LangGraph models an LLM agent as a graph: nodes are steps, edges are control flow, and everything operates over a shared, typed state. That structure is what lets you add cycles, branching, persistence, and human review around a non-deterministic model — the difference between a demo and something you can trust with users.
Curriculum
Shaped to your level; for 1:1, built around a graph you actually want to ship
1 · Mental model
Graphs vs chains, when a graph earns its complexity, and how to scope a LangGraph agent so it stays debuggable.
2 · State & nodes
Typed state, reducers, node functions, and keeping state clean as a graph grows.
3 · Edges & control flow
Conditional edges, branching, and cycles — including safe loops and re-explain patterns without infinite runs.
4 · Persistence & checkpointing
Checkpointers, resuming runs, session state, and durable long-running agents.
5 · Human-in-the-loop
Interrupts, approvals, and edits mid-run — putting a human in the graph where it matters.
6 · Multi-agent
Router, supervisor, and plan-and-execute topologies in LangGraph, and how to pick one.
7 · Tools, memory & RAG
Wiring tool calls, memory, and retrieval into the graph without turning it into spaghetti.
8 · Evals & observability
LangSmith traces, evals, and knowing a graph is production-ready. Testing non-deterministic flows.
9 · Ship it
Packaging the graph behind async FastAPI with streaming, plus cost and latency budgets.
Formats & Details
1:1 Private Mentoring
Tailored to your codebase, paced to you.
- 6–12 hours across sessions
- Built around your real graph
- Live code review + async follow-up
Team Workshop
Your team on one set of LangGraph patterns.
- 2–4 days, live online
- Hands-on labs + your use case
- Reference architecture to keep
Prerequisites
- Comfortable with Python
- Called an LLM API before
- No prior LangGraph needed
Frequently Asked Questions
What is LangGraph and why use it?
LangGraph is a library for stateful, graph-based LLM apps. You model an agent as nodes (steps) and edges (control flow) over shared state, which gives you cycles, branching, persistence, and human-in-the-loop — the reasons it holds up better than linear chains in production.
Do I need LangChain experience first?
No. We start from LLM basics and bring in LangChain concepts only where they matter. Prior experience helps but isn't required.
Is this 1:1 or a group?
Both. 1:1 mentoring is tailored to your codebase; team workshops run for groups.
How much does it cost?
Depends on format and duration — book a free intro call for a tailored proposal.
Related Training
Agentic AI Training →
The full agent stack, with LangGraph as the backbone.
RAG Training →
Grounding your agents with retrieval that actually works.
MCP Training →
Expose tools to your graph cleanly via MCP.
AI Architecture Mentoring →
Zoom out to whole-system design and reviews.