Drift//Monitor

Real-time behavioral drift detection for AI systems.

The Problem

LLM outputs degrade over long sessions. Tone shifts. Goals wander. Instructions erode. Content diverges from source material. This happens silently -- no existing tool monitors for it in real time.

Drift//Monitor is the andon cord for language models. It sits as a transparent proxy between your application and the LLM API, measuring output deviation from an established baseline using embedding distance and statistical process control.

Quick Start

pip install driftmonitor

import anthropic
from driftmonitor import DriftAwareClient, SessionConfig

# Wrap your existing Anthropic client -- zero code changes needed
base = anthropic.Anthropic()
client = DriftAwareClient(base, config=SessionConfig(name="My Session"))

# Use exactly like the base client
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello"}],
)
# Response passes through unchanged. Drift scores logged to SQLite.

# Check drift
print(client.latest_score)        # DriftScore(persona=5.2, goal=3.1, ...)
print(client.latest_severity)     # Severity.NOMINAL
print(client.composite_history)   # [2.1, 3.4, 5.2, ...]

How It Works

Three-Layer Architecture

Four Drift Axes

Severity Scale (SPC-based)

Operational Modes

from driftmonitor import DriftAwareClient, SessionConfig

# OBSERVE -- log everything, no intervention (default)
client = DriftAwareClient(base, config=SessionConfig(mode="observe"))

# ALERT -- notify operator on threshold breach
client = DriftAwareClient(base, config=SessionConfig(mode="alert"))

# GATE -- hold response until operator approves
client = DriftAwareClient(base, config=SessionConfig(mode="gate"))
response = client.messages.create(...)
if client.is_gated:
    client.gate_approve(operator="admin", reason="Acceptable drift")
    # or: client.gate_reject(operator="admin", reason="Too much drift")

REST API

# Install server dependencies
pip install driftmonitor[server]

# Launch the API
driftmonitor serve --port 8000

# Or with a custom database
driftmonitor serve --db /path/to/drift.db

Endpoints

CLI

# List sessions
driftmonitor sessions --db drift.db

# View scores for a session
driftmonitor scores <session-id> --limit 20

# Session summary
driftmonitor summary <session-id>

# Launch API server
driftmonitor serve --port 8000

# Version
driftmonitor version

Audit Log

Every drift event, mode change, gate decision, and correction is recorded in an immutable audit log with timestamps and operator attribution.

# Export for compliance
audit_json = client.audit.export_json(session_id="sess-001")
audit_csv = client.audit.export_csv(session_id="sess-001")

# Query entries
entries = client.audit.get_entries(action="gate_released", limit=50)

Custom Anchor

By default, the first assistant response becomes the drift baseline. You can provide a reference document instead:

client.set_anchor("Your reference text or system prompt here.")

Architecture

Your App
    |
    v
DriftAwareClient  ───>  EmbeddingEngine  ───>  SPCEngine
    |                    (sentence-transformers)  (control limits)
    |                         |                       |
    v                         v                       v
Anthropic API            DriftStore              DriftExplainer
    |                    (SQLite WAL)            (Claude Haiku)
    v                         |                       |
Response                      v                       v
(unchanged)              Audit Log              Corrections

Competitive Landscape

Development

# Clone and install
git clone https://github.com/AreteDriver/drift-monitor.git
cd drift-monitor
python -m venv .venv && source .venv/bin/activate
pip install -e ".[server,dev]"

# Run tests
pytest tests/ -v

# Lint
ruff check src/ tests/
ruff format src/ tests/

License

MIT

Built by AreteDriver -- standard work for AI behavior.