Datasaur
NLP data labeling platform with AI-assisted automation, quality workflows, and private LLM options
Argilla
Collaborative platform for building high-quality AI datasets
Argilla lets AI engineers and domain experts collaboratively create, annotate, and manage high‑quality datasets for NLP, LLM, and multimodal projects, with easy deployment on Hugging Face Spaces.
Overview
Overview
Argilla is a collaboration tool designed for AI teams and domain experts who need to build and maintain high‑quality datasets. It supports a range of annotation tasks—from classic text classification and NER to LLM preference tuning and multimodal labeling—allowing users to iterate quickly on data and models.
Capabilities & Deployment
The platform offers a programmatic workflow via a Python SDK, enabling automated dataset creation, record logging, and annotation retrieval. Users can launch a ready‑to‑use Argilla server with a single click on Hugging Face Spaces, or self‑host using Docker for full control. Integrated features such as filters, AI‑generated feedback suggestions, and semantic search make data curation efficient and engaging.
Community & Maintenance
Argilla is community‑driven, with active Discord channels, bi‑weekly meetups, and contributions from organizations like the Red Cross and Prolific. While the core codebase is stable and bug‑fixes continue, no new features are planned, making it a reliable foundation for teams that can extend it themselves.
Highlights
Programmatic workflow for continuous evaluation and model improvement
Human‑AI feedback loops with filters, suggestions, and semantic search
One‑click deployment on Hugging Face Spaces or self‑hosted Docker image
Python SDK for dataset creation, logging, and annotation retrieval
Pros
- Mature, stable codebase with ongoing bug‑fix support
- Supports text, LLM, and multimodal annotation tasks
- Strong community presence via Discord, meetups, and social channels
- Easy initial deployment through Hugging Face Spaces
Considerations
- No new feature development planned
- Advanced automation requires Python SDK knowledge
- Self‑hosting may need infrastructure expertise
- Limited built‑in analytics compared to enterprise‑grade platforms
Managed products teams compare with
When teams consider Argilla, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- AI teams needing collaborative dataset curation
- Researchers building custom NLP or multimodal datasets
- Organizations that want full control over data and model pipelines
- Projects that can leverage Hugging Face Spaces for quick hosting
Not ideal when
- Enterprises requiring dedicated commercial support and SLAs
- Users seeking a fully managed SaaS solution
- Teams without Python development resources
- Projects needing extensive built‑in reporting dashboards
How teams use it
Refugee request triage for humanitarian aid
Domain experts classified incoming messages, enabling the Red Cross to route assistance faster and improve response accuracy.
Customer support multi‑label classification
Loris.ai generated labeled samples via contrastive learning, reducing annotation time and boosting classifier performance.
Research data collection for academic studies
Prolific distributed annotation tasks to a workforce, gathering high‑quality labeled data at scale.
Fine‑tuning LLMs with curated feedback
Teams built preference‑tuned datasets (e.g., UltraFeedback) leading to higher benchmark scores for models like Notus.
Tech snapshot
Python59%
Jupyter Notebook21%
Vue8%
TypeScript8%
JavaScript3%
SCSS1%
Frequently asked questions
How do I deploy Argilla?
You can launch the Argilla server with a single click on Hugging Face Spaces or self‑host using the provided Docker image.
Is Argilla still maintained?
Core code is stable; maintainers will continue to issue bug‑fixes and security patches, but no new features are planned.
What annotation tasks are supported?
Text classification, named‑entity recognition, RAG, preference tuning, and multimodal tasks such as text‑to‑image labeling.
Can I integrate Argilla with my existing ML pipeline?
Yes, the Python SDK lets you create datasets, log records, and retrieve annotations programmatically.
Is there a community to get help?
Join the Argilla Discord, attend bi‑weekly meetups, or follow the project on Twitter and LinkedIn for support.
Project at a glance
Active- Stars
- 4,816
- Watchers
- 4,816
- Forks
- 471
LicenseApache-2.0
Repo age4 years old
Last commit2 days ago
Primary languagePython
Last synced yesterday