Hosted Quickstart

1. Create A Project#

Open the TraceLLM dashboard:

https://tracellm.in

Create an account, open the dashboard, and copy a project API key from API Keys.

Full API keys are shown only once. If you lose one, create a new key and revoke the old key.

2. Install The Node SDK#

npm install @use-tracellm/sdk-node

You can also use pnpm or yarn:

pnpm add @use-tracellm/sdk-node

3. Configure Your App#

Add these values to your application environment:

TRACELLM_ENDPOINT=https://api.tracellm.in
TRACELLM_API_KEY=trllm_your_project_key
TRACELLM_SERVICE_NAME=my-ai-service

The API key connects traces to your project and loads your project capture policy.

4. Send A Trace#

import { TraceLLM } from "@use-tracellm/sdk-node";
 
const tracellm = new TraceLLM({
  endpoint: process.env.TRACELLM_ENDPOINT,
  apiKey: process.env.TRACELLM_API_KEY,
  serviceName: process.env.TRACELLM_SERVICE_NAME ?? "my-ai-service"
});
 
const session = await tracellm.startSession({
  name: "Chat request",
  userId: "user_123",
  attributes: {
    workflow: "chat",
    provider: "openai",
    model: "gpt-4.1-mini"
  },
  input: "Explain vector databases in one paragraph."
});
 
const span = await session.startSpan({
  name: "openai.chat.complete",
  kind: "llm",
  attributes: {
    provider: "openai",
    model: "gpt-4.1-mini"
  },
  input: "Explain vector databases in one paragraph."
});
 
// Call your real LLM provider here.
const answer = "Vector databases store embeddings so apps can search by meaning.";
 
await span.end({
  status: "ok",
  usage: {
    inputTokens: 12,
    outputTokens: 18,
    totalTokens: 30
  },
  output: answer
});
 
await session.end({
  status: "ok",
  output: answer
});

5. Inspect The Trace#

Return to the TraceLLM dashboard and open Trace Explorer.

You should see:

a session named Chat request
an llm span named openai.chat.complete
duration and status
token usage if token capture is enabled
input/output content only if content capture is enabled

6. Configure Capture Policy#

Open Project Config to choose what TraceLLM stores:

sampling rate
prompt and output capture
input capture
output capture
token usage
metadata
errors
email redaction
API key redaction
ignored span kinds

Configuration is project-level, so SDKs can pick up changes without redeploying your app.

7. Optional: Export To Your Observability Stack#

Open Exports if you want to forward project traces to your own OTLP HTTP backend, such as SigNoz, Honeycomb, Grafana Tempo, Datadog, or an OpenTelemetry Collector.

TraceLLM remains the LLM workflow UI. External exports are for teams that also want the same trace data in their existing observability platform.

Updated Jul 13, 2026