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https://github.com/trevorhobenshield/twitter-api-client

Implementation of X/Twitter v1, v2, and GraphQL APIs
https://github.com/trevorhobenshield/twitter-api-client

api async automation bot client scrape search twitter twitter-api twitter-bot twitter-scraper x x-api x-bot x-scraper

Last synced: 26 days ago
JSON representation

Implementation of X/Twitter v1, v2, and GraphQL APIs

Host: GitHub
URL: https://github.com/trevorhobenshield/twitter-api-client
Owner: trevorhobenshield
License: mit
Created: 2023-03-11T18:09:08.000Z (over 1 year ago)
Default Branch: main
Last Pushed: 2024-04-05T21:39:54.000Z (7 months ago)
Last Synced: 2024-04-06T19:40:55.292Z (7 months ago)
Topics: api, async, automation, bot, client, scrape, search, twitter, twitter-api, twitter-bot, twitter-scraper, x, x-api, x-bot, x-scraper
Language: Python
Homepage: https://pypi.org/project/twitter-api-client
Size: 31 MB
Stars: 1,309
Watchers: 20
Forks: 170
Open Issues: 36
Metadata Files:
- Readme: readme.md
- License: LICENSE

Awesome Lists containing this project

README

## Implementation of X/Twitter v1, v2, and GraphQL APIs

[![PyPI Version](https://img.shields.io/pypi/v/twitter-api-client?color=4f46e5)](https://pypi.org/project/twitter-api-client)
[![Python Version](https://img.shields.io/pypi/pyversions/twitter-api-client?color=3b82f6)](https://pypi.org/project/twitter-api-client)

[![GitHub License](https://img.shields.io/github/license/trevorhobenshield/twitter-api-client?color=0891b2)](https://github.com/trevorhobenshield/twitter-api-client/blob/main/LICENSE)

## Table of Contents

* [Installation](#installation)
* [Automation](#automation)
* [Scraping](#scraping)
* [Get all user/tweet data](#get-all-usertweet-data)
* [Resume Pagination](#resume-pagination)
* [Search](#search)
* [Spaces](#spaces)
* [Live Audio Capture](#live-audio-capture)
* [Live Transcript Capture](#live-transcript-capture)
* [Search and Metadata](#search-and-metadata)
* [Automated Solvers](#automated-solvers)
* [Example API Responses](#example-api-responses)

### Installation

```bash
pip install twitter-api-client -U
```

### Automation

![](assets/account.gif)

