langchain-workflow-builder

name	langchain-workflow-builder
description	Builds LLM applications with LangChain including chains, agents, memory, tools, and RAG pipelines. Use when users request "LangChain setup", "LLM chain", "AI workflow", "conversational AI", or "RAG pipeline".

LangChain Workflow Builder

Build powerful LLM applications with chains, agents, and retrieval-augmented generation.

Core Workflow

1. Setup LangChain: Install and configure
2. Create chains: Build processing pipelines
3. Add memory: Enable conversation context
4. Define tools: Extend agent capabilities
5. Implement RAG: Add knowledge retrieval
6. Deploy: Production-ready setup

Installation

npm install langchain @langchain/openai @langchain/community

Basic Chains

Simple LLM Chain

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

const model = new ChatOpenAI({
  modelName: 'gpt-4-turbo-preview',
  temperature: 0.7,
});

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'You are a helpful assistant that {task}.'],
  ['human', '{input}'],
]);

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

// Usage
const result = await chain.invoke({
  task: 'summarizes text concisely',
  input: 'Summarize this article: ...',
});

Sequential Chain

// chains/sequential.ts
import { RunnableSequence } from '@langchain/core/runnables';

// Chain 1: Extract key points
const extractChain = ChatPromptTemplate.fromMessages([
  ['system', 'Extract the key points from the following text.'],
  ['human', '{text}'],
]).pipe(model).pipe(new StringOutputParser());

// Chain 2: Summarize key points
const summarizeChain = ChatPromptTemplate.fromMessages([
  ['system', 'Create a brief summary from these key points.'],
  ['human', '{keyPoints}'],
]).pipe(model).pipe(new StringOutputParser());

// Combined chain
const fullChain = RunnableSequence.from([
  {
    keyPoints: extractChain,
    originalText: (input) => input.text,
  },
  {
    summary: summarizeChain,
    keyPoints: (input) => input.keyPoints,
  },
]);

const result = await fullChain.invoke({ text: 'Long article...' });

Branching Chain

// chains/branching.ts
import { RunnableBranch } from '@langchain/core/runnables';

const classifyChain = ChatPromptTemplate.fromMessages([
  ['system', 'Classify the query as: question, complaint, or feedback'],
  ['human', '{query}'],
]).pipe(model).pipe(new StringOutputParser());

const questionChain = ChatPromptTemplate.fromMessages([
  ['system', 'Answer this question helpfully.'],
  ['human', '{query}'],
]).pipe(model).pipe(new StringOutputParser());

const complaintChain = ChatPromptTemplate.fromMessages([
  ['system', 'Respond empathetically to this complaint.'],
  ['human', '{query}'],
]).pipe(model).pipe(new StringOutputParser());

const feedbackChain = ChatPromptTemplate.fromMessages([
  ['system', 'Thank the user for their feedback.'],
  ['human', '{query}'],
]).pipe(model).pipe( ());

 routingChain = .([
  {
    : classifyChain,
    :  input.,
  },
  .([
    [ input..(), questionChain],
    [ input..(), complaintChain],
    feedbackChain, 
  ]),
]);

Memory & Conversation

Buffer Memory

// memory/conversation.ts
import { BufferMemory } from 'langchain/memory';
import { ConversationChain } from 'langchain/chains';

const memory = new BufferMemory({
  returnMessages: true,
  memoryKey: 'history',
});

const chain = new ConversationChain({
  llm: model,
  memory,
  prompt: ChatPromptTemplate.fromMessages([
    ['system', 'You are a helpful assistant.'],
    new MessagesPlaceholder('history'),
    ['human', '{input}'],
  ]),
});

// Conversation maintains context
await chain.invoke({ input: 'My name is Alice' });
await chain.invoke({ input: 'What is my name?' }); // Remembers Alice

Window Memory

// memory/window.ts
import { BufferWindowMemory } from 'langchain/memory';

const memory = new BufferWindowMemory({
  k: 5, // Keep last 5 exchanges
  returnMessages: true,
  memoryKey: 'history',
});

