FastAPI: High-Performance Python Backend Framework

🚀 Excited to Share My New Python Project! ✨Expense Tracking System using FastAPI and Streamlit✨ This web application allows users to record daily expenses, categorize them, and analyze spending patterns easily. 🎯 Project Highlights: • FastAPI Backend: Built a robust backend server using FastAPI and tested all APIs using Postman. • Streamlit Frontend: Developed an interactive UI where users can add, update, and view expenses. It also includes analytics features to compare spending across categories, displayed through bar graph for better insights. • Database: Used MySQL for storing and managing expense data. 💼 GitHub Project Link: https://lnkd.in/gr7KjFs9 🙏 A special thanks to #DhavalPatel sir, #HemanandVadivel sir, and the entire #Codebasics team for designing such an intuitive, project-based learning experience. #Python #FastAPI #Streamlit #DataAnalytics #SQL #ExpenseTracking #Learning #Codebasics

🚀 Just Released: robyn-ml-api (Ultra fast rust-based framework server API) I'm excited to share an open-source template for building production-ready ML APIs with Python. What is it? A modern, type-safe foundation for deploying Machine Learning models as APIs, powered by Robyn — one of the fastest Python web frameworks available. Next generation of FastAPI with a very close dev experience to it. If you use to love fastapi this framework, companied by these elements collected in this template with blow your mind Why I built it: Setting up ML APIs with proper lifecycle management, validation, and production tooling takes time. This template solves that with batteries included. Key features: ✅ Custom Router with automatic Pydantic validation & JSON serialization ✅ Event-driven lifespan for clean model loading/unloading ✅ Structured logging with correlation IDs ✅ ProcessPoolExecutor integration for CPU-bound inference ✅ Modern tooling: uv + Hatch, Docker, GitHub Actions CI ✅ Python 3.12+ with full type hints Get started in 3 commands: ``` git clone git@github.com:damvolkov/robyn-ml-api.git make install make dev ``` Whether you're deploying sklearn, PyTorch, or any ML model — this gives you a clean, fast starting point. ⭐ Star it, fork it, and let me know what you build! 🔗 [https://lnkd.in/e_UpCFNM] #Python #MachineLearning #API #OpenSource #MLOps #Robyn

FastAPI Changed How I Build APIs — Here's What I Learned After years with Django, I finally dove deep into #FastAPI. The difference is night and day for AI applications. What makes FastAPI special: ⚡ Async-first —> Handle 100 concurrent LLM calls without blocking 📝 Auto-documentation —> Swagger UI generated from your code ✅ Type validation —> Pydantic validates requests automatically 💉 Dependency Injection —> Clean, testable, reusable code 🚀 Performance —> One of the fastest Python frameworks What's in the repo: → Path & Query parameters with automatic validation → Pydantic models for request/response schemas → Field validators and custom validation logic → Dependency Injection patterns (Django doesn't have this!) → Async endpoints for non-blocking operations → Router organization for scalable projects The code progresses from basics to advanced patterns. Each file builds on the previous one. If you're building AI applications, APIs for LLMs, or just want a modern Python framework — this is the stack to learn. 🔗 GitHub repository: https://lnkd.in/eQt2p8pz Clone it and run `uvicorn main:app --reload`. You'll have a working API in 30 seconds. What framework are you using for your AI backends? 👇 #FastAPI #Python #API #WebDevelopment #AsyncPython #OpenSource #LearningInPublic

Today I explored FastAPI, a modern, high-performance Python framework for building APIs Q. What is FastAPI? FastAPI is a modern, high-performance web framework for building APIs with python, designed around: •  Speed •  Type safety •  Developer productivity It is built on top of: •  Starlette → handles request/response lifecycle and ASGI support •  Pydantic → handles data parsing, validation, and type enforcement This combination enables concurrent request handling, strict type safety, and clean API contracts. Q. Why is FastAPI fast to run? •  Uses ASGI instead of WSGI •  Supports async / await for non-blocking operations •  Powered by Uvicorn (ASGI server) enabling non-blocking I/O •  Handles multiple concurrent requests efficiently Compared to Flask’s WSGI-based synchronous request handling, FastAPI’s ASGI-based async model enables better concurrency and efficiency under high I/O workloads. Pydantic ensures: • Type correctness •  Data constraints •  Clean, predictable schemas Concepts I practiced: •  Field() for constraints & metadata •  field_validator →Validates or transforms an individual field’s value during model parsing. • before → raw input • after → post type conversion • model_validator  →Applies validation logic across multiple fields to enforce business rules at the model level. • computed_field → Defines a derived field whose value is calculated dynamically from other model fields. • Nested models → Use one Pydantic model inside another to represent structured, hierarchical data safely. Exporting models: • model_dump() • model_dump_json() Key Takeaway • Clear data contracts via explicit schemas • Safe data handling through strict type & business-rule validation • Self-documenting APIs with structured, predictable payloads • Scalable architecture that separates validation, logic, and transport layers #FastAPI #Pydantic #BackendEngineering #Python #APIDevelopment

