kubernetes-sigs/agent-sandbox - Open Source PR Review Scorecard

agent-sandbox enables easy management of isolated, stateful, singleton workloads, ideal for use cases like AI agent runtimes and reinforcement learning (RL).

C-Rank Grade: A (Welcoming) - 66/100

External PR Merge Rate: 66%

Response Time: 17h

First Timer Success: 52%

Frequently Asked Questions

Is kubernetes-sigs/agent-sandbox welcoming to first-time open-source contributors?

kubernetes-sigs/agent-sandbox has a recorded first-timer success rate of 52.3%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in kubernetes-sigs/agent-sandbox respond to incoming external pull requests in approximately 17.4 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 65.6 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 65.6 places kubernetes-sigs/agent-sandbox in the A tier.

What is the external contributor pull request merge rate for kubernetes-sigs/agent-sandbox?

The external contributor pull request merge rate for kubernetes-sigs/agent-sandbox is 66.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in kubernetes-sigs/agent-sandbox?

kubernetes-sigs/agent-sandbox currently has 16 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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agent-sandbox enables easy management of isolated, stateful, singleton workloads, ideal for use cases like AI agent runtimes and reinforcement learning (RL).

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AI Maintainer Review Guidelines

Review Persona

High-Friction Gatekeeper

Warmth Score
6.0/10
Patience Score
6.7/10
Nitpick Rate
52%

Rigorous architecture standards (80% critical review signals). Ensure PRs strictly follow guidelines before requesting review in kubernetes-sigs/agent-sandbox.

Top PR Submission Do's

  • •Add unit tests with >80% coverage for modified code paths
  • •Run repository linter and code formatters locally before pushing
  • •Link relevant GitHub issue ID in PR description pre-flight checklist

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit unlinked PRs without referencing an existing issue
  • •Do not mix refactoring and feature logic into a single commit
  • •Do not ping maintainers repeatedly within 24 hours of opening
Response Velocity
17 hours
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
66.0%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
52.3%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
91 core
Highly collaborative maintainer core
Diagnostic Health HUD
66.0%
Merge Gauge
52.3%
1st-Timer
Community Vibe59/100

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Active Good First Issues (5)

View on GitHub

I was investigating what the minimum version of Kubernetes that Agent Sandbox can run on today, and did not find an obvious answer in the repo. There was also not a known answer from the team in Slack. I will run the E2E test suite on older clusters to find which version tests fail on, and then submit a PR with documentation updates reporting the last working version as the minimum K8s version for Agent Sandbox.

📅 Opened Oct 2, 2026💬 1 comment
Quality: 60/100Contribute

Problem The published docs don't say which status conditions a Sandbox reports, what reasons they carry, or which one to check for a given question. The API reference only describes status.conditions as "the status conditions array". Today the only full list is in the Go doc comments in api/v1beta1/sandbox_types.go, and those have drifted from the controller: The Suspended comment names the deprecated PodNotTerminated reason. The ReconcilerError reason exists only as a string literal in the controller, not as a constant. Proposal Add a conditions page to the docs site with two parts. 1. How to use the conditions (hand-written). Map common questions to the condition to check and a kubectl command, for example: Question Check Command Is the sandbox ready to use? Ready=True kubectl wait sandbox/<name> --for=condition=Ready --timeout=2m Why isn't it ready? Ready reason and message, plus PodScheduled (e.g. Unschedulable) kubectl get sandbox <name> -o jsonpath='{range .status.cond

📅 Opened Oct 1, 2026💬 1 comment
Quality: 60/100Contribute

The docs site has a set of UI defects that are easier to track together than one at a time. Everything below was measured on the deployed site (https://agent-sandbox.sigs.k8s.io, Hugo 0.163.3, main, 2026-09-15). They are independent, so they can be picked off one at a time. Content unreachable The navbar never collapses. (#1659) At a 390px viewport, .navbar-collapse computes to display:flex at 983px wide and .navbar-toggler to display:none, so the document is 1118px wide and six of eight nav links are off-screen with no way to reach them. Cause: site/layouts/partials/navbar.html#L6-L9 is a fork of the Bootstrap 4 Docsy navbar (collapse navbar-collapse plus a data-toggle button), while current Docsy ships .td-navbar .navbar-collapse { display: flex !important } and .td-navbar .navbar-toggler { display: none } outside any media query. The fork gets neither behaviour. Fix: drop the custom partial, or move it to Docsy's current markup (td-navbar-nav-scroll, no toggler). Tables spill

📅 Opened Sep 15, 2026💬 2 comments
Quality: 100/100Contribute

What would you like to be added? A small, reproducible RLlib training and evaluation example that reuses the existing SandboxEnv integration. The project already provides: Ray-based sandboxed code execution examples (#742, #868). A Gymnasium-compatible SandboxEnv and a Hugging Face TRL PPO training notebook (#1350). An official Ray/KubeRay integration guide, including sandbox suspend/resume. Building on these existing integrations, an end-to-end example connecting SandboxEnv to RLlib for actual training and evaluation is still missing. Why is this needed? The roadmap lists Ray/RLlib integration as in progress. A runnable RLlib example would help users bridge the gap between the existing Gymnasium interface and a distributed training workflow. In particular, SandboxEnv exposes text-based action and observation spaces. Users still need to adapt these to spaces supported by their chosen RLlib algorithm, construct SDK clients within remote environment processes, and manage independent sa

📅 Opened Sep 15, 2026💬 1 comment
Quality: 50/100Contribute

What would you like to be added? Add a streaming download API to the supported SDKs so callers can copy a file into a destination without materializing the complete response in memory. The existing APIs should remain available and backward compatible: Python: Filesystem.read() and AsyncFilesystem.read() should continue to return bytes. Go: Files.Read() should continue to return []byte. A new API could write incrementally to a caller-provided sink, for example a Python binary file-like object / async sink and a Go io.Writer. The exact public API names can be decided during implementation. Why is this needed? The current download implementations buffer the whole response: Python sync read() accesses response.content. Python async read() also accesses response.content. Go Files.Read() uses io.ReadAll. This makes client memory usage proportional to file size even though both HTTP runtimes can deliver response bodies incrementally. It is especially limiting for agent workloads that need

📅 Opened Sep 13, 2026💬 1 comment
Quality: 50/100Contribute
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Contributor Compatibility & Review Speed Analysis for kubernetes-sigs/agent-sandbox

When evaluating whether to contribute to kubernetes-sigs/agent-sandbox, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of kubernetes-sigs/agent-sandbox acknowledge new external contributions in approximately 17 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 66.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to kubernetes-sigs/agent-sandbox

01

Is kubernetes-sigs/agent-sandbox welcoming to first-time open-source contributors?

kubernetes-sigs/agent-sandbox has a recorded first-timer success rate of 52.3%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~17 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 65.6 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 65.6 places kubernetes-sigs/agent-sandbox in the Welcoming tier.

04

What is the external contributor pull request merge rate for kubernetes-sigs/agent-sandbox?

The external pull request merge rate is 66.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in kubernetes-sigs/agent-sandbox?

Yes, kubernetes-sigs/agent-sandbox currently has 16 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for kubernetes-sigs/agent-sandbox are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.