Traceloop - LLM Reliability Platform

Spreadsheets stuffed with ad-hoc scores and gut-check metrics leave every result in doubt, wasting hours hunting down context across tabs.
Model quality drops quietly — your users feel it before you do. Traceloop catches the failures before they hit production.
Traceloop monitors what your model says, how fast it responds, and when things start to slip — so you can debug faster and deploy safely.
Traceloop turns noisy LLM logs into clear insights — instantly
Just one line of code gets you live visibility into prompts, responses, latency, and more — no setup, no hassle.
Traceloop runs trusted checks like faithfulness, relevance, and safety using built-in metrics — applied automatically to your real data. Get a baseline understanding of model quality without writing a single test.
Off-the-shelf metrics don’t always cut it. With Traceloop, you can define what quality means for your use case, annotate real examples, and train a custom evaluator that scores output the way you would.
Traceloop runs your standard and custom evaluations automatically — whether it’s on every pull request or in real time as your app runs. Catch issues early, enforce thresholds, and ship with confidence.
From startup to enterprise, cloud to air-gapped — we’ve got you covered.
SOC 2 & HIPAA compliant. Deploy Traceloop in the cloud, on-prem, or air-gapped.
Traceloop is built on OpenTelemetry and ships with OpenLLMetry, our open-source SDK — giving you transparency without lock-in.
Connect your LLMs in Python, TypeScript, Go, or Ruby using OpenLLMetry or our native OpenTelemetry-based gateway, Hub.
Traceloop supports 20+ providers (OpenAI, Anthropic, Gemini, Bedrock, Ollama), vector DBs (Pinecone, Chroma), and frameworks like LangChain, LlamaIndex, and CrewAI.
Check out our GitHub repository to see contributions and perhaps make your own!
‍Start tracking, testing, and improving your LLMs with Traceloop — one line of code, full observability.