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LangChain

Browse articles on LangChain — tutorials, guides, and in-depth comparisons.

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LangChain is the most widely used Python framework for building LLM-powered applications. Its modular abstractions — chains, retrievers, agents, and memory — let you compose complex AI workflows without reinventing common patterns.

LangChain Ecosystem

ToolRoleWhen to use
LangChainCore framework, chains, agentsConnecting LLMs to data and tools
LangGraphStateful multi-agent workflowsComplex agent orchestration
LangSmithObservability, eval, monitoringDebugging and testing in production
LlamaIndexData indexing, RAG patternsDocument-heavy applications
LangServeServe chains as REST APIDeploying chains as microservices

Quick Start — RAG Pipeline

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import PGVector
from langchain.chains import RetrievalQA

# Embedding model
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")

# Vector store (PostgreSQL + pgvector)
vectorstore = PGVector(
    connection_string="postgresql://user:pass@localhost/db",
    embedding_function=embeddings,
    collection_name="docs",
)

# RAG chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o"),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
)

answer = qa_chain.invoke("What is LangGraph used for?")

LCEL — LangChain Expression Language

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

# Compose chains with | operator
chain = (
    ChatPromptTemplate.from_template("Summarize: {text}")
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)

result = chain.invoke({"text": "Your document here..."})

Learning Path

  1. LCEL basics — prompt templates, model invocation, output parsers
  2. Retrieval chains — document loaders, text splitters, vector stores
  3. Agents — tool definitions, ReAct agent, tool calling
  4. LangGraph — stateful workflows, human-in-the-loop, multi-agent
  5. LangSmith — tracing, evaluation datasets, CI/CD testing
  6. Production — streaming, async, caching, error handling

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