scorable-otel-evaluation

name

scorable-otel-evaluation

description

Wire end-to-end OpenTelemetry-based tracing and evaluation for an LLM application — instrument the application with OTEL via OpenInference, ship traces to Scorable's OTLP endpoint, then use the Scorable CLI to query traces and create evaluation filters that auto-run an evaluator or judge against matching traces. Use when users want OTEL/OpenTelemetry tracing for their LLM app, want to monitor production LLM traffic, want auto-evaluation on a fraction of traffic, mention OTLP, OpenInference, Arize, pydantic-ai, openai-agents, LangChain, or "tracing my agent."

Add End-to-End OTEL Tracing + Evaluation with Scorable

These instructions guide you through instrumenting an LLM application with OpenTelemetry, shipping traces to Scorable, and configuring server-side evaluation filters that automatically score matching traces. The result: every (or a sampled subset of) production LLM call gets observed, traced, and scored — and the score lands back on the same trace as a child span carrying the OpenTelemetry GenAI evaluation attributes.

Execution Contract

You are responsible for completing the OTEL tracing + evaluation setup end-to-end in as few turns as possible.

• You MUST analyze the codebase to identify the LLM framework and entry points.
• You MUST install and use the Scorable CLI directly.
• You MUST add the OTEL instrumentation code yourself.
• You MUST run a test request and verify traces land in Scorable.
• You MUST create at least one evaluation filter via the CLI.
• You MUST verify the filter triggered an evaluation by inspecting the trace's spans.
• You MUST update project documentation for usage.
• You MUST NOT delegate technical steps to the user except where explicitly required (API key entry, secrets).
• You MUST continue until implementation is complete or a hard blocker is reached.

Overview

Your role is to:

1. Analyze the codebase to identify the LLM framework in use (OpenAI SDK, openai-agents, pydantic-ai, LangChain, Anthropic, LlamaIndex, etc.) and the entry points where requests are made.
2. Install and authenticate the Scorable CLI so you can manage filters and inspect traces.
3. Instrument the application using the OpenInference instrumentation that matches the framework, plus the OTLP exporter pointing at Scorable.
4. Send a test request and verify traces appear in Scorable using scorable otel-trace list.
5. Create an evaluation filter that runs an evaluator or judge against matching traces.
6. Verify the filter triggered by inspecting the resulting eval span on the trace.
7. Provide usage + next-step documentation.

If the user wants a custom judge but doesn't have one yet, generate it using the scorable-integration skill (its Step 3 covers scorable judge generate with the right intent prompts), then come back here with the resulting judge_id for filter creation.

Step 0: Explain the Process

Before performing any analysis or technical steps, brief the user clearly. Explain that you will:

• Analyze the codebase to identify the LLM framework and entry points
• Install the Scorable CLI and authenticate
• Add OTEL instrumentation to ship traces to Scorable's OTLP endpoint
• Send a test request and verify traces land in Scorable
• Create an evaluation filter that auto-scores matching traces
• Provide usage documentation

Mention that this setup is composable: any other observability backend that accepts OTLP HTTP/protobuf can be a second sink later — Scorable just receives the same OTLP traffic via OpenTelemetry, so they can keep their existing tracing if they have it.

---

Step 1: Analyze the Application

Identify:

• Framework — OpenAI SDK directly, openai-agents, pydantic-ai, LangChain / LangGraph, LlamaIndex, Anthropic SDK, Mastra, Vercel AI SDK, etc. Each has a corresponding OpenInference instrumentation library.
• Language — Python or TypeScript/JavaScript (instrumentation libraries exist for both).
• Entry points — where the application kicks off an LLM call. The OTEL setup needs to run before those calls execute.
• Existing observability — if the project already has OpenTelemetry configured (e.g. exports to Honeycomb, Datadog, Tempo), you'll add a second exporter rather than replace the setup.

