Traceloop

Traceloop is an LLM reliability and observability platform that helps teams monitor, evaluate, and debug large language model applications in production. Built on OpenTelemetry via the open-source OpenLLMetry SDK, it automatically captures traces, metrics, and quality scores from every LLM call—transforming noisy logs into clear, actionable insights. The platform is designed for developers and ML engineers who need to ship trustworthy LLM apps with confidence. Traceloop comes with built-in quality evaluations for faithfulness, relevance, and safety, applied automatically to real data without writing a single test. You can also define custom quality metrics by annotating examples and training an evaluator that scores outputs the way you would. These checks run automatically on every pull request or in real time as your app runs—catching regressions before they hit users. The platform offers a real-time monitoring dashboard, evaluation dashboard with test runs, CI/CD integration, prompt management and registry, and a smart proxy called Traceloop Hub for routing and observability. It supports 20+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Ollama), vector databases (Pinecone, Chroma), and frameworks like LangChain, LlamaIndex, and CrewAI. Deployment options include cloud, on-prem, and air-gapped, with SOC 2 and HIPAA compliance. Compared to alternatives, Traceloop stands out for its OpenTelemetry-based open-source core, which avoids vendor lock-in, and its generous free tier. While tools like LangSmith focus on LangChain users, Traceloop is framework-agnostic. Its combination of out-of-the-box evaluations and custom evaluator training makes quality assurance both automated and tailored—a strong choice for teams at any stage of LLM production.