Tavily Research Tool

uv add 'crewai[tools]' tavily-python

export TAVILY_API_KEY='your_tavily_api_key'

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

# Ensure TAVILY_API_KEY is set in your environment
# os.environ["TAVILY_API_KEY"] = "YOUR_API_KEY"

tavily_tool = TavilyResearchTool()

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,
)

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,
)

crew = Crew(agents=[researcher], tasks=[research_task])
print(crew.kickoff())

from crewai_tools import TavilyResearchTool

tavily_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)