If the framework is unfamiliar, default to looking it up in the OpenInference catalog: https://github.com/Arize-ai/openinference. Most popular SDKs are covered there.

If multiple LLM-using subsystems exist, help the user prioritize. Recommend starting with the most critical agent first — additional services can each get their own service.name and their own filter later.

---

Step 2: Install Scorable CLI & Authenticate

Install the Scorable CLI:

curl -sSL https://scorable.ai/cli/install.sh | sh

Or with npm:

npm install -g @root-signals/scorable-cli

Or run without installing via npx:

npx @root-signals/scorable-cli judge list

Then ask the user which authentication option they prefer:

Option A: Permanent API Key (Recommended)

Direct them to: https://scorable.ai/api-key-setup to create an API key, then set it via the CLI:

scorable auth set-key
# paste the key when prompted

# or alternatively:
scorable auth set-key <your-api-key>

Security: Use environment variables or the project's secret management. The same key is used both by the CLI and by the application's OTEL exporter. Read existing .env files if available, otherwise ask the user where they want to store the secret. Do not paste the key into this session.

Option B: Temporary API Key (Testing Only)

Get a free demo key (no registration required):

scorable auth demo-key

Warn the user appropriately that:

• Filters and evaluators created with it will be public and visible to everyone
• The key only works for a limited time
• For private setups, they should create a permanent key at https://scorable.ai/register

Option C: Existing API Key

If they have an account: https://scorable.ai/settings/api-keys

scorable auth set-key <your-api-key>
# or
export SCORABLE_API_KEY="sk-your-api-key"

Verify the CLI works before continuing:

scorable judge list

If this errors, do not move on — the OTEL exporter uses the same key.

---

Step 3: Instrument the Application with OTEL

Choose the OpenInference instrumentation library that matches the framework you identified in Step 1.

Python frameworks

See references/python-instrumentation.md for installation snippets and the canonical setup for OpenAI SDK, openai-agents, pydantic-ai, LangChain / LangGraph, Anthropic SDK, and LlamaIndex.

TypeScript / JavaScript frameworks

See references/typescript-instrumentation.md for OpenAI Node SDK, LangChain.js, and Vercel AI SDK setup.

Other languages

OpenTelemetry SDKs exist for Go, Java, Ruby, .NET, etc., but OpenInference auto-instrumentation coverage is currently strongest in Python and TypeScript. For other languages, point the OTLP HTTP/protobuf exporter at https://api.scorable.ai/otel/v1/traces with header Authorization: Api-Key <your-api-key> and instrument LLM calls manually with custom spans following the GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/ . Note that you must check this as this keeps constantly updating. For example Scorable expects for example gen_ai.output.messages and gen_ai.input.messages.

The OTLP exporter (universal Python snippet)

After picking the right framework instrumentor, add the exporter that ships spans to Scorable:

import os
from opentelemetry import trace
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
from opentelemetry.sdk.resources import Resource
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

exporter = OTLPSpanExporter(
    endpoint="https://api.scorable.ai/otel/v1/traces",
    headers={"Authorization": f"Api-Key {os.environ['SCORABLE_API_KEY']}"},
)

resource = Resource.create({"service.name": "my-agent"})
provider = TracerProvider(resource=resource)
provider.add_span_processor(BatchSpanProcessor(exporter))
trace.set_tracer_provider(provider)

Critical pieces:

• service.name is the most important resource attribute. Use a stable, descriptive name (e.g. customer-support-agent, code-review-bot, sales-bot-prod). It's the strongest filter target in Scorable. Per-environment naming (my-agent-prod vs my-agent-staging) is fine and recommended.
• Api-Key <key> header — the same key set in Step 2. Read it from an env var; never hardcode.
• BatchSpanProcessor batches spans before flush. For tests you may want SimpleSpanProcessor to see traces immediately.

