The full upstream README, mirrored here for reference. Install config, tool schemas, adoption signals, and an original overview live on the Ossfind listing page.
Given a query like "http client", ossfind returns open-source components ranked by whether you can actually ship a product on them — a blended, explainable score of fit · license · security · maintenance health · integration effort — served through both a web UI and an MCP tool over one ranking engine.
Its core promise: never recommend ("ship") a component whose safety evidence is unsafe, missing, or ambiguous. The engine fails closed.
New here? → GETTING_STARTED.md — install, try the offline demo, go live,
and connect it to an AI agent over MCP (Claude Code / Claude Desktop / Cursor config included), in
about five minutes.
-e/--ecosystem npm · pypi · github · huggingface · cargo · rubygems · all — -l/--license
declares your project's license so incompatible results are ranked AVOID — --json for
scripting — --no-color (also honours NO_COLOR). Colour is disabled automatically when
stdout is not a terminal.
Run the web app (offline demo mode, uses frozen fixtures):
By default, the web server binds exclusively to loopback (127.0.0.1).
HOST — bind host (default 127.0.0.1). Non-loopback hosts (e.g. HOST=0.0.0.0) require OSSFIND_WEB_TOKEN to be set; starting wide-open without a token is refused.PORT — server port (default 8787).OSSFIND_WEB_TOKEN — optional Bearer token requiring Authorization: Bearer <token> on /api/* endpoints.Run the MCP server (for AI agents):
Drop OSSFIND_FIXTURES=1 to hit live suppliers (npm registry, ecosyste.ms, deps.dev, OSV).
ossfind searches npm (default), PyPI, crates.io (Rust), RubyGems, GitHub repositories, Hugging Face models, or
all six at once (ecosystem: "all") — one query, results from every ecosystem merged and
safety-ranked together, so you don't have to guess where the answer lives (e.g. "video generation" →
PyPI's decord, a GitHub AI-model repo, and a Hugging Face model in the same result set). Pick the
ecosystem with the web/MCP selector, the ecosystem MCP tool argument, or &ecosystem=all on
/api/search.
Discovery is federated: a FederatedDiscoverer composes multiple source adapters per query
(parallel, per-source error isolation + timeouts, results merged and deduped by id). Enrichment routes
each candidate by its own id prefix (npm:/pypi:/cargo:/rubygems:/github:/huggingface:), so a mixed batch is
enriched correctly per-source. The safety-ranking layer is the same for every source — ossfind owns
the ranking, not the corpus. GitHub and Hugging Face are what surface AI-model repos/models (diffusers,
CogVideo, …) that aren't on any package registry.
npm needs no key — discovery uses the npm registry search API, with query expansion
(progressively shorter slices of the query, unioned) to recover the recall a conjunctive text
match loses. Optionally federate it with a local semantic index to bridge vocabulary the
registry cannot — marked says parser when you asked for a renderer:
The same optional index federates crates.io, RubyGems and PyPI. When an index has not been built, that ecosystem's search behaves exactly as before.
It does not help everywhere, and the eval says where. Measured on the labelled set:
npm and crates.io improve substantially (crates.io MRR 0.000 → 0.675, since crates.io's own
search ranks by name similarity and never returns serde for "serialization"). RubyGems is
neutral on MRR and slightly positive on recall. Rebuilding PyPI's index concentrated on the
top 8,000 packages measured slightly worse (0.611 → 0.597) than the broader 25,000-package
index, so the wider corpus stays — a hypothesis the harness rejected.
A RubyGems index originally measured much worse (MRR 0.500 → 0.250) by pushing rails out of
the enrichment shortlist. That was a shortlisting defect, not an index one, and is fixed: a
complete lexical match now counts as relevance evidence, so an adopted package whose
description contains every query word earns a slot regardless of its embedding score.
Measured on the labelled set, adding the index moved MRR 0.561 → 0.636, hit@3 60.5% → 67.4%
and noise@3 2.6% → 0.0%, with no per-query regressions — and made marked the top result for
"markdown to html renderer", which no lexical probe can reach. Note that npm run eval
therefore depends on a locally built index; without one the numbers are the registry-only ones.
