“Strong fit for tracing graph-based agent execution.”
“Useful for inspecting nested runs, tool calls, prompts and intermediate outputs.”
“Connects tracing with datasets and evaluations.”
“Watch out for: How well it handles custom orchestration outside the LangChain ecosystem.”
“Best for open-source flexibility”
“Traces LLM calls and non-LLM operations.”
“Useful session, span, prompt, latency and cost visibility.”
“Attractive for teams that want self-hosting and control over telemetry.”
“Watch out for: Whether its debugging experience gives you enough insight into complex branching and agent state.”
“Best for instrumentation flexibility”
“OpenTelemetry and OpenInference-oriented tracing.”
“Useful for inspecting spans and evaluating intermediate outputs.”
“Interesting when you have multiple frameworks or custom agent orchestration.”
“Watch out for: Whether the complete production debugging workflow meets your team's needs.”
“Best for evaluation-driven development”
“Connects traces to datasets, experiments and scoring.”
“Useful for comparing agent behavior across prompt or model changes.”
“Particularly interesting if you want to turn production failures into regression tests.”
“Watch out for: Whether its trace exploration is sufficiently detailed for low-level debugging.”
“Best for ML experimentation integration”
“Tracing and inspecting model and application operations.”
“Useful for connecting experiments, evaluations and application behavior.”
“Worth considering if your team already uses Weights & Biases.”
“Watch out for: Fit with your particular multi-agent orchestration and production incident workflows.”
“Prefer tools that support standard trace/span instrumentation, ideally with GenAI semantic conventions.”
“OpenTelemetry is worth considering as a portability layer even if you ultimately choose LangSmith, Langfuse, Phoenix or another backend.”