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Introduction

Integrate Tavily with Agno to enhance your AI agents with powerful web search capabilities. Agno provides a lightweight library for building agents with memory, knowledge, tools, and reasoning, making it easy to incorporate real-time web search and data extraction into your AI applications.

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Step-by-Step Integration Guide

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Step 1: Install Required Packages

Install the necessary Python packages:

pip install agno tavily-python

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Step 2: Set Up API Keys

Set these as environment variables in your terminal or add them to your environment configuration file:

export TAVILY_API_KEY=your_tavily_api_key
export OPENAI_API_KEY=your_openai_api_key

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Step 3: Initialize Agno Agent with Tavily Tools

from agno.agent import Agent
from agno.tools.tavily import TavilyTools
import os

# Initialize the agent with Tavily tools
agent = Agent(
    tools=[TavilyTools(
        search=True,                    # Enable search functionality
        max_tokens=8000,                # Increase max tokens for more detailed results
        search_depth="advanced",        # Use advanced search for comprehensive results
        format="markdown"               # Format results as markdown
    )],
    show_tool_calls=True
)

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Step 4: Example Use Cases

# Example 1: Basic search with default parameters
agent.print_response("Latest developments in quantum computing", markdown=True)

# Example 2: Market research with multiple parameters
agent.print_response(
    "Analyze the competitive landscape of AI-powered customer service solutions in 2024, "
    "focusing on market leaders and emerging trends",
    markdown=True
)

# Example 3: Technical documentation search
agent.print_response(
    "Find the latest documentation and tutorials about Python async programming, "
    "focusing on asyncio and FastAPI",
    markdown=True
)

# Example 4: News aggregation
agent.print_response(
    "Gather the latest news about artificial intelligence from tech news websites "
    "published in the last week",
    markdown=True
)

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Additional Use Cases

  1. Content Curation: Gather and organize information from multiple sources
  2. Real-time Data Integration: Keep your AI agents up-to-date with the latest information
  3. Technical Documentation: Search and analyze technical documentation
  4. Market Analysis: Conduct comprehensive market research and analysis