Record in aggregate · metrics 2.10.0

The state of PyPI.

Aggregate statistics across every inspected repository publishing to PyPI — how health distributes, where the download volume sits, and which practices are common or rare, measured against the whole record.

Inspected repositories
9,826 of 9,826 indexed
Monthly downloads under inspection
87.7B registry-reported
Median health index
69 Good
Good or better
58% index 65 and above

Health-index distribution

Latest health index of every inspected repository, in five-point intervals over the 1–100 scale.

Where the download volume sits

Combined monthly downloads by band, against repository counts.

18% of the monthly download volume under inspection flows through repositories below the good band — and the top 1% most-downloaded repositories carry 64% of the entire volume.

Repositories
25%21%20%
Download volume
40%27%
BandRepositoriesDownloads / moVolume share
Exceptional1,18035.3B40%
Excellent2,45023.3B27%
Good2,03713.4B15%
Moderate1,9407.8B8.9%
Weak8831.9B2.2%
At Risk9554.5B5.1%
Critical3811.4B1.6%

Score shapes

The distribution behind each category median, in ten-point bins — where the record clusters, and where a category separates repositories or saturates.

Vitality74
1100
Community & Adoption57
1100
Sustainability & Governance62
1100
Engineering Quality76
1100
Security53
1100
AI Readiness56
1100

Category profile

Median category score across the scope. Ticks mark the whole-record median. Categories are documented in the methodology wiki.

Vitality
74
Community & Adoption
57
Sustainability & Governance
62
Engineering Quality
76
Security
53
AI Readinessunweighted
56

The state of practice

Share of inspected repositories where the practice is publicly evident. Reports that predate a signal are excluded from its basis, never counted as missing.

Engineering & community practice

License detected
96% of 9,826
README
95% of 9,826
Automated tests
94% of 9,826
CI workflows
89% of 9,826
Documentation directory
67% of 9,826
Linter configuration
63% of 9,826
Contributing guide
46% of 9,826
Code of conduct
26% of 9,826
Security policy
23% of 9,826

Agent-era signals

One-command bootstrap
51% of 9,826
AI agent instructions
28% of 9,826
llms.txt
9.8% of 9,826

Signals read by the unweighted AI Readiness category.

Does popularity mean health?

Median health index (dot) and the middle half of repositories (band) per popularity bracket, on the shared 1–100 scale.

By GitHub stars

Under 100 stars 4,972
60 · 45–75
100 – 999 2,453
77 · 57–88
1,000 – 9,999 1,890
86 · 65–93
10,000 and more 511
94 · 84–97

By monthly downloads

Under 10K / month 2,329
65 · 53–78
10K – 1M 1,615
78 · 62–89
1M – 100M 1,413
78 · 53–92
100M and more 169
88 · 67–95

Security under the microscope

Average OpenSSF Scorecard result per check across the scope, weakest first, on Scorecard's 0–10 scale. Check results are mostly all-or-nothing, so the average tracks how much of the record passes. Checks Scorecard reports inconclusive are excluded from scoring, never counted as zero; each row's tooltip carries its basis.

CII-Best-Practices
0.1
Fuzzing
0.5
Signed-Releases
1.0
Branch-Protection
1.4
Pinned-Dependencies
1.6
SAST
1.7
Token-Permissions
1.9
Code-Review
2.6
Security-Policy
2.8
Dependency-Update-Tool
4.6
Maintained
5.9
Vulnerabilities
6.5
CI-Tests
6.8
Contributors
6.9
License
9.5
Binary-Artifacts
9.7
Dangerous-Workflow
9.7
Packaging
10.0

Red flags

Findings that adjust a rating downward rather than scoring into it. Each is reported as a count, as a share of the whole record, and as a rate among the repositories where it could be determined at all.

Abandonment
1,00510% of the record · 10% of 9,827 assessed
High-risk jurisdiction exposure
1491.5% of the record · 1.7% of 8,984 assessed
Malicious dependencies
5<0.1% of the record · <0.1% of 6,700 assessed
Inorganic growth
1<0.1% of the record · 0.2% of 501 assessed

A red flag needs its own evidence, so its basis is smaller than the record. Growth authenticity is assessed only where day-by-day history was collected; dependency findings only where a dependency graph resolved. Repositories the evidence cannot answer for are left out of the basis rather than counted as passing.

