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AI Agents with LangChain and LangGraph
Build real agents, step by step, with code that actually runs
A practical developer book on building AI agents with LangChain and LangGraph. Twenty chapters and six end-to-end projects, from your first LLM call to a shipped agent API with FastAPI and Docker. Every example in the book was run for real, and the real output is shown, so the code you read is code that works.
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Most agent tutorials show code that was never run. This book is different. Every example was executed, and the real output is printed next to it. You start with what an agent is and when not to use one, then build up through prompts, structured output, tools, memory, RAG, LangGraph state and routing, checkpointers, human-in-the-loop, multi-agent teams, evals, and deployment. The six projects at the end put it together: a personal task agent, a docs Q and A bot, a multi-tool research assistant, a support triage system with human approval, a multi-agent research team, and a capstone agent API you can deploy. Written for developers who know Python and want working agents, not theory. Companion to the AI Agent Engineering course at logicstack.org.
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Muhammad Saud
Muhammad Saud is a computer science graduate and founder of LogicStack Engineering Labs. He builds AI agent systems and teaches the AI Agent Engineering course at logicstack.org.
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