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This example shows how to create a reasoning team that can handle complex queries involving web search and financial data using the coordinate mode with reasoning capabilities.

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Code

cookbook/examples/teams/coordinate_mode/reasoning_team.py
from agno.agent import Agent
from agno.models.anthropic import Claude
from agno.models.openai import OpenAIChat
from agno.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.reasoning import ReasoningTools
from agno.tools.exa import ExaTools

web_agent = Agent(
    name="Web Search Agent",
    role="Handle web search requests",
    model=OpenAIChat(id="gpt-5-mini"),
    tools=[DuckDuckGoTools()],
    instructions=["Always include sources"],
)

finance_agent = Agent(
    name="Finance Agent",
    role="Handle financial data requests",
    model=OpenAIChat(id="gpt-5-mini"),  
    tools=[
        ExaTools(
            include_domains=["cnbc.com", "reuters.com", "bloomberg.com", "wsj.com"],
            show_results=True,
            text=False,
            highlights=False,
        )
    ],
    instructions=["Use tables to display data"],
)

team_leader = Team(
    name="Reasoning Team Leader",
    model=Claude(id="claude-3-7-sonnet-latest"),
    members=[
        web_agent,
        finance_agent,
    ],
    tools=[ReasoningTools(add_instructions=True)],
    markdown=True,
    show_members_responses=True,
)

team_leader.print_response(
    "Tell me 1 company in New York, 1 in San Francisco and 1 in Chicago and the stock price of each",
    stream=True,
    stream_intermediate_steps=True,
    show_full_reasoning=True,
)

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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

Install required libraries

pip install agno ddgs exa anthropic
3

Set environment variables

export OPENAI_API_KEY=****
export ANTHROPIC_API_KEY=****
export EXA_API_KEY=****
4

Run the agent

python cookbook/examples/teams/coordinate_mode/reasoning_team.py
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