Product guide

Generative AI with LangChain, 2nd Edition

Generative AI with LangChain is a second-edition technical book on building LLM applications and agents with Python, LangChain, and LangGraph. Its focus extends from workflows and retrieval-augmented generation to multi-agent design, evaluation, observability, and production deployment. The listing says a basic understanding of Python is required.

At a glance

Covers LangChain fundamentals, LangGraph workflows, and multi-agent architectures.
Topics include agentic RAG, hybrid search, re-ranking, and fact-checking pipelines.
Includes material on testing, evaluation, observability, and production deployment.
The listing says purchase of the print or Kindle book includes a free PDF eBook.

Price

$41.99Save 30%

Reference price: ~~$59.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Broad coverage links agent and RAG design with testing, monitoring, and deployment topics.
Includes examples and patterns for multi-agent systems, software-development agents, and data-analysis agents.
The listed topics include several model providers, including Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI.

Cons

A basic understanding of Python is required, so it is not positioned as a no-code introduction.

Technical specifications

8 specifications

Publication
Edition	2nd ed.
Publisher	Packt Publishing
Publication date	May 23, 2025
Language	English
Print length	476 pages
ISBN-13	978-1837022014

Dimensions and installation
Weight listed	1.82 pounds
Dimensions	7.5 x 1.09 x 9.25 inches

What to know before buying

The listing describes advanced LangChain and LangGraph coverage and names current LLM providers, but does not specify software-version compatibility. Readers relying on particular APIs or provider integrations should confirm that the examples match their environment.

Who is it for?

A good fit for

Python developers moving LLM prototypes toward deployed applications.
Readers learning LangGraph workflows, multi-agent design, and agentic RAG.
Engineering teams and researchers interested in evaluation, observability, and enterprise deployment practices.

Less suited to

Readers seeking a no-code introduction or a book that requires no Python background.
Readers primarily looking for a general machine-learning textbook rather than LangChain and LLM application development.

Customer reviews on Amazon

4.5 / 5 · 75 ratings

5 stars71%71%
4 stars15%15%
3 stars9%9%
2 stars4%4%
1 star1%1%

The supplied evidence contains aggregate ratings but no written customer feedback, so it does not establish which aspects readers liked or disliked. The rating distribution is mostly at the high end but also includes lower-star ratings. Treat that as a limited overall signal, not a guide to the quality of specific chapters, examples, or software-version fit.

Frequently asked questions

What does this book cover?

The listing describes LangChain and LangGraph, AI-agent design, multi-agent systems, RAG, testing and evaluation, observability, and deployment.

Is Python experience needed?

Yes. The listing states that a basic understanding of Python is required. Familiarity with machine learning is described as helpful.

Does it include a PDF eBook?

The listing says purchase of the print or Kindle book includes a free PDF eBook.

Who is the book aimed at?

It is presented for developers, researchers, engineering teams, and decision-makers learning about LangChain and LangGraph, with emphasis on enterprise deployment patterns.