vibrantlabsai/ragas

Health v2 · maintenance only

Adoption not part of the score

15901 stars · 1737 forks observed · 2026-10-02

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Ragas is a Python library for evaluating LLM applications with objective, LLM-driven metrics and systematic experiment loops. It also generates synthetic test datasets and integrates with frameworks like LangChain, LlamaIndex, and observability tools such as LangSmith and Arize Phoenix.

Use cases

• evaluate rag pipeline quality with metrics like faithfulness and answer relevance
• generate synthetic test datasets for llm application evaluation
• build continuous evaluation loops for chatbots and ai agents
• benchmark prompt or model changes against a baseline
• integrate llm evals into langchain or llamaindex workflows
• trace and log evaluation results to langsmith or arize phoenix
• evaluate amazon bedrock agents

When to choose

• you need systematic, repeatable evaluation of rag or agent applications instead of manual vibe checks
• you lack a golden test dataset and want automated test set generation
• you use langchain, llamaindex, or bedrock and want drop-in eval integration
• you want custom llm-based metrics defined with simple decorators

When to avoid

• you need deterministic unit testing of non-LLM code - use pytest instead
• you want a hosted eval dashboard with no code - this is a developer library
• your evaluation must run fully offline without any LLM judge, since many metrics require an evaluator LLM

Facets

library · maturity active

testing benchmarking machine-learning rag llm-inference data-generation analytics large-language-models machine-learning developer-tools artificial-intelligence python cross-platform llm-evaluation evals rag-evaluation test-set-generation llmops langchain llamaindex retrieval-augmented-generation

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markdown · JSON · MCP: product_card(name="vibrantlabsai/ragas")