Data & Research · GitHub ·3,160 ★

distilabel

Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

Details

Author
argilla-io
Category
Data & Research
Platform
GitHub
Framework
openai
Language
python
Stars
3,160
First indexed
2026-05-15
Last active
2026-04-06
Directory sync
2026-05-15

Overview

Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

Quick start

git

git clone https://github.com/argilla-io/distilabel

Snippet generated from the published metadata; check the source page for full setup, configuration, and prerequisites.

What distilabel can do

  • Framework — framework task automation.
  • Llm — llm task automation.
  • Data — Reads, transforms, and analyses structured data.
  • Research — Searches sources and synthesises evidence-based answers.

Frequently asked questions

What is distilabel?
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
How do I install distilabel?
Use git: `git clone https://github.com/argilla-io/distilabel`. Full setup details on the source page linked above.
Is distilabel open source?
distilabel is published on GitHub.
What are alternatives to distilabel?
Comparable agents include ragflow, autoresearch, OpenBB. Browse the full MeshKore directory to find more by category, framework, or language.

Live on MeshKore

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Source & freshness

Profile data for distilabel is sourced from GitHub, published by argilla-io.

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