# argilla-io/argilla

Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets

Repository: https://github.com/argilla-io/argilla
Canonical: https://ross.abutalabs.com/products/argilla
Homepage: https://argilla-io.github.io/argilla/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: human-in-the-loop, natural-language-processing, mlops, developer-tools, text-labeling, annotation-tool, nlp, machine-learning, active-learning, weak-supervision, weakly-supervised-learning, text-annotation, llm, ai, gpt-4, rlhf, langchain
Last push: 2026-08-24T22:15:29+00:00

## Health v2 (maintenance only)
Score: 79/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 40, longevity 100
- inputs: {"age_days": 1953, "days_push": 9, "days_rel": 540, "gap_med": 14.5, "n_releases_24m": 13}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5085, forks 503 (observed 2026-08-28T04:09:09.586888+00:00)

## What it is
Argilla is an open-source collaboration tool for AI engineers and domain experts to build, annotate, and curate high-quality datasets for NLP, LLM, and multimodal projects. It combines a Python SDK, a FastAPI server backed by Elasticsearch/OpenSearch, and a Vue.js annotation UI, deployable via Docker or Hugging Face Spaces.

## Use cases
- annotate text classification and NER datasets with domain experts
- collect human preference data for RLHF and LLM fine-tuning
- review and evaluate RAG system outputs
- build training datasets with human-in-the-loop workflows
- run active learning and weak supervision pipelines
- log model predictions for continuous evaluation
- self-host a data annotation platform for an ML team

## When to choose
- you need a free, self-hosted annotation tool with full data ownership
- you want programmatic dataset workflows via a Python SDK rather than manual labeling
- you're collecting human feedback for LLM tuning, RAG evaluation, or classic NLP tasks
- you want one-click deployment on Hugging Face Spaces

## When to avoid
- you need active feature development or new capabilities - the original team has moved on and only bug fixes are published
- you need synthetic data generation - use the companion distilabel library instead
- you need model training - Argilla only manages data, not training
- you need a fully managed commercial annotation workforce platform

## Facets
- artifact type: application
- maturity: maintenance
- function: data-science, machine-learning, nlp, rag, llm-training, self-hosted, sdk
- domain: machine-learning, data-science, large-language-models, developer-tools, self-hosted
- platform: python, self-hosted, cloud
- tags: data-annotation, human-in-the-loop, text-labeling, rlhf, active-learning, weak-supervision, dataset-curation, hugging-face, vuejs-ui, fastapi, annotation, natural-language-processing, retrieval-augmented-generation, docker, web-server

## Member repositories
- argilla-io/argilla (main) score 79

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:09.586888+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:02:33.663914+00:00, confidence not recorded.
  - readme: https://github.com/argilla-io/argilla (fetched 2026-08-28T04:09:09.586888+00:00, sha f570bea73294)
  - homepage: https://argilla-io.github.io/argilla/latest/ (fetched 2026-08-29T08:57:28.604406+00:00, sha 2ff382e775ea)
  - site_page: https://docs.argilla.io/latest/getting_started/quickstart (fetched 2026-08-29T08:57:28.689941+00:00, sha dfbe574474f5)
  - site_page: https://docs.argilla.io/latest/getting_started/faq (fetched 2026-08-29T08:57:28.707644+00:00, sha 5a9c3e75fb84)
  - site_page: https://docs.argilla.io/latest/community/developer (fetched 2026-08-29T08:57:28.709670+00:00, sha 29f5fbe80a41)
  - site_page: https://docs.argilla.io/latest/community/changelog (fetched 2026-08-29T08:57:28.711799+00:00, sha 15b0b7b0af99)
  - site_page: https://docs.argilla.io/latest/community/integrations/llamaindex_rag_github (fetched 2026-08-29T08:57:28.715244+00:00, sha 9b8e6f38412f)
- Data as of 2026-08-30T08:39:29.467469+00:00.