Where to place the setup

The setup must run once, before any LLM call. Common placements:

• A dedicated tracing.py (or tracing.ts) module imported from your app entrypoint
• The framework's startup hook (FastAPI startup event, Django app ready(), Next.js instrumentation.ts)
• __main__.py / top of the agent script for CLI tools

---

Step 4: Send a Test Request + Verify Trace Ingestion

After wiring up the instrumentation:

1. Run a single LLM request through the application's normal entry point.
2. Wait ~10 seconds for the BatchSpanProcessor to flush.
3. Verify the trace landed in Scorable using the CLI:

scorable otel-trace list --since 5m --service-name <your-service-name>

You should see the trace listed with its root span name and a span count.

Drilling in

scorable otel-trace spans <trace_id>
scorable otel-trace spans <trace_id> --output json | jq '.[].span.attributes'

The full payload shows you exactly what gen_ai.* attributes the framework emitted, which determines what your filter conditions can match against. Common attributes any spec-conformant instrumentor sets: gen_ai.agent.name, gen_ai.request.model, gen_ai.operation.name, gen_ai.tool.name, gen_ai.usage.input_tokens, .

If no trace shows up

• CLI works but no trace? OTEL export error. Check the application's stderr for OTLP exporter logs (the SDK logs export failures by default).
• Confirm the BatchSpanProcessor flushed. For an immediate test, swap to SimpleSpanProcessor temporarily — flushes synchronously.
• Confirm service.name. scorable otel-trace list --since 5m (without a --service-name filter) should show something. If it does, your filter value is off; if it doesn't, the export isn't reaching Scorable.
• Auth failure? A 401 from the exporter is logged at error level. Re-verify with scorable judge list.

Do not move on until at least one trace from the application is visible.

---

Step 5: Create an Evaluation Filter

A filter runs an evaluator or judge automatically against every matching trace. The filter criteria uses the same wire format as the CLI's --filter flag.

Decide the target

Two options:

• An existing evaluator — if the user has one (built with scorable evaluator create or pre-existing in their org), use --evaluator-id <uuid>. Single score per trace.
• A judge — bundle of evaluators producing per-evaluator scores. Use --judge-id <uuid>. If no judge exists, generate one using the scorable-integration skill (it walks through scorable judge generate with proper intent prompts), then come back here with the resulting judge_id.

Decide what to match

A solid first filter scopes to the service you just instrumented:

scorable otel-filter create \
  --name "<service-name>-<evaluator-name>" \
  --evaluator-id <uuid> \
  --filter-criteria '{"conditions":[{"column":"resource","type":"string","key":"service.name","operator":"=","value":"<your-service-name>"}]}' \
  --delay-seconds 10

Or via judge:

scorable otel-filter create \
  --name "<service-name>-quality-judge" \
  --judge-id <uuid> \
  --filter-criteria '{"conditions":[{"column":"resource","type":"string","key":"service.name","operator":"=","value":"<your-service-name>"}]}' \
  --delay-seconds 10

Key parameters

• --filter-criteria — JSON conditions, AND-combined. Scope to your service.name at minimum; narrow further with gen_ai.agent.name, gen_ai.request.model, etc. as needed.
• --delay-seconds — wait this long after the most recent span lands before triggering evaluation. 10s is a safe default; bump higher (30–60s) for long-running agents whose final span arrives much later than the first.
• --sampling-rate — between 0.0 and 1.0; default 1.0 (every match). Use 0.1 for 10% sampling in production.

Filter grammar reference

For the full grammar (operators, columns, attribute syntax, time windows), refer to:

scorable otel-filter create --help
scorable otel-trace list --help    # same column vocabulary

The --help of otel-trace list documents all filter columns, the GenAI semantic conventions, and operator semantics inline.

