deepfabric vs ragas
Synthetic training data and LLM application evaluation compared
deepfabric creates domain-specific synthetic training data and evaluates models on held-out tool-use tasks. ragas focuses on evaluating and improving LLM applications, with metrics, generated test data, and feedback from production data.
deepfabric: Generate and Evaluate Synthetic Training Data
DeepFabric generates domain-specific synthetic datasets for language-model training and agent evaluation. It combines topic planning, tool-use traces, validation, and evaluation in a Python library and CLI.
ragas: Evaluate and Improve LLM Applications
Ragas is a Python toolkit for evaluating and improving LLM applications with metrics, generated test data, and feedback from production data. It suits teams building evaluation workflows for retrieval-augmented generation and other LLM-powered systems.
| deepfabric | ragas | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 888 | 15.9k |
| Forks | 81 | 1.7k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- deepfabric builds datasets for model training and agent evaluation, while ragas assesses LLM application behavior, including RAG systems.
- deepfabric uses topic trees or graphs and can generate tool-use conversations with optional execution; ragas offers LLM-based and traditional metrics, custom checks, and test-data generation.
- deepfabric exports data for Hugging Face, TRL, Unsloth, and Axolotl workflows; ragas supports evaluation workflows alongside LLM frameworks and observability tools.
- deepfabric supports hosted and local model providers, while ragas describes LLM-based evaluation using a configured LLM and documents an OpenAI example.
- Both projects use Python and the Apache-2.0 license. ragas has 15.9k stars and 1.7k forks, compared with deepfabric's 888 stars and 81 forks.
Choose deepfabric if you…
- need domain-focused synthetic datasets for fine-tuning or agent evaluation.
- want to build tool-use examples with schema constraints and optional execution.
- need exports for Hugging Face or common fine-tuning workflows.
Choose ragas if you…
- need metrics and custom checks for evaluating an LLM application or RAG system.
- want to generate test datasets for evaluation scenarios.
- plan to use production data to guide ongoing evaluation and improvement.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.