Öffentliches RegisterExzellent
Software-GesundheitsberichtSchema 0.27.0 · Metriken 2.3.1 · 2026-08-01 12:48 UTC
vstorm-co / subagents-pydantic-ai
Subagent Delegation framework for Pydantic AI, enabling nested subagents that can spawn their own specialists on-the-fly, with smart sync/async/auto mode selection, runtime agent creation, and clean multi-agent architecture. Adds specialization, parallel execution, and task cancellation.
vstorm-co/subagents-pydantic-ai erreicht einen Gesundheitsindex von 88 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Engineering Quality (96/100) ab, am schwächsten bei Security (53/100). Zuletzt heute aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
88
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer standardisierten Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr gewichtetes Mittel, kalibriert auf die Verteilung des öffentlichen Registers, sodass die Stufen Perzentilbedeutung tragen; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze Gefährdet von 34.
88
Außergewöhnlich93-100Die Spitzengruppe des Registers (≈ obere 5 %); erfüllt im Wesentlichen alle geprüften Kriterien
Exzellent80-92Durchgehend stark; geringfügige Lücken
Gut65-79Gesund; Lücken sind begrenzt und beherrschbar
Mittel50-64Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Schwach35-49Wesentliche Schwächen in mehreren Bereichen
Gefährdet20-34Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-19Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Der gewichtete Gesamtwert 74 wird auf der veröffentlichten Indexskala auf 88 kalibriert (Register-Kalibrierung 2026-08-02).
Eigentümerschaft
VstormOrganisation
146 Follower34 öffentliche Reposseit März 2021
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | subagents-pydantic-ai | 0.2.13 | 229.008 | 24 | vor 0 Tagen | agentsaimulti-agentpydanticpydantic-aisubagentstask-delegationtoolset |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
86
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 13.2/36 | Commit-Rhythmus — 19/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 135 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 3 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 135 |
| human_commit_share | 0,94 |
| days_since_last_push | 0 |
| active_weeks_last_year | 19 |
Release-Disziplin
100Außergewöhnlich
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 24 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 0 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 7,7 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 24 |
| latest_release_tag | 0.2.13 |
| releases_from_tags | nein |
| days_since_latest_release | 0 |
| mean_days_between_releases | 7,7 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
65
Popularität & Verbreitung
39Schwach
Wie die Bewertung erfolgt
| 28.4/60 | Stars — 57 Stars |
| 10.3/25 | Forks — 18 Forks |
| 0/15 | Watcher — 2 Watcher |
Verwendete Eingangsdaten
| forks | 18 |
| stars | 57 |
| watchers | 2 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
77Gut
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (MIT) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 0/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 6.3/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| readme_badges | — |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | nein |
| readme_badge_services | — |
| has_pull_request_template | ja |
Ökosystem-Verbreitung (Downloads)
89Exzellent
Wie die Bewertung erfolgt
| 71.5/80 | Downloads pro Monat — 229.008 Downloads/Monat über pypi |
| 0/20 | Abhängige in der Registry — von diesem Ökosystem nicht ausgewiesen |
Verwendete Eingangsdaten
| packages | subagents-pydantic-ai |
| dependents | — |
| ecosystems | pypi |
| total_downloads | — |
| monthly_downloads | 229.008 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Abhängige in der Registry. Die verbleibenden Gewichte wurden renormalisiert.
