Maigret is a username-based OSINT reconnaissance tool that checks across thousands of websites for accounts associated with a given username, extracts profile information via the socid_extractor library, and generates multi-format reports (HTML, PDF, CSV, JSON, Markdown, XMind, Neo4j, etc.). It operates as a CLI tool, an embeddable Python library, a Flask web application with live scan streaming, and a Docker image. The architecture is a monolith with a clean separation between site database management (MaigretDatabase/MaigretSite models), asynchronous checking engine (AsyncioQueueGeneratorExecutor, checkers for aiohttp, curl_cffi, Cloudflare bypass), extraction enrichment, and report generation. External dependencies include aiohttp, socid_extractor, curl_cffi, aiodns, and potentially FlareSolverr and OpenAI-compatible APIs (optional). The tool supports proxy/Tor/I2P routing, self-validation of site configurations, and auto-update of its site database. It is actively maintained, with frequent releases and a large community, and is used by professionals in OSINT, academic research, cryptocurrency investigations, and privacy analysis.
Linkook is a Python CLI tool for OSINT-driven discovery of linked social media accounts and associated emails across multiple platforms. It takes a single username as input, scans a configurable set of websites (defined in a provider.json file), and recursively extracts linked accounts, usernames, and emails from discovered profiles. The tool supports breach checking via HudsonRock's database or Have I Been Pwned, and can export results to a Neo4j-compatible JSON format for graph visualization. The architecture is modular: a Provider class handles URL templates and regex extraction, a SiteScanner performs HTTP requests and keyword-based existence checks, and a ScannerManager manages concurrent scanning via a thread pool with a queue. The code is well-structured, includes unit tests, and demonstrates solid engineering practices with clean separation of concerns, error handling, and logging.