PASS  84/100  my-rag-app  "what is RAG?"
       ✓ precision:90  ✓ efficiency:80  ✓ redundancy:85  ✓ coverage:100

 WARN  61/100  my-rag-app  "what is dense passage retrieval?"
       ✗ precision:30  ✗ efficiency:45  ~ redundancy:70  ✓ coverage:100
       → Reduce TOP_K 10→5  · 2 near-duplicate chunks detected

 ─────────────────────────────────────────────────────────────
 Session  2 queries · avg 72/100  ↑ improving

The problem

You build a RAG pipeline. It looks fine in demos. You ship it. Users complain the answers are wrong or vague — but nothing in your logs tells you why.

The real issue is usually invisible: too many chunks retrieved, half of them never reaching the LLM, near-duplicate content eating your context window, no similarity scores to optimize against. RAGScope makes all of this visible — scored, labelled, and actionable — in your terminal, before you ship.

Quick start

# 1. Start RAGScope (no install needed)
npx ragscope start

# 2. Point your pipeline's OTel exporter at it
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4321

# 3. Run your test queries — scores appear instantly

That's it. No config files, no accounts, no data leaving your machine.

How it scores

Every query gets four sub-scores combined into a single 0–100:

Add --verbose for a full per-query breakdown with specific recommendations.

Integrations

RAGScope is source-agnostic. Traces arrive via two paths.

Path 1 — Any OTel-compatible tool

One line change: set the OTLP exporter URL to http://localhost:4321/v1/traces.

TraceAI / Traceloop (auto-instruments LangChain, LlamaIndex, OpenAI, Pinecone, Qdrant, Cohere…)

import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { instrument } from '@traceloop/node-server-sdk';

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: 'http://localhost:4321/v1/traces' }),
});
sdk.start();
instrument();

Vercel AI SDK

import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: 'http://localhost:4321/v1/traces' }),
});
sdk.start();

Phoenix (Arize) / OpenLLMetry — set PHOENIX_COLLECTOR_ENDPOINT=http://localhost:4321 or TRACELOOP_BASE_URL=http://localhost:4321.

Manual OpenTelemetry

import { trace } from '@opentelemetry/api';
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({ url: 'http://localhost:4321/v1/traces' }),
});
sdk.start();

const tracer = trace.getTracer('my-rag-app');

const span = tracer.startSpan('qdrant.query');
span.setAttribute('gen_ai.operation.name', 'retrieve');
span.setAttribute('gen_ai.retrieval.documents', JSON.stringify(docs));
span.end();

Path 2 — Langfuse

Set two env vars — RAGScope polls every 30 seconds, zero code changes:

LANGFUSE_PUBLIC_KEY=pk-lf-... \
LANGFUSE_SECRET_KEY=sk-lf-... \
npx ragscope start

Coming soon: LangSmith · Helicone adapters. Open an issue to vote or contribute.

CLI options

npx ragscope start [options]

  --port <n>     Port to listen on (default: 4321)
  --db <path>    Path to SQLite database file (default: in-memory)
  --verbose      Show full sub-score breakdown and recommendations per query

Works with

Why not just use Langfuse / Phoenix / Arize?

Those are excellent production monitoring tools — they record what happened after you ship.

RAGScope is a pre-ship quality gate — like ESLint or Lighthouse, you run it during development to catch retrieval problems before they reach users. Different job, smaller footprint, zero cloud dependency.

Roadmap

Now (v0.1.x)

• OTLP ingestion — works with any OTel-compatible source
• Langfuse polling adapter
• Four sub-scores: precision, efficiency, redundancy, coverage
• PASS / WARN / FAIL per query with rolling session average
• --verbose flag for full breakdown + recommendations

Next (v0.2)

• LangSmith adapter — poll runs via LangSmith API, zero code changes
• Helicone adapter — fetch requests via Helicone API
• Langfuse webhooks — real-time instead of 30s polling
• Audit report export — npx ragscope report writes a Markdown/JSON summary you can commit or share

Later

• Compare mode — npx ragscope compare v1 v2 diffs two pipeline versions side-by-side
• Python support — native Python instrumentation helpers
• Threshold config — .ragscope.json to set custom PASS/WARN/FAIL thresholds per project
• Span-level drill-down — --trace <id> to inspect a single trace in detail

Vote on features or propose new ones by opening an issue.

Contributing

Contributions are welcome. See CONTRIBUTING.md for setup and guidelines.

Good first issues: LangSmith adapter, Helicone adapter, audit report export, improving scoring heuristics.

Privacy

All data stays on your machine. No telemetry, no cloud, no accounts required.

License

Apache 2.0 — © 2026 Siddharth Pandey