Publisher's Synopsis
LangGraph in Action: A Hands-On, Step-by-Step Guide to Building Stateful AI Agents in Python
Unlock the power of LangGraph-a modern extension of LangChain-to orchestrate intelligent, stateful AI workflows. Designed for intermediate Python developers, this book teaches you how to build robust, context-aware agents that remember past interactions, make dynamic decisions, and scale in production.
Managing state is crucial for real-world AI applications. LangGraph's graph-based approach lets your agents carry information across turns and sessions, so they never lose track of user preferences or workflow progress. You'll learn not only why persistent context matters but exactly how to implement it.
Dive straight into code with a practical, step-by-step format:
LangGraph Fundamentals: Build your first LLM-driven Python agent, then transform it into a LangGraph flow with nodes and edges.
Graph-Based Workflows: Structure complex logic as reusable graphs, enabling loops, branches, and conditional paths beyond simple chains.
Stateful AI Applications: Implement persistent session state so each node can access and update memory, powering long-running and multi-step processes.
Agent Orchestration & Scaling: Coordinate multiple agents, integrate external APIs, and apply best practices for performance and horizontal scaling.
Real-World Case Studies: Follow hands-on examples-from context-aware chatbots to customer support and data-analysis pipelines-to see how LangGraph solves practical challenges.
Each chapter builds on the last, reinforcing concepts with working examples, clear explanations, and downloadable code. By the end, you'll have the skills to design, implement, and deploy production-grade AI agents with Python.
Whether you're automating customer support, building autonomous data pipelines, or exploring new AI workflows, LangGraph in Action gives you the roadmap and the hands-on practice to turn ideas into intelligent, scalable solutions. Start building your next-generation AI agents today!
