Datasets

import { zespan, withZespanTrace } from "@zespan/sdk";
import { randomUUID } from "node:crypto";

zespan.init({ apiKey: process.env.ZESPAN_API_KEY! });
const client = zespan.getClient();

// 1. Fetch the dataset's items
const items = await client.datasets.getItems("support-eval-set");

// 2. Create (or re-attach to) a named run — safe to call every time your job starts
const run = await client.datasets.createRun("support-eval-set", "gpt-4o-v2");

// 3. Run your pipeline per item under a known trace ID, then link it to the run
for (const item of items) {
  const traceId = randomUUID();
  // Any wrapped LLM/agent call inside this callback is tagged with traceId
  await withZespanTrace(() => mySupportAgent(item.input), { traceId });

  await run.link(item.id, traceId);
}

import zespan
from zespan import get_client, with_zespan_context

zespan.init(api_key="zsp_your_api_key_here")
client = get_client()

# 1. Fetch the dataset's items
items = client.datasets.get_items("support-eval-set")

# 2. Create (or re-attach to) a named run — safe to call every time your job starts
run = client.datasets.create_run("support-eval-set", "gpt-4o-v2")

# 3. Run your pipeline per item under a known trace ID, then link it to the run
for item in items:
    with with_zespan_context() as ctx:
        my_support_agent(item["input"])  # your own LLM/agent call, traced as usual
        trace_id = ctx["trace_id"]

    run.link(item["id"], trace_id)