# 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.

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- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies > Books, Music & Movies > Books & Comics
- Price: 41.99 USD
- Previous price: 59.99 USD
- Discount: 30%
- Price checked: 2026-11-14T05:50:25.263Z
- Rating: 4.2 / 5
- Reviews: 68

Prices and deal status are the latest recorded data, not a guarantee of availability. Confirm the price and availability with the store.

## 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.

## 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.

## 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

## Before you buy

- 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.

## 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 feedback

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.

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## Images

![Building AI Agents with LLMs, RAG, and Knowledge Graphs — 1](https://best-deals.pro/i/bdBTfJwM-Rst2sFKN1UuQ0UAAg50oydEC6kKrSSyViVhnGJUPIzmHmAYK8GeaIQkRq7bsVTuZ8TqZ6BnGN-2SAcNkfqLJB1v_NTbHYmQLRoMgs-yC0rt.jpg)