mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-05-05 01:02:37 +00:00
Add Tavily Research and get Research (#5483)
* Add Tavily Research and get Research - Added tavily research with docs to crew AI - Added tavily get research with docs to crew AI * Update `tavily-python` installation instructions and adjust version constraints - Changed installation command from `pip install` to `uv add` for `tavily-python` in multiple documentation files. - Updated version constraint for `tavily-python` in `pyproject.toml` from `>=0.7.14` to `~=0.7.14`. - Modified the `exclude-newer` date in `uv.lock` to `2026-04-23T07:00:00Z`. * Add Tavily Research Tool documentation in multiple languages - Introduced `TavilyResearchTool` documentation in English, Arabic, Korean, and Portuguese. - Updated `docs.json` to include paths for the new documentation files. - The `TavilyResearchTool` allows CrewAI agents to perform multi-step research tasks and generate cited reports using the Tavily Research API. * Fix Tavily research CI failures --------- Co-authored-by: lorenzejay <lorenzejaytech@gmail.com> Co-authored-by: Evan Rimer <evan.rimer@tavily.com> Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
This commit is contained in:
Mani
@@ -69,7 +69,7 @@ linkup-sdk = [
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"linkup-sdk>=0.2.2",
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]
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tavily-python = [
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"tavily-python>=0.5.4",
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"tavily-python~=0.7.14",
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]
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hyperbrowser = [
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"hyperbrowser>=0.18.0",
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@@ -197,6 +197,12 @@ from crewai_tools.tools.stagehand_tool.stagehand_tool import StagehandTool
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from crewai_tools.tools.tavily_extractor_tool.tavily_extractor_tool import (
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TavilyExtractorTool,
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)
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from crewai_tools.tools.tavily_get_research_tool.tavily_get_research_tool import (
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TavilyGetResearchTool,
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)
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from crewai_tools.tools.tavily_research_tool.tavily_research_tool import (
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TavilyResearchTool,
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)
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from crewai_tools.tools.tavily_search_tool.tavily_search_tool import TavilySearchTool
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from crewai_tools.tools.txt_search_tool.txt_search_tool import TXTSearchTool
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from crewai_tools.tools.vision_tool.vision_tool import VisionTool
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@@ -310,6 +316,8 @@ __all__ = [
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"StagehandTool",
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"TXTSearchTool",
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"TavilyExtractorTool",
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"TavilyGetResearchTool",
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"TavilyResearchTool",
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"TavilySearchTool",
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"VisionTool",
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"WeaviateVectorSearchTool",
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@@ -184,6 +184,12 @@ from crewai_tools.tools.stagehand_tool.stagehand_tool import StagehandTool
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from crewai_tools.tools.tavily_extractor_tool.tavily_extractor_tool import (
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TavilyExtractorTool,
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)
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from crewai_tools.tools.tavily_get_research_tool.tavily_get_research_tool import (
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TavilyGetResearchTool,
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)
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from crewai_tools.tools.tavily_research_tool.tavily_research_tool import (
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TavilyResearchTool,
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)
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from crewai_tools.tools.tavily_search_tool.tavily_search_tool import TavilySearchTool
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from crewai_tools.tools.txt_search_tool.txt_search_tool import TXTSearchTool
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from crewai_tools.tools.vision_tool.vision_tool import VisionTool
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@@ -293,6 +299,8 @@ __all__ = [
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"StagehandTool",
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"TXTSearchTool",
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"TavilyExtractorTool",
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"TavilyGetResearchTool",
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"TavilyResearchTool",
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"TavilySearchTool",
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"VisionTool",
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"WeaviateVectorSearchTool",
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@@ -9,7 +9,7 @@ The `TavilyExtractorTool` allows CrewAI agents to extract structured content fro
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To use the `TavilyExtractorTool`, you need to install the `tavily-python` library:
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```shell
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pip install 'crewai[tools]' tavily-python
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uv add 'crewai[tools]' tavily-python
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```
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You also need to set your Tavily API key as an environment variable:
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@@ -0,0 +1,44 @@
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# Tavily Get Research Tool
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## Description
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The `TavilyGetResearchTool` provides an interface to Tavily's research status endpoint through the Tavily Python SDK. It retrieves the current status and results of an existing Tavily research task by `request_id`.
