Quickstart

Run Pulse server

Start the trace service before instrumenting your app. For local development, single mode (SQLite) is the quickest path.

Install the binary:

curl -fsSL https://raw.githubusercontent.com/EK-LABS-LLC/trace-service/main/scripts/install.sh | bash -s -- pulse

Set required environment variables:

export BETTER_AUTH_SECRET='replace-with-32+char-secret'
export ENCRYPTION_KEY='replace-with-32+char-secret'
export BETTER_AUTH_URL='http://localhost:3000'

Start the service:

pulse

Install SDK

TypeScript

bun add @pulse/sdk

Python

pip install pulse-sdk

Works with Bun, Node, or any JavaScript/Python runtime.

CLI integrations (Claude Code, Opencode, OpenClaw)

If you want Pulse to capture coding-agent events in addition to SDK traces:

# Install CLI
curl -fsSL https://raw.githubusercontent.com/EK-LABS-LLC/trace-cli/main/install.sh | sh

# Configure trace service connection
pulse init

# Install integrations for detected agents
pulse connect

# Verify config + connectivity + integration status
pulse status

See CLI Reference for full command and config details.

Initialize

Call initPulse() once at application startup. You need an API key from the Pulse dashboard.

TypeScript

import { initPulse } from "@pulse/sdk";

initPulse({
  apiKey: "pulse_sk_...",
});

Python

from pulse_sdk import init_pulse

init_pulse({
  "api_key": "pulse_sk_...",
})

This starts background trace batches and registers shutdown handlers to flush remaining traces on exit.

Wrap your client

Use observe() to wrap your LLM client. The returned client behaves identically and tracing is captured as a side effect.

TypeScript

import { initPulse, observe, Provider } from "@pulse/sdk";
import OpenAI from "openai";

initPulse({ apiKey: "pulse_sk_..." });

const client = observe(
  new OpenAI({ apiKey: "sk-..." }),
  Provider.OpenAI
);

const res = await client.chat.completions.create({
  model: "gpt-4o",
  messages: [{ role: "user", content: "Hello" }],
});

Python

from pulse_sdk import init_pulse, observe, Provider
from openai import OpenAI

init_pulse({ "api_key": "pulse_sk_..." })

client = observe(
  OpenAI(api_key="sk-..."),
  Provider.OPENAI
)

res = client.chat.completions.create(
  model="gpt-4o",
  messages=[{"role": "user", "content": "Hello"}],
)

What gets captured

Field	Description
Request and response bodies	Full prompt and completion
Token counts	Input and output tokens
Latency	End-to-end request duration in milliseconds
Cost	Provider-reported or SDK-estimated when model pricing is available
Model	Requested and actual model used
Status	success or error
Provider	openai, anthropic, or openrouter

Supported providers

Provider	Client	Enum
OpenAI	openai	Provider.OpenAI
Anthropic	@anthropic-ai/sdk	Provider.Anthropic
OpenRouter	openai	Provider.OpenRouter

See Providers for detailed usage.

Next steps

CLI Reference

Configuration

Sessions & Metadata

REST API