Streamlit vs FastAPI vs A2A vs React
Health Universe supports four app runtimes. Each is the right tool for a different job:
A2A Agents — reusable clinical agents and workflows that run inside Navigator.
FastAPI — deterministic Python tools (e.g. calculators) that appear as Navigator tools.
React — bring-your-own-frontend standalone apps with a full custom UI via the typed SDK. Preferred for real products.
Streamlit — the fastest path for a simple Python tool or prototype.
What is Streamlit?
Streamlit is an open-source Python framework for building interactive data apps and dashboards with minimal code. It's optimized for rapid prototyping and visualization, making it ideal for healthcare professionals and data scientists who want to quickly turn scripts into shareable web apps.
What is FastAPI?
FastAPI is a modern, high-performance web framework for building APIs with Python. It's built on top of Starlette and Pydantic, offering robust support for data validation, async programming, and integration with backend systems. On Health Universe, FastAPI is the way to ship a deterministic Python tool — a calculator or transform — that Navigator can surface and call as a tool.
What are A2A Agents?
A2A (Agent-to-Agent) agents are specialized applications built using the Health Universe A2A SDK. These agents enable AI-driven workflows with features like document processing, progress tracking, inter-agent communication, and long-running background tasks. They're reusable clinical agents and workflows designed specifically for Health Universe's Navigator platform, and support advanced workflow orchestration.
What is the React runtime?
The React runtime lets you bring your own frontend. Instead of a generated UI, you build a fully custom single-page app (React/Vite) or server app (Next.js/Node) and talk to the platform through the typed @health-universe/react SDK — typed React Query hooks for routines, documents, patients, sources/bundles, chat threads, and clinical tools. It's the preferred runtime for building real, production clinical products where you need full control of the user experience. See Working in React.
Side-by-Side Comparison
Feature
Streamlit
FastAPI
A2A Agents
React
Primary Use Case
Simple Python tool / prototype; dashboards
Deterministic Python tools (calculators) surfaced as Navigator tools
Reusable AI-driven clinical agents & workflows
Bring-your-own-frontend standalone apps (full custom UI)
Language / Stack
Python
Python
Python (A2A SDK)
TypeScript / React / Next.js / Node
UI/UX Focus
Built-in widgets and layout for rapid UI
No native UI; surfaces as a Navigator tool
Navigator integration with automatic progress tracking
Fully custom UI — your own design system and routing
Data Access
Manual file upload and processing
Inputs passed in by Navigator/caller
Built-in document client with NestJS/S3 integration
Typed @health-universe/react SDK (patients, documents, routines, bundles, …)
Navigator Integration
Standalone apps
Appears as a tool Navigator can call
Native AI workflows + inter-agent communication
Standalone; can read Navigator threads via the SDK
Background Tasks
Limited; relies on session state
Request-response; should return quickly
Automatic background job management with progress persistence
Client-side React Query; long work runs server-side via routines/agents
Deployment Style
Static-ish standalone app
API-first; runs as a Python service
Workflow-native; designed for Navigator orchestration
Static SPA (React) or Node server (Next.js/Node), auto-detected
Best For
Prototypes, quick data tools, solo apps
Calculators and deterministic tools for Navigator
AI workflows, document analysis, complex healthcare processes
Production clinical products with bespoke UX
When to use each runtime
✅ Use A2A Agents when:
You are building a reusable clinical agent or workflow that runs inside Navigator.
You need long-running background tasks (analyzing large datasets, generating reports, etc.).
Your workflow involves multiple agents communicating to complete a complex process.
You want automatic progress tracking, document persistence, and artifact management in Health Universe threads.
✅ Use FastAPI when:
You are building a deterministic Python tool — a calculator, transform, or scorer — that should appear as a Navigator tool.
You want fine-grained control over request/response handling with robust validation (Pydantic).
Your tool can return a result quickly (Navigator tools should respond within ~15 seconds).
✅ Use React when:
You are building a standalone product with a full custom UI, not a single tool.
You want the complete clinical data surface (patients, documents, routines, sources/bundles, threads) through typed hooks.
You want client-side routing, your own design system, and purpose-built clinical workflows.
This is the preferred path for real products. See Working in React.
✅ Use Streamlit when:
You want the fastest path to a simple Python tool or prototype.
You need to rapidly iterate on a data-driven UI with minimal code.
You are focused on visualizing health data or models rather than building a polished product.
Choosing between them
A quick rule of thumb:
Need a product with a custom UI? → React.
Need a deterministic tool inside Navigator? → FastAPI.
Need a reusable AI agent/workflow? → A2A.
Need something quick and Python-only? → Streamlit.
FastAPI and Streamlit are the quickest ways to get a single capability live. A2A is the home for AI-driven, reusable workflows. React is where you build the full clinical product experience on top of the platform's data through the typed SDK.
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