# opendatalab/LabelLLM

The Open-Source Data Annotation Platform

Repository: https://github.com/opendatalab/LabelLLM
Canonical: https://ross.abutalabs.com/products/labelllm
Homepage: https://labelu-llm-demo.shlab.tech
Language: TypeScript
License: Apache-2.0
License Family: permissive
Last push: 2026-07-02T04:54:17+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 90, release rhythm 35, longevity 60
- inputs: {"age_days": 849, "days_push": 62, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1277, forks 132 (observed 2026-08-28T04:04:13.258039+00:00)

## What it is
LabelLLM is an open-source data annotation platform designed to streamline the labeling workflows needed for LLM development. It offers configurable annotation tasks, multimodal data support (audio, image, video), AI-assisted pre-annotation, and comprehensive task management with progress and quality monitoring.

## Use cases
- annotate conversation data for LLM fine-tuning
- label audio, image, and video datasets in one platform
- manage annotation teams with progress and quality tracking
- pre-annotate data with AI and have humans refine it
- set up custom annotation task configurations
- prepare training data for model development

## When to choose
- you are an independent developer or small/medium team preparing LLM training data
- you need multimodal annotation support under a unified self-hosted platform
- you want AI-assisted pre-annotation to speed up labeling
- you need task management with real-time progress and quality control

## When to avoid
- you only need simple image bounding-box annotation for computer vision
- you require a fully managed SaaS annotation service
- you need large-enterprise annotation workforce management features

## Facets
- artifact type: application
- maturity: active
- function: machine-learning, data-science, workflow-automation, developer-tools
- domain: machine-learning, artificial-intelligence, large-language-models
- platform: self-hosted, cross-platform
- tags: data-annotation, labeling, llm-training-data, multimodal, task-management, human-in-the-loop, data-engineering, web-server, docker

## Member repositories
- opendatalab/LabelLLM (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:13.258039+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 2026-08-30T05:02:50.977071+00:00, confidence not recorded.
  - readme: https://github.com/opendatalab/LabelLLM (fetched 2026-08-28T04:04:13.258039+00:00, sha 2b67207b00e2)
- Data as of 2026-08-30T08:39:29.467469+00:00.