jlfwong/speedscope

🔬 A fast, interactive web-based viewer for performance profiles.

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Summary Information

Updated 30 minutes ago
Added to GitGenius on September 9th, 2026
Created on November 22nd, 2017
Open Issues & Pull Requests: 159 (+0)
GitHub issues: Enabled
Number of forks: 320
Total Stargazers: 6,746 (+0)
Total Subscribers: 46 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.4 days
Mean response time: 173.8 days
90th percentile: 727.8 days
Tracked items: 36

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 94% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 0% of issues opened in the past year have been closed. Three people close 76% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 34
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 578 days
Stale 30+ days: 33
Stale 90+ days: 29

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • charity donation bounty (1)
  • import source (1)

Most active issues this week

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Detailed Description

Speedscope is a fast, interactive web-based viewer for performance profiles that enables developers to explore profiling data across multiple programming languages and environments.

The tool solves the problem of making sense of raw profiling data by providing an interactive visualization interface. Rather than requiring developers to parse complex profiling output manually, speedscope ingests profiles from a variety of profilers and presents them in an explorable format. This approach lets you drill down into performance data to identify bottlenecks, memory allocations, or other issues represented in the profile, regardless of which language or profiler generated the data.

Speedscope suits teams working across multiple languages and toolchains. It accepts profiles from JavaScript environments including Chrome, Firefox, Safari, and Node.js, as well as Ruby profilers like stackprof and rbspy, Python tools including py-spy and pyinstrument, Go's pprof, Java's async-profiler and Flight Recorder, PHP, Rust, Erlang/Elixir, Haskell, .NET Core, and native code profilers like macOS Instruments and Linux perf. The web-based interface means no installation is required beyond uploading a profile file. This breadth of format support makes it particularly valuable for organizations with polyglot codebases or developers who switch between languages frequently.

The project maintains active engagement with its user base through documented import workflows for each supported profiler and a clear process for contributing additional format support. Development activity shows responsiveness to feature requests and bug reports, with the maintainers actively triaging issues related to new import sources. The codebase demonstrates ongoing refinement of the visualization engine and parser infrastructure to handle diverse profiling formats reliably.