LangChain — Build LLM-Powered Applications

Core Concepts

Chains

Compose multiple LLM calls and transformations into a pipeline:

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

chain = ChatPromptTemplate.from_template("Summarize: {text}") | ChatOpenAI()
result = chain.invoke({"text": "..."})

RAG (Retrieval-Augmented Generation)

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

vectorstore = FAISS.from_documents(docs, OpenAIEmbeddings())
retriever = vectorstore.as_retriever()

Agents

from langchain.agents import create_tool_calling_agent
agent = create_tool_calling_agent(llm, tools, prompt)

Ecosystem

• LangSmith — Observability and testing platform
• LangGraph — Stateful multi-actor agent framework
• LangServe — Deploy chains as REST APIs

FAQ

Q: What is LangChain? A: The most popular framework for building applications with large language models. Chains, agents, RAG, memory, tool use, and integrations with 700+ providers.

Q: How do I install LangChain? A: Check the Quick Use section above for step-by-step installation instructions. Most assets can be set up in under 2 minutes.