Mar 20, 2026 at 6:30 AM 6 signals analysed No manual reviews · fully automatedTrust Signal Breakdown high 23 sub-signals across 6 dimensions
Vulnerability & Safety ×0.25 4.8 CVEs, dependency health, and supply chain integrity
2 of 3 sub-signals with data
Known CVEs 57% 4.8
1 CVE(s) found — 0 unpatched
via OSV.dev
Dependency Health no data —
Weight redistributed to sub-signals with data
Supply Chain 43% 4.8
7 transitive CVEs found (penalty: -0.21)
via npm provenance
Operational Reliability ×0.15 4.2 Uptime, latency, error rates, and incident history
4 of 4 sub-signals with data
Uptime 35% 5.0
99.90% over 1000 checks
via Health checks
Response Latency 25% 4.0
p99: 324ms, p50: 132ms
via Health checks
Error Rate 20% 4.0
0.10% error rate (1/1000)
via Health checks
Incident History 20% 3.0
2 incidents in last 90 days
via Incidents table
Maintenance Activity ×0.15 5.0 Commit recency, release cadence, issue response, CI/CD
3 of 4 sub-signals with data
Commit Recency 37% 5.0
via GitHub
Release Cadence 31% 5.0
via GitHub
Issue Response no data —
Weight redistributed to sub-signals with data
CI/CD Presence 31% 5.0
via GitHub Actions
Adoption ×0.15 4.7 Downloads, stars, dependents, and growth trajectory
2 of 4 sub-signals with data
Download Volume 55% 4.5
9,904,667 weekly downloads
via npm / PyPI
GitHub Stars 45% 5.0
130,274 stars
via GitHub
Dependent Packages no data —
Weight redistributed to sub-signals with data
Growth Trend no data —
Weight redistributed to sub-signals with data
Transparency ×0.15 4.5 License, documentation, security policy, changelog
4 of 4 sub-signals with data
Open Source 30% 5.0
Public repo with OSI-approved license (mit)
via GitHub
Documentation 25% 4.0
Good README (>2000 bytes with examples)
via GitHub
Security Policy 20% 5.0
SECURITY.md inherited from org .github repo
via GitHub
Changelog 25% 4.0
Releases exist but no CHANGELOG.md
via GitHub
Publisher Trust ×0.15 4.5 Track record, org maturity, community standing
4 of 4 sub-signals with data
Track Record 30% 5.0
Internal: 5.0 (14 services), External: 4.5 (17186 followers, 38584 stars)
via Fabric index
Org Maturity 30% 4.5
Organization, 3.1 years old
via GitHub
Community Standing 20% 5.0
230 public repositories
via GitHub
Cross-Platform 20% 3.0
Present on 2 platform(s): github, pypi
via Registry scan
About this scoreScored across 23 sub-signals in 6 dimensions Scoring engine v1 (beta) — actively being expanded Phase 1: Core sub-signal architecture (live) Phase 2: Permission scope & expanded collection (in progress)
Trust Assessment AI Assessment
langchain-text-splitters is an MIT-licensed text processing framework published by LangChain, providing document chunking and splitting functionality for LLM applications with 9.9M weekly downloads. The service shows strong operational reliability and maintenance with only 1 CVE detected across its history. Transparency scoring indicates limited public documentation of security practices, though the core infrastructure signals remain solid for a widely-adopted text processing utility.
Generated by Fabric AI · Mar 4, 2026 at 10:51 PM
Service Health (30d)
99.90%
p50: 132ms · p99: 324ms
Avg Latency
143ms
averaged across 30d health checks
Weekly Downloads
9.9M
PyPI weekly
Transparency & Compliance 4/5 passed
Incidents & Alerts last 90 days
Score History 90 snapshots
Mar 4 Mar 5
Community & Ecosystem adoption signals
Supply Chain & Dependencies trust chain
Showing 1 of 1 dependencies
Data Sources 6 indexed
◎
OSV.dev CVE database · vulnerability scanning for npm & PyPI packages
◈
GitHub API Commits, issues, releases, repo metadata, transparency checks
⬡
npm Registry Package metadata, weekly downloads, maintainers, dependencies
⬡
PyPI Package metadata, weekly downloads, dependency tree
△
HTTP Health Checks 15-min pings · uptime, latency, status monitoring
◎
PyPI Stats Download statistics and trends
Version History score per release
VERSION RELEASED SCORE DELTA
Showing 6 of 10 releases Show more →
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