Building a Python Tool to Fetch X (Twitter) Threads: Overcoming API Hurdles with xAI's Grok
From Tweepy Tantrums to Grok Giggles: Dodging Rate Limits Like a Pro (or at Least Trying)
Hey there, fellow coders and API enthusiasts! If you’ve ever tried to build a tool for scraping or fetching data from social media platforms like X (formerly Twitter), you know it’s not always smooth sailing. Rate limits, authentication woes, and ever-changing APIs can turn a simple project into a debugging nightmare. In this post, I’ll walk you through my journey of creating a Python script to pull the latest X threads based on a given topic. We started with the classic Tweepy library and the Twitter API, hit some roadblocks, and pivoted to using xAI’s Grok API for a more robust solution. By the end, you’ll have a working script and some tips to avoid common pitfalls.
The Goal: Fetching Threads on Demand
The idea was straightforward: Build a command-line Python tool that takes a topic (like “AI advancements”) and returns the latest threads from X. A “thread” here means a series of connected posts by the same user, starting from a root tweet and including all self-replies. We wanted to limit it to a certain number of threads (default: 5), filter by language (default: English), and display them neatly in the console.
Why threads? They’re gold for in-depth discussions—think tech breakdowns, news analyses, or opinion pieces. Manually scrolling through X is tedious, so automating it saves time.
Tweepy and the Twitter API – A Rate-Limited Reality
I kicked things off with Tweepy, a popular Python wrapper for the Twitter API v2. It’s straightforward for authentication and querying. Here’s the initial script I whipped up:
import tweepy
import argparse
from collections import OrderedDict
def get_threads(client, topic, num_threads=5, lang=’en’):
query = f”{topic} -is:reply -is:retweet lang:{lang}”
response = client.search_recent_tweets(
query=query,
tweet_fields=[’conversation_id’, ‘author_id’, ‘created_at’],
sort_order=’recency’,
max_results=100
)
if not response.data:
return []
conv_ids = list(OrderedDict.fromkeys([tweet.conversation_id for tweet in response.data]))
threads = []
for conv_id in conv_ids:
if len(threads) >= num_threads:
break
root_response = client.get_tweet(conv_id, tweet_fields=[’author_id’])
if not root_response.data:
continue
author_id = root_response.data.author_id
thread_query = f”conversation_id:{conv_id} from:{author_id}”
thread_response = client.search_recent_tweets(
query=thread_query,
tweet_fields=[’created_at’, ‘text’],
sort_order=’recency’,
max_results=100
)
if thread_response.data and len(thread_response.data) > 1:
sorted_tweets = sorted(thread_response.data, key=lambda t: t.created_at)
thread_texts = [tweet.text for tweet in sorted_tweets]
threads.append(thread_texts)
return threads
if __name__ == “__main__”:
parser = argparse.ArgumentParser(description=”Pull latest X threads based on a topic”)
parser.add_argument(”topic”, type=str, help=”The topic to search for”)
parser.add_argument(”--num_threads”, type=int, default=5, help=”Number of threads to return (default: 5)”)
parser.add_argument(”--lang”, type=str, default=”en”, help=”Language filter (default: en)”)
parser.add_argument(”--bearer_token”, type=str, required=True, help=”Your Twitter API Bearer Token”)
args = parser.parse_args()
client = tweepy.Client(bearer_token=args.bearer_token)
threads = get_threads(client, args.topic, args.num_threads, args.lang)
if not threads:
print(”No threads found for the topic.”)
else:
for i, thread in enumerate(threads, 1):
print(f”\nThread {i} (Length: {len(thread)} tweets):”)
print(”-” * 80)
for j, text in enumerate(thread, 1):
print(f”Tweet {j}: {text}”)
print(”-” * 40)This uses search_recent_tweets to find root tweets matching the topic, then fetches the full thread by querying the conversation ID. Seemed solid—until I ran it.
The Error Saga Begins
First hurdle: ModuleNotFoundError: No module named ‘tweepy’. Easy fix: pip install tweepy.
Next: The dreaded 429 Too Many Requests. Turns out, on the free Twitter API tier, you’re limited to like 1 request per 15 minutes for key endpoints. My script makes multiple calls (one for search, one per thread), so it bombed immediately. Even adding retries with time.sleep(900) felt hacky and impractical for real use.
