Registro público
Informe de salud del softwareesquema 0.11.0 · métricas 1.13.0 · 2026-07-17 01:03 UTC

openai / openai-agents-python

A lightweight, powerful framework for multi-agent workflows

PythonMIT★ 27.952 estrellas⑂ 4338 forksdesde mar 2025Ver en GitHub ↗

openai/openai-agents-python tiene un índice de salud de 79 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Vitality (100/100) y la más baja, Security (62/100). Se actualizó por última vez hoy. 2 personas concentran la mayor parte del trabajo reciente.

79
global / 100
Bueno

Índice de salud del software

Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.

79
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

Perfil de puntuación

Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.

Titularidad

OpenAIOrganización
126.532 seguidores264 repositorios públicosdesde oct 2015

Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicación
PyPIopenai-agents0.18.2-109hace 5 días

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

100Excelente · 22% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 0 días
36/36Cadencia de commits — 52/52 semanas con commits
18/18Volumen de commits — 1208 commits en el último año
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year1208
human_commit_share
days_since_last_push0
active_weeks_last_year52
Cómo se puntúa
27/27Publica versiones — 100 versiones publicadas
36/36Recencia de las versiones — última versión hace 5 días
27/27Cadencia de publicación — una versión cada ~6,7 días
0/10OpenSSF Scorecard: Signed-Releases — sin datos
Datos de entrada utilizados
releases_count100
latest_release_tagv0.18.2
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases6,7
Excluidos de la puntuación (sin datos o no aplicable): OpenSSF Scorecard: Signed-Releases. Los pesos restantes se han renormalizado.

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

76Bueno · 18% del índice global
Cómo se puntúa
60/60Estrellas — 27.952 estrellas
25/25Forks — 4338 forks
13/15Observadores — 220 observadores
Datos de entrada utilizados
forks4338
stars27.952
watchers220
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
22.5/22.5Licencia — licencia reconocida (MIT)
0/18Guía CONTRIBUTING
0/13.5Código de conducta
0/7.2Plantilla de issues
0/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

77Bueno · 24% del índice global
Cómo se puntúa
25.2/54Factor bus — la mitad de los commits recae en 2 contribuyente(s)
15.5/22.5Distribución de commits — el principal contribuyente firma el 31% de los commits
13.5/13.5Amplitud de contribuyentes — 100 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 36 contributing companies or organizations
Datos de entrada utilizados
bus_factor2
contributors_sampled100
top_contributor_share0,313
Cómo se puntúa
45.9/46.8Resolución de issues — 98% de issues cerradas
22.3/38.3Aceptación de PR — 1404/2410 PR decididos fusionados
4.5/15OpenSSF Scorecard: Code-Review — Found 9/29 approved changesets -- score normalized to 3
Datos de entrada utilizados
merged_prs1404
open_issues24
closed_issues1315
issue_closed_ratio0,982
closed_unmerged_prs1006
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
25/25Alcance del propietario — 126.532 seguidores de openai
25/25Trayectoria — 264 repos públicos, cuenta de ~10 años
Datos de entrada utilizados
followers126.532
owner_typeOrganization
is_verified
owner_loginopenai
public_repos264
account_age_days3939
Cómo se puntúa
25/25Publicado y resoluble — 1 paquete(s) en pypi
35/35Recencia de publicación — última publicación hace 5 días
20/20Historial de versiones — 109 versiones en el registro
20/20No obsoleto — activo, ni obsoleto ni retirado
Datos de entrada utilizados
packagesopenai-agents
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

77Bueno · 20% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 6 flujo(s) de trabajo
24/24Pruebas presentes
0/16Configuración de linter
0/9.6Hooks de pre-commit
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 23 out of 23 merged PRs checked by a CI test -- score normalized to 10
Datos de entrada utilizados
has_ci
has_tests
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentación

90Excelente
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — https://openai.github.io/openai-agents-python/
10/10Descripción del repositorio
10/10Topics — 7 topics
0/10Wiki
Datos de entrada utilizados
topicsagents, ai, framework, llm, python, openai, harness
has_wikino
homepagehttps://openai.github.io/openai-agents-python/
has_readme
has_docs_dir
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

62Moderado · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
2.5/2.5CI-Tests — 23 out of 23 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
2.2/7.5Code-Review — Found 9/29 approved changesets -- score normalized to 3
2.5/2.5Contributors — project has 36 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Licencia — license file detected
7.5/7.5Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
3.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — sin datos
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 77 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6,2
Excluidos de la puntuación (sin datos o no aplicable): branch_protection, signed_releases. Los pesos restantes se han renormalizado.

Preparación para IA

¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.

