As of May 08, 2026, LangSmith, Arize Phoenix, and W&B Weave all support tracing and evals, but their operational sweet spots differ. Read now.
Why Agentic Systems Break Traditional Monitoring
When an application is just a series of function calls, a stack trace and a latency graph tell you most of what you need. Agentic systems don't behave that way. A single user request can fan out into a loop of model calls, tool invocations, retries, and intermediate decisions, and the thing that went wrong is often a judgment the model made three steps back — not an exception you can catch. Observability for these systems has to capture the full chain of reasoning, the inputs and outputs at every hop, and some notion of whether each step was actually correct.
That is why tracing and evaluation have become the two pillars of agentic observability. Tracing reconstructs what happened; evals decide whether what happened was good. LangSmith, Arize Phoenix, and W&B Weave all cover both, which is why the choice between them comes down to how you work rather than a checklist of features.
What Tracing and Evals Actually Give You
Tracing records each span in an agent run — the prompt sent, the response received, the tool called, the tokens and time spent — and nests them so you can walk the tree from the top-level request down to the failing leaf. Good tracing is what turns "the agent gave a bad answer" into "the retrieval step returned the wrong document, so the model had nothing useful to work with."
Evals close the loop. Instead of eyeballing outputs, you define graders — exact-match checks, rule-based assertions, or model-graded judgments — and run them across a dataset so you can measure quality as you change prompts, models, or tools. The value is regression safety: when you tweak one part of an agent, evals tell you whether you improved things overall or fixed one case while breaking three others.
Matching a Platform to Your Team
Because all three tools do tracing and evals, the real decision is about operational fit — where each one sits most naturally in an existing workflow. Think about who owns the agent, what else they already use, and where the debugging happens day to day.
- LangSmith fits teams building around the LangChain ecosystem, where tight integration means traces and evals come with minimal wiring and the debugging experience is oriented around chains and agents.
- Arize Phoenix leans toward practitioners who want an open, self-hostable option and who care about inspecting embeddings, retrieval quality, and drift alongside standard tracing.
- W&B Weave is a natural extension for teams already running experiments in Weights & Biases, keeping agent traces and evals next to the model training and tracking work they do elsewhere.
How to Choose Without Overthinking It
Start from where your team already lives. If your stack is built on a particular framework or you already run a tracking platform, the tool that plugs into it with the least friction will get adopted and actually used — which matters more than any single capability. Instrument one real agent end to end, run a small eval set against it, and see whether the traces answer your actual debugging questions.
Since the feature gap on core tracing and evals is narrow, treat this as a reversible decision. Keep your instrumentation as decoupled from the vendor as you reasonably can, pick the platform that matches your current workflow, and revisit only if your operational needs shift.