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‎agentstack/_tools/dappier/__init__.py

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import os
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from typing import Optional, Literal
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from dappier import Dappier
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# Initialize the Dappier client
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client = Dappier(api_key=os.getenv("DAPPIER_API_KEY"))
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# --- Functions for AI Models ---
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def real_time_web_search(query: str) -> str:
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"""
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Perform a real-time web search. Access the latest news, stock market data, weather,
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travel information, deals, and more using this AI model. Use when no stock ticker symbol
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is provided.
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Args:
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query: The search query to retrieve real-time information.
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Returns:
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A formatted string containing real-time search results.
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"""
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try:
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return client.search_real_time_data_string(query=query, ai_model_id="am_01j06ytn18ejftedz6dyhz2b15")
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except Exception as e:
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return f"Error: {str(e)}"
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def stock_market_data_search(query: str) -> str:
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"""
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Perform a real-time stock market data search. Retrieve real-time financial news,
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stock prices, and trade updates with AI-powered insights using this model. Use only when a
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stock ticker symbol is provided.
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Args:
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query: The search query to retrieve real-time stock market information.
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Returns:
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A formatted string containing real-time financial search results.
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"""
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try:
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return client.search_real_time_data_string(query=query, ai_model_id="am_01j749h8pbf7ns8r1bq9s2evrh")
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except Exception as e:
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return f"Error: {str(e)}"
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# --- Functions for Data Models ---
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def get_sports_news(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered Sports News recommendations. Get real-time news, updates, and personalized
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content from top sports sources like Sportsnaut, Forever Blueshirts, Minnesota Sports Fan,
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LAFB Network, Bounding Into Sports, and Ringside Intel.
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Args:
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query: The input string for sports-related content recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended sports articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01j0pb465keqmatq9k83dthx34",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"
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def get_lifestyle_news(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered Lifestyle News recommendations. Access current lifestyle updates, analysis,
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and insights from leading lifestyle publications like The Mix, Snipdaily, Nerdable
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and Familyproof.
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Args:
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query: The input string for lifestyle-related content recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended lifestyle articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01j0q82s4bfjmsqkhs3ywm3x6y",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"
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def get_iheartdogs_content(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered iHeartDogs content recommendations. Tap into a dog care expert with access
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to thousands of articles covering pet health, behavior, grooming, and ownership from
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iHeartDogs.com.
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Args:
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query: The input string for dog care-related content recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended dog-related articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01j1sz8t3qe6v9g8ad102kvmqn",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"
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def get_iheartcats_content(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered iHeartCats content recommendations. Utilize a cat care specialist that
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provides comprehensive content on cat health, behavior, and lifestyle from iHeartCats.com.
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Args:
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query: The input string for cat care-related content recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended cat-related articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01j1sza0h7ekhaecys2p3y0vmj",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"
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def get_greenmonster_guides(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered GreenMonster guides and articles. Receive guidance for making conscious
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and compassionate choices benefiting people, animals, and the planet.
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Args:
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query: The input string for eco-friendly and conscious lifestyle recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended eco-conscious articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01j5xy9w5sf49bm6b1prm80m27",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"
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def get_wishtv_news(
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query: str,
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similarity_top_k: int = 9,
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ref: Optional[str] = None,
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num_articles_ref: int = 0,
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search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"] = "most_recent",
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) -> str:
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"""
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Fetch AI-powered WISH-TV news recommendations. Get recommendations covering sports,
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breaking news, politics, multicultural updates, Hispanic language content, entertainment,
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health, and education.
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Args:
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query: The input string for general news recommendations.
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similarity_top_k: Number of top similar articles to retrieve.
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ref: Optional site domain to prioritize recommendations.
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num_articles_ref: Minimum number of articles to return from the reference domain.
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search_algorithm: The search algorithm to use ('most_recent', 'semantic', 'most_recent_semantic', 'trending').
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Returns:
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A formatted string containing recommended news articles.
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"""
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try:
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return client.get_ai_recommendations_string(
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query=query,
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data_model_id="dm_01jagy9nqaeer9hxx8z1sk1jx6",
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similarity_top_k=similarity_top_k,
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ref=ref or "",
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num_articles_ref=num_articles_ref,
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search_algorithm=search_algorithm,
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)
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except Exception as e:
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return f"Error: {str(e)}"

