Public record
Software health reportschema 0.27.0 · metrics 1.13.0 · 2026-07-26 21:40 UTC

microsoft / graphrag

A modular graph-based Retrieval-Augmented Generation (RAG) system

Python · Jupyter NotebookMIT★ 34,856 stars⑂ 3,673 forkssince Mar 2024View on GitHub ↗

microsoft/graphrag holds a health index of 83 out of 100, placing it in the Good band. It scores highest on Community & Adoption (92/100) and lowest on AI Readiness (54/100). It was last updated today. 2 contributors account for most of its recent work.

83
overall / 100
Good

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.

83
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe 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.

Ownership

MicrosoftOrganization
125,928 followers8,222 public repossince Dec 2013

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIgraphrag3.1.174,788478 days ago
PyPIgraphrag-llm3.1.1-128 days ago
PyPIgraphrag-cache3.1.1-128 days ago
PyPIgraphrag-input3.1.1-128 days ago
PyPIgraphrag-common3.1.131,981128 days ago
PyPIgraphrag-storage3.1.1-128 days ago
PyPIgraphrag-vectors3.1.1-128 days ago
PyPIgraphrag-chunking3.1.1-128 days ago

Metrics by category

Vitality

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

87Excellent · 22% of overall
How it's scored
36/36Push recency — last push 0 days ago
15.9/36Commit cadence — 23/52 weeks with commits
17.4/18Commit volume — 85 commits in the last year
10/10OpenSSF Scorecard: Maintained — 14 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year85
human_commit_share1
days_since_last_push0
active_weeks_last_year23

Release discipline

100Excellent
How it's scored
27/27Ships releases — 41 releases published
36/36Release recency — latest release 8 days ago
27/27Release cadence — a release every ~18.9 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count41
latest_release_tagv3.1.1
releases_from_tagsno
days_since_latest_release8
mean_days_between_releases18.9
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?

92Excellent · 18% of overall
How it's scored
60/60Stars — 34,856 stars
25/25Forks — 3,673 forks
12.7/15Watchers — 196 watchers
Inputs used
forks3,673
stars34,856
watchers196
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
has_contributingyes
has_issue_templateno
has_code_of_conductyes
has_pull_request_templateyes
How it's scored
67/80Monthly downloads — 106,769 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesgraphrag, graphrag-llm, graphrag-cache, graphrag-input, graphrag-common, graphrag-storage, graphrag-vectors, graphrag-chunking
dependents
ecosystemspypi
total_downloads
monthly_downloads106,769
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?

80Good · 24% of overall
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
15.1/22.5Commit distribution — top contributor authored 33% of commits
13.5/13.5Contributor breadth — 49 contributors
10/10OpenSSF Scorecard: Contributors — project has 9 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled49
top_contributor_share0.329
How it's scored
46.5/46.8Issue resolution — 100% of issues closed
23.6/38.3PR acceptance — 746/1,209 decided PRs merged
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs746
open_issues4
closed_issues828
issue_closed_ratio0.995
closed_unmerged_prs463
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 125,928 followers of microsoft
25/25Track record — 8,222 public repos, account ~12 yr old
Inputs used
followers125,928
owner_typeOrganization
is_verified
owner_loginmicrosoft
public_repos8,222
account_age_days4,611
How it's scored
25/25Published & resolvable — 8 package(s) on pypi
35/35Publish recency — latest publish 8 days ago
20/20Version history — 47 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesgraphrag, graphrag-llm, graphrag-cache, graphrag-input, graphrag-common, graphrag-storage, graphrag-vectors, graphrag-chunking
ecosystemspypi
any_deprecatedno
min_days_since_publish8

Engineering Quality

Are baseline engineering and documentation practices in place?