Summary Memory

// memory/summary.ts
import { ConversationSummaryMemory } from 'langchain/memory';

const memory = new ConversationSummaryMemory({
  llm: model,
  memoryKey: 'history',
});
// Summarizes conversation to save tokens

Persistent Memory with Redis

// memory/redis.ts
import { BufferMemory } from 'langchain/memory';
import { RedisChatMessageHistory } from '@langchain/community/stores/message/redis';

const memory = new BufferMemory({
  chatHistory: new RedisChatMessageHistory({
    sessionId: `user:${userId}:session:${sessionId}`,
    client: redisClient,
    ttl: 3600, // 1 hour
  }),
  returnMessages: true,
  memoryKey: 'history',
});

Tools & Agents

Define Custom Tools

// tools/custom.ts
import { DynamicTool, DynamicStructuredTool } from '@langchain/core/tools';
import { z } from 'zod';

// Simple tool
const searchTool = new DynamicTool({
  name: 'search',
  description: 'Search the web for information',
  func: async (query: string) => {
    const results = await searchAPI.search(query);
    return JSON.stringify(results);
  },
});

// Structured tool with schema
const calculatorTool = new DynamicStructuredTool({
  name: 'calculator',
  description: 'Perform mathematical calculations',
  schema: z.object({
    expression: z.string().describe('Mathematical expression to evaluate'),
  }),
  func: async ({ expression }) => {
    try {
      const result = eval(expression); // Use safer math parser in production
      return String(result);
    }  {
       ;
    }
  },
});


 dbQueryTool =  ({
  : ,
  : ,
  : z.({
    : z.([, , ]),
    : z.(z.()).(),
    : z.().(),
  }),
  :  ({ table, filter, limit }) => {
     results =  db[table].({
      : filter,
      : limit,
    });
     .(results);
  },
});

Create Agent

// agents/react.ts
import { createReactAgent, AgentExecutor } from 'langchain/agents';
import { pull } from 'langchain/hub';

// Get standard ReAct prompt
const prompt = await pull('hwchase17/react');

// Create agent
const agent = await createReactAgent({
  llm: model,
  tools: [searchTool, calculatorTool, dbQueryTool],
  prompt,
});

// Create executor
const executor = new AgentExecutor({
  agent,
  tools: [searchTool, calculatorTool, dbQueryTool],
  verbose: true,
  maxIterations: 5,
});

// Run agent
const result = await executor.invoke({
  input: 'What is the square root of the number of users in our database?',
});

OpenAI Functions Agent

// agents/openai-functions.ts
import { createOpenAIFunctionsAgent, AgentExecutor } from 'langchain/agents';

const prompt = ChatPromptTemplate.fromMessages([
  ['system', 'You are a helpful assistant with access to tools.'],
  new MessagesPlaceholder('chat_history'),
  ['human', '{input}'],
  new MessagesPlaceholder('agent_scratchpad'),
]);

const agent = await createOpenAIFunctionsAgent({
  llm: new ChatOpenAI({ modelName: 'gpt-4-turbo-preview' }),
  tools: [searchTool, calculatorTool],
  prompt,
});

const executor = new AgentExecutor({
  agent,
  tools: [searchTool, calculatorTool],
  memory: new BufferMemory({
    returnMessages: true,
    memoryKey: 'chat_history',
  }),
});

RAG Pipeline

Document Loading

// rag/loader.ts
import { DirectoryLoader } from 'langchain/document_loaders/fs/directory';
import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
import { TextLoader } from 'langchain/document_loaders/fs/text';
import { JSONLoader } from 'langchain/document_loaders/fs/json';

const loader = new DirectoryLoader('./documents', {
  '.pdf': (path) => new PDFLoader(path),
  '.txt': (path) => new TextLoader(path),
  '.json': (path) => new JSONLoader(path),
});

const docs = await loader.load();

Text Splitting

// rag/splitter.ts
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';

const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 200,
  separators: ['\n\n', '\n', ' ', ''],
});

const chunks = await splitter.splitDocuments(docs);

