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This example demonstrates how to use the Exa research tool for complex, structured research tasks with automatic citation tracking.

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Code

cookbook/examples/agents/deep_research_agent_exa.py
from textwrap import dedent

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.tools.exa import ExaTools

agent = Agent(
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[ExaTools(research=True, research_model="exa-research-pro")],
    instructions=dedent("""
        You are an expert research analyst with access to advanced research tools.
        
        When you are given a schema to use, pass it to the research tool as output_schema parameter to research tool. 

        The research tool has two parameters:
        - instructions (str): The research topic/question 
        - output_schema (dict, optional): A JSON schema for structured output

        Example: If user says "Research X. Use this schema {'type': 'object', ...}", you must call research tool with the schema.

        If no schema is provided, the tool will auto-infer an appropriate schema.

        Present the findings exactly as provided by the research tool.
    """),
)

# Example 1: Basic research with simple string
agent.print_response(
    "Perform a comprehensive research on the current flagship GPUs from NVIDIA, AMD and Intel. Return a table of model name, MSRP USD, TDP watts, and launch date. Include citations for each cell."
)

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Usage

1

Create a virtual environment

Open the Terminal and create a python virtual environment.
python3 -m venv .venv
source .venv/bin/activate
2

Set your API key

export OPENAI_API_KEY=xxx
export EXA_API_KEY=xxx
3

Install libraries

pip install -U agno openai exa_py
4

Run Agent

python cookbook/examples/agents/deep_research_agent_exa.py
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