Tavily Search

AI-powered web search using Tavily API

Tavily is an AI-powered search engine that gives agents high-quality results with built-in answer synthesis.

Diagram

Overview

Tavily is an AI-powered search engine optimized for LLMs and AI agents. It provides high-quality, relevant search results with built-in answer synthesis.

Installation

bash

pip install "praisonai[tools]"

Environment Variables

bash

export TAVILY_API_KEY="${TAVILY_API_KEY:?Set TAVILY_API_KEY in your shell}"

Get your API key from Tavily.

How It Works

Diagram

Quick Start

Simple Usage

python

from praisonai_tools import TavilyTool

# Initialize
tavily = TavilyTool()

# Search
results = tavily.search("What is quantum computing?")
print(results)

With Configuration
Use the same tool with an agent — see Usage with Agent below, or pass env vars and options from the sections above.

Usage with Agent

python

from praisonaiagents import Agent
from praisonai_tools import TavilyTool

agent = Agent(
    name="Researcher",
    instructions="You are a research assistant. Use Tavily to search for information.",
    tools=[TavilyTool()]
)

response = agent.chat("Search for the latest AI news")
print(response)

Available Methods

search(query, max_results=5)

Search the web with AI-powered results.

python

from praisonai_tools import TavilyTool

tavily = TavilyTool()
results = tavily.search("Python best practices", max_results=3)

# Returns:
# {
#     "query": "Python best practices",
#     "answer": "AI-generated summary...",
#     "results": [
#         {"title": "...", "url": "...", "content": "...", "score": 0.95}
#     ]
# }

search_context(query)

Get search context optimized for RAG applications.

python

context = tavily.search_context("machine learning fundamentals")
# Returns a string with relevant context for LLM consumption

extract(urls)

Extract content from specific URLs.

python

content = tavily.extract("https://example.com,https://another.com")
# Returns list of extracted content from each URL

Configuration Options

python

tavily = TavilyTool(
    api_key="your_key",           # Optional: defaults to TAVILY_API_KEY env var
    search_depth="advanced",       # "basic" or "advanced"
    include_answer=True,           # Include AI-generated answer
    max_tokens=6000               # Max tokens for context
)

Function-Based Usage

python

from praisonai_tools import tavily_search

# Quick search without instantiating class
results = tavily_search("latest tech news", max_results=5)

CLI Usage

bash

# Set API key
export TAVILY_API_KEY=your_key

# Use with praisonai
praisonai --tools TavilyTool "Search for AI trends 2025"

Error Handling

python

from praisonai_tools import TavilyTool

tavily = TavilyTool()
results = tavily.search("my query")

if "error" in results:
    print(f"Error: {results['error']}")
else:
    print(f"Found {len(results['results'])} results")

Common Errors

Error	Cause	Solution
`TAVILY_API_KEY not configured`	Missing API key	Set `TAVILY_API_KEY` environment variable
`tavily-python not installed`	Missing dependency	Run `pip install tavily-python`
`Invalid API key`	Wrong API key	Verify your API key at tavily.com

Best Practices

Let TAVILY_API_KEY come from the environment

TavilyTool() defaults to the TAVILY_API_KEY env var. Set it in your shell or .env rather than passing api_key= inline.

Use search_context for RAG

search_context(query) returns text optimised for LLM consumption. Prefer it over raw search when feeding results straight into the agent.

Tune search_depth

search_depth="advanced" returns richer results at higher latency. Use "basic" for quick lookups to keep the agent responsive.

Related Tools

Exa Search

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DuckDuckGo

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Serper

Google search API