How it works: data gets encoded onto a lattice, a wave equation evolves it on a GPU (NVIDIA T4), and anomalies show up as regions where wave energy concentrates. It also returns the top features explaining WHY each point was flagged.

What's different from sklearn/PyOD: - Stateless: training + inference in one API call - GPU-accelerated: CUDA kernels, not Python loops - Explainable: per-feature contribution scores, not just a number - Works on anything: JSON, time series, text, tabular

Free tier: 100 scans/day, no key needed.

Python SDK: pip install WaveGuardClient MCP server: works with Claude Desktop / AI agents via Smithery Docs: https://github.com/gpartin/WaveGuardClient

Try it in 4 lines: from waveguard import WaveGuard wg = WaveGuard() result = wg.scan(training=[{"cpu": 45}, {"cpu": 50}], test=[{"cpu": 99}]) print(result.summary)