linkedin-api

linkedin-api

非官方Python库实现LinkedIn数据编程访问

这是一个非官方Python库,允许开发者使用普通LinkedIn账户编程访问个人资料、发送消息和搜索职位。无需官方API权限,仅需有效账户即可使用。该库提供全面的LinkedIn数据获取功能,但可能违反服务条款,主要用于教育和个人用途。它为获取LinkedIn结构化数据提供了简单接口,使用时需谨慎。

LinkedIn APIPythonVoyager数据抓取认证Github开源项目

Linkedin API for Python

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Programmatically search profiles, send messages, and find jobs. All with a regular Linkedin user account.

No "official" API access required - just use a valid Linkedin account!

Caution: This library is not officially supported by LinkedIn. Using it might violate LinkedIn's Terms of Service. Use it at your own risk.

Installation

Python >= 3.6 required

To install the linkedin_api package, use the following command:

pip install linkedin-api

Or, for bleading edge:

pip install git+https://github.com/tomquirk/linkedin-api.git

Quick Start

See all methods on the documentation website.

The following snippet demonstrates a few basic linkedin_api use cases:

from linkedin_api import Linkedin # Authenticate using any Linkedin account credentials api = Linkedin('reedhoffman@linkedin.com', '*******') # GET a profile profile = api.get_profile('billy-g') # GET a profiles contact info contact_info = api.get_profile_contact_info('billy-g') # GET 1st degree connections of a given profile connections = api.get_profile_connections('1234asc12304')

Commercial alternatives

This is a sponsored section

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End sponsored section

Documentation

For comprehensive documentation, including available methods and parameters, visit the documentation.

Learn more about how it works.

Disclaimer

This library is not endorsed or supported by LinkedIn. It is an unofficial library intended for educational purposes and personal use only. By using this library, you agree to not hold the author or contributors responsible for any consequences resulting from its usage.

Contributing

We welcome contributions! Learn how to find endpoints.

Development

Dependencies

  • Python 3.7
  • A valid Linkedin user account (don't use your personal account, if possible)
  • pipenv (optional)

Development installation

  1. Create a .env config file. An example is provided in .env.example - you include at least all of the settings set there.

  2. Install dependencies with pipenv:

    pipenv install --dev pipenv shell

Run tests

pipenv run test

Troubleshooting

I keep getting a CHALLENGE

Linkedin will throw you a curve ball in the form of a Challenge URL. We currently don't handle this, and so you're kinda screwed. We think it could be only IP-based (i.e. logging in from different location). Your best chance at resolution is to log out and log back in on your browser.

Known reasons for Challenge include:

  • 2FA
  • Rate-limit - "It looks like you’re visiting a very high number of pages on LinkedIn.". Note - n=1 experiment where this page was hit after ~900 contiguous requests in a single session (within the hour) (these included random delays between each request), as well as a bunch of testing, so who knows the actual limit.

Please add more as you come across them.

Search problems

  • Mileage may vary when searching general keywords like "software" using the standard search method. They've recently added some smarts around search whereby they group results by people, company, jobs etc. if the query is general enough. Try to use an entity-specific search method (i.e. search_people) where possible.

How it works

This project attempts to provide a simple Python interface for the Linkedin API.

Do you mean the legit Linkedin API?

NO! To retrieve structured data, the Linkedin Website uses a service they call Voyager. Voyager endpoints give us access to pretty much everything we could want from Linkedin: profiles, companies, connections, messages, etc. - anything that you can see on linkedin.com, we can get from Voyager.

This project aims to provide complete coverage for Voyager.

How does it work?

Deep dive

Voyager endpoints look like this:

https://www.linkedin.com/voyager/api/identity/profileView/tom-quirk

Or, more clearly

___________________________________ _______________________________ | base path | resource | https://www.linkedin.com/voyager/api /identity/profileView/tom-quirk

They are authenticated with a simple cookie, which we send with every request, along with a bunch of headers.

To get a cookie, we POST a given username and password (of a valid Linkedin user account) to https://www.linkedin.com/uas/authenticate.

Find new endpoints

We're looking at the Linkedin website and we spot some data we want. What now?

The following describes the most reliable method to find relevant endpoints:

  1. view source

  2. command-f/search the page for some keyword in the data. This will exist inside of a <code> tag.

  3. Scroll down to the next adjacent element which will be another <code> tag, probably with an id that looks something like

    <code style="display: none" id="datalet-bpr-guid-3900675"> {"request":"/voyager/api/identity/profiles/tom-quirk/profileView","status":200,"body":"bpr-guid-3900675"} </code>

The value of request is the url! 🤘

You can also use the network tab in you browsers developer tools, but you will encounter mixed results.

How Clients query Voyager

linkedin.com uses the Rest-li Protocol for querying data. Rest-li is an internal query language/syntax where clients (like linkedin.com) specify what data they want. It's conceptually similar to the GraphQL.

Here's an example of making a request for an organisation's name and groups (the Linkedin groups it manages):

/voyager/api/organization/companies?decoration=(name,groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url))&q=universalName&universalName=linkedin

The "querying" happens in the decoration parameter, which looks like the following:

( name, groups*~(entityUrn,largeLogo,groupName,memberCount,websiteUrl,url) )

Here, we request an organisation name and a list of groups, where for each group we want largeLogo, groupName, and so on.

Different endpoints use different parameters (and perhaps even different syntaxes) to specify these queries. Notice that the above query had a parameter q whose value was universalName; the query was then specified with the decoration parameter.

In contrast, the /search/cluster endpoint uses q=guided, and specifies its query with the guided parameter, whose value is something like

List(v->PEOPLE)

It could be possible to document (and implement a nice interface for) this query language - as we add more endpoints to this project, I'm sure it will become more clear if such a thing would be possible (and if it's worth it).

Release a new version

  1. Bump __version__ in __init__.py
  2. pipenv run build
  3. pipenv run publish

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