Lilly Tech Systems
Learn LangChain
Master the most popular framework for building LLM-powered applications. Learn chains, agents, memory, RAG, LangGraph, and LangSmith — from first install to production deployment.
Your Learning Path
Follow these lessons in order, or jump to any topic that interests you.
1. Introduction
What is LangChain? The ecosystem, key components, comparison with alternatives, and when to use it.
2. Installation & Setup
Install LangChain, configure API keys, build your first chain, and learn LCEL basics.
3. LLMs & Chat Models
ChatOpenAI, ChatAnthropic, Ollama. Configuration, streaming, caching, and fallback models.
4. Prompts & Templates
PromptTemplate, ChatPromptTemplate, few-shot prompts, output parsers, and structured output.
5. Chains
LCEL pipe operator, RunnableSequence, RunnableParallel, streaming, batch processing, and error handling.
6. Memory
Buffer, summary, window, entity, and vector store memory. Persisting and customizing memory.
7. RAG with LangChain
Document loaders, text splitters, embeddings, vector stores, retrievers, and full RAG chains.
8. Agents & Tools
ReAct agents, built-in tools, custom tools, agent executor, structured output, and multi-action agents.
9. LangGraph
Graph-based orchestration, StateGraph, conditional routing, human-in-the-loop, and multi-agent workflows.
10. LangSmith
LLM observability, tracing, debugging, evaluation datasets, prompt playground, and production monitoring.
11. Best Practices
Project structure, error handling, cost optimization, testing, production deployment, and common mistakes.
What You'll Learn
By the end of this course, you'll be able to:
Build LLM Chains
Compose multi-step pipelines that combine prompts, models, parsers, and tools using LCEL.
Create AI Agents
Build autonomous agents that use tools, reason step-by-step, and solve real-world tasks.
Implement RAG
Load documents, embed them, store in vector databases, and build retrieval-augmented generation systems.
Deploy to Production
Monitor with LangSmith, serve with LangServe, and follow best practices for reliable LLM applications.