Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-18 07:59 UTC

Cogensec / agentegrity

The open standard for AI agent integrity. Evaluate, enforce, and prove that autonomous agents are adversarially coherent, environmentally portable, and verifiably assured.

Python · TypeScriptApache-2.0★ 3 stars⑂ 1 forksince Mar 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

Cogensec/agentegrity holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (90/100) and lowest on Community & Adoption (36/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

80
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

80
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe 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.

The weighted overall 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

CogensecOrganization
8 followers2 public repossince Apr 2025

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIagentegrity0.10.01561346 days agoadversarial-aiagent-integrityagentegrityai-agentsai-governanceai-securityautonomous-agentsruntime-security

Metrics by category

Vitality

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

87Excellent · 21% of overall
How it's scored
36/36Push recencylast push 1 days ago
13.8/36Commit cadence20/52 weeks with commits
18/18Commit volume207 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year207
human_commit_share0.96
days_since_last_push1
active_weeks_last_year20

Release discipline

100Exceptional
How it's scored
27/27Ships releases6 releases published
36/36Release recencylatest release 46 days ago
27/27Release cadencea release every ~17.7 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count6
latest_release_tagv0.10.0
releases_from_tagsno
days_since_latest_release46
mean_days_between_releases17.7
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?

36Weak · 17% of overall
How it's scored
4.9/60Stars3 stars
0/25Forks1 forks
0/15Watchers0 watchers
Inputs used
forks1
stars3
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges7
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
29.3/80Monthly downloads156 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesagentegrity
dependents
ecosystemspypi
total_downloads
monthly_downloads156
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

53Moderate · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
0.2/22.5Commit distributiontop contributor authored 99% of commits
2.7/13.5Contributor breadth2 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.99
How it's scored
42/42Issue resolution100% of issues closed
20.5/30PR acceptance26/38 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 0/29 approved changesets -- score normalized to 0
Inputs used
merged_prs26
open_issues0
closed_issues2
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio1
closed_unmerged_prs12
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
6.9/25Owner reach8 followers of Cogensec
6.3/25Track record2 public repos, account ~1 yr old
Inputs used
followers8
owner_typeOrganization
is_verifiedno
owner_loginCogensec
public_repos2
account_age_days513

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 46 days ago
20/20Version history13 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesagentegrity
ecosystemspypi
any_deprecatedno
min_days_since_publish46

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
16/16Linter configpyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests1 out of 1 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttp://agentegrity.cogensec.com
10/10Repository description
10/10Topics20 topics
10/10Wiki
Inputs used
topicsagentic-security, agentegrity, adversarial-ai, ai-governance, ai-safety, ai-security, llm-security, runtime-security, agno, autogen, autogen-extension, aws-bedrock, aws-bedrock-agents, crewai, google-adk, langchain, langgraph, openai-agents, openai-agents-sdk, vercel-ai-sdk
has_wikiyes
homepagehttp://agentegrity.cogensec.com
docs_sitehttp://agentegrity.cogensec.com
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

70Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
2.5/2.5CI-Tests1 out of 1 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 0/29 approved changesets -- score normalized to 0
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflowno dangerous workflow patterns detected
7.5/7.5Dependency-Update-Toolupdate tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
5/5SASTSAST tool is run on all commits
5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
6.8/7.5Vulnerabilities1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.2
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
unassessed_packages30
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 1 resolved dependencies against OSV. 30 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

78Good · 4% of overall
How it's scored
45/45Agent instructionsCLAUDE.md
15/15Machine-readable docs (llms.txt)llms.txt served by the project website (https://agentegrity.cogensec.com/llms.txt)
40/40Legible commit history90 of 96 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://agentegrity.cogensec.com/llms.txt
legible_history_share0.938
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes14,720
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configpyproject.toml ([tool.ruff])
11/11Static type checkingclients/typescript/packages/claude-sdk/tsconfig.json, clients/typescript/packages/client/tsconfig.json, clients/typescript/packages/crewai/tsconfig.json, clients/typescript/packages/google-adk/tsconfig.json, clients/typescript/packages/langchain/tsconfig.json, clients/typescript/packages/openai-agents/tsconfig.json, clients/typescript/packages/vercel-ai/tsconfig.json
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
8/8Automated maintenance4 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configsclients/typescript/packages/claude-sdk/tsconfig.json, clients/typescript/packages/client/tsconfig.json, clients/typescript/packages/crewai/tsconfig.json, clients/typescript/packages/google-adk/tsconfig.json, clients/typescript/packages/langchain/tsconfig.json, clients/typescript/packages/openai-agents/tsconfig.json, clients/typescript/packages/vercel-ai/tsconfig.json
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0.04
How it's scored
27/45Type-checkable codePython with type-check config (clients/typescript/packages/claude-sdk/tsconfig.json, clients/typescript/packages/client/tsconfig.json, clients/typescript/packages/crewai/tsconfig.json, clients/typescript/packages/google-adk/tsconfig.json, clients/typescript/packages/langchain/tsconfig.json, clients/typescript/packages/openai-agents/tsconfig.json, clients/typescript/packages/vercel-ai/tsconfig.json)
55/55Manageable file sizes0/170 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes42,773
source_files_sampled170
oversized_source_files0
How it's scored
40/40API schema (OpenAPI/GraphQL/proto)schemas/openapi.yaml
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_filesschemas/openapi.yaml
interfaces_expected_of
Excluded from scoring (no data or not applicable): MCP server. Remaining weights renormalized.

Key facts

3GitHub stars
2contributors
207commits, last 12 months
1days since last push
6releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

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 · 2026-09-18 07:58 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests1 out of 1 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/29 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
9Vulnerabilities1 existing vulnerabilities detected
All dependencies 31

Full resolved dependency set from the GitHub dependency graph: 0 direct and 31 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
npm@agentegrity/client0.10.0indirect
npm@types/node^20.0.0indirect
npmtsx^4.7.0indirect
npmtypescript^5.4.0indirect
PyPIagnoindirect
PyPIanthropicindirect
PyPIautogen-agentchatindirect
PyPIautogen-coreindirect
PyPIboto3indirect
PyPIclaude-agent-sdkindirect
PyPIcoverageindirect
PyPIcrewaiindirect
PyPIcryptographyindirect
PyPIfastapiindirect
PyPIgoogle-adkindirect
PyPIhttpxindirect
PyPIjsonschemaindirect
PyPIlangchain-coreindirect
PyPImypyindirect
PyPIopenai-agentsindirect
PyPIopentelemetry-apiindirect
PyPIopentelemetry-exporter-otlpindirect
PyPIopentelemetry-sdkindirect
PyPIpytestindirect
PyPIpytest-asyncioindirect
PyPIpytest-covindirect
PyPIreferencingindirect
PyPIruffindirect
PyPIscipyindirect
PyPIstrands-agentsindirect
PyPIuvicornindirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 1 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 30 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

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 v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

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