> ## Documentation Index > Fetch the complete documentation index at: https://docs.agno.com/llms.txt > Use this file to discover all available pages before exploring further. # WebSearch Tools - Advanced Configuration > Demonstrates advanced WebSearchTools configuration with timelimit, region, and backend parameters for customized search behavior across multiple search engines. ```python websearch_tools_advanced.py theme={null} """ WebSearch Tools - Advanced Configuration ========================================= Demonstrates advanced WebSearchTools configuration with timelimit, region, and backend parameters for customized search behavior across multiple search engines. Parameters: - timelimit: Filter results by time ("d" = day, "w" = week, "m" = month, "y" = year) - region: Localize results (e.g., "us-en", "uk-en", "de-de", "fr-fr", "ru-ru") - backend: Search backend ("auto", "duckduckgo", "google", "bing", "brave", "yandex", "yahoo") """ from agno.agent import Agent from agno.models.openai import OpenAIChat from agno.tools.websearch import WebSearchTools # --------------------------------------------------------------------------- # Example 1: Time-limited search with auto backend # --------------------------------------------------------------------------- # Filter results to specific time periods # Past day - for breaking news daily_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( timelimit="d", # Results from past day backend="auto", ) ], instructions=["Search for the most recent information from today."], ) # Past week - for recent developments weekly_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( timelimit="w", # Results from past week backend="auto", ) ], instructions=["Search for recent information from the past week."], ) # Past month - for broader recent context monthly_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( timelimit="m", # Results from past month backend="auto", ) ], instructions=["Search for information from the past month."], ) # Past year - for yearly trends yearly_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( timelimit="y", # Results from past year backend="auto", ) ], instructions=["Search for information from the past year."], ) # --------------------------------------------------------------------------- # Example 2: Region-specific searches # --------------------------------------------------------------------------- # Localize search results based on region # US English us_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( region="us-en", backend="auto", ) ], instructions=["Provide US-localized search results."], ) # UK English uk_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( region="uk-en", backend="auto", ) ], instructions=["Provide UK-localized search results."], ) # German de_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( region="de-de", backend="auto", ) ], instructions=["Provide German-localized search results."], ) # French fr_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( region="fr-fr", backend="auto", ) ], instructions=["Provide French-localized search results."], ) # Russian ru_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( region="ru-ru", backend="auto", ) ], instructions=["Provide Russian-localized search results."], ) # --------------------------------------------------------------------------- # Example 3: Different backend options # --------------------------------------------------------------------------- # Use specific search engines as backends # DuckDuckGo backend duckduckgo_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="duckduckgo", timelimit="w", region="us-en", ) ], ) # Google backend google_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="google", timelimit="w", region="us-en", ) ], ) # Bing backend bing_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="bing", timelimit="w", region="us-en", ) ], ) # Brave backend brave_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="brave", timelimit="w", region="us-en", ) ], ) # Yandex backend yandex_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="yandex", timelimit="w", region="ru-ru", # Yandex works well with Russian region ) ], ) # Yahoo backend yahoo_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="yahoo", timelimit="w", region="us-en", ) ], ) # --------------------------------------------------------------------------- # Example 4: Combined configuration - Research assistant # --------------------------------------------------------------------------- # Combine all parameters for a powerful research assistant research_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="auto", # Auto-select best available backend timelimit="w", # Focus on recent results region="us-en", # US English results fixed_max_results=10, # Get more results timeout=20, # Longer timeout for thorough search ) ], instructions=[ "You are a research assistant that finds comprehensive, recent information.", "Always cite your sources and provide context for your findings.", "Focus on authoritative and reliable sources.", ], ) # --------------------------------------------------------------------------- # Example 5: News-focused agent with time and region filters # --------------------------------------------------------------------------- news_agent = Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="auto", timelimit="d", # Today's news only region="us-en", enable_search=False, # Disable general search enable_news=True, # Enable news search only ) ], instructions=[ "You are a news assistant that finds today's breaking news.", "Summarize the key points and provide source links.", ], ) # --------------------------------------------------------------------------- # Example 6: Multi-region comparison agent # --------------------------------------------------------------------------- # Create agents for different regions to compare perspectives def create_regional_agent(region: str, region_name: str) -> Agent: """Create a region-specific search agent.""" return Agent( model=OpenAIChat(id="gpt-4o"), tools=[ WebSearchTools( backend="auto", timelimit="w", region=region, ) ], instructions=[ f"You are a search assistant for {region_name}.", "Provide localized search results and perspectives.", ], ) # Create regional agents us_regional = create_regional_agent("us-en", "United States") uk_regional = create_regional_agent("uk-en", "United Kingdom") de_regional = create_regional_agent("de-de", "Germany") # --------------------------------------------------------------------------- # Run Examples # --------------------------------------------------------------------------- if __name__ == "__main__": # Example 1: Time-limited search print("\n" + "=" * 60) print("Example 1: Weekly time-limited search") print("=" * 60) weekly_agent.print_response("What are the latest AI developments?", markdown=True) # Example 2: Region-specific search (US) print("\n" + "=" * 60) print("Example 2: US region search") print("=" * 60) us_agent.print_response("What are trending tech topics?", markdown=True) # Example 3: DuckDuckGo backend with filters print("\n" + "=" * 60) print("Example 3: DuckDuckGo backend with time and region filters") print("=" * 60) duckduckgo_agent.print_response("What is quantum computing?", markdown=True) # Example 4: Research assistant print("\n" + "=" * 60) print("Example 4: Research assistant (combined configuration)") print("=" * 60) research_agent.print_response( "Find recent research on large language models", markdown=True ) # Example 5: News agent print("\n" + "=" * 60) print("Example 5: News-focused agent (daily news)") print("=" * 60) news_agent.print_response("What are today's top tech headlines?", markdown=True) # Example 6: Regional comparison print("\n" + "=" * 60) print("Example 6: US regional agent") print("=" * 60) us_regional.print_response("What is the economic outlook?", markdown=True) ``` ## Run the Example ```bash theme={null} uv pip install -U agno ddgs openai ``` ```bash Mac/Linux theme={null} export OPENAI_API_KEY="your_openai_api_key_here" ``` ```bash Windows theme={null} $Env:OPENAI_API_KEY="your_openai_api_key_here" ``` Save the code above as `websearch_tools_advanced.py`, then run: ```bash theme={null} python websearch_tools_advanced.py ``` Full source: [cookbook/91\_tools/websearch\_tools\_advanced.py](https://github.com/agno-agi/agno/blob/main/cookbook/91_tools/websearch_tools_advanced.py)