Stew wao (14 risultati)
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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EUR 50,14
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Paperback or Softback. Condizione: New. Build GenAI Agents with OpenAI + vLLM: Develop portable AI agents in Python with structured outputs, tool calling, OpenAI Agents SDK, vLLM, model swit. Book.
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Da: California Books, Miami, FL, U.S.A.California Books
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EUR 55,42
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Condizione: New.
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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EUR 67,70
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 66,16
EUR 3,84 spedizioneSpedito da Regno Unito a U.S.A.Quantità: Più di 20 disponibili
PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 58,13
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Paperback. Condizione: new. Paperback. AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, wh…ich is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable. Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 78,75
EUR 7,59 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand.
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
Contatta il venditoreVenditore con 4 stelleCondizione: Nuovo
EUR 84,44
EUR 3,46 spedizioneSpedito in U.S.A.Quantità: 4 disponibili
Condizione: New. Print on Demand.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 80,65
EUR 9,95 spedizioneSpedito da Germania a U.S.A.Quantità: 4 disponibili
Condizione: New. PRINT ON DEMAND.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 68,20
EUR 23,00 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or depl…oyment setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable.Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment 122 pp. Englisch.
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Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 77,99
EUR 32,11 spedizioneSpedito da Australia a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, wh…ich is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable. Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Da: CitiRetail, Stevenage, Regno UnitoCitiRetail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 71,57
EUR 43,22 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: new. Paperback. AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, wh…ich is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable. Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 68,20
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployme…nt setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable.Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and DeploymentLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 122 pp. Englisch.
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Da: preigu, Osnabrück, Germaniapreigu
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 61,25
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. Build GenAI Agents with OpenAI + vLLM | Develop portable AI agents in Python with structured outputs, tool calling, OpenAI Agents SDK, vLLM, model switching, CLI, API, and Docker deployment | Stew Wao | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9789349174245 | Verantwortliche Person für die E…U: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 73,88
EUR 61,22 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deploymen…t setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.Key LearningsBuild GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.Create portable AI agents by separating business logic from LLM and model APIs.Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.Run the same agent with OpenAI models or local LLM inference using vLLM.Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.Package AI agent as CLI tool and HTTP API for real applications and integrations.Deploy AI agents with Docker containers and environment variable. Table of ContentShipping GenAI Agent in MinutesBuilding Agent WorkflowsReliable and Structured Agent OutputSwitching Models without Rewriting AgentRunning vLLMDesigning Stable Business Logic across Multiple SDKsAgent Packaging and Deployment.
