Tavily Research Tool

The TavilyResearchTool lets CrewAI agents kick off Tavily research tasks, returning a synthesized, cited report (or a stream of progress events) instead of raw search results. Use it when an agent needs an investigative answer rather than a single web search.

Installation

To use the TavilyResearchTool, install the tavily-python library alongside crewai-tools:

uv add 'crewai[tools]' tavily-python

Environment Variables

Set your Tavily API key:

export TAVILY_API_KEY='your_tavily_api_key'

Get an API key at https://app.tavily.com/ (sign up, then create a key).

Example Usage

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())

Configuration Options

The TavilyResearchTool accepts the following arguments — all can be set on the tool instance (defaults for every call) or per-call via the agent’s tool input:

• input (str): Required. The research task or question to investigate.
• model (Literal[“mini”, “pro”, “auto”]): The Tavily research model. "auto" lets Tavily pick; "mini" is faster/cheaper; "pro" is the most capable. Defaults to "auto".
• output_schema (dict | None): Optional JSON Schema that structures the research output. Useful when you want strictly typed results.
• stream (bool): When True, the tool returns an iterator of SSE chunks emitting research progress and the final result instead of a single string. Defaults to False.
• citation_format (Literal[“numbered”, “mla”, “apa”, “chicago”]): Citation format for the report. Defaults to "numbered".

Advanced Usage

Configure defaults on the tool instance

from crewai_tools import TavilyResearchTool

tavily_tool = TavilyResearchTool(
    model="pro",                # use Tavily's most capable research model
    citation_format="apa",      # APA-style citations
)

Stream research progress

When stream=True, the tool returns a generator (or async generator from _arun) of SSE chunks so your application can surface incremental progress:

tavily_tool = TavilyResearchTool(stream=True)

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

Structured output via JSON Schema

Pass an output_schema when you need a typed result instead of a free-form report:

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)

Features

• End-to-end research: Returns a synthesized, cited report rather than raw search hits.
• Model selection: Trade off cost, speed, and depth via mini, pro, or auto.
• Streaming: Stream incremental progress and results as SSE chunks for responsive UIs.
• Structured output: Coerce results to a JSON Schema you define.
• Multiple citation styles: Choose from numbered, MLA, APA, or Chicago citations.
• Sync and async: Use either _run or _arun depending on your application’s runtime.

Refer to the Tavily API documentation for full details on the Research API.