LangChain.js Agents: Building AI Workflows That Use Tools and Memory

LangChain provides the building blocks for AI agents that can reason, use tools, and maintain memory.
Here's a practical introduction with real code.

What LangChain Adds

Beyond calling chat.completions.create directly, LangChain gives you:

• Chains: Composable sequences of LLM calls
• Agents: LLMs that decide which tools to call
• Memory: Conversation history management
• Tools: Functions the LLM can invoke

Setup

npm install langchain @langchain/openai @langchain/core

Simple Chain

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

const model = new ChatOpenAI({ model: 'gpt-4o-mini', temperature: 0 })
const parser = new StringOutputParser()

const prompt = ChatPromptTemplate.fromTemplate(
  'Summarize this code review in one sentence:\n\n{code}'
)

const chain = prompt.pipe(model).pipe(parser)

const summary = await chain.invoke({
  code: 'function add(a, b) { return a + b }',
})

Tools

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

const searchTool = tool(
  async ({ query }) => {
    const results = await searchWeb(query)
    return results.map(r => r.snippet).join('\n')
  },
  {
    name: 'web_search',
    description: 'Search the web for current information',
    schema: z.object({
      query: z.string().describe('The search query'),
    }),
  }
)

const calculatorTool = tool(
  async ({ expression }) => {
    return String(eval(expression))
  },
  {
    name: 'calculator',
    description: 'Evaluate mathematical expressions',
    schema: z.object({
      expression: z.string().describe('Math expression to evaluate'),
    }),
  }
)

Agent with Tools

import { createReactAgent } from '@langchain/langgraph/prebuilt'

const agent = createReactAgent({
  llm: model,
  tools: [searchTool, calculatorTool],
})

const result = await agent.invoke({
  messages: [{
    role: 'user',
    content: 'What is the current price of Bitcoin and what is 10% of that?',
  }],
})

console.log(result.messages.at(-1)?.content)
// Agent: searches for BTC price, then uses calculator for 10%

Memory

import { ConversationSummaryBufferMemory } from 'langchain/memory'
import { ConversationChain } from 'langchain/chains'

const memory = new ConversationSummaryBufferMemory({
  llm: model,
  maxTokenLimit: 2000,  // summarize older messages
  returnMessages: true,
})

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

// Messages persist across calls
await chain.invoke({ input: 'My name is Atlas' })
const response = await chain.invoke({ input: 'What is my name?' })
// response: 'Your name is Atlas'

RAG with LangChain

import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter'
import { OpenAIEmbeddings } from '@langchain/openai'
import { MemoryVectorStore } from 'langchain/vectorstores/memory'
import { createRetrievalChain } from 'langchain/chains/retrieval'
import { createStuffDocumentsChain } from 'langchain/chains/combine_documents'

// Split documents into chunks
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 500,
  chunkOverlap: 50,
})
const docs = await splitter.createDocuments([longText])

// Create vector store
const vectorStore = await MemoryVectorStore.fromDocuments(
  docs,
  new OpenAIEmbeddings()
)

// Build RAG chain
const retriever = vectorStore.asRetriever(3)
const questionAnswerChain = await createStuffDocumentsChain({ llm: model, prompt })
const ragChain = await createRetrievalChain({
  retriever,
  combineDocsChain: questionAnswerChain,
})

const answer = await ragChain.invoke({ input: 'What does the document say about X?' })

Streaming

const stream = await chain.stream({ input: 'Explain quantum computing' })

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

LangChain supports streaming through the entire chain, including retrieval steps.

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