Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-18 15:31 UTC

mihir0209 / AI_engine

Free AI inference router for developers - 21 providers, OpenAI-compatible

PythonMIT★ 4 stars⑂ 0 forkssince Aug 2025View on GitHub ↗
KindLibraryCommand-line toolNetwork servicehow this is determined

mihir0209/AI_engine holds a health index of 62 out of 100, placing it in the Moderate band. It scores highest on Engineering Quality (88/100) and lowest on Sustainability & Governance (34/100). It was last updated 5 days ago. A single contributor accounts for most of its recent work.

62
overall / 100
Moderate

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

62
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 58 is calibrated to 62 on the published index scale (record calibration 2026-08-02).

Ownership

Mihir PatilPersonal account
7 followers46 public repossince Feb 2024

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIai-synapse1.0.5109676 days agoaillmopenaianthropicinferencefree-aigroqgeminisdk

Metrics by category

Vitality

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

76Good · 21% of overall
How it's scored
36/36Push recency — last push 5 days ago
6.2/36Commit cadence — 9/52 weeks with commits
18/18Commit volume — 211 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year211
human_commit_share1
days_since_last_push5
active_weeks_last_year9
How it's scored
27/27Ships releases — 1 releases published
36/36Release recency — latest release 75 days ago
12.6/27Release cadence — cadence unknown (single release)
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count1
latest_release_tagv1.0.5
releases_from_tagsno
days_since_latest_release75
mean_days_between_releases—
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?

36Weak · 17% of overall
How it's scored
7.7/60Stars — 4 stars
0/25Forks — 0 forks
0/15Watchers — 0 watchers
Inputs used
forks0
stars4
watchers0
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges4
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com, shields.io
has_pull_request_templateno
How it's scored
27.2/80Monthly downloads — 109 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesai-synapse
dependents—
ecosystemspypi
total_downloads—
monthly_downloads109
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?

34At Risk · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0/22.5Commit distribution — top contributor authored 100% of commits
1.4/13.5Contributor breadth — 1 contributors
0/10OpenSSF Scorecard: Contributors — project has 0 contributing companies or organizations -- score normalized to 0
Inputs used
bus_factor1
contributors_sampled1
top_contributor_share1
How it's scored
0/42Issue resolution — no issues or no data
0/30PR acceptance — no decided pull requests or no data
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — Found 0/30 approved changesets -- score normalized to 0
Inputs used
merged_prs0
open_issues0
closed_issues0
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio—
closed_unmerged_prs0
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Issue resolution, PR acceptance, Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
6.5/25Owner reach — 7 followers of mihir0209
17.5/25Track record — 46 public repos, account ~2 yr old
Inputs used
followers7
owner_typeUser
is_verified—
owner_loginmihir0209
public_repos46
account_age_days976
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 76 days ago
20/20Version history — 6 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesai-synapse
ecosystemspypi
any_deprecatedno
min_days_since_publish76

Engineering Quality

Are baseline engineering and documentation practices in place?

88Excellent · 19% of overall
How it's scored
24/24CI workflows — 3 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: CI-Tests. Remaining weights renormalized.

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://github.com/mihir0209/AI_engine/tree/main/docs
10/10Repository description
10/10Topics — 20 topics
10/10Wiki
Inputs used
topicsai, ai-api, chatgpt, developer-tools, docker, fastapi, free-ai, free-llm, gemini, gpt4, gpt4free, groq, inference, llm, llm-api, openai, openrouter, python, router, self-hosted
has_wikiyes
homepage—
docs_sitehttps://github.com/mihir0209/AI_engine/tree/main/docs
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

55Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — branch protection not enabled on development/release branches
0/2.5CI-Tests — no data
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
0/7.5Code-Review — Found 0/30 approved changesets -- score normalized to 0
0/2.5Contributors — project has 0 contributing companies or organizations -- score normalized to 0
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 0 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
0/5SAST — no SAST tool detected
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): CI-Tests, 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_packages23
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:ai-synapse@1.0.5 runtime dependency closure — what installing the published package pulls in — 23 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.

69Good · 4% of overall
How it's scored
45/45Agent instructions — docs/archive/claude.md, docs/claude.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_filesdocs/archive/claude.md, docs/claude.md
agent_instruction_max_bytes3,857
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — ai_engine/py.typed, core/py.typed
10/10Reproducible environment — Dockerfile
10/10Demonstrated agent practice — 11 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
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontainerno
has_linter_configyes
typecheck_configsai_engine/py.typed, core/py.typed
agent_commit_share0.11
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (ai_engine/py.typed, core/py.typed)
54.3/55Manageable file sizes — 3/230 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes72,277
source_files_sampled230
oversized_source_files3
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — examples
Inputs used
example_dirsexamples
has_mcp_signalno
api_schema_files—
interfaces_expected_ofnetwork-service

Key facts

4GitHub stars
1contributors
211commits, last 12 months
5days since last push
1releases
1bus factor
0open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

OpenSSF Scorecard 4.4 / 10
4.4aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-10-18 15:31 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
n/aCI-Testsno pull request found
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/30 approved changesets -- score normalized to 0
0Contributorsproject has 0 contributing companies or organizations -- score normalized to 0
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 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
0SASTno SAST tool detected
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPIrequests>=2.28.0pyproject.toml
PyPIaiohttp>=3.8.0pyproject.toml
PyPIpython-dotenv>=0.19.0pyproject.toml
PyPIpydantic>=2.5.0pyproject.toml
PyPIhttpx>=0.28.0,<0.29.0pyproject.toml
All dependencies not collected

The resolved dependency set could not be collected for this report: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

Dependency advisories 0

Installing pypi:ai-synapse@1.0.5 pulls in 23 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 v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.