Data labeling and classification

ML and data teams use agents to turn large collections of unstructured inputs into datasets for training, evaluation, search, and automation. Agno applies the same Pydantic schema pattern across text, images, audio, video, and PDFs, with workflows for parallel labeling, review, and conditional adjudication.

from typing import Literal

from agno.agent import Agent
from pydantic import BaseModel, Field


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


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

result = agent.run("Broken on arrival, total waste of money.").content
# Classification(label='negative')

Agent.run() validates the response against the Pydantic model. Change the schema and instructions to extract fields, label spans, score responses, or rank preferences.

What you can build

Outcome	Input	Pattern
Classify feedback, support requests, or documents	Text or files	Classification
Extract contacts, line items, action items, or attributes	Any supported modality	Data extraction
Detect entities and PII spans	Text	Classification and span labeling
Search an image library in natural language	Images	Image Search
Build pairwise preference datasets	Prompt and two responses	Preference data
Score generated responses against a rubric	Prompt and response	LLM as judge
Review and adjudicate important labels	Any labeling task	Quality pipeline
Label images, audio, video, and PDFs	Media or files	Multimodal inputs

The data labeling cookbook contains 18 labeling patterns and a complete Image Search application. The patterns cover text, image, audio, video, and document inputs.

Run the examples

Create an environment and install the Google provider:

uv venv .venv --python 3.12
source .venv/bin/activate
uv pip install "agno[google]"
export GOOGLE_API_KEY="..."

The quality review workflow also uses Anthropic:

uv pip install "agno[anthropic]"
export ANTHROPIC_API_KEY="..."

Welcome

Get Started

Features

Use Cases

Data labeling and classification

What you can build

Run the examples

Developer Resources

​What you can build

​Run the examples

​Developer Resources

What you can build

Run the examples

Developer Resources