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_messages([
    ("system", "You are a helpful assistant."),
    ("human", "{input}"),
])
chain = prompt | llm
response = chain.invoke({"input": "Explain RAG in 2 sentences"})

GitHub: langchain-ai/langchain - 98K+ stars

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