公开记录
软件健康报告模式 0.14.0 · 指标 1.13.0 · 2026-07-18 22:08 UTC

microsoft / onnxruntime-genai

Generative AI extensions for onnxruntime

C++ · PythonMIT★ 1,086 星标⑂ 323 复刻始于 2023年11月在 GitHub 上查看 ↗

microsoft/onnxruntime-genai 的健康指数为 100 分中的 75 分,处于「良好」区间。 其得分最高的类别是Vitality(100/100),最低的是Security(50/100)。 最近一次更新在今天。 近期的大部分工作由 4 位贡献者完成。

75
总分 / 100
良好

软件健康指数

指标归入加权类别,统一采用 1–100 量表。总体分先取类别加权平均;当公开证据触发高风险司法辖区政策时,评级会按政策调整,并设置 49(有风险)的上限。AI 就绪度不计入总体分。

75
优秀85-100堪称典范;基本满足所有检验标准
良好70-84健康;仅有轻微不足
中等50-69可接受,但存在明显不足;建议进行审查
存在风险30-49存在重大薄弱环节;采用时应保持审慎
危急1-29问题严重(项目被弃置、仅有单一维护者、缺乏基本工程规范)
活力社区与采用可持续性与治理工程质量安全AI 就绪度

评分画像

每条轴代表一个类别。形状比平均值更重要——健康的对象会填满整个图形,而“一峰一谷”式画像意味着某一维度的优势正掩盖另一维度的风险。

所有权

Microsoft组织
125,335 关注者8,203 个公开仓库始于 2013年12月

该仓库由组织支持——共同承担、可问责的托管责任,可延续于任何单一维护者之后。

按类别列示的指标

活力

项目是否仍有生命——是否仍在编写代码,是否仍在发布版本?

100优秀 · 占总体的 22%
评分方式
36/36推送新近度 — 最近一次推送于 0 天前
36/36提交节奏 — 52 周中有 52 周有提交
18/18提交量 — 最近一年 348 次提交
0/10OpenSSF Scorecard:Maintained — 无数据
所用输入
commits_last_year348
human_commit_share
days_since_last_push0
active_weeks_last_year52

发布纪律

100优秀
评分方式
27/27有发布版本 — 已发布 29 个发布版本
36/36发布时效 — 最近一次发布版本于 50 天前
27/27发布节奏 — 约每 21.4 天发布一次
0/10OpenSSF Scorecard:Signed-Releases — 无数据
所用输入
releases_count29
latest_release_tagv0.14.0
releases_from_tags
days_since_latest_release50
mean_days_between_releases21.4

社区与采用

项目是否拥有用户、下载量与关注度,并具备欢迎贡献者参与的配置?

72良好 · 占总体的 18%
评分方式
49.2/60星标 — 1,086 个星标
20.9/25复刻 — 323 个复刻
9.3/15关注者 — 48 位关注者
所用输入
forks323
stars1,086
watchers48
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

社区健康

65中等
评分方式
22.5/22.5README
22.5/22.5许可证 — 可识别的许可证(MIT)
0/18CONTRIBUTING 指南
13.5/13.5行为准则
0/7.2议题模板
0/6.3PR 模板
所用输入
has_readme
has_license
has_contributing
has_issue_template
has_code_of_conduct
has_pull_request_template

可持续性与治理

项目能否在其成员之外延续——巴士系数、响应能力、由谁支持,以及软件包的维护状况?

80良好 · 占总体的 24%
评分方式
43.2/54巴士系数 — 4 位贡献者贡献了半数提交
18.3/22.5提交分布 — 头号贡献者编写了 19% 的提交
13.5/13.5贡献者广度 — 100 位贡献者
0/10OpenSSF Scorecard:Contributors — 无数据
所用输入
bus_factor4
contributors_sampled100
top_contributor_share0.187
评分方式
34.6/46.8议题解决 — 74% 的议题已关闭
31.9/38.3PR 接受 — 已裁定的 PR 中 1,300/1,559 已合并
0/15OpenSSF Scorecard:Code-Review — 无数据
所用输入
merged_prs1,300
open_issues145
closed_issues415
issue_closed_ratio0.741
closed_unmerged_prs259
评分方式
30/30所有权背书 — 组织持有
0/20已验证域名
25/25所有者影响力 — microsoft 有 125,335 位关注者
25/25既往记录 — 8,203 个公开仓库,账户约 12 年
所用输入
followers125,335
owner_typeOrganization
is_verified
owner_loginmicrosoft
public_repos8,203
account_age_days4,603

工程质量

基础的工程与文档实践是否到位?