Vector Store

// rag/vectorstore.ts
import { OpenAIEmbeddings } from '@langchain/openai';
import { PineconeStore } from '@langchain/pinecone';
import { Pinecone } from '@pinecone-database/pinecone';

const embeddings = new OpenAIEmbeddings();

const pinecone = new Pinecone();
const index = pinecone.Index(process.env.PINECONE_INDEX!);

// Create vector store from documents
const vectorStore = await PineconeStore.fromDocuments(chunks, embeddings, {
  pineconeIndex: index,
  namespace: 'documents',
});

// Or connect to existing
const existingStore = await PineconeStore.fromExistingIndex(embeddings, {
  pineconeIndex: index,
  namespace: 'documents',
});

RAG Chain

// rag/chain.ts
import { createRetrievalChain } from 'langchain/chains/retrieval';
import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';

const retriever = vectorStore.asRetriever({
  k: 5, // Return top 5 results
  searchType: 'similarity',
});

const prompt = ChatPromptTemplate.fromMessages([
  [
    'system',
    `Answer questions based on the following context. If the answer is not in the context, say so.

Context: {context}`,
  ],
  ['human', '{input}'],
]);

const combineDocsChain = await createStuffDocumentsChain({
  llm: model,
  prompt,
});

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

const result = await ragChain.invoke({
  input: 'What is our refund policy?',
});
// { answer: '...', context: [Document, Document, ...] }

Conversational RAG

// rag/conversational.ts
import { createHistoryAwareRetriever } from 'langchain/chains/history_aware_retriever';

const contextualizePrompt = ChatPromptTemplate.fromMessages([
  ['system', 'Given chat history and question, reformulate as standalone question.'],
  new MessagesPlaceholder('chat_history'),
  ['human', '{input}'],
]);

const historyAwareRetriever = await createHistoryAwareRetriever({
  llm: model,
  retriever,
  rephrasePrompt: contextualizePrompt,
});

const qaPrompt = ChatPromptTemplate.fromMessages([
  ['system', 'Answer based on context:\n\n{context}'],
  new MessagesPlaceholder('chat_history'),
  ['human', '{input}'],
]);

const questionAnswerChain = await createStuffDocumentsChain({
  llm: model,
  prompt: qaPrompt,
});

const conversationalRagChain = await createRetrievalChain({
  retriever: historyAwareRetriever,
  combineDocsChain: questionAnswerChain,
});

Streaming

// streaming/stream.ts
const stream = await chain.stream({ input: 'Tell me a story' });

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

// With callbacks
const result = await chain.invoke(
  { input: 'Tell me a story' },
  {
    callbacks: [
      {
        handleLLMNewToken(token: string) {
          process.stdout.write(token);
        },
      },
    ],
  }
);

Production Setup

// config/langchain.ts
import { ChatOpenAI } from '@langchain/openai';

export const model = new ChatOpenAI({
  modelName: process.env.OPENAI_MODEL || 'gpt-4-turbo-preview',
  temperature: 0.7,
  maxTokens: 4096,
  timeout: 60000,
  maxRetries: 3,
  cache: true,
});

// With rate limiting
import Bottleneck from 'bottleneck';

const limiter = new Bottleneck({
  minTime: 100, // 10 requests per second
  maxConcurrent: 5,
});

export async function invokeWithRateLimit(chain: any, input: any) {
  return limiter.schedule(() => chain.invoke(input));
}

Best Practices

1. Use LCEL: LangChain Expression Language for composability
2. Add memory strategically: Balance context vs tokens
3. Define clear tool descriptions: Help the agent choose correctly
4. Chunk documents properly: Optimize for retrieval
5. Use streaming: Better UX for long responses
6. Cache embeddings: Reduce API costs
7. Monitor token usage: Track and optimize
8. Handle errors gracefully: Retry and fallback

Output Checklist

Every LangChain implementation should include:

• Model configuration with retries
• Prompt templates with variables
• Chain composition with LCEL
• Memory for conversation context
• Tools with clear descriptions
• Agent with iteration limits
• RAG with proper chunking
• Streaming for UX
• Error handling
• Rate limiting for production