---

Step 6: Verify the Filter Triggered

1. Send another test request through the instrumented application.
2. Wait delay_seconds + ~5s for the eval to run.
3. List traces and find the matching one:

scorable otel-trace list --since 5m --service-name <your-service-name>

4. Inspect spans on the trace. The evaluation result lands as a child span parented to the original root, named evaluate <evaluator-name>:

scorable otel-trace spans <trace_id>

5. For full attributes:

scorable otel-trace spans <trace_id> --output json

The eval span carries:

• gen_ai.evaluation.name — the evaluator/judge that ran
• gen_ai.evaluation.score.value — numeric score (0–1)
• gen_ai.evaluation.explanation — the justification text
• scorable.evaluation = true — Scorable's marker indicating this is an eval span (not user data)
• resource.service.name = scorable.evaluation — distinct service so eval spans don't accidentally match customer service-scoped filters

Querying by evaluation result

Once filters start producing eval spans, the CLI can target them too:

# Every trace that received any evaluation
scorable otel-trace list --since 24h \
  --filter 'gen_ai.evaluation.name;string;gen_ai.evaluation.name;=;<evaluator-name>'

# Low-scoring runs from the last 24h, exported as CSV for review
scorable otel-trace list --since 24h --output csv \
  --filter 'gen_ai.evaluation.score.value;number;gen_ai.evaluation.score.value;<;0.5' > low-scores.csv

If no eval span appears within the expected window:

• Run scorable otel-filter list and confirm the filter is is_active: true and matches by service.name.
• Confirm the trace's service.name (in resource.service.name attribute) actually matches the filter value — typos here are the most common cause of silent non-firing.
• The filter only fires after delay_seconds. For long delays, just wait longer.
• Filters only evaluate traces ingested after the filter was created — older traces are not backfilled.

---

Step 7: Provide Next Steps

After verification:

1. Sampling for production — recommend --sampling-rate 0.1 (or lower) to control evaluator cost. Sampling rate is part of the filter; recreate the filter to change it.
2. Multiple filters per service — separate filters for separate concerns (one for truthfulness, one for toxicity, one for tool-call correctness) keep results readable. They run independently against the same trace.
3. Frontend visibility — traces and their eval child spans are visible at https://scorable.ai/traces. Scores show up on the trace detail view alongside the original spans.
4. Everyday CLI usage:
   • scorable otel-trace list --since 1h --has-error — traces that errored in the last hour
   • scorable otel-trace list --since 7d --filter 'gen_ai.evaluation.score.value;number;gen_ai.evaluation.score.value;<;0.3' — flag low-scoring traces from the past week
   • scorable otel-trace spans <trace_id> --output json | jq — drill into one trace for debugging
   • scorable otel-filter list — review currently active filters
5. Documentation entry — add a short README/CONTRIBUTING note describing:
   • The service.name value the application reports
   • Where the OTEL setup lives (tracing.py or equivalent)
   • The active filter(s) and what they evaluate
6. Link to docs:
   • OTEL setup: https://docs.scorable.ai/usage/otel
   • GenAI semantic conventions (the source of truth for span attribute names): https://opentelemetry.io/docs/specs/semconv/registry/attributes/gen-ai/
   • OpenInference instrumentation libraries catalog: https://github.com/Arize-ai/openinference

---

Key Implementation Notes

• Instrumentation runs once, before LLM calls — get this wrong and traces silently disappear.
• Use a stable service.name — the strongest filter target. Per-environment naming (my-agent-prod, my-agent-staging) is a good default.
• OpenInference libraries are framework-specific — pick the one matching your framework. Don't double-instrument; OpenAI auto-instrumentation alongside pydantic-ai's own tracing creates duplicate spans.
• API key from env vars — same key for the CLI and the OTEL exporter. Same security posture as the existing Scorable integration skill.
• Eval spans carry scorable.evaluation = true — handy for distinguishing your application's spans from Scorable-emitted eval spans when querying or building dashboards.
• Filters apply to new traces only — they don't backfill historical data, so create filters as part of the rollout, not after the fact.
• For private custom judges, follow scorable-integration — that skill covers scorable judge generate with proper intent prompts, then return here with the judge_id for filter creation.