Grok Cheat Sheet — xAI's Real-Time Reasoning Model

import os
import json
import time
from datetime import datetime, timedelta
from openai import OpenAI
from tenacity import retry, stop_after_attempt, wait_exponential

# Initialize
client = OpenAI(
    api_key=os.environ["XAI_API_KEY"],
    base_url="https://api.x.ai/v1"
)

# Simple in-memory cache
query_cache = {}
CACHE_TTL = 3600  # 1 hour

@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=2, min=4))
def query_grok_with_cache(prompt: str, use_web: bool = True) -> str:
    """Query Grok with caching and retry logic."""
    
    cache_key = hash((prompt, use_web))
    
    # Check cache
    if cache_key in query_cache:
        cached_time, cached_response = query_cache[cache_key]
        if datetime.now() - cached_time < timedelta(seconds=CACHE_TTL):
            print(f"[CACHE HIT] {prompt[:50]}...")
            return cached_response
    
    print(f"[QUERY] {prompt[:50]}... (timeout: 90s)")
    
    try:
        response = client.chat.completions.create(
            model="grok-3",
            messages=[{"role": "user", "content": prompt}],
            temperature=1.0,  # REQUIRED for reasoning
            max_tokens=6000,
            timeout=90
        )
        
        content = response.choices[0].message.content
        
        # Cache result
        query_cache[cache_key] = (datetime.now(), content)
        
        return content
    
    except Exception as e:
        if "rate_limit" in str(e).lower():
            print("[RATE_LIMITED] Backing off...")
            raise
        raise

# Example: Multi-turn market analysis
if __name__ == "__main__":
    print("=== Grok Real-Time Market Analysis ===")
    
    # Turn 1: Get current data
    analysis = query_grok_with_cache(
        "What's the current state of AI chip stocks (NVIDIA, Intel, AMD)? "
        "Include today's price movement and market sentiment."
    )
    print(f"\n[ANALYSIS]\n{analysis}\n")
    
    # Turn 2: Follow-up with latest trends
    trends = query_grok_with_cache(
        "Based on recent AI chip developments, which sector is most likely to grow 50% in 2026?"
    )
    print(f"[TRENDS]\n{trends}\n")
    
    # Turn 3: Cached (returns instantly)
    cached_again = query_grok_with_cache(
        "What's the current state of AI chip stocks (NVIDIA, Intel, AMD)? "
        "Include today's price movement and market sentiment."
    )
    print(f"[CACHED RESULT]\n{cached_again}")