Ragas
FreeSupercharge Your LLM Application Evaluations
FreeFree tier
About Ragas
Ragas is an open-source toolkit for evaluating and optimizing Large Language Model (LLM) applications. It provides objective metrics, intelligent test generation, and data-driven insights to replace time-consuming subjective assessments with efficient evaluation workflows. Ragas supports both LLM-based and traditional metrics, automatically creates comprehensive test datasets, integrates seamlessly with frameworks like LangChain and observability tools, and enables building feedback loops from production data to continuously improve LLM applications.
Key Features
Objective Metrics: Evaluate LLM applications with precision using both LLM-based and traditional metrics.
Test Data Generation: Automatically create comprehensive test datasets covering a wide range of scenarios, including production-aligned test set generation.
Seamless Integrations: Works with popular LLM frameworks like LangChain and major observability tools.
Build feedback loops: Leverage production data to continually improve LLM applications.
Custom aspect evaluation with DiscreteMetric for specific output aspects.
Quickstart templates for RAG evaluation, with upcoming templates for agent evals, benchmark LLMs, prompt evals, and workflow evals.
Python and asyncio based, easily extensible.
Pros & Cons
Pros
- Open-source and free to use
- Provides objective, data-driven evaluation metrics
- Automates test dataset generation from production data
- Seamless integration with LangChain and observability tools
- Supports custom metric creation via DiscreteMetric
- Quickstart templates accelerate setup and project creation
- Actively maintained with a large community (14.9k stars on GitHub)
Cons
- Requires LLM API keys (e.g., OpenAI) and incurs usage costs
- Currently focused on RAG evaluation; other templates are marked as coming soon
- Python-only toolkit; limited to Python environments
- LLM-based metrics may introduce latency and additional cost
Best For
Evaluating RAG (Retrieval-Augmented Generation) systemsEvaluating AI agentsBenchmarking and comparing LLMsEvaluating prompt variationsEvaluating complex workflowsGeneral LLM application evaluation and optimization
FAQ
What is Ragas?
Ragas is an open-source toolkit for evaluating and optimizing Large Language Model (LLM) applications. It provides objective metrics, test data generation, and seamless integrations with frameworks like LangChain.
How do I install Ragas?
Install via pip: pip install ragas, or from source: pip install git+https://github.com/vibrantlabsai/ragas.
How do I get started with Ragas?
Use the ragas quickstart command to clone a complete example project: for example, 'ragas quickstart rag_eval' to create a RAG evaluation project.
What metrics does Ragas provide?
Ragas offers pre-built metrics such as Aspect Critique using DiscreteMetric, and supports both LLM-based and traditional metrics for evaluating LLM applications.
Is Ragas free?
Yes, Ragas is open-source and free to use.