Tracing

TechFabric Experiments ingests OpenInference/OTLP traces, so any instrumentation library that emits OpenInference semantic conventions (OpenAI, LangGraph, LlamaIndex, Vercel AI SDK, and the wider tooling) works without modification. Point its OTLP exporter at your ingest endpoint. Spans tagged with an experiment and variant power per-variant runtime analytics and online evals.

Ingest endpoint and authentication

Traces are ingested at the edge.

POST https://otlp.experiments.techfabric.com/<org>/v1/traces
Authorization: Bearer fxit_...
Content-Type: application/json | application/x-protobuf

• OTLP/HTTP with JSON or protobuf payloads is supported. gRPC (:4317) is not, configure exporters with OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf (or http/json).
• Ingest tokens are org-scoped, write-only, and rotatable. Mint them in Settings → Ingest tokens; the secret is shown once. Tokens are bound to your organization. A token cannot write into another org's path, and revocation takes effect at the edge immediately.
• Browser (cross-origin) ingestion requires explicitly configured allowed origins; server-side ingestion is unaffected by CORS.

First-party SDK tracing

The Node and Web SDKs include a tracer that emits OpenInference spans and stamps the active experiment assignment automatically.

import { createTracer } from '@fabricorg/experiments-sdk-node';

const tracer = createTracer({
  ingestUrl: 'https://otlp.experiments.techfabric.com',
  tenantId: 'my-org',
  ingestToken: process.env.FX_INGEST_TOKEN,
});
tracer.setAssignmentFromTreatment(treatment); // fx.experiment_id / fx.variant_key

const answer = await tracer.trace(
  { name: 'ChatCompletion', kind: 'LLM', model: 'gpt-4o', input: question },
  async (span) => {
    const out = await llm.complete(question);
    span.setOutput(out.text);
    span.setTokens(out.usage.prompt, out.usage.completion);
    return out.text;
  },
);

In the browser, import from @fabricorg/experiments-sdk-web/tracing, a separate entry point, so the experimentation tag's size budget is unaffected.

Where spans go

Ingested spans land in two places:

1. Hot store (Postgres), powers the interactive Studio views below.
2. Lakehouse (R2 → Auto Loader → Delta), the system of record for analytics; join spans against exposures and conversions in your Databricks workspace.

Hot-store data is pruned on a rolling window (default 14 days, legal holds honored); the lakehouse copy is retained per your warehouse policy.

Studio views

• Traces: filterable list (kind, errors-only, experiment, session) with duration, token, and cost columns.
• Waterfall: per-trace span tree with proportional timing bars, input/output values, attributes, and per-span dollar cost.
• Sessions: multi-trace conversation view in chronological order.
• Experiment detail → Traces: recent traces attributed to each variant of a running experiment.
• Annotations: attach human labels (score 0–1 plus explanation) to any span; online eval scores from sampled live traffic appear alongside.

Privacy controls

Per-organization redaction policies apply at the ingestion edge, before storage: drop input.value/output.value entirely, or strip all non-essential attributes to an allowlist. Structural fields (span kind, token counts, model name, session and experiment ids) always survive so analytics keep working on redacted data.