The pulse

How recently each inspected repository last saw a push, at inspection time.

Half of the inspected repositories saw a push within 3 days of inspection.

Push recency
72%
Last pushRepositoriesShare
Pushed within 30 days7,04272%
31 – 90 days8218.4%
91 – 365 days8768.9%
Over a year1,08711%

Stewardship & resilience

Who stands behind the inspected repositories, and how many people the code depends on. Both are read by the governance category.

5,797Organization-stewarded median 78
4,029Personal accounts median 59

Maintainer bus factor

71% of inspected repositories depend on a single maintainer for the majority of their commits — including 984 with over a million monthly downloads.

1 maintainer
6,924
2 maintainers
1,615
3–5 maintainers
1,004
6+ maintainers
251

The dependency iceberg

Declared direct dependencies against the full resolved graph (direct plus transitive), across the 7,616 reports with a collected dependency graph.

The median repository declares 2 direct dependencies — and resolves to 26 packages in total.

Resolved packages per repository

0
577
1 – 5
1,295
6 – 20
1,640
21 – 50
1,090
51 – 200
1,422
201 – 500
721
501 – 1,000
396
Over 1,000
475

License landscape

The most common detected licenses across the scope (SPDX identifiers).

MIT
3,927
Apache-2.0
2,326
Custom license
1,299
BSD-3-Clause
679
GPL-3.0
409
No license detected
387
AGPL-3.0
231
BSD-2-Clause
144
LGPL-3.0
98
MPL-2.0
89

Most relied upon

The most-downloaded repositories under inspection publishing to PyPI — the records the figures above weigh heaviest. The rest is covered by the full catalogue · tag index.

PyPI
98Exceptionalhealth index
pypa/packaging
Core utilities for Python packages
Python★ 746↓ 2.1B/moAug 4, 2026
Custom licenseAug 4, 2026 · metrics 2.10.0
PyPI
98Exceptionalhealth index
urllib3/urllib3
urllib3 is a user-friendly HTTP client library for Python
Python★ 4,052↓ 1.9B/moAug 28, 2026
MITAug 28, 2026 · metrics 2.10.0
PyPI
83Excellenthealth index
kjd/idna
Internationalized Domain Names for Python (IDNA 2008 and UTS #46)
Python★ 288↓ 1.7B/moAug 4, 2026
BSD-3-ClauseAug 4, 2026 · metrics 2.10.0
PyPI
98Exceptionalhealth index
psf/requests
A simple, yet elegant, HTTP library.
Python★ 54.2K↓ 1.7B/moAug 4, 2026
Apache-2.0Aug 4, 2026 · metrics 2.10.0
PyPI
97Exceptionalhealth index
python/typing_extensions
Backported and experimental type hints for Python
Python★ 582↓ 1.6B/moSep 9, 2026
Custom licenseSep 9, 2026 · metrics 2.10.0
PyPI
94Exceptionalhealth index
jawah/charset_normalizer
Truly universal encoding detector in pure Python.
Python · Cython★ 793↓ 1.5B/moSep 9, 2026
MITSep 9, 2026 · metrics 2.10.0

Reading these figures

  • Every figure is computed from the latest published inspection of each repository, under the versioned methodology (currently metrics 2.10.0). See the methodology · band scale.
  • Statistics describe the inspected record — software admitted for inspection, not a random sample of all open source. Admission follows the public-interest criteria.
  • Download figures come from package registries; registries that publish no monthly number (Maven Central, Go, NuGet, RubyGems) contribute no volume rather than zero. Coverage per ecosystem is documented in supported ecosystems.
  • Where a signal is unavailable in a report — an uncollected dependency graph, an inconclusive Scorecard check, a report predating a signal — the repository is excluded from that figure's basis, never counted against it.
  • Health indices are signals of publicly visible practice, not audits or warranties — how to read them is covered by the health index.
  • Figures computed 2026-09-11 07:30 UTC; the aggregate is recomputed hourly. The underlying data is available as JSON.