Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 15:21 UTC

zilliztech / memsearch

A persistent, unified memory layer for all your AI agents (e.g. Claude Code, Codex, DSH), backed by Markdown and Milvus.

Python · JavaScript · ShellMIT★ 2,584 stars⑂ 244 forkssince Feb 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

zilliztech/memsearch holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (96/100) and lowest on Security (56/100). It was last updated today. A single contributor accounts for most of its recent work.

91
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.

91
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 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

ZillizOrganization
1,044 followers75 public repossince Apr 2016

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPImemsearch0.4.1934,9364718 days ago

Metrics by category

Vitality

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

91Excellent · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
20.8/36Commit cadence — 30/52 weeks with commits
18/18Commit volume — 459 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year459
human_commit_share1
days_since_last_push0
active_weeks_last_year30

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 31 releases published
36/36Release recency — latest release 18 days ago
27/27Release cadence — a release every ~7.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count31
latest_release_tagv0.4.19
releases_from_tagsno
days_since_latest_release18
mean_days_between_releases7.5
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

75Good · 17% of overall
How it's scored
55.3/60Stars — 2,584 stars
19.9/25Forks — 244 forks
5.3/15Watchers — 10 watchers
Inputs used
forks244
stars2,584
watchers10
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges13
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

65Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
4/22.5Commit distribution — top contributor authored 82% of commits
13.5/13.5Contributor breadth — 25 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled25
top_contributor_share0.823
How it's scored
31.5/42Issue resolution — 75% of issues closed
24.3/30PR acceptance — 321/396 decided PRs merged
7.2/13Newcomer PR acceptance — 5/9 first-time contributors' PRs merged in 30d
3/15OpenSSF Scorecard: Code-Review — Found 6/30 approved changesets -- score normalized to 2
Inputs used
merged_prs321
open_issues30
closed_issues90
prs_merged_7d5
prs_decided_7d9
prs_merged_30d34
prs_decided_30d39
issue_closed_ratio0.75
closed_unmerged_prs75
first_time_authors_30d8
first_time_prs_merged_30d5
first_time_prs_decided_30d9
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
21.7/25Owner reach — 1,044 followers of zilliztech
25/25Track record — 75 public repos, account ~10 yr old
Inputs used
followers1,044
owner_typeOrganization
is_verifiedno
owner_loginzilliztech
public_repos75
account_age_days3,803

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 18 days ago
20/20Version history — 47 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmemsearch
ecosystemspypi
any_deprecatedno
min_days_since_publish18

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://zilliztech.github.io/memsearch/
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsagent-memory, claude-code, claude-code-plugin, memory, openclaw, rag, agent, embeddings, milvus, semantic-search, ai-agents, hybrid-search, long-term-memory, opencode, skills, codex, deepseek, deepseek-harness, dsh, dsh-plugin
has_wikiyes
homepagehttps://zilliztech.github.io/memsearch/
docs_sitehttps://zilliztech.github.io/memsearch/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

56Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
2.2/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 6/30 approved changesets -- score normalized to 2
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities — 82 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.9
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.
How it's scored
17.9/35Direct dependencies free of known advisories — 1 affected: setuptools 80.10.2 (moderate 6.1)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
40/40No advisories left outstanding — no advisory has been public longer than 90 days
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages37
unassessed_packages0
affected_by_severitymoderate 1
direct_affected_packages1
Matched the pypi:memsearch@0.4.19 runtime dependency closure — what installing the published package pulls in — 37 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.

65Good · 4% of overall
How it's scored
45/45Agent instructions — AGENT.md, CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://milvus.io/llms.txt)
40/40Legible commit history — 85 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://milvus.io/llms.txt
legible_history_share0.85
agent_instruction_filesAGENT.md, CLAUDE.md
agent_instruction_max_bytes10,011
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — lockfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
54.4/55Manageable file sizes — 1/90 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes75,904
source_files_sampled90
oversized_source_files1

Key facts

2,584GitHub stars
25contributors
459commits, last 12 months
0days since last push
31releases
1bus factor
30open issues
PyPIpackage ecosystems

Data collection warnings

  • 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

More detail

Star and fork history 0 ★ / 244 ⇿
0Stars
244Forks
31Releases

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.

050100150200250243122026-022026-052026-09
Major 0Minor 3Patch 28
OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-09-10 15:20 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 6/30 approved changesets -- score normalized to 2
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
0Vulnerabilities82 existing vulnerabilities detected
Direct dependencies 11
RegistryPackageVersion constraintManifest
PyPIpymilvus>=2.5.0,!=2.6.10pyproject.toml
PyPIpymilvus>=2.6.11pyproject.toml
PyPImilvus-lite>=2.5.0pyproject.toml
PyPImilvus-lite>=3.1.1pyproject.toml
PyPIclick>=8.1pyproject.toml
PyPIwatchdog>=4.0pyproject.toml
PyPIpathspec>=0.12pyproject.toml
PyPIsetuptools>=78.1.1,<81pyproject.toml
PyPItomli_w>=1.0pyproject.toml
PyPItomli>=2.0pyproject.toml
PyPIopenai>=1.0pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 1

Installing pypi:memsearch@0.4.19 pulls in 37 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
setuptools80.10.2directmoderate283.0.0

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.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

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.