Your agents made 14,302 tool calls today.
You watched five of them.

import { AgentGraph } from "@agentgraph/sdk";
import { stripe, postmark, openai } from "./tools";

const ag = new AgentGraph({ apiKey: process.env.AG_KEY });

export async function checkout(order: Order) {
  const trace = ag.trace({
    agentId: "checkout-agent",
    sessionId: order.sessionId,
    userId: order.userId,
  });

  // each step records latency + cost; pass a StepType + name
  const ctx = await trace.step("retrieval", "fetch_context", async (span) => {
    span.setInput({ orderId: order.id });
    const docs = await fetchContext(order);
    span.setOutput({ docs });
    return docs;
  });

  const plan = await trace.step("llm_call", "plan", async (span) => {
    span.setModel("gpt-4o").setInput({ ctx });
    return openai.complete(ctx);
  });

  await trace.step("tool_call", "stripe.charge", () => stripe.charge(order));
  await trace.step("tool_call", "postmark.send", () => postmark.send(order));

  await trace.flush(); // POST /v1/ingest
  return { ok: true, plan };
}

# Python SDK — coming soon.
#
# We're building a native Python client with the same
# trace / step API as the TypeScript SDK.
#
# Until then, any Python service can ingest traces by
# POSTing to /v1/ingest directly — see the curl tab.

# no SDK? POST a trace directly.
curl -X POST https://agentgraph.dev/v1/ingest \
  -H "Authorization: Bearer $AG_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "agentId": "checkout-agent",
    "env": "prod",
    "status": "completed",
    "steps": [
      {
        "type": "llm_call",
        "name": "plan",
        "model": "gpt-4o",
        "input": { "ctx": "..." },
        "tokenUsage": { "prompt": 1820, "completion": 412, "total": 2232 }
      },
      {
        "type": "tool_call",
        "name": "stripe.charge",
        "toolName": "stripe",
        "latencyMs": 328,
        "costUsd": 0.004,
        "input": { "amount": 8240, "currency": "usd" }
      }
    ]
  }'