oak-events

Privacy-first events from Luxonis OAK cameras for AI agents.

An OAK camera detects people, tracks them and measures depth on its own chip. oak-events turns that into one JSON line per event ("a person entered the doorway zone", "something 40 cm closer has stood in cell r2c5 for a minute") and serves those lines to agents through an MCP server.

Agents never receive a picture. There is no image tool, no snapshot command, and no code path that writes an image file.

Why

Giving an agent a camera usually means giving it a video feed. Most agent questions do not need one: is anyone at the bench? did something on the shelf move? is the camera alive? Those are answerable from detections and a coarse depth grid, which carry far less about the people in view than frames do.

Privacy model

Stated plainly, so you can check it against the code:

1. Colour frames stay on the camera. The colour sensor feeds the on-chip detection network. Its frames are never linked to a host output queue. Only two streams reach the host: detection results (label, confidence, 3-D position, normalized box) and the stereo depth map (src/oak_events/camera.py).
2. Depth maps are reduced and dropped. Each depth frame becomes a grid of cell medians (default 6 x 8) in memory, then is discarded. No depth map is stored.
3. Only JSON lines are written, to stdout and/or a daily .jsonl file. A test scans the repo and a temporary run directory for image files.
4. DepthAI telemetry, crash-dump upload and auto-calibration are off by default. oak_events.privacy forces DEPTHAI_TELEMETRY=0, DEPTHAI_DISABLE_CRASHDUMP_COLLECTION=1 and DEPTHAI_AUTOCALIBRATION=OFF before depthai is imported. Only OAK_EVENTS_ALLOW_TELEMETRY=1 leaves DepthAI's own defaults in place.
5. The MCP server only reads the JSONL files. It never opens the camera.
6. No model download at runtime. You point --model at a local NNArchive you fetched once.

What the events still reveal: when someone was present, for how long, how far from the camera, and when objects moved. Treat the event files as sensitive and keep them local.

Supported hardware

Requires DepthAI v3 (depthai>=3.9) and Python 3.10+.

Quickstart

pip install 'oak-events[camera,mcp]'

# 1. Check the setup (works without a camera or depthai installed)
oak-events doctor

# 2. Fetch a detection model ONCE (see below), then run
oak-events run --model ~/models/yolov6-nano.rvc2.tar.xz --out-dir ~/.local/share/oak-events/events

# 3. Let an agent read the events over MCP
oak-events mcp --events-dir ~/.local/share/oak-events/events

Fetching a model once

oak-events never downloads at runtime. Fetch an NNArchive from the Luxonis model zoo once, with DepthAI itself, and keep the file:

import depthai as dai

path = dai.getModelFromZoo(
    dai.NNModelDescription(model="luxonis/yolov6-nano:r2-coco-512x288", platform="RVC2")
)
print(path)  # copy this .tar.xz somewhere stable and pass it to --model

This one step contacts the Luxonis model hub. Check the exact model slug on the Luxonis model zoo; any detection NNArchive whose classes include person works (use --person-label if the class has another name). For RVC4, use platform="RVC4".

Zones

By default everything is reported for one zone named view. A zones file names regions by normalized image box and/or distance:

{"zones": [
  {"name": "doorway", "bbox": [0.0, 0.0, 0.35, 1.0]},
  {"name": "desk", "bbox": [0.4, 0.3, 1.0, 1.0], "z_mm": [400, 2500]}
]}

bbox is [x0, y0, x1, y1] in 0..1 with the origin top-left; z_mm is [min, max] distance in millimetres (either side may be null). Numbers must be finite: NaN and Infinity are rejected. Each zone gets its own person tracker and depth-change grid. The depth map is aligned to the colour camera only approximately, so leave some margin.

Commands

Optional checks, all off unless you set them:

• --model-sha256 HEX: compare the model archive's SHA-256 with HEX at start and refuse to run on a mismatch (camera event error, exit code 3, camera not opened). The hash is computed and recorded in the camera up event on every start, with or without this option. The reference hash is in docs/MODEL_CARD.md.
• --confidence-stats: add per-zone detection-confidence statistics to each alive event (see the spot-check procedure in the model card).
• --access-log FILE: append one JSON line per MCP tool call to FILE (mode 0600): time, tool name, argument names with the size of each value, result count, success, and the process id of the MCP client. Argument values and results are not written, and there is no image content to write. FILE must be outside the events directory.

Event schema

Every line is a JSON object with ts (ISO-8601 local time with offset), kind, and the fields below.

In confidence, n counts per-frame person detections in the zone at or above --person-confidence and below counts those under it; mean, min and max describe the accepted ones (null when n is 0). Each alive event covers the time since the previous one.

Example:

{"ts": "2026-10-11T09:14:03-07:00", "kind": "person_enter", "zone": "doorway", "count": 1, "nearest_mm": 1840}

Depth-change detection pauses while a person is present in that zone: people are not scenery.

MCP

oak-events mcp --events-dir DIR runs a stdio MCP server (official mcp Python SDK). Tools, all returning JSON text:

• recent_events(limit=50, kind=None) - latest events, oldest first
• zone_state() - per zone: person present or not, latest person and depth events
• camera_health() - latest camera and alive events with their age and a status (ok, stale, no_data, or the last camera state), plus the latest privacy event and private (true, false, or null if no run has written one)

See examples/claude-desktop-mcp.json for a client config, and examples/openclaw-stream.md / examples/webhook.md for wiring the event stream into automations.

Running it for real

Run oak-events run under a supervisor that restarts it on exit (systemd, launchd, a container restart policy). The process exits with a camera event explaining why: not_found, error, pipeline_stopped.

PoE notes

• Bootloader: RVC2 PoE devices with a bootloader older than 0.0.29 can fail discovery; 0.0.29 fixes it. oak-events doctor shows the version when the device is not in use.
• The device can take from ~30 s to a few minutes to free up after a previous host disconnects (2.5 min observed on an OAK-D PoE). oak-events run retries discovery for up to 4 minutes (240 s), then asks DepthAI to reconnect up to 5 times before giving up.
• macOS launchd: observed, cause not proven: under a launchd job the device could fail with X_LINK_DEVICE_NOT_FOUND while the same command worked from a terminal. Running it under a supervisor process that has macOS local-network permission worked. If you see this, check System Settings > Privacy & Security > Local Network.
• Prefer --device-id over addresses; the id survives network changes.

Security and AI documentation

• Threat model: trust boundaries, threats (STRIDE, MITRE ATLAS, OWASP LLM Top 10) and residual risk.
• Model card: the reference detector, its limits, failure modes and open-model risk.
• Data flow, retention and privacy impact: what leaves the camera, what is stored, retention and redaction.
• Software bill of materials: CycloneDX SBOM and licence summary for 0.1.2.
• Shared responsibility: who owns which security control (CloudLab Works, you, the model provider), per AI-CAIQ question.
• Risk assessment: AI risk register and AI impact assessment.
• Hardening: running oak-events as a dedicated user, with a sandboxed, read-only MCP server.
• CloudLab Works security policies: security baseline, security and AI governance policy (including acceptable use of oak-events), incident response plan, supply chain policy and the security program index.

Report vulnerabilities as described in SECURITY.md.

Development

python3 -m venv .venv
.venv/bin/pip install -e '.[mcp]' pytest
.venv/bin/pytest -q

Tests need neither a camera nor depthai. See CONTRIBUTING.md.

License

MIT. See LICENSE.

oak-events is an independent community project, not affiliated with or endorsed by Luxonis. OAK and DepthAI are Luxonis trademarks, used here only to say which hardware and SDK the tool works with.