{
  "schema_version": "openagent.resource.v1",
  "id": "res_ragas",
  "slug": "ragas",
  "status": "published",
  "identity": {
    "name": "Ragas",
    "one_liner": "Open-source evaluation framework for LLM applications and RAG workflows.",
    "short_description": "Ragas is an Apache-2.0 evaluation framework for LLM applications, especially retrieval-augmented generation workflows that need structured quality checks."
  },
  "classification": {
    "resource_type": "tool",
    "primary_category": "tools",
    "subcategories": [
      "evals",
      "rag",
      "retrieval",
      "python",
      "quality"
    ]
  },
  "positioning": {
    "why_it_matters": "Many agent products use retrieval or long-context workflows. Ragas gives builders a practical evaluation layer for checking answers, context, retrieval quality, and application behavior.",
    "best_for": [
      "Teams evaluating RAG and LLM applications",
      "Developers building repeatable quality checks for retrieval workflows",
      "Builders comparing evaluation frameworks around agent knowledge systems"
    ],
    "not_for": [
      "Pure browser automation tests",
      "Teams that need production tracing more than evaluation datasets"
    ],
    "use_cases": [
      "self-hosted-ai"
    ],
    "target_audience": [
      "developer",
      "agent_builder"
    ],
    "maturity": "active"
  },
  "decision_signals": {
    "deployment_modes": [
      "self_hosted",
      "cloud"
    ],
    "open_source": true,
    "local_first": false,
    "self_hostable": false,
    "has_api": false,
    "has_gui": false,
    "supports_mcp": false,
    "supports_docker": false
  },
  "facts": {
    "license": "Apache-2.0",
    "pricing_model": "open_source",
    "github_stars": 14187,
    "github_forks": 1452,
    "github_repo_full_name": "vibrantlabsai/ragas",
    "last_verified_at": "2026-06-02"
  },
  "capabilities": {
    "core_capabilities": [
      "rag"
    ],
    "interfaces": [
      "repo",
      "docs"
    ]
  },
  "links": {
    "primary_url": "https://github.com/vibrantlabsai/ragas",
    "items": [
      {
        "type": "github",
        "label": "GitHub",
        "url": "https://github.com/vibrantlabsai/ragas"
      },
      {
        "type": "homepage",
        "label": "Homepage",
        "url": "https://docs.ragas.io"
      }
    ]
  },
  "media": {
    "thumbnail_brief": {
      "resource_type": "tool",
      "visual_motif": "clean utility panel and geometric control surface",
      "background_style": "minimal editorial surface with restrained open-source accent color",
      "title_overlay": "Ragas",
      "subtitle": "Open-source evaluation framework for LLM applications and RAG workflows.",
      "avoid": [
        "noisy poster layout",
        "large marketing slogans",
        "random gradient blobs"
      ]
    }
  },
  "tags": {
    "category": [
      "tool",
      "open-source"
    ],
    "capability": [
      "rag"
    ],
    "constraint": [
      "open-source"
    ],
    "scenario": [
      "self-hosted-ai"
    ]
  },
  "relationships": {},
  "machine_readable": {
    "canonical_url": "https://www.openagent.bot/tools/ragas",
    "json_url": "https://www.openagent.bot/tools/ragas.json",
    "markdown_url": "https://www.openagent.bot/tools/ragas.md"
  },
  "seo": {
    "title": "Ragas: open-source LLM and RAG evaluation framework",
    "description": "Ragas profile: RAG evaluation, LLM app quality checks, official links, command line, and OpenAgent structured data."
  },
  "editorial": {
    "trust_note": "Verified from source links and project metadata.",
    "core_strengths": [
      {
        "title": "Rag",
        "description": "Ragas surfaces rag as a core capability in its published project metadata and source links.",
        "why_it_matters": "This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs."
      }
    ],
    "use_case_notes": [
      {
        "title": "Self hosted ai",
        "description": "Use it as a candidate for self hosted ai when the project facts, license, and official links match your deployment requirements."
      }
    ],
    "compare_notes": [
      {
        "title": "When to choose Ragas",
        "summary": "Compare it with nearby tools by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need."
      }
    ],
    "getting_started": [
      {
        "label": "Review the repository",
        "url": "https://github.com/vibrantlabsai/ragas",
        "type": "github"
      },
      {
        "label": "Homepage",
        "url": "https://docs.ragas.io",
        "type": "homepage"
      }
    ],
    "command_line": [
      {
        "label": "Install or run",
        "command": "pip install ragas"
      }
    ],
    "seo_article": {
      "intro": "Ragas is an Apache-2.0 evaluation framework for LLM applications, especially retrieval-augmented generation workflows that need structured quality checks.",
      "what_it_is": "Ragas is listed on OpenAgent.bot as a tools resource for open AI builders.",
      "why_it_matters": "Many agent products use retrieval or long-context workflows. Ragas gives builders a practical evaluation layer for checking answers, context, retrieval quality, and application behavior.",
      "how_it_works": "Start from the official source links, then validate the project against your deployment needs, license requirements, and maintenance expectations.",
      "getting_started": [
        {
          "label": "Review the repository",
          "url": "https://github.com/vibrantlabsai/ragas",
          "type": "github"
        },
        {
          "label": "Homepage",
          "url": "https://docs.ragas.io",
          "type": "homepage"
        }
      ]
    }
  },
  "timestamps": {
    "created_at": "2026-06-02T00:00:00.000Z",
    "updated_at": "2026-06-02T00:00:00.000Z",
    "published_at": "2026-06-02T00:00:00.000Z"
  }
}