81Good · 20% of overall
How it's scored
24/24CI workflows — 10 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-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_configno
has_precommit_configno

Documentation

100Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://microsoft.github.io/graphrag/
10/10Repository description
10/10Topics — 7 topics
10/10Wiki
Inputs used
topicsgraphrag, rag, llm, llms, gpt, gpt-4, gpt4
has_wikiyes
homepagehttps://microsoft.github.io/graphrag/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

73Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3.8/7.5Branch-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
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 9 contributing companies or organizations
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
7.5/7.5Maintained — 14 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
5/5SAST — SAST tool is run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 85 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6.6
Excluded from scoring (no data or not applicable): packaging, signed_releases. Remaining weights renormalized.
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages113
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:graphrag@3.1.1 runtime dependency closure — what installing the published package pulls in — 113 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score.

54Moderate · 0% of overall
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 99 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.99
agent_instruction_files
agent_instruction_max_bytes
How it's scored
0/18One-command bootstrap
22/22Automated tests
0/11Lint / format config
11/11Static type checking — packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed
10/10Reproducible environment — Dockerfile, lockfile
10/10Demonstrated agent practice — 7 of the last 100 commits agent-authored or agent-credited
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_files
has_devcontainerno
has_linter_configno
typecheck_configspackages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed
agent_commit_share0.07
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed)
55/55Manageable file sizes — 0/573 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes22,070
source_files_sampled573
oversized_source_files0
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — notebooks
Inputs used
example_dirsnotebooks
has_mcp_signalno
api_schema_files

Key facts

34,856GitHub stars
49contributors
85commits, last 12 months
0days since last push
41releases
2bus factor
4open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • Could not fetch pypi package 'graphrag-monorepo' from its registry
  • Could not fetch pypi package 'unified-copilot' from its registry

More detail

Star and fork history 0 ★ / 3,673 ⇿
0Stars
3,673Forks
15Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

2,5002,7503,0003,2503,5003,7503,673122025-082026-012026-07
Major 1Minor 3Patch 10
OpenSSF Scorecard 6.6 / 10
6.6aggregate

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 · 2026-07-26 21:40 UTC

10Binary-Artifactsno binaries found in the repo
5Branch-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
10Code-Reviewall changesets reviewed
10Contributorsproject has 9 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained14 commit(s) and 8 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
10SASTSAST tool is run on all commits
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities85 existing vulnerabilities detected
Direct dependencies 72
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All dependencies 427

Full resolved dependency set from the GitHub dependency graph: 106 direct and 321 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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Dependency advisories 0

