Traceloop

Traceloop is an observability and reliability platform for LLM apps, giving teams the tracing, evaluation and monitoring they need to spot issues early and ship faster.

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LLM observabilityTraceloop tutorialOpenTelemetry LLM tracingAI app monitoringRAG quality monitoringLLM drift detection

Features of Traceloop

One loop: evaluation, monitoring and tracing from dev to production

Native OpenTelemetry & OpenLLMetry ingestion for full-stack LLM data

Track token cost, latency and error trends in one dashboard

Built-in quality metrics—relevance, faithfulness and more

Replay any request to debug non-deterministic failures

Drift & regression alerts before users notice

SDKs for Python, TypeScript; Go & Ruby in Beta

Cloud or self-hosted OTLP/Collector—fits your existing stack

Use Cases of Traceloop

A/B test prompts or models before every release

Monitor RAG answer quality in prod and catch relevance drops

Trace every agent tool call to find timeouts or external errors

Replay the exact context that caused a bad live answer

Stream LLM metrics into your current OpenTelemetry pipeline

Watch token spend and latency to keep costs down

Run on-prem with custom retention for regulated data

FAQ about Traceloop

QWhat is Traceloop?

Traceloop is an observability and reliability platform for LLM/GenAI apps that provides tracing, monitoring and evaluation.

QWhich metrics can Traceloop track?

Latency, token cost, errors and quality shifts—paired with full trace data for root-cause analysis.

QDoes Traceloop work with OpenTelemetry?

Yes. It’s built on OpenTelemetry/OpenLLMetry and ships data to any OTLP endpoint.

QWhich languages are supported?

Python and TypeScript SDKs are GA; Go and Ruby are in Beta.

QIs there a free plan?

Yes. The Free Forever tier includes ~50K spans/mo, 5 seats and 24h data retention.

QHow is Enterprise different from the free plan?

Enterprise adds higher quotas, unlimited seats, custom retention and on-prem deployment.

QCan Traceloop debug non-deterministic LLM issues?

Absolutely—use traces, replays and evaluations to pinpoint drift, regressions or anomalous outputs.

QWho should use Traceloop?

AI engineers, platform teams and SREs who need production-grade quality and stability for LLMs.

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