Registry › Compare › Claude Mem vs Notebooklm Py

Claude Mem vs Notebooklm Py

Both are Grade A and 1.2 points apart, so choose on what you weigh most. Claude Mem leads on maintenance and adoption; Notebooklm Py leads on supply-chain integrity and transparency.

A Claude Mem 90.2

Persistent Context Across Sessions for Every Agent – Captures everything your agent does during sessions, compresses it with AI, and injects relevant context back into future sessions. Works with Claude Code, OpenClaw, Codex, Gemini, Hermes, Copilot, OpenCode + More

thedotmack/claude-mem · #4 overall · #4 Agent Skills · coverage B (3/5)

Choose Claude Mem if maintenance and adoption matter most.

+2.0Adoption: 35.5k weekly downloads against 27.4k
+1.9Maintenance: last push today, against 2d ago

A Notebooklm Py 89.0

Unofficial Python API and agentic skill for Google Gemini Notebook. Full programmatic access to Gemini Notebook's features—including capabilities the web UI doesn't expose—via Python, CLI, and AI agents like Claude Code, Codex, and OpenClaw.

teng-lin/notebooklm-py · #6 overall · #6 Agent Skills · coverage B (3/5)

Choose Notebooklm Py if supply-chain integrity and transparency matter most.

+1.3Safety / Integrity: 92% of recent commits signed, against 91%
+0.9Transparency: OSSF Scorecard 5.6 against 4.6

Where they differ

17.8

Safety / IntegrityNotebooklm Py +1.3

19.1

12.4

TransparencyNotebooklm Py +0.9

13.3

20.0

MaintenanceClaude Mem +1.9

18.1

18.2

AdoptionClaude Mem +2.0

16.2

+3.8

Runtime calibrationNotebooklm Py +0.5

+4.3

1 dimension identical: Identity 18.0 · Full evidence table

An independent, evidence-based trust comparison of Claude Mem and Notebooklm Py, two Agent Skills projects in the HVTracker registry. Scores come from public, checkable signals — supply-chain provenance, OSSF Scorecard, maintenance, and adoption — not popularity.

Full evidence

Signal	Claude Memthedotmack/claude-mem	Notebooklm Pyteng-lin/notebooklm-py
HVTrust score	90.2	89.0
Evidence grade	A	A
Coverage grade	B	B
Overall rank	#4	#6
Rank in Agent Skills	#4	#6
GitHub stars	115.5k	20.2k
Last updated	today	2d ago
Build provenance	Yes	Yes
OSSF Scorecard	4.6 / 10	5.6 / 10
License	Apache-2.0	MIT
Downloads	36k/wk	27k/wk
Trust dimensions (points earned)
Safety / integrity / 25	17.8	19.1
Identity & provenance / 18	18.0	18.0
Transparency / 17	12.4	13.3
Maintenance / 20	20.0	18.1
Adoption / 20	18.2	16.2
Runtime capability surface (full matrix)
MCP server	Implemented	Implemented
External providers	1 — Anthropic	—
Requires API keys	No	No
Plugin surface	plugins	—
Provenance drift	Match	Match

Open in the live compare tool → Claude Mem profile Notebooklm Py profile More Agent Skills →

How to read this: HVTrust (0–100) weighs supply-chain signals (provenance, OSSF Scorecard, signed commits, open license) alongside real-world adoption, scaled by an evidence-confidence factor. Grade bands: A ≥ 80, B ≥ 65, C ≥ 50, D < 50. Signals refresh daily. Full methodology v4.4 →