Maigret

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Maigret collects a dossier on a person by username only, checking for accounts on a huge number of sites and gathering all the available information from web pages. No API keys required. AI profiling (demo).

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Contents

• In one minute
• Main features
• Demo
• Installation
• Usage
• Contributing
• Commercial Use
• About

In one minute

Ensure you have Python 3.10 or higher.

pip install maigret
maigret YOUR_USERNAME

No install? Try the community Telegram bot or a Cloud Shell.

Want a web UI? See how to launch it.

See also: Quick start.

Main features

• Supports 3,000+ sites (see full list). A default run checks the 500 highest-ranked sites by traffic; pass -a to scan everything, or --tags to narrow by category/country.
• Embeddable in Python projects — import maigret and run searches programmatically (see library usage).
• Extracts all available information about the account owner from profile pages and site APIs, including links to other accounts.
• Performs recursive search using discovered usernames and other IDs.
• Allows filtering by tags (site categories, countries).
• Detects and partially bypasses blocks, censorship, and CAPTCHA.
• Fetches an auto-updated site database from GitHub each run (once per 24 hours), and falls back to the built-in database if offline.
• Works with Tor and I2P websites; able to check domains.
• Ships with a web interface for browsing results as a graph and downloading reports in every format from a single page.
• Optional AI analysis mode (--ai) that turns raw findings into a short investigation summary using an OpenAI-compatible API.

For the complete feature list, see the features documentation.

Used by

Professional OSINT and social-media analysis tools built on Maigret:

Demo

Video

Reports

PDF report, HTML report

Full console output

Installation

Already ran the In one minute steps? You're set. Below are alternative methods.

Don't want to install anything? Use the community Telegram bot.

Windows

Download maigret_standalone.exe from Releases. You can launch it two ways:

• Double-click it — Maigret will ask for a username, run a default search, and wait at the end so the report links stay visible.
• Run it from a terminal — open Command Prompt (press Win+R, type cmd, hit Enter) or PowerShell to pass extra options:

cd %USERPROFILE%Downloads
maigret_standalone.exe USERNAME
maigret_standalone.exe USERNAME --html       :: also save an HTML report
maigret_standalone.exe --help                :: list all options

Video guide: https://youtu.be/qIgwTZOmMmM.

Cloud Shells

Run Maigret in the browser via cloud shells or Jupyter notebooks:

Local installation (pip)

# install from pypi
pip3 install maigret

# usage
maigret username

From source

# or clone and install manually
git clone https://github.com/soxoj/maigret && cd maigret

# build and install
pip3 install .

# usage
maigret username

Docker

Two image variants are published:

• soxoj/maigret:latest — CLI mode (default)
• soxoj/maigret:web — auto-launches the web interface

# official image (CLI)
docker pull soxoj/maigret

# CLI usage
docker run -v /mydir:/app/reports soxoj/maigret:latest username --html

# Web UI (open http://localhost:5000)
docker run -p 5000:5000 soxoj/maigret:web

# Web UI on a custom port
docker run -e PORT=8080 -p 8080:8080 soxoj/maigret:web

# manual build
docker build -t maigret .                  # CLI image (default target)
docker build --target web -t maigret-web . # Web UI image

Troubleshooting

Build errors? See the troubleshooting guide.

PDF reports (--pdf) are an optional extra — install with pip install 'maigret[pdf]'. They need system-level graphics libraries on Linux/macOS; see the PDF reports section for per-OS install steps.

Usage

Examples

# make HTML, PDF, and XMind reports
maigret user --html
maigret user --pdf
maigret user --xmind # legacy XML with a manifest for XMind 2022+ readers

# machine-readable exports
maigret user --json ndjson   # newline-delimited JSON (also: --json simple)
maigret user --csv
maigret user --txt
maigret user --graph         # interactive D3 graph (HTML)
maigret user --neo4j         # Neo4j Cypher script (graph database)

# search on sites marked with tags photo & dating
maigret user --tags photo,dating

# search on sites marked with tag us
maigret user --tags us

# highlight sites whose page also mentions specific keywords
maigret user --keywords python rust
# keyword-matched sites are shown with "[++]" in bright green

# search for three usernames on all available sites
maigret user1 user2 user3 -a

# AI-assisted investigation summary (needs OPENAI_API_KEY)
maigret user --ai

--neo4j writes a *_neo4j.cypher script of the results graph; import it with cypher-shell -u neo4j -p < report_user_neo4j.cypher or paste it into the Neo4j Browser. Re-imports are idempotent. See the Neo4j export docs.

