Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-10 10:46 UTC

Trampoline-AI / predict-rlm

Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.

PythonMIT★ 431 stars⑂ 47 forkssince Mar 2026View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

Trampoline-AI/predict-rlm holds a health index of 80 out of 100, placing it in the Excellent band. It scores highest on Vitality (86/100) and lowest on Community & Adoption (55/100). It was last updated 1 day ago. A single contributor accounts for most of its recent work.

80
overall / 100
Excellent

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.

80
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 68 is calibrated to 80 on the published index scale (record calibration 2026-08-02).

Ownership

Trampoline AIOrganization
16 followers5 public repossince Apr 2023

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIpredict-rlm0.8.02,3442435 days agoaidspylanguage-modelsreasoningrlmtool-use

Metrics by category

Vitality

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

86Excellent · 21% of overall
How it's scored
36/36Push recency — last push 1 days ago
12.5/36Commit cadence — 18/52 weeks with commits
18/18Commit volume — 351 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year351
human_commit_share1
days_since_last_push1
active_weeks_last_year18

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 24 releases published
36/36Release recency — latest release 35 days ago
27/27Release cadence — a release every ~7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count24
latest_release_tagv0.8.0
releases_from_tagsno
days_since_latest_release35
mean_days_between_releases7
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?

55Moderate · 17% of overall
How it's scored
42.7/60Stars — 431 stars
13.9/25Forks — 47 forks
1.7/15Watchers — 3 watchers
Inputs used
forks47
stars431
watchers3
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_badges7
has_contributingno
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateno
How it's scored
44.9/80Monthly downloads — 2,344 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagespredict-rlm
dependents—
ecosystemspypi
total_downloads—
monthly_downloads2,344
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?

56Moderate · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
6.1/22.5Commit distribution — top contributor authored 73% of commits
5.4/13.5Contributor breadth — 4 contributors
10/10OpenSSF Scorecard: Contributors — project has 3 contributing companies or organizations -- score normalized to 10
Inputs used
bus_factor1
contributors_sampled4
top_contributor_share0.729
How it's scored
22.6/42Issue resolution — 54% of issues closed
22.8/30PR acceptance — 35/46 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
3/15OpenSSF Scorecard: Code-Review — Found 2/9 approved changesets -- score normalized to 2
Inputs used
merged_prs35
open_issues6
closed_issues7
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.538
closed_unmerged_prs11
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
0/20Verified domain
8.8/25Owner reach — 16 followers of Trampoline-AI
12.5/25Track record — 5 public repos, account ~3 yr old
Inputs used
followers16
owner_typeOrganization
is_verifiedno
owner_loginTrampoline-AI
public_repos5
account_age_days1,255

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 35 days ago
20/20Version history — 24 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespredict-rlm
ecosystemspypi
any_deprecatedno
min_days_since_publish35

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 2 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-commit hooks
0/6.4.editorconfig
16/20OpenSSF Scorecard: CI-Tests — 5 out of 6 merged PRs checked by a CI test -- score normalized to 8
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://trampoline.ai
10/10Repository description
10/10Topics — 3 topics
0/10Wiki
Inputs used
topicsdspy, llm, rlm
has_wikino
homepagehttps://trampoline.ai
docs_sitehttps://trampoline.ai
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

57Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0.8/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2/2.5CI-Tests — 5 out of 6 merged PRs checked by a CI test -- score normalized to 8
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
1.5/7.5Code-Review — Found 2/9 approved changesets -- score normalized to 2
2.5/2.5Contributors — project has 3 contributing companies or organizations -- score normalized to 10
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 5 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
1/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not 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_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate4.9
Excluded from scoring (no data or not applicable): Signed-Releases. Remaining weights renormalized.
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
19/25Indirect dependencies free of known advisories — 1 affected: diskcache 5.6.3 (moderate)
37.2/40No advisories left outstanding — 1 advisory-carrying package(s) unaddressed past 90 days; oldest published 210 days ago
Inputs used
sourceosv
advisories2
affected_packages1
assessed_packages70
unassessed_packages0
affected_by_severitymoderate 1
direct_affected_packages0
Matched the pypi:predict-rlm@0.8.0 runtime dependency closure — what installing the published package pulls in — 70 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.

78Good · 4% of overall
How it's scored
45/45Agent instructions — AGENTS.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://trampoline.ai/llms.txt)
40/40Legible commit history — 93 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://trampoline.ai/llms.txt
legible_history_share0.93
agent_instruction_filesAGENTS.md
agent_instruction_max_bytes8,388
How it's scored
18/18One-command bootstrap — Makefile, examples/appworld/Makefile, examples/spreadbench/Makefile, examples/terminal_bench/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
10/10Reproducible environment — Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
2/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 2
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, examples/appworld/Makefile, examples/spreadbench/Makefile, examples/terminal_bench/Makefile
has_devcontainerno
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
52.3/55Manageable file sizes — 12/245 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes196,284
source_files_sampled245
oversized_source_files12
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, sample
Inputs used
example_dirsexamples, notebooks, sample
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

431GitHub stars
4contributors
351commits, last 12 months
1days since last push
24releases
1bus factor
6open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 47 ⇿
0Stars
47Forks
20Releases

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.

010203040504572026-042026-062026-08
Major 0Minor 6Patch 7
OpenSSF Scorecard 4.9 / 10
4.9aggregate

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 · 2026-09-10 10:45 UTC

10Binary-Artifactsno binaries found in the repo
1Branch-Protectionbranch protection is not maximal on development and all release branches
8CI-Tests5 out of 6 merged PRs checked by a CI test -- score normalized to 8
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
2Code-ReviewFound 2/9 approved changesets -- score normalized to 2
10Contributorsproject has 3 contributing companies or organizations -- score normalized to 10
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
2Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 2
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 6
RegistryPackageVersion constraintManifest
PyPIdeno>=2pyproject.toml
PyPIdspy>=3.2.1,<3.3pyproject.toml
PyPInest-asyncio>=1.6.0pyproject.toml
PyPIpygments>=2.0.0pyproject.toml
PyPIpydantic>=2.8.2,<3pyproject.toml
PyPItenacity>=8.2.0pyproject.toml
All dependencies 22

Full resolved dependency set from the GitHub dependency graph: 6 direct and 16 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIdeno—direct
PyPIdspy—direct
PyPInest-asyncio—direct
PyPIpydantic—direct
PyPIpygments—direct
PyPItenacity—direct
PyPIformulas—indirect
PyPIgepa—indirect
PyPIipykernel—indirect
PyPIkaleido—indirect
PyPIlitellm—indirect
PyPIopenai—indirect
PyPIopenpyxl—indirect
PyPIplotly—indirect
PyPIpymupdf—indirect
PyPIpytest—indirect
PyPIpytest-asyncio—indirect
PyPIpytest-cov—indirect
PyPIpytest-testdox—indirect
PyPIruff—indirect
PyPItqdm—indirect
PyPIwebsockets—indirect
Dependency advisories 1

Installing pypi:predict-rlm@0.8.0 pulls in 70 packages, direct and transitive: 1 carry known advisories, of which 0 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
diskcache5.6.3indirectmoderate2—

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.