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116524e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | /**
* Example: instrument a GLM agent pipeline with Kayba tracing (TypeScript).
*
* Mirrors the Python example in examples/tracing_glm_example.py.
*
* Run: npx tsx example.ts
*/
import "dotenv/config";
import OpenAI from "openai";
import kayba, { SpanType } from "./src/index";
// ── Kayba tracing setup ────────────────────────────────────────────────
kayba.configure({
apiKey: process.env.KAYBA_SDK_KEY,
baseUrl: process.env.KAYBA_BASE_URL,
folder: "ts-sdk-examples",
});
// ── OpenAI client (GLM-compatible endpoint) ────────────────────────────
const client = new OpenAI({
baseUrl: process.env.OPENAI_BASE_URL,
apiKey: process.env.OPENAI_API_KEY,
});
const MODEL = "glm-5.1";
// ── Traced helper functions ────────────────────────────────────────────
const llmCall = kayba.trace(
async (messages: OpenAI.ChatCompletionMessageParam[]) => {
const response = await client.chat.completions.create({
model: MODEL,
messages,
temperature: 0.7,
});
return response.choices[0].message.content ?? "";
},
{ name: "llm_call", spanType: SpanType.LLM },
);
const researchAgent = kayba.trace(
async (topic: string) => {
const span = kayba.startSpan({
name: "build_prompt",
spanType: SpanType.TOOL,
inputs: { topic },
});
const messages: OpenAI.ChatCompletionMessageParam[] = [
{
role: "system",
content: "You are a research assistant. List 3 key facts.",
},
{ role: "user", content: `Research this topic: ${topic}` },
];
span.end({ outputs: { message_count: messages.length }, status: "OK" });
return await llmCall(messages);
},
{ name: "research_agent", spanType: SpanType.AGENT },
);
const summariserAgent = kayba.trace(
async (facts: string) => {
const span = kayba.startSpan({
name: "build_prompt",
spanType: SpanType.TOOL,
inputs: { facts_length: facts.length },
});
const messages: OpenAI.ChatCompletionMessageParam[] = [
{
role: "system",
content:
"You are a summariser. Condense the following facts into one concise paragraph.",
},
{ role: "user", content: facts },
];
span.end({ outputs: { message_count: messages.length }, status: "OK" });
return await llmCall(messages);
},
{ name: "summariser_agent", spanType: SpanType.AGENT },
);
const runPipeline = kayba.trace(
async (topic: string) => {
const facts = await researchAgent(topic);
console.log(`\n--- Research Agent ---\n${facts}`);
const summary = await summariserAgent(facts);
console.log(`\n--- Summariser Agent ---\n${summary}`);
return summary;
},
{ name: "pipeline", spanType: SpanType.CHAIN },
);
// ── Main ───────────────────────────────────────────────────────────────
async function main() {
console.log("Running TypeScript tracing example...\n");
const result = await runPipeline("The history of the Silk Road");
console.log(`\n--- Final result ---\n${result}`);
// Give MLflow time to flush traces to Kayba
console.log("\nFlushing traces...");
await new Promise((r) => setTimeout(r, 3000));
console.log("Done!");
}
main().catch(console.error);
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