Quickstart

pip install uselemma-tracing

// instrumentation.ts (Next.js) or tracer.ts (Node.js)
import { registerOTel } from "@uselemma/tracing";
registerOTel();

import { agent } from "@uselemma/tracing";
import { Agent, run } from "@openai/agents";

const assistant = new Agent({
  name: "Assistant",
  instructions: "You are a helpful assistant.",
});

const myAgent = agent("my-agent", async (input: string) => {
  const result = await run(assistant, input);
  return result.finalOutput;
});

const { result, runId } = await myAgent("What is the capital of France?");
console.log(result);  // Paris
console.log(runId);

// instrumentation.ts (Next.js) or tracer.ts (Node.js)
import { registerOTel } from "@uselemma/tracing";
import { registerInstrumentations } from "@opentelemetry/instrumentation";
import { OpenAIInstrumentation } from "@arizeai/openinference-instrumentation-openai";

const provider = registerOTel();
registerInstrumentations({
  instrumentations: [new OpenAIInstrumentation()],
  tracerProvider: provider,
});

import { agent } from "@uselemma/tracing";
import OpenAI from "openai";

const openai = new OpenAI();

const myAgent = agent("my-agent", async (input: string) => {
  const response = await openai.chat.completions.create({
    model: "gpt-4o-mini",
    messages: [{ role: "user", content: input }],
  });
  return response.choices[0].message.content ?? "";
});

const { result, runId } = await myAgent("What is the capital of France?");
console.log(result);
console.log(runId);

from uselemma_tracing import register_otel, instrument_openai

register_otel()
instrument_openai()  # adds per-call child spans with prompt/completion/tokens

from uselemma_tracing import agent, TraceContext
from openai import AsyncOpenAI

client = AsyncOpenAI()

async def run_agent(user_message: str, ctx: TraceContext) -> str:
    response = await client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": user_message}],
    )
    return response.choices[0].message.content or ""

myAgent = agent("my-agent", run_agent)

res = await myAgent("What is the capital of France?")
print(res.result)   # Paris
print(res.run_id)   # look this up in the Lemma dashboard

from uselemma_tracing import register_otel, instrument_anthropic

register_otel()
instrument_anthropic()  # adds per-call child spans with prompt/completion/tokens

from uselemma_tracing import agent, TraceContext
import anthropic

client = anthropic.AsyncAnthropic()

async def run_agent(user_message: str, ctx: TraceContext) -> str:
    message = await client.messages.create(
        model="claude-haiku-4-5",
        max_tokens=256,
        messages=[{"role": "user", "content": user_message}],
    )
    return message.content[0].text

myAgent = agent("my-agent", run_agent)

res = await myAgent("What is the capital of France?")
print(res.result)
print(res.run_id)