Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-10-18 16:09 UTC

LibreChat-AI / rag-api

ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector

PythonMIT★ 913 stars⑂ 408 forkssince Mar 2024View on GitHub ↗

LibreChat-AI/rag-api holds a health index of 65 out of 100, placing it in the Good band. It scores highest on Engineering Quality (81/100) and lowest on Security (21/100). It was last updated 13 days ago. A single contributor accounts for most of its recent work.

65
overall / 100
Good

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.

65
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 60 is calibrated to 65 on the published index scale (record calibration 2026-08-02).

Ownership

LibreChat-AIOrganization · verified domain
250 followers14 public repossince May 2024

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

Metrics by category

Vitality

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

71Good · 21% of overall
How it's scored
28.8/36Push recency — last push 13 days ago
8.3/36Commit cadence — 12/52 weeks with commits
14.6/18Commit volume — 41 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year41
human_commit_share0.99
days_since_last_push13
active_weeks_last_year12
How it's scored
27/27Ships releases — 12 releases published
36/36Release recency — latest release 78 days ago
19.8/27Release cadence — a release every ~74.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count12
latest_release_tagv0.9.0
releases_from_tagsno
days_since_latest_release78
mean_days_between_releases74.7

Community & Adoption

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

63Moderate · 17% of overall
How it's scored
48/60Stars — 913 stars
21.7/25Forks — 408 forks
4.3/15Watchers — 7 watchers
Inputs used
forks408
stars913
watchers7
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (MIT)
0/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

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

60Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
7.9/22.5Commit distribution — top contributor authored 65% of commits
13.5/13.5Contributor breadth — 34 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled34
top_contributor_share0.651
How it's scored
33/42Issue resolution — 79% of issues closed
17/30PR acceptance — 105/185 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs105
open_issues24
closed_issues88
prs_merged_7d0
prs_decided_7d0
prs_merged_30d4
prs_decided_30d4
issue_closed_ratio0.786
closed_unmerged_prs80
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
17.3/25Owner reach — 250 followers of LibreChat-AI
13.4/25Track record — 14 public repos, account ~2 yr old
Inputs used
followers250
owner_typeOrganization
is_verifiedyes
owner_loginLibreChat-AI
public_repos14
account_age_days891

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows — 4 workflow(s)
24/24Tests present
16/16Linter config
9.6/9.6Pre-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_configyes
How it's scored
30/30README
0/25Documentation directory
15/15Documentation / homepage site — https://librechat.ai/
10/10Repository description
10/10Topics — 12 topics
0/10Wiki
Inputs used
topicsapi, api-rest, embeddings, fastapi, langchain, pgvector, postgresql, psql, python, rag, vector, vector-database
has_wikino
homepagehttps://librechat.ai/
docs_sitehttps://librechat.ai/
has_readmeyes
has_docs_dirno
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestsrequirements.lite.txt, requirements.txt
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories30
affected_packages2
assessed_packages40
unassessed_packages6
affected_by_severitycritical 2
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 40 resolved dependencies against OSV. 6 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. 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.

52Moderate · 4% of overall
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://librechat.ai/llms.txt)
40/40Legible commit history — 91 of 99 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://librechat.ai/llms.txt
legible_history_share0.919
agent_instruction_files—
agent_instruction_max_bytes—
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format config
0/11Static type checking
10/10Reproducible environment — devcontainer, Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_files—
has_devcontaineryes
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
55/55Manageable file sizes — 0/48 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes56,019
source_files_sampled48
oversized_source_files0

Key facts

913GitHub stars
34contributors
41commits, last 12 months
13days since last push
12releases
1bus factor
24open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard timed out after 240s; skipping Scorecard checks

More detail

Star and fork history 0 ★ / 408 ⇿
0Stars
408Forks
12Releases

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.

010020030040050040572024-032025-072026-10
Major 0Minor 9Patch 3

Each point covers 3 days.

All dependencies 46

Full resolved dependency set from the GitHub dependency graph: 0 direct and 46 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaiofiles24.1.0indirect
PyPIasyncpg0.29.0indirect
PyPIboto3—indirect
PyPIchardet5.2.0indirect
PyPIcryptography50.0.1indirect
PyPIdocx2txt0.9indirect
PyPIfastapi0.115.12indirect
PyPIhttpx0.27.0indirect
PyPIlangchain1.3.10indirect
PyPIlangchain-aws1.6.0indirect
PyPIlangchain-community0.4.2indirect
PyPIlangchain-core1.4.8indirect
PyPIlangchain-google-genai4.2.5indirect
PyPIlangchain-huggingface1.2.2indirect
PyPIlangchain-mongodb0.11.0indirect
PyPIlangchain-ollama1.1.0indirect
PyPIlangchain-openai1.3.2indirect
PyPIlangchain-text-splitters1.1.2indirect
PyPImarkdown3.8.2indirect
PyPImongomock4.3.0indirect
PyPImsoffcrypto-tool—indirect
PyPInetworkx3.2.1indirect
PyPIopencv-python-headless4.9.0.80indirect
PyPIopenpyxl3.1.5indirect
PyPIpandas2.2.1indirect
PyPIpgvector0.2.5indirect
PyPIpsycopg2-binary2.9.9indirect
PyPIpydantic—indirect
PyPIpyjwt2.13.0indirect
PyPIpymongo—indirect
PyPIpypandoc1.15indirect
PyPIpypdf6.19.0indirect
PyPIpytest9.0.3indirect
PyPIpytest-asyncio1.3.0indirect
PyPIpython-dotenv1.2.2indirect
PyPIpython-magic0.4.27indirect
PyPIpython-multipart0.0.32indirect
PyPIpython-pptx1.0.2indirect
PyPIrapidocr-onnxruntime1.4.4indirect
PyPIsentence-transformers3.1.1indirect
PyPIsqlalchemy2.0.41indirect
PyPItenacity—indirect
PyPItestcontainers—indirect
PyPIunstructured0.27.8indirect
PyPIuvicorn0.28.0indirect
PyPIxlrd2.0.2indirect
Dependency advisories 2

This repository publishes no package the index resolves, so its own dependency graph was assessed — 40 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct. 6 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
pyjwt2.13.0indirectcritical282.15.0
sentence-transformers3.1.1indirectcritical25.6.0

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.

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.