argilla
The Argilla python server SDK
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a deployed Argilla Server instance (easiest via Hugging Face Spaces); the SDK is a client library only and cannot function standalone.
- 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 · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 is permissive, allowing commercial and private use with minimal restrictions; you must retain license and copyright notices in distributions.
last release 2025-03-10 (522 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 260,978 downloads/mo, #8,389 on PyPI
Alternatives
Verify before relying
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()- 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).
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 on it.
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
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.
Requires a deployed Argilla Server instance (easiest via Hugging Face Spaces); the SDK is a client library only and cannot function standalone.
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()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
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packageshttpxpydantichuggingface_hubtqdmrichdatasetspillowstandardwebhooks |
| Maintenance | Aging 522 days since the last release |
| First released | |
| Downloads | 260,978 / month, #8,389 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: argilla-2.8.0-py3-none-any.whl
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