Bring Rag
to LangChain

Integrate Ragas with your AI agent to bring professional grade RAG (Retrieval-Augmented Generation) evaluation and tracking into your chat interface. By subscribing to this server, the AI can seamlessly manage datasets and measure LLM performance on demand.

What you can do

• Dataset Management — Upload, list, and organize evaluation datasets directly inside your environment.
• Run Evaluations — Automatically trigger Ragas evaluations on your RAG pipelines and fetch detailed scoring.
• Track Experiments — Monitor and compare iterative improvements by viewing tracked metrics across different agent versions.
• Project Organization — Associate evaluations with specific projects within your Ragas dashboard.

How it works

1. Enable the server integration.
2. Provide your Ragas Application URL and your generated Application Token.
3. Instruct your AI to initiate evaluations or query historical metrics natively from your IDE or chat.

Who is this for?

• AI & ML Engineers — Run pipeline evaluations without context switching to a separate dashboard or writing Python evaluation scripts each time.
• QA Specialists for LLMs — Rapidly examine datasets and benchmark results to ensure hallucination rates remain low.
• Data Scientists — Compare multiple RAG configuration experiments side-by-side using unified metrics.