An OSINT tool to investigate GitHub profiles, tracking usernames, emails, identities, and repositories.
GitFive is a Python-based OSINT tool for investigating GitHub user profiles. It tracks usernames, email addresses, identities, and repositories to gather comprehensive public data about GitHub accounts. The tool solves the problem of fragmented GitHub intelligence by providing a unified, efficient way to analyze account history and connections.
Security researchers, criminal investigators, and open-source intelligence analysts who need to profile GitHub users for investigations or security assessments.
Developers choose GitFive for its low API consumption, multi-processing optimizations, and comprehensive feature set—enabling deep GitHub investigations without hitting rate limits or sacrificing depth of data.
🐙 Track down GitHub users.
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
Uses multi-processing and async scraping to minimize API calls, staying under GitHub's rate limits as emphasized in the README.
Integrates username history, email mapping, and repository analysis to provide a holistic view of GitHub accounts, including identity discovery.
Implements multi-processing to bypass Python's GIL and async operations for efficient scraping and data processing.
Supports JSON export for results, enabling further analysis or integration with other tools, as shown in the usage examples.
Requires Git and Python 3.10+ installation, along with pipx for dependency isolation, which can be challenging for users unfamiliar with Python environments.
Cloning and analyzing all repositories can be slow and consume significant disk space and bandwidth, especially for targets with many repos.
Only works with publicly available GitHub data, so it cannot investigate private profiles or access non-public information, limiting its usefulness in some scenarios.
The author explicitly forbids use in automated paid services without permission, restricting its applicability in commercial or automated contexts.