Documentation Index
Fetch the complete documentation index at: https://docs.ag2.ai/llms.txt
Use this file to discover all available pages before exploring further.
The Tavily AI search integration allows users to perform real-time web
searches within the AG2 framework. Follow these steps to integrate
TavilySearchTool with AG2 Agents.
Configuring Your Tavily API Key
-
Create a Tavily Account:
- Visit Tavily AI
- Click
Sign Up and create an account
-
Get API Key:
- Navigate to Tavily API
- Generate API key under API Keys
-
Set Environment Variable:
export TAVILY_API_KEY="your_api_key_here"
Package Installation
To get started with the Tavily Search integration in AG2, follow these
steps:
Install AG2 with openai extra since we use openai in our examples:
pip install -U "ag2[openai]"
Note: If you have been using autogen or ag2, all you need to
do is upgrade it using:
pip install -U "autogen[openai]"
or
pip install -U "ag2[openai]"
as autogen, and ag2 are aliases for the same PyPI package.
You’re all set! Now you can start using Tavily AI Search with AG2.
Implementation
The TavilySearchTool enables agents to perform real time AI Powered
web search.
Imports
import os
from autogen import AssistantAgent, UserProxyAgent
from autogen.tools.experimental import TavilySearchTool
Agent Configuration
Configure an assistant agent and user proxy to be used for LLM
recommendation and execution respectively.
os.environ["AUTOGEN_USE_DOCKER"] = "False"
from autogen import LLMConfig
config_list = LLMConfig(api_type="openai", model="gpt-4o-mini")
assistant = AssistantAgent(
name="assistant",
llm_config=config_list,
)
user_proxy = UserProxyAgent(name="user_proxy", human_input_mode="NEVER")
tavily_search_tool = TavilySearchTool(tavily_api_key=os.getenv("TAVILY_API_KEY"))
# Register the tool for LLM recommendation and execution.
tavily_search_tool.register_for_llm(assistant)
tavily_search_tool.register_for_execution(user_proxy)
Start the Conversation
With the setup complete, you can now use the assistant to fetch live web
search results.
response = user_proxy.initiate_chat(
recipient=assistant,
message="What happened with stock prices after deepseek was launched? Please search the web.",
max_turns=2,
)
print(f"Final Answer: {response.summary}")