FastAPI
High-performance asynchronous Python APIs, Pydantic type validation, OpenAPI documentation generation, and real-time streaming.
1 / Async Python APIs
FastAPI became my choice when a Python backend needed async performance and strict type validation. In Phoenix and Vigil, the async endpoint handling was essential — retrieval queries and sandbox container operations involved I/O waits that would have blocked traditional synchronous frameworks.
2 / Type Safety with Pydantic
FastAPI's integration with Pydantic models provided the kind of runtime type validation that Python typically lacks. In Phoenix, every retrieval query payload was validated against Pydantic schemas before entering the Hybrid RAG pipeline, catching malformed requests at the API boundary rather than as exceptions deep inside vector search logic. The automatic OpenAPI documentation generation from type annotations also eliminated the need for manually maintaining API documentation.
3 / Real-Time Streaming
In Vigil, FastAPI's streaming response support enabled real-time container log streaming — agent sandbox execution logs were forwarded to the frontend as they were generated, rather than buffered until completion. The async architecture handled multiple concurrent sandbox sessions without blocking, which was critical for running parallel agent evaluation tests.
4 / Compared to Django
FastAPI and Django serve different roles in my toolkit. Django is batteries-included for full-stack applications where I need an ORM, admin panel, and template rendering. FastAPI is purpose-built for performance-critical APIs where async I/O, type validation, and minimal framework overhead matter. I would not use FastAPI to build a content management system, and I would not use Django to build a real-time retrieval engine.