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@breadcrumb-sdk/ai-sdk

v0.0.3

Published

Trace Vercel AI SDK calls with Breadcrumb. Pass `experimental_telemetry` to any `generateText`, `streamText`, or `generateObject` call and it shows up in your dashboard automatically.

Readme

@breadcrumb-sdk/ai-sdk

Trace Vercel AI SDK calls with Breadcrumb. Pass experimental_telemetry to any generateText, streamText, or generateObject call and it shows up in your dashboard automatically.

Install

npm install @breadcrumb-sdk/core @breadcrumb-sdk/ai-sdk

Quick start

import { init } from "@breadcrumb-sdk/core";
import { initAiSdk } from "@breadcrumb-sdk/ai-sdk";
import { generateText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

const bc = init({ apiKey: "bc_...", baseUrl: "https://your-breadcrumb-instance.com" });
const { telemetry } = initAiSdk(bc);

const { text } = await generateText({
  model: anthropic("claude-opus-4-6"),
  prompt: "What is TypeScript?",
  experimental_telemetry: telemetry("answer-question"),
});

Each call with telemetry() shows up as its own trace in the dashboard. No other setup needed.

Grouping calls into one trace

If you want multiple calls to appear under a single trace, wrap them in bc.trace():

await bc.trace("chat-request", async () => {
  await generateText({ ..., experimental_telemetry: telemetry("plan") });
  await generateText({ ..., experimental_telemetry: telemetry("respond") });
});

You can also mix in manual steps — for example a retrieval step that isn't an LLM call:

await bc.trace("rag-pipeline", async () => {
  const docs = await bc.span("retrieve", async (span) => {
    span.set({ metadata: { source: "vector-db", top_k: 5 } });
    return await vectorSearch(query);
  }, { type: "retrieval" });

  await generateText({
    model,
    prompt: `Context: ${docs.join("\n")}\n\nQuestion: ${query}`,
    experimental_telemetry: telemetry("generate"),
  });
});

API

initAiSdk(bc)

Takes your bc instance and returns the telemetry helper. Call once after init().

const { telemetry } = initAiSdk(bc);

telemetry(functionId, metadata?)

Returns the config to pass to experimental_telemetry.

experimental_telemetry: telemetry("my-step")
experimental_telemetry: telemetry("my-step", { userId: "u_123", temperature: 0.7 })

| Parameter | Type | Description | |-----------|------|-------------| | functionId | string | Name shown in the trace UI | | metadata | Record<string, string \| number \| boolean \| ...[]> | Extra data attached to the span |

Compatibility

Works with AI SDK 5 and AI SDK 6.

License

AGPL-3.0 — see LICENSE for details.