Tavily Search

from praisonaiagents import Agent, tavily

agent = Agent(name="Researcher", tools=[tavily])
agent.start("Search for the latest on agent frameworks")

pip install praisonaiagents tavily-python

export TAVILY_API_KEY=your_tavily_api_key
export OPENAI_API_KEY=your_openai_api_key

from praisonaiagents import Agent
from praisonaiagents import tavily

agent = Agent(
    name="SearchAgent",
    role="Web Researcher",
    goal="Find information on the web",
    tools=[tavily]
)

result = agent.start("What are the latest AI trends in 2025?")
print(result)

from praisonaiagents import tavily

# Simple search
results = tavily("Python programming best practices")
print(results)

from praisonaiagents import tavily_search

results = tavily_search(
    query="AI trends 2025",
    max_results=5,
    search_depth="advanced",  # "basic" or "advanced"
    include_answer=True,      # Get LLM-generated answer
    topic="general"           # "general", "news", or "finance"
)

print(results.get("answer"))  # AI-generated answer
for r in results.get("results", []):
    print(f"- {r['title']}: {r['url']}")

from praisonaiagents import tavily_extract

content = tavily_extract(
    urls=["https://praison.ai/docs", "https://example.com"],
    extract_depth="basic"  # "basic" or "advanced"
)

for result in content.get("results", []):
    print(f"URL: {result['url']}")
    print(f"Content: {result['raw_content'][:500]}...")

from praisonaiagents import tavily_crawl

results = tavily_crawl(
    url="https://praison.ai/docs",
    max_depth=2,
    max_breadth=10,
    limit=20,
    instructions="Find all pages about agents"
)

for page in results.get("results", []):
    print(f"- {page['url']}")

from praisonaiagents import tavily_map

sitemap = tavily_map(
    url="https://praison.ai/docs",
    max_depth=2,
    limit=50
)

for url in sitemap.get("results", []):
    print(url)

from praisonaiagents import Agent
from praisonaiagents import tavily

agent = Agent(
    name="SearchAgent",
    role="Web Researcher",
    goal="Find and analyze information from the web",
    tools=[tavily]
)

result = agent.start("Research the latest developments in quantum computing")
print(result)

from praisonaiagents import TavilyTools

# Initialize with custom API key (optional)
tools = TavilyTools(api_key="your_api_key")  # or uses TAVILY_API_KEY env var

# Search
results = tools.search("AI news", max_results=5, include_answer=True)

# Extract
content = tools.extract(["https://example.com"])

# Crawl
pages = tools.crawl("https://praison.ai/docs", max_depth=2)

# Map
sitemap = tools.map("https://praison.ai/docs")

import asyncio
from praisonaiagents import Agent, Task, AgentTeam
from praisonaiagents import tavily_search_async

agent = Agent(
    name="AsyncSearchAgent",
    role="Search Specialist",
    tools=[tavily_search_async]
)

task = Task(
    description="Search for AI trends",
    agent=agent,
    async_execution=True
)

agents = AgentTeam(agents=[agent], tasks=[task])
result = asyncio.run(agents.astart())