Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-05 22:17 UTC

cdeust / Cortex

Cross-platform persistent memory MCP for Codex, Gemini CLI, Claude Code, and other local MCP hosts. 36 cited neuroscience mechanisms, local-first SQLite/PostgreSQL, hybrid retrieval, decay-based consolidation, and reproducible benchmarks. Claude adds optional automatic lifecycle hooks.

PythonMIT★ 71 stars⑂ 11 forkssince Mar 2026View on GitHub ↗
KindCommand-line toolLibraryMCP serverhow this is determined
#mcp-server#model-context-protocol#agent-memory-system#causal-inference#claude-code#cognitive-architecture#cognitive-science#neuroscience#persistent-memory#predictive-coding#retrieval-augmented-generation#vector-search#hopfield-network#long-term-memory#episodic-memory#llm-memory#memory#agent-skills#codex#gemini-cli

cdeust/Cortex holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (87/100) and lowest on Sustainability & Governance (57/100). It was last updated today. A single contributor accounts for most of its recent work.

89
overall / 100
Excellent

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.

89
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)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

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 75 is calibrated to 89 on the published index scale (record calibration 2026-08-02).

Ownership

ClementPersonal account
31 followers57 public repossince Jan 2013

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIhypermnesia-mcp4.19.12,447302 days agoclaude-codecodexcognitive-profilinggemini-climcpmemory

Metrics by category

Vitality

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

85Excellent · 21% of overall

Development activity

81Excellent
How it's scored
36/36Push recency — last push 0 days ago
16.6/36Commit cadence — 24/52 weeks with commits
18/18Commit volume — 1,235 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,235
human_commit_share0.71
days_since_last_push0
active_weeks_last_year24

Release discipline

90Excellent
How it's scored
27/27Ships releases — 95 releases published
36/36Release recency — latest release 2 days ago
27/27Release cadence — a release every ~5.7 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count95
latest_release_tagv4.19.1
releases_from_tagsno
days_since_latest_release2
mean_days_between_releases5.7

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

62Moderate · 17% of overall

Popularity & adoption

40Weak
How it's scored
29.9/60Stars — 71 stars
8.3/25Forks — 11 forks
1.7/15Watchers — 3 watchers
Inputs used
forks11
stars71
watchers3
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, www.bestpractices.dev
has_pull_request_templateyes

Ecosystem adoption (downloads)

56Moderate
How it's scored
45.2/80Monthly downloads — 2,447 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packageshypermnesia-mcp
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,447
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?

57Moderate · 23% of overall

Maintainer resilience (bus factor)

12Critical
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
2.7/13.5Contributor breadth — 2 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled2
top_contributor_share0.999

Issue & PR responsiveness

78Good
How it's scored
40.2/42Issue resolution — 96% of issues closed
27.6/30PR acceptance — 283/308 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/18 approved changesets -- score normalized to 0
Inputs used
merged_prs283
open_issues6
closed_issues129
prs_merged_7d6
prs_decided_7d6
prs_merged_30d30
prs_decided_30d32
issue_closed_ratio0.956
closed_unmerged_prs25
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

57Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
10.8/25Owner reach — 31 followers of cdeust
24.8/25Track record — 57 public repos, account ~13 yr old
Inputs used
followers31
owner_typeUser
is_verified—
owner_logincdeust
public_repos57
account_age_days4,994
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 2 days ago
20/20Version history — 30 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packageshypermnesia-mcp
ecosystemspypi
any_deprecatedno
min_days_since_publish2

Engineering Quality

Are baseline engineering and documentation practices in place?

86Excellent · 19% of overall

Engineering practices

84Excellent
How it's scored
24/24CI workflows — 10 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 26 out of 26 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://ai-architect.tools
10/10Repository description
10/10Topics — 20 topics
0/10Wiki
Inputs used
topicsmcp-server, model-context-protocol, agent-memory-system, causal-inference, claude-code, cognitive-architecture, cognitive-science, neuroscience, persistent-memory, predictive-coding, retrieval-augmented-generation, vector-search, hopfield-network, long-term-memory, episodic-memory, llm-memory, memory, agent-skills, codex, gemini-cli
has_wikino
homepagehttps://ai-architect.tools
docs_sitehttps://ai-architect.tools
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

85Excellent · 16% of overall

Security posture

81Excellent
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 26 out of 26 merged PRs checked by a CI test -- score normalized to 10
1.8/2.5CII-Best-Practices — badge detected: Silver
0/7.5Code-Review — Found 0/18 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
5/5Fuzzing — project is fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
5/5Pinned-Dependencies — all dependencies are pinned
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate8.1
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages94
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:hypermnesia-mcp@4.19.1 runtime dependency closure — what installing the published package pulls in — 94 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.

87Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://ai-architect.tools/llms.txt)
40/40Legible commit history — 69 of 71 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://ai-architect.tools/llms.txt
legible_history_share0.972
agent_instruction_filesCLAUDE.md
agent_instruction_max_bytes9,999

Verify loop (build / test / typecheck)

100Exceptional
How it's scored
18/18One-command bootstrap — Makefile, docs/arxiv-thermodynamic/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — pyrightconfig.json
10/10Reproducible environment — devcontainer, Dockerfile, lockfile
10/10Demonstrated agent practice — 73 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 27 of the last 100 commits are automated dependency updates
10/10OpenSSF Scorecard: Pinned-Dependencies — all dependencies are pinned
Inputs used
has_nixno
has_testsyes
lockfilespackage-lock.json, uv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, docs/arxiv-thermodynamic/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configspyrightconfig.json
agent_commit_share0.73
toolchain_manifests—
dependency_bot_commit_share0.27

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (pyrightconfig.json)
54.9/55Manageable file sizes — 3/1,252 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes115,902
source_files_sampled1,252
oversized_source_files3

Machine-readable interfaces

33At Risk
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto). Remaining weights renormalized.

Key facts

71GitHub stars
2contributors
1,235commits, last 12 months
0days since last push
95releases
1bus factor
6open issues
npm, PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch npm package 'hypermnesia-mcp' from its registry
  • pypi download statistics for 'hypermnesia-mcp' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for hypermnesia-mcp carried forward from the previous scan (stats endpoint unavailable this scan)

More detail

Star and fork history 0 ★ / 11 ⇿
0Stars
11Forks
64Releases

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.

0246810121112026-032026-052026-07
Major 1Minor 25Patch 38
OpenSSF Scorecard 8.1 / 10
8.1aggregate

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 · 2026-09-05 22:16 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal 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
10CI-Tests26 out of 26 merged PRs checked by a CI test -- score normalized to 10
7CII-Best-Practicesbadge detected: Silver
0Code-ReviewFound 0/18 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
10Fuzzingproject is fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 29 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
10Pinned-Dependenciesall dependencies are pinned
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPImcp>=2.0.0pyproject.toml
PyPIanyio>=4.5pyproject.toml
PyPIpydantic>=2.0.0pyproject.toml
PyPIpydantic-settings>=2.0.0pyproject.toml
PyPInumpy>=1.24.0pyproject.toml
PyPIsentence-transformers>=3.0.0pyproject.toml
PyPIflashrank>=0.2.0pyproject.toml
PyPIpython-dateutil>=2.8.2pyproject.toml
All dependencies 250

Full resolved dependency set from the GitHub dependency graph: 10 direct and 240 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIflashrank0.2.10direct
PyPImcp2.0.0direct
PyPInumpy2.2.6direct
PyPInumpy2.4.6direct
PyPInumpy2.5.1direct
PyPIpydantic2.13.4direct
PyPIpydantic-settings2.14.2direct
PyPIpython-dateutil2.9.0.post0direct
PyPIsentence-transformers5.6.1direct
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npm@anthropic-ai/claude-code-linux-x64-musl2.1.220indirect
npm@anthropic-ai/claude-code-win32-arm642.1.220indirect
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PyPIscipy1.17.1indirect
PyPIscipy1.18.0indirect
PyPIsetproctitle1.3.7indirect
PyPIsetuptools84.0.0indirect
PyPIshellingham1.5.4indirect
PyPIsix1.17.0indirect
PyPIsqlite-vec0.1.9indirect
PyPIsse-starlette3.4.6indirect
PyPIstarlette1.3.1indirect
PyPIsympy1.14.0indirect
PyPItexttable1.7.0indirect
PyPItextual8.2.8indirect
PyPIthreadpoolctl3.6.0indirect
PyPItokenizers0.22.2indirect
PyPItoml0.10.2indirect
PyPItomli2.4.1indirect
PyPItomlkit0.15.1indirect
PyPItoolz1.1.0indirect
PyPItorch2.13.0indirect
PyPItorch2.13.0+cpuindirect
PyPItqdm4.70.0indirect
PyPItransformers5.14.1indirect
PyPItree-sitter0.26.0indirect
PyPItree-sitter-language-pack1.14.3indirect
PyPItrove-classifiers2026.6.1.19indirect
PyPItruststore0.10.4indirect
PyPItyper0.27.1indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspection0.4.2indirect
PyPItzdata2026.3indirect
PyPIuc-micro-py2.0.0indirect
PyPIurllib32.7.0indirect
PyPIuvicorn0.52.1indirect
PyPIxarray2025.6.1indirect
PyPIxarray2026.7.0indirect
PyPIxxhash3.8.1indirect
PyPIyarl1.24.5indirect
PyPIzipp4.1.0indirect
Dependency advisories 0

Installing pypi:hypermnesia-mcp@4.19.1 pulls in 94 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.

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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.