Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-15 05:11 UTC

vstorm-co / pydantic-deepagents

Open-source, self-hosted Claude Code - a terminal AI assistant and the Python framework behind it. Tool-calling, sandboxed execution, multi-agent teams, skills, checkpoints, unlimited context - on Pydantic AI, any model.

PythonMIT★ 1,062 stars⑂ 133 forkssince Nov 2025View on GitHub ↗

KindLibraryCommand-line toolNetwork servicehow this is determined

#pydantic-ai #agent-framework #pydantic #python #anthropic #llms #mcp #ai-agents #subagents #claude-code #cli #coding-agent #deep-research #docker-sandbox #playwright #tui #vstorm

vstorm-co/pydantic-deepagents holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (92/100) and lowest on Sustainability & Governance (69/100). It was last updated 23 days ago. A single contributor accounts for most of its recent work.

overall / 100

Exceptional

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.

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)

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 80 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

VstormOrganization · verified domain

163 followers39 public repossince Mar 2021

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

Package ecosystems

Registry	Package	Version	Downloads / mo	Versions	Last publish	Tags
PyPI	pydantic-deep	0.3.43	141,905	58	40 days ago	agent-framework agentic ai-agent autonomous-agent claude cli deep-agent llm mcp multi-agent openai pydantic-ai subagents terminal-assistant tool-calling

Metrics by category

Vitality

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

89Excellent · 21% of overall

Development activity

81Excellent

How it's scored

28.8/36	Push recency — last push 23 days ago
24.2/36	Commit cadence — 35/52 weeks with commits
18/18	Commit volume — 677 commits in the last year
10/10	OpenSSF Scorecard: Maintained — 30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10

Inputs used

commits_last_year	677
human_commit_share	0.97
days_since_last_push	23
active_weeks_last_year	35

Release discipline

100Exceptional

How it's scored

27/27	Ships releases — 66 releases published
36/36	Release recency — latest release 40 days ago
27/27	Release cadence — a release every ~4.5 days
0/10	OpenSSF Scorecard: Signed-Releases — no data

Inputs used

releases_count	66
latest_release_tag	0.3.43
releases_from_tags	no
days_since_latest_release	40
mean_days_between_releases	4.5

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?

77Good · 17% of overall

Popularity & adoption

72Good

How it's scored

49.1/60	Stars — 1,062 stars
17.7/25	Forks — 133 forks
5.6/15	Watchers — 11 watchers

Inputs used

forks	133
stars	1,062
watchers	11
growth_state	unverified
growth_factor_pct	100
growth_unverified_reason	no_history

Community health

77Good

How it's scored

22.5/22.5	README
22.5/22.5	License — recognized license (MIT)
18/18	CONTRIBUTING guide
0/13.5	Code of conduct
0/7.2	Issue template
6.3/6.3	PR template

Inputs used

has_readme	yes
has_license	yes
readme_badges	18
has_contributing	yes
has_issue_template	no
has_code_of_conduct	no
readme_badge_services	coveralls.io, github.com, shields.io, www.bestpractices.dev
has_pull_request_template	yes

Ecosystem adoption (downloads)

86Excellent

How it's scored

68.7/80	Monthly downloads — 141,905 downloads/month across pypi
0/20	Registry dependents — not reported by this ecosystem

Inputs used

packages	pydantic-deep
dependents	—
ecosystems	pypi
total_downloads	—
monthly_downloads	141,905
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?

69Good · 23% of overall

Maintainer resilience (bus factor)

28At Risk

How it's scored

9/54	Bus factor — 1 contributor(s) cover half of all commits
2.8/22.5	Commit distribution — top contributor authored 87% of commits
13.5/13.5	Contributor breadth — 14 contributors
3/10	OpenSSF Scorecard: Contributors — project has 1 contributing companies or organizations -- score normalized to 3

Inputs used

bus_factor	1
contributors_sampled	14
top_contributor_share	0.874

Issue & PR responsiveness

74Good

How it's scored

36.3/42	Issue resolution — 86% of issues closed
26.7/30	PR acceptance — 113/127 decided PRs merged
0/13	Newcomer PR acceptance — no first-time contributor's PR decided in 30d
1.5/15	OpenSSF Scorecard: Code-Review — Found 2/12 approved changesets -- score normalized to 1

Inputs used

merged_prs	113
open_issues	10
closed_issues	64
prs_merged_7d	0
prs_decided_7d	0
prs_merged_30d	0
prs_decided_30d	0
issue_closed_ratio	0.865
closed_unmerged_prs	14
first_time_authors_30d	0
first_time_prs_merged_30d	0
first_time_prs_decided_30d	0

Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

89Excellent

How it's scored

30/30	Ownership backing — organization-owned
20/20	Verified domain
15.9/25	Owner reach — 163 followers of vstorm-co
22.7/25	Track record — 39 public repos, account ~5 yr old

Inputs used

followers	163
owner_type	Organization
is_verified	yes
owner_login	vstorm-co
public_repos	39
account_age_days	2,013

