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.
Traceloop is Apache 2.0 licensed with 2,000+ GitHub stars and backed by Battery Ventures. The Traceloop Cloud product adds a managed dashboard with prompt analytics, quality scoring, and A/B testing for prompts. For teams invested in the OpenTelemetry ecosystem, OpenLLMetry is the most natural path to LLM observability — no new vendors, no data silos, just additional instrumentation on existing infrastructure.