WaveGuard

<p align="center"> <img src="https://img.shields.io/pypi/v/WaveGuardClient?style=for-the-badge&color=blueviolet" alt="PyPI"> <img src="https://img.shields.io/badge/API-v3.3.0_stateless-brightgreen?style=for-the-badge" alt="v3.3.0"> <img src="https://img.shields.io/badge/GPU-CUDA_accelerated-76B900?style=for-the-badge&logo=nvidia" alt="CUDA"> <img src="https://img.shields.io/badge/MCP-Claude_Desktop-orange?style=for-the-badge" alt="MCP"> <a href="https://smithery.ai/servers/emergentphysicslab/waveguard"><img src="https://smithery.ai/badge/emergentphysicslab/waveguard" alt="Smithery"></a> </p>

<h1 align="center">WaveGuard Python SDK</h1>

<p align="center"> <strong>Anomaly detection powered by wave physics. Not machine learning.</strong><br> One API call. Fully stateless. Works on any data type. </p>

<p align="center"> <a href="#benchmarks">Benchmarks</a> • <a href="#quickstart">Quickstart</a> • <a href="#use-cases">Use Cases</a> • <a href="#examples">Examples</a> • <a href="#mcp-server-claude-desktop">MCP / Claude</a> • <a href="docs/api-reference.md">API Reference</a> </p>

---

What is WaveGuard?

WaveGuard is a general-purpose anomaly detection API. Send it any data — server metrics, financial transactions, log files, sensor readings, time series — and get back anomaly scores, confidence levels, and explanations of which features triggered the alert.

No training pipelines. No model management. No state. One API call.

Your data  →  WaveGuard API (GPU)  →  Anomaly scores + explanations

Under the hood, it uses GPU-accelerated wave physics instead of machine learning. You don't need to know or care about the physics — it's all server-side.

Modal dashboard vs API endpoints

If you look at Modal, you will see deployed functions (for example fastapi_app, gpu_scan, gpu_fingerprint). Those are compute/runtime units, not the HTTP route list.

To see all live API endpoints, use:

• OpenAPI docs: https://gpartin--waveguard-api-fastapi-app.modal.run/docs
• OpenAPI JSON: https://gpartin--waveguard-api-fastapi-app.modal.run/openapi.json

<details> <summary><strong>How does it actually work?</strong></summary>

Your data is encoded onto a 64³ lattice and run through coupled wave equation simulations on GPU. Normal data produces stable wave patterns; anomalies produce divergent ones. A 52-dimensional statistical fingerprint is compared between training and test data. Everything is torn down after each call — nothing is stored.

The key advantage over ML: no training data requirements (2+ samples is enough), no model drift, no retraining, no hyperparameter tuning. Same API call works on structured data, text, numbers, and time series.

</details>

Benchmarks (v2.2)

WaveGuard v2.2 vs scikit-learn across 6 real-world scenarios (10 training + 10 test samples each).

TL;DR: WaveGuard v2.2 wins 4 of 6 scenarios and averages 0.76 F1 — competitive with sklearn methods while requiring zero ML expertise.

F1 Score (balanced precision-recall)

Scenario	WaveGuard	IsolationForest	LOF	OneClassSVM
Server Metrics (IT Ops)	0.87	0.71	0.87	0.62
Financial Fraud	0.83	0.74	0.77	0.77
IoT Sensors (Industrial)	0.87	0.69	0.69	0.65
Network Traffic (Security)	0.82	0.61	0.77	0.61
Time-Series (Monitoring)	0.46	0.77	0.80	0.67
Sparse Features (Logs)	0.72	0.90	0.82	0.78
Average	0.76	0.74	0.79	0.68

What's new in v2.2

Multi-resolution scoring tracks each feature's local lattice energy in addition to global fingerprint distance. This catches subtle per-feature anomalies (like 3 of 10 IoT sensors drifting) that v2.1's global averaging missed. IoT F1 improved from 0.30 → 0.87.

When to choose WaveGuard over sklearn

Choose WaveGuard when...	Choose sklearn when...
False alarms are expensive (alert fatigue, SRE pages)	You need to catch every possible anomaly
You have no ML expertise on the team	You have data scientists who can tune models
You need a zero-config API call	You can manage model lifecycle (train/save/load)
Data schema changes frequently	Feature engineering is stable
Your AI agent needs anomaly detection (MCP)	Everything runs locally, no API calls

<details> <summary><strong>Reproduce these benchmarks</strong></summary>

pip install WaveGuardClient scikit-learn
python benchmarks/benchmark_vs_sklearn.py

Results saved to benchmarks/benchmark_results.json. Benchmarks use deterministic random seeds for reproducibility.

