CrewAI

pip install 'crewai[tools]'
pip install pydantic

import os

# Set your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["TAVILY_API_KEY"] = "your-tavily-api-key"

import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilySearchTool

# Initialize the Tavily search tool
tavily_tool = TavilySearchTool()

# Create an agent that uses the tool
researcher = Agent(
    role='News Researcher',
    goal='Find trending information about AI agents',
    backstory='An expert News researcher specializing in technology, focused on AI.',
    tools=[tavily_tool],
    verbose=True
)

# Create a task for the agent
research_task = Task(
    description='Search for the top 3 Agentic AI trends in 2025.',
    expected_output='A JSON report summarizing the top 3 AI trends found.',
    agent=researcher
)

# Form the crew and execute the task
crew = Crew(
    agents=[researcher],
    tasks=[research_task],
    verbose=True
)

result = crew.kickoff()
print(result)

from crewai_tools import TavilySearchTool

# You can configure the tool with specific parameters
tavily_search_tool = TavilySearchTool(
    search_depth="advanced",
    max_results=10,
    include_answer=True
)

import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilyExtractorTool

# Initialize the Tavily extractor tool
tavily_tool = TavilyExtractorTool()

# Create an agent that uses the tool
extractor_agent = Agent(
    role='Web Page Content Extractor',
    goal='Extract key information from the given web pages',
    backstory='You are an expert at extracting relevant content from websites using the Tavily Extract.',
    tools=[tavily_tool],
    verbose=True
)

# Define a task for the agent
extract_task = Task(
    description='Extract the main content from the URL https://en.wikipedia.org/wiki/Lionel_Messi .',
    expected_output='A JSON string containing the extracted content from the URL.',
    agent=extractor_agent
)

# Create and run the crew
crew = Crew(
    agents=[extractor_agent],
    tasks=[extract_task],
    verbose=False
)

result = crew.kickoff()
print(result)

from crewai_tools import TavilyExtractorTool

# You can configure the tool with specific parameters
tavily_extract_tool = TavilyExtractorTool(
    extract_depth="advanced",
    include_images=True,
    timeout=45
)

import os
from crewai import Agent, Task, Crew
from crewai_tools import TavilyResearchTool

# Initialize the Tavily research tool
tavily_tool = TavilyResearchTool()

# Create an agent that uses the tool
researcher = Agent(
    role="Research Analyst",
    goal="Investigate questions and produce concise, well-cited briefings.",
    backstory=(
        "You are a meticulous analyst who delegates web research to the Tavily "
        "Research tool, then synthesizes the findings into short briefings."
    ),
    tools=[tavily_tool],
    verbose=True,
)

# Create a task for the agent
research_task = Task(
    description=(
        "Investigate notable open-source agent orchestration frameworks released "
        "in the last six months and summarize their differentiators."
    ),
    expected_output="A bulleted briefing with citations.",
    agent=researcher,
)

# Form the crew and execute the task
crew = Crew(agents=[researcher], tasks=[research_task])
print(crew.kickoff())

from crewai_tools import TavilyResearchTool

# You can configure the tool with specific defaults for every call
tavily_research_tool = TavilyResearchTool(
    model="pro",                # use Tavily's most capable research model
    citation_format="apa",      # APA-style citations
)

tavily_tool = TavilyResearchTool(stream=True)

for chunk in tavily_tool.run(input="Summarize recent advances in retrieval-augmented generation."):
    print(chunk)

output_schema = {
    "type": "object",
    "properties": {
        "summary": {"type": "string"},
        "key_points": {"type": "array", "items": {"type": "string"}},
        "sources": {"type": "array", "items": {"type": "string"}},
    },
    "required": ["summary", "key_points", "sources"],
}

tavily_tool = TavilyResearchTool(output_schema=output_schema)