Fastapi Mcp In Python
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Fastapi With Mcp
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Author : Drake Duncan
language : en
Publisher: Independently Published
Release Date : 2025-06-11
Fastapi With Mcp written by Drake Duncan and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-11 with Computers categories.
Unlock the power of intelligent API development with FastAPI and Model-Text Protocols (MCP). This comprehensive and practical guide teaches backend developers, API engineers, and AI system architects how to integrate FastAPI, Python's blazing-fast web framework, with MCP (Model-Text Protocol) to create scalable, agentic, and intelligent applications. Whether you're building structured LLM workflows, chaining agent tasks, or deploying microservice-style AI pipelines, this book provides a hands-on roadmap using the most up-to-date tools and practices. Inside You'll Learn: How to build clean, production-ready FastAPI endpoints integrated with LLM-based agents Understanding and implementing the MCP message structure for model-to-agent communication Managing tools, prompts, and agent logic with reusable patterns and scalable design Running background tasks, cron jobs, and real-time agent calls Observability, logging, and tracing with tools like OpenTelemetry and Prometheus Dockerizing and deploying your FastAPI + MCP stack to Render, Fly.io, or a VPS Full project: Build and deploy a multi-agent system with FastAPI, MCP, and a front-end interface (Streamlit or React) Whether you're building agent-driven AI backends, experimenting with LangChain-style chaining, or just getting started with structured LLM protocols in Python, this book delivers expert-level guidance without the fluff. Who This Book Is For: Backend developers and Python programmers working with APIs Engineers deploying AI agents or LLM workflows using FastAPI Professionals seeking real-world, containerized deployment strategies AI system architects adopting structured agent messaging and tool chaining Technologies Covered: FastAPI - MCP Protocol - Uvicorn & Gunicorn - Docker - Poetry - OpenTelemetry - Prometheus - Jaeger - APScheduler - Pydantic - JWT - Streamlit - React - LLM Toolchains Drake Duncan is a software engineer, backend architect, and technical author with deep expertise in Python-based systems, distributed architecture, and modern API design. With years of hands-on experience building production systems and AI-driven services, Drake is known for writing clear, structured, and practical programming books that help developers level up with confidence.
Fastapi Mcp In Python
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Author : TODD. CHANDLER
language : en
Publisher: Independently Published
Release Date : 2025-05-22
Fastapi Mcp In Python written by TODD. CHANDLER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with Computers categories.
FastAPI & MCP in Python: Build High-Performance AI Agent Services Are you ready to build next-generation AI agent services-fast, reliable, and production-ready? If you want to design robust agent pipelines, integrate powerful tools, and scale your AI solutions with confidence, this book delivers everything you need. This comprehensive guide brings together FastAPI-the leading Python web framework for modern APIs-and the Model Context Protocol (MCP), an emerging standard for agent communication. You'll move step-by-step from foundational concepts to advanced, real-world implementations. No abstract theory-just proven, practical strategies, clear explanations, and fully working code. What sets this book apart? Essential Building Blocks: Learn how to structure your FastAPI projects, manage dependencies, and implement security best practices. Hands-On Agent Pipelines: Follow detailed walkthroughs for creating MCP-compliant services, from tool definition and request-reply handlers to streaming, error handling, and tool orchestration. Real-World Features: Discover how to add authentication, logging, monitoring with Prometheus, and tracing with OpenTelemetry. Explore advanced concurrency patterns, serverless deployment with AWS Lambda, and resilient error recovery. Testing and CI/CD: Master automated unit, integration, and contract testing. Streamline delivery with ready-to-use CI/CD pipelines for quality and speed. Actionable Guides: Put knowledge into practice with hands-on projects: weather alert tools, research assistant pipelines, interactive dashboards, and more. Are you looking for a guide that balances clarity, depth, and hands-on practicality? This book is packed with examples, concise tips, and solutions to common challenges faced by modern Python developers building agentic systems. Take your AI services from idea to production-order your copy of "FastAPI & MCP in Python: Build High-Performance AI Agent Services" today and start building with confidence.
Building Mcp Clients In Python
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Author : Timothy Kertzmann
language : en
Publisher: Independently Published
Release Date : 2025-06-16
Building Mcp Clients In Python written by Timothy Kertzmann and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-16 with Computers categories.
