AO3 Wrapped 2025 is not an official Archive of Our Own feature. It is a fandom-made year-in-review format that turns a reader’s AO3 history—or an author’s 2025 publishing records—into statistics, rankings, charts, and shareable graphics. Different notebooks, spreadsheets, and community projects produce different versions, so there is no single canonical AO3 Wrapped generator.
The most important limitation is that a reader report normally measures works visited or recorded in AO3 history, not verified reading time, comprehension, completion, or genuine preference. Before running any automated tool, decide what data you are comfortable sharing, inspect the code, and avoid entering an AO3 password into an untrusted notebook.
What does “AO3 Wrapped 2025” mean?
The phrase refers to a personal year-end summary modeled on Spotify Wrapped. A typical report may show the fandoms you visited most, the relationships or characters that appeared most often, your most-viewed works, total words encountered, reading patterns, or the number of kudos you left.
Some versions are reader-focused and analyze an AO3 history page. Others are writer-focused and summarize works published during 2025. A person may also combine both: for example, a graphic showing the fandoms they read most alongside the works they posted, their chapter count, and their annual word total.
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That distinction matters because “my AO3 Wrapped” can describe several different data sources:
- AO3 history: pages and works recorded in an account’s history.
- Manual reading logs: a spreadsheet, Notion database, or other tracker maintained by the user.
- Author records: works, chapters, word counts, completion status, and fandoms published during the year.
- Community graphics: a manually assembled collection of favorite statistics and observations.
None of these should be described as an official AO3 dashboard. AO3’s official year-end statistics report archive-wide activity, not a personalized Wrapped report for each account.
Is there an official AO3 Wrapped 2025?
No. AO3 did not provide a native “Wrapped” dashboard for individual users. The official Archive of Our Own and OTW statistics published for 2025 describe the archive as a whole: comments, page views, bookmarks, traffic patterns, and year-over-year changes.
Those statistics provide useful context, but they cannot tell you how much you personally read. OTW reported that monthly comments in 2025 were generally one to two million above the corresponding 2020 levels, and December 2025 exceeded five million comments for the first time. It also reported a daily page-view peak of 141 million on June 1, although that spike was likely associated with bot traffic. These are archive-wide measurements, not individual reading totals.
The same report documented events that can affect the archive and, indirectly, personal data collection: a bookmark-storage outage on July 3–4, planned downtime for a collections update on September 29, strong weekend traffic, and a record 879 million page views during the first week of 2026. A temporary outage or an incomplete history export can make a personal report look different from what a user remembers.
What can an AO3 Wrapped report show?
The exact output depends on the implementation and the quality of the source data. Common categories include:
| Statistic | What it may represent | Important limitation |
|---|---|---|
| Works visited | Works appearing in AO3 history during the selected period | A visit is not proof that the work was finished or even read in full. |
| Total words | The word totals associated with works included by the tool | Rereads, incomplete reads, deleted works, and processing rules can affect the total. |
| Top fandoms | Fandom tags appearing most often in the analyzed records | Multi-fandom works and tag-cleaning rules can change rankings. |
| Relationships and characters | Relationship and character tags found in the source records | Tags are metadata, not a measurement of how strongly you prefer a character or pairing. |
| Ratings and warnings | AO3 rating and archive-warning categories among included works | These describe the work’s posted metadata, not your personal response to it. |
| Kudos | Kudos left, received, or associated with analyzed works, depending on the tool | The report must state which direction is being counted. |
| Reading frequency | Monthly, weekly, or yearly patterns based on dates in the data | It may reflect visits or log entries rather than time spent reading. |
| Most-viewed works | Works that appear most prominently in the relevant history or activity data | “Most viewed” can be misleading if the source is personal history rather than AO3’s public view counter. |
Older open-source AO3 Wrapped projects list outputs such as works, tags, relationships, characters, fandoms, categories, warnings, ratings, fanfic counts, kudos, and total words. Newer community notebooks may add visual cards, monthly charts, reading streaks, or custom rankings.
