Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 17:03 UTC

mlc-ai / xgrammar

Fast, Flexible and Portable Structured Generation

C++ · PythonApache-2.0★ 1,845 stars⑂ 193 forkssince Jun 2024View on GitHub ↗
KindLibraryhow this is determined
#large-language-models#structured-generation

mlc-ai/xgrammar holds a health index of 93 out of 100, placing it in the Exceptional band. It scores highest on Vitality (94/100) and lowest on Security (46/100). It was last updated today. 2 contributors account for most of its recent work.

93
overall / 100
Exceptional

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.

93
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 79 is calibrated to 93 on the published index scale (record calibration 2026-08-02).

Ownership

mlc-aiOrganization
2,185 followers39 public repossince May 2022

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIxgrammar0.2.58,170,5423436 days agomachine-learninginference
npm@mlc-ai/web-xgrammar0.1.278092245 days agomachine-learningllmnlp

Metrics by category

Vitality

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

94Exceptional · 21% of overall

Development activity

96Exceptional
How it's scored
36/36Push recency — last push 0 days ago
31.8/36Commit cadence — 46/52 weeks with commits
18/18Commit volume — 246 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 7 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year246
human_commit_share1
days_since_last_push0
active_weeks_last_year46

Release discipline

90Excellent
How it's scored
27/27Ships releases — 34 releases published
36/36Release recency — latest release 12 days ago
27/27Release cadence — a release every ~18.2 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count34
latest_release_tagv0.2.6rc1
releases_from_tagsno
days_since_latest_release12
mean_days_between_releases18.2

Community & Adoption

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

81Excellent · 17% of overall

Popularity & adoption

79Good
How it's scored
53/60Stars — 1,845 stars
19/25Forks — 193 forks
6.7/15Watchers — 17 watchers
Inputs used
forks193
stars1,845
watchers17
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

70Good
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno

Ecosystem adoption (downloads)

100Exceptional
How it's scored
80/80Monthly downloads — 8,171,351 downloads/month across npm, pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesxgrammar, @mlc-ai/web-xgrammar
dependents—
ecosystemsnpm, pypi
total_downloads—
monthly_downloads8,171,351
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?

75Good · 23% of overall

Maintainer resilience (bus factor)

62Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
13.1/22.5Commit distribution — top contributor authored 42% of commits
13.5/13.5Contributor breadth — 62 contributors
10/10OpenSSF Scorecard: Contributors — project has 14 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled62
top_contributor_share0.419

Issue & PR responsiveness

70Good
How it's scored
31.3/42Issue resolution — 75% of issues closed
24.2/30PR acceptance — 516/640 decided PRs merged
8.7/13Newcomer PR acceptance — 4/6 first-time contributors' PRs merged in 30d
6/15OpenSSF Scorecard: Code-Review — Found 12/30 approved changesets -- score normalized to 4
Inputs used
merged_prs516
open_issues45
closed_issues132
prs_merged_7d2
prs_decided_7d3
prs_merged_30d18
prs_decided_30d57
issue_closed_ratio0.746
closed_unmerged_prs124
first_time_authors_30d5
first_time_prs_merged_30d4
first_time_prs_decided_30d6

Ownership & stewardship

74Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
24/25Owner reach — 2,185 followers of mlc-ai
20.2/25Track record — 39 public repos, account ~4 yr old
Inputs used
followers2,185
owner_typeOrganization
is_verifiedno
owner_loginmlc-ai
public_repos39
account_age_days1,556

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 2 package(s) on npm, pypi
35/35Publish recency — latest publish 36 days ago
20/20Version history — 34 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesxgrammar, @mlc-ai/web-xgrammar
ecosystemsnpm, pypi
any_deprecatedno
min_days_since_publish36

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
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

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://xgrammar.mlc.ai/docs
10/10Repository description
10/10Topics — 2 topics
0/10Wiki
Inputs used
topicslarge-language-models, structured-generation
has_wikino
homepagehttps://xgrammar.mlc.ai/docs
docs_sitehttps://xgrammar.mlc.ai/docs
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

Security posture

41Weak
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
3/7.5Code-Review — Found 12/30 approved changesets -- score normalized to 4
2.5/2.5Contributors — project has 14 contributing companies or organizations
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 7 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 — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 56 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate4.1

Dependency advisories

65Good
How it's scored
8.1/35Direct dependencies free of known advisories — 1 affected: transformers 4.57.6 (critical 9.6)
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
32.3/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 246 days ago
Inputs used
sourceosv
advisories7
affected_packages1
assessed_packages25
unassessed_packages0
affected_by_severitycritical 1
direct_affected_packages1
Matched the pypi:xgrammar@0.2.5 runtime dependency closure — what installing the published package pulls in — 25 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.

