LangGraph Mastery

Why LangGraph exists and when it beats vanilla LangChain, CrewAI, AutoGen or Pydantic AI. Understand the core primitives — nodes, edges, state, checkpoints — and pick the right tool for your build.

Build a working LangGraph in 20 minutes. Master TypedDict and Pydantic state schemas, custom reducers, pure-Python nodes vs Runnables, and the static edges that wire it all together.

Make your graph think. Branch, loop, retry intelligently with conditional edges, self-loops with bounded recursion, parallel fan-out using Send, and runtime-constructed graphs.

Build your first true agent — Plan → Act → Reflect. Use `create_react_agent` for fast prototypes, then graduate to custom graphs for full control over the reasoning loop.

Give your agent superpowers — connect databases, REST APIs, Slack, GitHub, Stripe. Master tool design, error handling, and the subtle differences between OpenAI, Anthropic and Gemini function calling.

Make agents that remember — across messages, sessions and weeks. Master the 4 checkpointers (InMemory / SQLite / Postgres / Redis), threads, and the new Store abstraction for cross-thread user memory.

Orchestrate teams of agents that actually cooperate. Master the 3 multi-agent patterns, the `langgraph-supervisor` package, agent-to-agent handoffs with Command, and how to avoid infinite handoffs.

Build agents your boss will actually trust in production. Add approval gates, time-travel debugging, and streaming HITL UIs that let humans review, edit or reject agent actions in real time.

Ship agents you can debug at 2am. Integrate LangSmith for tracing, use LangGraph Studio for visual debugging, build eval datasets, and apply LLM-as-judge to catch regressions before users do.

Ship a real agent the world can use. Deploy to LangGraph Cloud or self-host on Docker. Apply security, cost control and caching. Pick 1 of 5 industry capstones and submit for AI-graded review across 6 dimensions.