Installing pypi:graphrag@3.1.1 pulls in 113 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
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          "oid": "64a95d3d512fc5765511573cc803de2e30fc2a25",
          "body": "* build(deps): refresh lockfile and bump runtime constraints\n\n- Refresh uv.lock within existing ranges (ruff 0.15.21, pyright 1.1.411)\n- Bump azure-search-documents ~=12.0, pyarrow ~=25.0, aiofiles ~=25.1, lancedb ~=0.34.0, litellm ==1.92.0, pandas ~=3.0 (+ pandas-stubs)\n- Hold graspologic-native on\n[…]\nithub-actions\n- unified-search-app remains excluded (separate project at repo root)\n\n* Bump dependencies.\n\n* Fix type check and tests.\n\n* Fix drift search.\n\n* Fix streaming.\n\n* Fix context management.",
          "is_bot": false,
          "headline": "Dep sweep (#2441)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-07-17T15:38:45Z",
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          "oid": "dac4f721ddc14adae9d3183cdc99e7c6ad7d9fca",
          "body": "- Resolves #2381",
          "is_bot": false,
          "headline": "Fix .strip call (#2431)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-07-10T21:00:01Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "94a2095c6fb35765c9d0a2def9b4cdad26a0408b",
          "body": "…… (#2430)\n\nLoosen service_tier type to str | None to allow for more flexibility in service tier representation.\n\n- Resolves #2389",
          "is_bot": false,
          "headline": "Loosen service_tier type to str | None to allow for more flexibility …",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-07-10T20:56:28Z",
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        },
        {
          "oid": "274101505799e24d08e43b52cc3c9f07efe01a23",
          "body": "- Resolves #2254",
          "is_bot": false,
          "headline": "Fix logging bug. (#2429)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-07-10T20:52:09Z",
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          "oid": "6d02c2355c3fed4c49007572fbe951d73258a37f",
          "body": null,
          "is_bot": false,
          "headline": "Update getting started docs. (#2367)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-05-28T16:53:15Z",
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          "oid": "8679794df31cd766e81800e21b0cae361037490c",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.1.0 (#2366)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-05-28T15:51:39Z",
          "body_truncated": false,
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          "oid": "f7016ddfd000e5e191faedf2176b51a4a8c6f22a",
          "body": "- Resolves #2331",
          "is_bot": false,
          "headline": "Update litellm dependency. (#2365)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
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        {
          "oid": "de531f0a697d2f35c4f85cb8511141507278404e",
          "body": "* feat: native CosmosTableProvider with namespace partitioning\n\nReplace the parquet-decomposition approach in AzureCosmosStorage with a\nnative CosmosTableProvider that implements TableProvider directly:\n\n- CosmosTableProvider: stores DataFrame rows as Cosmos documents with\n  /namespace partition key\n[…]\natching\n\nUse transactional batches for delete operations instead of\none-at-a-time delete_item calls, mirroring the _batch_upsert pattern.\nFalls back to individual deletes on CosmosBatchOperationError.",
          "is_bot": false,
          "headline": "feat: native CosmosTableProvider with namespace partitioning (#2354)",
          "author_name": "disamhembere",
          "author_login": "disamhembere",
          "committed_at": "2026-05-13T19:25:45Z",
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        },
        {
          "oid": "0da2a4dd3e1402bd97a7ddd4e6cace8c87a55c8b",
          "body": "Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>",
          "is_bot": false,
          "headline": "Release v3.0.9 (#2321)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-04-13T19:21:05Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "080e665c23372334db75d66b743cf4c0ffcf9fc3",
          "body": "parquet reader impl\n\nCo-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>",
          "is_bot": false,
          "headline": "parquet reader implementation as a new input type (#2320)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-04-09T20:03:13Z",
          "body_truncated": false,
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        },
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          "oid": "ebc8d61f462b8d25d4ebe317e8b1086c177eadfa",
          "body": "- Addresses #2286\n- Resolves #2263",
          "is_bot": false,
          "headline": "Support client side json validation. (#2306)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-04-07T15:09:40Z",
          "body_truncated": false,
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        },
        {
          "oid": "b1f0e6c345832d64fe55a199e110b778dc9e62bf",
          "body": "- Addresses #2258 and #2287\n- Resolves #2249\n\nCo-authored-by: Br1an67 <932039080@qq.com>",
          "is_bot": false,
          "headline": "Fix broken documentation links. (#2305)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-03-30T13:46:02Z",
          "body_truncated": false,
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          "oid": "74239e8e142ee63c46c201e2c880ffc07829998b",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.0.8 (#2299)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-03-27T18:45:08Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "df2a8b32e07b2bc077a86db30ca69834abfe0ccc",
