "@langchain/core/runnables";

const parallel = RunnableParallel.from({
  joke: jokeChain,
  poem: poemChain,
  fact: factChain,
});

const results = await parallel.invoke({ topic: "space" });
// { joke: "...", poem: "...", fact: "..." }

RAG (Retrieval Augmented Generation)

Document Loading

typescript

import { TextLoader } from "langchain/document_loaders/fs/text";
import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf";
import { WebLoader } from "langchain/document_loaders/web/cheerio";

// Load text file
const textLoader = new TextLoader("./document.txt");
const textDocs = await textLoader.load();

// Load PDF
const pdfLoader = new PDFLoader("./document.pdf");
const pdfDocs = await pdfLoader.load();

// Load web page
const webLoader = new WebLoader("https://example.com/article");
const webDocs = await webLoader.load();

Text Splitting

typescript

import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
});

const chunks = await splitter.splitDocuments(docs);

Vector Store

typescript

import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "langchain/vectorstores/memory";

const embeddings = new OpenAIEmbeddings();

// Create vector store
const vectorStore = await MemoryVectorStore.fromDocuments(
  chunks,
  embeddings
);

// Search for similar documents
const results = await vectorStore.similaritySearch(
  "What is the main topic?",
  3  // Return top 3 results
);

RAG Chain

typescript

import { ChatOpenAI } from "@langchain/openai";
import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
import { createRetrievalChain } from "langchain/chains/retrieval";

const model = new ChatOpenAI();

// Create retriever from vector store
const retriever = vectorStore.asRetriever();

// Create RAG prompt
const ragPrompt = ChatPromptTemplate.fromTemplate(`
Answer the question based on the following context:

Context: {context}

Question: {input}
`);

// Build RAG chain
const combineDocsChain = await createStuffDocumentsChain({
  llm: model,
  prompt: ragPrompt,
});

const ragChain = await createRetrievalChain({
  retriever,
  combineDocsChain,
});

// Query
const response = await ragChain.invoke({
  input: "What is discussed in the document?",
});

console.log(response.answer);

Memory

Conversation Buffer Memory

typescript

import { BufferMemory } from "langchain/memory";
import { ConversationChain } from "langchain/chains";

const memory = new BufferMemory();

const chain = new ConversationChain({
  llm: model,
  memory: memory,
});

await chain.call({ input: "Hi, I'm Bob" });
await chain.call({ input: "What's my name?" });
// Remembers: "Your name is Bob"

Message History

typescript

import { ChatMessageHistory } from "@langchain/community/stores/message/in_memory";

const messageHistory = new ChatMessageHistory();

await messageHistory.addUserMessage("Hello!");
await messageHistory.addAIMessage("Hi there! How can I help?");

const messages = await messageHistory.getMessages();

Callbacks and Streaming

Custom Callbacks

typescript

import { BaseCallbackHandler } from "@langchain/core/callbacks/base";

class MyHandler extends BaseCallbackHandler {
  name = "MyHandler";

  async handleLLMStart(llm, prompts) {
    console.log("LLM starting with prompts:", prompts);
  }

  async handleLLMEnd(output) {
    console.log("LLM finished:", output);
  }

  async handleLLMError(error) {
    console.error("LLM error:", error);
  }
}

const model = new ChatOpenAI({
  callbacks: [new MyHandler()],
});

Streaming with Callbacks

typescript

const model = new ChatOpenAI({
  streaming: true,
  callbacks: [
    {
      handleLLMNewToken(token) {
        process.stdout.write(token);
      },
    },
  ],
});

await model.invoke("Write a story about a robot.");

Output Parsers

String Parser

typescript

import { StringOutputParser } from "@langchain/core/output_parsers";

const parser = new StringOutputParser();
const chain = prompt.pipe(model).pipe(parser);

JSON Parser

typescript

import { JsonOutputParser } from "@langchain/core/output_parsers";

const parser = new JsonOutputParser();

List Parser

typescript

import { CommaSeparatedListOutputParser } from "@langchain/core/output_parsers";

const parser = new CommaSeparatedListOutputParser();
const chain = prompt.pipe(model).pipe(parser);

const result = await chain.invoke({ topic: "colors" });
// ["red", "blue", "green", ...]

LangGraph Integration

LangChain agents are built on top of LangGraph for advanced orchestration:

typescript

import { StateGraph, END } from "@langchain/langgraph";

// Define state
interface AgentState {
  messages: BaseMessage[];
  next: string;
}

// Create graph
const graph = new StateGraph<AgentState>({
  channels: {
    messages: { value: (a, b) => [...a, ...b] },
    next: { value: (_, b) => b },
  },
});

// Add nodes
graph.addNode("agent", agentNode);
graph.addNode("tools", toolsNode);

// Add edges
graph.addEdge("agent", "tools");
graph.addConditionalEdges("tools", shouldContinue);

// Compile
const app = graph.compile();

LangSmith Integration

Monitor and debug LLM applications:

typescript

// Set environment variables
process.env.LANGCHAIN_TRACING_V2 = "true";
process.env.LANGCHAIN_API_KEY = "your-api-key";
process.env.LANGCHAIN_PROJECT = "my-project";

// All LangChain operations are now traced
const result = await chain.invoke({ input: "Hello" });
// View traces at smith.langchain.com

