Master LangChain end-to-end — LCEL, chains, RAG, tools, agents, memory, and deployment. From your first chain to production-grade applications.
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# LCEL chain composition
prompt = ChatPromptTemplate.from_template(
"Summarize: {text}"
)
chain = prompt | ChatOpenAI() | StrOutputParser()
result = chain.invoke({
"text": document
})
# ✓ Clean, composable, production-readyReal projects, not toy demos. Every module leads to something you can ship.
Master the LangChain Expression Language — compose chains, add fallbacks, parallelize, and stream responses.
Build a complete retrieval system with document loaders, splitters, embeddings, and vector store retrieval.
Create agents that use custom tools, browse the web, query databases, and take real-world actions.
Deploy LangChain apps with LangSmith observability, error handling, caching, and rate limiting.
14 modules · 198+ lessons · 30+ hours of content
You can write Python and want to build AI-powered applications — chatbots, RAG, agents, automation.
Evaluating LangChain vs alternatives? This shows you exactly what LangChain can do and where it shines.
Your proof-of-concept works. Now you need production patterns — memory, streaming, error handling, monitoring.
“Built an AI email assistant for a client using LangChain+LCEL. Course paid for itself 10x over.”
“The LCEL module is the best explanation of chain composition I've found anywhere. Better than the official docs.”
“Went from calling APIs to building AI RAG pipelines in a week. The LangChain module alone is worth it.”