Crates.io Package Intelligence — Rust Crate Health & Downloads (AI)

Before you add a Rust dependency, know what you're pulling in. Give this Actor a list of crate names and it turns each one into a clear intelligence record: how often it's downloaded, how many dependencies it drags in, when it was last released, its minimum Rust version and license — and an AI risk read that tells you plainly whether a crate is active, stale, or effectively abandoned.

You bring the crate names. It hands back a dependency due-diligence sheet.

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The flow

1. Paste crate names               2. The Actor gathers facts            3. Get an AI risk read
   (serde, tokio, clap,       →       crates.io API                 →      downloads · deps · age ·
    time, ...)                         (public, no API key)                 license · maintenance · flags

1. Paste Rust crate names into the crates field — one per line.
2. The Actor pulls public data for each: the crates.io API for metadata, versions, downloads, categories, keywords, license and dependencies. No API key, no scraping of login-walled pages.
3. The AI layer reads the facts and returns a maintenance signal, a popularity tier, concrete risk flags and a two-sentence summary — grounded only in the data, never invented.

Note: crates.io requires a descriptive User-Agent header on every request (or it returns 403). This Actor sends one automatically.

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What you get per crate

For every crate, the Actor returns:

1. Identity & release — latestVersion, summary, versionCount, lastRelease (ISO timestamp) and lastReleaseAgeDays (how long since the newest release).
2. Reach — totalDownloads (all-time) and recentDownloads (last ~90 days) from crates.io, so you can tell a ubiquitous library from a weekend experiment.
3. Footprint — dependenciesCount (runtime + build deps of the latest version), rustVersion (declared MSRV), edition, categories, keywords, and license.
4. Maintenance signals — yanked, and the repository, documentation and homepage URLs.

Then the AI layer (OpenAI gpt-4o-mini, a fast and low-cost model) adds:

• maintenanceSignal — active, maintained, stale, or abandoned, from release recency.
• popularityTier — niche, growing, popular, or ubiquitous, from download volume.
• riskFlags — short, data-backed phrases: "unmaintained (last release >18mo)", "yanked release", "no license declared", "heavy dependency tree", "pre-1.0 (unstable API)".
• aiSummary — two factual sentences on the crate and its risk posture.
• suggestedAlternatives — only well-known, widely-trusted substitutes for the same job, else [].

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Three ways people use it

• Rust dependency due diligence — before a new crate lands in Cargo.toml, run a crate analysis to see downloads, license, dependency count and whether it's still maintained. Catch a yanked release, a missing license or a pre-1.0 unstable API before it's in your build.
• Supply-chain risk — audit an existing dependency list for Rust dependency risk: surface unmaintained crates, stale projects and heavy dependency trees across your whole stack in one run. A standing input for your supply chain security reviews.
• Library selection — comparing candidates for the same job? Line them up and let download tier, maintenance signal and suggestedAlternatives make the call obvious.

Keywords: crates.io analysis, Rust dependency risk, supply chain, unmaintained crates.

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Input

Field	Type	Notes
crates	array	Required. crates.io crate names, one per line (e.g. serde, tokio). Version pins and crates.io/crates/... URLs are tolerated and normalised.
maxResults	integer	Cap how many crates to analyze. Default 200.
maxConcurrency	integer	Crates processed in parallel. Default 6.
model	string	OpenAI model override. Default gpt-4o-mini.

Minimal input example

{
  "crates": ["serde", "tokio", "clap"]
}

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Output example

{
  "crate": "serde",
  "latestVersion": "1.0.229",
  "summary": "A generic serialization/deserialization framework",
  "license": "MIT OR Apache-2.0",
  "rustVersion": "1.56",
  "edition": "2015",
  "categories": ["No standard library", "Encoding"],
  "keywords": ["no_std", "serde", "serialization"],
  "totalDownloads": 1515377748,
  "recentDownloads": 354264210,
  "lastRelease": "2026-07-18T23:05:13.266456Z",
  "lastReleaseAgeDays": 86,
  "versionCount": 316,
  "dependenciesCount": 2,
  "yanked": false,
  "documentation": "https://docs.rs/serde",
  "repository": "https://github.com/serde-rs/serde",
  "homepage": "https://serde.rs",
  "maintenanceSignal": "active",
  "popularityTier": "ubiquitous",
  "riskFlags": [],
  "suggestedAlternatives": ["serde_json", "rkyv"],
  "aiSummary": "serde is a ubiquitous, permissively licensed serialization framework with over a billion all-time downloads and an actively maintained release line. It carries a light dependency tree and shows no significant supply-chain risk."
}

A crate that is not found on crates.io comes back with the same shape plus an error field (and is not charged).

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Pay-per-event pricing

This Actor uses pay-per-event billing with a single event:

Event	Price	Charged when
package-analyzed	$0.10	Once per crate successfully analyzed (facts gathered + AI risk read produced).

The Actor logs its OpenAI token usage and the estimated AI cost per crate at the end of every run, so the event price always stays safely above the model cost.

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Data source

Everything comes from public, key-less sources: the crates.io API (https://crates.io/api/v1/crates/<crate>) for metadata, versions, downloads, categories, keywords and license, and the dependencies endpoint (https://crates.io/api/v1/crates/<crate>/<version>/dependencies) for the latest version's dependency list. A descriptive User-Agent header is sent on every request as crates.io requires. A missing dependency list is handled gracefully — the record still ships.

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Setup

The AI layer calls the OpenAI API. Set the environment variable / secret OPENAI_API_KEY on the Actor. The fact-gathering step (downloads, dependencies, license, maintenance) needs no key.

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Use it from an AI agent (Apify MCP server)

This Actor is available through the Apify MCP server, so AI agents (Claude, ChatGPT, or anything that speaks MCP) can call it as a tool — hand it a list of crates and read back the intelligence records — with no custom integration code.

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Run it locally

npm install
# put your input in storage/key_value_stores/default/INPUT.json
export OPENAI_API_KEY=sk-...
apify run -p

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Related actors

• PyPI Package Intelligence — The Python twin of this Actor: downloads, dependencies, maintenance and AI risk for any PyPI package.
• npm Package Intelligence — The Node.js twin: downloads, dependencies, maintenance and AI risk for any npm package.
• GitHub Repo Analyzer — Health, activity and maintenance signals for any GitHub repository.
• CVE Vulnerability Lookup — Look up known vulnerabilities and severity for packages and software.

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Part of the No Niner toolkit — no-code web scraping, enrichment & AI data tools, all pay-per-use. Browse the full set at noniner.io.