Public record
Software health reportschema 0.16.0 · metrics 2.10.0 · 2026-07-20 22:53 UTC

ratel-ai / ratel

Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 and semantic retrieval. Progressive Disclosure. No vector DB.

Rust · Python · TypeScriptMIT★ 233 stars⑂ 9 forkssince Nov 2025View on GitHub ↗

ratel-ai/ratel holds a health index of 75 out of 100, placing it in the Good band. It scores highest on Engineering Quality (90/100) and lowest on Security (36/100). It was last updated today. A single contributor accounts for most of its recent work.

75
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.

75
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 65 is calibrated to 75 on the published index scale (record calibration 2026-08-02).

Ownership

RatelOrganization
23 followers4 public repossince Nov 2025

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?

78Good · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
6.2/36Commit cadence9/52 weeks with commits
17.6/18Commit volume89 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year89
human_commit_share
days_since_last_push0
active_weeks_last_year9
How it's scored
27/27Ships releases49 releases published
36/36Release recencylatest release 0 days ago
27/27Release cadencea release every ~1 days
0/10OpenSSF Scorecard: Signed-ReleasesProject has not signed or included provenance with any releases.
Inputs used
releases_count49
latest_release_tagsdk-ts-v0.5.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases1

Community & Adoption

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

64Moderate · 17% of overall
How it's scored
38.4/60Stars233 stars
7.5/25Forks9 forks
0/15Watchers2 watchers
Inputs used
forks9
stars233
watchers2
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

85Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized 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_badges
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services
has_pull_request_templateno

Sustainability & Governance

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

49Weak · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
6.3/22.5Commit distributiontop contributor authored 72% of commits
10.8/13.5Contributor breadth8 contributors
6/10OpenSSF Scorecard: Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
Inputs used
bus_factor1
contributors_sampled8
top_contributor_share0.722
How it's scored
26.7/42Issue resolution64% of issues closed
23.9/30PR acceptance71/89 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
1.5/15OpenSSF Scorecard: Code-ReviewFound 3/30 approved changesets -- score normalized to 1
Inputs used
merged_prs71
open_issues8
closed_issues14
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0.636
closed_unmerged_prs18
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
9.9/25Owner reach23 followers of ratel-ai
6.5/25Track record4 public repos, account ~0 yr old
Inputs used
followers23
owner_typeOrganization
is_verified
owner_loginratel-ai
public_repos4
account_age_days250
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Engineering Quality

Are baseline engineering and documentation practices in place?

90Excellent · 19% of overall
How it's scored
24/24CI workflows13 workflow(s)
24/24Tests present
16/16Linter configbiome.json
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests12 out of 12 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://www.ratel.sh/
10/10Repository description
10/10Topics16 topics
10/10Wiki
Inputs used
topicsaccuracy, agents, claude-skills, context, harness, llm, llm-routing, mcp, mcp-server, memory, optimization, rag, skills, token-optimization, tool-calling, tool-selection
has_wikiyes
homepagehttps://www.ratel.sh/
docs_sitehttps://www.ratel.sh/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

36Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionbranch protection not enabled on development/release branches
2.5/2.5CI-Tests12 out of 12 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0.8/7.5Code-ReviewFound 3/30 approved changesets -- score normalized to 1
1.5/2.5Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-ReleasesProject has not signed or included provenance with any releases.
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities33 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated18
scorecard_versionv5.5.0
checks_inconclusive0
scorecard_aggregate3.5

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.

86Excellent · 4% of overall
How it's scored
45/45Agent instructionsAGENTS.md, CLAUDE.md
15/15Machine-readable docs (llms.txt)llms.txt present
0/40Legible commit historyno data
Inputs used
has_llms_txtyes
llms_txt_url
legible_history_share
agent_instruction_filesAGENTS.md, CLAUDE.md
agent_instruction_max_bytes16,168
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.
How it's scored
0/18One-command bootstrap
22/22Automated tests
11/11Lint / format configbiome.json
11/11Static type checkingexamples/ai-sdk/tsconfig.json, examples/mcp-chat/tsconfig.json, examples/telemetry-ts/tsconfig.json, src/sdk/python/ratel_ai/py.typed, src/sdk/ts/tsconfig.json, src/telemetry/python/ratel_ai_telemetry/py.typed, src/telemetry/ts-otlp/tsconfig.json, src/telemetry/ts/tsconfig.json
10/10Reproducible environmentlockfile
0/10Demonstrated agent practiceno data
0/8Automated maintenanceno data
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfilesCargo.lock, pnpm-lock.yaml, uv.lock
has_dockerfileno
typed_languageyes
bootstrap_files
has_devcontainerno
has_linter_configyes
typecheck_configsexamples/ai-sdk/tsconfig.json, examples/mcp-chat/tsconfig.json, examples/telemetry-ts/tsconfig.json, src/sdk/python/ratel_ai/py.typed, src/sdk/ts/tsconfig.json, src/telemetry/python/ratel_ai_telemetry/py.typed, src/telemetry/ts-otlp/tsconfig.json, src/telemetry/ts/tsconfig.json
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Excluded from scoring (no data or not applicable): Demonstrated agent practice, Automated maintenance. Remaining weights renormalized.
How it's scored
45/45Type-checkable codeRust (statically typed)
54.5/55Manageable file sizes1/112 source files over 60KB
Inputs used
primary_languageRust
largest_source_bytes90,237
source_files_sampled112
oversized_source_files1
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples
Inputs used
example_dirsexamples
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

233GitHub stars
8contributors
89commits, last 12 months
0days since last push
49releases
1bus factor
8open issues
crates.io, npmpackage ecosystems

Data collection warnings

  • GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository

More detail

OpenSSF Scorecard 3.5 / 10
3.5aggregate

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 · 2026-07-20 22:53 UTC

10Binary-Artifactsno binaries found in the repo
0Branch-Protectionbranch protection not enabled on development/release branches
10CI-Tests12 out of 12 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
1Code-ReviewFound 3/30 approved changesets -- score normalized to 1
6Contributorsproject has 2 contributing companies or organizations -- score normalized to 6
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
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities33 existing vulnerabilities detected
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

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 v0.16.0 — full methodology · metrics wiki.

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