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MonitoringFreemiumOpen Source
TRACELOOP
OpenTelemetry-native observability for LLM applications
Apache-2.0
ABOUT
LLM applications are notoriously difficult to debug and monitor because each API call, vector search, and prompt template interaction happens inside framework abstractions with no visibility. When an AI agent returns wrong answers or degrades over time, developers have no way to trace the root cause. Traceloop instruments the entire LLM stack via OpenTelemetry — from the user request through prompt construction, retrieval, LLM inference, and response — providing traces, metrics, and evaluation in a unified dashboard.
INSTALL
pip install traceloop-sdkINTEGRATION GUIDE
1. Trace end-to-end LLM inference requests across model providers, vector databases, and prompt templates to debug failures
2. Monitor token usage, latency, and cost per request across all LLM calls in production
3. Detect regressions in response quality by comparing traces against evaluation metrics over time
4. Instrument RAG pipelines to see which documents were retrieved and how they influenced the final response
5. Set up alerts on LLM performance degradation using OpenTelemetry-standard metrics and thresholds
TAGS
llmobservabilitytracingopentelemetrymonitoringpythonevaluation