agentscope-ai/agentscope-java - Open Source PR Review Scorecard

Build distributed, production-grade, long-running agents.

C-Rank Grade: A (Welcoming) - 64/100

External PR Merge Rate: 37%

Response Time: -

First Timer Success: 27%

Frequently Asked Questions

Is agentscope-ai/agentscope-java welcoming to first-time open-source contributors?

agentscope-ai/agentscope-java has a recorded first-timer success rate of 27.5%. 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 agentscope-ai/agentscope-java respond to incoming external pull requests in approximately 0.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 63.8 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 63.8 places agentscope-ai/agentscope-java in the A tier.

What is the external contributor pull request merge rate for agentscope-ai/agentscope-java?

The external contributor pull request merge rate for agentscope-ai/agentscope-java is 37.1%, based on public PR activity from non-core contributors.

Are there Good First Issues available in agentscope-ai/agentscope-java?

agentscope-ai/agentscope-java currently has 17 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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agentscope-ai/agentscope-java

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Build distributed, production-grade, long-running agents.

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AI Maintainer Review Guidelines

Review Persona

Strict Quality Architect

Warmth Score
6.6/10
Patience Score
7.2/10
Nitpick Rate
43%

Rigorous architecture standards (66% critical review signals). Ensure PRs strictly follow guidelines before requesting review in agentscope-ai/agentscope-java.

Top PR Submission Do's

  • •Add unit tests with >80% coverage for modified code paths
  • •Use conventional commit messages and clean branch names
  • •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
-
No reviewer activity recorded

Average Response Latency

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

Merge Efficiency
37.1%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
27.5%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
117 core
Highly collaborative maintainer core
Diagnostic Health HUD
37.1%
Merge Gauge
27.5%
1st-Timer
Community Vibe23/100

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Active Good First Issues (5)

View on GitHub

Background Some deployments host multiple tenants on shared infrastructure, and tenant isolation is a common requirement in that model. Today the framework has no tenant concept: neither the session/state identity (userId + sessionId) nor any storage backend carries a tenant dimension. Introducing a framework-level tenant_id would be a significant cross-cutting change, so before any design work we want to collect real demand and use cases first. Current position No design has been chosen. Options range from doing nothing (tenant isolation handled entirely by the surrounding system) to a storage-level tenant column or full framework-level identity. The direction will be driven by the feedback collected here. Please comment with your use case, even if it only partially matches the questions above.

📅 Opened Sep 29, 2026💬 1 comment
Quality: 90/100Contribute

问题现象 长任务(多轮工具调用)执行中途,客户端/网关因空闲超时切断 SSE 连接后,本轮已完成的全部进度丢失。 会话状态回退到本轮 call 开始前的样子,"就像从来没有对话过"。 ask_user 挂起场景:回答后 resume 的新一轮若断连,用户回答也一起丢失,会话看起来停留在 ask 阶段。 复现步骤 启动需要多轮工具调用的任务(如查询+导出)。 在工具执行间隙或模型长时间无 token 输出时断开客户端(或等待网关空闲超时)。 重新连接同一 sessionId 继续对话 → 上一轮全部进度丢失。 根因分析 AgentState 落盘时机:仅 ReActAgent.call() 整体结束(成功/失败/空)时保存一次(ReActAgent.java L1195-1225, saveStateToSession L475-497)。 流式出口:sink.onCancel(lifecycleDisposable)(ReActAgent.java L1093)——客户端 cancel 时直接 dispose 生命周期 Mono,无保存钩子。 Harness 层 Transcript

📅 Opened Sep 21, 2026💬 6 comments
Quality: 90/100Contribute

期望在转化为Agui Message时,能包含该 reasoning message

📅 Opened Aug 27, 2026💬 8 comments
Quality: 35/100Contribute

定义返回结构: @DaTa public static class Test { String name; int age; } 调用: agent.call(msg,Test.class) 报错: {"message":"{\n "error": {\n "message": "Invalid schema for response_format 'generate_response': In context=(), 'additionalProperties' is required to be supplied and to be false.",\n "type": "invalid_request_error",\n "param": "response_format",\n "code": null\n }\n}","retcode":40000}) 备注:只有 openai 的 api 有这个问题, gemini ,qwen 都没有这个问题 'additionalProperties' is required to be supplied and to be false

📅 Opened Aug 4, 2026💬 3 comments
Quality: 40/100Contribute

先撤销刚才的源码修改: 已撤销。以下是你可以提 issue 的完整描述: Issue: 环境信息 AgentScope v2.0.0 模型:deepseek-v4-pro(通过 Anthropic 协议接入的第三方模型) 场景:多轮对话,ReActAgent 问题现象 多轮对话时,Anthropic API 报 400 错误: The `content[].thinking` in the thinking mode must be passed back to the API. 根因 AnthropicMessageConverter.java 第131-137行,将 ThinkingBlock 转换为普通 TextBlockParam 传回 API: } else if (block instanceof ThinkingBlock thinkingBlock) { contentBlocks.add( ContentBlockParam.ofText( TextBlockParam.builder() .text(thinkingBlock.getThinking()) .build())); } 问题在于: 模型上一轮回复产生了 thinking 块,AgentScope 存入对话历史 下一轮请求时,thinking 块被转成普通 text 类型传回 API 但 Anthropic API 要求 thinking 内容必须用** thinking

📅 Opened Jul 27, 2026💬 2 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for agentscope-ai/agentscope-java

When evaluating whether to contribute to agentscope-ai/agentscope-java, 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 agentscope-ai/agentscope-java acknowledge new external contributions in approximately -. Out of all submitted pull requests from non-core authors in the last 180-day window, 37.1% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to agentscope-ai/agentscope-java

01

Is agentscope-ai/agentscope-java welcoming to first-time open-source contributors?

agentscope-ai/agentscope-java has a recorded first-timer success rate of 27.5%. 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 ~-. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 63.8 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 63.8 places agentscope-ai/agentscope-java in the Welcoming tier.

04

What is the external contributor pull request merge rate for agentscope-ai/agentscope-java?

The external pull request merge rate is 37.1%. 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 agentscope-ai/agentscope-java?

Yes, agentscope-ai/agentscope-java currently has 17 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 agentscope-ai/agentscope-java 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.