Deep Research Agent

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

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."
)

Usage

1. Create a virtual environment

   Open the Terminal and create a python virtual environment.

   Mac

   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

   Mac

   python cookbook/examples/agents/deep_research_agent_exa.py