LangChain overview for Node.js

LangChain.js is a framework for LLM applications in TypeScript and Node.js. It standardizes how you wire prompts, models, tools, document s, embeddings, and retrievers into reusable pipelines and agents.

Project	Role

createAgent

, s, retrieversUse Deep Agents for complex multi-step tasks out of the box. Use LangChain's createAgent

when you want a minimal harness you compose with middleware. Reach for LangGraph when you need custom stateful workflows, branching, or fine-grained control over the agent loop.

Install the core packages first (install guide):

npm i langchain @langchain/core @langchain/openai zod

Provider-specific integrations live in separate packages:

langchain

• createAgent

, tool

, and high-level chain helperszod

• tool input schemas when defining tools with tool()

@langchain/core

• prompts, output parsers, Runnable interface, LCEL@langchain/openai

• ChatOpenAI

, OpenAIEmbeddings

@langchain/textsplitters

• document chunking (used in the For raw API access, see the Chat Completions and OpenAI Responses API posts. For provider-agnostic text and agents, see the Vercel AI SDK and OpenAI Agents SDK posts.

Tool	Best for
Raw openai package
Minimal calls, full control, least abstraction

generateText

, streaming, embeddings, tool loopscreateAgent

, swappable vector storesReach for LangChain when RAG or multi-step LLM pipelines grow beyond a few manual API calls.

langchain

, @langchain/core

, @langchain/openai

, and zod

installedOPENAI_API_KEY

set in the environmentDocument - a chunk of text with optional metadata. s produce Document

instances; splitters break long sources into retrieval-friendly pieces.

import { Document } from '@langchain/core/documents';

const doc = new Document({
  pageContent: 'LangChain helps compose LLM pipelines.',
  metadata: { source: 'intro' }
});

Runnable - any component with .invoke()

, .stream()

, or .batch()

. Prompts, models, parsers, and composed chains are all Runnables.

LCEL (LangChain Expression Language) - chain Runnables with .pipe()

. Data flows left to right: prompt → model → parser. The same .invoke()

, .stream()

, and .batch()

interface applies to every Runnable in the chain.

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

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'Answer in one sentence.'],
  ['human', '{question}']
]);

const model = new ChatOpenAI({ model: 'gpt-5.5' });
const chain = prompt.pipe(model).pipe(new StringOutputParser());

const answer = await chain.invoke({ question: 'What is LangChain?' });
console.log(answer);

Agents - LangChain's current high-level agent API is createAgent. Pass a model string or chat model, optional tools (with

zod

schemas), and an optional checkpointer for conversation memory (@langchain/langgraph

):

import { createAgent } from 'langchain';

const agent = createAgent({
  model: 'gpt-5.5',
  tools: []
});

const result = await agent.invoke({
  messages: [{ role: 'user', content: 'What is LangChain?' }]
});

@langchain/langgraph

thread_id

; long-term memory via createAgent

with tools and middleware; for production agents you may also prefer the LANGSMITH_TRACING=true

); optional The same LCEL chain supports streaming and batch invocation:

for await (const chunk of await chain.stream({ question: 'What is LCEL?' })) {
  process.stdout.write(chunk);
}

const answers = await chain.batch([
  { question: 'What is a Runnable?' },
  { question: 'What is a retriever?' }
]);

Runnable LCEL scripts for this post live in the langchain-overview-nodejs-demo

folder. Get access via code demos.

source & further reading

dev.to — original article PyPI Supply Chain, OWASP LLM Top 10, & eBPF Cloud-Native Security Claude LLM Execution Harnesses, RAG Rerank, & Browser-based Edge AI Local Inference Powers Browser Sign Language, Open-Source Agent Infra, & AI Engineering Guides