Actualis reads the logs your coding agents already write, and turns them into clear answers about exposure, activity and cost. Nothing is instrumented, nothing is intercepted, nothing leaves the machine.
AGPL-3.0 · macOS, Linux, Windows · Python standard library only
$ actualis --days 30 EXPOSURE 20 critical credentials in agent context 54 distinct secrets seen · oldest 37 days ACTIVITY 4,812 commands actually executed 9 flagged for review COST $418.22 across 6 projects cache hit rate 71% · $1.34 median per ticket
Illustrative output. Your figures come from your own logs.
What it answers
They are just not in a form you can read. Actualis does the reading.
API keys, tokens and service credentials that were pasted into a prompt, printed by a command, or read out of a file. Grouped by fingerprint, with first seen, last seen, how many times it was used, and which projects.
Every shell command that really ran, not what was proposed. Destructive and network-reaching commands are surfaced separately, with the permission mode in force at the time and every denial recorded.
Token spend by project, branch, model and ticket, with cache read and write priced separately. Subagent usage is attributed to the run that spawned it rather than lost in the total.
How it works
Claude Code and Codex both write a full transcript of every session to disk. Actualis parses those files where they already sit. There is no agent to install, no proxy to route through, no wrapper around your CLI, and nothing to configure before the first run.
Actualis opens your transcripts read-only and never modifies them. It does not sit between you and your agent, cannot block a command, and cannot change a result. If it stopped running tomorrow, nothing about how you work would change.
Before trusting a transcript, Actualis checks the code signature of the agent that produced it against the expected publisher — Anthropic for Claude Code, OpenAI for Codex. A tampered or unsigned binary is reported as such rather than quietly accepted.
Any figure can be expanded to the records behind it. Ask
--why of a finding and it shows the evidence;
ask --explain of a metric and it shows the
arithmetic, the rate applied, and what was excluded.
On your menu bar
The tray app watches in the background and stays out of the way. When a credential you have not seen before turns up in agent context, the mark takes an amber ring and you get one notification — once, per credential, never a backlog dumped at launch.
State is carried by form as well as colour, so the mark still reads without it.
Private by construction
This is not a policy commitment. It is a property of how the tool is built, and you can verify it yourself in an afternoon.
The CLI is written against the Python standard library and imports no HTTP client, no socket, no telemetry SDK. There is no account, no API key and no opt-out to find, because there is nothing to opt out of.
Detected credentials are fingerprinted, never stored in full and never printed. Shared output is redacted by default, and the share command shows you exactly what it will disclose before it writes anything.
Licensed AGPL-3.0. The whole CLI is a single auditable file with no third-party dependencies, so “read the source” is a real option rather than a formality.
Bug reports carry the version and platform and nothing else. Your findings, transcripts and project names are yours to share deliberately or not at all.
Honest about limits
A measurement tool that overstates its reach is worse than none. These are the boundaries, stated plainly.
Contact
Questions about Actualis, licensing, or using it across a team. For anything that belongs in the open, the issue tracker is usually faster.
Support
No paid tier, no seat count, no telemetry pitch. If it found a credential before someone else did, you can put something behind the next release.