The biggest accuracy issue: history is not the same as reading
AO3 history records access to pages. A work may appear because you read it, skimmed it, opened it accidentally, reread it, checked the tags, or returned to it without finishing. Consequently, a report based on history should use language such as:
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- “works visited in 2025,” not “works I read in 2025”;
- “words associated with recorded works,” not “words I definitely completed”;
- “reported by this tool,” not “verified by AO3”;
- “most common tags in my history,” not “my objectively favorite tropes.”
Your data may also be incomplete if you read while logged out, used more than one account, cleared your history, browsed on an account with history disabled, or kept a separate manual log. Deleted, hidden, or changed works can further affect a later analysis.
How to make an AO3 Wrapped 2025
There is no one required method. Choose based on convenience, privacy, repeatability, and technical ability.
Option 1: Use a Google Colab notebook
The most widely discussed 2025 workflow used a Google Colab notebook linked from a Reddit community post. Colab runs notebook code in a browser, which makes it easier to use than installing Python locally. The general process was:
- Open the notebook and read every instruction before running a cell.
- Supply the requested AO3 session information or cookies, if the notebook requires authenticated access.
- Select 2025 as the analysis year.
- Allow the notebook to retrieve and process the relevant history pages.
- Save the resulting statistics or visual cards locally.
This is the convenient, polished route, but it has the greatest trust and availability concerns. It depends on a third-party notebook, browser behavior, authentication details, notebook dependencies, and AO3 responding normally. Users in the associated discussion reported redirects, blocking, Google Colab dependency errors, trouble saving generated images, and uncertainty about local execution.
If you choose the browser-based route, treat a Google Colab AO3 Wrapped notebook as code you are executing—not as an official AO3 service. Do not paste your AO3 password into a notebook. Review cells that request cookies, tokens, file uploads, or external network access, and stop if the instructions are unclear.
Option 2: Analyze a CSV file
A separate GitHub-maintained Colab workflow is designed around a CSV file such as ao3_history_2025.csv. It can connect to Google Drive if desired and allows users to configure settings including:
- the year being analyzed;
- the number of top fandoms to display;
- scraping limits;
- request delays; and
- the output categories and visualizations.
This approach is easier to repeat because the source data can be archived and analyzed again without collecting the same pages every time. It also separates data collection from analysis: a user can prepare or obtain the CSV first, then inspect or process it later.
A spreadsheet can serve the same purpose without automated scraping. An AO3 reading tracker spreadsheet or Google Sheets dashboard may require manual entry on a log tab while calculating summaries elsewhere. This is slower, but it avoids handing authenticated session data to a third-party scraper and gives you control over exactly what counts as “read.”
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Option 3: Run a local Python script
A local script is the most transparent option for a technically comfortable user. You can inspect the repository, review dependencies, keep the data on your own computer, and adjust the analysis. GitHub is useful here because it provides a place to inspect versioned code, issues, documentation, and changes.
The trade-off is setup. The older open-source ao3-wrapped project is described as simple and potentially buggy. Its installation requires Python-related dependencies and an AO3 API fork. A local script can also fail when AO3 changes its pages, when dependencies become incompatible, or when authentication and rate limits interfere.
Before running an AO3 Wrapped GitHub script, check the repository’s recent activity, installation instructions, dependency files, issue reports, and the code that handles authentication. “Hosted on GitHub” does not mean “audited,” official, or guaranteed safe.
Option 4: Build a manual Google Sheets or Notion Wrapped
Manual tracking is the best choice when privacy and interpretability matter more than automation. Create columns for the work title, author, URL, fandom, relationship, characters, rating, warnings, date started, date finished, and whether the work was completed. You can then use pivot tables, formulas, or dashboard charts to calculate your 2025 totals.
