Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-04 03:38 UTC

ljxpython / graphharbor

Enterprise-grade, open-source LangGraph Agent Server powered by PostgreSQL checkpoints & Redis distributed workers. Subagent traceability & SSE resilience.

PythonMIT★ 0 stars⑂ 0 forkssince Aug 2026forkView on GitHub ↗
KindCommand-line toolLibraryMCP serverNetwork servicehow this is determined
#agent-server#ai-agent#langgraph#langgraph-api#langsmith#multi-agent#postgresql#python#redis#sse-stream

ljxpython/graphharbor holds a health index of 59 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (75/100) and lowest on Community & Adoption (24/100). It was last updated today. A single contributor accounts for most of its recent work.

59
overall / 100
Moderate

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.

59
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 56 is calibrated to 59 on the published index scale (record calibration 2026-08-02).

Ownership

李佳欣Personal account
41 followers54 public repossince Apr 2019

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
PyPIgraphharbor0.13.0.post381,846260 days agoagent-protocolagent-serveragentsai-agentsclilangchainlanggraphlanggraph-apilanggraph-sdklangsmithllmopen-sourcepostgresredisself-hosteduvicorn
PyPIgraphharbor-runtime0.13.0.post382,017260 days agoagent-protocolagent-serveragentsai-agentsasgicheckpointlangchainlanggraphlanggraph-apilanggraph-checkpointlanggraph-sdklangsmithlangsmith-deploymentsllmmulti-agentopen-sourcepostgrespostgresqlredisruntimeself-hosteduvicorn

Metrics by category

Vitality

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

71Good · 21% of overall

Development activity

58Moderate
How it's scored
36/36Push recency — last push 0 days ago
4.8/36Commit cadence — 7/52 weeks with commits
16.9/18Commit volume — 75 commits in the last year
0/10OpenSSF Scorecard: Maintained — project was created within the last 90 days. Please review its contents carefully
Inputs used
commits_last_year75
human_commit_share0.221
days_since_last_push0
active_weeks_last_year7

Release discipline

90Excellent
How it's scored
27/27Ships releases — 4 releases published
36/36Release recency — latest release 0 days ago
27/27Release cadence — a release every ~13.2 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count4
latest_release_tagv0.13.0.post38
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases13.2

Community & Adoption

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

24At Risk · 17% of overall

Popularity & adoption

1Critical
How it's scored
0/60Stars — 0 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars0
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

25At Risk
How it's scored
0/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeno
has_licenseno
readme_badges7
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

60Moderate
How it's scored
47.8/80Monthly downloads — 3,863 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesgraphharbor, graphharbor-runtime
dependents—
ecosystemspypi
total_downloads—
monthly_downloads3,863
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?

59Moderate · 23% of overall

Maintainer resilience (bus factor)

26At Risk
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
5.4/22.5Commit distribution — top contributor authored 76% of commits
5.4/13.5Contributor breadth — 4 contributors
6/10OpenSSF Scorecard: Contributors — project has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled4
top_contributor_share0.761

Issue & PR responsiveness

67Good
How it's scored
0/42Issue resolution — no issues or no data
30/30PR acceptance — 2/2 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs2
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

58Moderate
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
11.7/25Owner reach — 41 followers of ljxpython
24.7/25Track record — 54 public repos, account ~7 yr old
Inputs used
followers41
owner_typeUser
is_verified—
owner_loginljxpython
public_repos54
account_age_days2,735
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 2 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 26 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesgraphharbor, graphharbor-runtime
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

75Good · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

50Moderate
How it's scored
0/30README
25/25Documentation directory
15/15Documentation / homepage site — https://pypi.org/project/graphharbor/
0/10Repository description
10/10Topics — 10 topics
0/10Wiki
Inputs used
topicsagent-server, ai-agent, langgraph, langgraph-api, langsmith, multi-agent, postgresql, python, redis, sse-stream
has_wikino
homepagehttps://pypi.org/project/graphharbor/
docs_sitehttps://pypi.org/project/graphharbor/
has_readmeno
has_docs_diryes
has_descriptionno

Security

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

41Weak · 16% of overall

Security posture

41Weak
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
1.5/2.5Contributors — project has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
0/7.5Maintained — project was created within the last 90 days. Please review its contents carefully
0/5Packaging — no data
4.5/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 9
0/5SAST — no SAST tool detected
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
6.8/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 21 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate4.1
Excluded from scoring (no data or not applicable): CI-Tests, Packaging. 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.

65Good · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.langchain.com/llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.langchain.com/llms.txt
legible_history_share—
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes8,817
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

60Moderate
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 77
8/8Automated maintenance — 6 of the last 77 commits are automated dependency updates
9/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 9
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_share0.078

Code legibility for models

54Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
53.5/55Manageable file sizes — 3/107 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes137,489
source_files_sampled107
oversized_source_files3

Machine-readable interfaces

20At Risk
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
20/20MCP server
0/40Runnable examples
Inputs used
example_dirs—
has_mcp_signalyes
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

0GitHub stars
4contributors
75commits, last 12 months
0days since last push
4releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Community profile unavailable
  • Could not fetch pypi package 'graphharbor-workspace' from its registry
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
  • deps.dev does not index pypi:graphharbor@0.13.0.post38; advisories assessed against the repository dependency graph instead

More detail

OpenSSF Scorecard 4.1 / 10
4.1aggregate

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 · 2026-10-04 03:38 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintainedproject was created within the last 90 days. Please review its contents carefully
n/aPackagingpackaging workflow not detected
9Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 9
0SASTno SAST tool detected
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
9Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities21 existing vulnerabilities detected
Direct dependencies 20
RegistryPackageVersion constraintManifest
PyPIanyio>=4.0libs/langgraph-runtime-pg/pyproject.toml
PyPIsqlalchemy>=2.0.51libs/langgraph-runtime-pg/pyproject.toml
PyPIasyncpg>=0.29libs/langgraph-runtime-pg/pyproject.toml
PyPIredis>=5.0libs/langgraph-runtime-pg/pyproject.toml
PyPIalembic>=1.14libs/langgraph-runtime-pg/pyproject.toml
PyPIpsycopg>=3.3.4libs/langgraph-runtime-pg/pyproject.toml
PyPIstructlog>=24.0libs/langgraph-runtime-pg/pyproject.toml
PyPIcroniter>=1.0libs/langgraph-runtime-pg/pyproject.toml
PyPIorjson>=3.9libs/langgraph-runtime-pg/pyproject.toml
PyPIPyJWT>=2.8libs/langgraph-runtime-pg/pyproject.toml
PyPIstarlette>=0.37libs/langgraph-runtime-pg/pyproject.toml
PyPIlanggraph-checkpoint-postgres==3.1.2libs/langgraph-runtime-pg/pyproject.toml
PyPIlanggraph==1.2.11libs/langgraph-runtime-pg/pyproject.toml
PyPIclick>=8.1.7libs/langhost/pyproject.toml
PyPIpython-dotenv>=0.8.0libs/langhost/pyproject.toml
PyPIpyfiglet>=1.0.0libs/langhost/pyproject.toml
PyPIlanggraph-cli>=0.4.0,<0.5libs/langhost/pyproject.toml
PyPImcp>=1.23,<2libs/langhost/pyproject.toml
PyPIuvicorn>=0.51.0libs/langhost/pyproject.toml
PyPIgraphharbor-runtime==0.13.0.post38libs/langhost/pyproject.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

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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.