Fonnx is a Flutter plugin that enables cross-platform, on-device machine learning inference by wrapping the ONNX Runtime engine. It provides a unified Dart API that abstracts away the complexities of native FFI (for iOS, Android, macOS, Windows, Linux) and WebAssembly (for web), allowing developers to run models like OpenAI Whisper, Silero VAD, Magika, and various text embedding models without cloud dependencies. The codebase exhibits high software craftsmanship. Its architecture uses conditional Dart imports to cleanly separate native and web backends, employs Dart isolates for offloading heavy inference tasks, and implements a custom RPC protocol for web workers. A standout feature is its robust native memory management, including a C finalizer tested with sanitizers to prevent leaks during Flutter hot-restarts. While primarily a software library, it bridges the gap between ML research and production by bundling specific, optimized ONNX models and providing a deterministic test corpus with golden outputs. It is an excellent resource for Flutter developers building privacy-focused, offline-first AI applications, though its reliance on pinned native assets and specific build tooling introduces a moderate technical drift risk.