Teams can generate structured data using Pydantic models, just like individual agents. This feature is perfect for coordinated data extraction, analysis, and report generation where multiple agents work together to produce a structured result.

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Example

Let’s create a Stock Research Team that produces a structured StockReport.

stock_team.py
from typing import List
from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.yfinance import YFinanceTools

class StockAnalysis(BaseModel):
    symbol: str
    company_name: str
    analysis: str

class CompanyAnalysis(BaseModel):
    company_name: str
    analysis: str

class StockReport(BaseModel):
    symbol: str = Field(..., description="Stock ticker symbol")
    company_name: str = Field(..., description="Full company name")
    current_price: str = Field(..., description="Current stock price")
    analysis: str = Field(..., description="Comprehensive analysis combining multiple perspectives")
    recommendation: str = Field(..., description="Investment recommendation: Buy, Hold, or Sell")

# Create specialized agents
stock_searcher = Agent(
    name="Stock Searcher",
    model=OpenAIChat("gpt-4o"),
    response_model=StockAnalysis,
    role="Searches for current stock information and price data.",
    tools=[
        YFinanceTools(
            stock_price=True,
            analyst_recommendations=True,
        )
    ],
)

company_info_agent = Agent(
    name="Company Info Searcher", 
    model=OpenAIChat("gpt-4o"),
    role="Researches company fundamentals and recent news.",
    response_model=CompanyAnalysis,
    tools=[
        YFinanceTools(
            stock_price=False,
            company_info=True,
            company_news=True,
        )
    ],
)

# Create team with structured output
stock_research_team = Team(
    name="Stock Research Team",
    mode="coordinate",
    model=OpenAIChat("gpt-4o"),
    members=[stock_searcher, company_info_agent],
    response_model=StockReport,
    markdown=True,
    show_members_responses=True,
)

stock_research_team.print_response("Give me a comprehensive stock report for NVDA")

The team will coordinate between its members and produce a structured StockReport object:

StockReport(
│   symbol='NVDA',
│   company_name='NVIDIA Corporation',
│   current_price='$875.42',
│   analysis='NVIDIA continues to dominate the AI chip market with strong demand for its H100 and upcoming H200 GPUs. The company has shown exceptional growth in data center revenue, driven by enterprise AI adoption and cloud provider expansion. Recent partnerships with major tech companies strengthen its market position, though competition from AMD and Intel is intensifying.',
│   recommendation='Buy'
)

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Using a Parser Model

You can use an additional model to parse and structure the output from your primary model. This approach is particularly effective when the primary model is optimized for reasoning tasks, as such models may not consistently produce detailed structured responses.

team = Team(
    name="Stock Research Team",
    mode="coordinate",
    model=Claude(id="claude-sonnet-4-20250514"),
    members=[stock_searcher, company_info_agent],
    response_model=StockReport,
    parser_model=OpenAIChat(id="gpt-4o"),
)

You can also provide a custom parser_model_prompt to your Parser Model.

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Streaming Structured Output

Teams support streaming with structured output, where the content event contains the complete structured result as a single event.

streaming_team.py
from typing import List
from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.team.team import Team
from agno.tools.yfinance import YFinanceTools

class MarketAnalysis(BaseModel):
    sector: str = Field(..., description="Market sector being analyzed")
    key_trends: List[str] = Field(..., description="Major trends affecting the sector")
    top_performers: List[str] = Field(..., description="Best performing stocks in the sector")
    market_outlook: str = Field(..., description="Overall market outlook and predictions")
    risk_factors: List[str] = Field(..., description="Key risks to consider")

# Create research agents
trend_analyst = Agent(
    name="Trend Analyst",
    model=OpenAIChat("gpt-4o"),
    role="Analyzes market trends and sector performance.",
    tools=[YFinanceTools(stock_price=True, analyst_recommendations=True)]
)

risk_assessor = Agent(
    name="Risk Assessor", 
    model=OpenAIChat("gpt-4o"),
    role="Identifies and evaluates market risks and opportunities.",
    tools=[YFinanceTools(company_news=True, company_info=True)]
)

# Create streaming team
market_research_team = Team(
    name="Market Research Team",
    mode="coordinate", 
    model=OpenAIChat("gpt-4o"),
    members=[trend_analyst, risk_assessor],
    response_model=MarketAnalysis,
    markdown=True,
    show_members_responses=True,
)

# Stream the team response
market_research_team.print_response(
    "Analyze the technology sector for Q1 2024", 
    stream=True, 
    stream_intermediate_steps=True
)

When streaming with teams and structured output, you’ll see intermediate steps from individual team members, but the final structured result is delivered as a single complete chunk rather than being streamed progressively.

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Developer Resources