Package maintenance

100Exceptional

How it's scored

25/25	Published & resolvable — 1 package(s) on pypi
35/35	Publish recency — latest publish 40 days ago
20/20	Version history — 58 published versions
20/20	Not deprecated — active, not deprecated or yanked

Inputs used

packages	pydantic-deep
ecosystems	pypi
any_deprecated	no
min_days_since_publish	40

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional

How it's scored

24/24	CI workflows — 5 workflow(s)
24/24	Tests present
16/16	Linter config — pyproject.toml ([tool.ruff])
9.6/9.6	Pre-commit hooks
0/6.4	.editorconfig
20/20	OpenSSF Scorecard: CI-Tests — 12 out of 12 merged PRs checked by a CI test -- score normalized to 10

Inputs used

has_ci	yes
has_tests	yes
has_editorconfig	no
has_linter_config	yes
has_precommit_config	yes

Documentation

90Excellent

How it's scored

30/30	README
25/25	Documentation directory
15/15	Documentation / homepage site — https://vstorm-co.github.io/pydantic-deepagents/
10/10	Repository description
10/10	Topics — 17 topics
0/10	Wiki

Inputs used

topics	pydantic-ai, agent-framework, pydantic, python, anthropic, llms, mcp, ai-agents, subagents, claude-code, cli, coding-agent, deep-research, docker-sandbox, playwright, tui, vstorm
has_wiki	no
homepage	https://vstorm-co.github.io/pydantic-deepagents/
docs_site	https://vstorm-co.github.io/pydantic-deepagents/
has_readme	yes
has_docs_dir	yes
has_description	yes

Security

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

71Good · 16% of overall

Security posture

64Moderate

How it's scored

7.5/7.5	Binary-Artifacts — no binaries found in the repo
4.5/7.5	Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5	CI-Tests — 12 out of 12 merged PRs checked by a CI test -- score normalized to 10
1.2/2.5	CII-Best-Practices — badge detected: Passing
0.8/7.5	Code-Review — Found 2/12 approved changesets -- score normalized to 1
0.8/2.5	Contributors — project has 1 contributing companies or organizations -- score normalized to 3
10/10	Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5	Dependency-Update-Tool — update tool detected
0/5	Fuzzing — project is not fuzzed
2.5/2.5	License — license file detected
7.5/7.5	Maintained — 30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10
5/5	Packaging — packaging workflow detected
0/5	Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0.5/5	SAST — SAST tool is not run on all commits -- score normalized to 1
5/5	Security-Policy — security policy file detected
0/7.5	Signed-Releases — no data
0/7.5	Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5	Vulnerabilities — 0 existing vulnerabilities detected

Inputs used

source	openssf_scorecard
checks_evaluated	17
scorecard_version	v5.5.0
checks_inconclusive	1
scorecard_aggregate	6.4

Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional

How it's scored

35/35	Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25	Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40	No advisories left outstanding — no advisory carries a publication date

Inputs used

source	osv
advisories	0
affected_packages	0
assessed_packages	30
unassessed_packages	0
affected_by_severity	none
direct_affected_packages	0

Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:pydantic-deep@0.3.43 runtime dependency closure — what installing the published package pulls in — 30 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.

79Good · 4% of overall

Agent context & guidance

100Exceptional

How it's scored

45/45	Agent instructions — CLAUDE.md, apps/harbor/AGENTS.md, docs/api/agent.md, docs/concepts/agents.md
15/15	Machine-readable docs (llms.txt) — llms.txt served by the project website (https://vstorm-co.github.io/pydantic-deepagents/llms.txt)
40/40	Legible commit history — 90 of 97 human commits state their intent (structured subject or explanatory body)

Inputs used

has_llms_txt	yes
llms_txt_url	https://vstorm-co.github.io/pydantic-deepagents/llms.txt
legible_history_share	0.928
agent_instruction_files	CLAUDE.md, apps/harbor/AGENTS.md, docs/api/agent.md, docs/concepts/agents.md
agent_instruction_max_bytes	19,342

Verify loop (build / test / typecheck)

79Good

How it's scored

18/18	One-command bootstrap — Makefile
22/22	Automated tests
11/11	Lint / format config — pyproject.toml ([tool.ruff])
0/11	Static type checking
10/10	Reproducible environment — Dockerfile
10/10	Demonstrated agent practice — 28 of the last 100 commits agent-authored or agent-credited
8/8	Automated maintenance — 3 of the last 100 commits are automated dependency updates
0/10	OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0

Inputs used

has_nix	no
has_tests	yes
lockfiles	—
has_dockerfile	yes
typed_language	no
bootstrap_files	Makefile
has_devcontainer	no
has_linter_config	yes
typecheck_configs	—
agent_commit_share	0.28
toolchain_manifests	—
dependency_bot_commit_share	0.03

Code legibility for models

53Moderate

How it's scored

0/45	Type-checkable code — Python without a type-check config
53.1/55	Manageable file sizes — 10/295 source files over 60KB

Inputs used

primary_language	Python
largest_source_bytes	134,208
source_files_sampled	295
oversized_source_files	10

Machine-readable interfaces

60Moderate

How it's scored

0/40	API schema (OpenAPI/GraphQL/proto)
20/20	MCP server
40/40	Runnable examples — examples

Inputs used

example_dirs	examples
has_mcp_signal	yes
api_schema_files	—
interfaces_expected_of	network-service

Key facts

1,062GitHub stars

14contributors

677commits, last 12 months

23days since last push

66releases

1bus factor

10open issues

PyPIpackage ecosystems

Data collection warnings

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

More detail

Star and fork history 0 ★ / 133 ⇿

0Stars

133Forks

62Releases

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.

