SourceForge Data API: Extract Structured JSON in 2026

This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Use AlterLab's Extract API to turn any public SourceForge page into typed JSON. Define a JSON schema that lists the fields you need (repo_name, stars, forks, language, description, last_updated). POST the URL and schema to /v1/extract and receive validated JSON without writing parsers.

Why use SourceForge data?

SourceForge hosts millions of open‑source projects. Teams use its public data for:

• Training models that predict project health or language trends
• Building analytics dashboards for ecosystem monitoring
• Gathering competitive intelligence on tool adoption and release frequency

What data can you extract?

Publicly visible fields on a project page include:

• repo_name – the short identifier shown in the URL
• stars – number of users who have starred the project
• forks – count of repository forks
• language – primary programming language detected by SourceForge
• description – short summary displayed under the title
• last_updated – timestamp of the most recent commit or release

All of these are plain text in the HTML. By supplying a schema you tell AlterLab exactly which pieces to return as typed strings.

The extraction approach

Parsing raw HTML with regex or XPath is fragile. Site updates break selectors, and anti‑bot measures (CAPTCHAs, rate limits) require constant maintenance. A data API handles:

• Automatic retries with rotating proxies
• JavaScript rendering when needed
• Structured output that conforms to your schema This lets you focus on the data pipeline, not the scraping layer.

Quick start with AlterLab Extract API

First install the Python SDK (see the Getting started guide for setup). Then call the extract endpoint with your schema.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "repo_name": {
      "type": "string",
      "description": "The repo name field"
    },
    "stars": {
      "type": "string",
      "description": "The stars field"
    },
    "forks": {
      "type": "string",
      "description": "The forks field"
    },
    "language": {
      "type": "string",
      "description": "The language field"
    },
    "description": {
      "type": "string",
      "description": "The description field"
    },
    "last_updated": {
      "type": "string",
      "description": "The last updated field"
    }
  }
}

result = client.extract(
    url="https://sourceforge.net/p/example/wiki/Home/",
    schema=schema,
)
print(result.data)

The same request works with cURL:

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://sourceforge.net/p/example/wiki/Home/",
    "schema": {
      "properties": {
        "repo_name": {"type": "string"},
        "stars": {"type": "string"},
        "forks": {"type": "string"}
      }
    }
  }'

Both examples return a JSON object matching the schema, ready for downstream consumption.

Define your schema

The schema parameter drives validation. AlterLab checks that each extracted value matches the declared type and returns a 422 if conversion fails. You can add constraints such as minimum for numeric strings or pattern for identifiers. Example with numeric validation:

schema = {
  "type": "object",
  "properties": {
    "stars": {
      "type": "string",
      "pattern": "^[0-9]+$",
      "description": "Star count as digits"
    },
    "forks": {
      "type": "string",
      "pattern": "^[0-9]+$",
      "description": "Fork count as digits"
    }
  }
}

If the page contains non‑numeric text, the API returns an error instead of malformed data. This prevents silent pipeline failures.

Handle pagination and scale

SourceForge lists projects across many pages. To collect data at scale:

1. Enumerate page URLs (e.g., https://sourceforge.net/directory/?page=2)
2. Fire concurrent extract requests using asyncio or a thread pool
3. Respect the rate limit hint returned in the X-RateLimit-Remaining header
4. Store results in a queue or database as they arrive

Below is a simple async batch that processes ten pages:

import asyncio
import alterlab

async def extract_page(client, page_num):
    url = f"https://sourceforge.net/directory/?page={page_num}"
    schema = {
        "type": "object",