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
64
Maintainer-Resilienz (Bus-Faktor)
29Gefährdet
Wie die Bewertung erfolgt
| 9/54 | Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab |
| 3.2/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 86 % der Commits |
| 6.8/13.5 | Breite der Beitragenden — 5 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 5 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 1 |
| contributors_sampled | 5 |
| top_contributor_share | 0,86 |
Wie die Bewertung erfolgt
| 38.5/42 | Issue-Lösungsquote — 92 % der Issues geschlossen |
| 23.9/30 | PR-Annahme — 39/49 entschiedene PRs gemergt |
| 0/13 | Newcomer PR acceptance — kein PR eines Erstbeitragenden in 30 Tagen entschieden |
| 3/15 | OpenSSF Scorecard: Code-Review — Found 2/7 approved changesets -- score normalized to 2 |
Verwendete Eingangsdaten
| merged_prs | 39 |
| open_issues | 1 |
| closed_issues | 11 |
| prs_merged_7d | — |
| prs_decided_7d | — |
| prs_merged_30d | — |
| prs_decided_30d | — |
| issue_closed_ratio | 0,917 |
| closed_unmerged_prs | 10 |
| first_time_authors_30d | — |
| first_time_prs_merged_30d | — |
| first_time_prs_decided_30d | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): newcomer_pr_acceptance. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 15.6/25 | Reichweite des Inhabers — 146 Follower von vstorm-co |
| 22/25 | Kontohistorie — 34 öffentliche Repos, Kontoalter ca. 5 Jahre |
Verwendete Eingangsdaten
| followers | 146 |
| owner_type | Organization |
| is_verified | — |
| owner_login | vstorm-co |
| public_repos | 34 |
| account_age_days | 1.968 |
Paketpflege
100Außergewöhnlich
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 0 Tagen |
| 20/20 | Versionshistorie — 24 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | subagents-pydantic-ai |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 0 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
96
Engineering-Praktiken
94Außergewöhnlich
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 3 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 9 out of 9 merged PRs checked by a CI test -- score normalized to 10 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Außergewöhnlich
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://vstorm-co.github.io/subagents-pydantic-ai/ |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 17 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | ai-agent-framework, aiagentsframework, anthropic, artificial-intelligence, chatgpt, claude-code, framework, genai, llm, mcp, open-source, openai, pydantic, pydantic-ai, python, subagents, vstorm |
| has_wiki | ja |
| homepage | https://vstorm-co.github.io/subagents-pydantic-ai/ |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
53
Sicherheitslage
53Mittel
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.5/2.5 | CI-Tests — 9 out of 9 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 1.5/7.5 | Code-Review — Found 2/7 approved changesets -- score normalized to 2 |
| 2.5/2.5 | Contributors — project has 5 contributing companies or organizations |
| 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 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 3 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 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 1.5/7.5 | Vulnerabilities — 8 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 16 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 2 |
| scorecard_aggregate | 5,3 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Trägt ein bewusst kleines Gewicht (4 %): Agenten-Tooling ist ein echtes Pflegesignal, doch ein Repository ohne jedes Signal kann weiterhin 100/100 erreichen.
80
AI-Agentenkontext & Leitlinien
85Exzellent
Wie die Bewertung erfolgt
| 45/45 | Agentenanweisungen — AGENTS.md |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 40/40 | Lesbare Commit-Historie — 73 von 94 menschlichen Commits benennen ihre Absicht (strukturierter Betreff oder erläuternder Text) |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | 0,777 |
| agent_instruction_files | AGENTS.md |
| agent_instruction_max_bytes | 7.166 |
AI-Verify-Loop (Build / Test / Typprüfung)
90Exzellent
Wie die Bewertung erfolgt
| 18/18 | Bootstrap mit einem Befehl — Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 11/11 | Statische Typprüfung — src/subagents_pydantic_ai/py.typed |
| 10/10 | Reproduzierbare Umgebung — lockfile |
| 10/10 | Belegte Agentenpraxis — 15 der letzten 100 Commits von Agenten verfasst oder ihnen zugeschrieben |
| 8/8 | Automatisierte Wartung — 6 der letzten 100 Commits sind automatisierte Abhängigkeits-Updates |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | uv.lock |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | src/subagents_pydantic_ai/py.typed |
| agent_commit_share | 0,15 |
| toolchain_manifests | — |
| dependency_bot_commit_share | 0,06 |
AI-Code-Lesbarkeit für Modelle
80Exzellent
Wie die Bewertung erfolgt
| 27/45 | Typprüfbarer Code — Python mit Typprüfungs-Konfiguration (src/subagents_pydantic_ai/py.typed) |
| 53.3/55 | Handhabbare Dateigrößen — 1/32 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 167.980 |
| source_files_sampled | 32 |
| oversized_source_files | 1 |
Maschinenlesbare AI-Schnittstellen
40Schwach
Wie die Bewertung erfolgt
| 0/40 | API-Schema (OpenAPI/GraphQL/proto) |
