websearch_tools_advanced.py
"""
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
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
websearch_tools_advanced.py, then run:python websearch_tools_advanced.py