Telemetry

Repowise collects completely anonymous usage telemetry to help us decide what to build and fix. It is impossible to tie any event to you, your machine, or your code. There are no usernames, no IP addresses, no file paths, and no source content anywhere in what we send.

This page documents exactly what is collected, what is never collected, how to verify it yourself, and how to turn it off.

Why we collect it

A tiny amount of anonymous data answers questions we otherwise have to guess at:

• Which commands do people actually run? (where to invest)
• Which CLI versions and Python versions are in use? (what we can drop support for)
• What share of runs fail, and on which command? (where the bugs are)
• Which OSes matter? (what to test on)
• Which MCP tools do coding agents reach for, and how often do answers come back stale? (where the retrieval work pays off)

That is the whole purpose. We do not sell it, and we do not use it to identify anyone.

What is collected

Every event, whatever its type, carries the same anonymous envelope:

Field	Example	Purpose
event	command_run / mcp_tool_call	Event type
anon_id	194639…d48a	Random per-install id (see below)
session_id	e2ecb2…1158	Random per-process id, groups one run
cli_version	0.21.0	Version adoption
os	darwin / linux / windows	Platform priorities
arch	arm64 / x86_64	Architecture priorities
python_version	3.12	major.minor only
is_ci	true / false	Separate automation from humans

command_run — once per CLI invocation

Field	Example	Purpose
properties.command	init, update, health	Which command ran
properties.subcommand	decision.add, telemetry.status	A known subcommand name only
properties.flags	["--resume", "--provider"]	Option names only, never values
properties.status	ok / error / usage_error / interrupted	Success, failure, a mis-invocation (bad/unknown flag), or user-cancelled (Ctrl-C)
properties.error_type	LLMProviderError	Exception class name only
properties.duration_ms	71840	Performance in the wild

For init and update, a few coarse buckets and enums describe the shape of the run so we can see the size of repos people index and whether docs generation is used. Never exact counts, never repo or file names:

Field	Example	Purpose
properties.file_count_bucket	500-999	Repo size distribution (a range, not a count)
properties.top_language	python	Which languages to prioritise
properties.docs_mode	true / false	AI docs vs index-only adoption
properties.provider / properties.model	anthropic / claude-opus	Which models generate docs
properties.embedder	openai	Which embedders are in use
properties.pages_bucket	100-499	Docs volume (a range)

mcp_tool_call — once per MCP tool call

Emitted by the MCP server (repowise mcp / repowise serve) so we can see which tools coding agents actually use and how often answers come back stale or degraded. Only the tool name and coarse enums/booleans — never the question, query, results, paths, or repo/symbol names:

Field	Example	Purpose
properties.tool	get_answer, search_codebase	Which tool was called
properties.status	ok / error	Tool error rate
properties.duration_ms	820	Tool latency in the wild
properties.confidence	high / medium / low	Answer confidence distribution
properties.retrieval_quality	strong / weak	Retrieval quality distribution
properties.results_bucket	1-3, 4-10	Result count for searches (a range)
properties.index_behind	true / false	How often served content is stale
properties.embedder_degraded	true / false	How often search runs on mock vectors

Example command_run payload:

{
  "event": "command_run",
  "anon_id": "194639251e854669ae2f56081620d48a",
  "session_id": "e2ecb2aeb4df422883c5af451f2b1158",
  "cli_version": "0.21.0",
  "os": "darwin",
  "arch": "arm64",
  "python_version": "3.12",
  "is_ci": false,
  "properties": {
    "command": "init",
    "subcommand": null,
    "flags": ["--resume"],
    "status": "ok",
    "error_type": null,
    "duration_ms": 71840
  }
}

What is never collected

• Source code or file contents
• File paths or directory names
• Repository names, package names, symbol/function names
• Generated documentation text
• Flag values (only the flag name is recorded)
• Environment variable values
• API keys or credentials
• Error messages or stack traces (only the exception class name)
• IP addresses (the server does not record the request source)
• Usernames, hostnames, email, or anything personally identifiable

How the anonymous id works

anon_id is a random UUID generated once and stored in ~/.repowise/platform.json. It is not derived from your hostname, username, or any machine identifier, so it cannot be reversed to a person. It only lets us count distinct installs. Delete the file and a new, unrelated id is generated; nothing links the two.

Verify it yourself

Run any command with REPOWISE_TELEMETRY_DEBUG=1 to print the exact payload to stderr without sending anything:

REPOWISE_TELEMETRY_DEBUG=1 repowise status

How to opt out

Any one of these disables all telemetry:

repowise telemetry disable        # persisted to ~/.repowise/platform.json
export REPOWISE_TELEMETRY_DISABLED=1
export DO_NOT_TRACK=1             # the cross-tool standard (https://consoledonottrack.com)

Check the current state anytime:

repowise telemetry status

Re-enable with repowise telemetry enable.

Data retention

Events are retained for 90 days and then deleted. Only aggregate figures (e.g. "X% of runs are on Windows") are ever used or shared.