AgentTracker

AgentTracker is a dependency-free status board for anyone running their own always-on local agents, the kind of scheduled scripts you wire up with launchd or cron to do work for you every day. It reads each agent’s real on-disk state (scheduler status, run logs, output artifacts) and renders a self-contained dark-theme HTML dashboard, so at a glance you can tell which agents ran, which stalled, and which failed.

The board is a deterministic view of real signals, not a generative model. It is read-only: no servers, no frameworks, no network calls, no mutation, just Python stdlib and a single HTML file you open in a browser. The companion face and one-line summary are a friendly presentation of those same on-disk signals.

The screenshot and committed demo.html are --demo renders made from fabricated sample data. Your live index.html never leaves your machine.

Why

Background agents fail silently. A cron job that stops firing, a delivery step that no-ops, a connector that goes blind — you don’t notice until you needed the output. AgentTracker turns “I hope my agents ran” into a glanceable status board, with honest states derived from ground truth rather than hardcoded green checkmarks.

What it shows

• Per-agent status cards — HEALTHY / STALE / DEGRADED / FAILED, each derived from real signals (is the scheduled job loaded? did it run today? did delivery succeed?).
• Key facts per agent — last run, freshness, output counts, delivery channel.
• Merged activity timeline — recent runs across all agents in one feed.
• Auto-refresh every 5 minutes.

Status model

State	Meaning
HEALTHY	Scheduled job loaded and a clean run recorded today
STALE	Job loaded but no run yet today (e.g. before its fire time)
DEGRADED	Ran today but a downstream step (delivery/connector) failed
FAILED	Job not loaded, or today’s run errored / produced nothing

Usage

python3 gen-dashboard.py && open index.html      # render from YOUR agents' real state
python3 gen-dashboard.py --demo --out=demo.html  # render a sample board with fake data

📺 See it without installing anything: open demo.html (raw HTML, sample data) — or view it rendered via htmlpreview.

The generator (gen-dashboard.py) is the whole tool. It ships wired to two example agents (a daily-brief job and a second scheduled job) by reading their launchd labels, run-logs, and output files. Adapt the *_state() functions to point at your own agents’ logs and artifacts. The committed demo.html is generated with --demo (fabricated data) so the live view never exposes real machine state.

Architecture

Your local agents (launchd/cron jobs) leave real state on disk — launchctl status, run logs, output artifacts. The generator reads that state (Python stdlib, no network, no mutation), maps it to an honest status model, and renders a self-contained HTML board.

🖼️ View / edit the diagram: drag assets/architecture.excalidraw onto excalidraw.com, or open the live version.

local agents ──▶ on-disk state ──reads──▶ gen-dashboard.py ──▶ index.html (live, git-ignored)
(launchd/cron)   (launchctl,            (status model +        demo.html  (sample, public)
                  logs, artifacts)       HTML render)          + runs at login, refresh 5m

Design principles

1. Ground truth, not assumptions. Every field traces to a file or a launchctl line. The dashboard’s job is to surface real failures, not to look reassuring.
2. Zero dependencies. Python stdlib only; the output is one portable HTML file.
3. Read-only. It never mutates agent state — it only observes.

License

MIT

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