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waybackpy

3.0.4 open-source

Waybackpy is a Python package and CLI tool that interfaces with the Wayback Machine APIs.

Summary

waybackpy is an open-source Python package and command-line tool that interfaces with the Internet Archive's Wayback Machine APIs, covering the Save, CDX, and Availability endpoints for archiving pages, listing snapshots, and finding the closest capture to a given date. It can be installed from PyPI and used either as a library import or run directly from the terminal, which suits OSINTOSINTOpen-source intelligence, collecting and analyzing information from publicly available sources. Investigators use it to learn about people, infrastructure or threats. researchers and web recon practitioners who need archived versions of URLs as part of a broader investigation rather than a standalone archiving workflow. It sits in the reconnaissance space alongside other URL-archive and OSINT utilities, wrapping the Wayback Machine's own APIs rather than replacing them. First committed in 2020 and maintained by 15 contributors across 10 tracked releases, though there have been no commits in the past year.

Waybackpy is a Python package and CLI tool that interfaces with the Wayback Machine APIs.

How waybackpy helps

Has this page ever been captured before, and can I force a fresh snapshot right now?

sends the URL to the Save API, which either archives it on the spot or confirms an existing recent capture, returning the archived URL either way

What did this page look like on or around a specific date?

queries the Availability API with the target date and gets back the closest snapshot's timestamp and URL, noting how far off that snapshot actually falls from the date requested

What is the full history of snapshots the Wayback Machine holds for this URL?

queries the CDX Server API and returns the list of captures with their timestamps, so every archived version can be walked through in order

Which pages on this domain were archived, and how many times each?

pulls the CDX records across the domain and lists the captured URLs and their capture counts, giving a picture of what recon already exists without visiting the live site

Can I pull that snapshot history sorted or narrowed down instead of the raw dump?

applies sorting to the CDX results so the captures come back ordered rather than requiring the reader to post-process the raw list

Languages

Not documented

Score card

  1. Shipping velocity releases a month, last 12 months
  2. Documentation quality release-notes rubric, 0–100
  3. Breaking-change discipline how rarely a release breaks users, and notice given
  4. Maintenance risk quiet time against its own cadence, maintainers
  5. Innovation & surface new capabilities, API and MCP surface
  6. Community health
  7. Contributor trend distinct committers, this year against last
  8. Maintainer concentration bus factor now against a year ago
  9. Issue backlog trend issues closed per issue opened, 12 months
  10. Responsiveness median time to a maintainer’s first reply

Nine scores for waybackpy, each built from the releases, notes and API history this site tracks — and, on Researcher and Business, where it stands among every measured tool in its category and how that has moved.

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Each score is 0–100 on a published rubric, derived from data this site already shows. A dimension we cannot measure says so — it is never scored 0 and never enters a rank.

How this is scored
  • Shipping velocity — releases a month over the last 12 months, on a log scale where 16 a month scores 100. Needs 3 releases in the window.
  • Documentation quality — the release-notes rubric published on the analytics page, unchanged.
  • Breaking-change discipline — the share of releases carrying a breaking change (none scores 100, half or more scores 0). Only projects that label breaking changes are measured: a clean record cannot be told from an unlabelled one.
  • Maintenance risk — quiet time measured against the tool's own release cadence (quiet longer than 90 days and three typical gaps scores 0), plus the number of active maintainers and whether contributors are growing, where known. Higher is lower risk.
  • Innovation & surface — new capabilities in the feature ledger this year, API and MCP operations added, and the share of commits that are features: the mean of whichever we hold.
  • Community health (open-source tools only) — each is a direction, not a size: contributors this year against last, the bus factor now against a year ago, and issues closed per issue opened, where steady scores 50, doubled 100 and halved 0; plus the median time to a maintainer's first reply (an hour scores about 100, a month or more 0). A commercial card has no repository and carries none of the four.
  • Documented delivery (commercial tools) — when a vendor's documented new capabilities have slowed against its own baseline, that counts against the overall standing as heavily as two low dimensions. Steady delivery, or too little history to judge, leaves the standing alone; it is never a sixth score.
  • Open-source tools are scored on repository signals; commercial tools on their published changelog. Ranks compare tools within one category and one profile and need at least five measured peers; the overall standing needs three ranked dimensions. Best-in-breed or consolidator is a shape, not a quality, and is never scored.

Release-note detail over time

See how much detail waybackpy puts in its release notes, quarter by quarter, and compare it with tracked tools in the same category. This history uses up to ten retained, reviewed notes per tool; sparse or blank older quarters may reflect that limit.

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Releases

6 entries

  1. February 20221
  2. January 20221
  3. January 20213
  4. October 20201

    6 entries

    Project health

    Issue, contributor and maintainer activity in akamhy/waybackpy, 2023-10 – 2026-09, as of 2026-09-25. Twelve-month figures first; the trend lines run the whole window.

    1. Contributors 0 ±0 vs prior 12 months
    2. Maintainers 0 +0 joined · −0 left, last 12 months
    3. Bus factor 0 committers behind half the commits
    4. Community-filed 100% of issues opened, last 12 months
    5. Median time to close 3.1 years issues closed, last 12 months
    6. Open backlog 19 of 88 issues ever filed
    1. Issues opened / month 0 · 0 communitymaintainers
    2. Open backlog 19
    3. Contributors / month 0
    4. Commits / month 0
    5. Stars 605
    Issue themes, last 12 months
    • bugs 25%
    • other 75%

    Counts from the project's public repository and git history; no individuals are identified. Maintainers are the committers covering 80% of the last year's commits. how we measure this

    Libraries its release ships

    What its latest release locks, checked against known advisories.

    None of its release's images build from a lockfile we can read.

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