</details>

Real-World Validation: Crypto Crash Detection

WaveGuard powers CryptoGuard, a crypto risk scanner. Backtested against 7 historical crashes (LUNA, FTX, Celsius, 3AC, UST, SOL/FTX, TITAN):

Method	Recall	Avg Lead Time	False Positive Rate
WaveGuard	100% (7/7)	27.4 days	6.1%
Z-score baseline	100% (7/7)	28.4 days	29.9%
Rolling volatility	86% (6/7)	15.5 days	4.0%

WaveGuard flagged FTT (FTX token) at CAUTION on October 16, 2022 — 23 days before the 94% crash — while z-score analysis showed nothing unusual.

5× fewer false alarms than statistical baselines with the same recall. Full results: CryptoGuard backtest.

Install

pip install WaveGuardClient

That's it. The only dependency is requests. All physics runs server-side on GPU.

Quickstart

The same scan() call works on any data type. Here are three different industries — same API:

Detect a compromised server

from waveguard import WaveGuard

wg = WaveGuard(api_key="YOUR_KEY")

result = wg.scan(
    training=[
        {"cpu": 45, "memory": 62, "disk_io": 120, "errors": 0},
        {"cpu": 48, "memory": 63, "disk_io": 115, "errors": 0},
        {"cpu": 42, "memory": 61, "disk_io": 125, "errors": 1},
    ],
    test=[
        {"cpu": 46, "memory": 62, "disk_io": 119, "errors": 0},    # ✅ normal
        {"cpu": 99, "memory": 95, "disk_io": 800, "errors": 150},   # 🚨 anomaly
    ],
)

for r in result.results:
    print(f"{'🚨' if r.is_anomaly else '✅'}  score={r.score:.1f}  confidence={r.confidence:.0%}")

Flag a fraudulent transaction

result = wg.scan(
    training=[
        {"amount": 74.50, "items": 3, "session_sec": 340, "returning": 1},
        {"amount": 52.00, "items": 2, "session_sec": 280, "returning": 1},
        {"amount": 89.99, "items": 4, "session_sec": 410, "returning": 0},
    ],
    test=[
        {"amount": 68.00, "items": 2, "session_sec": 300, "returning": 1},     # ✅ normal
        {"amount": 4200.00, "items": 25, "session_sec": 8, "returning": 0},     # 🚨 fraud
    ],
)

Catch a security event in logs

result = wg.scan(
    training=[
        "2026-02-24 10:15:03 INFO  Request processed in 45ms [200 OK]",
        "2026-02-24 10:15:04 INFO  Request processed in 52ms [200 OK]",
        "2026-02-24 10:15:05 INFO  Cache hit ratio=0.94 ttl=300s",
    ],
    test=[
        "2026-02-24 10:20:03 INFO  Request processed in 48ms [200 OK]",                  # ✅ normal
        "2026-02-24 10:20:04 CRIT  xmrig consuming 98% CPU, port 45678 open",             # 🚨 crypto miner
        "2026-02-24 10:20:05 WARN  GET /api/users?id=1;DROP TABLE users-- from 185.x.x",  # 🚨 SQL injection
    ],
    encoder_type="text",
)

Same client. Same scan() call. Any data.

Use Cases

WaveGuard works on any structured, numeric, or text data. If you can describe "normal," it can detect deviations.

Industry	What You Scan	What It Catches
DevOps	Server metrics (CPU, memory, latency)	Memory leaks, DDoS attacks, runaway processes
Fintech	Transactions (amount, velocity, location)	Fraud, money laundering, account takeover
Security	Log files, access events	SQL injection, crypto miners, privilege escalation
IoT / Manufacturing	Sensor readings (temp, pressure, vibration)	Equipment failure, calibration drift
E-commerce	User behavior (session time, cart, clicks)	Bot traffic, bulk purchase fraud, scraping
Healthcare	Lab results, vitals, biomarkers	Abnormal readings, data entry errors
Time Series	Metric windows (latency, throughput)	Spikes, flatlines, seasonal breaks

The API doesn't know your domain. It just knows what "normal" looks like (your training data) and flags anything that deviates. This makes it general — you bring the context, it brings the detection.

