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11h · 48 lessons
Agentic Systems with LangGraph
Multi-agent orchestration, memory, and long-horizon tasks.
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About this course
Master graph-based agent orchestration: routing, supervision, human-in-the-loop, and durable execution.
What you'll learn
- Model multi-step agent workflows as explicit LangGraph state machines
- Compose tools, memory and human-in-the-loop checkpoints safely
- Debug non-deterministic runs with tracing and replay
- Ship a production agent with retries, timeouts and cost caps
Requirements
- Intermediate Python
- Prior exposure to LLM APIs (OpenAI / Anthropic)
- Basic understanding of async and message-passing
Curriculum
6 modules · 24 lessons
Module 1Why graphs beat chains4 lessons
Module 1
Why graphs beat chains
- 01.01The limits of linear chains
- 01.02State machines for LLM apps
- 01.03Anatomy of a LangGraph app
- 01.04Setting up your workspace
Module quiz · 3 questions
Module 2Nodes, edges and state4 lessons
Module 2
Nodes, edges and state
- 02.01Typed state with Pydantic
- 02.02Conditional edges
- 02.03Cycles and termination
- 02.04Parallel branches
Module quiz · 3 questions
Module 3Tools and memory4 lessons
Module 3
Tools and memory
- 03.01Tool calling patterns
- 03.02Short vs long-term memory
- 03.03Vector memory with pgvector
- 03.04Sharing state across sessions
Module quiz · 3 questions
Module 4Human in the loop4 lessons
Module 4
Human in the loop
- 04.01Interrupts and approvals
- 04.02Editable intermediate state
- 04.03Streaming partial results
- 04.04Approval UIs
Module quiz · 3 questions
Module 5Observability & evals4 lessons
Module 5
Observability & evals
- 05.01Tracing every step
- 05.02Replay and time-travel debugging
- 05.03Golden-set evaluations
- 05.04Regression alerts
Module quiz · 3 questions
Module 6Shipping to production4 lessons
Module 6
Shipping to production
- 06.01Concurrency and rate limits
- 06.02Cost caps and budgets
- 06.03Deploying on serverless
- 06.04Capstone: research agent
Module quiz · 3 questions