Tavily Search

pip install "praisonai[tools]"

export TAVILY_API_KEY=your_api_key_here

from praisonai_tools import TavilyTool

# Initialize
tavily = TavilyTool()

# Search
results = tavily.search("What is quantum computing?")
print(results)

from praisonaiagents import Agent
from praisonai_tools import TavilyTool

agent = Agent(
    name="Researcher",
    instructions="You are a research assistant. Use Tavily to search for information.",
    tools=[TavilyTool()]
)

response = agent.chat("Search for the latest AI news")
print(response)

from praisonai_tools import TavilyTool

tavily = TavilyTool()
results = tavily.search("Python best practices", max_results=3)

# Returns:
# {
#     "query": "Python best practices",
#     "answer": "AI-generated summary...",
#     "results": [
#         {"title": "...", "url": "...", "content": "...", "score": 0.95}
#     ]
# }

context = tavily.search_context("machine learning fundamentals")
# Returns a string with relevant context for LLM consumption

content = tavily.extract("https://example.com,https://another.com")
# Returns list of extracted content from each URL

tavily = TavilyTool(
    api_key="your_key",           # Optional: defaults to TAVILY_API_KEY env var
    search_depth="advanced",       # "basic" or "advanced"
    include_answer=True,           # Include AI-generated answer
    max_tokens=6000               # Max tokens for context
)

from praisonai_tools import tavily_search

# Quick search without instantiating class
results = tavily_search("latest tech news", max_results=5)

# Set API key
export TAVILY_API_KEY=your_key

# Use with praisonai
praisonai --tools TavilyTool "Search for AI trends 2025"

from praisonai_tools import TavilyTool

tavily = TavilyTool()
results = tavily.search("my query")

if "error" in results:
    print(f"Error: {results['error']}")
else:
    print(f"Found {len(results['results'])} results")