LangGraph in 45 Minutes: Everything You Need to Ship Production Agents

Most LangGraph tutorials show you a hello-world graph and stop. This one shows you the checkpoint-size math, the failure modes, and the 18-line production skeleton. A complete, in-depth guide to LangGraph 1.x — the framework behind production agent systems. Every section includes runnable code (current for v1), an animated diagram, practical tips learned in production, and one deeper insight most tutorials skip: the Pregel model behind the runtime, why reducers are a concurrency contract, why interrupted nodes re-run from the top, and how your checkpoint database secretly becomes a regression-test suite. WHAT YOU'LL LEARN Design state with TypedDict and reducers (and the 4 questions to ask of every key) Route with conditional edges and the Command API — and cap runaway loops Get crash recovery, memory, and time travel from one compile() argument Pause agents for human approval with interrupt() / Command(resume=...) Stream tokens, step updates, and custom progress from one socket Build agents with create_agent + middleware (LangChain v1) Coordinate multi-agent teams: supervisor, subgraphs, Send fan-out Production: RetryPolicy math, CachePolicy, LangSmith traces, deployment CHAPTERS Intro: why LangGraph exists + roadmap State, StateGraph & reducers Routing: conditional edges & Command Persistence: checkpoints, threads, time travel Human-in-the-loop: interrupt() & resume Streaming: the four modes Agents: ReAct & create_agent Multi-agent: supervisor, subgraphs, Send Production: retries, caching, LangSmith, deploy CAPSTONE: RefundDesk — all 8 concepts in one system The four ideas to keep + build curriculum References: Pregel (Malewicz et al., 2010), ReAct (Yao et al., 2022), Chandy-Lamport snapshots (1985), AutoGen (Wu et al., 2023), MapReduce (Dean & Ghemawat, 2004), Google SRE (Beyer et al., 2016). #langgraph #langchain #aiagents #llm #python