Building MCP Clients in Python: A FastAPI Developer's Guide Are you ready to master a modern standard that is transforming how AI models, APIs, and automation tools connect seamlessly? This book offers a hands-on, practical guide to building robust MCP (Model Context Protocol) clients in Python using FastAPI-one of the fastest, most developer-friendly frameworks available. Building MCP Clients in Python explores the fundamentals of MCP, a protocol designed to standardize communication between intelligent systems and external tools. You'll learn how to build scalable, secure, and maintainable clients that leverage real-time streaming, structured tool discovery, typed schemas, and seamless integration with large language models and external APIs. With a focus on practical implementation, this guide walks you through every step-from setting up your environment and crafting your first client, to advanced features like authentication, error handling, testing, deployment, and extending MCP capabilities. What sets this book apart is its comprehensive coverage structured into clearly focused chapters, including: Foundations of MCP and FastAPI: Understand MCP's architecture and why FastAPI is ideal for modern API development. Deep Dive into MCP Protocol: Learn about JSON-RPC, Server-Sent Events, schema generation, and tool discovery. Building and Connecting MCP Clients: Step-by-step guidance to create clients, connect securely, invoke operations, and handle streaming data. Advanced Features and Security: Implement authentication, error handling, caching, and best security practices for reliable clients. Testing, Deployment, and Full-Stack Solutions: Strategies for writing tests, deploying with Docker and Kubernetes, and building full-stack MCP-powered systems. Ecosystem and Community: Stay current with MCP tools, libraries, and standards. Whether you are a Python developer aiming to integrate cutting-edge AI workflows, build automated business tools, or explore scalable API design, this book equips you with practical knowledge and runnable examples to accelerate your projects confidently. Ready to build the future of AI-powered integrations? Start your journey with Building MCP Clients in Python and turn complex protocols into reliable, production-ready applications.
Mastering Mcp Clients In Python With Fastapi
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Author : Cameron McLucas
language : en
Publisher: Independently Published
Release Date : 2025-06-05
Mastering Mcp Clients In Python With Fastapi written by Cameron McLucas and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-05 with Computers categories.
Mastering MCP Clients in Python with FastAPI: The Essential Tutorial for AI Engineers Tired of patching together fragile AI agent scripts that break at the slightest mistake? What if you could build rock-solid, production-ready MCP clients in Python that scale and stay reliable? Core Promise Mastering MCP Clients in Python with FastAPI equips you with a hands-on blueprint to design, implement, and secure AI agent integrations. You'll learn how to enforce consistent schemas, handle tool calls seamlessly, and maintain full context and traceability across services-transforming your prototypes into robust, maintainable systems. Key Learnings & Benefits - Craft Stable Schemas: Design Pydantic models for ToolInvokeRequest and ToolInvokeResponse so every message between your agent and tools is validated, versioned, and error-free (Chapters 3 & 12.3). - Build FastAPI Endpoints: Learn how to set up FastAPI routes, dependency injection, and middleware to expose /invoke-tool and /receive-tool-response endpoints with minimal boilerplate (Chapters 4 & 5). - Integrate with AI & Data Stores: Seamlessly connect to LLMs for planning and summarization, and run semantic searches with vector databases like Pinecone-all while preserving context chaining and trace IDs (Chapters 6 & 12.1-12.2). - Secure Your Pipeline: Implement mutual TLS, JWT HTTPBearer authentication, and fine-grained permission filters so only authorized agents access sensitive data (Chapter 7). - Monitor Performance: Incorporate Prometheus metrics, Grafana dashboards, and distributed tracing via OpenTelemetry to pinpoint bottlenecks and errors-before your users do (Chapter 8). - Test & Deploy with Confidence: Develop pytest unit tests, fastapi TestClient integration tests, and GitHub Actions CI workflows. Then package your MCP client in Docker, deploy it on Kubernetes, and configure autoscaling for high availability (Chapters 9 & 10). - Adopt Advanced Patterns: Embed MCP clients in long-running "agent runner" loops, implement synchronous vs. asynchronous invocation strategies, and leverage LLMs for dynamic tool selection (Chapter 11). Don't let brittle integrations slow you down. Get your copy of Mastering MCP Clients in Python with FastAPI today and start building resilient, scalable AI agents that make every tool invocation reliable-right from the first line of code.
Hands On Apis For Ai And Data Science
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Author : Ryan Day
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-03-04
Hands On Apis For Ai And Data Science written by Ryan Day and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-04 with Computers categories.
