Maigret searches for accounts across more than 2,500 websites using a single username input. It aggregates profile links, photos, bios, and contact details into a structured report, without requiring API keys or registration on each target site. The tool is named after the fictional detective and fits the workflow of security researchers, journalists, and investigators who need a fast, reproducible way to map a person's online presence.
Getting started requires Python and a single pip install. From there, you run a username through the CLI and Maigret queries its built-in site database, filters out false positives, and outputs results as HTML, PDF, JSON, or plain text. The site database is maintained by the community and updated regularly, so coverage stays current as new platforms emerge. Investigators can rerun the same handle over time, compare report deltas, and keep an auditable trail for case notes and incident response timelines.
Maigret builds on the original Sherlock project but extends it substantially: smarter false-positive filtering, richer metadata extraction per site, graph-based relationship mapping, and a Telegram bot interface for teams that prefer chat-driven workflows. Because you self-host it, all query data stays under your control, and no third-party service sees the usernames you are investigating. For teams with strict data handling requirements, that boundary matters.