*As of Fall 2023 login by username/password is unstable. Using cookies is now recommended.*

```python
from twitter.account import Account

## sign-in with credentials
email, username, password = ..., ..., ...
account = Account(email, username, password)

## or, resume session using cookies
# account = Account(cookies={"ct0": ..., "auth_token": ...})

## or, resume session using cookies (JSON file)
# account = Account(cookies='twitter.cookies')

account.tweet('test 123')
account.untweet(123456)
account.retweet(123456)
account.unretweet(123456)
account.reply('foo', tweet_id=123456)
account.quote('bar', tweet_id=123456)
account.schedule_tweet('schedule foo', 1681851240)
account.unschedule_tweet(123456)

account.tweet('hello world', media=[
{'media': 'test.jpg', 'alt': 'some alt text', 'tagged_users': [123]},
{'media': 'test.jpeg', 'alt': 'some alt text', 'tagged_users': [123]},
{'media': 'test.png', 'alt': 'some alt text', 'tagged_users': [123]},
{'media': 'test.jfif', 'alt': 'some alt text', 'tagged_users': [123]},
])

account.schedule_tweet('foo bar', '2023-04-18 15:42', media=[
{'media': 'test.gif', 'alt': 'some alt text'},
])

account.schedule_reply('hello world', '2023-04-19 15:42', tweet_id=123456, media=[
{'media': 'test.gif', 'alt': 'some alt text'},
])

account.dm('my message', [1234], media='test.jpg')

account.create_poll('test poll 123', ['hello', 'world', 'foo', 'bar'], 10080)

# tweets
account.like(123456)
account.unlike(123456)
account.bookmark(123456)
account.unbookmark(123456)
account.pin(123456)
account.unpin(123456)

# users
account.follow(1234)
account.unfollow(1234)
account.mute(1234)
account.unmute(1234)
account.enable_follower_notifications(1234)
account.disable_follower_notifications(1234)
account.block(1234)
account.unblock(1234)

# user profile
account.update_profile_image('test.jpg')
account.update_profile_banner('test.png')
account.update_profile_info(name='Foo Bar', description='test 123', location='Victoria, BC')

# topics
account.follow_topic(111)
account.unfollow_topic(111)

# lists
account.create_list('My List', 'description of my list', private=False)
account.update_list(222, 'My Updated List', 'some updated description', private=False)
account.update_list_banner(222, 'test.png')
account.delete_list_banner(222)
account.add_list_member(222, 1234)
account.remove_list_member(222, 1234)
account.delete_list(222)
account.pin_list(222)
account.unpin_list(222)

# refresh all pinned lists in this order
account.update_pinned_lists([222, 111, 333])

# unpin all lists
account.update_pinned_lists([])

# get timelines
timeline = account.home_timeline()
latest_timeline = account.home_latest_timeline(limit=500)

# get bookmarks
bookmarks = account.bookmarks()

# get DM inbox metadata
inbox = account.dm_inbox()

# get DMs from all conversations
dms = account.dm_history()

# get DMs from specific conversations
dms = account.dm_history(['123456-789012', '345678-901234'])

# search DMs by keyword
dms = account.dm_search('test123')

# delete entire conversation
account.dm_delete(conversation_id='123456-789012')

# delete (hide) specific DM
account.dm_delete(message_id='123456')

# get all scheduled tweets
scheduled_tweets = account.scheduled_tweets()

# delete a scheduled tweet
account.delete_scheduled_tweet(12345678)

# get all draft tweets
draft_tweets = account.draft_tweets()

# delete a draft tweet
account.delete_draft_tweet(12345678)

# delete all scheduled tweets
account.clear_scheduled_tweets()

# delete all draft tweets
account.clear_draft_tweets()

# example configuration
account.update_settings({
"address_book_live_sync_enabled": False,
"allow_ads_personalization": False,
"allow_authenticated_periscope_requests": True,
"allow_dm_groups_from": "following",
"allow_dms_from": "following",
"allow_location_history_personalization": False,
"allow_logged_out_device_personalization": False,
"allow_media_tagging": "none",
"allow_sharing_data_for_third_party_personalization": False,
"alt_text_compose_enabled": None,
"always_use_https": True,
"autoplay_disabled": False,
"country_code": "us",
"discoverable_by_email": False,
"discoverable_by_mobile_phone": False,
"display_sensitive_media": False,
"dm_quality_filter": "enabled",
"dm_receipt_setting": "all_disabled",
"geo_enabled": False,
"include_alt_text_compose": True,
"include_mention_filter": True,
"include_nsfw_admin_flag": True,
"include_nsfw_user_flag": True,
"include_ranked_timeline": True,
"language": "en",
"mention_filter": "unfiltered",
"nsfw_admin": False,
"nsfw_user": False,
"personalized_trends": True,
"protected": False,
"ranked_timeline_eligible": None,
"ranked_timeline_setting": None,
"require_password_login": False,
"requires_login_verification": False,
"sleep_time": {
"enabled": False,
"end_time": None,
"start_time": None
},
"translator_type": "none",
"universal_quality_filtering_enabled": "enabled",
"use_cookie_personalization": False,
})

# example configuration
account.update_search_settings({
"optInFiltering": True, # filter nsfw content
"optInBlocking": True, # filter blocked accounts
})

notifications = account.notifications()

account.change_password('old pwd','new pwd')

