LangChain Has a Free LLM Framework — Build AI Applications with Chains, Agents, and RAG

A developer wanted to build a chatbot that answers questions about company docs. Raw OpenAI API calls needed: document chunking, embedding generation, vector search, prompt engineering, memory management. Weeks of work. LangChain is a framework for building LLM applications. Chains, agents, RAG, memory - pre-built components that snap together. What LangChain Offers for Free Chains - Compose LLM calls into workflows Agents - LLMs that use tools (search, calculator, APIs) RAG - Retrieval-Augmented Generation pipelines Memory - Conversation memory (buffer, summary, vector) Document Loaders - PDF, CSV, HTML, Notion, GitHub, Confluence Vector Stores - Integrate with Pinecone, Chroma, pgvector, Qdrant LLM Support - OpenAI, Anthropic, Ollama, Hugging Face, Cohere LangSmith - Observability and tracing (free tier) Quick Start from langchain_openai import ChatOpenAI from langchain_core.prompts import ChatPromptTemplate llm = ChatOpenAI ( model = " gpt-4 " ) prompt = ChatPromptTemplate . from_me