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OpenVidu Platform#

Debugging WebRTC with an AI agent and Grafana MCP

Debugging WebRTC with an AI agent and Grafana MCP: read-only Grafana, a broken deployment, and an agent that works through the metrics to find each root cause

What if you gave an AI agent nothing but read-only access to your Grafana, pointed it at a WebRTC deployment it had never seen, and asked what was broken? No shell, no source code, no config files, nothing but the dashboards and logs any on-call engineer would stare at. Could it actually find the root cause?

That is the experiment we ran at OpenVidu. We took a real OpenVidu deployment, broke it on purpose in five different ways, and handed a blind Claude Code session a single vague complaint and a link to Grafana. This post walks through what it found, where it shone and where it fell flat, and it ships with a companion repo so you can reproduce every bit of it yourself.

Choosing the right level of abstraction in self-hosted WebRTC solutions in 2026: OpenVidu Meet vs OpenVidu Platform

OpenVidu Meet vs OpenVidu Platform

In this blog post we explore how different levels of abstraction are needed in the WebRTC arena, and which choices do you have when using the OpenVidu WebRTC ecosystem.

Why Abstraction Matters

People are diverse, and that’s what makes life interesting. In the world of real-time communications (RTC), diversity means that different users require entirely different levels of abstraction to get the job done, from a "batteries included" scalable meeting application, to an extremely customizable media processing pipeline with access to low-level media SDKs and APIs.