# PyPI Python Packages Scraper - Versions, Authors & Licenses (`scrapers_lat/pypi-packages-scraper`) Actor
Scrape Python packages by name or keyword: version, summary, author and maintainer contacts, license, repository links, requires-python, release date, keywords, classifiers and dependencies. Export to JSON, CSV or Excel.
- **URL**: https://apify.com/scrapers\_lat/pypi-packages-scraper.md
- **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community)
- **Categories:** Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $6.80 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## PyPI Python Packages Scraper
> Look up Python packages by exact name or search the index by keyword, and export clean structured data: version, summary, author and maintainer contacts, license, project and repository links, requires-python, release date, keywords, classifiers and dependencies. Great for dependency research, package discovery and ecosystem analysis.
**๐ฅ [Input](https://apify.com/scrapers_lat/pypi-packages-scraper/input-schema) ยท ๐ค [Output](https://apify.com/scrapers_lat/pypi-packages-scraper/output-schema) ยท ๐ฐ [Pricing](https://apify.com/scrapers_lat/pypi-packages-scraper/pricing) ยท โถ๏ธ [Examples](https://apify.com/scrapers_lat/pypi-packages-scraper/examples)**
Version & release package metadata
Author & license contacts and links
JSON / CSV / Excel output formats
### Who is it for
| Use case | Who benefits |
|---|---|
| Dependency research | Engineers comparing packages before adopting one |
| Package discovery | Teams finding libraries for a given need |
| Ecosystem analysis | Analysts tracking authors, licenses and dependencies |
| Dashboards | Builders feeding package stats into their own apps |
### How to use it
1. Add exact **Package Names** (for example `requests`, `numpy`), or **Search Queries** (for example `machine learning`), or both.
2. Set **Max Packages To Scrape**.
3. Run it and export as JSON, CSV or Excel, or pull it through the Apify API.
You are only ever charged for real package records. An empty run costs nothing.
### Frequently Asked Questions
**Can I look up several packages at once?**
Yes. Package Names and Search Queries are both lists, so one run can cover many packages and searches.
**How does the keyword search work?**
Each query is matched against package names across the whole index, ranked best-first, and the top matches are fetched with full metadata.
**Do I get author and maintainer contacts?**
Yes, when the package publishes them: author, maintainer and their email addresses are captured as standard.
**How fresh is the data?**
Versions and metadata are read live at run time, so each record reflects the moment of the run (see observedAt).
### Related scrapers
- [NPM Packages Scraper](https://apify.com/scrapers_lat/npm-packages-scraper)
- [Crates.io Rust Packages Scraper](https://apify.com/scrapers_lat/crates-io-scraper)
- [GitHub Repositories Scraper](https://apify.com/scrapers_lat/github-repositories-scraper)
### More scrapers at scrapers.lat
This actor is built and maintained by [scrapers.lat](https://scrapers.lat), where we publish scrapers for public platforms: finance, news, real estate, jobs, e-commerce and government data. Browse the full catalog or ask us for a custom scraper at [scrapers.lat](https://scrapers.lat).
---
> This actor is an independent tool and has no affiliation with the Python Package Index or the Python Software Foundation. It only accesses publicly available package data. Use the results in accordance with the source's terms.
# Actor input Schema
## `packageNames` (type: `array`):
Exact PyPI project names to look up (for example 'requests', 'numpy', 'django'). Each returns full metadata.
## `searchQueries` (type: `array`):
Keywords to match against package names across the whole index (for example 'machine learning', 'async http'). Best matches are fetched with full metadata.
## `maxPackages` (type: `integer`):
Maximum number of packages to collect across all names and searches.
## Actor input object example
```json
{
"packageNames": [
"requests",
"numpy",
"flask"
],
"searchQueries": [
"fastapi"
],
"maxPackages": 30
}
````
# Actor output Schema
## `results` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"packageNames": [
"requests",
"numpy",
"flask"
],
"searchQueries": [
"fastapi"
],
"maxPackages": 30
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/pypi-packages-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`๐พ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"packageNames": [
"requests",
"numpy",
"flask",
],
"searchQueries": ["fastapi"],
"maxPackages": 30,
}
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/pypi-packages-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("๐พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# ๐ Want to learn more ๐? Go to โ https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"packageNames": [
"requests",
"numpy",
"flask"
],
"searchQueries": [
"fastapi"
],
"maxPackages": 30
}' |
apify call scrapers_lat/pypi-packages-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/pypi-packages-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "PyPI Python Packages Scraper - Versions, Authors & Licenses",
"description": "Scrape Python packages by name or keyword: version, summary, author and maintainer contacts, license, repository links, requires-python, release date, keywords, classifiers and dependencies. Export to JSON, CSV or Excel.",
"version": "0.1",
"x-build-id": "nBXvvhO8tHh2cefYa"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~pypi-packages-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-pypi-packages-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~pypi-packages-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-pypi-packages-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~pypi-packages-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-pypi-packages-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"packageNames": {
"title": "Package Names",
"type": "array",
"description": "Exact PyPI project names to look up (for example 'requests', 'numpy', 'django'). Each returns full metadata.",
"items": {
"type": "string"
}
},
"searchQueries": {
"title": "Search Queries",
"type": "array",
"description": "Keywords to match against package names across the whole index (for example 'machine learning', 'async http'). Best matches are fetched with full metadata.",
"items": {
"type": "string"
}
},
"maxPackages": {
"title": "Max Packages To Scrape",
"minimum": 1,
"maximum": 100000,
"type": "integer",
"description": "Maximum number of packages to collect across all names and searches."
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```