Supported Data Types

All auto-detected from data shape. No configuration needed:

Type	Example	Use When
JSON objects	{"cpu": 45, "memory": 62}	Structured records with named fields
Numeric arrays	[1.0, 1.2, 5.8, 1.1]	Feature vectors, embeddings
Text strings	"ERROR segfault at 0x0"	Logs, messages, free text
Time series	[100, 102, 98, 105, 99]	Metric windows, sequential readings

Examples

Every example is a runnable Python script that hits the live API:

#	Example	Industry	What It Shows
🏭	IoT Predictive Maintenance	Manufacturing	Detect bearing failure, leaks, overloads from sensor data
🔒	Network Intrusion Detection	Cybersecurity	Catch port scans, C2 beacons, DDoS, data exfiltration
🤖	MCP Agent Demo	AI/Agents	Claude calls WaveGuard via MCP — zero ML knowledge
01	Quickstart	General	Minimal scan in 10 lines
02	Server Monitoring	DevOps	Memory leak + DDoS detection
03	Log Analysis	Security	SQL injection, crypto miner detection
04	Time Series	Monitoring	Latency spikes, flatline detection
06	Batch Scanning	E-commerce	20 transactions, fraud flagging
07	Error Handling	Production	Retry logic, exponential backoff

pip install WaveGuardClient
python examples/iot_predictive_maintenance.py

MCP Server (Claude Desktop)

The first physics-based anomaly detector available as an MCP tool. Give any AI agent the ability to detect anomalies — zero ML knowledge required.

Quick setup

{
  "mcpServers": {
    "waveguard": {
      "command": "uvx",
      "args": ["--from", "WaveGuardClient", "waveguard-mcp"]
    }
  }
}

Then ask Claude: "Are any of these sensor readings anomalous?" — it calls waveguard_scan automatically.

Available MCP tools

Tool	Description
waveguard_scan	Detect anomalies in any structured data
waveguard_scan_timeseries	Auto-window time-series and detect anomalous segments
waveguard_health	Check API status and GPU availability

See the MCP Agent Demo for a working example, or the MCP Integration Guide for full setup.

Azure Migration

Azure Anomaly Detector retires October 2026. WaveGuard is a drop-in replacement:

# Before (Azure) — 3+ API calls, stateful, time-series only
client = AnomalyDetectorClient(endpoint, credential)
model = client.train_multivariate_model(request)   # minutes
result = client.detect_multivariate_batch_anomaly(model_id, data)
client.delete_multivariate_model(model_id)

# After (WaveGuard) — 1 API call, stateless, any data type
wg = WaveGuard(api_key="YOUR_KEY")
result = wg.scan(training=normal_data, test=new_data)  # seconds

See Azure Migration Guide for details.

API Reference

wg.scan(training, test, encoder_type=None, sensitivity=None)

Parameter	Type	Description
training	list	2+ examples of normal data
test	list	1+ samples to check
encoder_type	str	Force: "json", "numeric", "text", "timeseries" (default: auto)
sensitivity	float	0.5–3.0, lower = more sensitive (default: 1.0)

Returns ScanResult with .results (per-sample) and .summary (aggregate).

wg.health() / wg.tier()

Health check (no auth) and subscription tier info.

Advanced intelligence methods (v3.3.0)

• wg.counterfactual(...)
• wg.trajectory_scan(...)
• wg.instability(...)
• wg.phase_coherence(...)
• wg.interaction_matrix(...)
• wg.cascade_risk(...)
• wg.mechanism_probe(...)
• wg.action_surface(...)
• wg.multi_horizon_outlook(...)

These map directly to /v1/* intelligence endpoints and return the raw JSON payload for maximal compatibility with rapidly evolving server-side response schemas.

Error Handling

from waveguard import WaveGuard, AuthenticationError, RateLimitError

try:
    result = wg.scan(training=data, test=new_data)
except AuthenticationError:
    print("Bad API key")
except RateLimitError:
    print("Too many requests — back off and retry")

Full API reference: docs/api-reference.md

Project Structure

WaveGuardClient/
├── waveguard/              # Python SDK package
│   ├── __init__.py         # Public API exports
│   ├── client.py           # WaveGuard client class
│   └── exceptions.py       # Exception hierarchy
├── mcp_server/             # MCP server for Claude Desktop
│   └── server.py           # stdio + HTTP transport
├── benchmarks/             # Reproducible benchmarks vs sklearn
│   ├── benchmark_vs_sklearn.py
│   └── benchmark_results.json
├── examples/               # 9 runnable examples
├── docs/                   # Documentation
│   ├── getting-started.md
│   ├── api-reference.md
│   ├── mcp-integration.md
│   └── azure-migration.md
├── tests/                  # Test suite
├── pyproject.toml          # Package config (pip install -e .)
└── CHANGELOG.md

Development

git clone https://github.com/gpartin/WaveGuardClient.git
cd WaveGuardClient
pip install -e ".[dev]"
pytest

Links

• Live API: https://gpartin--waveguard-api-fastapi-app.modal.run
• Interactive Docs (Swagger): https://gpartin--waveguard-api-fastapi-app.modal.run/docs
• PyPI: https://pypi.org/project/WaveGuardClient/
• Smithery: https://smithery.ai/servers/emergentphysicslab/waveguard

License

MIT — see LICENSE.