Book
LangChain ni yoru AI Agent Kaihatsu Koza (A Course in AI Agent Development with LangChain)
Yoshiki Kanda
Summary
A Japanese-language book on AI agent development with LangChain, by Yoshiki Kanda. It explains the basics of AI agent development and how to build practical AI agents with LangChain. It starts with setting up an environment on Google Colaboratory, which is free to use, and goes on to using libraries, working with LLMs, developing AI agents, designing agents, and application examples. The tools used include Google Colab, LangChain, and Claude Code. The book also covers best practices, advanced agent patterns, industrial application cases, and overviews of LangGraph, LangMem, and LangSmith, and it has exercises at the end of the chapters.
Target Readers
- Engineers who are interested in developing AI agents
- Engineers who want to learn AI agent development with LangChain from the basics
- Developers who want to learn implementation patterns for applications that integrate LLMs by working through exercises
Tags
Colophon
- Publisher
- Shoeisha
- ISBN
- 978-4-7981-9304-5
- Published
- Jan 2026
- List price
- ¥3,520incl. taxMay differ from the actual selling price on Amazon
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Prerequisites
- Recommended
Context Engineering
Understood through LLM Principles, RAG, and Agent Development
Hirosato Gamo
Reason: Once you grasp how LLMs process input and generate responses, plus the fundamentals of prompt design and context construction, you can see what abstractions a framework like LangChain takes off your hands. This book's insights on issues like Lost in the Middle and the volatility of conversation history give you criteria for designing context when building agents with LangChain.
Next Books
- Recommended
Building Applications with AI Agents
Designing and Implementing Multiagent Systems
Michael Albada
Reason: Having gone through the basic design and implementation patterns of agents with LangChain, you start to see the design challenges of multi-agent systems that go beyond a single agent. Practical AI Agent Development builds up production-facing perspectives—orchestration, evaluation, monitoring, security—that framework learning alone doesn't cover.