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Pass structured data to teams using Pydantic models. You can either pass a model instance directly or set input_schema to validate dictionaries automatically.

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Passing Pydantic Models Directly

Pass a Pydantic model instance to input: 
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 ResearchProject(BaseModel):
    topic: str
    focus_areas: list[str] = Field(min_length=1)
    max_sources: int = Field(ge=1, le=20, default=10)

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

finance_agent = Agent(
    name="Finance Researcher",
    role="Research financial data",
    tools=[YFinanceTools()]
)

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

# Pass the model instance directly
project = ResearchProject(
    topic="AI Agents",
    focus_areas=["multi-agent systems", "tool use"],
    max_sources=15
)

response = team.run(input=project)
Validation happens when you create the model instance. Invalid data raises a Pydantic ValidationError before the team runs.

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Using input_schema

Set input_schema on the team to validate dictionaries automatically: 
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 ResearchProject(BaseModel):
    topic: str
    focus_areas: list[str] = Field(min_length=1)
    max_sources: int = Field(ge=1, le=20, default=10)

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

finance_agent = Agent(
    name="Finance Researcher",
    role="Research financial data",
    tools=[YFinanceTools()]
)

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

# Pass a dict - validated against ResearchProject
response = team.run(
    input={
        "topic": "AI Agents",
        "focus_areas": ["multi-agent systems", "tool use"],
        "max_sources": 15
    }
)
This is useful when input comes from external sources like API requests or configuration files.

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Validation Errors

Invalid input raises a Pydantic ValidationError: 
from pydantic import BaseModel, Field, ValidationError

class ResearchProject(BaseModel):
    topic: str
    focus_areas: list[str] = Field(min_length=1)

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

try:
    team.run(input={"topic": "AI", "focus_areas": []})  # Empty list
except ValidationError as e:
    print(e)
    # focus_areas: List should have at least 1 item

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When to Use Each Approach

ApproachUse when
Pass model directlyYou’re building the input in code
Use input_schemaInput comes from external sources (APIs, files, user input)

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Common Patterns

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Multi-Topic Research

from pydantic import BaseModel, Field

class ComparisonProject(BaseModel):
    title: str
    items_to_compare: list[str] = Field(min_length=2, max_length=5)
    comparison_criteria: list[str]
    output_format: str = Field(default="table")

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

response = team.run(
    input={
        "title": "AI Framework Comparison",
        "items_to_compare": ["Agno", "LangChain", "CrewAI"],
        "comparison_criteria": ["performance", "ease of use", "documentation"],
        "output_format": "table"
    }
)

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Scoped Analysis

from pydantic import BaseModel, Field
from datetime import date

class AnalysisScope(BaseModel):
    company: str
    analysis_type: str = Field(description="financial, competitive, or market")
    start_date: date | None = None
    end_date: date | None = None
    include_competitors: bool = True

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

response = team.run(
    input={
        "company": "NVIDIA",
        "analysis_type": "competitive",
        "include_competitors": True
    }
)

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Nested Configuration

from pydantic import BaseModel, Field

class Source(BaseModel):
    name: str
    priority: int = Field(ge=1, le=3, default=2)

class ResearchConfig(BaseModel):
    topic: str
    sources: list[Source]
    depth: int = Field(ge=1, le=10, default=5)

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

response = team.run(
    input={
        "topic": "Quantum Computing",
        "sources": [
            {"name": "HackerNews", "priority": 1},
            {"name": "Financial Reports", "priority": 2}
        ],
        "depth": 7
    }
)