Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-16 18:08 UTC

ScrapeGraphAI / Scrapegraph-ai

Python scraper based on AI

PythonMIT★ 31,032 stars⑂ 3,122 forkssince Jan 2024View on GitHub ↗
KindLibraryhow this is determined
#scraping#scraping-python#llm#web-crawler#web-scraping#ai-scraping#crawler#markdown#rag#web-crawlers#ai-crawler#ai-search#large-language-model#web-data-extraction#web-search#web-scraper#data-extraction#web-data#webscraping#firecrawl-alternative

ScrapeGraphAI/Scrapegraph-ai holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Community & Adoption (92/100) and lowest on Security (66/100). It was last updated 9 days ago. A single contributor accounts for most of its recent work.

94
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.

94
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 81 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

ScrapeGraphAIOrganization
616 followers30 public repossince May 2024

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIscrapegraphai2.2.4-4879 days agoaiartificial-intelligencegptgraphlangchainllmmachine-learningnatural-language-processingnlpopenairagscrapegraphscrapegraphaiscrapingweb-scrapingweb-scraping-libraryweb-scraping-toolwebscraping

Metrics by category

Vitality

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

90Excellent · 21% of overall

Development activity

84Excellent
How it's scored
28.8/36Push recency — last push 9 days ago
27/36Commit cadence — 39/52 weeks with commits
18/18Commit volume — 192 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year192
human_commit_share1
days_since_last_push9
active_weeks_last_year39

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 9 days ago
27/27Release cadence — a release every ~2.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tagv2.2.4
releases_from_tagsno
days_since_latest_release9
mean_days_between_releases2.1
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

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

92Excellent · 17% of overall

Popularity & adoption

98Exceptional
How it's scored
60/60Stars — 31,032 stars
25/25Forks — 3,122 forks
12.5/15Watchers — 178 watchers
Inputs used
forks3,122
stars31,032
watchers178
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
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
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges2
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateno

Sustainability & Governance

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

69Good · 23% of overall

Maintainer resilience (bus factor)

43Weak
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
10.8/22.5Commit distribution — top contributor authored 52% of commits
13.5/13.5Contributor breadth — 97 contributors
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled97
top_contributor_share0.52

Issue & PR responsiveness

79Good
How it's scored
41.4/42Issue resolution — 99% of issues closed
25.7/30PR acceptance — 524/612 decided PRs merged
9.3/13Newcomer PR acceptance — 5/7 first-time contributors' PRs merged in 30d
3/15OpenSSF Scorecard: Code-Review — Found 4/17 approved changesets -- score normalized to 2
Inputs used
merged_prs524
open_issues6
closed_issues430
prs_merged_7d0
prs_decided_7d1
prs_merged_30d14
prs_decided_30d17
issue_closed_ratio0.986
closed_unmerged_prs88
first_time_authors_30d7
first_time_prs_merged_30d5
first_time_prs_decided_30d7

Ownership & stewardship

66Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
20.1/25Owner reach — 616 followers of ScrapeGraphAI
15.5/25Track record — 30 public repos, account ~2 yr old
Inputs used
followers616
owner_typeOrganization
is_verifiedno
owner_loginScrapeGraphAI
public_repos30
account_age_days841

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 9 days ago
20/20Version history — 487 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesscrapegraphai
ecosystemspypi
any_deprecatedno
min_days_since_publish9

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall

Engineering practices

88Excellent
How it's scored
24/24CI workflows — 4 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
14/20OpenSSF Scorecard: CI-Tests — 8 out of 11 merged PRs checked by a CI test -- score normalized to 7
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://scrapegraphai.com
10/10Repository description
10/10Topics — 20 topics
0/10Wiki
Inputs used
topicsscraping, scraping-python, llm, web-crawler, web-scraping, ai-scraping, crawler, markdown, rag, web-crawlers, ai-crawler, ai-search, large-language-model, web-data-extraction, web-search, web-scraper, data-extraction, web-data, webscraping, firecrawl-alternative
has_wikino
homepagehttps://scrapegraphai.com
docs_sitehttps://scrapegraphai.com
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

66Good · 16% of overall

Security posture

58Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
1.8/2.5CI-Tests — 8 out of 11 merged PRs checked by a CI test -- score normalized to 7
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 4/17 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 10 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 — 30 commit(s) and 1 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
3.5/5SAST — SAST tool is not run on all commits -- score normalized to 7
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 — 140 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.8
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
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_packages97
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:scrapegraphai@2.2.4 runtime dependency closure — what installing the published package pulls in — 97 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.

82Excellent · 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://scrapegraphai.com/llms.txt)
40/40Legible commit history — 94 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://scrapegraphai.com/llms.txt
legible_history_share0.94
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes5,217

Verify loop (build / test / typecheck)

71Good
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/11Static type checking
10/10Reproducible environment — Dockerfile, lockfile
10/10Demonstrated agent practice — 28 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
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.28
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

55Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
54.8/55Manageable file sizes — 1/266 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes76,219
source_files_sampled266
oversized_source_files1

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 — examples, notebooks
Inputs used
example_dirsexamples, notebooks
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

31,032GitHub stars
97contributors
192commits, last 12 months
9days since last push
100releases
1bus factor
6open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'scrapegraphai' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced

More detail

Star and fork history 0 ★ / 3,122 ⇿
0Stars
3,122Forks
23Releases

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.

2,0002,2002,4002,6002,8003,0003,2003,122292026-052026-072026-09
Major 0Minor 1Patch 11
OpenSSF Scorecard 5.8 / 10
5.8aggregate

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 · 2026-09-16 18:07 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during GetBranch(pre/beta): error during branchesHandler.query: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
7CI-Tests8 out of 11 merged PRs checked by a CI test -- score normalized to 7
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 4/17 approved changesets -- score normalized to 2
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 1 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
7SASTSAST tool is not run on all commits -- score normalized to 7
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities140 existing vulnerabilities detected
Direct dependencies 23
RegistryPackageVersion constraintManifest
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PyPIlangchain-classic>=1.0.0pyproject.toml
PyPIlangchain-openai>=1.1.6pyproject.toml
PyPIlangchain-mistralai>=1.1.1pyproject.toml
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PyPItqdm>=4.67.1pyproject.toml
PyPIminify-html>=0.18.1pyproject.toml
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PyPIpydantic>=2.12.5pyproject.toml
PyPIscrapegraph-py>=2.0.0pyproject.toml
All dependencies 227

Full resolved dependency set from the GitHub dependency graph: 23 direct and 204 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

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PyPIuuid-utils0.12.0indirect
PyPIuvicorn0.38.0indirect
PyPIvirtualenv20.35.4indirect
PyPIwatchdog6.0.0indirect
PyPIwcwidth0.2.14indirect
PyPIwidgetsnbextension4.0.15indirect
PyPIwin32-setctime1.2.0indirect
PyPIxxhash3.6.0indirect
PyPIyarl1.22.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:scrapegraphai@2.2.4 pulls in 97 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.

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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.