Back to Details

xai-x-search

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

xAI X (Twitter) Search

xAI X（Twitter）搜索

Real-time Twitter/X search using Grok's native X integration - a capability unique to xAI.
借助Grok原生的X集成实现Twitter/X实时搜索——这是xAI独有的功能。

Quick Start

快速开始

python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("XAI_API_KEY"),
    base_url="https://api.x.ai/v1"
)
python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("XAI_API_KEY"),
    base_url="https://api.x.ai/v1"
)

Simple X search

Simple X search

response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "Search X for what people are saying about Tesla stock today" }] ) print(response.choices[0].message.content)
undefined
response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "Search X for what people are saying about Tesla stock today" }] ) print(response.choices[0].message.content)
undefined

Search Capabilities

搜索功能

1. Topic Search

1. 主题搜索

python
def search_x_topic(topic: str) -> str:
    """Search X for posts about a topic."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"Search X for recent posts about {topic}. Summarize the main discussions and sentiment."
        }]
    )
    return response.choices[0].message.content
python
def search_x_topic(topic: str) -> str:
    """Search X for posts about a topic."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"Search X for recent posts about {topic}. Summarize the main discussions and sentiment."
        }]
    )
    return response.choices[0].message.content

2. Ticker/Stock Search

2. 股票代码搜索

python
def search_stock_mentions(ticker: str) -> str:
    """Search X for stock ticker mentions."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Search X for mentions of ${ticker} stock.
            Find:
            - Recent discussions
            - Sentiment (bullish/bearish)
            - Key influencer opinions
            - Breaking news mentions
            Return structured analysis."""
        }]
    )
    return response.choices[0].message.content
python
def search_stock_mentions(ticker: str) -> str:
    """Search X for stock ticker mentions."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Search X for mentions of ${ticker} stock.
            Find:
            - Recent discussions
            - Sentiment (bullish/bearish)
            - Key influencer opinions
            - Breaking news mentions
            Return structured analysis."""
        }]
    )
    return response.choices[0].message.content

3. Account Monitoring

3. 账户监控

python
def monitor_account(handle: str) -> str:
    """Get recent posts from a specific X account."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"Search X for the most recent posts from @{handle}. Summarize their latest activity."
        }]
    )
    return response.choices[0].message.content
python
def monitor_account(handle: str) -> str:
    """Get recent posts from a specific X account."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"Search X for the most recent posts from @{handle}. Summarize their latest activity."
        }]
    )
    return response.choices[0].message.content

4. Trending Topics

4. 热门话题

python
def get_trending() -> str:
    """Get current trending topics on X."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": "What are the current trending topics on X? List the top 10 with brief descriptions."
        }]
    )
    return response.choices[0].message.content
python
def get_trending() -> str:
    """Get current trending topics on X."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": "What are the current trending topics on X? List the top 10 with brief descriptions."
        }]
    )
    return response.choices[0].message.content

Agent Tools API (Advanced)

Agent Tools API（进阶用法）

For more control, use the Agent Tools API:
python
undefined
如需更多控制，可使用Agent Tools API：
python
undefined

Using Responses API with x_search tool

Using Responses API with x_search tool

response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "Search X for posts about Bitcoin from the last 24 hours" }], tools=[{ "type": "x_search", "x_search": { "enabled": True, "date_range": { "start": "2025-12-04", "end": "2025-12-05" } } }] )
undefined
response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "Search X for posts about Bitcoin from the last 24 hours" }], tools=[{ "type": "x_search", "x_search": { "enabled": True, "date_range": { "start": "2025-12-04", "end": "2025-12-05" } } }] )
undefined

Filter by Handles

按账户筛选

python
undefined
python
undefined

Search only specific accounts

Search only specific accounts

response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "What are these financial analysts saying about the market?" }], tools=[{ "type": "x_search", "x_search": { "enabled": True, "allowed_x_handles": [ "jimcramer", "elonmusk", "chaikinadx", "unusual_whales" ] } }] )
undefined
response = client.chat.completions.create( model="grok-4-1-fast", messages=[{ "role": "user", "content": "What are these financial analysts saying about the market?" }], tools=[{ "type": "x_search", "x_search": { "enabled": True, "allowed_x_handles": [ "jimcramer", "elonmusk", "chaikinadx", "unusual_whales" ] } }] )
undefined

Common Use Cases

常见使用场景

Financial News Monitoring

财经新闻监控

python
def monitor_financial_news() -> dict:
    """Monitor financial news on X."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": """Search X for breaking financial news in the last hour.
            Focus on:
            - Market-moving news
            - Earnings announcements
            - Fed/economic news
            - Major analyst calls

            Return as JSON:
            {
                "breaking_news": [...],
                "market_sentiment": "bullish/bearish/neutral",
                "key_events": [...]
            }"""
        }]
    )
    return response.choices[0].message.content
python
def monitor_financial_news() -> dict:
    """Monitor financial news on X."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": """Search X for breaking financial news in the last hour.
            Focus on:
            - Market-moving news
            - Earnings announcements
            - Fed/economic news
            - Major analyst calls

            Return as JSON:
            {
                "breaking_news": [...],
                "market_sentiment": "bullish/bearish/neutral",
                "key_events": [...]
            }"""
        }]
    )
    return response.choices[0].message.content

Earnings Reaction Tracking

财报反应追踪

python
def track_earnings_reaction(ticker: str) -> str:
    """Track X reaction to earnings announcement."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Search X for reaction to ${ticker} earnings.
            Analyze:
            - Overall sentiment
            - Key concerns raised
            - Positive highlights mentioned
            - Notable influencer reactions
            - Volume of discussion"""
        }]
    )
    return response.choices[0].message.content
python
def track_earnings_reaction(ticker: str) -> str:
    """Track X reaction to earnings announcement."""
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Search X for reaction to ${ticker} earnings.
            Analyze:
            - Overall sentiment
            - Key concerns raised
            - Positive highlights mentioned
            - Notable influencer reactions
            - Volume of discussion"""
        }]
    )
    return response.choices[0].message.content