Run maigret --help for all options. Docs: CLI options, more examples. Running into 403s or timeouts? See TROUBLESHOOTING.md.

Web interface

Maigret has a built-in web UI with a results graph and downloadable reports.

maigret --web 5000

Open http://127.0.0.1:5000, enter a username, and view results.

Python library

Maigret can be embedded in your own Python projects. The CLI is a thin wrapper around an async function you can call directly — build custom pipelines, feed results into your own tooling, or run it inside a larger OSINT workflow.

See the full library usage guide for a working example, async patterns, and how to filter sites by tag.

Useful CLI flags

• --parse URL — parse a profile page, extract IDs/usernames, and use them to kick off a recursive search.
• --permute — generate likely username variants from two or more inputs (e.g. john doe → johndoe, j.doe, …) and search for all of them.
• --self-check [--auto-disable] — verify usernameClaimed / usernameUnclaimed pairs against live sites for maintainers auditing the database.
• --ai / --ai-model — run the AI analysis over the search results and stream a short investigation summary to the terminal.

AI analysis

<img src="https://asciinema.org/a/979404.svg" alt="asciicast" class="ra0-md-img" loading="lazy" />

--ai collects the search results, builds an internal Markdown report, and sends it to an OpenAI-compatible chat completion endpoint to produce a short, neutral investigation summary (likely real name, location, occupation, interests, languages, confidence, follow-up leads). Per-site progress is suppressed and the model's output is streamed to stdout.

export OPENAI_API_KEY=sk-...
maigret user --ai

# pick a different model
maigret user --ai --ai-model gpt-4o-mini

The key can also be set as openai_api_key in settings.json. The endpoint defaults to https://api.openai.com/v1, but openai_api_base_url in settings.json can point to any OpenAI-compatible API (Azure OpenAI, OpenRouter, a local server, …). See the settings docs for the full list of options.

Tor / I2P / proxies

Maigret can route checks through a proxy, Tor, or I2P — useful for .onion / .i2p sites and for bypassing WAFs that block datacenter IPs.

# any HTTP/SOCKS proxy
maigret user --proxy socks5://127.0.0.1:1080

# Tor (default gateway socks5://127.0.0.1:9050)
maigret user --tor-proxy socks5://127.0.0.1:9050

# I2P (default gateway http://127.0.0.1:4444)
maigret user --i2p-proxy http://127.0.0.1:4444

Start your Tor / I2P daemon before running the command — Maigret does not manage these gateways.

Cloudflare bypass

> Experimental. The Cloudflare webgate is under active development; the configuration schema, CLI behaviour, and the set of routed sites may change without backwards-compatibility guarantees.

A subset of sites in the database require a real browser to solve a JavaScript challenge. Maigret can offload these checks to a local FlareSolverr instance:

docker run -d -p 8191:8191 --name flaresolverr ghcr.io/flaresolverr/flaresolverr:latest
maigret --cloudflare-bypass <username>

The bypass is opt-in (--cloudflare-bypass or cloudflare_bypass.enabled in settings.json) and only fires for sites whose protection field matches. See the feature docs for backend options and configuration.

Contributing

Add or fix new sites surgically in data.json (no json.load/json.dump), then run ./utils/update_site_data.py to regenerate sites.md and the database metadata, and open a pull request. For more details, see the CONTRIBUTING guide and development docs. Release history: CHANGELOG.md.

Commercial Use

The open-source Maigret is MIT-licensed and free for commercial use without restriction — but site checks break over time and need active maintenance.

For serious commercial use — with a daily-updated site database or a username-check API — reach out: 📧 maigret@soxoj.com

• Private site database — 5 000+ sites, updated daily (separate from the public open-source database)
• Username check API — integrate Maigret into your product

About

Disclaimer

For educational and lawful purposes only. You are responsible for complying with all applicable laws (GDPR, CCPA, etc.) in your jurisdiction. The authors bear no responsibility for misuse.

Feedback

Open an issue · GitHub Discussions · Telegram

SOWEL classification

OSINT techniques used:

• SOTL-2.2. Search For Accounts On Other Platforms
• SOTL-6.1. Check Logins Reuse To Find Another Account
• SOTL-6.2. Check Nicknames Reuse To Find Another Account

License

MIT © Maigret