Are you ready to grow your skills in AI and data science? A great place to start is learning to build and use APIs in real-world data and AI projects. API skills have become essential for AI and data science success, because they are used in a variety of ways in these fields. With this practical book, data scientists and software developers will gain hands-on experience developing and using APIs with the Python programming language and popular frameworks like FastAPI and StreamLit. As you complete the chapters in the book, you'll be creating portfolio projects that teach you how to: Design APIs that data scientists and AIs love Develop APIs using Python and FastAPI Deploy APIs using multiple cloud providers Create data science projects such as visualizations and models using APIs as a data source Access APIs using generative AI and LLMs
Mcp With Ai
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Author : 서지영
language : ko
Publisher: (주)도서출판길벗
Release Date : 2025-08-02
Mcp With Ai written by 서지영 and has been published by (주)도서출판길벗 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-02 with Computers categories.
공개된 MCP 서버도 사용해보고, 나만의 MCP 서버도 만들고, 누구나 커서와 클로드 데스크톱으로 MCP 서버를 쉽게 만들어볼 수 있다! MCP는 LLM 기반 에이전트와 도구들을 연결할 수 있도록 설계된 프로토콜로, 다양한 플랫폼과 함께 사용하거나 서버-클라이언트 구조를 통해 모듈형 AI 시스템을 직접 구축할 수 있습니다. MCP의 등장으로 인해 앞으로는 모델 중심의 단순한 응답 생성이 아닌 시스템 중심의 복합적 문제 해결 구조로 바뀔 것입니다. 또한 다양한 AI 에이전트들이 각자의 역할을 분담하고 유기적으로 협력하면서 복잡한 요청에도 더욱 유연하고 지능적으로 대응할 수 있게 될 것입니다. 이 책은 MCP의 기본 구조와 철학, 동작 원리 등을 빠르게 살펴보고 클로드 데스크톱, 커서 AI, 스미더리 같은 도구를 사용해 간단한 MCP 서버-클라이언트 구조를 직접 구현해보면서 MCP를 경험해볼 수 있는 MCP 입문서입니다. MCP를 이해하고 다양한 에이전트를 연결해보고 싶은 분들께 효율적인 출발점이 될 수 있도록 구성했습니다.
Ai Agent Pydanticai
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Author : อนุชิต ชโลธร
language : th
Publisher: สำนักพิมพ์ก๊อปวาง
Release Date :
Ai Agent Pydanticai written by อนุชิต ชโลธร and has been published by สำนักพิมพ์ก๊อปวาง this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
เปลี่ยนไอเดียในหัว...ให้กลายเป็น AI Agent ที่ใช้งานได้จริง! ในยุคที่ Generative AI และ Large Language Models (LLMs) กำลังเปลี่ยนแปลงโลก, นักพัฒนาทุกคนต่างมองหาเครื่องมือที่จะช่วยสร้างแอปพลิเคชันอัจฉริยะได้อย่างรวดเร็วและมีประสิทธิภาพ แต่ความท้าทายสำคัญคือการจัดการกับผลลัพธ์จาก LLM ที่มักจะ "ไร้ระเบียบ" และนำไปใช้งานต่อได้ยาก PydanticAI คือคำตอบสุดท้าย ที่จะเชื่อมโลกของ AI ที่ยืดหยุ่นเข้ากับโลกของซอฟต์แวร์ที่ต้องการความแม่นยำและโครงสร้างที่ชัดเจน หนังสือเล่มนี้ไม่ใช่แค่คู่มือการใช้ไลบรารี แต่เป็น "ตำราอาหาร" (Cookbook) ที่รวบรวม "สูตรสำเร็จ" สำหรับการสร้าง AI Agent ในทุกสถานการณ์ที่คุณต้องเจอ ตั้งแต่พื้นฐานจนถึงระดับโปรดักชัน สิ่งที่คุณจะได้เรียนรู้จากหนังสือเล่มนี้ - เริ่มต้นอย่างมั่นคง: ทำความเข้าใจปัญหาของ LLM และเรียนรู้ว่า PydanticAI เข้ามาแก้ปัญหาได้อย่างไร พร้อมสร้าง Agent ตัวแรกของคุณในไม่กี่นาที - เครื่องมือครบมือ: สร้าง Tools ให้ Agent ของคุณสามารถเชื่อมต่อกับ API ภายนอก, ทำงานกับฐานข้อมูล (SQL) หรือแม้กระทั่งทำงานร่วมกับ Agent อื่นๆ - สร้าง Chatbot อัจฉริยะ: เรียนรู้วิธีสร้าง Chatbot ที่จดจำบทสนทนาและมีบุคลิก (Persona) ที่น่าสนใจ- ระบบถาม-ตอบ (RAG) จากเอกสารของคุณ: สอนให้ Agent เรียนรู้และตอบคำถามจากข้อมูลเฉพาะทาง, เอกสารภายในองค์กร หรือไฟล์ PDF ของคุณเอง - สถาปัตยกรรมขั้นสูง (Multi-agent & Graphs): ออกแบบระบบที่ซับซ้อนให้ Agent หลายตัวทำงานร่วมกันอย่างเป็นระบบผ่าน Workflow และ Graph - เชื่อมต่อกับโลกภายนอก (A2A & AG-UI Protocols): เรียนรู้มาตรฐานเปิดอย่าง Agent2Agent (A2A) และ Agent-User Interaction (AG-UI) เพื่อให้ Agent ของคุณสามารถสื่อสารกับ Agent อื่นๆ และเชื่อมต่อกับแอปพลิเคชัน Frontend ได้อย่างเป็นระบบ - ทดสอบและประเมินผล: วัดประสิทธิภาพของ Agent อย่างเป็นวิทยาศาสตร์ด้วย pydantic-evals เพื่อให้มั่นใจในคุณภาพก่อนนำไปใช้งานจริง - Monitoring และ Debug: ติดตามการทำงานของ Agent ทุกฝีก้าวด้วย Logfire และ OpenTelemetry เพื่อหาจุดบกพร่องได้อย่างรวดเร็ว - นำไปใช้งานจริง (Deployment): เรียนรู้วิธีการ Deploy Agent ของคุณในรูปแบบต่างๆ ทั้ง API ด้วย FastAPI, เว็บแอปด้วย Streamlit และ CLI ไม่ว่าคุณจะเป็นนักพัฒนาที่เพิ่งก้าวเข้าสู่โลกของ AI หรือวิศวกรที่ต้องการยกระดับการสร้าง AI Application ให้เป็นระบบและพร้อมสำหรับสเกลใหญ่ หนังสือเล่มนี้คือเข็มทิศที่จะนำทางคุณไปสู่การเป็นมืออาชีพด้านการสร้าง AI Agent ได้อย่างแน่นอน พร้อมหรือยังที่จะเปลี่ยน "Prompt" ให้กลายเป็น "Product"? หยิบหนังสือเล่มนี้แล้วเริ่มสร้างสรรค์ผลงานที่น่าทึ่งไปพร้อมกัน!