```

### Scraping

#### Get all user/tweet data

Two special batch queries `scraper.tweets_by_ids` and `scraper.users_by_ids` should be preferred when applicable. These endpoints are more much more efficient and have higher rate limits than their unbatched counterparts. See the table below for a comparison.

| Endpoint | Batch Size | Rate Limit |
|---------------|----------------|---------------|
| tweets_by_ids | ~220 | 500 / 15 mins |
| tweets_by_id | 1 | 50 / 15 mins |
| users_by_ids | ~220 | 100 / 15 mins |
| users_by_id | 1 | 500 / 15 mins |

![](assets/scrape.gif)

*As of Fall 2023 login by username/password is unstable. Using cookies is now recommended.*

```python
from twitter.scraper import Scraper

## sign-in with credentials
email, username, password = ..., ..., ...
scraper = Scraper(email, username, password)

## or, resume session using cookies
# scraper = Scraper(cookies={"ct0": ..., "auth_token": ...})

## or, resume session using cookies (JSON file)
# scraper = Scraper(cookies='twitter.cookies')

## or, initialize guest session (limited endpoints)
# from twitter.util import init_session
# scraper = Scraper(session=init_session())

# user data
users = scraper.users(['foo', 'bar', 'hello', 'world'])
users = scraper.users_by_ids([123, 234, 345]) # preferred
users = scraper.users_by_id([123, 234, 345])
tweets = scraper.tweets([123, 234, 345])
likes = scraper.likes([123, 234, 345])
tweets_and_replies = scraper.tweets_and_replies([123, 234, 345])
media = scraper.media([123, 234, 345])
following = scraper.following([123, 234, 345])
followers = scraper.followers([123, 234, 345])
scraper.tweet_stats([111111, 222222, 333333])

# get recommended users based on user
scraper.recommended_users()
scraper.recommended_users([123])

# tweet data
tweets = scraper.tweets_by_ids([987, 876, 754]) # preferred
tweets = scraper.tweets_by_id([987, 876, 754])
tweet_details = scraper.tweets_details([987, 876, 754])
retweeters = scraper.retweeters([987, 876, 754])
favoriters = scraper.favoriters([987, 876, 754])

scraper.download_media([
111111,
222222,
333333,
444444,
])

# trends
scraper.trends()
```

#### Resume Pagination

**Pagination is already done by default**, however there are circumstances where you may need to resume pagination from
a specific cursor. For example, the `Followers` endpoint only allows for 50 requests every 15 minutes. In this case, we
can resume from where we left off by providing a specific cursor value.

```python
from twitter.scraper import Scraper

email, username, password = ..., ..., ...
scraper = Scraper(email, username, password)

user_id = 44196397
cursor = '1767341853908517597|1663601806447476672' # example cursor
limit = 100 # arbitrary limit for demonstration
follower_subset, last_cursor = scraper.followers([user_id], limit=limit, cursor=cursor)

# use last_cursor to resume pagination
```

#### Search

![](assets/search.gif)

```python
from twitter.search import Search

email, username, password = ..., ..., ...
# default output directory is `data/search_results` if save=True
search = Search(email, username, password, save=True, debug=1)

res = search.run(
limit=37,
retries=5,
queries=[
{
'category': 'Top',
'query': 'paperswithcode -tensorflow -tf'
},
{
'category': 'Latest',
'query': 'test'
},
{
'category': 'People',
'query': 'brasil portugal -argentina'
},
{
'category': 'Photos',
'query': 'greece'
},
{
'category': 'Videos',
'query': 'italy'
},
],
)
```

**Search Operators Reference**

https://developer.twitter.com/en/docs/twitter-api/v1/rules-and-filtering/search-operators

https://developer.twitter.com/en/docs/twitter-api/tweets/search/integrate/build-a-query

### Spaces

#### Live Audio Capture

Capture live audio for up to 500 streams per IP

![](assets/spaces-audio.gif)

```python
from twitter.scraper import Scraper
from twitter.util import init_session

session = init_session() # initialize guest session, no login required
scraper = Scraper(session=session)

rooms = [...]
scraper.spaces_live(rooms=rooms) # capture live audio from list of rooms
```