          "body": "…HSA-7p94-766c-hgjp) (#2298)",
          "is_bot": false,
          "headline": "bump nltk to resolve [CVE-2025-14009](https://github.com/advisories/G…",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-03-27T15:52:22Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ac142c5ed46cce90e7472748815ec01fecbdf9b8",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.0.7 (#2291)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-03-24T17:15:48Z",
          "body_truncated": false,
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        },
        {
          "oid": "f0fd676729553c763851d7f748e3aa0451d337f7",
          "body": "- Response to #2289",
          "is_bot": false,
          "headline": "Pin litellm dependency (#2290)",
          "author_name": "Derek Worthen",
          "author_login": "dworthen",
          "committed_at": "2026-03-24T16:30:11Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "3502c22209a0fc8b86a164063fd9fbb40c6c0e7d",
          "body": null,
          "is_bot": false,
          "headline": "reconfigure vector store size (#2281)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-03-17T21:09:55Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ae2508d37a18f05f5ba72798769344febe0df2d7",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.0.6 (#2267)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-03-06T20:02:49Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bb9afeb898ad2cc886093b05f1f11c3740bfe819",
          "body": "* fix cooccurences\n\n* unused async fixes",
          "is_bot": false,
          "headline": "nlp streaming (#2264)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-03-03T21:59:21Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bfd42c1ecd2c4bbc17ff6e0674f8fb6e1ceabffa",
          "body": "* filter phantom relationships\n\n* fix flush size",
          "is_bot": false,
          "headline": "remove relationships with phantom entities (#2261)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-03-03T13:18:34Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "1ceb00c4dea349157d7d2fb4e01fc5e40b948eb7",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.0.5 (#2252)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-27T21:17:11Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "6f26d0ec6fdc88e035b4661ff6c839ae423c9257",
          "body": "* vectors bulk load_documents\n\n* vector load\n\n* add upsert work into dictionary\n\n* fic dictionary",
          "is_bot": false,
          "headline": "vector load_documents in batches (#2251)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-27T20:59:11Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "64c55522fd7ecc5044f302d0f8b7ac72bad598f3",
          "body": "* fix\n\n* fix test with inline content\n\n* fix format",
          "is_bot": false,
          "headline": "fix csv file reader (#2248)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-25T01:10:59Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "a82d186fd2ca128ebbfa04f7b3675a338f855320",
          "body": "* Release v3.0.4\n\n* release v3.0.4",
          "is_bot": false,
          "headline": "Release v3.0.4 (#2247)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-02-24T22:51:02Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0a29d278255a7f2a129c355a0b5c6936bd399114",
          "body": "Co-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>",
          "is_bot": false,
          "headline": "Release v3.0.3 (#2245)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-02-24T20:54:28Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "47fae953930235e11ede77140346e0d0cd24ef0b",
          "body": "add stats write",
          "is_bot": false,
          "headline": "write stats per workflow (#2244)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-23T22:17:21Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "1cedb796b3e26f432743e9884b62f6c23c8a37b0",
          "body": "* streaming create_final_documents\n\n* add semversioner",
          "is_bot": false,
          "headline": "streaming create_final_documents (#2243)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-23T17:16:39Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ba9169a2328598bf80220554bd86e0aa77050f55",
          "body": "* add streaming\n\n* add patch\n\n* fix spelling\n\n* add improvements and test",
          "is_bot": false,
          "headline": "streaming finalize_graph  (#2240)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-23T16:57:52Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "cd0c405887cdb53d96c5598ea03083080ba232e2",
          "body": "…s (#2236)\n\n* feat(graphrag-vectors): add filtering, timestamps, and CRUD operations\n\nImplement the vector store enhancements from the graphrag-vectors-design spec:\n\nNew modules:\n- filtering.py: Pydantic-based filter expression system with F builder,\n  operator overloads, JSON serialization, client-\n[…]\nuet, embeddings.text_unit_text.parquet)\n- Add CPY001, SLF001, DTZ005 to notebook lint ignores in pyproject.toml\n\n* refactor: extract model/tokenizer creation from generate_text_embeddings into callers",
          "is_bot": false,
          "headline": "feat(graphrag-vectors): add filtering, timestamps, and CRUD operation…",
          "author_name": "Nathan Evans",
          "author_login": "natoverse",
          "committed_at": "2026-02-20T23:39:08Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "97045b55fe5f672dec3c8ac514862251d524459d",
          "body": "* addd streaming\n\n* fixes",
