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Open source
24 alternativesdistilabel
Synthetic data and AI feedback pipelines can be built with Distilabel, a framework based on research papers.
About distilabel
Distilabel is an open-source framework for building pipelines that generate synthetic data and collect AI feedback based on research methods. It covers predictive NLP tasks such as classification and extraction, as well as language-model tasks such as instruction following, dialogue generation, and judging, and provides a unified API for getting feedback from different LLM providers.
It installs with pip and requires Python 3.9+. Optional extras add support for specific providers and for exporting datasets to Argilla. The original authors have moved on to other projects, and community collaborators are maintaining the project, with current fixes available on the develop branch.
Key features
- Build programmatic pipelines for synthetic data generation and AI feedback
- Support classification and extraction for predictive NLP projects
- Support instruction following, dialogue generation, and judging workflows
- Connect to multiple LLM providers through one unified API
- Export generated datasets to Argilla
- Install optional extras for Anthropic, Cohere, Groq, and others
Best for: Engineers building synthetic datasets for fine-tuning LLMs or for traditional NLP tasks.
Good for
- Implements methods based on verified research papers
- Works with multiple LLM providers through a unified API
- Open-source and community-driven with meetups, Discord channels, and a public roadmap
Watch out for
- Original authors have moved on; community collaborators are working toward the next release
- Requires Python 3.9 or newer
- Specific LLM integrations require installing the matching extra
distilabel at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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distilabel FAQ
How do I install Distilabel?
Run pip install distilabel --upgrade. Python 3.9 or newer is required.
Which LLM integrations are listed?
Extras are listed for Anthropic, Cohere, Groq, Hugging Face Inference Endpoints, transformers, LiteLLM, and llama-cpp, plus export to Argilla.
Who maintains Distilabel now?
The original authors moved on to other projects. Community collaborators maintain it, and the develop branch has the latest fixes and improvements.
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