66中等 · 占总体的 20%

工程实践

60中等
评分方式
24/24CI 工作流 — 15 个工作流
24/24存在测试
0/16Linter 配置
0/9.6Pre-commit 钩子
0/6.4.editorconfig
0/20OpenSSF Scorecard:CI-Tests — 无数据
所用输入
has_ci
has_tests
has_editorconfig
has_linter_config
has_precommit_config

文档

75良好
评分方式
30/30README
25/25文档目录
0/15文档 / 主页站点
10/10仓库描述
0/10主题标签
10/10Wiki
所用输入
topics
has_wiki
homepage
has_readme
has_docs_dir
has_description

安全

可见的安全与供应链实践是否稳固,且不存在未解决的高风险司法辖区暴露?

50中等 · 占总体的 16%

安全态势

50中等
评分方式
30/30安全策略(SECURITY.md)
0/25Dependabot 配置
0/25依赖锁定文件
20/20CodeQL 工作流
所用输入
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-lintrunner.txt
has_codeql_workflow
has_security_policy
has_dependabot_config

AI 就绪度

该仓库在多大程度上具备与 AI 编码代理协同开发与维护的条件?这是一枚独立的实验性徽章——权重为 0.0,因此单独呈现,不影响总体健康评分。

67中等 · 占总体的 0%
评分方式
45/45代理指令 — .github/copilot-instructions.md, .github/instructions/python-model-builder.instructions.md
0/15机器可读文档(llms.txt)
0/40可读的提交历史 — 无数据
所用输入
has_llms_txt
legible_history_share
agent_instruction_files.github/copilot-instructions.md, .github/instructions/python-model-builder.instructions.md
agent_instruction_max_bytes5,931
已排除计分(无数据或不适用):可读的提交历史。 其余权重已重新归一化。
评分方式
0/18一条命令的引导启动
22/22自动化测试
0/11Lint / 格式化配置
11/11静态类型检查 — C++(静态类型)
10/10可复现环境 — Dockerfile
0/10已体现的代理实践 — 无数据
0/8自动化维护 — 无数据
0/10OpenSSF Scorecard:Pinned-Dependencies — 无数据
所用输入
has_nix
has_tests
lockfiles
has_dockerfile
typed_language
bootstrap_files
has_devcontainer
has_linter_config
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
已排除计分(无数据或不适用):已体现的代理实践, 自动化维护。 其余权重已重新归一化。
评分方式
45/45可类型检查的代码 — C++(静态类型)
54/55可控的文件大小 — 采样的 447 个源文件中有 8 个超过 60KB
所用输入
primary_languageC++
largest_source_bytes215,793
source_files_sampled447
oversized_source_files8

机器可读接口

40存在风险
评分方式
0/40API 模式(OpenAPI/GraphQL/proto)
0/20MCP 服务器
40/40可运行示例 — example, examples
所用输入
example_dirsexample, examples
has_mcp_signal
api_schema_files

关键数据

1,086GitHub 星标
100贡献者
348最近 12 个月提交数
0距最近推送天数
29发布版本数
4巴士系数(bus factor)
145开放议题
PyPI软件包生态系统数

