LangChain Has a Free Framework for Building LLM-Powered Applications

LangChain connects LLMs to your data, tools, and APIs. RAG, agents, chains, and memory — the building blocks for AI applications beyond simple chat.

Beyond ChatGPT Wrappers

Calling an LLM API is easy. Building a production AI app is hard:

• How do you search YOUR documents? (RAG)
• How do you give the LLM access to tools? (Agents)
• How do you maintain conversation history? (Memory)
• How do you chain multiple LLM calls? (Chains)

LangChain provides abstractions for all of this.

What You Get for Free

RAG (Retrieval-Augmented Generation):

from langchain_community.document_loaders import PDFLoader
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain.chains import RetrievalQA

# Load your documents
loader = PDFLoader('company_docs.pdf')
docs = loader.load()

# Create vector store
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())

# Ask questions about your data
qa = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model='gpt-4o'),
    retriever=vectorstore.as_retriever(),
)

result = qa.invoke('What is our refund policy?')

The LLM answers using YOUR documents, not its training data.

Agents (LLM + Tools):

from langchain.agents import create_react_agent
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun

agent = create_react_agent(
    llm=ChatOpenAI(model='gpt-4o'),
    tools=[DuckDuckGoSearchRun(), WikipediaQueryRun()],
)

result = agent.invoke('What happened in tech news today?')

The agent decides which tools to use, calls them, and synthesizes results.

LangChain Expression Language (LCEL)

from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

chain = (
    ChatPromptTemplate.from_template('Tell me a joke about {topic}')
    | ChatOpenAI(model='gpt-4o')
    | StrOutputParser()
)

result = chain.invoke({'topic': 'programming'})

Pipe syntax for composable, readable chains.

Quick Start

pip install langchain langchain-openai
export OPENAI_API_KEY=sk-...

LangSmith (Observability)

LangSmith traces every LLM call, shows token usage, latency, and lets you debug chains visually. Free tier included.

If you're building AI features beyond simple chat — LangChain gives you the building blocks.

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