Intermediate

LangGraph for AI Research

Research work is full of multi-step model pipelines: an eval that generates, grades and aggregates hundreds of items, a run that must not restart when a process dies, a judge whose uncertain verdicts need a person. This course builds each on LangGraph's current API with a stand-in model, so the code runs on Colab without an API key. Then it does the arithmetic: a binomial error bar on the pass rate, judge bias, reliability as p^n, cost per run and wall-clock time under a concurrency cap. It ends by comparing LangGraph with the plain loop the book builds from scratch.

ToolChecked against LangGraph v1.2 on 2026-09-30.

49 min read

9 lessons

Rod Rivera

Professor

Desk

First lesson open

Start the first lesson

Every lesson is free. Lessons marked open need no account; the rest open once you register.

LangGraphevaluationAI researchcheckpointinghuman in the loop

What You'll Learn

Build an eval harness as a graph that fans out one branch per item with Send, grades each answer, and aggregates a pass rate with a Wald and a Wilson 95% interval
Design a State where every field has one owner, and add a reducer wherever parallel branches write the same key, so LangGraph merges results instead of raising InvalidUpdateError
Route a judge's low-confidence verdicts to a separate path with a conditional edge, and derive how much an unreviewed noisy judge biases the headline pass rate
Checkpoint a long run to SQLite so a crashed process resumes with only the unfinished items, and choose a durability mode knowing what each one loses
Pause a run with interrupt() so a person reviews the judge's borderline cases, then resume it with Command(resume=...) under the same thread_id
Wrap a per-item pipeline as a subgraph, execute a model's tool calls with ToolNode, and stream node-by-node updates from a long run
Derive a graph's end-to-end reliability as p^n, what a retry policy buys and what it cannot fix, and the cost and wall-clock time of a run from tokens, prices and a concurrency cap
Decide when LangGraph earns its place over a plain loop you write yourself, with reasons you could defend in an interview or a design review

Prerequisites

Comfort reading Python; every lesson runs real code, and each listing runs on Colab's Python 3.12
No prior LangGraph or LangChain experience; lesson 01 builds the current API from the ground up
Helpful but not required: the probability in chapters 3 and 16 of the profrod.ai book (reliability over n steps, error bars on a pass rate)