Classification and span labeling

from typing import Literal

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


class Classification(BaseModel):
    label: Literal["positive", "negative", "neutral"] = Field(
        ..., description="The assigned sentiment label"
    )


agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions="You classify product reviews by sentiment.",
    output_schema=Classification,
)

result = agent.run("It works as described, nothing special.").content
# Classification(label='neutral')

from typing import List, Literal

from pydantic import BaseModel, Field

Aspect = Literal["food", "service", "value", "atmosphere", "cleanliness"]


class Tagging(BaseModel):
    tags: List[Aspect] = Field(
        ..., description="Every aspect the reviewer commented on; empty if none"
    )

from typing import List

from pydantic import BaseModel, Field


class HierarchicalTag(BaseModel):
    parent: str = Field(..., description="Top-level category")
    child: str = Field(..., description="Sub-category under the parent")


class Tagging(BaseModel):
    tags: List[HierarchicalTag]

from typing import List, Literal

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


class Entity(BaseModel):
    text: str = Field(..., description="Exact substring from the input")
    label: Literal["PERSON", "ORG", "LOCATION", "DATE"]


class Entities(BaseModel):
    entities: List[Entity]


agent = Agent(
    model=OpenAIResponses(id="gpt-5.5"),
    instructions=(
        "Extract all named entities. Return the exact substring as it "
        "appears, with its label. Do not paraphrase or normalize."
    ),
    output_schema=Entities,
)

text = "On March 3rd, Sarah Johnson left Acme Corp to join Lumen Labs."
result = agent.run(text).content

for e in result.entities:
    start = text.find(e.text)
    print(e.label, e.text, start, start + len(e.text))