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## Installation
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To use the `TavilyGetResearchTool`, you need to install the `tavily-python` library:
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```shell
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uv add 'crewai[tools]' tavily-python
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```
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## Environment Variables
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Ensure your Tavily API key is set as an environment variable:
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```bash
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export TAVILY_API_KEY='your_tavily_api_key'
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```
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## Example
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```python
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from crewai_tools import TavilyGetResearchTool
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tavily_get_research_tool = TavilyGetResearchTool()
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status_result = tavily_get_research_tool.run(
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request_id="Your Request ID Here"
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)
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print(status_result)
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```
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## Arguments
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The `TavilyGetResearchTool` accepts the following arguments during initialization or when calling the `run` method:
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- `request_id` (str): Existing Tavily research request ID to retrieve.
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## Response Format
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The tool returns a JSON string containing the current research task status and any available results from Tavily.
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@@ -0,0 +1 @@
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@@ -0,0 +1,120 @@
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from __future__ import annotations
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import json
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import os
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from typing import Any
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from crewai.tools import BaseTool, EnvVar
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from dotenv import load_dotenv
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from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
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load_dotenv()
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try:
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from tavily import AsyncTavilyClient, TavilyClient # type: ignore[import-untyped]
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TAVILY_AVAILABLE = True
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except ImportError:
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TAVILY_AVAILABLE = False
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class TavilyGetResearchToolSchema(BaseModel):
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"""Input schema for TavilyGetResearchTool."""
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request_id: str = Field(
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...,
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description="Existing Tavily research request ID to fetch status and results for.",
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)
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class TavilyGetResearchTool(BaseTool):
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"""Tool that uses the Tavily Research status endpoint to retrieve results."""
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model_config = ConfigDict(arbitrary_types_allowed=True)
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_client: Any | None = PrivateAttr(default=None)
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_async_client: Any | None = PrivateAttr(default=None)
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name: str = "Tavily Get Research"
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description: str = (
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"A tool that retrieves the status and results of an existing Tavily "
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"research task by request ID. It returns Tavily responses as JSON."
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)
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args_schema: type[BaseModel] = TavilyGetResearchToolSchema
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package_dependencies: list[str] = Field(default_factory=lambda: ["tavily-python"])
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env_vars: list[EnvVar] = Field(
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default_factory=lambda: [
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EnvVar(
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name="TAVILY_API_KEY",
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description="API key for Tavily research service",
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required=True,
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),
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]
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)
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def __init__(self, **kwargs: Any):
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super().__init__(**kwargs)
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if TAVILY_AVAILABLE:
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api_key = os.getenv("TAVILY_API_KEY")
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self._client = TavilyClient(api_key=api_key)
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self._async_client = AsyncTavilyClient(api_key=api_key)
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else:
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try:
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import subprocess
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import click
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except ImportError as e:
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raise ImportError(
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"The 'tavily-python' package is required. 'click' and "
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"'subprocess' are also needed to assist with installation "
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"if the package is missing. Please install 'tavily-python' "
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"manually (e.g., 'pip install tavily-python') and ensure "
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"'click' and 'subprocess' are available."
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) from e
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if click.confirm(
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"You are missing the 'tavily-python' package, which is required "
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"for TavilyGetResearchTool. Would you like to install it?"
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):
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try:
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subprocess.run(["uv", "add", "tavily-python"], check=True) # noqa: S607
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raise ImportError(
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"'tavily-python' has been installed. Please restart your "
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"Python application to use the TavilyGetResearchTool."
