Wao Stew (14 results)
- Softcover
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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Paperback or Softback. Condition: 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.
- Softcover
Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
Seller: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
Seller: PBShop.store UK, Fairford, GLOS, United KingdomPBShop.store UK
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
- Softcover
- Print on Demand
Seller: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Paperback. Condition: 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, whi…ch 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.
- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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£ 67.47
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Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
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£ 72.34
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Condition: New. Print on Demand.
- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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Condition: New. PRINT ON DEMAND.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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£ 60.23
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Taschenbuch. Condition: 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 deplo…yment 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.
- Softcover
- Print on Demand
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contact seller5-star sellerCondition: New
£ 67.02
£ 27.51 shippingShips from Australia to U.S.A.Quantity: 1 available
Paperback. Condition: 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, whi…ch 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.
- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
Contact seller5-star sellerCondition: New
£ 59.49
£ 37.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: 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, whi…ch 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.
- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
Contact seller5-star sellerCondition: New
£ 60.23
£ 51.44 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: 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 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 DeploymentLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 122 pp. Englisch.
- Softcover
- Print on Demand
Seller: preigu, Osnabrück, Germanypreigu
Contact seller5-star sellerCondition: New
£ 54.09
£ 60.02 shippingShips from Germany to U.S.A.Quantity: 5 available
Taschenbuch. Condition: 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 EU…: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
- Softcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 65.25
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Taschenbuch. Condition: 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 deployment… 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.
