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Building generative AI agents = using LangGraph, AutoGen, and CrewAI /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Building generative AI agents/ by Tom Taulli, Gaurav Deshmukh.
其他題名:	using LangGraph, AutoGen, and CrewAI /
作者:	Taulli, Tom.
其他作者:	Deshmukh, Gaurav.
出版者:	Berkeley, CA :Apress : : 2025.,
面頁冊數:	ix, 275 p. :ill., digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Machine Learning. -
電子資源:	https://doi.org/10.1007/979-8-8688-1134-0
ISBN:	9798868811340

Building generative AI agents = using LangGraph, AutoGen, and CrewAI /
Taulli, Tom.

Building generative AI agentsusing LangGraph, AutoGen, and CrewAI /[electronic resource] :by Tom Taulli, Gaurav Deshmukh. - Berkeley, CA :Apress :2025. - ix, 275 p. :ill., digital ;24 cm.

Chapter 1: Introduction to Generative AI Agents -- Chapter 2: Generative AI Foundations -- Chapter 3: Types of Agents -- Chapter 4: Open AI GPT Agents and the Assistants API -- Chapter 5: Development Agents -- Chapter 6: Crew AI -- Chapter 7: AutoGen -- Chapter 8: LangChain -- Chapter 9: LangGraph -- Chapter 10: Haystack -- Chapter 11: Takeaways.

The dawn of AI agents is upon us. Tech visionaries like Bill Gates, Andrew Ng, and Vinod Khosla have highlighted the monumental potential of this powerful technology. This book will provide the knowledge and tools necessary to build generative AI agents using the most popular frameworks, such as AutoGen, LangChain, LangGraph, CrewAI, and Haystack. Recent breakthroughs in large language models have opened up unprecedented possibilities. After years of gradual progress in machine learning and deep learning, we are now witnessing novel approaches capable of understanding, reasoning, and generating content in ways that promise to revolutionize nearly every industry. This platform shift is as significant as the advent of mainframes, PCs, cloud computing, mobile technology, and social media. It's why the world's largest technology companies - like Microsoft, Apple, Google, and Meta - are making enormous investments in this category. While chatbots like ChatGPT, Claude, and Gemini have demonstrated remarkable potential, the years ahead will see the rise of generative AI agents capable of executing complex tasks on behalf of users. These agents already exhibit capabilities such as running test suites, searching the web for documentation, writing software, answering questions based on vast organized information, and performing intricate web-based tasks across multiple domains. They can autonomously investigate cybersecurity incidents and address complex customer support needs. By integrating skills, knowledge bases, planning frameworks, memory, and feedback loops, these systems can handle many tasks and improve over time. Building Generative AI Agents serves as a high-quality guide for developers to understand when and where AI agents can be useful, their advantages and disadvantages, and practical advice on designing, building, deploying, and monitoring them.

ISBN: 9798868811340

Standard No.: 10.1007/979-8-8688-1134-0doiSubjects--Topical Terms:

1137723
Machine Learning.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Building generative AI agents = using LangGraph, AutoGen, and CrewAI /
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