Product guide

Building AI Agents with LLMs, RAG, and Knowledge Graphs

Building AI Agents with LLMs, RAG, and Knowledge Graphs is a practical guide to developing agent systems that combine language models with retrieval, structured knowledge, and tool use. Its stated topics range from LLM foundations and Python examples to RAG, knowledge graphs, multi-agent workflows, deployment, and monitoring.

At a glance

Covers LLM fundamentals, retrieval-augmented generation, reinforcement learning, and knowledge graphs.
Describes building agents that plan, use tools, retrieve information, and connect with APIs and databases.
Includes Python examples and case studies, according to the listing.
Topics include single- and multi-agent systems, application development, deployment, and monitoring.

Price

$41.99Save 30%

Reference price: ~~$59.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Brings RAG, knowledge graphs, tool use, and agent orchestration together in one 560-page guide.
Its described progression spans core LLM concepts through deployment and monitoring.
The listing specifies Python examples and identifies basic Python and generative AI knowledge as useful prerequisites.

Cons

The stated prerequisites mean readers without basic Python and generative AI knowledge may need introductory material first.

Technical specifications

8 specifications

Publication details
Publisher	Packt Publishing
Publication date	July 11, 2025
Language	English
Print length	560 pages
ISBN-10	183508706X
ISBN-13	978-1835087060

Dimensions and installation
Weight listed	2.12 pounds
Dimensions	7.5 x 1.27 x 9.25 inches

What to know before buying

The listing describes a DRM-free PDF and Packt Reader access, but says email sign-up and proof of purchase are required for access. Confirm the selected edition and access terms before buying.

Who is it for?

A good fit for

Data scientists and researchers learning to build and deploy LLM-based agents.
Readers with basic Python and generative AI knowledge who want coverage of RAG, knowledge graphs, and multi-agent systems.
Experienced data scientists exploring LLM applications and agent architectures.

Less suited to

Readers seeking a beginner-level introduction without prior Python or generative AI knowledge.
Buyers looking for a reference limited to one framework, since the described scope includes multiple techniques and topics.

Customer reviews on Amazon

4.2 / 5 · 68 ratings

5 stars69%69%
4 stars11%11%
3 stars4%4%
2 stars9%9%
1 star7%7%

Aggregate customer ratings lean positive, with many five-star ratings, but a substantial minority are lower. Without written feedback, the scores do not establish what readers liked or disliked. They offer limited guidance on whether this book's broad coverage suits a particular reader, so consider the stated Python and generative AI prerequisites and topic list.

Frequently asked questions

What topics does the book cover?

The listing describes LLM fundamentals, RAG, knowledge graphs, reinforcement learning, tool use, APIs and databases, agent orchestration, and production deployment and monitoring.

Does it include practical examples?

The listing says it includes Python examples and real-world case studies. Basic Python and generative AI knowledge are recommended.

Is a digital version included?

The listing claims a DRM-free PDF and access to Packt's Reader. It also says email sign-up and proof of purchase are required, so confirm the selected edition and access requirements.