Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 22:04 UTC

evidentlyai / evidently

Evidently is ​​an open-source ML and LLM observability framework. Evaluate, test, and monitor any AI-powered system or data pipeline. From tabular data to Gen AI. 100+ metrics.

Jupyter Notebook · PythonApache-2.0★ 7,800 stars⑂ 896 forkssince Nov 2020View on GitHub ↗
KindCommand-line toolLibraryNetwork servicehow this is determined

evidentlyai/evidently holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on Vitality (87/100) and lowest on Security (40/100). It was last updated 7 days ago. 3 contributors account for most of its recent work.

90
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.

90
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 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

Evidently AIOrganization
361 followers10 public repossince Nov 2020

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIevidently0.7.21-152155 days ago

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 7 days ago
22.2/36Commit cadence32/52 weeks with commits
18/18Commit volume118 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year118
human_commit_share1
days_since_last_push7
active_weeks_last_year32
How it's scored
27/27Ships releases100 releases published
27/36Release recencylatest release 155 days ago
27/27Release cadencea release every ~24.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count100
latest_release_tagv0.7.21
releases_from_tagsno
days_since_latest_release155
mean_days_between_releases24.1

Community & Adoption

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

83Excellent · 17% of overall
How it's scored
60/60Stars7,800 stars
24.6/25Forks896 forks
9.6/15Watchers54 watchers
Inputs used
forks896
stars7,800
watchers54
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_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

80Excellent · 23% of overall
How it's scored
36/54Bus factor3 contributor(s) cover half of all commits
17/22.5Commit distributiontop contributor authored 24% of commits
13.5/13.5Contributor breadth89 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor3
contributors_sampled89
top_contributor_share0.243
How it's scored
21.8/42Issue resolution52% of issues closed
27.4/30PR acceptance1,238/1,354 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs1,238
open_issues238
closed_issues257
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.519
closed_unmerged_prs116
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 domainverified-domain status not read for this organization
18.4/25Owner reach361 followers of evidentlyai
19/25Track record10 public repos, account ~5 yr old
Inputs used
followers361
owner_typeOrganization
is_verified
owner_loginevidentlyai
public_repos10
account_age_days2,086
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 155 days ago
20/20Version history152 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesevidently
ecosystemspypi
any_deprecatedno
min_days_since_publish155

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall
How it's scored
24/24CI workflows5 workflow(s)
24/24Tests present
16/16Linter configruff.toml
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage sitehttps://discord.gg/xZjKRaNp8b
10/10Repository description
10/10Topics14 topics
10/10Wiki
Inputs used
topicsdata-drift, jupyter-notebook, pandas-dataframe, machine-learning, model-monitoring, html-report, mlops, data-science, hacktoberfest, data-quality, data-validation, generative-ai, llm, llmops
has_wikiyes
homepagehttps://discord.gg/xZjKRaNp8b
docs_sitehttps://discord.gg/xZjKRaNp8b
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

40Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
25/25Dependency lockfilespnpm-lock.yaml
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilespnpm-lock.yaml
manifestspyproject.toml, requirements.dev.txt, requirements.min.txt, ui/package.json
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages74
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:evidently@0.7.21 runtime dependency closure — what installing the published package pulls in — 74 packages. 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.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history85 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.85
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocker/Makefile
22/22Automated tests
11/11Lint / format configruff.toml
11/11Static type checkingui/packages/evidently-ui-lib/tsconfig.json, ui/service/tsconfig.json, ui/standalone/tsconfig.json
10/10Reproducible environmentdevcontainer, Dockerfile, lockfile
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfilespnpm-lock.yaml
has_dockerfileyes
typed_languageno
bootstrap_filesdocker/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configsui/packages/evidently-ui-lib/tsconfig.json, ui/service/tsconfig.json, ui/standalone/tsconfig.json
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable codeJupyter Notebook with type-check config (ui/packages/evidently-ui-lib/tsconfig.json, ui/service/tsconfig.json, ui/standalone/tsconfig.json)
54.4/55Manageable file sizes11/1,078 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes3,525,811
source_files_sampled1,078
oversized_source_files11
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examplescookbook, examples, notebooks
Inputs used
example_dirscookbook, examples, notebooks
has_mcp_signalno
api_schema_files
interfaces_expected_ofnetwork-service

Key facts

7,800GitHub stars
89contributors
118commits, last 12 months
7days since last push
100releases
3bus factor
238open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (2026/08/12 22:02:53 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 896 ⇿
0Stars
896Forks
100Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

02004006008001,000893152020-122023-102026-08
Major 0Minor 6Patch 80

Each point covers 6 days.

Direct dependencies 27
RegistryPackageVersion constraintManifest
PyPIplotly>=5.10.0,<6pyproject.toml
PyPIstatsmodels>=0.14.0pyproject.toml
PyPIscikit-learn>=1.1.1pyproject.toml
PyPIpandas>=1.3.5pyproject.toml
PyPInumpy>=1.23.0pyproject.toml
PyPInltk>=3.6.7pyproject.toml
PyPIscipy>=1.10.0pyproject.toml
PyPIrequests>=2.32.0pyproject.toml
PyPIPyYAML>=6.0.1pyproject.toml
PyPIpydantic>=1.10.16pyproject.toml
PyPIlitestar>=2.19.0pyproject.toml
PyPItyping-inspect>=0.9.0pyproject.toml
PyPIuvicorn>=0.22.0pyproject.toml
PyPIwatchdog>=3.0.0pyproject.toml
PyPItyper>=0.3pyproject.toml
PyPIrich>=13pyproject.toml
PyPIiterative-telemetry>=0.0.5pyproject.toml
PyPIdynaconf>=3.2.4pyproject.toml
PyPIcertifi>=2024.7.4pyproject.toml
PyPIurllib3>=1.26.19pyproject.toml
PyPIfsspec>=2024.9.0pyproject.toml
PyPIujson>=5.4.0pyproject.toml
PyPIdeprecation>=2.1.0pyproject.toml
PyPIuuid6>=2024.7.10pyproject.toml
PyPIcryptography>=43.0.1pyproject.toml
PyPIopentelemetry-proto>=1.25.0pyproject.toml
npm@biomejs/biome1.9.4ui/package.json
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 0

Installing pypi:evidently@0.7.21 pulls in 74 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

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 v0.31.0 — full methodology · metrics wiki.

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