Best LangChain Books

IntermediatePaid★ Top Pick

LLM Engineer's Handbook

Paul Iusztin, Maxime Labonne · Packt · 523 pages · ~13 hrs to read

The most complete treatment of building production LLM applications — covers LangChain, RAG pipelines, fine-tuning, and deployment as part of an end-to-end engineering workflow. The best single book for serious AI engineers.

BeginnerPaid

Generative AI with LangChain

Ben Auffarth · Packt · 377 pages · ~9 hrs to read

Covers the breadth of LangChain use cases including text generation, code generation, chatbots, and multimodal applications — good overview for engineers exploring what LangChain can do.

BeginnerPaid

LangChain in your Pocket

Mehul Gupta · Self-published / Packt · 152 pages · ~4 hrs to read

A fast, no-frills walkthrough of LangChain's core building blocks — chains, agents, memory, RAG — aimed at developers who just want working code quickly. Thinner than the Packt titles above but genuinely current given how fast LangChain's API has moved, and one of the few dedicated LangChain-only books that exists.

Teaches how to embed LLMs in real applications using LangChain for orchestration, covering memory, prompts, tools, vector databases and fine-tuning, with a Streamlit front end. Suits developers new to LLM app building.

IntermediatePaid★ Top Pick

LLM Engineer's Handbook

Paul Iusztin, Maxime Labonne · Packt · 523 pages · ~13 hrs to read

The most complete treatment of building production LLM applications — covers LangChain, RAG pipelines, fine-tuning, and deployment as part of an end-to-end engineering workflow. The best single book for serious AI engineers.

IntermediatePaid

Prompt Engineering for Generative AI

James Phoenix & Mike Taylor · O'Reilly · 430 pages · ~11 hrs to read

Covers prompting principles for both LLMs and diffusion models, with a substantial LangChain section on building chains, agents, and output parsers around those prompts. Good for engineers who want the prompting theory that most LangChain-specific books skip past.

Walks through building LLM applications with LangChain and LangGraph, including RAG and cognitive architectures built as LangGraph graphs. Suits developers with Python or JavaScript who want to move from prototypes to production agents.

Covers prompt engineering, advanced RAG, LangGraph agentic workflows, tool-based agents, multi-agent systems and the Model Context Protocol. Assumes Python experience but welcomes readers new to building with LLMs.