> ## Documentation Index
> Fetch the complete documentation index at: https://agno-v2-codex-docs-audit-20260719-0149.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Data labeling and classification
> Label and classify text, images, audio, video, and PDFs.
Agents can:
* Turn text, images, audio, video, and PDFs into structured records.
* Assign labels, label sets, taxonomy paths, and labeled spans.
* Score and rank model outputs for evals and preference data.
* Add a reviewer and an adjudicator when label quality matters.
Each task follows a similar pattern: an agent with an `output_schema`.
`Agent.run()` accepts text and media inputs. Set `output_schema` to validate the response against a Pydantic model.
## Example
```python theme={null}
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("Broken on arrival, total waste of money.").content
# Classification(label='negative')
```
Swap the schema and instructions and the same pattern covers data extraction, span labeling, scoring, and preference ranking.
If you're looking to jump straight into code, the [data labeling cookbook](https://github.com/agno-agi/agno/tree/v2.7.4/cookbook/data_labeling) contains 40+ runnable recipes across 18 data labeling patterns.
## Data labeling workflows
Pick the page that matches what you need.
| Workload | Input | Output | Page |
| -------------------- | ------------------------ | ------------------------------- | ----------------------------------------------------------------- |
| Data extraction | Any modality | Typed Pydantic object | [Data extraction](/use-cases/data-labeling/structured-extraction) |
| Classification | Any modality | One label, label set, or spans | [Classification](/use-cases/data-labeling/classification) |
| Scoring / evaluation | Prompt + response | Rubric scores | [LLM as judge](/use-cases/data-labeling/llm-as-judge) |
| Preference ranking | Prompt + two responses | Winner + rationale | [Preference data](/use-cases/data-labeling/preference-data) |
| Non-text input | Image, audio, video, PDF | Any of the above | [Multimodal inputs](/use-cases/data-labeling/multimodal-inputs) |
| Reviewed labels | Any input | Adjudicated label + audit trail | [Quality pipeline](/use-cases/data-labeling/quality-pipeline) |
## Model choice
The cookbooks use `gemini-3.5-flash` for text, image, audio, video, and PDF examples. A replacement model must support the input modality and structured output used by the recipe.
To run the examples, install the Google provider and set your API key:
```bash theme={null}
pip install -U "agno[google]"
export GOOGLE_API_KEY=***
```
The [Quality pipeline](/use-cases/data-labeling/quality-pipeline) runs its second labeler, reviewer, and adjudicator on Anthropic for provider diversity. That page needs a second provider and key:
```bash theme={null}
pip install -U "agno[anthropic]"
export ANTHROPIC_API_KEY=***
```
## Explore
Turn any modality into a typed object, with optional per-field confidence.
Single-label, multi-label, hierarchical, and span labeling.
Score outputs against a rubric. The same machinery, used for evals.
Rank A vs B for RLHF and DPO datasets.
Feed images, audio, video, and PDFs into any labeler.
Two labelers, a reviewer, and an adjudicator with an audit trail.
## Developer Resources
* [Data labeling cookbook](https://github.com/agno-agi/agno/tree/v2.7.4/cookbook/data_labeling)
* [Structured output](/input-output/structured-output/agent)
* [Multimodal agents](/multimodal/overview)