Common Integrations

Vector Stores

typescript

// Pinecone
import { Pinecone } from "@pinecone-database/pinecone";
import { PineconeStore } from "@langchain/pinecone";

// Chroma
import { Chroma } from "@langchain/community/vectorstores/chroma";

// Supabase
import { SupabaseVectorStore } from "@langchain/community/vectorstores/supabase";

// Weaviate
import { WeaviateStore } from "@langchain/weaviate";

// Qdrant
import { QdrantVectorStore } from "@langchain/qdrant";

Document Loaders

typescript

// File loaders
import { TextLoader } from "langchain/document_loaders/fs/text";
import { JSONLoader } from "langchain/document_loaders/fs/json";
import { CSVLoader } from "@langchain/community/document_loaders/fs/csv";

// Web loaders
import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio";
import { PlaywrightWebBaseLoader } from "@langchain/community/document_loaders/web/playwright";

// API loaders
import { NotionLoader } from "@langchain/community/document_loaders/web/notion";
import { GitHubLoader } from "@langchain/community/document_loaders/web/github";

Tools

typescript

// Search
import { TavilySearchResults } from "@langchain/community/tools/tavily_search";
import { SerpAPI } from "@langchain/community/tools/serpapi";

// Code execution
import { PythonREPL } from "@langchain/community/tools/python";

// APIs
import { WikipediaQueryRun } from "@langchain/community/tools/wikipedia";
import { Calculator } from "@langchain/community/tools/calculator";

Environment Variables

bash

# OpenAI
OPENAI_API_KEY=sk-...

# Anthropic
ANTHROPIC_API_KEY=sk-ant-...

# Google
GOOGLE_API_KEY=...

# LangSmith (tracing)
LANGCHAIN_TRACING_V2=true
LANGCHAIN_API_KEY=...
LANGCHAIN_PROJECT=my-project

# Vector stores
PINECONE_API_KEY=...
PINECONE_ENVIRONMENT=...

Project Statistics

16.7k GitHub stars
3k forks
1,055 contributors
7,264 commits
548+ releases
48.9k dependent projects
95.6% TypeScript

Resources

Repository: https://github.com/langchain-ai/langchainjs
Documentation: https://docs.langchain.com/oss/javascript/langchain/overview
LangSmith: https://smith.langchain.com
LangGraph: https://langchain-ai.github.io/langgraphjs/
Discord: https://discord.gg/langchain
License: MIT

Best Practices

Model Selection

Use gpt-4 or claude-sonnet-4-20250514 for complex reasoning
Use gpt-3.5-turbo or claude-haiku for simple tasks (cost-effective)
Use streaming for better UX in chat applications

Memory Management

Use MemorySaver for conversation persistence
Clear memory when starting new topics
Consider token limits when storing history

RAG Optimization

Chunk documents appropriately (1000-2000 chars)
Use overlap (10-20% of chunk size)
Rerank results for better relevance
Consider hybrid search (semantic + keyword)

Error Handling

typescript

try {
  const result = await chain.invoke(input);
} catch (error) {
  if (error.message.includes("rate limit")) {
    // Implement retry with backoff
  } else if (error.message.includes("context length")) {
    // Reduce input size
  }
}

Testing

typescript

// Use LangSmith for evaluation
import { evaluate } from "langsmith/evaluation";

await evaluate(
  (input) => chain.invoke(input),
  {
    data: "my-dataset",
    evaluators: [accuracy, relevance],
  }
);

Troubleshooting

"API key not found"

bash

export OPENAI_API_KEY=sk-...
# Or set in code
const model = new ChatOpenAI({ openAIApiKey: "sk-..." });

"Context length exceeded"

Reduce input size
Use text splitter for long documents
Implement summarization for conversation history

"Rate limit exceeded"

Implement exponential backoff
Use caching for repeated queries
Consider batch processing

"Module not found"

bash

# Install specific provider package
npm install @langchain/openai

# Or community package
npm install @langchain/community

Search AI Tools

Install this agent skill to your Project

SKILL.md

LangChain.js

When to Use

Core Concepts

Agents

Chains

Tools

Memory

Retrieval

Messages

Structured Output

Installation

Core Packages

Provider Packages

Package Structure

Supported Environments

Basic Usage

Chat Models

Using Anthropic

Streaming Responses

Prompt Templates

Template Variables

Structured Output

With Zod Schema

JSON Mode

Tools and Function Calling

Defining Tools

Using Tools with Agents

Building Agents

Simple Agent (Under 10 Lines)

Agent with Memory

Chains

Simple Chain

Runnable Sequence

Parallel Execution

RAG (Retrieval Augmented Generation)

Document Loading

Text Splitting

Vector Store

RAG Chain

Memory

Conversation Buffer Memory

Message History

Callbacks and Streaming

Custom Callbacks

Streaming with Callbacks

Output Parsers

String Parser

JSON Parser

List Parser

LangGraph Integration

LangSmith Integration

Common Integrations

Vector Stores

Document Loaders

Tools

Environment Variables

Project Statistics

Resources

Best Practices

Model Selection

Memory Management

RAG Optimization

Error Handling

Testing

Troubleshooting

"API key not found"

"Context length exceeded"

"Rate limit exceeded"

"Module not found"