kontinuum-AI-anomaly 0.1.0a3

Anomaly / novelty monitor for agent action streams, built on kontinuum-core

kontinuum-AI-anomaly

A novelty & anomaly monitor for agent action streams. Point it at what your agent does — not what a home does — and it learns the agent's normal rhythm, then tells you when a step doesn't fit.

Built on kontinuum-core, a neuro-inspired learning engine. This package is the layer on top that turns the engine's raw signal into a usable verdict, keeps a history, and can alert you.

🇩🇪 Eine deutsche Version dieser README findest du in README.de.md.

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Why this exists

kontinuum-core was written to learn the behaviour of a home — lights, sensors, rooms. But its core idea (learn a stream of events, flag what's surprising) isn't specific to homes. An autonomous agent — a bot, a scraper, an "openclaw" worker — also produces a stream of actions with a normal rhythm. When that rhythm breaks, you usually want to know.

Core gives you a raw per-event "surprise" flag, but on its own that flag is jittery on short runs and has no memory of what was odd last week. This package adds exactly the parts core deliberately leaves out:

Layer	Module	What it adds
Ingestion	AgentMonitor	Feeds named agent actions into the engine, hiding the token/room mechanics core requires.
Scoring	scoring	Turns the jittery raw flag into a stable verdict: novelty-first, plus an adaptive threshold for known actions once there's enough data.
History	history	A ledger — "what was flagged this week?" — which core does not keep.
Recurrence	recurrence	Windowed counts of every action (not just flagged ones), so recurring misbehaviour novelty fires on only once stays visible: new-established, rate-spike, gone-silent.
Alerting	alerting	Routes anomalies to a webhook, a log, or a callback back into your agent.
Correlation	correlation	Watches several agents/streams at once and links related anomalies.
Feedback	feedback	Renders the engine's state as a prompt so the agent's own LLM can reflect.
Dashboard	dashboard	A tiny self-contained HTML view.
Orchestration	AnomalyWatch	Wires all of the above together behind one observe() call.

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Install

pip install kontinuum-AI-anomaly

Requires Python ≥ 3.9 and kontinuum-core >= 0.6.3 (pulled in automatically).

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Quick start

from kontinuum_ai_anomaly import AnomalyWatch

watch = AnomalyWatch(agent_id="openclaw")

# Let it learn the agent's normal rhythm.
rhythm = ["plan", "act", "observe", "reflect", "done"]
for _ in range(20):
    for action in rhythm:
        watch.observe(action)

# A rehearsed step is unremarkable...
watch.observe("plan").is_anomaly        # -> False

# ...a never-seen step is flagged.
verdict = watch.observe("escalate")
print(verdict.is_anomaly, verdict.score, verdict.reasons)
# True 0.72 ['[novelty] never-seen action']

That output is real, not illustrative — it's what the snippet above prints on a fresh install.

Each observe() returns an AnomalyScore with: action, is_anomaly, score (0–1 severity), surprise, threshold, is_novel, reasons, and strategy. Call .as_dict() for a JSON-friendly form.

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Alerting into your agent

from kontinuum_ai_anomaly import AnomalyWatch, AlertRouter, WebhookSink, LogSink

watch = AnomalyWatch(
    agent_id="openclaw",
    history_path="anomaly_history.json",   # persist the ledger
    router=AlertRouter([
        LogSink(),
        WebhookSink("https://example.com/hooks/openclaw"),
    ]),
)

Only actions that cross the anomaly bar are recorded and routed, so your webhook stays quiet during normal operation. Use CallbackSink to hand the anomaly straight back to your agent's own code.

See examples/openclaw_demo.py for an end-to-end run that rehearses a rhythm, injects a novelty, alerts, and renders a dashboard.

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Command line

The package ships a small CLI for driving it without writing code. Every number it prints is measured from real input — it never fabricates a metric.

# Stream actions (one per line) and report what's flagged + the real metrics.
python -m kontinuum_ai_anomaly watch actions.txt --history anomaly_history.json

# Print the ledger's real summary + long-horizon patterns.
python -m kontinuum_ai_anomaly report --history anomaly_history.json

# Render the ledger to a self-contained HTML dashboard.
python -m kontinuum_ai_anomaly dashboard --history anomaly_history.json --out dash.html

# Report recurring behaviour (new-established / rate-spike / gone-silent).
python -m kontinuum_ai_anomaly watch actions.txt --recurrence recurrence.json
python -m kontinuum_ai_anomaly recurrence --recurrence recurrence.json

kontinuum-AI-anomaly is also installed as a console script.

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What it's good at — and what it isn't

Being honest about this saves you from trusting the wrong signal:

• Reliable: novelty — a genuinely never-seen action is flagged immediately and confidently.
• Weaker: order/sequence anomalies (right actions, wrong order). The engine needs a lot of events before this sharpens, and stays in a cold_start learning phase under 100 events. SequenceStrategy helps but don't expect miracles on short runs.
• Not a thing it does: it does not train or change your agent. It's an external observer that produces a signal; what you do with that signal is up to you.

The design reflects this: scoring is novelty-first, and the adaptive threshold for known actions only switches on once there's enough data to be trustworthy.

For the hard-won details of how core actually ingests events — and the traps that cost real debugging time — see docs/INSIGHTS.md. Those notes came out of reverse- engineering core's ingestion path and aren't documented anywhere else.

Documentation

• docs/USAGE.md — a runnable, end-to-end openclaw example.
• docs/API.md — the full public API at a glance.
• docs/SCORING.md — how the scoring decides, and how to tune it.
• docs/INSIGHTS.md — field notes on core's ingestion path.
• CONTRIBUTING.md — dev setup and how to run the tests.

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License

AGPL-3.0. kontinuum-core is AGPL-3.0 and this package imports it, so the copyleft and network-service obligations propagate here too. If you build on this, keep that in mind.