Major 0Minor 1Patch 61

OpenSSF Scorecard 6.4 / 10

6.4aggregate

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 · 2026-09-15 05:11 UTC

10	Binary-Artifacts	no binaries found in the repo
6	Branch-Protection	branch protection is not maximal on development and all release branches
10	CI-Tests	12 out of 12 merged PRs checked by a CI test -- score normalized to 10
5	CII-Best-Practices	badge detected: Passing
1	Code-Review	Found 2/12 approved changesets -- score normalized to 1
3	Contributors	project has 1 contributing companies or organizations -- score normalized to 3
10	Dangerous-Workflow	no dangerous workflow patterns detected
10	Dependency-Update-Tool	update tool detected
0	Fuzzing	project is not fuzzed
10	License	license file detected
10	Maintained	30 commit(s) and 10 issue activity found in the last 90 days -- score normalized to 10
10	Packaging	packaging workflow detected
0	Pinned-Dependencies	dependency not pinned by hash detected -- score normalized to 0
1	SAST	SAST tool is not run on all commits -- score normalized to 1
10	Security-Policy	security policy file detected
n/a	Signed-Releases	no releases found
0	Token-Permissions	detected GitHub workflow tokens with excessive permissions
10	Vulnerabilities	0 existing vulnerabilities detected

Direct dependencies 8

Registry	Package	Version constraint	Manifest
PyPI	pydantic-ai-slim	`>=2.0.0`	pyproject.toml
PyPI	pydantic-ai-todo	`>=0.2.7`	pyproject.toml
PyPI	pydantic-ai-backend	`>=0.2.25`	pyproject.toml
PyPI	summarization-pydantic-ai	`>=0.1.11`	pyproject.toml
PyPI	subagents-pydantic-ai	`>=0.2.18`	pyproject.toml
PyPI	pydantic-ai-shields	`>=0.3.4`	pyproject.toml
PyPI	pydantic	`>=2.0`	pyproject.toml
PyPI	chardet	`>=5.0.0`	pyproject.toml

All dependencies 43

Full resolved dependency set from the GitHub dependency graph: 8 direct and 35 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

Registry	Package	Version	Relation
PyPI	chardet	`—`	direct
PyPI	pydantic	`—`	direct
PyPI	pydantic-ai-backend	`—`	direct
PyPI	pydantic-ai-shields	`—`	direct
PyPI	pydantic-ai-slim	`—`	direct
PyPI	pydantic-ai-todo	`—`	direct
PyPI	subagents-pydantic-ai	`—`	direct
PyPI	summarization-pydantic-ai	`—`	direct
PyPI	agent-client-protocol	`—`	indirect
PyPI	bandit	`—`	indirect
PyPI	build	`—`	indirect
PyPI	coverage	`—`	indirect
PyPI	fastapi	`—`	indirect
PyPI	griffe	`—`	indirect
PyPI	html2text	`—`	indirect
PyPI	liteparse	`—`	indirect
PyPI	logfire	`—`	indirect
PyPI	mkdocs	`—`	indirect
PyPI	mkdocs-llmstxt	`—`	indirect
PyPI	mkdocs-material	`—`	indirect
PyPI	mkdocstrings	`—`	indirect
PyPI	mkdocstrings-python	`—`	indirect
PyPI	mypy	`—`	indirect
PyPI	pillow	`—`	indirect
PyPI	playwright	`—`	indirect
PyPI	pre-commit	`—`	indirect
PyPI	prompt-toolkit	`—`	indirect
PyPI	py-key-value-aio	`—`	indirect
PyPI	pydantic-ai	`—`	indirect
PyPI	pydantic-deep	`—`	indirect
PyPI	pyright	`—`	indirect
PyPI	pytest	`—`	indirect
PyPI	pytest-asyncio	`—`	indirect
PyPI	python-dotenv	`—`	indirect
PyPI	python-multipart	`—`	indirect
PyPI	pyyaml	`—`	indirect
PyPI	rich	`—`	indirect
PyPI	ruff	`—`	indirect
PyPI	textual	`—`	indirect
PyPI	twine	`—`	indirect
PyPI	typer	`—`	indirect
PyPI	types-pyyaml	`—`	indirect
PyPI	uvicorn	`—`	indirect

Dependency advisories 0

Installing pypi:pydantic-deep@0.3.43 pulls in 30 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.

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.

Software health index

Score profile

Ownership

Package ecosystems

Metrics by category

Key facts

Data collection warnings

More detail

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