@node-llm/monitor-otel 📡

OpenTelemetry integration for NodeLLM Monitor. Bridging the gap between standard OTel tracing and AI-specific observability.

Features

• 🔌 Zero-Code Instrumentation: Automatically capture AI spans from libraries like Vercel AI SDK.
• 🧠 AI-Aware: Extracts model names, token usage, cost, and tool calls from OTel attributes.
• 🎯 Native Routing: Forwards AI spans directly to your NodeLLM Monitor store.
• 🔍 Streaming Support: Captures Time-to-First-Token (TTFT) and average tokens/sec.

Installation

pnpm add @node-llm/monitor @node-llm/monitor-otel

Usage

1. Basic Setup

import { NodeTracerProvider } from "@opentelemetry/sdk-trace-node";
import { Monitor } from "@node-llm/monitor";
import { NodeLLMSpanProcessor } from "@node-llm/monitor-otel";

// 1. Initialize your monitor store
const monitor = Monitor.memory();

// 2. Add the SpanProcessor to your OTel provider
const provider = new NodeTracerProvider();
provider.addSpanProcessor(new NodeLLMSpanProcessor(monitor.getStore()));
provider.register();

2. Instrumented Libraries (e.g., Vercel AI SDK)

import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const result = await generateText({
  model: openai("gpt-4o"),
  prompt: "What is quantum gravity?",
  experimental_telemetry: { isEnabled: true } // Enable OTel spans
});

The spans emitted by ai will be automatically intercepted, processed, and saved to your NodeLLM Monitor dashboard.

Configuration

NodeLLMSpanProcessor accepts an optional options object:

new NodeLLMSpanProcessor(store, {
  captureContent: true, // Set to false to hide prompts/responses from logs
  filter: (span) => span.name.includes("my-app"), // Custom span filtering
  onError: (err, span) => console.error("Failed to process AI span", err)
});

License

MIT