| 0/20 | MCP-Server |
| 40/40 | Lauffähige Beispiele — examples |
Verwendete Eingangsdaten
| example_dirs | examples |
| has_mcp_signal | nein |
| api_schema_files | — |
Eckdaten
57GitHub-Sterne
5Mitwirkende
135Commits, letzte 12 Monate
0Tage seit letztem Push
24Releases
1Bus-Faktor
1offene Issues
PyPIPaket-Ökosysteme
Warnungen zur Datenerhebung
- 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
- deps.dev does not index pypi:subagents-pydantic-ai@0.2.13; advisories assessed against the repository dependency graph instead
Weitere Details
Stern- und Fork-Verlauf 0 ★ / 18 ⇿
0Sterne
18Forks
17Releases
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Major 0Minor 2Patch 15
OpenSSF Scorecard 5.3 / 10
5.3Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 10 | CI-Tests | 9 out of 9 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 2 | Code-Review | Found 2/7 approved changesets -- score normalized to 2 |
| 10 | Contributors | project has 5 contributing companies or organizations |
| 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 3 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 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 0 | Security-Policy | security policy file not detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 2 | Vulnerabilities | 8 existing vulnerabilities detected |
Direkte Abhängigkeiten 2
| Registry | Paket | Versionsvorgabe | Manifest |
|---|---|---|---|
| PyPI | pydantic | >=2.0 | pyproject.toml |
| PyPI | pydantic-ai-slim | >=2.0.0 | pyproject.toml |
Alle Abhängigkeiten nicht erhoben
Der aufgelöste Abhängigkeitssatz konnte für diesen Bericht nicht erhoben werden: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
JSON-Rohbericht maschinenlesbar
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"body": "Every one of these lived where the tooling cannot see: two call sites that\ndisagree, or documentation describing behaviour the code does not implement.\nLint, both type checkers and 100% branch coverage were green throughout.\n\nThe one that matters is run isolation. `soft_cancel_task` and\n`hard_cancel\n[…]\n Sync and background mode recorded different errors for the same\n exhausted budget.\n\nVerified against pydantic-deep (2785 passed) since the capability gained\nfields and the toolset gained validation.",
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"body": "A one-shot specialist was labelled `oneshot-{task_id}`, so an operator reading\nlogs or a `TaskHandle` learned nothing about what it was for -- least helpful\nexactly when a fan-out of them is running. The caller now supplies the label and\nit becomes `TaskHandle.subagent_name`.\n\nNaming a one-shot does\n[…]\nes this release.\n\nPorted from #57 by Gabriel, which was written against 0.2.11 and could not be\nrebased cleanly onto the toolset refactor in this branch.\n\nCo-authored-by: Gby56 <gab.marquet@gmail.com>",
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"body": "…rement\n\nFollow-up to the refactor, taking the changes that need a downstream update.\n\n- `SubAgentToolset.answer_task()` / `steer_task()`: the Python halves of the\n `answer_subagent` and `send_message_to_subagent` tools. An application that\n drives delegation itself had no way to reach a running s\n[…]\ncol.subagents`. The library never read it, so every\n application declared a dict for nothing. Dropping a protocol requirement only\n widens what satisfies it, so existing deps classes are unaffected.",
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"body": "pydantic-ai 2.0 turned AgentRunResult.usage from a method into a property\nreturning RunUsage. The post-run observability step captured it via\n`result.usage()`, which now raises inside the task-complete `try` and flips a\nsuccessful subagent run to FAILED — so every delegated task errored.\n\n- Read usa\n[…]\nt /\n FakeResult test stubs expose `usage` as a property.\n- Require pydantic-ai-slim>=2.0, bump to 0.2.8, add the changelog entry.\n\nCo-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>",
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"note": null,
"notes": [],
"value": 77,
"inputs": {
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"has_license": true,
"readme_badges": null,
"has_contributing": true,
"has_issue_template": false,
"has_code_of_conduct": false,
"readme_badge_services": [],
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"name": "README",
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{
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{
"code": "license_spdx",
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{
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{
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]
},
{
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]
},
{
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"notes": [],
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"inputs": {
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{
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{
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{
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}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v2.3.1, Schema v0.27.0 — vollständige Methodik · Metriken-Wiki.
Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken — PyPI.