Generative Ai With Langchain
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Author : Ben Auffarth
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-05-23
Generative Ai With Langchain written by Ben Auffarth and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-23 with Computers categories.
Go beyond foundational LangChain documentation with detailed coverage of LangGraph interfaces, design patterns for building AI agents, and scalable architectures used in production—ideal for Python developers building GenAI applications Key Features Bridge the gap between prototype and production with robust LangGraph agent architectures Apply enterprise-grade practices for testing, observability, and monitoring Build specialized agents for software development and data analysis Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis second edition tackles the biggest challenge facing companies in AI today: moving from prototypes to production. Fully updated to reflect the latest developments in the LangChain ecosystem, it captures how modern AI systems are developed, deployed, and scaled in enterprise environments. This edition places a strong focus on multi-agent architectures, robust LangGraph workflows, and advanced retrieval-augmented generation (RAG) pipelines. You'll explore design patterns for building agentic systems, with practical implementations of multi-agent setups for complex tasks. The book guides you through reasoning techniques such as Tree-of -Thoughts, structured generation, and agent handoffs—complete with error handling examples. Expanded chapters on testing, evaluation, and deployment address the demands of modern LLM applications, showing you how to design secure, compliant AI systems with built-in safeguards and responsible development principles. This edition also expands RAG coverage with guidance on hybrid search, re-ranking, and fact-checking pipelines to enhance output accuracy. Whether you're extending existing workflows or architecting multi-agent systems from scratch, this book provides the technical depth and practical instruction needed to design LLM applications ready for success in production environments.What you will learn Design and implement multi-agent systems using LangGraph Implement testing strategies that identify issues before deployment Deploy observability and monitoring solutions for production environments Build agentic RAG systems with re-ranking capabilities Architect scalable, production-ready AI agents using LangGraph and MCP Work with the latest LLMs and providers like Google Gemini, Anthropic, Mistral, DeepSeek, and OpenAI's o3-mini Design secure, compliant AI systems aligned with modern ethical practices Who this book is for This book is for developers, researchers, and anyone looking to learn more about LangChain and LangGraph. With a strong emphasis on enterprise deployment patterns, it’s especially valuable for teams implementing LLM solutions at scale. While the first edition focused on individual developers, this updated edition expands its reach to support engineering teams and decision-makers working on enterprise-scale LLM strategies. A basic understanding of Python is required, and familiarity with machine learning will help you get the most out of this book.
