Classification and span labeling

Assign one label, a set of labels, a taxonomy path, or marked spans.

Classification is extraction where the schema is a closed set. Use a Literal for one label, a List[Literal] for many.

from typing import Literal

from agno.agent import Agent
from agno.models.google import Gemini
from pydantic import BaseModel, Field


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


agent = Agent(
    model=Gemini(id="gemini-3.5-flash"),
    instructions="You classify product reviews by sentiment.",
    output_schema=Classification,
)

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

The Literal constrains the model to the closed set. There is no invalid label to clean up downstream.

Multi-label

A review can touch several aspects. Return any subset.

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"
    )

Hierarchical

For a taxonomy, return the path instead of a flat label.

from typing import List, Literal

from pydantic import BaseModel, Field


Parent = Literal["sports", "politics", "tech", "business", "health"]


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


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

Span labeling

Asking the model to count characters is unreliable. Have it return the exact substring and locate offsets in Python.

from typing import List, Literal

from agno.agent import Agent
from agno.models.google import Gemini
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=Gemini(id="gemini-3.5-flash"),
    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)
    end = start + len(e.text) if start >= 0 else None
    print(e.label, e.text, start, end)

str.find() returns -1 when the returned text is not a verbatim substring, which is the signal that the model paraphrased despite the instructions. Guard the offset before you use it. find() also returns the first match only. When the same substring appears more than once, track the search offset or match occurrences in order before assigning spans.

The same shape drives PII redaction: detect the spans, then replace each with its tag in post-processing.

Choosing the shape

You need	Schema
Exactly one label	Literal[...]
Any subset of labels	List[Literal[...]]
A taxonomy path	A model with parent / child
Marked substrings	A model with text + label, offsets in Python

Other modalities

Image, audio, video, and document classification follow the same schema pattern with a different input argument. See Multimodal inputs.

Modality	Cookbook
Image	image_classification
Audio	audio_classification
Video	video_classification
Document	document_classification

Next steps

Task	Guide
Extract fields instead of labels	Data extraction
Score outputs against a rubric	LLM as judge
Add reviewer agreement	Quality pipeline

Developer Resources

• Text classification cookbook
• Span labeling cookbook