Structured Output for Teams

from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools

class ResearchReport(BaseModel):
    title: str
    summary: str = Field(description="Executive summary of findings")
    key_insights: list[str] = Field(description="Top 3-5 insights")
    recommendation: str

news_agent = Agent(
    name="News Researcher",
    role="Research tech news and trends",
    tools=[HackerNewsTools()]
)

finance_agent = Agent(
    name="Finance Analyst",
    role="Analyze financial data and stocks",
    tools=[YFinanceTools()]
)

team = Team(
    name="Research Team",
    model=OpenAIResponses(id="gpt-5.2"),
    members=[news_agent, finance_agent],
    output_schema=ResearchReport,
)

response = team.run("Research NVIDIA - analyze stock performance and recent news")

# response.content is a validated ResearchReport object
report: ResearchReport = response.content
print(report.title)
print(report.summary)
print(report.recommendation)

team = Team(
    model=OpenAIResponses(id="gpt-5.2"),
    members=[news_agent, finance_agent],
)

# Different schemas for different requests
report = team.run("Analyze AI market", output_schema=MarketReport)
comparison = team.run("Compare NVDA vs AMD", output_schema=StockComparison)

from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team import Team
from agno.tools.hackernews import HackerNewsTools
from agno.tools.yfinance import YFinanceTools

# Member schemas
class NewsInsights(BaseModel):
    headlines: list[str]
    sentiment: str = Field(description="positive, negative, or neutral")

class FinanceInsights(BaseModel):
    price: float
    change_percent: float
    recommendation: str

# Team schema
class CombinedReport(BaseModel):
    summary: str
    market_sentiment: str
    stock_outlook: str
    final_recommendation: str

news_agent = Agent(
    name="News Analyst",
    role="Research news",
    tools=[HackerNewsTools()],
    output_schema=NewsInsights,
)

finance_agent = Agent(
    name="Finance Analyst",
    role="Analyze stocks",
    tools=[YFinanceTools()],
    output_schema=FinanceInsights,
)

team = Team(
    model=OpenAIResponses(id="gpt-5.2"),
    members=[news_agent, finance_agent],
    output_schema=CombinedReport,
)

response = team.run("Full analysis of NVDA")
report: CombinedReport = response.content

class CompetitiveAnalysis(BaseModel):
    company: str
    market_position: str = Field(description="Leader, challenger, or follower")
    technical_strengths: list[str] = Field(description="From technical research")
    financial_strengths: list[str] = Field(description="From financial analysis")
    combined_outlook: str

class InvestmentRecommendation(BaseModel):
    ticker: str
    action: str = Field(description="buy, hold, or sell")
    price_target: float | None = None
    reasoning: str = Field(description="Synthesized reasoning from all analysts")
    risk_factors: list[str]
    confidence: float = Field(ge=0, le=1)

class CompanyComparison(BaseModel):
    companies: list[str]
    winner: str
    comparison_criteria: list[str]
    scores: dict[str, dict[str, int]]  # company -> criterion -> score
    summary: str

team = Team(
    model=OpenAIResponses(id="gpt-5.2"),
    members=[...],
    output_schema=MySchema,
    use_json_mode=True,
)