Upgrading to Twitter’s Basic tier ($100/month) was an option, but who wants to pay that for a side project? Time for a rethink.
Pivoting to xAI’s Grok: A Smarter, Agentic Approach
Enter xAI’s Grok API. As an AI built by xAI, Grok has built-in tools for searching X (thanks to its integration), and it’s designed for agentic workflows—meaning it can handle multi-step reasoning like searching for threads without me micromanaging API calls. Plus, no insane rate limits to worry about (though it has its own usage quotas based on your subscription).
I refactored the script to use the xai-sdk library, leveraging Grok’s chat interface with a system prompt to guide it on fetching threads. Here’s the evolved version:
import os
import argparse
from xai_sdk import Client
from xai_sdk.chat import user, system
from xai_sdk.tools import x_search
from dotenv import load_dotenv
def get_threads(client, topic, num_threads=5, lang=’en’):
chat = client.chat.create(
model=”grok-4-fast”,
tools=[x_search()],
)
chat.append(system(
“You are a helpful assistant that fetches the latest X (Twitter) threads on a given topic. “
“Use the x_search tool to find recent posts that start threads (non-replies). “
“Look for conversations with multiple self-replies from the same author. “
“Fetch full thread content using available tools if needed. “
“Return up to {num_threads} threads, each as a list of tweet texts in order. “
“Format the output clearly, e.g., Thread 1: Tweet 1: ..., Tweet 2: ..., etc. “
“Filter for language: {lang}.”.format(num_threads=num_threads, lang=lang)
))
chat.append(user(”Pull the latest threads on the topic: {topic}”.format(topic=topic)))
response = chat.sample()
return response.content
if __name__ == “__main__”:
parser = argparse.ArgumentParser(description=”Pull latest X threads using Grok API”)
parser.add_argument(”topic”, type=str, help=”The topic to search for”)
parser.add_argument(”--num_threads”, type=int, default=5, help=”Number of threads to return (default: 5)”)
parser.add_argument(”--lang”, type=str, default=”en”, help=”Language filter (default: en)”)
parser.add_argument(”--api_key”, type=str, required=False, help=”Your xAI API key (overrides .env)”)
args = parser.parse_args()
load_dotenv()
api_key = args.api_key or os.getenv(”XAI_API_KEY”)
if not api_key:
raise ValueError(”xAI API key is required. Set XAI_API_KEY in .env file or use --api_key.”)
client = Client(api_key=api_key)
threads_content = get_threads(client, args.topic, args.num_threads, args.lang)
print(threads_content)You can also grab this Python script from: https://github.com/rod-trent/JunkDrawer/blob/main/GetXThreads/GetXThreads.py
Key Improvements
Grok Handles the Heavy Lifting: Instead of direct API calls, we prompt Grok to use its x_search tool internally. It searches for relevant posts, identifies threads, and formats the output.
API Key Management: Added python-dotenv to load keys from a .env file in the working directory. No more hardcoding sensitive info! Just add XAI_API_KEY=your_key_here to .env.
Error Handling: If no key is found, it raises a clear error. Install with pip install xai-sdk python-dotenv.
To get started, grab an xAI API key from https://console.x.ai/ (sign up is quick, and there are free tiers with limits). For pricing and docs, check https://x.ai/api.
Tackling More Errors Along the Way
During development, I hit another ModuleNotFoundError for xai_sdk. Fix: pip install xai-sdk python-dotenv. If you’re on Windows like me (from the tracebacks), ensure your Python path is set up correctly.
Also, remember: xAI’s API isn’t free forever—monitor your usage. But for occasional thread fetching, it’s way more forgiving than Twitter’s free tier.
Wrapping Up: Why This Matters
This project highlights a key lesson in API development: Flexibility is king. Starting with Tweepy taught me about direct API interactions, but switching to Grok showed how AI agents can abstract away complexity. Now, fetching threads is as simple as:
python GetXThreads.py “climate change” --num_threads 3If you’re building similar tools, consider hybrid approaches—combine traditional APIs with AI for resilience. Got questions or improvements? Drop a comment below. Happy coding!