1 / 2Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph
Learning LangChain is a practical guide to building AI and LLM applications with LangChain and LangGraph. It moves from chatbot fundamentals and retrieval-augmented generation to agent workflows, deployment, monitoring and evaluation, for readers who already understand Python or JavaScript.
At a glance
- Covers building a chatbot that works with user-provided documents and conversation history.
- Explains retrieval-augmented generation, LangGraph agent architecture, and integrations with third-party APIs and tools.
- Includes topics for deploying, monitoring, testing and evaluating AI applications.
Reference price: $112.94
Price checked: Nov 13, 2026
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Pros and cons
Pros
- Connects core LLM application concepts to hands-on chatbot and agent-building topics.
- Coverage extends beyond prototyping to deployment, monitoring, reliability and security considerations.
- The listing identifies Python or JavaScript familiarity as suitable prior programming knowledge.
Technical specifications
9 specifications| Publisher | O'Reilly Media |
|---|---|
| Publication date | March 25 2025 |
| Edition | 1st |
| Language | English |
| Print length | 294 pages |
| ISBN-10 | 1098167287 |
| ISBN-13 | 978-1098167288 |
| Weight listed | 502 g |
|---|---|
| Dimensions | 17.78 x 1.57 x 23.34 cm |
Who is it for?
A good fit for
- Developers familiar with Python or JavaScript who are new to building LLM applications.
- Readers looking for a guided introduction to LangChain, LangGraph, RAG and tool-using agents.
Less suited to
- Readers seeking a no-code introduction or a book focused only on machine-learning theory.
Customer reviews on Amazon
- 5 stars
69% 69% - 4 stars
13% 13% - 3 stars
5% 5% - 2 stars
4% 4% - 1 star
9% 9%
Aggregate customer ratings lean positive, with a substantial share in the five-star category, while lower-star ratings show that experiences are not uniformly favourable. The available rating data does not explain the reasons behind either response, so it cannot establish how well the tutorials work for a particular reader or programming background.
Frequently asked questions
What does this book cover?
The listing describes tutorials on LLM application foundations, document-based chatbots, RAG, LangGraph agents, third-party tools, deployment, monitoring, testing and evaluation.
What programming background is suggested?
The listing targets developers who understand Python or JavaScript, while positioning them as beginners to AI application development.
Does it discuss production deployment?
Yes. The described material includes deployment and post-deployment considerations such as latency, reliability, security and monitoring.
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