volatilityfoundation

    volatilityfoundation/volatility3

    Volatility 3.0 development

    backend
    digital-investigation
    forensics
    incident-response
    malware
    memory
    Python
    NOASSERTION
    4.6K stars
    729 forks
    4.6K GitHub watchers
    Updated Oct 30, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    1 release in the last 90 days, the latest 43 days ago, with 81 contributors on GitHub.

    Gained 36% more stars in the last 90 days than in the 90 days before.

    Use Cases & Benefits

    • Extracts digital artifacts from volatile memory (RAM) samples for forensic analysis and incident response.
    • Offers a comprehensive, system-independent framework with updated symbol tables for accurate memory analysis across Windows, Mac, and Linux.
    • Use for investigating malware behavior by analyzing runtime memory states in compromised systems.
    • Use for conducting incident response to identify unauthorized activities from memory dumps during security breaches.
    • Use for digital forensic research and education to understand memory extraction techniques and volatile data analysis.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Other

    GitHub could not identify this license; read the LICENSE file before depending on it.

    Community files

    • README: present

    Watch-outs

    • License not identified by GitHub; read the LICENSE file before depending on it.

    General guidance from repository metadata, not legal advice. How we score

    From the README

    Volatility is the world's most widely used framework for extracting digital artifacts from volatile memory (RAM) samples. The extraction techniques are performed completely independent of the system being investigated but offer visibility into the runtime state of the system. The framework is intended to introduce people to the techniques and complexities associated with extracting digital artifacts from volatile memory samples and provide a platform for further work into this exciting area of research.

    In 2019, the Volatility Foundation released a complete rewrite of the framework, Volatility 3. The project was intended to address many of the technical and performance challenges associated with the original code base that became apparent over the previous 10 years. Another benefit of the rewrite is that Volatility 3 could be released under a custom license that was more aligned with the goals of the Volatility community, the Volatility Software License (VSL). See the LICENSE file for more details.

    Some also require/accept other options. Run vol <plugin> -h for more information on a particular command.

    Volatility 3 requires Python 3.8.0 or later and is published on the PyPi registry.

    If you want to use the latest development version of Volatility 3 we recommend you manually clone this repository and install an editable version of the project. We recommend you use a virtual environment to keep installed dependencies separate from system packages.

    Excerpt from the volatilityfoundation/volatility3 README on GitHub (NOASSERTION license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    729+44

    Open issues

    153+14

    Compared with peers in Backend

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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