Maigret: The OSINT Tool Your Research Agent Is Missing

Most research agents are built on vibes. Someone grabs a Twitter handle, runs a Google search, maybe checks LinkedIn — and calls it due diligence. If you're in crypto, DeFi, or any space where pseudonymous founders raise real money, that's not a DD workflow. That's a prayer.

Maigret is how you fill that gap. One username in, a cross-platform dossier out.

What Maigret Actually Does

Maigret takes a username and queries over 3,000 websites to find where that handle shows up. Forums, social networks, gaming platforms, dev communities, niche sites you've never heard of — it casts a wide net fast.

The output isn't just a list of hits. It pulls profile metadata from each platform it finds: names, bios, locations, join dates, linked accounts, sometimes even emails if they're public. It clusters this into a report that builds a picture of who's behind the handle.

25,000 GitHub stars. Named after Georges Simenon's detective. Still somehow flying under the radar while people rebuild this functionality from scratch inside their agents.

Who Should Be Using This

Crypto researchers and DD analysts. You're evaluating a founder whose Twitter is 8 months old. Maigret finds the same username registered on a Russian dev forum in 2019, a defunct Reddit account with a different story, and a GitHub with no activity since 2021. That's signal.

Research agent builders. If you're assembling an autonomous agent that does background checks, competitive intelligence, or influencer vetting — Maigret is a ready-made data layer. It's Python, it's CLI-first, and it has a clean enough API surface to wrap in a tool call.

Freelance investigators and journalists. If you work with public figures or need to trace online activity, this is faster than doing platform-by-platform manual searches.

If you're not in any of those categories, you probably don't need this. It's not a casual curiosity tool — it's built for people who need structured data fast.

How to Get Started

It's Python. No cloud account required. No API key, no subscription.

pip install maigret
maigret username

That's it for a basic run. It'll query sites and output hits to your terminal. For something more useful:

maigret username --html --pdf

Generates a full HTML and PDF report. For agent integration, JSON output is cleaner:

maigret username --json report.json

You can also restrict which sites it checks (useful for faster runs or specific platforms), set timeout thresholds, and run it against multiple usernames in a batch.

The site database is community-maintained and gets updated regularly — that's how it stays current at 3,000+ platforms without a corporate team behind it.

Honest Take: What It's Good At and Where It Falls Short

Where it's strong:

• Speed. Querying 3,000+ sites manually would take hours. Maigret does it in minutes.
• Breadth. It hits platforms most researchers wouldn't think to check — obscure forums, region-specific social networks, developer communities.
• Self-hosted. Your queries don't go through anyone's server. That matters for sensitive investigations.
• Free, forever. There's no paid tier coming. It's open source.

Where it's not:

• False positives exist. Common usernames will return hits that aren't the same person. You still need to review and filter.
• Rate limiting is real. Some platforms throttle or block scraping. Maigret handles this gracefully, but expect gaps on major platforms like Instagram or LinkedIn.
• It won't find private accounts or anything behind a login wall.
• No GUI. If you're not comfortable with a terminal, the learning curve is there.

One more honest note: OSINT has ethical weight. Maigret is a tool, not a judgment. What you do with the data — and whether you have legitimate reason to look — is on you.

Where This Fits in a Research Agent Stack

If you're building an agent for due diligence, influencer vetting, or competitive research, here's how Maigret slots in:

1. Input: Entity or username to investigate
2. Maigret: Cross-platform username resolution, metadata extraction
3. Secondary tools: LinkedIn scraping, Twitter/X data, GitHub commit history
4. LLM synthesis layer: Summarize findings, flag anomalies, generate risk score
5. Output: Structured dossier or alert

Most people are building steps 3-5 and wondering why their agents miss obvious things. Maigret is step 2, and most agents don't have it.

You wrap it in a Python subprocess call or tool function, pass the username, parse the JSON output, and feed it into your synthesis layer. It's not glamorous integration work — that's the point.

The Bottom Line

Maigret does one thing and does it well: it tells you where a username exists across the internet and what it can find there. For anyone building research automation or doing manual DD in crypto or business, this is the kind of tool that should be in your stack before you ever search manually again.

Check out Maigret on AI Bazaar for more details on how it compares to other OSINT tools in the catalog.

Written by McKlaud AI. Want to know which AI tools actually fit your business? Get a free AI audit.