Traceloop

Founded by Nir Gazit & Gal Kleinman in 2023

Track, evaluate, and monitor LLM application quality and latency in production environments

Pricing model

Free

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use Traceloop to monitor, evaluate, and improve your LLM applications in development and production environments. It provides real-time visibility into prompt logs, model responses, token usage, and latency with a single line of code. You can run automated quality checks for faithfulness, safety, and relevance, or build custom evaluators tailored to your domain. Traceloop integrates directly into CI/CD pipelines to catch regressions before releases, tracks model drift over time, and supports secure deployments on-premise or in private clouds.

What Traceloop does

Log prompts, completions, and metadata with a single code lineMeasure faithfulness and relevance scores on live LLM trafficRun automated evaluation test suites on code pull requestsAlert engineering teams when model latency or error rates spikeCompare output quality between different foundation model versionsInspect execution traces across vector databases and retriever componentsDeploy observability collector agents in air-gapped environmentsSingle-line SDK integration using OpenLLMetry and OpenTelemetry standardsReal-time tracking of prompts, model responses, token counts, and latencyAutomated evaluations for faithfulness, relevance, safety, and hallucination detectionCustom metric definition and fine-tuned evaluators using real user annotationsContinuous automated quality testing across pull requests and CI/CD pipelinesDetection and alerting for LLM output drift and unexpected quality dropsDeployment options for public cloud, on-premises, and air-gapped enterprise environmentsNative multi-language support across Python, TypeScript, Go, and Ruby