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)
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except subprocess.CalledProcessError as e:
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raise ImportError(
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f"Attempted to install 'tavily-python' but failed: {e}. "
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"Please install it manually to use the TavilyGetResearchTool."
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) from e
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else:
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raise ImportError(
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"The 'tavily-python' package is required to use the "
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"TavilyGetResearchTool. Please install it with: uv add tavily-python"
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)
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@staticmethod
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def _stringify_response(response: Any) -> str:
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if isinstance(response, str):
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return response
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return json.dumps(response, indent=2)
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def _run(self, request_id: str) -> str:
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"""Synchronously retrieves Tavily research task status and results."""
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if not self._client:
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raise ValueError(
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"Tavily client is not initialized. Ensure 'tavily-python' is "
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"installed and API key is set."
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)
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return self._stringify_response(self._client.get_research(request_id))
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async def _arun(self, request_id: str) -> str:
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"""Asynchronously retrieves Tavily research task status and results."""
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if not self._async_client:
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raise ValueError(
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"Tavily async client is not initialized. Ensure 'tavily-python' is "
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"installed and API key is set."
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)
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return self._stringify_response(
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await self._async_client.get_research(request_id)
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)
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@@ -0,0 +1,132 @@
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# Tavily Research Tool
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## Description
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The `TavilyResearchTool` provides an interface to Tavily Research through the Tavily Python SDK. It creates research tasks from an `input` prompt and can optionally stream Server-Sent Events (SSE) when `stream=True`.
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## Installation
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To use the `TavilyResearchTool`, you need to install the `tavily-python` library:
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```shell
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uv add 'crewai[tools]' tavily-python
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```
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## Environment Variables
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Ensure your Tavily API key is set as an environment variable:
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```bash
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export TAVILY_API_KEY='your_tavily_api_key'
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```
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## Example
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Here's how to initialize and use the `TavilyResearchTool` within a CrewAI agent:
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```python
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from crewai import Agent, Task, Crew
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from crewai_tools import TavilyResearchTool
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# Initialize the tool
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tavily_research_tool = TavilyResearchTool()
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# Create an agent that uses the tool
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researcher = Agent(
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role="Research Analyst",
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goal="Produce structured research reports",
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backstory="An expert analyst who uses Tavily Research for deep web research.",
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tools=[tavily_research_tool],
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verbose=True,
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)
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# Create a task for the agent
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research_task = Task(
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description="Research the latest developments in AI infrastructure startups.",
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expected_output="A detailed report with citations and supporting sources.",
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agent=researcher,
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)
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# Run the crew
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crew = Crew(
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agents=[researcher],
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tasks=[research_task],
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verbose=2,
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)
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result = crew.kickoff()
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print(result)
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# Direct tool usage: create a structured research task
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structured_result = tavily_research_tool.run(
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input="Research the latest developments in AI infrastructure startups.",
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model="pro",
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output_schema={
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"properties": {
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"summary": {
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"type": "string",
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"description": "A concise summary of the research findings",
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},
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"key_trends": {
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"type": "array",
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"description": "The major trends identified in the research",
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"items": {"type": "string"},
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},
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"companies": {
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"type": "array",
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"description": "Notable companies mentioned in the research",
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"items": {
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"type": "object",
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"description": "A company entry",
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"properties": {
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"name": {
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"type": "string",
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"description": "The company name",
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},
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"focus": {
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"type": "string",
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"description": "The company's main area of focus",
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},
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"notable_update": {
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"type": "string",
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"description": "A notable recent update about the company",
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},
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},
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"required": ["name", "focus", "notable_update"],
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},
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},
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},
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"required": ["summary", "key_trends", "companies"],
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},
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citation_format="apa",
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)
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print(structured_result)
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# Direct tool usage: stream research updates
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stream = tavily_research_tool.run(
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input="Research the latest developments in AI infrastructure startups.",
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model="mini",
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stream=True,
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)
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for chunk in stream:
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print(chunk.decode("utf-8", errors="replace"), end="")
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```
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## Arguments
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The `TavilyResearchTool` accepts the following arguments during initialization or when calling the `run` method:
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- `input` (str): The research task or question to investigate.
