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Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-15 16:55 UTC

cognizant-ai-lab / neuro-san

Neuro AI System of Agent Networks

PythonApache-2.0★ 136 Sterne⑂ 43 Forksseit Nov. 2024Auf GitHub ansehen ↗

cognizant-ai-lab/neuro-san erreicht einen Gesundheitsindex von 79 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (100/100) ab, am schwächsten bei AI Readiness (42/100). Zuletzt heute aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.

79
gesamt / 100
Gut

Software-Gesundheitsindex

Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.

79
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

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.

Eigentümerschaft

Cognizant AI LabOrganisation
188 Follower18 öffentliche Reposseit Dez. 2018

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlichtTags
PyPIneuro-san0.6.79-129vor 0 Tagenllmlangchainagentmulti-agent

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

100Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
36/36Commit-Rhythmus — 52/52 Wochen mit Commits
18/18Commit-Volumen — 3.078 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year3.078
human_commit_share
days_since_last_push0
active_weeks_last_year52

Release-Disziplin

100Exzellent
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 100 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 0 Tagen
27/27Release-Rhythmus — ein Release etwa alle 0,9 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count100
latest_release_tag0.6.79
releases_from_tagsnein
days_since_latest_release0
mean_days_between_releases0,9

Community & Verbreitung

Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?

68Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
34.6/60Stars — 136 Stars
13.5/25Forks — 43 Forks
5.3/15Watcher — 10 Watcher
Verwendete Eingangsdaten
forks43
stars136
watchers10
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (Apache-2.0)
18/18CONTRIBUTING-Leitfaden
13.5/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductja
has_pull_request_templatenein

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

68Mittel · 24 % des Gesamtindex
Wie die Bewertung erfolgt
9/54Bus-Faktor — 1 Beitragende decken die Hälfte aller Commits ab
10.7/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 53 % der Commits
13.5/13.5Breite der Beitragenden — 21 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor1
contributors_sampled21
top_contributor_share0,526
Wie die Bewertung erfolgt
32.3/46.8Issue-Lösungsquote — 69 % der Issues geschlossen
35.9/38.3PR-Annahme — 709/756 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs709
open_issues102
closed_issues227
issue_closed_ratio0,69
closed_unmerged_prs47
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
16.4/25Reichweite des Inhabers — 188 Follower von cognizant-ai-lab
21.3/25Kontohistorie — 18 öffentliche Repos, Kontoalter ca. 7 Jahre
Verwendete Eingangsdaten
followers188
owner_typeOrganization
is_verified
owner_logincognizant-ai-lab
public_repos18
account_age_days2.778

Paketpflege

100Exzellent
Wie die Bewertung erfolgt
25/25Veröffentlicht & auflösbar — 1 Paket(e) auf pypi
35/35Veröffentlichungsaktualität — letzte Veröffentlichung vor 0 Tagen
20/20Versionshistorie — 129 veröffentlichte Versionen
20/20Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen
Verwendete Eingangsdaten
packagesneuro-san
ecosystemspypi
any_deprecatednein
min_days_since_publish0

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

88Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 6 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration — .flake8, .pylintrc
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — keine Daten
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_confignein

Dokumentation

100Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://decisionai.ml/neuro-san
10/10Repository-Beschreibung
10/10Topics — 14 Topics
10/10Wiki
Verwendete Eingangsdaten
topicsagents, ai, ai-agents, mas, multi-agent-systems, mutli-agent, ai-agents-framework, langchain, multi-agent-framework, agentic-ai, agentic-framework, llms, aaosa, sly-data
has_wikija
homepagehttps://decisionai.ml/neuro-san
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

67Mittel · 16 % des Gesamtindex
Wie die Bewertung erfolgt
30/30Sicherheitsrichtlinie (SECURITY.md)
0/25Dependabot-Konfiguration
0/25Lockfiles für Abhängigkeiten — veröffentlichte Bibliothek — Lockfiles sind Sache der Anwendung, hier nicht erwartet
20/20CodeQL-Workflow
Verwendete Eingangsdaten
sourcefile_signals
lockfiles
manifestspyproject.toml, requirements-build.txt, requirements.txt, setup.py
has_codeql_workflowja
has_security_policyja
has_dependabot_confignein
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lockfiles für Abhängigkeiten. Die verbleibenden Gewichte wurden renormalisiert.

AI Readiness

Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.

42Gefährdet · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration — .flake8, .pylintrc
0/11Statische Typprüfung
10/10Reproduzierbare Umgebung — Dockerfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfileja
typed_languagenein
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configs
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
55/55Handhabbare Dateigrößen — 0/519 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes36.180
source_files_sampled519
oversized_source_files0
Wie die Bewertung erfolgt
40/40API-Schema (OpenAPI/GraphQL/proto) — neuro_san/api/grpc/agent.proto, neuro_san/api/grpc/chat.proto, neuro_san/api/grpc/concierge.proto, neuro_san/api/grpc/google/api/annotations.proto, neuro_san/api/grpc/google/api/http.proto, neuro_san/api/grpc/mime_data.proto
20/20MCP-Server
0/40Lauffähige Beispiele
Verwendete Eingangsdaten
example_dirs
has_mcp_signalja
api_schema_filesneuro_san/api/grpc/agent.proto, neuro_san/api/grpc/chat.proto, neuro_san/api/grpc/concierge.proto, neuro_san/api/grpc/google/api/annotations.proto, neuro_san/api/grpc/google/api/http.proto, neuro_san/api/grpc/mime_data.proto

Eckdaten

136GitHub-Sterne
21Mitwirkende
3.078Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
1Bus-Faktor
102offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

Weitere Details

Alle Abhängigkeiten 62

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 62 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
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PyPIcmarkgfmindirekt
PyPIcoverage7.6.1indirekt
PyPIcroniterindirekt
PyPIflake87.3.0indirekt
PyPIgrpcioindirekt
PyPIgrpcio-toolsindirekt
PyPIhttpxindirekt
PyPIidnaindirekt
PyPIjanusindirekt
PyPIjson-repairindirekt
PyPIjsonschemaindirekt
PyPIlangchainindirekt
PyPIlangchain-anthropicindirekt
PyPIlangchain-awsindirekt
PyPIlangchain-classicindirekt
PyPIlangchain-communityindirekt
PyPIlangchain-coreindirekt
PyPIlangchain-google-genaiindirekt
PyPIlangchain-mcp-adaptersindirekt
PyPIlangchain-nvidia-ai-endpointsindirekt
PyPIlangchain-ollamaindirekt
PyPIlangchain-openaiindirekt
PyPIlangchain-openrouterindirekt
PyPIlangfuseindirekt
PyPIleaf-commonindirekt
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PyPImcpindirekt
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