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A fan-created Sheets template reported that only the log tab required manual updating while other dashboard elements were automated. Third-party Notion products also marketed fanfiction trackers and AO3 Wrapped templates in late 2025. These are organizational tools, not AO3 integrations or AO3 endorsements. A fanfiction tracker template can be useful if you want to maintain cleaner data throughout 2026, but check what information the template collects and where it is stored.
Manual data also lets you answer questions an automated history report cannot: How many works did I finish? Which works did I abandon? Which rereads were intentional? How many words did I actually write rather than merely post?
How to avoid overwhelming AO3
History analysis can require many page requests, especially for a large account. The 2025 community workflow warned users not to run the notebook repeatedly, and the older open-source project notes that processing a large history may take considerable time.
Use a modest request rate and a meaningful delay between requests. If AO3 starts returning errors, redirects, or blocking responses:
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- Stop the notebook or script.
- Do not immediately rerun it with more aggressive settings.
- Increase delays if you resume later, or switch to a CSV or manual workflow.
- Do not attempt to bypass access controls, rate limits, or anti-abuse systems.
- Check AO3’s own status and communications before assuming your account or code is broken.
AO3’s 2025 communications discussed anti-spam rate limiting and the ongoing need to balance protection against abuse with accessibility. A personal year-end graphic is not worth repeatedly hammering the archive.
Privacy and security checklist
Some automated workflows request authenticated AO3 session information or browser cookies. A cookie can function as proof that a browser is logged in, so treat it as sensitive account material.
- Never enter your AO3 password into an untrusted notebook or script.
- Do not share cookies or session tokens in screenshots, chats, public notebooks, or issue reports.
- Read the notebook cells before executing them, especially cells that upload files or send data to external services.
- Prefer a local, reviewed script or manually prepared CSV when you do not trust a hosted workflow.
- Use the smallest necessary dataset. Do not upload unrelated browser exports or account files.
- Delete temporary files and revoke active sessions if you believe credentials or cookies were exposed.
- Stop when the workflow behaves unexpectedly. Redirects, repeated login prompts, unexplained downloads, or requests for unrelated credentials are reasons to exit.
The community discussions establish that users have privacy concerns and that local execution is an alternative. They do not amount to a formal security audit of any particular notebook. No third-party generator should be described as secure merely because it is popular or widely shared.
AO3 Wrapped for writers: posted words are not written words
An author’s version should distinguish at least three totals:
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- Words posted in 2025: words in chapters uploaded during the year.
- Total work word count: the current length of a work, which may include chapters posted in earlier years.
AO3’s 2025 release notes changed the interpretation of yearly word counts on the Statistics page: only words in chapters posted during that year count toward that year’s total. That means a personal spreadsheet or older Wrapped graphic may not match the current native Statistics figure, especially for a multiyear work or a chapter uploaded after a long drafting period.
A writer-focused Wrapped can include works posted, words posted, chapters, completed works, works still in progress, fandoms, relationships, and progress toward personal goals. It should state the counting rule. “I posted 40,000 words” is a different claim from “I wrote 40,000 words,” and neither necessarily equals the current total length of the author’s works.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.AO3 Wrapped community collections
A community-run AO3 Wrapped collection exists for fanworks connected to participants’ 2025 goals. Its profile describes moderation, minimum requirements for some submissions, participation through an associated AO3 Wrapped Discord server, and a prohibition on AI-generated or commissioned works.
The collection is evidence of organized fandom participation, not evidence that AO3 or the OTW operates the Wrapped trend. Being hosted on Archive of Our Own does not make a collection an official platform feature. The profile identifies AO3Wrapped, StrixXinXiang, and AKWayne as maintainers and says it became active on January 22, 2025. A related collection calls itself the “Official AO3 Wrapped Community Collection,” but the available evidence supports describing it as a community collection rather than an official AO3 product.
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Discord may be useful if you want to participate in that community, compare formats, or find announcements from maintainers. It is not required to generate a personal report.
