# 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.

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- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies &gt; Books, Music & Movies &gt; Books & Comics
- Price: 41.99 USD
- Previous price: 59.99 USD
- Discount: 30%
- Price checked: 2026-11-14T05:50:10.084Z
- Rating: 4.5 / 5
- Reviews: 75

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 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.

## 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.

## 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

## Before you buy

- 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.

## 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 feedback

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.

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## Add-ons

- [Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents](https://best-deals.pro/visit/building-ai-agents-llms-rag-knowledge-graphs)
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## Images

![Generative AI with LangChain, 2nd Edition — 1](https://best-deals.pro/i/ynKFiNfV8hkZ26xg_4OLuT0vnSYCNyLBiPwsKJZ55eyDQWT4LI1NvquIrnjt7A2l8-kHPMsIXxbpKVpiylaeOsCSypPHcWiF-_iwT07Htm1U8ojWJm4o.jpg)
