LangChain.js Has a Free Framework That Chains LLM Calls — Build AI Agents With RAG in 50 Lines

The LLM Problem You call the OpenAI API. You get a response. Now what? How do you add memory to a conversation? How do you give the LLM access to your documents? How do you chain multiple LLM calls together? How do you let the LLM use tools? LangChain.js solves all of this. Chains, agents, RAG, memory — all composable. What LangChain.js Gives You Simple Chat import { ChatOpenAI } from ' @langchain/openai ' ; const model = new ChatOpenAI ({ model : ' gpt-4o ' }); const response = await model . invoke ( ' Explain Docker in one sentence ' ); console . log ( response . content ); RAG (Retrieval-Augmented Generation) import { ChatOpenAI , OpenAIEmbeddings } from ' @langchain/openai ' ; import { MemoryVectorStore } from ' langchain/vectorstores/memory ' ; import { RecursiveCharacterTextSplitter } from ' langchain/text_splitter ' ; // 1. Load and split documents const splitter = new RecursiveCharacterTextSplitter ({ chunkSize : 1000 }); const docs = await splitter . createDocuments ([ yourDoc