multi_agent_team.py
"""
Multi-Agent Team - Investment Research Team
============================================
This example shows how to create a team of agents that work together.
Each agent has a specialized role, and the team leader coordinates.
We'll build an investment research team with opposing perspectives:
- Bull Agent: Makes the case FOR investing
- Bear Agent: Makes the case AGAINST investing
- Lead Analyst: Synthesizes into a balanced recommendation
This adversarial approach produces better analysis than a single agent.
Key concepts:
- Team: A group of agents coordinated by a leader
- Members: Specialized agents with distinct roles
- The leader delegates, synthesizes, and produces final output
Example prompts to try:
- "Should I invest in NVIDIA?"
- "Analyze Tesla as a long-term investment"
- "Is Apple overvalued right now?"
"""
from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.google import Gemini
from agno.team.team import Team
from agno.tools.yfinance import YFinanceTools
# ---------------------------------------------------------------------------
# Storage Configuration
# ---------------------------------------------------------------------------
team_db = SqliteDb(db_file="tmp/agents.db")
# ---------------------------------------------------------------------------
# Bull Agent — Makes the Case FOR
# ---------------------------------------------------------------------------
bull_agent = Agent(
name="Bull Analyst",
role="Make the investment case FOR a stock",
model=Gemini(id="gemini-3.5-flash"),
tools=[YFinanceTools(all=True)],
db=team_db,
instructions="""\
You are a bull analyst. Your job is to make the strongest possible case
FOR investing in a stock. Find the positives:
- Growth drivers and catalysts
- Competitive advantages
- Strong financials and metrics
- Market opportunities
Be persuasive but grounded in data. Use the tools to get real numbers.\
""",
add_datetime_to_context=True,
add_history_to_context=True,
num_history_runs=5,
)
# ---------------------------------------------------------------------------
# Bear Agent — Makes the Case AGAINST
# ---------------------------------------------------------------------------
bear_agent = Agent(
name="Bear Analyst",
role="Make the investment case AGAINST a stock",
model=Gemini(id="gemini-3.5-flash"),
tools=[YFinanceTools(all=True)],
db=team_db,
instructions="""\
You are a bear analyst. Your job is to make the strongest possible case
AGAINST investing in a stock. Find the risks:
- Valuation concerns
- Competitive threats
- Weak spots in financials
- Market or macro risks
Be critical but fair. Use the tools to get real numbers to support your concerns.\
""",
add_datetime_to_context=True,
add_history_to_context=True,
num_history_runs=5,
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
multi_agent_team = Team(
name="Multi-Agent Team",
model=Gemini(id="gemini-3.5-flash"),
members=[bull_agent, bear_agent],
instructions="""\
You lead an investment research team with a Bull Analyst and Bear Analyst.
## Process
1. Send the stock to BOTH analysts
2. Let each make their case independently
3. Synthesize their arguments into a balanced recommendation
## Output Format
After hearing from both analysts, provide:
- **Bull Case Summary**: Key points from the bull analyst
- **Bear Case Summary**: Key points from the bear analyst
- **Synthesis**: Where do they agree? Where do they disagree?
- **Recommendation**: Your balanced view (Buy/Hold/Sell) with confidence level
- **Key Metrics**: A table of the important numbers
Be decisive but acknowledge uncertainty.\
""",
db=team_db,
show_members_responses=True,
add_datetime_to_context=True,
add_history_to_context=True,
num_history_runs=5,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
# First analysis
multi_agent_team.print_response(
"Should I invest in NVIDIA (NVDA)?",
stream=True,
)
# Follow-up question — team remembers the previous analysis
multi_agent_team.print_response(
"How does AMD compare to that?",
stream=True,
)
# ---------------------------------------------------------------------------
# More Examples
# ---------------------------------------------------------------------------
"""
When to use Teams vs single Agent:
Single Agent:
- One coherent task
- No need for opposing views
- Simpler is better
Team:
- Multiple perspectives needed
- Specialized expertise
- Complex tasks that benefit from division of labor
- Adversarial reasoning (like this example)
Other team patterns:
1. Research → Analysis → Writing pipeline
researcher = Agent(role="Gather information")
analyst = Agent(role="Analyze data")
writer = Agent(role="Write report")
2. Checker pattern
worker = Agent(role="Do the task")
checker = Agent(role="Verify the work")
3. Specialist routing
classifier = Agent(role="Route to specialist")
specialists = [finance_agent, legal_agent, tech_agent]
"""
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno google-genai sqlalchemy yfinance
3
Export your Google API key
export GOOGLE_API_KEY="your_google_api_key_here"
$Env:GOOGLE_API_KEY="your_google_api_key_here"
4
Run the example
Save the code above as
multi_agent_team.py, then run:python multi_agent_team.py