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- `model` (Literal["mini", "pro", "auto"], optional): The Tavily research model to use. Defaults to `"auto"`.
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- `output_schema` (dict[str, Any], optional): A JSON Schema used to structure the research output. Tavily expects top-level `properties` and optional `required` keys, and each property should include a `description`.
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- `stream` (bool, optional): Whether to return Tavily's streaming SSE chunk generator. Defaults to `False`.
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- `citation_format` (Literal["numbered", "mla", "apa", "chicago"], optional): Citation format for the report. Defaults to `"numbered"`.
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## Response Format
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The tool returns:
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- A JSON string when creating a non-streaming research task
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- A byte generator of SSE chunks when `stream=True`
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Refer to the Tavily Research API documentation for the full response structure and streaming event format.
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@@ -0,0 +1,200 @@
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from __future__ import annotations
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from collections.abc import AsyncGenerator, Generator
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import json
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import os
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from typing import Any, Literal, cast
|
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from crewai.tools import BaseTool, EnvVar
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from dotenv import load_dotenv
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from pydantic import BaseModel, ConfigDict, Field, PrivateAttr
|
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load_dotenv()
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try:
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from tavily import ( # type: ignore[import-untyped, import-not-found, unused-ignore]
|
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AsyncTavilyClient,
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TavilyClient,
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||||
)
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TAVILY_AVAILABLE = True
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except ImportError:
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TAVILY_AVAILABLE = False
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class TavilyResearchToolSchema(BaseModel):
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"""Input schema for TavilyResearchTool."""
|
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input: str = Field(
|
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...,
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description="The research task or question to investigate.",
|
||||
)
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model: Literal["mini", "pro", "auto"] = Field(
|
||||
default="auto",
|
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description="The model used by the Tavily research agent.",
|
||||
)
|
||||
output_schema: dict[str, Any] | None = Field(
|
||||
default=None,
|
||||
description="Optional JSON Schema that structures the research output.",
|
||||
)
|
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stream: bool = Field(
|
||||
default=False,
|
||||
description="Whether to stream research progress and results as SSE chunks.",
|
||||
)
|
||||
citation_format: Literal["numbered", "mla", "apa", "chicago"] = Field(
|
||||
default="numbered",
|
||||
description="Citation format for the research report.",
|
||||
)
|
||||
|
||||
|
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class TavilyResearchTool(BaseTool):
|
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"""Tool that uses the Tavily Research API to create research tasks."""
|
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|
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model_config = ConfigDict(arbitrary_types_allowed=True)
|
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_client: Any | None = PrivateAttr(default=None)
|
||||
_async_client: Any | None = PrivateAttr(default=None)
|
||||
name: str = "Tavily Research"
|
||||
description: str = (
|
||||
"A tool that creates Tavily research tasks and can stream research "
|
||||
"progress and results. It returns Tavily responses as JSON or SSE chunks."
