Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 16:44 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★ 7,923 stars⑂ 918 forkssince Nov 2020View on GitHub ↗
KindCommand-line toolLibraryNetwork servicehow this is determined

evidentlyai/evidently holds a health index of 88 out of 100, placing it in the Excellent band. It scores highest on Vitality (89/100) and lowest on Security (36/100). It was last updated 6 days ago. 3 contributors account for most of its recent work.

88
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.

88
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 74 is calibrated to 88 on the published index scale (record calibration 2026-08-02).

Ownership

Evidently AIOrganization
369 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
PyPIevidently0.7.23-1536 days ago

Metrics by category

Vitality

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

89Excellent · 21% of overall
How it's scored
36/36Push recency — last push 6 days ago
20.1/36Commit cadence — 29/52 weeks with commits
18/18Commit volume — 103 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year103
human_commit_share1
days_since_last_push6
active_weeks_last_year29

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 6 days ago
27/27Release cadence — a release every ~42.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv0.7.23
releases_from_tagsno
days_since_latest_release6
mean_days_between_releases42.1

Community & Adoption

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

83Excellent · 17% of overall
How it's scored
60/60Stars — 7,923 stars
24.7/25Forks — 918 forks
9.7/15Watchers — 56 watchers
Inputs used
forks918
stars7,923
watchers56
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized 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?

74Good · 23% of overall
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
17.1/22.5Commit distribution — top contributor authored 24% of commits
13.5/13.5Contributor breadth — 88 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled88
top_contributor_share0.242
How it's scored
21.7/42Issue resolution — 52% of issues closed
27.4/30PR acceptance — 1,240/1,358 decided PRs merged
0/13Newcomer PR acceptance — 0/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs1,240
open_issues241
closed_issues257
prs_merged_7d2
prs_decided_7d2
prs_merged_30d2
prs_decided_30d4
issue_closed_ratio0.516
closed_unmerged_prs118
first_time_authors_30d2
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
18.5/25Owner reach — 369 followers of evidentlyai
19.2/25Track record — 10 public repos, account ~5 yr old
Inputs used
followers369
owner_typeOrganization
is_verifiedno
owner_loginevidentlyai
public_repos10
account_age_days2,122

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 6 days ago
20/20Version history — 153 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesevidently
ecosystemspypi
any_deprecatedno
min_days_since_publish6

Engineering Quality

Are baseline engineering and documentation practices in place?

85Excellent · 19% of overall
How it's scored
24/24CI workflows — 5 workflow(s)
24/24Tests present
16/16Linter config — ruff.toml
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no 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 site — https://discord.gg/xZjKRaNp8b
10/10Repository description
10/10Topics — 14 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?

36Weak · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
25/25Dependency lockfiles — pnpm-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
How it's scored
15.4/35Direct dependencies free of known advisories — 1 affected: nltk 3.10.3 (high 7.0)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories1
affected_packages1
assessed_packages78
unassessed_packages0
affected_by_severityhigh 1
direct_affected_packages1
Matched the pypi:evidently@0.7.23 runtime dependency closure — what installing the published package pulls in — 78 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.

67Good · 4% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.evidentlyai.com/llms.txt)
40/40Legible commit history — 84 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.evidentlyai.com/llms.txt
legible_history_share0.84
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
18/18One-command bootstrap — docker/Makefile
22/22Automated tests
11/11Lint / format config — ruff.toml
11/11Static type checking — ui/packages/evidently-ui-lib/tsconfig.json, ui/service/tsconfig.json, ui/standalone/tsconfig.json
10/10Reproducible environment — devcontainer, Dockerfile, lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no 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 code — Jupyter Notebook with type-check config (ui/packages/evidently-ui-lib/tsconfig.json, ui/service/tsconfig.json, ui/standalone/tsconfig.json)
54.4/55Manageable file sizes — 11/1,079 source files over 60KB
Inputs used
primary_languageJupyter Notebook
largest_source_bytes3,525,811
source_files_sampled1,079
oversized_source_files11
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — cookbook, examples, notebooks
Inputs used
example_dirscookbook, examples, notebooks
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

7,923GitHub stars
88contributors
103commits, last 12 months
6days since last push
100releases
3bus factor
241open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'evidently' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/17 16:42:42 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 ★ / 918 ⇿
0Stars
918Forks
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.

02004006008001,000915152020-122023-102026-09
Major 0Minor 6Patch 81

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 1

Installing pypi:evidently@0.7.23 pulls in 78 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
nltk3.10.3directhigh1—

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 statistics — PyPI.