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argilla-io/

argilla

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Argilla is an open-source Python tool to build and manage high-quality AI datasets with an emphasis on human-in-the-loop annotation and feedback. It provides an SDK installation, dataset creation, and examples for text classification tasks.

5.1kstars
498forks
28issues
Apache-2.0license
2021since
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Reviewgenerated from repository data · Aug 5, 2026

What it is

Argilla is a collaboration tool for AI engineers and domain experts who need to build high-quality datasets for their projects.

How it works

The project provides a Python SDK to interact with an Argilla server. Installation is via pip, and users instantiate an Argilla client with api_url and api_key. Datasets are created using a Settings object that defines guidelines, fields, and questions, then a Dataset is created with a client. Records can be added to datasets using dataset.records.log with a mapping for fields. The README includes a minimal end-to-end example for a text classification task and shows a dependency installation command for datasets.

Getting started

Install the SDK:

pip install argilla

Create a client:

import argilla as rg

client = rg.Argilla(api_url="https://[your-owner-name]-[your_space_name].hf.space", api_key="owner.apikey")

Define settings and a dataset, then create it:

settings = rg.Settings(
    guidelines="Classify the reviews as positive or negative.",
    fields=[
        rg.TextField(
            name="review",
            title="Text from the review",
            use_markdown=False,
        ),
    ],
    questions=[
        rg.LabelQuestion(
            name="my_label",
            title="In which category does this article fit?",
            labels=["positive", "negative"],
        )
    ],
)
dataset = rg.Dataset(
    name=f"my_first_dataset",
    settings=settings,
    client=client,
)
dataset.create()

Add records:

pip install datasets
from datasets import load_dataset

data = load_dataset("imdb", split="train[:100]").to_list()
dataset.records.log(records=data, mapping={"text": "review"})

Recent releases

Latest releases include:

  • v2.8.0 (2025-03-11): Better OAuth integration and other improvements and bug fixes.
  • v2.7.1 (2025-02-06): BUGFIX: Prevent sending auth headers for public requests.
  • v2.7.0 (2025-01-21): Similarity score feature in search.
  • v2.6.0 (2024-12-18): Push to Hugging Face Hub from Argilla UI.
  • v2.5.0 (2024-11-29): Webhooks to manage real-time information.

Traction

Stars: 5067

Behind the repo

Not provided in the README excerpt.

Caveats

License: Apache-2.0 Created: 2021-04-28 Last push: 2026-08-03

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