Product guide

An Illustrated Guide to AI Agents: Concepts and Code for Building Agents with LLMs, Tools, and Memory

Code Building LLMs: An Illustrated Guide to AI Agents by Maarten Grootendorst and Jay Alammar combines visual explanations of agent architecture with a practical Python project. It covers reasoning models, tools, memory, planning, reflection, evaluation, and multi-agent systems, while positioning core concepts as a way to build understanding amid changing frameworks.

At a glance

Uses hundreds of illustrations to explain AI agent components and processes.
Builds a Python TinyAgent incrementally, including reasoning, memory, tool use, planning, and reflection.
Topics include multimodal and reasoning models, multi-agent collaboration, evaluation, distillation, quantization, and reinforcement learning.
The publisher describes the repository as a maintained companion for current example code and additional material.

Price

$53.35Save 33%

Reference price: ~~$79.99~~

Price checked: Nov 13, 2026

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Pros and cons

Pros

Pairs conceptual diagrams with a step-by-step Python agent project, serving readers who want both visual intuition and implementation practice.
The stated audience spans developers, researchers, technical leaders, students, and readers who prefer to skip code.
Emphasizes durable agent fundamentals rather than chasing fast-changing frameworks and releases.

Technical specifications

8 specifications

Book details
Publisher	O'Reilly Media
Publication date	October 6, 2026
Edition	1st
Language	English
Print length	448 pages
ISBN-13	979-8341662698

Dimensions and installation
Dimensions	7 x 0.91 x 9.19 inches
Weight listed	1.72 pounds

What to know before buying

Listing details to confirm

The printed examples are described as instructional, while the authors recommend using the companion repository for runnable, maintained code because libraries and APIs evolve. The listing gives a publication date of October 6, 2026, so buyers should confirm edition availability and timing if purchasing before then.

Who is it for?

A good fit for

Developers who want to assemble and understand a Python AI agent component by component.
Readers seeking illustrated explanations of agent tools, memory, planning, reflection, and evaluation.
Technical leaders, students, or curious readers who want the conceptual material without writing code.
Research and machine-learning readers interested in model internals, training methods, and evaluation.

Less suited to

Readers looking only for a current framework-specific tutorial, since the book emphasizes fundamentals and points to a companion repository for evolving code.
Readers seeking a book focused solely on AI theory without an implementation project.

Customer reviews on Amazon

4.9 / 5 · 10 ratings

5 stars87%87%
4 stars13%13%
3 stars0%0%
2 stars0%0%
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Only aggregate ratings are provided, with a strongly positive pattern in a small sample. That is a limited signal rather than detailed evidence about the illustrations, Python project, or depth of coverage, so buyers should use the stated contents and audience to judge fit.

Frequently asked questions

Does the book include practical coding?

Yes. The description says readers build a working Python agent called TinyAgent across the chapters, adding reasoning, memory, tools, planning, and reflection.

Can I use it without writing code?

The publisher says the illustrations and conceptual explanations carry the story on their own, and that readers can skip the code without losing the conceptual material.

What topics does it cover?

Listed subjects include reasoning LLMs, memory, tools, planning, reflection, evaluation, multimodal models, multi-agent collaboration, distillation, quantization, and reinforcement learning.

Should I run code printed in the book?

The authors describe printed code as instructional and recommend the maintained repository for runnable examples and updates as libraries and APIs change.