Public record
Software health reportschema 0.11.0 · metrics 1.13.0 · 2026-07-17 01:03 UTC

openai / openai-agents-python

A lightweight, powerful framework for multi-agent workflows

PythonMIT★ 27,952 stars⑂ 4,338 forkssince Mar 2025View on GitHub ↗

openai/openai-agents-python holds a health index of 79 out of 100, placing it in the Good band. It scores highest on Vitality (100/100) and lowest on Security (62/100). It was last updated today. 2 contributors account for most of its recent work.

79
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

79
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

Ownership

OpenAIOrganization
126,532 followers264 public repossince Oct 2015

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIopenai-agents0.18.2-1095 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

100Excellent · 22% of overall
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 1,208 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,208
human_commit_share
days_since_last_push0
active_weeks_last_year52

Release discipline

100Excellent
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 5 days ago
27/27Release cadence — a release every ~6.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.18.2
releases_from_tagsno
days_since_latest_release5
mean_days_between_releases6.7
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

76Good · 18% of overall
How it's scored
60/60Stars — 27,952 stars
25/25Forks — 4,338 forks
13/15Watchers — 220 watchers
Inputs used
forks4,338
stars27,952
watchers220
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingno
has_issue_templateno
has_code_of_conductno
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

77Good · 24% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.5/22.5Commit distribution — top contributor authored 31% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 36 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled100
top_contributor_share0.313
How it's scored
45.9/46.8Issue resolution — 98% of issues closed
22.3/38.3PR acceptance — 1,404/2,410 decided PRs merged
4.5/15OpenSSF Scorecard: Code-Review — Found 9/29 approved changesets -- score normalized to 3
Inputs used
merged_prs1,404
open_issues24
closed_issues1,315
issue_closed_ratio0.982
closed_unmerged_prs1,006
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 126,532 followers of openai
25/25Track record — 264 public repos, account ~10 yr old
Inputs used
followers126,532
owner_typeOrganization
is_verified
owner_loginopenai
public_repos264
account_age_days3,939
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 5 days ago
20/20Version history — 109 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesopenai-agents
ecosystemspypi
any_deprecatedno
min_days_since_publish5

Engineering Quality

Are baseline engineering and documentation practices in place?

77Good · 20% of overall
How it's scored
24/24CI workflows — 6 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 23 out of 23 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://openai.github.io/openai-agents-python/
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicsagents, ai, framework, llm, python, openai, harness
has_wikino
homepagehttps://openai.github.io/openai-agents-python/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

62Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
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
2.5/2.5License — 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 — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 77 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): branch_protection, signed_releases. Remaining weights renormalized.

AI Readiness

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.

83Good · 0% of overall
How it's scored
45/45Agent instructions — 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
15/15Machine-readable docs (llms.txt) — llms.txt present
0/40Legible commit history — no data
Inputs used
has_llms_txtyes
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
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
18/18One-command bootstrap — Makefile, examples/sandbox/extensions/temporal/justfile
22/22Automated tests
0/11Lint / format config
11/11Static type checking — pyrightconfig.json, src/agents/py.typed, src/agents/sandbox/py.typed
10/10Reproducible environment — Dockerfile, lockfile
0/10Demonstrated agent practice — no data
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
7/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 7
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
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
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.
How it's scored
27/45Type-checkable code — Python with type-check config (pyrightconfig.json, src/agents/py.typed, src/agents/sandbox/py.typed)
52.5/55Manageable file sizes — 37/799 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes253,668
source_files_sampled799
oversized_source_files37
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
20/20MCP server
40/40Runnable examples — examples, notebooks
Inputs used
example_dirsexamples, notebooks
has_mcp_signalyes
api_schema_files

Key facts

27,952GitHub stars
100contributors
1,208commits, last 12 months
0days since last push
100releases
2bus factor
24open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 6.2 / 10
6.2aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-17 01:03 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-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/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities77 existing vulnerabilities detected
Direct dependencies 7
RegistryPackageVersion constraintManifest
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All dependencies 204

Full resolved dependency set from the GitHub dependency graph: 9 direct and 195 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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Raw JSON report machine-readable
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        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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                "docs/ja/agents.md",
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              ],
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            },
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                "points": 45,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
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                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": "llms.txt present",
                "points": 15,
                "status": "met",
                "details": [
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              },
              {
                "key": "legible_commit_history",
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                "points": 0,
                "status": "excluded",
                "details": [
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              }
            ]
          },
          {
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            "note": "Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.",
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                "params": {
                  "components": [
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              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
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              {
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                "points": 22,
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              },
              {
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              },
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              {
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                "max_points": 10
              },
              {
                "key": "automated_maintenance",
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                "detail": "dependency automation configured, none observed in the sampled commits",
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                ],
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              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
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                "status": "partial",
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                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "good",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 80,
            "inputs": {
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              "source_files_sampled": 799,
              "oversized_source_files": 37
            },
            "components": [
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                "key": "type_checkable_code",
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                "status": "partial",
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                    }
                  }
                ],
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              },
              {
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                ],
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            ]
          },
          {
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            "notes": [],
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            "inputs": {
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              ],
              "has_mcp_signal": true,
              "api_schema_files": []
            },
            "components": [
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                "points": 0,
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              },
              {
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                "status": "met",
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              },
              {
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            ]
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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",
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  "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"
}

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v1.13.0, schema v0.11.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.