#### Live Transcript Capture

**Raw transcript chunks**

![](assets/spaces-transcript-02.gif)

```python
from twitter.scraper import Scraper
from twitter.util import init_session

session = init_session() # initialize guest session, no login required
scraper = Scraper(session=session)

# room must be live, i.e. in "Running" state
scraper.space_live_transcript('1zqKVPlQNApJB',
frequency=2) # word-level live transcript. (dirty, on-the-fly transcription before post-processing)
```

**Processed (final) transcript chunks**

![](assets/spaces-transcript-01.gif)

```python
from twitter.scraper import Scraper
from twitter.util import init_session

session = init_session() # initialize guest session, no login required
scraper = Scraper(session=session)

# room must be live, i.e. in "Running" state
scraper.space_live_transcript('1zqKVPlQNApJB', frequency=1) # finalized live transcript. (clean)
```

#### Search and Metadata

```python
from twitter.scraper import Scraper
from twitter.util import init_session
from twitter.constants import SpaceCategory

session = init_session() # initialize guest session, no login required
scraper = Scraper(session=session)

# download audio and chat-log from space
spaces = scraper.spaces(rooms=['1eaJbrAPnBVJX', '1eaJbrAlZjjJX'], audio=True, chat=True)

# pull metadata only
spaces = scraper.spaces(rooms=['1eaJbrAPnBVJX', '1eaJbrAlZjjJX'])

# search for spaces in "Upcoming", "Top" and "Live" categories
spaces = scraper.spaces(search=[
{
'filter': SpaceCategory.Upcoming,
'query': 'hello'
},
{
'filter': SpaceCategory.Top,
'query': 'world'
},
{
'filter': SpaceCategory.Live,
'query': 'foo bar'
}
])
```

### Automated Solvers

> This requires installation of the [proton-api-client](https://pypi.org/project/proton-api-client) package

To set up automated email confirmation/verification solvers, add your Proton Mail credentials below as shown.
This removes the need to manually solve email challenges via the web app. These credentials can be used
in `Scraper`, `Account`, and `Search` constructors.

E.g.

```python
from twitter.account import Account
from twitter.util import get_code
from proton.client import ProtonMail

proton_username, proton_password = ..., ...
proton = lambda: get_code(ProtonMail(proton_username, proton_password))

email, username, password = ..., ..., ...
account = Account(email, username, password, proton=proton)
```

