Artificial Intelligence

LangChain, LangGraph & LangSmith

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

Level: IntermediateArtificial Intelligence

What You'll Learn

LangChain core: chains, prompts, models, parsers
Memory, tools, and retrieval components
LangGraph: stateful multi-step agent workflows
Conditional edges and cyclic agent loops
LangSmith: tracing, debugging, and evaluation
LangServe: deploying chains as REST APIs
LangChain Hub: reusing community prompts
Production patterns: caching, fallbacks, streaming

What's Included

🧭AI Tutor🔒

Interactive learning path with AI-generated lessons read aloud by your personal tutor

🧪Practice Lab🔒

Step-by-step lab instructions with AI guidance — run in your own IDE or sandbox.

🎙️Mock Interviews🔒

AI interviewer asks tailored questions, gives real-time feedback and a score report

📝Evaluation🔒

MCQ questions (theory, practical, code) to test your knowledge and earn a badge

⚔️Challenge AI🔒

Compete head-to-head against AI on real production problems — judged on correctness, performance, scalability, code quality, and edge cases

Course Syllabus

Module 1: LangChain 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

Module 2: Memory, 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

Module 3: LangGraph — 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

Module 4: LangSmith — 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

Module 5: LangServe, 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

You're all set

Everything in this course is unlocked.

Continue Learning →
✓AI Tutor
✓Practice Lab
✓Mock Interviews
✓Evaluation
✓Challenge AI