OpenLLMetry by Traceloop takes a fundamentally different approach to LLM observability compared to purpose-built platforms like Langfuse or Helicone. Instead of requiring a proprietary SDK and dashboard, it extends the OpenTelemetry standard — the industry-standard observability framework — with LLM-specific semantic conventions. This means LLM traces, spans, and metrics flow into whatever observability backend your team already uses: Datadog, Grafana, Jaeger, New Relic, Honeycomb, or any OTEL-compatible collector.
Installation requires just two lines: pip install traceloop-sdk and Traceloop.init(). The SDK automatically instruments calls to OpenAI, Anthropic, Cohere, Bedrock, VertexAI, HuggingFace, plus vector databases like Pinecone, ChromaDB, Qdrant, and Weaviate, and frameworks including LangChain, LlamaIndex, Haystack, and CrewAI. Each LLM call is captured as a span with prompt content, token usage, latency, model parameters, and cost estimation — all without modifying application code.
OpenLLMetry is Apache-2.0 licensed and has grown to roughly 7.2K GitHub stars. The current Traceloop product is framed as an LLM reliability platform with monitoring and evaluation dashboards, CI/CD integration, prompt management, and a Free Forever cloud tier up to 50K spans/month, 5 seats, and 24 hours of retention. For teams invested in the OpenTelemetry ecosystem, it remains a natural path to LLM observability and reliability workflows without forcing a wholesale backend migration.
