TavilySearch Tool

Guide for using the TavilySearch tool with PraisonAI agents.

The TavilySearch tool lets an agent run AI-optimised web search through the Tavily API.

Overview

The TavilySearch tool is a tool that allows you to search the web using the TavilySearch.

pip install "langchain-community>=0.2.11" tavily-python
export TAVILY_API_KEY="${TAVILY_API_KEY:?Set TAVILY_API_KEY in your shell}"

from praisonaiagents import Agent, AgentTeam
from langchain_community.tools import TavilySearchResults

def search_tool(query: str):
    tool = TavilySearchResults(
        max_results=5,
        search_depth="advanced",
        include_answer=True,
        include_raw_content=True,
        include_images=True
    )
    return tool.run(query)

data_agent = Agent(instructions="I am looking for the top google searches on AI tools of 2025", tools=[search_tool])
editor_agent = Agent(instructions="Analyze the data and rank the tools based on their popularity")

agents = AgentTeam(agents=[data_agent, editor_agent])
agents.start()

How It Works

Getting Started

1. Simple Usage

   1. Install dependencies (see Overview above)
   2. Set required API keys in your environment
   3. Run the agent example in Overview
2. With Configuration

   Use the same tool with an agent — see the Overview example, or pass env vars from the sections above.

Best Practices

Keep TAVILY_API_KEY in the environment

Set TAVILY_API_KEY in your shell or .env. The tool reads it automatically — never hard-code the key.

Cap max_results

TavilySearchResults(max_results=5) controls how many hits return. Lower it so the agent processes fewer tokens.

Enable include_answer for direct answers

Set include_answer=True when the agent needs a synthesised answer rather than raw links — it saves a follow-up LLM step.

Related Tools

Tavily

Tavily tool (praisonai-tools)

Exa Search

Exa LangChain integration

DuckDuckGo

Privacy-focused search