          "is_bot": false,
          "headline": "generate_text_embeddings streaming (#2241)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-20T22:53:32Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "bf1034a7eba75d15a1e1ea48d152dcc90a22387d",
          "body": "* addd streaming to create_final_text_units - also removed pandas from its internal workflow logic\n\n* fix comment\n\n* fix test\n\n* fix typo\n\n* add suggestion\n\n* add csv tests data\n\n* fix spell",
          "is_bot": false,
          "headline": "Streaming create_final_text_units (#2238)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-20T14:18:00Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "ac7ce32b61390402fd83e381cf40837a61ad4fe1",
          "body": "* add manual release instructions\n\n* create streaming\n\n* fix deleted file\n\n* addd file\n\n* fix check\n\n* add consistency\n\n* fix logic",
          "is_bot": false,
          "headline": "Streaming create communities (#2237)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-19T18:39:58Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "6d9f0dcf3004a83ef55398eada11fecd75f7307a",
          "body": "* add release doc\n\n* fix spellcheck",
          "is_bot": false,
          "headline": "add release doc (#2235)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-19T00:14:38Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "20d30fd93b4f3260e8f9522925063bcff4791133",
          "body": "* work in progress\n\n* cosmosdb output error fix\n\n* semserver update\n\n* remove unnecessary code\n\n* clean code\n\n* remove unnecessary prints",
          "is_bot": false,
          "headline": "Cosmosdb communities bug (#2232)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-02-17T21:59:36Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "79d7a70c945f7f6b2623072895145c8da0dd83ae",
          "body": "* add smoke tests for output csv\n\n* change text fixture test\n\n* change test\n\n* add semver\n\n* run format\n\n---------\n\nCo-authored-by: Nathan Evans <github@talkswithnumbers.com>",
          "is_bot": false,
          "headline": "modify smoke tests to include csv table provider (#2229)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-13T22:23:26Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "71445a3f44c17294e11ffac88017ab06b28a9a67",
          "body": "* add streaming read\n\n* fix formatting\n\n* create csv streaming write\n\n* add parquettable to simulate streaming\n\n* add sample rows return\n\n* add load_input_documents and truncate mode to Table\n\n* add semver\n\n* addd csv output reader fix\n\n* run formatter\n\n* add csv config",
          "is_bot": false,
          "headline": "load_input_documents and create_base_text_units streaming (#2221)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-13T20:59:40Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "9032b796541dcf7688ab7dc7f9f371201ec6c7c3",
          "body": null,
          "is_bot": false,
          "headline": "Release v3.0.2 (#2230)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-13T18:39:28Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "e1c92cc00608f49fbdcc48efb8f803bdd1644b1e",
          "body": "* Replace NX-based compute_degree with DataFrame-only implementation\n\n- Add graphrag.graphs package with compute_degree operating directly on\n  relationships DataFrames instead of building NetworkX graphs\n- Update finalize_entities and finalize_relationships to use the new\n  utility, eliminating NX \n[…]\nc.py\n- Add side-by-side modularity tests (9 tests comparing DF vs NX)\n\n* Add semversioner patch for NetworkX removal\n\n* Spelling\n\n* Spelling config\n\n* Fix British English spellings to American English",
          "is_bot": false,
          "headline": "Remove networkx (#2228)",
          "author_name": "Nathan Evans",
          "author_login": "natoverse",
          "committed_at": "2026-02-13T17:28:32Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "8d4080c4146c540c2cb4f0b392ff6587664fa782",
          "body": "* add profiling\n\n* add unit test for profiling\n\n* fix property name",
          "is_bot": false,
          "headline": "add memory profiling (#2227)",
          "author_name": "Dayenne Souza",
          "author_login": "dayesouza",
          "committed_at": "2026-02-11T23:15:07Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "0321329a0f445184a374f05b6f7b6cd0592e7edb",
          "body": "…#2226)\n\n* Add async iterator support to InputReader and use in load workflows\n\nInputReader now implements __aiter__ so it can be used as `async for doc in reader`. The core iteration logic is in _iterate_files(), and read_files() delegates to the iterator for batch loading. Both load_input_documents and load_update_documents workflows now use the async iterator with dataclasses.asdict for DataFrame construction.\n\n* Format",
          "is_bot": false,
          "headline": "Add async iterator support to InputReader and use in load workflows (…",
          "author_name": "Nathan Evans",
          "author_login": "natoverse",
          "committed_at": "2026-02-11T22:34:04Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "e48bb041104fb9edcc24af41ca6b80954b3fdaad",
          "body": "* Move document ID, human_readable_id, and raw_data setup from create_final_documents into load workflows\n\nConsolidates core document field initialization (id string cast, human_readable_id index, raw_data default) into load_input_documents and load_update_documents so that create_final_documents only handles the text unit join. Also applies the same setup in the run_pipeline input_documents bypass paths.\n\n* Remove overzealous input document assignment\n\n* Semver\n\n* Format",