GitHub uses the repo search API. Set an optional GITHUB_TOKEN in .env.local for higher rate
limits.
Hugging Face needs no key — discovery uses the public models search API.
crates.io (Rust) and RubyGems need no key — discovery uses their public search APIs,
with licence/vulnerability/health enrichment from ecosyste.ms, OSV and deps.dev like any package
ecosystem. crates.io ranks by name similarity, so "serialization" never returns serde from the
registry alone; federate a local index (INDEX_MAX=6000 INDEX_DB_PATH=.cache/index/cargo.db npm run index:build cargo) to fix that.
Licence expressions: an SPDX expression whose operands are all permissive resolves to a
permissive licence — Rust's near-universal MIT OR Apache-2.0 is a real choice, not an audit
item. Any copyleft operand keeps the conservative treatment G4 requires: GPL-3.0 OR MIT
never ships into a permissive project, and a WITH exception or unrecognized operand is left
for manual audit.
Health evidence is attributed only when the repository claim is corroborated. A package's
repository URL is self-declared, and typosquats name the real project's repo to inherit its
OpenSSF score — five PyPI packages claiming github.com/psf/requests were reported SHIP 92/100
on the real project's 8.1. The claim is now checked against the package name and fails closed
(G17).
GitHub and Hugging Face components fail-closed to at most "caution" (never "ship") — a raw repo's or model's dependency CVEs can't be verified the way a published package's can; Hugging Face also has no OpenSSF-style health score, so it relies on the existing missing-scorecard cap. License (SPDX) is still enriched and gated for both.
PyPI discovery uses a self-hosted local index by default (no key, no third-party service). Build/refresh it once:
The index is node:sqlite FTS5 (BM25) over name/description/keywords, semantically reranked by the
embedding model. Select the discovery source with OSSFIND_PYPI_DISCOVERY=index|libraries|auto
(default auto: local index if built, else libraries.io).
libraries.io is the fallback for PyPI (used when no local index exists). It needs a free key in
a gitignored .env.local (LIBRARY_IO_API_KEY=…, LIBRARIES_IO_API_KEY also accepted), loaded via
node --env-file=.env.local …. Without index or key, PyPI discovery degrades to empty (never crashes).
Live mode stores successful supplier responses on disk to reduce repeat requests and avoid supplier rate limits. Fixture mode remains local and does not use this cache.
OSSFIND_CACHE_DIR — cache directory (default .cache/http/).OSSFIND_CACHE_TTL — cache lifetime in seconds for discovery, license, and health data (default 3600).OSSFIND_SECURITY_TTL — cache lifetime in seconds for OSV vulnerability data (default 300).OSSFIND_CONCURRENCY — maximum concurrent upstream enrichment requests (default 4).OSSFIND_NO_CACHE=1 — disable the live-response cache.Security responses may be up to OSSFIND_SECURITY_TTL seconds stale; tune this value down when
stricter vulnerability-data freshness is required.
Supplier APIs are free but rate-limited; review each supplier's terms before commercial use.
ossfind includes an in-memory, privacy-preserving usage collector that tracks aggregate operational health and supplier rate limits.
You can inspect usage metrics at any time without sending data anywhere:
usage_stats to receive the metrics snapshot and a formatted summary of top suppliers, cache hit rates, rate-limit headroom, and latency percentiles (p50/p95).GET /api/usage to retrieve the JSON snapshot. When OSSFIND_WEB_TOKEN is set, /api/usage requires the same Authorization: Bearer <token> header as /api/search.2xx, 4xx, 5xx), 429 counts, error counts, and latest rate-limit headroom (remaining, limit, reset, retryAfter) per approved supplier host.npm, pypi, github, huggingface), verdict distribution (ship, caution, avoid), result count summary (min, max, mean), error counts, and latency percentiles (p50, p95)..cache/telemetry/install-id.0.1.0) and ISO 8601 timestamp.Remote telemetry is off by default. Absolutely no network calls are made unless both switches are explicitly set:
OSSFIND_TELEMETRY=1 or OSSFIND_TELEMETRY_URL is omitted, telemetry is completely inert.https://; unencrypted http:// URLs are rejected.OSSFIND_FIXTURES=1 or during automated test runs.OSSFIND_TELEMETRY (or set OSSFIND_TELEMETRY=0) or unset OSSFIND_TELEMETRY_URL.discover → enrich → fit → rank, wired in src/pipeline/orchestrator.ts:
| Stage | Module | Source |
|---|---|---|
| Discover | src/adapters/discovery.ts | npm registry search |
| Enrich | src/adapters/enrichment.ts | ecosyste.ms (license/repo), deps.dev + OpenSSF Scorecard (health), OSV (vulns) |
| Fit | src/fit/embeddings.ts · src/fit/tfidf.ts | local embedding model (live) / deterministic TF-IDF (tests), both with a keyword+coverage guard |
| Rank ★ | src/ranking/rank.ts | pure, deterministic, explainable blend + verdict |
The HTTP layer is injectable (src/http/client.ts), so every test replays frozen fixtures in
fixtures/raw/ — no test touches the network.
