spb/tools-spboucher-ai

1{2  "slug": "ragas",3  "name": "Ragas",4  "aliases": ["RAGAS", "ragas (PyPI)"],5  "vendor": { "name": "Vibrant Labs (formerly Exploding Gradients)", "slug": "vibrant-labs", "website": "https://www.ragas.io" },6  "homepage_url": "https://www.ragas.io/",7  "docs_url": "https://docs.ragas.io/en/stable/",8  "repo_url": "https://github.com/vibrantlabsai/ragas",9  "product_type": "library",10  "product_status": "active",11  "summary": "Open-source Python library for evaluating LLM and RAG applications with LLM-driven and custom metrics, experiments, dataset management and result tracking",12  "description": "Ragas is an Apache-2.0 Python library for systematic evaluation of LLM applications, originally focused on retrieval-augmented generation and now framed around an experiments workflow: change the application, run an evaluation, review results and iterate. It ships a library of LLM-driven metrics (faithfulness, context precision and recall and answer relevancy are among the best known from its RAG origins) and lets you define custom metrics with simple decorators; built-in dataset management and result tracking keep runs comparable. Integrations cover LangChain and LlamaIndex among other frameworks, and the GitHub repository moved from the explodinggradients organization to vibrantlabsai, reflecting the maintaining company's rename to Vibrant Labs.\n\nRagas suits teams who want a code-first evaluation harness inside Python rather than a hosted platform; it is commonly paired with a tracing backend such as Langfuse. Because most metrics use an LLM as judge, evaluation incurs model costs and inherits judge biases, and metric definitions have changed between major versions, so results should be compared within one version.",13  "capabilities": ["Library of LLM-driven evaluation metrics for RAG and agents", "Custom metrics via decorators", "Experiments workflow with dataset management and result tracking", "Integrations with LangChain, LlamaIndex and other frameworks", "Test-set generation for RAG evaluation"],14  "workflows": ["Score a RAG pipeline on faithfulness and context recall over a golden set", "Compare two retriever configurations in an experiment", "Log Ragas scores to a tracing platform"],15  "advantages": ["Code-first and framework-agnostic", "Apache-2.0 license", "Well-known RAG metric definitions"],16  "limitations": ["LLM-as-judge costs and biases", "Metric semantics changed across versions", "Hosted companion product is still emerging"],17  "prerequisites": ["Python 3.9+", "An LLM provider for judge metrics"],18  "audiences": ["Developers evaluating RAG systems", "Researchers benchmarking retrieval pipelines"],19  "alternatives": ["promptfoo", "langfuse", "langsmith", "braintrust", "dspy"],20  "categories": [{ "slug": "llm-ops", "primary": true }],21  "tags": ["rag", "evaluation", "llm", "python", "open-source"],22  "use_cases": ["evaluate-llm-apps"],23  "operating_systems": ["macos", "linux", "windows"],24  "execution_modes": ["local"],25  "interfaces": ["library"],26  "deployment_models": ["embedded"],27  "integrations": ["LangChain", "LlamaIndex", "Langfuse", "OpenAI", "Anthropic"],28  "data_formats": ["JSON", "CSV", "Hugging Face datasets"],29  "languages": ["Python"],30  "source_model": "open-source",31  "license_spdx": "Apache-2.0",32  "pricing_model": ["open-source", "free"],33  "self_hostable": true,34  "sources": [35    { "url": "https://www.ragas.io/", "type": "official_site", "title": "Ragas", "supports": ["name", "summary"], "checked_at": "2026-11-02" },36    { "url": "https://docs.ragas.io/en/stable/", "type": "official_docs", "title": "Ragas documentation", "supports": ["capabilities", "integrations", "vendor"], "checked_at": "2026-11-02", "note": "Links to Vibrant Labs as the maintaining company" },37    { "url": "https://github.com/vibrantlabsai/ragas", "type": "repository", "supports": ["license_spdx", "languages"], "checked_at": "2026-11-02", "note": "github.com/explodinggradients/ragas redirects here; repository metadata reports Apache-2.0" }38  ],39  "confidence": "medium",40  "uncertainty_note": "The company rename from Exploding Gradients to Vibrant Labs is inferred from the repository redirect and the docs footer; no announcement page was located. Specific metric names are from the project's documented history rather than the current landing page.",41  "discovery_method": "editorial-research",42  "depth": "standard",43  "editorial_status": "reviewed",44  "visibility": "public",45  "featured": false,46  "author": "batch-A"47}48