Competitor Analysis

竞品分析

python
def compare_sentiment(tickers: list) -> str:
    """Compare X sentiment across multiple stocks."""
    ticker_str = ", ".join([f"${t}" for t in tickers])
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Compare X sentiment for: {ticker_str}
            For each, provide:
            - Current sentiment score (-1 to +1)
            - Key themes being discussed
            - Notable mentions
            Return as structured comparison."""
        }]
    )
    return response.choices[0].message.content
python
def compare_sentiment(tickers: list) -> str:
    """Compare X sentiment across multiple stocks."""
    ticker_str = ", ".join([f"${t}" for t in tickers])
    response = client.chat.completions.create(
        model="grok-4-1-fast",
        messages=[{
            "role": "user",
            "content": f"""Compare X sentiment for: {ticker_str}
            For each, provide:
            - Current sentiment score (-1 to +1)
            - Key themes being discussed
            - Notable mentions
            Return as structured comparison."""
        }]
    )
    return response.choices[0].message.content

Search Parameters

搜索参数

ParameterDescriptionMax
allowed_x_handles
Only search these accounts10
excluded_x_handles
Exclude these accounts10
date_range.start
Start date (ISO8601)-
date_range.end
End date (ISO8601)-
include_media
Analyze images/videos-
参数描述最大值
allowed_x_handles
仅搜索指定账户10
excluded_x_handles
排除指定账户10
date_range.start
开始日期（ISO8601格式）-
date_range.end
结束日期（ISO8601格式）-
include_media
分析图片/视频-

Rate Limits & Costs

速率限制与成本

MetricValue
Cost per search$0.005 ($5/1,000)
Max handles filter10
Date rangeAny
指标数值
每次搜索成本$0.005（即1000次搜索5美元）
最大账户筛选数量10
日期范围无限制

Best Practices

最佳实践

1. Be Specific

1. 明确搜索需求

python
undefined
python
undefined

Bad - too vague

Bad - too vague

"Search X for stocks"
"Search X for stocks"

Good - specific query

Good - specific query

"Search X for posts about $AAPL stock price movement today from verified financial accounts"
undefined
"Search X for posts about $AAPL stock price movement today from verified financial accounts"
undefined

2. Request Structured Output

2. 请求结构化输出

python
undefined
python
undefined

Request JSON for easier parsing

Request JSON for easier parsing

content = """Search X for $NVDA sentiment. Return JSON: { "sentiment": "bullish/bearish/neutral", "score": -1 to 1, "key_posts": [...], "influencer_opinions": [...] }"""
undefined
content = """Search X for $NVDA sentiment. Return JSON: { "sentiment": "bullish/bearish/neutral", "score": -1 to 1, "key_posts": [...], "influencer_opinions": [...] }"""
undefined

3. Use Handle Filters for Quality

3. 使用账户筛选提升内容质量

python
undefined
python
undefined

Filter to trusted sources

Filter to trusted sources

financial_handles = [ "DeItaone", # Breaking news "unusual_whales", # Options flow "Fxhedgers", # Market news "zaborsky" # Analysis ]
undefined
financial_handles = [ "DeItaone", # Breaking news "unusual_whales", # Options flow "Fxhedgers", # Market news "zaborsky" # Analysis ]
undefined

4. Combine with Other Data

4. 结合其他数据使用

python
undefined
python
undefined

Combine X sentiment with price data

Combine X sentiment with price data

x_sentiment = search_stock_mentions("AAPL") price_data = finnhub_client.get_quote("AAPL")
x_sentiment = search_stock_mentions("AAPL") price_data = finnhub_client.get_quote("AAPL")

Analyze together

Analyze together

undefined
undefined

Limitations

局限性

  1. Sarcasm detection - May misinterpret sarcastic posts
  2. Bot content - Cannot always filter bot posts
  3. Historical depth - Best for recent data
  4. Rate limits - $5/1,000 searches
  1. 讽刺检测 - 可能会误解带有讽刺意味的帖子
  2. 机器人内容 - 无法完全过滤机器人发布的内容
  3. 历史数据深度 - 更适合处理近期数据
  4. 速率限制 - 1000次搜索5美元

Error Handling

错误处理

python
def safe_x_search(query: str) -> dict:
    """X search with error handling."""
    try:
        response = client.chat.completions.create(
            model="grok-4-1-fast",
            messages=[{"role": "user", "content": query}],
            timeout=30
        )
        return {
            "success": True,
            "data": response.choices[0].message.content
        }
    except Exception as e:
        return {
            "success": False,
            "error": str(e)
        }
python
def safe_x_search(query: str) -> dict:
    """X search with error handling."""
    try:
        response = client.chat.completions.create(
            model="grok-4-1-fast",
            messages=[{"role": "user", "content": query}],
            timeout=30
        )
        return {
            "success": True,
            "data": response.choices[0].message.content
        }
    except Exception as e:
        return {
            "success": False,
            "error": str(e)
        }

Related Skills

相关技能

  • xai-sentiment
    - Sentiment analysis
  • xai-stock-sentiment
    - Stock-specific sentiment
  • xai-agent-tools
    - Advanced tool usage
  • xai-sentiment
    - 情感分析
  • xai-stock-sentiment
    - 股票专属情感分析
  • xai-agent-tools
    - 进阶工具使用

References

参考资料