4 posts tagged with "Agents"

If you’re building agents with tools like Claude Code or Cursor, or you prefer working in the terminal, you’ve probably hit this friction already. Your agent runs, something breaks partway through, and now you have to context-switch to a web UI to understand what happened. You search for the right trace, click through LLM calls, and then try to carry that context back into your editor.

vLLora’s MCP Server removes that context switch. Your coding agent becomes the interface for inspecting traces, understanding failures, and debugging agent behavior — without leaving your editor or terminal.

LLMs behave like black boxes. You send them a request, hope the prompt is right, hope your agent didn't mutate it, hope the framework packaged it correctly — and then hope the response makes sense. In simple one-shot queries this usually works fine. But when you're building agents, tools, multi-step workflows, or RAG pipelines, it becomes very hard to see what the model is actually receiving. A single unexpected message, parameter, or system prompt change can shift the entire run.

Today we're introducing Debug Mode for LLM requests in vLLora that makes this visible — and editable.

Here’s what debugging looks like in practice:

Voice agents built with LiveKit Agents enable real-time, multimodal AI interactions that can handle voice, video, and text. These agents power everything from customer support bots to telehealth assistants, and debugging them requires visibility into the complex pipeline of speech-to-text, language model, and text-to-speech interactions.

In this video, we go over how you can debug voice agents built using LiveKit Agents with vLLora. You'll see how to trace every model call, tool execution, and response as your agent processes real-time audio streams.

Developers building coding agents need visiblity into how context is flowing through the agent, how much context is used, what tools are being called. vLLora enables you to debug all of this in real time.