GitHub API's 'sort=updated' Flaw: Why Your Engineering Project Management Software Might Be Miscounting PRs

The Hidden Flaw in GitHub's PR API: A Critical Challenge for Data-Driven Teams

In the world of software development, reliable data is the bedrock of effective decision-making. From tracking sprint velocity to measuring progress against GitHub OKRs, accurate metrics are paramount. That's why a recent discussion in the GitHub Community has sent ripples through teams relying on precise data synchronization: a significant inconsistency in the GitHub Pull Request API, specifically concerning the sort=updated parameter. Developers leveraging this endpoint for their engineering project management software and internal tooling are encountering non-monotonic results, leading to critical data synchronization issues and potentially flawed insights.

The Core Inconsistency: updated_at vs. The Actual Sort Key

The original poster, zakhij, meticulously detailed a problem with the GET /repos/{owner}/{repo}/pulls?state=all&sort=updated&direction=desc endpoint. Contrary to expectations, this API call does not strictly sort pull requests by their updated_at field, as returned in the response body. Instead, zakhij's investigation suggests the API sorts by the most recent entry in the more limited /issues/{n}/events endpoint. This is a crucial distinction, as the /issues/{n}/events endpoint does not capture all activities that update the PR's updated_at timestamp.

This divergence means that common activities like adding comments or submitting reviews—which undeniably update a PR's updated_at field (tracked by the richer /issues/{n}/timeline)—do not necessarily affect its sort position. The result is a seemingly random ordering where a PR with a very recent updated_at might appear far from its expected position, surrounded by much older entries. Imagine a critical PR appearing at the bottom of a list when it should be at the top, simply because its most recent activity was a comment.

Zakhij provided a clear example, demonstrating the issue with a paginated curl request:

for page in $(seq 1 22); do
    echo "===== page $page ====="
    curl -sS \
        -H "Authorization: Bearer $TOKEN" \
        -H "Accept: application/vnd.github.v3+json" \
        -H "X-GitHub-Api-Version: 2022-11-28" \
        "https://api.github.com/repos/$REPO/pulls?state=all&sort=updated&direction=desc&per_page=100&page=$page" \
        | jq -r '.[] | "\(.number)	\(.updated_at)"'
done

This script revealed instances where PRs with updated_at timestamps from 2026 were interleaved with PRs from 2023 or 2024, defying the expected descending sort order. The 'smoking gun' was the observation that the sort position matched the timestamp from the /issues/{n}/events endpoint, not the more recent updated_at value from the response body. This is a fundamental breach of API contract and a significant challenge for any system attempting to maintain accurate, time-based records.

The Impact: Flawed Metrics, Broken Syncs, and Misleading Insights

For dev teams, product managers, and CTOs, the implications of this API inconsistency are profound. Any client using sort=updated for binary-search counting, cursor-by-timestamp pagination, or windowed synchronization will silently overcount, undercount, or skip records. There's no way to detect this divergence from the API response alone.

• Inaccurate Progress Tracking: If your engineering project management software relies on this endpoint to track recent activity or calculate velocity, your metrics will be skewed. Projects might appear stalled when they're active, or active when they're not.
• Flawed OKR Reporting: For organizations tracking GitHub OKRs, particularly those tied to PR activity or resolution times, the underlying data could be fundamentally unreliable. This undermines confidence in data-driven decisions and makes it difficult to assess team performance accurately.
• Broken Incremental Syncs: Platforms that perform incremental data synchronization—fetching only new or updated records since the last sync—will inevitably miss critical updates or re-process old data, leading to stale datasets and inefficient resource utilization.
• Reduced Productivity: Developers building integrations spend valuable time debugging what appears to be their own logic errors, only to discover a subtle API inconsistency. This is a direct hit to productivity and morale.

Workarounds for Reliable GitHub Data

Fortunately, the GitHub Community discussion didn't just highlight the problem; it also offered practical workarounds. SarthkDeshmukh, another developer facing this frustration, shared valuable insights:

1. Embrace the GraphQL API: The most robust solution is to switch to GitHub's GraphQL API. The pullRequests(orderBy: {field: UPDATED_AT, direction: DESC}) query provides genuinely reliable sorted results, as it appears to handle the UPDATED_AT field consistently for sorting. This requires a shift in how you interact with the API but offers superior data integrity.
2. Client-Side Sorting: If your dataset is small enough (e.g., fetching PRs for a single repository, or within a specific time window), consider fetching all relevant PRs and performing the sort client-side. This offloads the sorting logic to your application, ensuring the order aligns with the updated_at values you receive.
3. Filter by State First: For REST API users who cannot immediately switch to GraphQL, filtering by state (e.g., state=open or state=closed) before sorting can reduce the dataset. While not a complete fix for the sorting inconsistency, it might make the issue less pronounced by limiting the scope of potentially mis-sorted items.

The critical lesson here, echoed by SarthkDeshmukh, is one that technical leaders and delivery managers should internalize: never fully trust API-side sorting for anything where order is business-critical. Always validate or re-sort on your side. This principle is vital for building resilient integrations and ensuring the accuracy of your engineering project management software and reporting tools.

Beyond the Bug: A Call for Robust Integrations and Data Awareness

This GitHub API inconsistency serves as a potent reminder of the complexities inherent in building robust integrations. While we expect APIs to behave predictably, real-world systems can have subtle divergences that impact data integrity. For organizations committed to data-driven development, this means:

• Prioritizing Data Validation: Implement checks and balances within your systems to validate data fetched from external APIs, especially when relying on specific sorting or filtering parameters.
• Investing in Flexible Tooling: Choose engineering project management software and integration platforms that offer flexibility in how data is consumed and processed, allowing for workarounds like GraphQL or client-side sorting.
• Fostering API Literacy: Encourage your development teams to delve deeper into API documentation and community discussions. Understanding the nuances, and even the "known inconsistencies," can save countless hours of debugging.

At devActivity, we understand the importance of accurate data for driving productivity and informed decision-making. Insights like these are crucial for building resilient systems that truly empower development teams and leadership. By being aware of these challenges and implementing robust strategies, you can ensure your data accurately reflects your team's hard work and progress.