### Example API Responses

UserTweetsAndReplies

```json
{
"entryId": "homeConversation-1648726807301218305-1648801924760711169-1648811419998228480",
"sortIndex": "1648811419998228480",
"content": {
"entryType": "TimelineTimelineModule",
"__typename": "TimelineTimelineModule",
"items": [
{
"entryId": "homeConversation-1648811419998228480-0-tweet-1648726807301218305",
"dispensable": true,
"item": {
"itemContent": {
"itemType": "TimelineTweet",
"__typename": "TimelineTweet",
"tweet_results": {
"result": {
"__typename": "Tweet",
"rest_id": "1648726807301218305",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjozMzgzNjYyOQ==",
"rest_id": "33836629",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": true,
"profile_image_shape": "Circle",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Tue Apr 21 06:49:15 +0000 2009",
"default_profile": false,
"default_profile_image": false,
"description": "Building a kind of JARVIS @ OреոΑӏ. Previously Director of AI @ Tesla, CS231n, PhD @ Stanford. I like to train large deep neural nets 🧠🤖💥",
"entities": {
"description": {
"urls": []
},
"url": {
"urls": [
{
"display_url": "karpathy.ai",
"expanded_url": "https://karpathy.ai",
"url": "https://t.co/0EcFthjJXM",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 7312,
"followers_count": 701921,
"friends_count": 809,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 9207,
"location": "Stanford",
"media_count": 633,
"name": "Andrej Karpathy",
"normal_followers_count": 701921,
"pinned_tweet_ids_str": [
"1599152286672248832"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/33836629/1407117611",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/1296667294148382721/9Pr6XrPB_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "karpathy",
"statuses_count": 8067,
"translator_type": "none",
"url": "https://t.co/0EcFthjJXM",
"verified": true,
"want_retweets": false,
"withheld_in_countries": []
},
"smart_blocked_by": false,
"smart_blocking": false
}
}
},
"unmention_data": {},
"edit_control": {
"edit_tweet_ids": [
"1648726807301218305"
],
"editable_until_msecs": "1681923877000",
"is_edit_eligible": true,
"edits_remaining": "5"
},
"edit_perspective": {
"favorited": false,
"retweeted": false
},
"is_translatable": false,
"views": {
"count": "409371",
"state": "EnabledWithCount"
},
"source": "Twitter Web App",
"quoted_status_result": {
"result": {
"__typename": "Tweet",
"rest_id": "1647434714947395585",
"has_birdwatch_notes": false,
"core": {
"user_results": {
"result": {
"__typename": "User",
"id": "VXNlcjozMTA4MzUx",
"rest_id": "3108351",
"affiliates_highlighted_label": {},
"has_graduated_access": true,
"is_blue_verified": false,
"profile_image_shape": "Square",
"legacy": {
"can_dm": false,
"can_media_tag": true,
"created_at": "Sun Apr 01 06:22:13 +0000 2007",
"default_profile": false,
"default_profile_image": false,
"description": "Sign up for our newsletters and alerts: https://t.co/QevH0DLisA | Got a tip? https://t.co/iXIigdOLPr | For WSJ customer support: https://t.co/DZgH9n4vAI",
"entities": {
"description": {
"urls": [
{
"display_url": "wsj.com/newsletters",
"expanded_url": "http://wsj.com/newsletters",
"url": "https://t.co/QevH0DLisA",
"indices": [
40,
63
]
},
{
"display_url": "wsj.com/tips",
"expanded_url": "http://wsj.com/tips",
"url": "https://t.co/iXIigdOLPr",
"indices": [
77,
100
]
},
{
"display_url": "customercenter.wsj.com",
"expanded_url": "http://customercenter.wsj.com",
"url": "https://t.co/DZgH9n4vAI",
"indices": [
129,
152
]
}
]
},
"url": {
"urls": [
{
"display_url": "wsj.com",
"expanded_url": "http://wsj.com",
"url": "https://t.co/9rMrYLEXTt",
"indices": [
0,
23
]
}
]
}
},
"fast_followers_count": 0,
"favourites_count": 1137,
"followers_count": 20521959,
"friends_count": 1087,
"has_custom_timelines": true,
"is_translator": false,
"listed_count": 128849,
"location": "New York, NY",
"media_count": 45523,
"name": "The Wall Street Journal",
"normal_followers_count": 20521959,
"pinned_tweet_ids_str": [
"1648690341581651971"
],
"possibly_sensitive": false,
"profile_banner_url": "https://pbs.twimg.com/profile_banners/3108351/1680557947",
"profile_image_url_https": "https://pbs.twimg.com/profile_images/971415515754266624/zCX0q9d5_normal.jpg",