79Good · 4% of overall

Agent context & guidance

55Moderate
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.modular.com/llms.txt)
40/40Legible commit history — 98 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.modular.com/llms.txt
legible_history_share0.98
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

82Excellent
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
11/11Static type checking — python/xgrammar/py.typed, web/example/tsconfig.json, web/tsconfig.json
10/10Reproducible environment — lockfile
10/10Demonstrated agent practice — 6 of the last 100 commits agent-authored or agent-credited
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
lockfilespackage-lock.json
has_dockerfileno
typed_languageyes
bootstrap_filesdocs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configspython/xgrammar/py.typed, web/example/tsconfig.json, web/tsconfig.json
agent_commit_share0.06
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

95Exceptional
How it's scored
45/45Type-checkable code — C++ (statically typed)
49.9/55Manageable file sizes — 14/152 source files over 60KB
Inputs used
primary_languageC++
largest_source_bytes179,869
source_files_sampled152
oversized_source_files14

Machine-readable interfaces

100Exceptional
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — example, examples
Inputs used
example_dirsexample, examples
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

1,845GitHub stars
62contributors
246commits, last 12 months
0days since last push
34releases
2bus factor
45open issues
npm, PyPI, RubyGemspackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 193 ⇿
0Stars
193Forks
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.

0408012016020019242024-112025-102026-08
Major 0Minor 1Patch 32

Each point covers 2 days.

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 · 2026-08-27 17:02 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
4Code-ReviewFound 12/30 approved changesets -- score normalized to 4
10Contributorsproject has 14 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 7 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
0Vulnerabilities56 existing vulnerabilities detected
Direct dependencies 9
RegistryPackageVersion constraintManifest
PyPIapache-tvm-ffi>=0.1.10pyproject.toml
PyPIpydantic—pyproject.toml
PyPItorch>=1.10.0pyproject.toml
PyPItransformers>=4.38.0pyproject.toml
PyPItriton—pyproject.toml
PyPInumpy—pyproject.toml
PyPItyping-extensions>=4.9.0pyproject.toml
RubyGemsjekyll-remote-theme—site/Gemfile
RubyGemsjekyll-sass-converter—site/Gemfile
All dependencies 480

Full resolved dependency set from the GitHub dependency graph: 9 direct and 471 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIapache-tvm-ffi—direct
PyPInumpy—direct
PyPIpydantic—direct
PyPItorch—direct
PyPItransformers—direct
PyPItriton—direct
PyPItyping-extensions—direct
RubyGemsjekyll-remote-theme—direct
RubyGemsjekyll-sass-converter—direct
npm@babel/code-frame7.27.1indirect
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npm@babel/helper-globals7.28.0indirect
npm@babel/helper-module-imports7.27.1indirect
npm@babel/helper-module-transforms7.28.3indirect
npm@babel/helper-plugin-utils7.27.1indirect
npm@babel/helper-string-parser7.27.1indirect
npm@babel/helper-validator-identifier7.28.5indirect
npm@babel/helper-validator-option7.27.1indirect
npm@babel/helpers7.28.4indirect
npm@babel/parser7.28.5indirect
npm@babel/plugin-syntax-async-generators7.8.4indirect
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PyPIautodoc-pydantic—indirect
PyPIautodocsumm—indirect
PyPIcohere-melody0.13.0indirect
PyPIfuro—indirect
PyPIipykernel—indirect
PyPIipywidgets—indirect
PyPIjupyter-client—indirect
PyPIlinkify-it-py—indirect
PyPImarkdown—indirect
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PyPInbsphinx—indirect
PyPInbstripout—indirect
PyPIpandoc—indirect
PyPIpillow—indirect
PyPIscikit-build-core—indirect
PyPIsentencepiece—indirect
PyPIsetuptools81.0.0indirect
PyPIsphinx—indirect
PyPIsphinx-autobuild—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-reredirects—indirect
PyPIsphinx-tabs—indirect
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PyPIsphinxcontrib-httpdomain—indirect
PyPIsphinxcontrib-mermaid—indirect
PyPItiktoken—indirect
PyPItomli—indirect
PyPIurllib3—indirect
Dependency advisories 1

Installing pypi:xgrammar@0.2.5 pulls in 25 packages, direct and transitive: 1 carry known advisories, of which 1 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
transformers4.57.6directcritical75.5.0

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.

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99huggingface/transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. Exceptional100ray-project/rayRay is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.Exceptional98huggingface/datasets🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation toolsExceptional97Unstructured-IO/unstructuredConvert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.Exceptional96keras-team/keras-hubPretrained model hub for Keras 3.Exceptional100deepset-ai/haystackOpen-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.Exceptional

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, npm.