Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-09 17:26 UTC

LazyAGI / LazyLLM

Easiest and laziest way for building multi-agent LLMs applications.

PythonApache-2.0★ 3,866 stars⑂ 404 forkssince Jun 2024View on GitHub ↗
KindCommand-line toolLibraryNetwork servicehow this is determined

LazyAGI/LazyLLM holds a health index of 89 out of 100, placing it in the Excellent band. It scores highest on Vitality (95/100) and lowest on Security (45/100). It was last updated 2 days ago. 2 contributors account 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

LazyAGIOrganization
253 followers10 public repossince Jun 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIlazyllm1.2.29,469477 days ago

Metrics by category

Vitality

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

95Exceptional · 21% of overall
How it's scored
36/36Push recencylast push 2 days ago
35.3/36Commit cadence51/52 weeks with commits
18/18Commit volume385 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year385
human_commit_share1
days_since_last_push2
active_weeks_last_year51
How it's scored
27/27Ships releases34 releases published
36/36Release recencylatest release 7 days ago
27/27Release cadencea release every ~16.8 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count34
latest_release_tagv1.2.2
releases_from_tagsno
days_since_latest_release7
mean_days_between_releases16.8

Community & Adoption

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

83Excellent · 17% of overall
How it's scored
58.2/60Stars3,866 stars
21.7/25Forks404 forks
12.1/15Watchers149 watchers
Inputs used
forks404
stars3,866
watchers149
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
0/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingno
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

76Good · 23% of overall
How it's scored
25.2/54Bus factor2 contributor(s) cover half of all commits
12.2/22.5Commit distributiontop contributor authored 46% of commits
13.5/13.5Contributor breadth44 contributors
10/10OpenSSF Scorecard: Contributorsproject has 5 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled44
top_contributor_share0.457
How it's scored
32/42Issue resolution76% of issues closed
24.9/30PR acceptance927/1,115 decided PRs merged
7.8/13Newcomer PR acceptance3/5 first-time contributors' PRs merged in 30d
10.5/15OpenSSF Scorecard: Code-ReviewFound 22/30 approved changesets -- score normalized to 7
Inputs used
merged_prs927
open_issues33
closed_issues105
prs_merged_7d3
prs_decided_7d3
prs_merged_30d39
prs_decided_30d47
issue_closed_ratio0.761
closed_unmerged_prs188
first_time_authors_30d4
first_time_prs_merged_30d3
first_time_prs_decided_30d5
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
17.3/25Owner reach253 followers of LazyAGI
11.9/25Track record10 public repos, account ~2 yr old
Inputs used
followers253
owner_typeOrganization
is_verified
owner_loginLazyAGI
public_repos10
account_age_days796
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 7 days ago
20/20Version history47 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packageslazyllm
ecosystemspypi
any_deprecatedno
min_days_since_publish7

Engineering Quality

Are baseline engineering and documentation practices in place?

74Good · 19% of overall
How it's scored
24/24CI workflows3 workflow(s)
24/24Tests present
16/16Linter config.flake8
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests0 out of 30 merged PRs checked by a CI test -- score normalized to 0
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 sitehttps://docs.lazyllm.ai/
10/10Repository description
10/10Topics14 topics
0/10Wiki
Inputs used
topicsllms, ai-agent, deep-learning, langchain, llamaindex, rag, agents, data, finetuning, framework, llm, documentation-tool, knowlege-graph, lazyllm
has_wikino
homepagehttps://docs.lazyllm.ai/
docs_sitehttps://docs.lazyllm.ai/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

45Weak · 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
0/2.5CI-Tests0 out of 30 merged PRs checked by a CI test -- score normalized to 0
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-ReviewFound 22/30 approved changesets -- score normalized to 7
2.5/2.5Contributorsproject has 5 contributing companies or organizations
0/10Dangerous-Workflowdangerous 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 2 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-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities97 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate3.1

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages31
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:lazyllm@1.2.2 runtime dependency closure — what installing the published package pulls in — 31 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.

64Moderate · 4% of overall
How it's scored
45/45Agent instructionsdocs/lazyllm-skill/assets/agent/agent.md, docs/lazyllm-skill/references/agent.md, lazyllm/AGENTS.md, lazyllm/common/AGENTS.md, lazyllm/components/AGENTS.md, lazyllm/flow/AGENTS.md, lazyllm/module/AGENTS.md, lazyllm/module/llms/onlinemodule/AGENTS.md, lazyllm/tools/AGENTS.md, lazyllm/tools/agent/AGENTS.md, lazyllm/tools/rag/AGENTS.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history98 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.98
agent_instruction_filesdocs/lazyllm-skill/assets/agent/agent.md, docs/lazyllm-skill/references/agent.md, lazyllm/AGENTS.md, lazyllm/common/AGENTS.md, lazyllm/components/AGENTS.md, lazyllm/flow/AGENTS.md, lazyllm/module/AGENTS.md, lazyllm/module/llms/onlinemodule/AGENTS.md, lazyllm/tools/AGENTS.md, lazyllm/tools/agent/AGENTS.md, lazyllm/tools/rag/AGENTS.md
agent_instruction_max_bytes15,837
How it's scored
18/18One-command bootstrapMakefile
22/22Automated tests
11/11Lint / format config.flake8
0/11Static type checking
0/10Reproducible environment
10/10Demonstrated agent practice12 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.12
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54/55Manageable file sizes15/846 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes476,471
source_files_sampled846
oversized_source_files15
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examplescookbook, examples
Inputs used
example_dirscookbook, examples
has_mcp_signalno
api_schema_files
interfaces_expected_ofnetwork-service

Key facts

3,866GitHub stars
44contributors
385commits, last 12 months
2days since last push
34releases
2bus factor
33open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 15 authors (cap 12)

More detail

Star and fork history 0 ★ / 404 ⇿
0Stars
404Forks
34Releases

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.

010020030040039992024-062025-072026-08
Major 0Minor 7Patch 21

Each point covers 2 days.

OpenSSF Scorecard 3.1 / 10
3.1aggregate

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 · 2026-08-09 17:23 UTC

10Binary-Artifactsno binaries found in the repo
3Branch-Protectionbranch protection is not maximal on development and all release branches
0CI-Tests0 out of 30 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 22/30 approved changesets -- score normalized to 7
10Contributorsproject has 5 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 2 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
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities97 existing vulnerabilities detected
Direct dependencies 16
RegistryPackageVersion constraintManifest
PyPItomlpyproject.toml
PyPItqdmpyproject.toml
PyPIfilelockpyproject.toml
PyPIpyyamlpyproject.toml
PyPIpackagingpyproject.toml
PyPIfastapi>=0.111.0pyproject.toml
PyPIloguru>=0.7.2pyproject.toml
PyPIpydantic>=2.11.7,<3.0.0pyproject.toml
PyPIrequests>=2.32.2pyproject.toml
PyPIuvicorn>=0.23.2pyproject.toml
PyPIcloudpickle>=3.0.0pyproject.toml
PyPIdocstring-parser>=0.16,<0.17pyproject.toml
PyPIjson5>=0.9.25,<0.10.0pyproject.toml
PyPIpyjwt>=2.8.0pyproject.toml
PyPIpsutil>=6.0.0,<7.0.0pyproject.toml
PyPIdeepdiff>=8.6.1pyproject.toml
All dependencies 197

Full resolved dependency set from the GitHub dependency graph: 32 direct and 165 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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Dependency advisories 0

Installing pypi:lazyllm@1.2.2 pulls in 31 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.

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 v0.31.0 — full methodology · metrics wiki.

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