rank() is pure and deterministic. It computes five 0–1 sub-scores, an overall 0–100, and a verdict
(ship / caution / avoid) with hard, fail-closed rules that override any high score:
avoid if critical — regardless of a future fixedIn.-or-later, +, AND/OR,
case) — can never ship.caution; the engine will not claim "no vulnerabilities" when OSV data is missing.Every result carries a non-empty, human-readable reasons[] explaining the drivers.
npm run gates)Thirteen executable gates, each mapped to the defect/decision that spawned it (see PIPELINE_LOG.md),
each proven to reject a known-bad input (not just accept a good one):
G1 contract · G2 determinism · G3 critical-CVSS fact (v3.0/v3.1/v4) · G4 license SPDX fact ·
G5 offline · G6 version-relevance fact · G7 evidence completeness · G8 federation provenance ·
G9 Python project-context honesty · G10 scaffold snippet integrity · G11 Python stub structural
honesty · G12 recipe resolution honesty · G13 adoption cannot override safety · G14 cache
preserves response bodies · G15 suggested ESM import matches declared exports · G16 recall survives
discovery · G17 health evidence belongs to the package.
Every gate after G7 exists because a real bug got past a green test suite — found by an independent
adversarial audit, by building a real project against the published package (G14, G15), or by the
relevance eval (G16).
Every component has been independently audited — each audit by an agent that did not write the code — and every one found real bugs the green test suite had missed. All are fixed and gated.
AUDIT_REPORT.md — the safety layer: 5 blockers (all fixed).REAUDIT_REPORT.md — independent re-audit confirming all 5 closed.CACHE_AUDIT.md — the live cache: key-collision + stale safety signal (both fixed).FEDERATION_AUDIT.md — federation/GitHub/all-ecosystem: 2 structural holes (→ G8).AUDIT_AGENT_LAYER_A.md — npm/PyPI manifest, compat, MCP, ZIP/range: false "compatible" blocker (→ G9).AUDIT_AGENT_LAYER_B.md — scaffold + stub parser: code injection into generated snippets and
fabricated exports from docstrings (→ G10, G11).AUDIT_AGENT_LAYER_C.md — recipes + typeshed: fail-open on supplier errors, false "ready" (→ G12).Xenova/all-MiniLM-L6-v2 via
@huggingface/transformers, mean-pooled, cached per package under .cache/embeddings/) ranks by
meaning. Fixture/test mode uses deterministic TF-IDF so tests stay offline and exact. Force either
with OSSFIND_FIT=embeddings|tfidf; live falls back to TF-IDF if the model can't load.groupId:artifactId collides with the ecosystem:name id convention.npm run eval scores a labelled set of 43 queries
(MRR, hit@k, recall, noise@3) and diffs against a saved baseline. Registry search matches
conjunctively, so a natural-language query excludes terse-description packages — discovery probes
with progressively shorter slices of the query and unions the results, and fit is scored before the
budgeted enrichment step so a wide pool costs no more than a narrow one. The harness has already
rejected one plausible change that measured worse, and caught a defect where a rate-limited search
was reported as "no results".searchHybrid). It does not scale to the full ~928k
corpus by API crawl — use ecosyste.ms bulk dumps + an ANN index for that (deliberately not built).