|
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)
|
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args_schema: type[BaseModel] = TavilyResearchToolSchema
|
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model: Literal["mini", "pro", "auto"] = Field(
|
||||
default="auto",
|
||||
description="Default model used for new Tavily research tasks.",
|
||||
)
|
||||
output_schema: dict[str, Any] | None = Field(
|
||||
default=None,
|
||||
description="Default JSON Schema used to structure research output.",
|
||||
)
|
||||
stream: bool = Field(
|
||||
default=False,
|
||||
description="Whether new Tavily research tasks should stream responses by default.",
|
||||
)
|
||||
citation_format: Literal["numbered", "mla", "apa", "chicago"] = Field(
|
||||
default="numbered",
|
||||
description="Default citation format for Tavily research results.",
|
||||
)
|
||||
package_dependencies: list[str] = Field(default_factory=lambda: ["tavily-python"])
|
||||
env_vars: list[EnvVar] = Field(
|
||||
default_factory=lambda: [
|
||||
EnvVar(
|
||||
name="TAVILY_API_KEY",
|
||||
description="API key for Tavily research service",
|
||||
required=True,
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
def __init__(self, **kwargs: Any):
|
||||
super().__init__(**kwargs)
|
||||
if TAVILY_AVAILABLE:
|
||||
api_key = os.getenv("TAVILY_API_KEY")
|
||||
self._client = TavilyClient(api_key=api_key)
|
||||
self._async_client = AsyncTavilyClient(api_key=api_key)
|
||||
else:
|
||||
try:
|
||||
import subprocess
|
||||
|
||||
import click
|
||||
except ImportError as e:
|
||||
raise ImportError(
|
||||
"The 'tavily-python' package is required. 'click' and "
|
||||
"'subprocess' are also needed to assist with installation "
|
||||
"if the package is missing. Please install 'tavily-python' "
|
||||
"manually (e.g., 'pip install tavily-python') and ensure "
|
||||
"'click' and 'subprocess' are available."
|
||||
) from e
|
||||
|
||||
if click.confirm(
|
||||
"You are missing the 'tavily-python' package, which is required "
|
||||
"for TavilyResearchTool. Would you like to install it?"
|
||||
):
|
||||
try:
|
||||
subprocess.run(["uv", "add", "tavily-python"], check=True) # noqa: S607
|
||||
raise ImportError(
|
||||
"'tavily-python' has been installed. Please restart your "
|
||||
"Python application to use the TavilyResearchTool."
|
||||
)
|
||||
except subprocess.CalledProcessError as e:
|
||||
raise ImportError(
|
||||
f"Attempted to install 'tavily-python' but failed: {e}. "
|
||||
"Please install it manually to use the TavilyResearchTool."
|
||||
) from e
|
||||
else:
|
||||
raise ImportError(
|
||||
"The 'tavily-python' package is required to use the "
|
||||
"TavilyResearchTool. Please install it with: uv add tavily-python"
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _stringify_response(response: Any) -> str:
|
||||
if isinstance(response, str):
|
||||
return response
|
||||
return json.dumps(response, indent=2)
|
||||
|
||||
def _run(
|
||||
self,
|
||||
input: str,
|
||||
model: Literal["mini", "pro", "auto"] | None = None,
|
||||
output_schema: dict[str, Any] | None = None,
|
||||
stream: bool | None = None,
|
||||
citation_format: Literal["numbered", "mla", "apa", "chicago"] | None = None,
|
||||
) -> str | Generator[bytes, None, None]:
|
||||
"""Synchronously creates Tavily research tasks or streams results."""
|
||||
if not self._client:
|
||||
raise ValueError(
|
||||
"Tavily client is not initialized. Ensure 'tavily-python' is "
|
||||
"installed and API key is set."
|
||||
)
|
||||
|
||||
use_stream = self.stream if stream is None else stream
|
||||
result = self._client.research(
|
||||
input=input,
|
||||
model=self.model if model is None else model,
|
||||
output_schema=self.output_schema
|
||||
if output_schema is None
|
||||
else output_schema,
|
||||
stream=use_stream,
|
||||
citation_format=(
|
||||
self.citation_format if citation_format is None else citation_format
|
||||
),
|
||||
)
|
||||
|
||||
if use_stream:
|
||||
return cast(Generator[bytes, None, None], result)
|
||||
|
||||
return self._stringify_response(result)
|
||||
|
||||
async def _arun(
|
||||
self,
|
||||
input: str,
|
||||
model: Literal["mini", "pro", "auto"] | None = None,
|
||||
output_schema: dict[str, Any] | None = None,
|
||||
stream: bool | None = None,
|
||||
citation_format: Literal["numbered", "mla", "apa", "chicago"] | None = None,
|
||||
) -> str | AsyncGenerator[bytes, None]:
|
||||
"""Asynchronously creates Tavily research tasks or streams results."""
