Connectez Ragas à ChatGPT, Claude et Gemini

• 01 / 03Évaluer vos RAG
• 02 / 03Gérer vos jeux de données Ragas
• 03 / 03Comparer vos expériences RAG

Jour le plus rapide

698ms

Jour le plus lent

1115ms

Tendance sur 14 jours

En ralentissement+43%

Ragas Connector

Tout est prêt. Choisissez votre client MCP et suivez les instructions de configuration.

Lien du connecteurhttps://edge.vinkius.com/vk_preview_mQUV7klN4rh09rRcp9ld3cwceCP25oL5Lk8res4B/mcpCopier

Claude Desktop

Suivez les étapes ci-dessous pour vous connecter en quelques secondes.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Ragas capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopier

{
  "mcpServers": {
    "ragas-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_mQUV7klN4rh09rRcp9ld3cwceCP25oL5Lk8res4B/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Les instructions pas à pas pour chaque client sont dans le guide. Comment connecter

Configuration guidée pour Claude ? Configurer Ragas avec Claude et les autres IA

Voyez tous les clients IA avec lesquels ce connector fonctionne ↑

• 01

  Ingénieurs IA et ML

  Pour évaluer les pipelines sans changer d'interface ni réécrire de scripts Python.

• 02

  Spécialistes QA pour LLM

  Pour analyser rapidement les datasets et limiter les taux d'hallucination.

• 03

  Data Scientists

  Pour comparer côte à côte différentes configurations RAG via des métriques unifiées.

• 01

  How do I use Ragas with my AI client?

  You connect the MCP by providing your Ragas Application URL and Application Token. Once connected, you can simply ask your agent to run evaluations or fetch metrics.

• 02

  What metrics can this MCP provide?

  The MCP can access Ragas metrics such as faithfulness and answer relevancy to score your RAG pipelines.

• 03

  Do I need to write Python code to run evaluations?

  No. This MCP allows your agent to trigger evaluations and manage datasets through natural language commands instead of manual scripting.

• 04

  Can I manage my existing Ragas datasets?

  Yes. The MCP includes capabilities to list, get details for, and organize your existing datasets.

• 05

  Can I compare different versions of my agent?

  Yes. You can use the capability to track and compare metrics across different experiments and agent versions.

• 06

  How do I secure an App Token for Ragas?

  Log into your provided Ragas dashboard. In your project's settings or dedicated security section, you will find the ability to generate a new Application Token. Copy it immediately, as it may only appear once.

• 07

  What format is required to upload a dataset?

  The capability uses common array formats through the MCP wrapper. When passing data, the AI maps arrays containing question, ground_truth and contexts natively matching Ragas base requirements.

• 08

  Does the server evaluate prompts automatically during testing?

  Yes. When triggering evaluations, Ragas uses its own sophisticated metrics (like Faithfulness, Answer Relevance) running internally. The MCP server simply pipes these generated reports back to your chat.

• 

  Langfuse (LLM Tracing & Evals) Connecteur IA

  Monitor LLM apps via Langfuse — track traces, manage prompt templates, and audit evaluation scores.

  Voir
• 

  Neptune.ai (ML Experiment Tracking) Connecteur IA

  Manage ML experiments via Neptune.ai — track training runs, monitor metrics, and audit model versions.

  Voir
• 

  LiteLLM (LLM Proxy & Spend Tracking) Connecteur IA

  Manage your LLM gateway via LiteLLM — generate API keys, track spending, and orchestrate model fallback paths.

  Voir
• 

  Comet ML Connecteur IA

  Manage machine learning experiments via Comet — track model metrics, audit project workspaces, and inspect ML

  Voir

Suggestions

• 

  LangSmith (LLM Observability & Hub) Connecteur IA

  Monitor LLM apps via LangSmith — track traces, audit prompt templates, and manage evaluation datasets.

  Voir
• 

  Datadog AI (LLM Observability) Connecteur IA

  Monitor LLM performance via Datadog — track token usage, audit prompts, and monitor AI model metrics directly

  Voir
• 

  New Relic AI (LLM Observability) Connecteur IA

  Monitor and audit LLM telemetry via New Relic AI — track token costs, p95 latency, and user feedback.

  Voir
• 

  MLflow (ML Lifecycle Management) Connecteur IA

  Manage ML lifecycle via MLflow — track training runs, monitor metrics, and audit the model registry.

  Voir