argilla

What it is and what it does

Argilla is a Python SDK that connects to a remote Argilla Server to enable collaborative dataset curation and annotation. It provides a programmatic interface for creating datasets with custom fields and labeling tasks, then logging records and managing them through filtering, AI-assisted suggestions, and semantic search. The package is designed for AI teams who need to improve model quality by focusing on data quality—it bridges the gap between raw data collection and model training by letting domain experts and engineers work together on labeling and validation workflows.

The SDK depends on eight runtime packages including httpx for HTTP communication, pydantic for data validation, huggingface_hub for integration with Hugging Face, and datasets for loading external data sources. It requires Python 3.9 or later and is distributed as a pure-Python wheel, making installation straightforward. However, it is fundamentally a client library: you must deploy an Argilla Server separately (typically via Hugging Face Spaces) before the SDK can be used.

Use it for:

• Build and label datasets for text classification, span labeling, or generative tasks by creating custom field and question schemas in code.
• Curate and filter existing datasets to improve quality before fine-tuning language models or evaluating model outputs.
• Collaborate with domain experts to annotate and validate data for specialized domains (e.g., medical, legal, customer support).
• Monitor and improve LLM pipeline outputs by logging model predictions and collecting human feedback through a shared interface.
• Integrate external datasets (e.g., from Hugging Face) and apply AI-assisted suggestions to accelerate labeling workflows.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Argilla is a Python SDK client for a server-based collaboration platform that helps teams build and curate high-quality datasets for AI projects, with support for labeling, filtering, and semantic search workflows.

Yes, if you have a deployed Argilla Server and need programmatic dataset management and team collaboration for AI projects. The SDK is straightforward to install and integrates well with Hugging Face tools. However, be aware that maintenance is aging (522 days since last release), so expect slower updates; verify compatibility with your runtime dependencies before committing to production use. Not worth installing if you only need local-only data labeling or have no server deployment plan.

Install

argilla on PyPI

pip install argilla

uv add argilla

poetry add argilla

Installing argilla

Before you install

Low install friction with a wheel distribution and eight common runtime dependencies. Maintenance status is aging—the latest release was 522 days ago—so expect slower response to issues, though the package remains functional.

License in practice

Apache 2.0 is permissive, allowing commercial and private use with minimal restrictions; you must retain license and copyright notices in distributions.

Quickstart

pip install argilla

import argilla as rg
client = rg.Argilla(api_url="https://your-space.hf.space", api_key="owner.apikey")
settings = rg.Settings(guidelines="...", fields=[rg.TextField(name="text")], questions=[rg.LabelQuestion(name="label", labels=["a", "b"])])
dataset = rg.Dataset(name="my_dataset", settings=settings, client=client)
dataset.create()

Requires a deployed Argilla Server instance (easiest via Hugging Face Spaces); the SDK is a client library only and cannot function standalone.

Verify before relying

• Whether the aging maintenance status (522 days since last release) affects stability or security in production use.
• Performance characteristics when working with large datasets or concurrent team members.
• Specific version compatibility with the eight runtime dependencies (httpx, pydantic, huggingface_hub, tqdm, rich, datasets, pillow, standardwebhooks).

Package facts

Evidence: argilla-2.8.0-py3-none-any.whl

Tags

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