alibaba/anolisa - Open Source PR Review Scorecard

ANOLISA (Agentic Nexus Operating Layer & Interface System Architecture) | Agentic OS with runtime, security, observability, and Tokenless response compression for lower token usage and cost.

C-Rank Grade: A (Welcoming) - 67/100

External PR Merge Rate: 78%

Response Time: 2d

First Timer Success: 64%

Frequently Asked Questions

Is alibaba/anolisa welcoming to first-time open-source contributors?

alibaba/anolisa has a recorded first-timer success rate of 64.1%. 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 alibaba/anolisa respond to incoming external pull requests in approximately 40.3 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 67.4 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 67.4 places alibaba/anolisa in the A tier.

What is the external contributor pull request merge rate for alibaba/anolisa?

The external contributor pull request merge rate for alibaba/anolisa is 77.8%, based on public PR activity from non-core contributors.

Are there Good First Issues available in alibaba/anolisa?

alibaba/anolisa does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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alibaba/anolisa

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Active (30d)A•Welcoming(67/100)Rust

ANOLISA (Agentic Nexus Operating Layer & Interface System Architecture) | Agentic OS with runtime, security, observability, and Tokenless response compression for lower token usage and cost.

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
8.4/10
Patience Score
8.6/10
Nitpick Rate
25%

Highly welcoming maintainers in alibaba/anolisa. Prompt code reviews with positive guidance for new contributors.

Top PR Submission Do's

  • •Ensure code complies with the project coding style
  • •Keep PRs scoped to a single concern
  • •Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit PRs without linking an issue
  • •Do not break existing tests without fixing them
  • •Do not mix unrelated refactors in a single PR
Response Velocity
1 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
77.8%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
64.1%
Strong first-timer PR acceptance rate

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
32 core
Highly collaborative maintainer core
Diagnostic Health HUD
77.8%
Merge Gauge
64.1%
1st-Timer
Community Vibe50/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for alibaba/anolisa
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/alibaba/anolisa)](https://getmerged.abhishekco.de/alibaba/anolisa?utm_source=github&utm_medium=badge)

Active Good First Issues (5)

View on GitHub

Component sight What problem are you trying to solve? 是否能增加指定 agent 单 session 的内存占用曲线、cpu占用曲线监测,可标注区分 llm 调用段和toolcall段和空闲段 此处 tool call 时间段定义为,harness识别到需要toolcall发起调用为起点,toolcall返回调用结果到harness为终点的时间段,可直观将session时间段划分为:llm调用段、toolcall段、agent未触发时间段(例如人类在打字思考的时间) 期待定时采集进程mem/cpu占用,并新增toolcall段的起点/终点时间戳 What would a useful outcome look like? dashboard 显示 agent 单 session 的内存占用曲线、cpu占用曲线监测,可标注区分 llm 调用段和toolcall段和空闲段。原始数据可拉取。 资源采样点最小字段 字段 类型 约束 timestamp_ns int64 纳秒精度,必须与 genai_events.start_timestamp_ns / end_timestamp_ns 同一时钟源(CLOCK_REALTIME 或等价),否则曲线与时段条

📅 Opened Aug 25, 2026💬 1 comment
Quality: 90/100Contribute

背景 当前 Agent 看板(/health,AgentHealthPage)的数据来源是 扫描 /proc 目录发现当前运行中的进程(HealthChecker → AgentScanner::scan())。这导致: 只能看到此刻正在运行的 Agent 进程,大量 Agent 类型缺失 实际使用中经常只显示 Claude 一个卡片,Qoder、Codex、Cursor 等间歇性运行的 Agent 根本看不到 看板的信息价值大打折扣——用户期望看到"这台机器上有哪些 Agent",而非"此刻哪些进程在跑" 期望行为 Agent 看板的 Agent 列表从 SQLite 数据库查询,展示所有曾被采集到的 Agent: 数据源从 /proc 进程扫描 → SQLite 查询(genai_events.db + trajectories.db) 展示所有出现过的 agent_name,无论当前是否在运行 每个 Agent 显示最后活跃时间、累计调用量等统计信息 当前实现 现有数据流(需要改变) /proc 扫描 → AgentScanner::scan() → HealthChecker → HealthStore(内存)→ GET /api/agent-health

