Action guide
DocString and Review Agent in LangGraph
Your agent never knows when to stop. LangGraph with typed state, nodes, edges, and conditional loops. Real design instead of a prayer loop.
Get the full guide
Free newsletter unlocks the full guide and subscriber links. Same library working engineers use. No pedigree bingo.
Free. No spam. Unsubscribe anytime.
Why subscribe
A while loop with an LLM is not an architecture. You need typed state, nodes, and edges so retries and 'done' are first-class citizens. Not vibes.
For: Engineers building multi-step agent flows who need code that passes review and on-call.
- A state model for review-style loops with real exit criteria
- Routing patterns for conditional behavior you can test
- Clarity on when LangGraph earns its complexity over plain chains
- Graph-shaped implementation with typed state
- Examples of pass/fail and retry you can adapt
- Control patterns that do not grow spaghetti with every feature
- Names the same control problems as any distributed workflow
Subscribe free to unlock the full guide and all future updates.
What you’ll learn
How LangGraph models a pipeline you can draw: a TypedDict state every node reads and updates, nodes that return only the keys that changed, fixed edges that always fire, and conditional edges that branch (or loop back) from a router function-using a small write docstring → review → pass or retry story you can follow in the full visual guide.
When you subscribe to the newsletter, you get access to the full online guide alongside course and issue updates.
Explore the other action guides
Each guide kills one sharp problem. You leave with steps you can type, not inspiration quotes.
-
AI Agent Architecture Simplified
'Just add an agent' turned into spaghetti. Here is the ReAct loop you can actually fit in your head. Reason. Act. Observe. Then wire state and tools like a real system.
View guide →
-
Attention: Explained for Engineers
Attention still looks like matrix soup. Here is what Q, K, and V actually do. Why it scales ugly. How to use that when you size context and money.
View guide →
-
Bayes' Theorem Made Simple
Bayes shows up in papers and evals and you freeze. Here is a visual path through priors, likelihoods, and evidence. Plain vocabulary. No stats degree required.
View guide →
-
Build a HackerNews MCP Server From Scratch
From zero to a real MCP server. FastMCP. Real tools. A desktop client path. MCP reads like any other service you ship because it is one.
View guide →
-
Build a Research Agent in LangChain
The smallest LangChain stack for a ReAct research loop. Chat model. Tools. Scratchpad prompt. Executor. Skip drowning in framework trivia.
View guide →
-
How LLMs Tokenize Text
Billing and limits disagree with your word count. Rare names and compounds blow up. Tokenization hits cost and quality like a tax you did not see coming.
View guide →
-
How MCP Works
How host, client, and server really connect in MCP. Integrate and debug like you own the wire, not like a tourist squinting at JSON.
View guide →
-
Prompt LLMs Like a Pro by Context Activation
Longer prompts made outputs worse. Word choice wakes the right slice of training. Stop building walls of text nobody reads.
View guide →
-
Setting Up AI Projects in Python
Your AI repo outgrew the demo script. Env files, pyproject, src layout, and a clean split between HTTP surfaces and agent code.
View guide →
-
Tests That Mean Something
Outputs drift. Your suite shrugs. Here are unit tests that pin behavior you actually care about. Skip the ceremony that only pads ego.
View guide →
-
Understand RAG From First Principles
Naive RAG parrots garbage. Your pile of docs is huge. The model’s window is not. Every extra token hits the invoice. Own the data path or keep paying for lies.
View guide →
-
Write System Prompts for AI Agents Like a Pro
When your system prompt is a novel and the agent still veers: what is already baked into models, and what to add so your instructions carry real signal.
View guide →