How to Run Maigret in Google Colab and Cloud Notebooks: A Complete Setup Guide

You can run Maigret in Google Colab by cloning the soxoj/maigret repository, installing the package via pip, and executing the CLI commands directly in notebook cells, with the bundled example.ipynb providing a one-click launch option.

Maigret is a Python command-line tool that enumerates public accounts across thousands of websites using a single username search. Because the codebase consists of pure Python modules without system-specific dependencies, you can deploy it seamlessly in cloud notebook environments like Google Colab, Kaggle, and Binder. This guide explains the exact installation steps and execution patterns using the official source files from the repository.

Why Maigret Works in Cloud Environments

The Maigret architecture relies entirely on Python standard libraries and packages like aiohttp for asynchronous HTTP requests. According to maigret/maigret.py, the CLI entry point parses arguments and orchestrates the search workflow using only Python-native operations. The executor logic in maigret/executors.py manages HTTP requests and rate limiting, while maigret/sites.py handles the JSON-style site database entirely in memory. This design requires only Python 3.8+ and network access, making it compatible with any cloud notebook providing a Linux-based Python kernel.

Running Maigret in Google Colab

Google Colab provides a ready-to-use Linux environment with git and pip pre-installed, allowing immediate execution of Maigret.

One-Click Setup Using the Official Notebook

The repository includes an example.ipynb notebook in the root directory that demonstrates the complete installation and execution flow. You can launch this directly in Colab via the badge link provided in the README, which automatically clones the repository, installs dependencies, and prepares the execution environment.

Manual Installation and Execution

To set up Maigret manually in a new Colab notebook, execute these cells in sequence.

First, clone the repository and install the package:

# Clone the repository

!git clone https://github.com/soxoj/maigret

# Install the package from the cloned source

!pip install ./maigret/

# Optional: Clean the output for better readability

from IPython.display import clear_output
clear_output()

Next, define your target username:

username = "example_user"  # Replace with your target username

Finally, execute the investigation using the Maigret CLI:

# Run Maigret with all engines enabled, limiting to first 10 results

!maigret {username} -a -n 10

The output displays a table of discovered URLs with status indicators (found, not-found, or blocked). To export results, append flags like --json out.json or --csv out.csv to the command.

Cloud Platform-Specific Installation Methods

While the git-clone method works universally, other platforms support direct installation from the GitHub URL:

• Kaggle Kernels: Use the percentage-pip magic for cleaner dependency resolution:

%pip install git+https://github.com/soxoj/maigret.git

• Binder: Add the following line to your requirements.txt file before building the environment:

git+https://github.com/soxoj/maigret.git

• Azure Machine Learning Notebooks: Execute the standard pip install in a shell cell:

!pip install git+https://github.com/soxoj/maigret.git

After installation on any platform, invoke Maigret using the same !maigret command syntax shown in the Colab examples.

Core Architecture Components

Understanding the key source files helps troubleshoot cloud deployments and customize behavior:

• maigret/maigret.py: The CLI driver that parses arguments, builds the search plan, and coordinates report generation.
• maigret/executors.py: Manages asynchronous HTTP requests, rate limiting, and retry logic using aiohttp.
• maigret/sites.py: Contains the site database defining URL templates, HTTP methods, and response parsing rules for each supported platform.
• maigret/settings.py: Stores default configuration values for concurrency, timeouts, and output formats that you can override via CLI flags or a settings.yaml file.
• maigret/result.py: Normalizes executor responses into uniform Result objects containing URL, status, and tags.
• maigret/report.py: Handles formatting for JSON, CSV, HTML, and PDF output generation.

Summary

• Maigret requires only Python 3.8+ and network access, making it ideal for cloud notebooks with no GPU requirements.
• Install in Google Colab by cloning soxoj/maigret and running !pip install ./maigret/.
• Use the bundled example.ipynb for automated setup or follow the manual cell-by-cell installation.
• Alternative cloud platforms (Kaggle, Binder, Azure ML) support direct git-based pip installation using platform-specific syntax.
• Core functionality resides in maigret/maigret.py, maigret/executors.py, and maigret/sites.py, with no system-level dependencies that block sandboxed environments.

Frequently Asked Questions

Can I run Maigret in Google Colab without installing anything?

No, you must install the package first because Maigret is not pre-installed in Colab's default environment. However, installation takes only seconds using !pip install ./maigret/ after cloning the repository, and the included example.ipynb automates this entire setup process.

Does Maigret require GPU acceleration in cloud notebooks?

No, Maigret performs only HTTP requests and text parsing, requiring no GPU resources. The maigret/executors.py module uses CPU-based asynchronous I/O through aiohttp, so you should use a standard CPU runtime in Colab or Kaggle to avoid unnecessary GPU charges.

How do I save Maigret results when running in Google Colab?

Add output flags to your execution command, such as !maigret {username} -a --json results.json. The file saves to the Colab filesystem, where you can download it via the file browser panel or mount Google Drive to persist results between sessions.

Is it safe to run Maigret in a shared cloud environment like Google Colab?

Yes, Maigret performs passive OSINT queries without exploiting vulnerabilities or sending malicious payloads. The tool only queries public profile URLs as defined in maigret/sites.py, making it safe for shared cloud notebook environments, though you should respect rate limits and terms of service for the sites being queried.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}