|
||||
if not self._async_client:
|
||||
raise ValueError(
|
||||
"Tavily async client is not initialized. Ensure 'tavily-python' is "
|
||||
"installed and API key is set."
|
||||
)
|
||||
|
||||
use_stream = self.stream if stream is None else stream
|
||||
result = await self._async_client.research(
|
||||
input=input,
|
||||
model=self.model if model is None else model,
|
||||
output_schema=self.output_schema
|
||||
if output_schema is None
|
||||
else output_schema,
|
||||
stream=use_stream,
|
||||
citation_format=(
|
||||
self.citation_format if citation_format is None else citation_format
|
||||
),
|
||||
)
|
||||
|
||||
if use_stream:
|
||||
return cast(AsyncGenerator[bytes, None], result)
|
||||
|
||||
return self._stringify_response(result)
|
||||
@@ -9,7 +9,7 @@ The `TavilySearchTool` provides an interface to the Tavily Search API, enabling
|
||||
To use the `TavilySearchTool`, you need to install the `tavily-python` library:
|
||||
|
||||
```shell
|
||||
pip install 'crewai[tools]' tavily-python
|
||||
uv add 'crewai[tools]' tavily-python
|
||||
```
|
||||
|
||||
## Environment Variables
|
||||
|
||||
@@ -25039,6 +25039,243 @@
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
{
|
||||
"description": "A tool that retrieves the status and results of an existing Tavily research task by request ID. It returns Tavily responses as JSON.",
|
||||
"env_vars": [
|
||||
{
|
||||
"default": null,
|
||||
"description": "API key for Tavily research service",
|
||||
"name": "TAVILY_API_KEY",
|
||||
"required": true
|
||||
}
|
||||
],
|
||||
"humanized_name": "Tavily Get Research",
|
||||
"init_params_schema": {
|
||||
"$defs": {
|
||||
"EnvVar": {
|
||||
"properties": {
|
||||
"default": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"default": null,
|
||||
"title": "Default"
|
||||
},
|
||||
"description": {
|
||||
"title": "Description",
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"title": "Name",
|
||||
"type": "string"
|
||||
},
|
||||
"required": {
|
||||
"default": true,
|
||||
"title": "Required",
|
||||
"type": "boolean"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"description"
|
||||
],
|
||||
"title": "EnvVar",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"description": "Tool that uses the Tavily Research status endpoint to retrieve results.",
|
||||
"properties": {},
|
||||
"required": [],
|
||||
"title": "TavilyGetResearchTool",
|
||||
"type": "object"
|
||||
},
|
||||
"name": "TavilyGetResearchTool",
|
||||
"package_dependencies": [
|
||||
"tavily-python"
|
||||
],
|
||||
"run_params_schema": {
|
||||
"description": "Input schema for TavilyGetResearchTool.",
|
||||
"properties": {
|
||||
"request_id": {
|
||||
"description": "Existing Tavily research request ID to fetch status and results for.",
|
||||
"title": "Request Id",
|
||||
"type": "string"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"request_id"
|
||||
],
|
||||
"title": "TavilyGetResearchToolSchema",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
{
|
||||
"description": "A tool that creates Tavily research tasks and can stream research progress and results. It returns Tavily responses as JSON or SSE chunks.",
|
||||
"env_vars": [
|
||||
{
|
||||
"default": null,
|
||||
"description": "API key for Tavily research service",
|
||||
"name": "TAVILY_API_KEY",
|
||||
"required": true
|
||||
}
|
||||
],
|
||||
"humanized_name": "Tavily Research",
|
||||
"init_params_schema": {
|
||||
"$defs": {
|
||||
"EnvVar": {
|
||||
"properties": {
