Vigil — monitor everything an AI model or agent does, from the outside, with guarantees

pip install vigil-monitor · MIT · numpy only · Python 3.9+ · open research for the AI community

Vigil watches AI models and agents without needing anything from the model vendor: no reasoning traces, no logprobs, no weights. It treats the deployed system as a scientific instrument: conserved quantities are checked, causes are established by intervention, behaviour is compared with the declared objective, every alarm carries a stated false-alarm rate, and a silent change of the model behind an API is detected from the outside.

In one minute

An AI agent reads documents, calls tools and answers. Vigil records what it did, checks that its permissions, secrets and budget balanced, replays it with pieces of its context removed to find out what caused each action, compares its behaviour with the goal it was given, and wraps every alarm in a stated false-alarm rate. A separate canary check tells you when the model behind the API has been silently swapped. A gateway can block a disallowed tool call before the agent ever sees it.

Who it helps

Use cases

How an injection gets caught, step by step

A clean run reads zero

What "guaranteed" means here

Every instrument says how hard it is to fool

Scorecard of live results

What it does

Results on live models in detail (16 September 2026)

Two open-weight agents on vLLM, 20 injected and 20 clean runs per condition. Every number below is reproducible from the scripts in experiments/ and the JSON in experiments/results/.

Replay attribution named the injected document as the cause in 16/16 (Qwen3-8B) and 15/16 (Qwen3-32B) of the runs where the model followed the injection, every one escalated as high, with zero false alarms on clean runs.

Injections split across documents, base64-encoded, or hidden in a tool result were followed by the 32B agent every time and caught every time by three independent instruments.

The model behind the endpoint was swapped from Qwen3-32B to Qwen3-8B. The paired per-canary test caught it; a pooled divergence did not. Drift marks the calibration set stale so every downstream guarantee is reported as unbacked.

With owned weights, a linear probe on the residual stream detects injections and generalises to unseen phrasings.

The gateway stripped 13/13 injected transfers before the agent saw them and unlocked one after a human approval; the real LLM judge was consequence-invariant; grounding surfaced that half the model's claims went beyond thin evidence.

The dashboard over 24 hours of stored verdicts: conservation accounts, escalations, regret and disagreement, the fingerprint band breaking at hour 20 and marking calibration stale, coverage, infrastructure, recent escalations.

Quick start

from vigilai import Monitor, ModelFingerprint, Store, from_spans
from vigilai.adapters import OpenAICompatibleRunner

runner = OpenAICompatibleRunner("https://api.example/v1", "model-name", tools_schema, api_key="…")
m = Monitor(runner=runner, alpha=0.05)
m.store = Store("vigil.db")                                   # persist every verdict and signal
m.calibrate([from_spans(s, tool_meta=meta) for s in known_good_span_sets])
m.attach_fingerprint(ModelFingerprint().fit(baseline_canary_responses))

v = m.assess(from_spans(new_spans, tool_meta=meta), output=answer_text, evidence=retrieved_chunks)
if v.escalate:
    route_to_human(v.severity, v.reasons, v.attribution.top1())

vigil-dashboard --db vigil.db --port 8020      # dashboard at /, Prometheus text at /metrics

To enforce rather than observe, put the gateway in front of the model:

from vigilai import AgentPolicy, PolicyGateway, serve_proxy
gw = PolicyGateway({"agentA": AgentPolicy("agentA", {"model-name"}, {"search", "read", "summarize"}, {"read"})}, tool_meta)
serve_proxy(gw, upstream="https://api.example", port=8010)     # agents call http://gateway:8010/v1/chat/completions

Design principles

1. Evasion-hard quantities first. Scopes used, secrets moved, budgets spent and actions under intervention must be violated to be beaten; text-based signals can merely appear clean.
2. Instruments vote independently and their disagreement is reported; each publishes its adversarial robustness and the attack that defeats it (vigilai.ROBUSTNESS).
3. Guarantees are explicit. Conformal calibration on known-good runs; a fingerprint change marks it stale.
4. Trust is derived from provenance, never declared upward; reasoning blocks are secrets by default.
5. Standards in, standards out. OpenTelemetry gen_ai and OpenInference spans in; decision spans and Prometheus metrics out. Vigil sits on top of any tracing platform; it does not replace it.

Limits, stated plainly

Replay needs a reproducible environment. The regret residual needs a correct feasible menu (it is skipped and declared when absent). Lexical grounding is gameable by paraphrase (plug in an entailment scorer). Nothing here sees inside a hosted model. The dashboard and proxy have no authentication and must sit behind an authenticated ingress. Samples are 20 runs per condition: enough to show mechanism, not to quote population rates.

Layout

vigilai/ package · tests/ 43 tests · experiments/ live experiments, node setup, plots, demo dashboard · experiments/results/ dated JSON and logs · docs/img/ figures and logo.

Licence and community

MIT. Built as open research for the AI community; features from the wider observability field were adopted with thanks rather than compared against. Contributions, replications and disagreements welcome.