          "is_bot": false,
          "headline": "Streamline workflows (#2225)",
          "author_name": "Nathan Evans",
          "author_login": "natoverse",
          "committed_at": "2026-02-11T22:01:44Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "8832370208e4daea46c771a65dacc9e7b92b2425",
          "body": "* add notebook example support for each package\n\n* add notebook example support for each package\n\n* semversioner change\n\n* feedback implemented for notebooks\n\n* feedback implemented for notebooks\n\n* feedback implemented for notebooks\n\n---------\n\nCo-authored-by: Gaudy Blanco <gaudy-microsoft@MacBook-Pro-m4-Gaudy-For-Work.local>",
          "is_bot": false,
          "headline": "Add notebook example support for each package (#2205)",
          "author_name": "gaudyb",
          "author_login": "gaudyb",
          "committed_at": "2026-02-11T20:54:08Z",
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                "key": "sast",
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              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file detected",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "no releases found",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 7.5
              },
              {
                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "detected GitHub workflow tokens with excessive permissions",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "85 existing vulnerabilities detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "dependency_advisories",
            "band": "excellent",
            "name": "Dependency advisories",
            "note": "Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:graphrag@3.1.1 runtime dependency closure — what installing the published package pulls in — 113 packages. Reachability is not analyzed.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "no_advisories_left_outstanding"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              },
              {
                "code": "advisories_scope_published",
                "params": {
                  "package": "pypi:graphrag@3.1.1",
                  "assessed": 113
                }
              },
              {
                "code": "advisories_reachability",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "source": "osv",
              "advisories": 0,
              "affected_packages": 0,
              "assessed_packages": 113,
              "unassessed_packages": 0,
              "affected_by_severity": "none",
              "direct_affected_packages": 0
            },
            "components": [
              {
                "key": "direct_dependencies_free_of_known_advisories",
                "name": "Direct dependencies free of known advisories",
                "detail": "no direct dependency carries a known advisory",
                "points": 35,
                "status": "met",
                "details": [
                  {
                    "code": "no_direct_advisories",
                    "params": {}
                  }
                ],
                "max_points": 35
              },
              {
                "key": "indirect_dependencies_free_of_known_advisories",
                "name": "Indirect dependencies free of known advisories",
                "detail": "no indirect dependency carries a known advisory",
                "points": 25,
                "status": "met",
                "details": [
                  {
                    "code": "no_indirect_advisories",
                    "params": {}
                  }
                ],
                "max_points": 25
              },
              {
                "key": "no_advisories_left_outstanding",
                "name": "No advisories left outstanding",
                "detail": "no advisory carries a publication date",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "advisories_no_publication_date",
                    "params": {}
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "malicious_dependencies",
            "band": "excellent",
            "name": "Malicious dependencies",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "source": "osv",
              "meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
              "packages": [],
              "red_flag": false,
              "assessed_packages": 113,
              "malicious_packages": 0,
              "direct_malicious_packages": 0,
              "withdrawn_malicious_packages": 0,
              "installable_malicious_packages": 0
            },
            "components": [
              {
                "key": "no_dependency_reported_as_a_malicious_package",
                "name": "No dependency reported as a malicious package",
                "detail": "no dependency is reported as a malicious package",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "no_malicious_dependencies",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "excellent",
            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "review_only_matches": 0,
              "assessed_self_published_locations": 15
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "moderate",
        "name": "AI Readiness",
        "value": 54,
