Public record
Software health reportschema 0.12.0 · metrics 2.10.0 · 2026-07-18 01:45 UTC

zilliztech / memsearch

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

Python · ShellMIT★ 2,250 stars⑂ 196 forkssince Feb 2026View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

zilliztech/memsearch holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Engineering Quality (95/100) and lowest on Security (46/100). It was last updated 4 days ago. 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

ZillizOrganization
1,024 followers72 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
PyPImemsearch0.4.14102,306424 days ago

Metrics by category

Vitality

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

88Excellent · 21% of overall
How it's scored
36/36Push recencylast push 4 days ago
15.9/36Commit cadence23/52 weeks with commits
18/18Commit volume401 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 13 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year401
human_commit_share
days_since_last_push4
active_weeks_last_year23

Release discipline

100Exceptional
How it's scored
27/27Ships releases26 releases published
36/36Release recencylatest release 4 days ago
27/27Release cadencea release every ~5.1 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count26
latest_release_tagv0.4.14
releases_from_tagsno
days_since_latest_release4
mean_days_between_releases5.1
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?

74Good · 17% of overall
How it's scored
54.4/60Stars2,250 stars
19.1/25Forks196 forks
5/15Watchers9 watchers
Inputs used
forks196
stars2,250
watchers9
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized 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_badges
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

68Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
3.6/22.5Commit distributiontop contributor authored 84% of commits
13.5/13.5Contributor breadth18 contributors
3/10OpenSSF Scorecard: Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
Inputs used
bus_factor1
contributors_sampled18
top_contributor_share0.838
How it's scored
29.7/42Issue resolution71% of issues closed
24.1/30PR acceptance271/338 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-ReviewFound 2/26 approved changesets -- score normalized to 0
Inputs used
merged_prs271
open_issues26
closed_issues63
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.708
closed_unmerged_prs67
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
21.6/25Owner reach1,024 followers of zilliztech
25/25Track record72 public repos, account ~10 yr old
Inputs used
followers1,024
owner_typeOrganization
is_verified
owner_loginzilliztech
public_repos72
account_age_days3,748
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 4 days ago
20/20Version history42 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesmemsearch
ecosystemspypi
any_deprecatedno
min_days_since_publish4

Engineering Quality

Are baseline engineering and documentation practices in place?

95Exceptional · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests24 out of 26 merged PRs checked by a CI test -- score normalized to 9
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 sitehttps://zilliztech.github.io/memsearch/
10/10Repository description
10/10Topics20 topics
10/10Wiki
Inputs used
topicsagent-memory, claude-code, claude-code-plugin, memory, openclaw, progressive-disclosure, rag, agent, embeddings, milvus, semantic-search, ai-agents, harness, hybrid-search, long-term-memory, opencode, reranker, skills, codex, codex-cli
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?

46Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
2.2/7.5Branch-Protectionbranch protection is not maximal on development and all release branches
2.2/2.5CI-Tests24 out of 26 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0/7.5Code-ReviewFound 2/26 approved changesets -- score normalized to 0
0.8/2.5Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 13 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
7.5/7.5Token-PermissionsGitHub workflow tokens follow principle of least privilege
0/7.5Vulnerabilities64 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.6
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.

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.

61Moderate · 4% of overall
How it's scored
45/45Agent instructionsAGENT.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
0/40Legible commit historyno data
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share
agent_instruction_filesAGENT.md, CLAUDE.md
agent_instruction_max_bytes9,401
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency 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_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
0/45Type-checkable codePython without a type-check config
55/55Manageable file sizes0/68 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes45,248
source_files_sampled68
oversized_source_files0

Key facts

2,250GitHub stars
18contributors
401commits, last 12 months
4days since last push
26releases
1bus factor
26open issues
PyPIpackage ecosystems

Data collection warnings

  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

OpenSSF Scorecard 4.6 / 10
4.6aggregate

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 · 2026-07-18 01:44 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests24 out of 26 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 2/26 approved changesets -- score normalized to 0
3Contributorsproject has 1 contributing companies or organizations -- score normalized to 3
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 13 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
0Vulnerabilities64 existing vulnerabilities detected
Direct dependencies 8
RegistryPackageVersion constraintManifest
PyPIpymilvus>=2.5.0,!=2.6.10pyproject.toml
PyPImilvus-lite>=2.5.0pyproject.toml
PyPIclick>=8.1pyproject.toml
PyPIwatchdog>=4.0pyproject.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

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 v0.12.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.