LinkedInWebScraper 1.1.1

A library for scraping LinkedIn job postings.

LinkedInWebScraper

LinkedInWebScraper is a production-minded Python library and scheduled job runner for collecting LinkedIn job listings, normalizing the data, persisting run history, and exporting reusable datasets.

Highlights

• Canonical package namespace under linkedin_web_scraper
• Typed programmatic config for single scrapes and TOML runtime config for CLI and scheduled runs
• Managed artifacts under artifacts/jobs, artifacts/logs, and artifacts/state
• SQLite-backed persistence through a clean application storage port
• Package CLI with scrape once, scrape daily, export, and --dry-run
• Optional OpenAI enrichment built on the current Responses API
• Runnable examples under examples/
• Auto release automation that waits for green CI and Docs runs on main

Install

pip install LinkedInWebScraper
pip install LinkedInWebScraper[openai]
pip install -e .[dev]

Quickstart

from linkedin_web_scraper import (
    JobScraperConfig,
    LinkedInJobScraper,
    RemoteType,
    configure_logging,
)

logger = configure_logging(filename="example.log")
config = JobScraperConfig(
    position="Data Analyst",
    location="San Francisco",
    remote=RemoteType.REMOTE,
)

jobs = LinkedInJobScraper(logger=logger, config=config).run()
print(jobs.head())

Examples

Run the example scripts from examples/:

python examples/example.py
python examples/example_advanced_config.py
python examples/example_openai.py

The OpenAI example requires OPENAI_API_KEY in the environment.

CLI Runtime

linkedin-webscraper scrape once --dry-run
linkedin-webscraper scrape daily
linkedin-webscraper export --run-id <run-id>

Use runtime.example.toml as the template for a real runtime.toml. The root runtime scripts remain available for the daily and once workflows:

python main.py
python process_ds_jobs.py

Docs

• Getting Started
• Configuration
• Runtime and Deployment
• Release and Automation
• Validation
• API Reference

Development

Run the local gate before risky pushes or merges:

python -m tox -e preflight

For a faster smoke-only path:

python -m tox -e smoke

The detailed validation matrix and release flow live in docs/development/validation.md and docs/development/release-and-automation.md.

License

This project is licensed under the MIT License.