‎agentstack/_tools/dappier/config.json

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{
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"name": "dappier",
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"url": "https://www.dappier.com/",
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"category": "search",
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"env": {
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"DAPPIER_API_KEY": null
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},
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"dependencies": ["dappier>=0.3.5"],
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"tools": [
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"real_time_web_search",
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"stock_market_data_search",
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"get_sports_news",
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"get_lifestyle_news",
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"get_iheartdogs_content",
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"get_iheartcats_content",
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"get_greenmonster_guides",
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"get_wishtv_news"
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],
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"cta": "Create an API key at https://platform.dappier.com/profile/api-keys/"
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}

‎docs/llms.txt

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## Search
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- [Perplexity](/tools/tool/perplexity)
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- [Dappier](/tools/tool/dappier)
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## Memory / State
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‎docs/tools/community.mdx

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## Search
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- [Perplexity](/tools/tool/perplexity)
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- [Dappier](/tools/tool/dappier)
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## Memory / State
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‎docs/tools/tool/dappier.mdx

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---
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title: Dappier
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description: Real-time web and content search for agents
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icon: search
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---
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Dappier is a real time search that connects any AI to proprietary, real-time data — including web search, news, sports, stock market data, and premium publisher content.
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## Description
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Dappier Real-Time Search provides instant access to live web search results and AI-powered recommendations with:
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- Real-Time Web Search offering up-to-the-minute results from Google, financial markets, and global news
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- Specialized Content Models trained on curated datasets for domains like sports, lifestyle, pet care, sustainability, and multicultural news
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- Intelligent Query Routing that automatically selects the appropriate model based on user input
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### Core Features:
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- Web Search - Perform real-time web lookups across news, stocks, travel, weather, and more
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- Stock Market Data - Retrieve live financial news, stock prices, and trades
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- Content Recommendations - Get semantically matched articles tailored to user interests
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- Domain-Specific Models - Specialized AI trained on lifestyle, pets, sports, and green living
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### Output Formats:
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- Summarized real-time search results
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- Curated lists of recommended articles
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- Live financial and stock market insights
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- Structured query-to-content responses
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## Available Models and Functions
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> Explore various AI models and data models available at [Dappier Marketplace](https://marketplace.dappier.com/marketplace).
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### AI Models
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| Function | Model | Description | Arguments |
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|:---|:---|:---|:---|
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| `real_time_web_search` | `am_01j06ytn18ejftedz6dyhz2b15` | Perform a real-time web search across Google, news, weather, and travel data. | `query: str` |
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| `stock_market_data_search` | `am_01j749h8pbf7ns8r1bq9s2evrh` | Perform a real-time stock market data search including stock prices and financial news. | `query: str` |
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### Data Models
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| Function | Model | Description | Arguments |
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|:---|:---|:---|:---|
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| `get_sports_news` | `dm_01j0pb465keqmatq9k83dthx34` | Get real-time sports news and updates from top sports sources. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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| `get_lifestyle_news` | `dm_01j0q82s4bfjmsqkhs3ywm3x6y` | Access real-time lifestyle news and insights from popular publications. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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| `get_iheartdogs_content` | `dm_01j1sz8t3qe6v9g8ad102kvmqn` | Fetch dog care articles on health, behavior, and grooming from iHeartDogs. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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| `get_iheartcats_content` | `dm_01j1sza0h7ekhaecys2p3y0vmj` | Fetch cat care content on health, lifestyle, and behavior from iHeartCats. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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| `get_greenmonster_guides` | `dm_01j5xy9w5sf49bm6b1prm80m27` | Access eco-conscious lifestyle articles from GreenMonster. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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| `get_wishtv_news` | `dm_01jagy9nqaeer9hxx8z1sk1jx6` | Get news updates on politics, entertainment, and multicultural topics from WISH-TV. | `query: str`, `similarity_top_k: int`, `ref: Optional[str]`, `num_articles_ref: int`, `search_algorithm: Literal["most_recent", "semantic", "most_recent_semantic", "trending"]` |
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## Installation
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```bash
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agentstack tools add dappier
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```
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Set the environment variable
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```env
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DAPPIER_API_KEY=...
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```
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## Usage
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Dappier can be configured for different behaviors by modifying `src/tools/dappier_tool.py`.

‎examples/stock_market_research/.env.example

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#AGENTOPS_API_KEY=...
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#OPENAI_API_KEY=...
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# Tools
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#DAPPIER_API_KEY=...

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