数据采集警告

  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

更多细节

全部依赖 49

来自 GitHub 依赖图的完整解析依赖集合:0 个直接依赖与 49 个间接(传递)软件包。仓库提交锁文件时,传递闭包才是完整的。

注册表软件包版本关系
NuGetMicrosoft.Direct3D.DXC1.7.2308.12间接
NuGetMicrosoft.Extensions.AI.Abstractions9.8.0间接
NuGetMicrosoft.ML.OnnxRuntime间接
NuGetMicrosoft.ML.OnnxRuntimeGenAI0.14.1间接
NuGetMicrosoft.ML.OnnxRuntimeGenAI.Cuda0.14.1间接
NuGetMicrosoft.ML.OnnxRuntimeGenAI.DirectML0.14.1间接
NuGetMicrosoft.NET.Test.Sdk17.5.0间接
NuGetNAudio2.2.1间接
NuGetSystem.CommandLine2.0.1间接
NuGetSystem.Memory4.5.5间接
NuGetxunit2.4.1间接
NuGetxunit.runner.visualstudio2.4.3间接
PyPIargparse间接
PyPIclang-format20.1.8间接
PyPIcoloredlogs间接
PyPIdatasets间接
PyPIflatbuffers间接
PyPIgnureadline间接
PyPIhuggingface-hub间接
PyPIlintrunner0.12.7间接
PyPIlintrunner-adapters0.12.5间接
PyPImypy间接
PyPInumpy间接
PyPIonnx间接
PyPIonnx-ir间接
PyPIonnxruntime1.26.0间接
PyPIonnxruntime1.27.0间接
PyPIonnxruntime1.28.0.dev20260612004间接
PyPIonnxruntime-directml1.25.0.dev20260125001间接
PyPIonnxruntime-ep-webgpu0.2.0.dev20260611间接
PyPIonnxruntime-genai间接
PyPIonnxruntime-gpu1.26.0间接
PyPIpackaging间接
PyPIpandas间接
PyPIprotobuf间接
PyPIprotobuf6.33.5间接
PyPIpytest间接
PyPIruff0.12.12间接
PyPIsentencepiece间接
PyPIsetuptools间接
PyPIsympy间接
PyPIsympy1.12间接
PyPItorch间接
PyPItorch2.12.0+cpu间接
PyPItorch2.6.0+cu124间接
PyPItorch2.7.1间接
PyPItorch2.7.1+cpu间接
PyPItransformers间接
PyPIwheel间接
原始 JSON 报告 机器可读
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            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 3
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "moderate",
        "name": "AI Readiness",
        "value": 67,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "good",
            "name": "Agent context & guidance",
            "note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "legible_commit_history"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 75,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": null,
              "agent_instruction_files": [
                ".github/copilot-instructions.md",
                ".github/instructions/python-model-builder.instructions.md"
              ],
              "agent_instruction_max_bytes": 5931
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": ".github/copilot-instructions.md, .github/instructions/python-model-builder.instructions.md",
                "points": 45,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": ".github/copilot-instructions.md, .github/instructions/python-model-builder.instructions.md"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "moderate",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "demonstrated_agent_practice",
                    "automated_maintenance"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 60,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": true,
              "typed_language": true,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": false,
              "typecheck_configs": [],
              "agent_commit_share": null,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": null
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": "C++ (statically typed)",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "statically_typed_language",
                    "params": {
                      "language": "C++"
                    }
                  }
                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "Dockerfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Dockerfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no data",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "OpenSSF Scorecard unavailable",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "excellent",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 99,
            "inputs": {
              "primary_language": "C++",
              "largest_source_bytes": 215793,
              "source_files_sampled": 447,
              "oversized_source_files": 8
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "C++ (statically typed)",
                "points": 45,
                "status": "met",
                "details": [
                  {
                    "code": "statically_typed_language",
                    "params": {
                      "language": "C++"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "8/447 source files over 60KB",
                "points": 54,
                "status": "partial",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 447,
                      "oversized": 8
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "example",
                "examples"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "example, examples",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "example, examples"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "OpenSSF Scorecard timed out after 240s; skipping Scorecard checks"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-18T22:08:44.647717Z",
  "schema_version": "0.14.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/microsoft/onnxruntime-genai.svg",
  "full_name": "microsoft/onnxruntime-genai",
  "license_state": "standard",
  "license_spdx": "MIT"
}

评分是信号,而非担保。 评分反映的是 GitHub 上公开可见的实践——不是代码审计,也不是安全保证。

缺失数据将被剔除并重新归一化权重,绝不按零分计。方法论已版本化并公开:指标 v1.13.0、模式 v0.14.0—— 完整方法论 · 指标知识库.

单项结果在整体记录中的位置: 汇总统计.