🦜Generative AI

LangChain, LangGraph and LangSmith: full course guide

Master the full LangChain ecosystem — build production chains, stateful agentic workflows with LangGraph, and debug and evaluate with LangSmith. One course, every tool.

113 pages5 modules36 topics

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What is inside this guide

01LangChain Foundations

  • LangChain architecture and core abstractions
  • Chat models, LLMs, and model I/O
  • Prompt templates and few-shot prompting
  • Output parsers: JSON, Pydantic, structured
  • Building your first LCEL chain
  • Runnable interface and chaining patterns

02Memory, Tools & Retrieval

  • Conversation memory types: buffer, summary, window
  • Document loaders and text splitters
  • Embeddings and vector store retrievers
  • Retrieval-augmented generation (RAG) with LangChain
  • Tool calling and custom tools
  • Agents: ReAct, OpenAI Functions, XML agent

03LangGraph — Stateful Agents

  • Why LangGraph: limitations of linear chains
  • Graphs, nodes, and edges fundamentals
  • State management and shared state schema
  • Conditional edges and branching logic
  • Cyclic graphs and agent loop patterns
  • Human-in-the-loop with interrupt and resume
  • Parallel node execution and subgraphs
  • Building a multi-step research agent with LangGraph

04LangSmith — Debug & Evaluate

  • LangSmith setup and project tracing
  • Reading and interpreting trace trees
  • Comparing runs: A/B testing prompts
  • Creating evaluation datasets from traces
  • Automated evaluators: criteria, embedding distance
  • Feedback loops and human annotation
  • Monitoring production chains for regressions
  • Cost and latency dashboards

05LangServe, Hub & Production

  • LangServe: serving chains as FastAPI endpoints
  • Streaming responses with LangServe
  • LangChain Hub: finding and versioning prompts
  • Caching: in-memory and Redis-backed
  • Fallback chains for reliability
  • Rate limiting and retry strategies
  • Secrets management and environment config
  • Capstone: production RAG API with tracing and eval

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