Most performance claims in the JavaScript tooling space have the shelf life of milk. Someone installs a few packages on a quiet Tuesday morning, captures the terminal output, and publishes a dramatic bar chart. By the next sprint, one of the tools has shipped a patch that invalidates the whole thing. The post remains indexed by search engines. The chart keeps getting shared. The numbers, however, are already lying to you.

This is the rot that infects nearly all package manager benchmarks. They are event photography when what we need is a live feed.

A project called depjs/canary treats this problem as a machine responsibility rather than a content calendar task. It is a living benchmark that watches npm, pnpm, Yarn, and dep, then re-runs its entire suite the moment any of them publishes a new version. The results are public, continuous, and unavoidable. When something breaks, the repository stays red until it heals. There is no cherry-picking, no hiding behind an old blog post, and no assumption that last month’s winner still holds the crown.

Why Speed Claims Need an Expiration Date

JavaScript package managers do not stand still. The gap between minor versions can include rewritten resolution algorithms, altered hoisting strategies, or changes to how the global cache is keyed. A benchmark that captures npm 10.2.1 and pnpm 8.11.0 tells you almost nothing about how those same tools behave two releases later. Yet the web is full of definitive statements like “Tool X is three times faster” based on exactly that kind of frozen snapshot.

Worse, many tests ignore the conditions that define real developer pain. A package manager might blaze through an install in a warm, well-rehearsed environment and then crawl on a CI runner that starts with an empty disk. Without testing both extremes, the benchmark becomes a press release rather than usable engineering data.

How the Canary Automates Comparison

Every two hours, a job polls the npm registry. If a new version of npm, pnpm, Yarn, or dep appears, the canary wakes up. It does not wait for a human to notice the changelog. It immediately executes a full test matrix that pits all four managers against five popular, real-world packages. The selection includes heavy hitters like React, Next.js, and Vite, codebases that actual developers install every day. These are not synthetic micro-projects designed to flatter one particular tool.

This release-triggered approach matters because it ties measurement directly to change. If the benchmark only ran on a nightly schedule, it might miss a mid-day hotfix or swallow a regression for hours. By running specifically on new versions, the canary asks a direct question each time: did this release make things better or worse?

The Four Scenarios That Stress Different Muscles

The test matrix is built around four distinct setups that map directly to workflows you will recognize.

  • Cold cache, no lockfile. This is the fresh clone on a new laptop, or the first install after deleting node_modules. Nothing is cached. Nothing is pinned. The package manager has to resolve, fetch, and write everything from scratch.
  • Warm cache, with lockfile. This is the happy path for continuous integration when things go right. The lockfile exists locally, and the cache still holds the tarballs from a previous run. The tool should move fast because most decisions are already made.
  • Cold cache, with lockfile. Here the lockfile is present, but the cache has been wiped. The manager can skip dependency resolution, yet it still has to download every byte across the network. This isolates network speed from resolution speed.
  • Warm cache, no lockfile. The cache is hot, but the lockfile is gone. The package manager must re-resolve the dependency tree before it can even begin extracting files. This tests the efficiency of the solver and the metadata parser under ideal network conditions.

Each scenario is executed five times, and the canary keeps the median result. That single choice kills a lot of noise. One transient network hiccup or a brief spike in registry latency cannot hijack the narrative. The outlier gets ignored; the typical experience gets recorded.

Smoke Tests Beat Empty Timers

Raw speed is easy to fake if you do not verify the outcome. A package manager could skip postinstall steps, corrupt a few symlinks, or install the wrong versions and still post an impressive timestamp. The canary refuses to stop at the timer. After the installation finishes, it actually exercises the installed code.

For example, it boots up an Express application and confirms that the server starts listening on the expected port. If the code does not run, the benchmark fails outright. The smoke test transforms the suite from a race into an audit. It answers the question that speed alone cannot: does the installation actually work?

Radical Honesty as a Feature

The author of the canary wrote three rules into the process that most benchmark authors treat as optional.

Same playing field. Flags are used to normalize behavior across tools. If one package manager hides a performance flaw behind a default setting, the benchmark exposes it rather than letting the tool look good by accident.

Genuine cold starts. Before every single repetition, not just the first one, the npm cache and the pnpm store get wiped. That word "every" is doing heavy labor. Many benchmarks clear the cache once, then run five installs in a row. The second through fifth runs are not truly cold, and the numbers inflate accordingly. The canary starts from zero each time.

Public failure states. When a new release breaks something, the repository remains in a red failure state. It sits there on the front page, ugly and unresolved, until a fix ships. There is no silent suppression to keep the dashboard looking green. This policy forces visibility. A user evaluating tools can see not just which one is fastest, but which one stayed reliable over time.

Inspectability Is Non-Negotiable

A benchmark that you cannot reproduce is a campaign slogan. The canary addresses this with a single bash script that lets anyone run any slice of the suite locally. You do not need to trust a cloud provider’s networking or a maintainer’s hand-tweaked environment. If you suspect the numbers are off, you can generate your own.

That transparency also makes the project useful for maintainers. When a regression hits, a downstream developer can pull the script, bisect the tool’s releases, and hand the upstream team a