|
||||
"default": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"default": null,
|
||||
"title": "Default"
|
||||
},
|
||||
"description": {
|
||||
"title": "Description",
|
||||
"type": "string"
|
||||
},
|
||||
"name": {
|
||||
"title": "Name",
|
||||
"type": "string"
|
||||
},
|
||||
"required": {
|
||||
"default": true,
|
||||
"title": "Required",
|
||||
"type": "boolean"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"name",
|
||||
"description"
|
||||
],
|
||||
"title": "EnvVar",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
"description": "Tool that uses the Tavily Research API to create research tasks.",
|
||||
"properties": {
|
||||
"citation_format": {
|
||||
"default": "numbered",
|
||||
"description": "Default citation format for Tavily research results.",
|
||||
"enum": [
|
||||
"numbered",
|
||||
"mla",
|
||||
"apa",
|
||||
"chicago"
|
||||
],
|
||||
"title": "Citation Format",
|
||||
"type": "string"
|
||||
},
|
||||
"model": {
|
||||
"default": "auto",
|
||||
"description": "Default model used for new Tavily research tasks.",
|
||||
"enum": [
|
||||
"mini",
|
||||
"pro",
|
||||
"auto"
|
||||
],
|
||||
"title": "Model",
|
||||
"type": "string"
|
||||
},
|
||||
"output_schema": {
|
||||
"anyOf": [
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"default": null,
|
||||
"description": "Default JSON Schema used to structure research output.",
|
||||
"title": "Output Schema"
|
||||
},
|
||||
"stream": {
|
||||
"default": false,
|
||||
"description": "Whether new Tavily research tasks should stream responses by default.",
|
||||
"title": "Stream",
|
||||
"type": "boolean"
|
||||
}
|
||||
},
|
||||
"required": [],
|
||||
"title": "TavilyResearchTool",
|
||||
"type": "object"
|
||||
},
|
||||
"name": "TavilyResearchTool",
|
||||
"package_dependencies": [
|
||||
"tavily-python"
|
||||
],
|
||||
"run_params_schema": {
|
||||
"description": "Input schema for TavilyResearchTool.",
|
||||
"properties": {
|
||||
"citation_format": {
|
||||
"default": "numbered",
|
||||
"description": "Citation format for the research report.",
|
||||
"enum": [
|
||||
"numbered",
|
||||
"mla",
|
||||
"apa",
|
||||
"chicago"
|
||||
],
|
||||
"title": "Citation Format",
|
||||
"type": "string"
|
||||
},
|
||||
"input": {
|
||||
"description": "The research task or question to investigate.",
|
||||
"title": "Input",
|
||||
"type": "string"
|
||||
},
|
||||
"model": {
|
||||
"default": "auto",
|
||||
"description": "The model used by the Tavily research agent.",
|
||||
"enum": [
|
||||
"mini",
|
||||
"pro",
|
||||
"auto"
|
||||
],
|
||||
"title": "Model",
|
||||
"type": "string"
|
||||
},
|
||||
"output_schema": {
|
||||
"anyOf": [
|
||||
{
|
||||
"additionalProperties": true,
|
||||
"type": "object"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"default": null,
|
||||
"description": "Optional JSON Schema that structures the research output.",
|
||||
"title": "Output Schema"
|
||||
},
|
||||
"stream": {
|
||||
"default": false,
|
||||
"description": "Whether to stream research progress and results as SSE chunks.",
|
||||
"title": "Stream",
|
||||
"type": "boolean"
|
||||
}
|
||||
},
|
||||
"required": [
|
||||
"input"
|
||||
],
|
||||
"title": "TavilyResearchToolSchema",
|
||||
"type": "object"
|
||||
}
|
||||
},
|
||||
{
|
||||
"description": "A tool that performs web searches using the Tavily Search API. It returns a JSON object containing the search results.",
|
||||
"env_vars": [
|
||||
|
||||
Reference in New Issue
Block a user