> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/speedyapply/JobSpy/llms.txt
> Use this file to discover all available pages before exploring further.

# Scraping jobs

> How to use scrape_jobs() to collect job postings from multiple boards concurrently.

`scrape_jobs()` is the single entry point for all job scraping in JobSpy. It accepts parameters for every supported job board and returns a unified Pandas DataFrame.

## How concurrent scraping works

Internally, `scrape_jobs()` uses a `ThreadPoolExecutor` to scrape all requested sites at the same time. Each site runs in its own thread, so scraping five boards takes roughly as long as scraping the slowest one — not the sum of all five.

```python theme={null}
from concurrent.futures import ThreadPoolExecutor, as_completed
# JobSpy manages this for you — no setup required on your end
```

## Basic usage

```python theme={null}
from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin", "zip_recruiter"],
    search_term="software engineer",
    location="San Francisco, CA",
    results_wanted=20,
)
print(f"Found {len(jobs)} jobs")
print(jobs.head())
```

## Choosing which sites to scrape

The `site_name` parameter accepts a string, a list of strings, or a `Site` enum (or list of `Site` enums).

<CodeGroup>
  ```python All sites (default) theme={null}
  from jobspy import scrape_jobs

  # Omitting site_name scrapes all supported boards
  jobs = scrape_jobs(
      search_term="data scientist",
      location="New York, NY",
  )
  ```

  ```python Specific sites (strings) theme={null}
  from jobspy import scrape_jobs

  jobs = scrape_jobs(
      site_name=["indeed", "glassdoor"],
      search_term="data scientist",
      location="New York, NY",
  )
  ```

  ```python Single site (string) theme={null}
  from jobspy import scrape_jobs

  jobs = scrape_jobs(
      site_name="linkedin",
      search_term="data scientist",
      location="New York, NY",
  )
  ```

  ```python Site enum theme={null}
  from jobspy import scrape_jobs
  from jobspy.model import Site

  jobs = scrape_jobs(
      site_name=[Site.INDEED, Site.LINKEDIN, Site.ZIP_RECRUITER],
      search_term="data scientist",
      location="New York, NY",
  )
  ```
</CodeGroup>

The supported values for `site_name` are:

| String value      | Board        |
| ----------------- | ------------ |
| `"linkedin"`      | LinkedIn     |
| `"indeed"`        | Indeed       |
| `"glassdoor"`     | Glassdoor    |
| `"zip_recruiter"` | ZipRecruiter |
| `"google"`        | Google Jobs  |
| `"bayt"`          | Bayt         |
| `"naukri"`        | Naukri       |
| `"bdjobs"`        | BDJobs       |

## Controlling the number of results

The `results_wanted` parameter sets how many job results to retrieve **per site**. If you scrape three sites with `results_wanted=20`, you may receive up to 60 results total.

```python theme={null}
jobs = scrape_jobs(
    site_name=["indeed", "linkedin", "zip_recruiter"],
    search_term="product manager",
    location="Austin, TX",
    results_wanted=25,  # up to 25 results from each site
)
```

<Note>
  All job board endpoints are capped at around 1,000 jobs per search, regardless of `results_wanted`.
</Note>

## Filtering by recency

Use `hours_old` to limit results to jobs posted within the last N hours.

```python theme={null}
jobs = scrape_jobs(
    site_name=["indeed", "linkedin"],
    search_term="devops engineer",
    location="Seattle, WA",
    results_wanted=30,
    hours_old=24,  # only jobs posted in the last 24 hours
)
```

<Note>
  ZipRecruiter and Glassdoor round `hours_old` up to the next full day.
</Note>

## Controlling log output

The `verbose` parameter controls how much JobSpy prints during scraping.

| Value | Behavior              |
| ----- | --------------------- |
| `0`   | Errors only (default) |
| `1`   | Errors and warnings   |
| `2`   | All logs              |

```python theme={null}
jobs = scrape_jobs(
    site_name="indeed",
    search_term="backend engineer",
    location="Chicago, IL",
    verbose=0,  # silent except for errors
)
```

## Full example

```python theme={null}
import csv
from jobspy import scrape_jobs

jobs = scrape_jobs(
    site_name=["indeed", "linkedin", "zip_recruiter", "google"],
    search_term="software engineer",
    google_search_term="software engineer jobs near San Francisco, CA since yesterday",
    location="San Francisco, CA",
    results_wanted=20,
    hours_old=72,
    country_indeed="USA",
    verbose=1,
)

print(f"Found {len(jobs)} jobs")
jobs.to_csv("jobs.csv", quoting=csv.QUOTE_NONNUMERIC, escapechar="\\", index=False)
```

<Tip>
  The `google_search_term` parameter is the only way to filter Google Jobs results. Copy the query string from the Google Jobs search box after applying filters in your browser.
</Tip>