Why two AO3 Wrapped reports may disagree
Different users—or the same user using two tools—can receive different results for legitimate reasons:
- one report analyzes history while another uses a manual reading log;
- the user read while logged out or used multiple accounts;
- the history was cleared or was incomplete;
- one tool counts every work visit while another removes duplicates;
- rereads are counted once, multiple times, or not at all;
- deleted or hidden works are unavailable to the tool;
- multi-fandom and relationship tags are processed differently;
- the selected year is based on visit date, publication date, completion date, or log-entry date;
- the tool applies different scraping limits and delays; or
- AO3’s native annual word-count rules differ from an older personal spreadsheet.
For a credible report, put the methodology on the graphic or in its caption: identify the data source, date range, whether the figures represent visits or completed works, how duplicates and rereads were treated, and whether the report covers reading, writing, or both.
Which method should you choose?
| Your priority | Best fit | Why |
|---|---|---|
| Fastest visual result | Colab notebook | Little local setup and commonly designed for shareable outputs. |
| Less repeated scraping | CSV-based workflow | Data can be saved, reviewed, and analyzed again. |
| Maximum control | Local Python script | You can inspect code and keep processing on your own machine, but setup is harder. |
| Privacy and personal definitions | Manual spreadsheet or Notion tracker | No automated account access and complete control over what counts. |
| Author-year review | AO3 Statistics plus a personal publishing log | Separates AO3’s annual posted-word rule from drafting and completion data. |
A practical, responsible workflow
- Define the report. Decide whether you want visits, completed works, manually logged reading, works posted, or drafting progress.
- Choose the least invasive data source. Use a manual log or existing CSV if it answers your question; do not scrape merely because automation is available.
- Set the date rule. For example, use activity recorded from January 1 through December 31, 2025, in your chosen time zone.
- Review the code or template. Look for authentication, uploads, external requests, dependencies, and storage behavior.
- Run once at a restrained rate. Do not repeatedly refresh a failed scraper or reduce delays to force a result.
- Check a sample manually. Compare several reported works, tags, and word totals with the underlying records.
- Label uncertainty. Say “based on my AO3 history” and disclose missing accounts, logged-out reading, or incomplete logs.
- Save the output without exposing private data. Crop usernames, private notes, URLs, cookies, and account identifiers before sharing.
Frequently Asked Questions
Can AO3 generate my personal Wrapped automatically?
No. AO3 Wrapped is a community-made format. AO3’s official statistics describe the archive overall, while personal reports generally require a notebook, CSV, script, or manual tracker.
Does AO3 Wrapped show how much I actually read?
Usually not. An automated history report generally shows works visited or recorded. It cannot reliably establish that you finished a work, understood it, or spent a particular amount of time reading.
Is it safe to give an AO3 cookie to a Colab notebook?
Treat that as a serious security decision. Do not share cookies casually, do not enter your password into third-party code, inspect notebook cells, and prefer a local or manual workflow if you cannot verify what the notebook does. The community notebooks discussed here have not been formally security-audited in the available evidence.
Why does my AO3 author word count differ from my Wrapped total?
The figures may use different rules. AO3’s 2025 Statistics behavior counts words in chapters posted during that year, while a personal Wrapped may count current work length, words drafted, manually logged words, or another definition.
Can I make an AO3 Wrapped without scraping my account?
Yes. Use a manually maintained Google Sheets, Notion, or other reading log, or analyze a CSV you already have. This takes more preparation but gives you greater control over privacy and what counts as reading.
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The Bottom Line
AO3 Wrapped 2025 is a community trend, not an official AO3 feature. Use a Colab notebook for convenience, a CSV or local script for more control, or a manual tracker for the clearest privacy and accuracy boundaries. Whatever method you choose, label the result honestly: it is a summary of recorded history or publishing data—not a verified measure of everything you read, finished, or wrote.
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