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RAG systems have multiple failure modes. When answers are wrong, the problem could be poor retrieval, LLM hallucination, or a weak system prompt. Without visibility into each step, you’re debugging blind. Observability helps you:
  • Find root causes — Determine if bad answers stem from missing chunks or generation errors.
  • Improve accuracy — Identify failing queries and adjust prompts, or the search configuration.
  • Track latency — Measure time spent in retrieval vs generation to optimize the right component.
  • Collect feedback — Use thumbs up/down signals from users to surface problem areas.
Agentset integrates with observability tools like Langfuse and Helicone to trace your RAG pipeline.

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Tracing with Langfuse

Wrap your RAG function with the @observe decorator to automatically trace each invocation. LLM calls are traced automatically when using the Langfuse OpenAI wrapper. 
import { Agentset } from "agentset";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const agentset = new Agentset();
const ns = agentset.namespace("YOUR_NAMESPACE_ID");

async function ragBot(question: string) {
  const results = await ns.search(question);
  const context = results.map((r) => r.text).join("\n\n");

  const { text } = await generateText({
    model: openai("gpt-5.1"),
    system: `Answer based on this context:\n\n${context}`,
    prompt: question,
    experimental_telemetry: { isEnabled: true },
  });

  return text;
}

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Logging search results

To inspect retrieval quality separately, log search results as a span within your trace. 
import { Langfuse } from "langfuse";

const langfuse = new Langfuse();

async function ragBot(question: string) {
  const trace = langfuse.trace({ name: "rag-bot" });

  const retrievalSpan = trace.span({ name: "retrieval", input: question });
  const results = await ns.search(question);
  retrievalSpan.end({ output: results });

  // Continue with LLM call...
}

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Tracing with Helicone

Helicone traces LLM calls through a proxy. Change your OpenAI base URL to route requests through Helicone. 
import { Agentset } from "agentset";
import { generateText } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const agentset = new Agentset();
const ns = agentset.namespace("YOUR_NAMESPACE_ID");

const openai = createOpenAI({
  baseURL: "https://oai.helicone.ai/v1",
  headers: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});

async function ragBot(question: string) {
  const results = await ns.search(question);
  const context = results.map((r) => r.text).join("\n\n");

  const { text } = await generateText({
    model: openai("gpt-5.1"),
    system: `Answer based on this context:\n\n${context}`,
    prompt: question,
  });

  return text;
}

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Next steps

  • Search — Configure search parameters
  • Ranking — Improve retrieval quality with reranking
  • Data Segregation — Isolate data for multi-tenant applications