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Pass structured data to agents 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

class ResearchRequest(BaseModel):
    topic: str
    max_sources: int = Field(ge=1, le=20, default=5)
    focus_areas: list[str] = Field(default_factory=list)

agent = Agent(model=OpenAIResponses(id="gpt-5.2"))

# Pass the model instance directly
request = ResearchRequest(
    topic="AI Agents",
    max_sources=10,
    focus_areas=["multi-agent systems", "tool use"]
)

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

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Using input_schema

Set input_schema on the agent to validate dictionaries automatically: 
from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIResponses

class ResearchRequest(BaseModel):
    topic: str
    max_sources: int = Field(ge=1, le=20, default=5)
    focus_areas: list[str] = Field(default_factory=list)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    input_schema=ResearchRequest,
)

# Pass a dict - validated against ResearchRequest
response = agent.run(
    input={
        "topic": "AI Agents",
        "max_sources": 10,
        "focus_areas": ["multi-agent systems", "tool use"]
    }
)
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
from agno.agent import Agent
from agno.models.openai import OpenAIResponses

class OrderRequest(BaseModel):
    product_id: str
    quantity: int = Field(gt=0)

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    input_schema=OrderRequest,
)

try:
    agent.run(input={"product_id": "SKU-123", "quantity": -5})
except ValidationError as e:
    print(e)
    # quantity: Input should be greater than 0

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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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API Request Handler

from pydantic import BaseModel, Field
from agno.agent import Agent
from agno.models.openai import OpenAIResponses

class SummaryRequest(BaseModel):
    text: str = Field(min_length=1, max_length=50000)
    max_length: int = Field(ge=50, le=500, default=200)
    style: str = Field(default="concise")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    input_schema=SummaryRequest,
)

# In your API endpoint
def summarize(request_data: dict):
    response = agent.run(input=request_data)  # Auto-validated
    return {"summary": response.content}

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Configuration-Driven Tasks

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

class ResearchConfig(BaseModel):
    topic: str
    depth: int = Field(ge=1, le=10, default=5)
    include_sources: bool = True
    output_format: str = Field(default="markdown")

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    tools=[HackerNewsTools()],
    input_schema=ResearchConfig,
)

# Load config from file or environment
config = {
    "topic": "LLM frameworks",
    "depth": 7,
    "include_sources": True
}

response = agent.run(input=config)

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Nested Models

from pydantic import BaseModel
from agno.agent import Agent
from agno.models.openai import OpenAIResponses

class Author(BaseModel):
    name: str
    email: str

class ArticleRequest(BaseModel):
    title: str
    author: Author
    tags: list[str]

agent = Agent(
    model=OpenAIResponses(id="gpt-5.2"),
    input_schema=ArticleRequest,
)

response = agent.run(
    input={
        "title": "Getting Started with Agno",
        "author": {"name": "Jane Doe", "email": "[email protected]"},
        "tags": ["tutorial", "agents"]
    }
)