Tool overview
Ragas is listed under AI Governance Safety & Evaluation AI tools.
What is Ragas?
Ragas is an open-source Python framework for evaluating RAG pipelines and LLM applications. It provides component and end-to-end metrics, experiment workflows, integrations with popular AI frameworks, and local execution for teams building repeatable evaluation systems.
Best for
RAG and LLM teams building repeatable evaluation datasets and metrics
Who is it for?
Decision note
Best for teams that need programmable, local evaluation of RAG and LLM pipelines and can design meaningful test datasets.
Key features
Component-level and end-to-end RAG evaluation metrics
Python APIs for datasets, experiments, and scoring
Integrations with LLM and observability frameworks
Local and CI-friendly open-source execution
Use cases
Evaluate retrieval relevance and faithfulness
Compare RAG pipelines and model configurations
Build repeatable LLM quality benchmarks
Run evaluation suites during development and CI
Pros
- Open source under Apache 2.0
- Runs locally in Python workflows
- Broad ecosystem integration for RAG evaluation
Limitations
Scores depend on dataset quality, metric assumptions, judge models, and implementation details. Teams should combine automated metrics with domain review and production monitoring.
Pricing details
Billing options
Pricing note
The Ragas framework is free and open source under Apache 2.0. Model-provider or infrastructure charges may apply when evaluations call external services. Consulting or enterprise assistance is available by contacting Vibrant Labs.
Supported languages
- English
Integrations
LangChain
LlamaIndex
LangSmith
LLM providers
Observability and experiment frameworks
Please log in to join the discussion.