83Bueno · 0% del índice global
Cómo se puntúa
45/45Instrucciones para agentes — AGENTS.md, CLAUDE.md, docs/agents.md, docs/ja/agents.md, docs/ko/agents.md, docs/ref/agent.md, docs/ref/realtime/agent.md, docs/zh/agents.md
15/15Documentación legible por máquinas (llms.txt) — llms.txt presente
0/40Historial de commits legible — sin datos
Datos de entrada utilizados
has_llms_txt
legible_history_share
agent_instruction_filesAGENTS.md, CLAUDE.md, docs/agents.md, docs/ja/agents.md, docs/ko/agents.md, docs/ref/agent.md, docs/ref/realtime/agent.md, docs/zh/agents.md
agent_instruction_max_bytes22.095
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
18/18Arranque con un solo comando — Makefile, examples/sandbox/extensions/temporal/justfile
22/22Pruebas automatizadas
0/11Configuración de lint / formato
11/11Verificación estática de tipos — pyrightconfig.json, src/agents/py.typed, src/agents/sandbox/py.typed
10/10Entorno reproducible — Dockerfile, lockfile
0/10Práctica demostrada con agentes — sin datos
5/8Mantenimiento automatizado — automatización de dependencias configurada, no observada en los commits muestreados
7/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
Datos de entrada utilizados
has_nixno
has_tests
lockfilesuv.lock
has_dockerfile
typed_languageno
bootstrap_filesMakefile, examples/sandbox/extensions/temporal/justfile
has_devcontainerno
has_linter_configno
typecheck_configspyrightconfig.json, src/agents/py.typed, src/agents/sandbox/py.typed
agent_commit_share
toolchain_manifests
dependency_bot_commit_share0
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes. Los pesos restantes se han renormalizado.
Cómo se puntúa
27/45Código verificable por tipos — Python con configuración de verificación de tipos (pyrightconfig.json, src/agents/py.typed, src/agents/sandbox/py.typed)
52.5/55Tamaños de archivo manejables — 37/799 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes253.668
source_files_sampled799
oversized_source_files37
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
20/20Servidor MCP
40/40Ejemplos ejecutables — examples, notebooks
Datos de entrada utilizados
example_dirsexamples, notebooks
has_mcp_signal
api_schema_files

Datos clave

27.952estrellas de GitHub
100contribuidores
1208commits en los últimos 12 meses
0días desde el último push
100versiones publicadas
2factor bus
24issues abiertas
PyPIecosistemas de paquetes

Más detalle

OpenSSF Scorecard 6.2 / 10
6.2agregado

Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-17 01:03 UTC

10Binary-Artifactsno binaries found in the repo
n/dBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests23 out of 23 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3Code-ReviewFound 9/29 approved changesets -- score normalized to 3
10Contributorsproject has 36 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
7Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 7
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/dSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities77 existing vulnerabilities detected
Dependencias directas 7
RegistroPaqueteRestricción de versiónManifiesto
PyPIopenai>=2.45.0,<3pyproject.toml
PyPIpydantic>=2.12.2, <3pyproject.toml
PyPIgriffelib>=2, <3pyproject.toml
PyPItyping-extensions>=4.12.2, <5pyproject.toml
PyPIrequests>=2.0, <3pyproject.toml
PyPIwebsockets>=15.0, <17pyproject.toml
PyPImcp>=1.19.0, <2pyproject.toml
Todas las dependencias 204

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 9 paquetes directos y 195 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

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                "detail": "dependency not pinned by hash detected -- score normalized to 7",
                "points": 3.5,
                "status": "partial",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
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                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file detected",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
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                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
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                "points": 0,
                "status": "missed",
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              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
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            "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",
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                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 7
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
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                  }
                ],
                "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": "good",
        "name": "AI Readiness",
        "value": 83,
        "weight": 0,
        "metrics": [
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            "note": "Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.",
            "notes": [
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                "code": "weights_renormalized",
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            "inputs": {
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              "legible_history_share": null,
              "agent_instruction_files": [
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                "CLAUDE.md",
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                "docs/zh/agents.md"
              ],
              "agent_instruction_max_bytes": 22095
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "AGENTS.md, CLAUDE.md, docs/agents.md, docs/ja/agents.md, docs/ko/agents.md, docs/ref/agent.md, docs/ref/realtime/agent.md, docs/zh/agents.md",
                "points": 45,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "AGENTS.md, CLAUDE.md, docs/agents.md, docs/ja/agents.md, docs/ko/agents.md, docs/ref/agent.md, docs/ref/realtime/agent.md, docs/zh/agents.md"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": "llms.txt present",
                "points": 15,
                "status": "met",
                "details": [
                  {
                    "code": "llms_txt_present",
                    "params": {}
                  }
                ],
                "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": "good",
            "name": "Verify loop (build / test / typecheck)",
            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
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                "max_points": 18
              },
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                "key": "automated_tests",
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                "status": "met",
                "details": [],
                "max_points": 22
              },
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                "points": 0,
                "status": "missed",
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              },
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                "status": "met",
                "details": [
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              },
              {
                "key": "demonstrated_agent_practice",
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                "points": 0,
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                "details": [
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                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "dependency automation configured, none observed in the sampled commits",
                "points": 5,
                "status": "partial",
                "details": [
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                    "code": "dependency_bot_config_only",
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                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 7",
                "points": 7,
                "status": "partial",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "good",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 80,
            "inputs": {
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                  }
                ],
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              },
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                "status": "partial",
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            ]
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            "key": "ai_interfaces",
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            "notes": [],
            "value": 60,
            "inputs": {
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              ],
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            },
            "components": [
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                "points": 0,
                "status": "missed",
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                "max_points": 40
              },
              {
                "key": "mcp_server",
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                "points": 20,
                "status": "met",
                "details": [],
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              },
              {
                "key": "runnable_examples",
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            ]
          }
        ],
        "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": [],
  "report_type": "repository",
  "generated_at": "2026-07-17T01:03:53.340306Z",
  "schema_version": "0.11.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/o/openai/openai-agents-python.svg",
  "full_name": "openai/openai-agents-python",
  "license_state": "standard",
  "license_spdx": "MIT"
}

Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.

Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.11.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadasPyPI.