7 Best LLM Observability Tools for Debugging and Tracing

LLM observability tools offer crucial solutions for debugging and optimizing large language model (LLM) applications, which operate probabilistically and often produce semantically incorrect outputs without traditional errors. These tools provide structured tracing, step-level inspection, and replay capabilities, allowing for comprehensive visibility into model behavior. Unlike conventional application performance monitoring, LLM observability captures complete prompt and completion bodies, token-level cost attribution, and semantic quality scores to address the non-deterministic nature of LLM outputs. Key platforms like Galileo, LangSmith, Arize AI and Phoenix, Langfuse, Helicone, Braintrust, and Portkey offer varied features and strengths, such as hierarchical tracing, evaluation integration, and session management, each catering to different use cases and deployment preferences. These tools enhance debugging efficiency, reduce incident resolution times, and support systematic quality improvement through features like session threading, root-cause analysis, cost tracking, and intelligent routing, ultimately enabling engineering teams to maintain high reliability and performance in AI systems.