"profile_interstitial_type": "",
"screen_name": "WSJ",
"statuses_count": 404295,
"translator_type": "regular",
"url": "https://t.co/9rMrYLEXTt",
"verified": true,
"verified_type": "Business",
"want_retweets": false,
"withheld_in_countries": []
},
"smart_blocked_by": false,
"smart_blocking": false
}
}
},
"card": {
"rest_id": "https://t.co/eDupI8Jpey",
"legacy": {
"binding_values": [
{
"key": "photo_image_full_size_large",
"value": {
"image_value": {
"height": 419,
"width": 800,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=800x419"
},
"type": "IMAGE"
}
},
{
"key": "thumbnail_image",
"value": {
"image_value": {
"height": 150,
"width": 267,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=280x150"
},
"type": "IMAGE"
}
},
{
"key": "description",
"value": {
"string_value": "iPhone thieves across the country are locking people out of their Apple accounts and draining their bank accounts—sometimes before victims even know what happen",
"type": "STRING"
}
},
{
"key": "domain",
"value": {
"string_value": "www.wsj.com",
"type": "STRING"
}
},
{
"key": "thumbnail_image_large",
"value": {
"image_value": {
"height": 320,
"width": 569,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=800x320_1"
},
"type": "IMAGE"
}
},
{
"key": "summary_photo_image_small",
"value": {
"image_value": {
"height": 202,
"width": 386,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=386x202"
},
"type": "IMAGE"
}
},
{
"key": "thumbnail_image_original",
"value": {
"image_value": {
"height": 720,
"width": 1280,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=orig"
},
"type": "IMAGE"
}
},
{
"key": "site",
"value": {
"scribe_key": "publisher_id",
"type": "USER",
"user_value": {
"id_str": "3108351",
"path": []
}
}
},
{
"key": "photo_image_full_size_small",
"value": {
"image_value": {
"height": 202,
"width": 386,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=386x202"
},
"type": "IMAGE"
}
},
{
"key": "summary_photo_image_large",
"value": {
"image_value": {
"height": 419,
"width": 800,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=800x419"
},
"type": "IMAGE"
}
},
{
"key": "thumbnail_image_small",
"value": {
"image_value": {
"height": 81,
"width": 144,
"url": "https://pbs.twimg.com/card_img/1644698702035185664/Q7MqVdeE?format=jpg&name=144x144"
},
"type": "IMAGE"
}
},
{
"key": "thumbnail_image_x_large",
"value": {
"image_value": {
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"url": "https://pbs.twimg.com/profile_images/1646934608196558849/ML64e9i3_bigger.png"
},
"description": "Bored Box",
"userLabelType": "BusinessLabel",
"userLabelDisplayType": "Badge"
}
},
"has_nft_avatar": false,
"is_blue_verified": true,
"legacy": {}
}
}
},
{
"periscope_user_id": "1xnQrqqoLdGEY",
"start": 1685493161000,
"twitter_screen_name": "greg16676935420",
"display_name": "greg",
"avatar_url": "https://pbs.twimg.com/profile_images/1581014308397502464/NPogKMyk_normal.jpg",
"is_verified": true,
"is_muted_by_admin": false,
"is_muted_by_guest": false,
"user_results": {
"rest_id": "1356434353623093249",
"result": {
"__typename": "User",
"identity_profile_labels_highlighted_label": {},
"has_nft_avatar": false,
"is_blue_verified": true,
"legacy": {}
}
}
},
{
"periscope_user_id": "1oNQlLXrndrQw",
"twitter_screen_name": "zachxbt",
"display_name": "ZachXBT",
"avatar_url": "https://pbs.twimg.com/profile_images/1656044798090854429/2v6CCeiE_normal.jpg",
"is_verified": true,
"is_muted_by_admin": false,
"is_muted_by_guest": false,
"user_results": {
"rest_id": "3012852462",
"result": {
"__typename": "User",
"identity_profile_labels_highlighted_label": {},
"has_nft_avatar": false,
"is_blue_verified": true,
"legacy": {}
}
}
}
]
}
}
}
}
```

AudioSpaceSearch

```json
{
"data": {
"search_by_raw_query": {
"audio_spaces_grouped_by_section": {
"sections": [
{
"name": "Live",
"items": [
{
"kind": "Audiospace",
"followed_participants_results": [],
"space": {
"rest_id": "1MYGNgPoldnJw"
}
},
{
"kind": "Audiospace",
"followed_participants_results": [],
"space": {
"rest_id": "1YqGoAdvOjbxv"
}
},
{
"kind": "Audiospace",
"followed_participants_results": [],
"space": {
"rest_id": "1OwGWwdNlmpGQ"
}
}
],
"destination": "Live"
}
]
}
}
}
}
```