        "weight": 0,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "at_risk",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.99,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "99 of 100 human commits state their intent (structured subject or explanatory body)",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 99,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "moderate",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 58,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [
                "uv.lock"
              ],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": false,
              "has_linter_config": false,
              "typecheck_configs": [
                "packages/graphrag-cache/graphrag_cache/py.typed",
                "packages/graphrag-common/graphrag_common/py.typed",
                "packages/graphrag-llm/graphrag_llm/py.typed",
                "packages/graphrag-storage/graphrag_storage/py.typed",
                "packages/graphrag/graphrag/py.typed"
              ],
              "agent_commit_share": 0.07,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": "packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed"
                    }
                  }
                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "Dockerfile, lockfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "Dockerfile, lockfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "7 of the last 100 commits agent-authored or agent-credited",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "agent_authored_commits",
                    "params": {
                      "count": 7,
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "dependency automation configured, none observed in the sampled commits",
                "points": 5,
                "status": "partial",
                "details": [
                  {
                    "code": "dependency_bot_config_only",
                    "params": {}
                  }
                ],
                "max_points": 8
              },
              {
                "key": "openssf_scorecard_pinned_dependencies",
                "name": "OpenSSF Scorecard: Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "ai_code_legibility",
            "band": "good",
            "name": "Code legibility for models",
            "note": null,
            "notes": [],
            "value": 82,
            "inputs": {
              "primary_language": "Python",
              "largest_source_bytes": 22070,
              "source_files_sampled": 573,
              "oversized_source_files": 0
            },
            "components": [
              {
                "key": "type_checkable_code",
                "name": "Type-checkable code",
                "detail": "Python with type-check config (packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed)",
                "points": 27,
                "status": "partial",
                "details": [
                  {
                    "code": "typecheck_config_language",
                    "params": {
                      "files": "packages/graphrag-cache/graphrag_cache/py.typed, packages/graphrag-common/graphrag_common/py.typed, packages/graphrag-llm/graphrag_llm/py.typed, packages/graphrag-storage/graphrag_storage/py.typed, packages/graphrag/graphrag/py.typed",
                      "language": "Python"
                    }
                  }
                ],
                "max_points": 45
              },
              {
                "key": "manageable_file_sizes",
                "name": "Manageable file sizes",
                "detail": "0/573 source files over 60KB",
                "points": 55,
                "status": "met",
                "details": [
                  {
                    "code": "oversized_source_files",
                    "params": {
                      "kb": 60,
                      "sampled": 573,
                      "oversized": 0
                    }
                  }
                ],
                "max_points": 55
              }
            ]
          },
          {
            "key": "ai_interfaces",
            "band": "at_risk",
            "name": "Machine-readable interfaces",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "example_dirs": [
                "notebooks"
              ],
              "has_mcp_signal": false,
              "api_schema_files": []
            },
            "components": [
              {
                "key": "api_schema_openapi_graphql_proto",
                "name": "API schema (OpenAPI/GraphQL/proto)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 40
              },
              {
                "key": "mcp_server",
                "name": "MCP server",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "runnable_examples",
                "name": "Runnable examples",
                "detail": "notebooks",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "notebooks"
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          }
        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
    ],
    "metrics_version": "1.13.0"
  },
  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "Could not fetch pypi package 'graphrag-monorepo' from its registry",
    "Could not fetch pypi package 'unified-copilot' from its registry"
  ],
  "report_type": "repository",
  "generated_at": "2026-07-26T21:40:55.520352Z",
  "schema_version": "0.27.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/m/microsoft/graphrag.svg",
  "full_name": "microsoft/graphrag",
  "license_state": "standard",
  "license_spdx": "MIT"
}

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 v1.13.0, schema v0.27.0 — full methodology · metrics wiki.

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