Building Data Science Applications With Fastapi
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Author : Francois Voron
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-10-08
Building Data Science Applications With Fastapi written by Francois Voron and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-08 with Computers categories.
Get well-versed with FastAPI features and best practices for testing, monitoring, and deployment to run high-quality and robust data science applications Key FeaturesCover the concepts of the FastAPI framework, including aspects relating to asynchronous programming, type hinting, and dependency injectionDevelop efficient RESTful APIs for data science with modern PythonBuild, test, and deploy high performing data science and machine learning systems with FastAPIBook Description FastAPI is a web framework for building APIs with Python 3.6 and its later versions based on standard Python-type hints. With this book, you'll be able to create fast and reliable data science API backends using practical examples. This book starts with the basics of the FastAPI framework and associated modern Python programming language concepts. You'll be taken through all the aspects of the framework, including its powerful dependency injection system and how you can use it to communicate with databases, implement authentication and integrate machine learning models. Later, you'll cover best practices relating to testing and deployment to run a high-quality and robust application. You'll also be introduced to the extensive ecosystem of Python data science packages. As you progress, you'll learn how to build data science applications in Python using FastAPI. The book also demonstrates how to develop fast and efficient machine learning prediction backends and test them to achieve the best performance. Finally, you'll see how to implement a real-time face detection system using WebSockets and a web browser as a client. By the end of this FastAPI book, you'll have not only learned how to implement Python in data science projects but also how to maintain and design them to meet high programming standards with the help of FastAPI. What you will learnExplore the basics of modern Python and async I/O programmingGet to grips with basic and advanced concepts of the FastAPI frameworkImplement a FastAPI dependency to efficiently run a machine learning modelIntegrate a simple face detection algorithm in a FastAPI backendIntegrate common Python data science libraries in a web backendDeploy a performant and reliable web backend for a data science applicationWho this book is for This Python data science book is for data scientists and software developers interested in gaining knowledge of FastAPI and its ecosystem to build data science applications. Basic knowledge of data science and machine learning concepts and how to apply them in Python is recommended.
Getting Started With Fastapi
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Author : Andrés Cruz Yoris
language : en
Publisher: Andres Cruz
Release Date :
Getting Started With Fastapi written by Andrés Cruz Yoris and has been published by Andres Cruz this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
FastAPI is a great web framework for creating web APIs with Python; It offers us multiple features with which it is possible to create modular, well-structured, scalable APIs with many options such as validations, formats, typing, among others. When you install FastAPI, two very important modules are installed: Pydantic that allows the creation of models for data validation. Starlette, which is a lightweight ASGI tooltip, used to create asynchronous (or synchronous) web services in Python. With these packages, we have the basics to create APIs, but we can easily extend a FastAPI project with other modules to provide the application with more features, such as the database, template engines, among others. FastAPI is a high-performance, easy-to-learn, start-up framework; It is ideal for creating all kinds of sites that not only consist of APIs, but we can install a template manager to return complete web pages. This book is mostly practical, we will learn the basics of FastAPI, knowing its main features based on a small application that we will extend chapter after chapter and whose content you can see below: Chapter 1: We present some essential commands to develop in FastApi , we will prepare the environment and we will give an introduction to the framework . Chapter 2: One of the main factors in FastApi is the creation of resources for the API through functions, in this section we will deal with the basics of this, introducing routing between multiple files as well as the different options for the arguments and parameters of these routes. Chapter 3: In this section, learn how to handle HTTP status codes from API methods and also handle errors/exceptions from API methods. Chapter 4: In this section we will see how to create sample data to use from the automatic documentation that FastAPI offers for each of the API methods. Chapter 5: In this chapter we will see how to implement the upload of files, knowing the different existing variants in FastAPI. Chapter 6: In this chapter we will see how to connect a FastAPI application to a relational database such as MySQL. Chapter 7: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 8: In this chapter we will see installing and using a template engine in Python, specifically Jinja, with which we can return responses in HTML format. Chapter 9: In this chapter we will learn how to use dependencies. Chapter 10: In this chapter we will see how to use middleware to intercept requests to API methods and execute some procedure before the request or after generating the response. Chapter 11: In this chapter we will see how to create a user module, to register users, login, generate access tokens and logout. Chapter 12: In this chapter we will learn about some particularities and functionalities of FastAPI such as the use of annotations and the Ellipsis operator. Chapter 13: In this chapter we will see how to implement unit tests. Chapter 14: In this chapter we will know some general aspects applied to FastAPI.