Why I chose FastAPI over Django for my Scalable RAG Support Agent Project. I am certified in Django. I love its "batteries-included" philosophy. But when I architected my recent RAG Support Agent, I ditched it for FastAPI. Here is the engineering reasoning: Asynchronous by Default: AI agents involve heavy I/O waiting (waiting for Vector DBs, waiting for LLMs). FastAPI’s async/await support handles this natively without blocking the main thread. Performance: Built on Starlette and Pydantic, FastAPI is simply faster. For a real-time agent, latency is the enemy. Simplicity: I didn't need a heavy ORM or template engine. I needed a lightweight, high-performance API wrapper around my logic. There is no "Best Framework." There is only the Right Framework for the specific problem you are solving. Django is for rapid monolithic development. FastAPI is for high-performance microservices. What’s your go-to Python framework in 2026? #AI #RAG #Python #FastAPI #Django #SystemDesign #BackendDevelopment

Designing a Short URL (Convert API) – A System Design Perspective One of the most common and practical system design problems is building a Short URL / Convert API. High-level approach: Accept a long URL Generate a unique ID Encode it using Base62 (a–z, A–Z, 0–9) Store the mapping for fast redirection ❓ Why are short URLs often 7 characters long? Base62 allows 62⁷ ≈ 3.5 trillion unique combinations Keeps URLs short and user-friendly Scales efficiently for millions to billions of requests Enables fast lookups using DB + cache (Redis) Small design decisions like ID length directly impact scalability, performance, and user experience. Currently exploring more real-world backend and system design problems using Python, Django, and FastAPI. #SystemDesign #BackendEngineering #APIDesign #Python #FastAPI #Django #ScalableSystems

🚀 12 Insane Things You Can Do with FastAPI FastAPI isn’t just another Python framework — it’s a production-ready backend powerhouse ⚡ From automatic Swagger docs to async-first performance, FastAPI lets you: ✅ Build blazing-fast APIs ✅ Validate data effortlessly with Pydantic ✅ Scale microservices cleanly ✅ Secure APIs with OAuth2 & JWT ✅ Ship production code with minimal boilerplate If you’re building modern APIs, AI backends, or microservices, FastAPI is hard to beat. Pythonic. Performant. Developer-friendly. Are you using FastAPI in production yet? 👀 #FastAPI #Python #BackendDevelopment #APIs #Microservices #SoftwareEngineering

🚀 Why I’m Enjoying Working with FastAPI FastAPI has quickly become one of my favorite frameworks for building modern backend applications with Python. Its speed, simplicity, and powerful features make API development both efficient and enjoyable. What I really like about FastAPI: ✅ High performance – Built on Starlette and Pydantic, delivering near Node.js and Go-level speed ✅ Clean & readable code – Less boilerplate, more productivity ✅ Automatic API documentation – Swagger UI and ReDoc out of the box ✅ Strong data validation – Thanks to Pydantic models ✅ Perfect for scalable systems – Ideal for microservices and async applications I’ve been using FastAPI for building REST APIs, authentication systems, and database-driven applications, and it has significantly improved my development workflow. If you’re a Python developer looking for a modern, production-ready API framework, FastAPI is definitely worth exploring. 💡 Always learning, always building. #FastAPI #Python #BackendDevelopment #APIs #WebDevelopment #Programming #SoftwareEngineering

designing for scale later doesn’t mean you stop writing performant code. you may see posts or articles that encourage optimizing for scale as the need emerges. i support this, but keeping things simple doesn't mean that you can't have performance in mind. there are some fundamentals that keep your systems performant without going an “extra mile”. It could be as simple as: - not doing database entries in a for loop, instead using insertMany for bulk writes to the database (you don’t want 200 trips to your database if you can write 200 items at 𝗼𝗻𝗰𝗲). - properly using async/await to run independent operations in parallel instead of awaiting every step. this is useful when there are multiple things that need to undergo the same process. this way, the nth item doesn't have to wait for the first hundred before it gets to its turn. with javascript or typescript, you can use Promise.all() (or asyncio.gather() in python) to resolve everything at the end if you need the final data for a later step. practices like these keep your code performant and even scale as your system grows. #softwareengineering #mvp #typescript #python #mongodb

Async Python is great for high-performance Agents. Until it crashes your Container. I’ve been refining the backend infrastructure for my Agentic workflow—moving from local scripts to a containerized FastAPI + PostgreSQL architecture to handle concurrent LLM requests. I set up the docker-compose.yml, leveraged the default network for service discovery, and injected DB credentials via runtime environment variables for security. The infrastructure was solid. The containers spun up perfectly. But the application crashed on startup with: sqlalchemy.exc.MissingGreenlet: greenlet_spawn has not been called The Architectural Mismatch: In AgentOps, we enforce async everywhere to handle parallel tool execution without blocking. But bridging the gap between Sync ORM definitions (SQLModel) and Async DB drivers (asyncpg) is a common blind spot. The error occurred because SQLModel.metadata.create_all() is synchronous/blocking, but I was forcing it through an asynchronous engine. The Fix: It wasn't enough to just "await" the call. I had to explicitly bridge the contexts: await conn.run_sync(SQLModel.metadata. create_all) Why this distinction matters: If you don't handle these sync-to-async bridges correctly, you aren't just getting errors; you risk blocking the Event Loop. For an AI Agent system waiting on multiple real-time streams, that latency is unacceptable. Building production-ready agents isn't just about the Prompt; it's about ensuring the underlying system is non-blocking and architecturally sound. #AgentOps #BackendArchitecture #Docker #FastAPI #AsyncIO #AgentOps #ProductionEngineering #Python #SoftwareEngineering