📅 Opened Aug 21, 2026💬 2 comments
Quality: 90/100Contribute

背景 当前 Dashboard 的默认落地页(/)渲染的是 Agent 可观测(ConversationList),即 LLM 调用会话列表。但对于日常运维场景,用户打开 Dashboard 第一眼更想看的是 Agent 看板(/health)——它展示所有 Agent 的在线/离线/挂起状态概览,信息密度更高、更适合作为首页。 期望行为 访问 Dashboard 根路径 / 时,默认展示 Agent 看板(/health 对应的 AgentHealthPage) 如果 agent_health capability 不可用,回退到当前行为(agent_observability → /,否则 /sessions) 当前实现 dashboard/src/App.tsx 中的路由逻辑: const LOCAL_DEFAULT_PATH = "/sessions"; function defaultPath(capabilities: string[]): string { return capabilities.includes("agent_observability") ? "/" : LOCAL_DEFAULT_PATH; } // Route "/" <Route path="/" element={ capabilities.includes("agent_observability") ? <ConversationList /> : <Navigate to={fallbackPath} replace /> } /> 修改建

📅 Opened Aug 20, 2026💬 0 comments
Quality: 90/100Contribute

背景 当前 Agent 看板(/health 页面)以进程(PID)为粒度展示卡片:每个被发现的 Agent 进程独占一张卡片。当用户同时运行多个同类型 Agent 实例时(如开了 3 个终端各跑一个 claude-code),看板会出现 3 张几乎相同的卡片,信息密度低且不易快速掌握全局状态。 期望行为 按 agent_name(Agent 类型)聚合展示: 同一 agent_name 的所有进程合并为一张聚合卡片 卡片标题显示 agent 类型名(如 "Claude Code"),附带进程计数徽章(如 "×3") 聚合卡片的状态取所有子进程中最严重的状态(优先级:hung > unhealthy > offline > healthy > no_port > unknown) 展开卡片可查看各子进程详情(PID、端口、延迟、状态) 单进程 Agent 保持现有卡片样式不变(无需展开) 示意 ┌─────────────────────────────────────────────┐ │ 🟢

📅 Opened Aug 18, 2026💬 3 comments
Quality: 90/100Contribute

Component anolisa What problem are you trying to solve? Issue #2086 and PR #2171 improved the error for a missing local raw-repository index: local raw repository index not found at /tmp/.../repo/v1/index.toml; check the raw repository URL in repo.toml or, if supplied, the one-off --repo <URL> override That diagnostic identifies the missing index, but users still cannot tell which repo.toml supplied the stale URL. ANOLISA discovers repository configuration from multiple locations (user/site, packaged data, and the development tree), so "check repo.toml" is not enough to recover without inspecting the source code or asking for support. A real user-mode failure was caused by ~/.config/anolisa/repo.toml retaining a file:// URL for an already removed temporary repository. Moving that config aside and retrying allowed ANOLISA to provision its default repository config and complete the install. Reproduction In a temporary user-mode filesystem layout, write repo.toml with [backends.raw].bas

📅 Opened Aug 10, 2026💬 5 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for alibaba/anolisa

When evaluating whether to contribute to alibaba/anolisa, 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 alibaba/anolisa acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 77.8% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to alibaba/anolisa

01

Is alibaba/anolisa welcoming to first-time open-source contributors?

alibaba/anolisa has a recorded first-timer success rate of 64.1%. 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 ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 67.4 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 67.4 places alibaba/anolisa in the Welcoming tier.

04

What is the external contributor pull request merge rate for alibaba/anolisa?

The external pull request merge rate is 77.8%. 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 alibaba/anolisa?

alibaba/anolisa does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for alibaba/anolisa 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.