The best LLM tracing tools (2026)
Five tracing platforms ranked for debugging multi-step LLM and agent applications: span depth, search, OpenTelemetry support, cost attribution, and the path from failure to regression test.
An LLM log tells you what one model call returned. A trace tells you what the application did: the user request, routing decision, retrieved context, model calls, tool arguments, retries, intermediate outputs, latency, token usage, and the final answer, connected in the order they happened.
That distinction matters once an application becomes more than one prompt. When an agent calls the wrong tool or a RAG pipeline answers from weak context, the final output is only the symptom. The useful debugging unit is the full execution path.
We ranked these tools on the tracing workflow itself. Broad infrastructure monitoring and attractive aggregate dashboards count for less here than whether an engineer can open one bad run, find the first failing step, and turn that discovery into a test that prevents the failure from returning.
What separates a good LLM trace
| Capability | What we looked for |
|---|---|
| Trace structure | Nested spans for models, tools, retrieval, agent handoffs, and custom application steps |
| Instrumentation | Provider wrappers, framework integrations, manual SDKs, and OpenTelemetry support |
| Debugging | Search and filtering by user, session, model, metadata, score, error, latency, and cost |
| Streaming and async | Correct parent-child context across streamed responses and background work |
| Cost attribution | Token, latency, and estimated cost on each span and rolled up to the request |
| Evaluation handoff | A direct route from a bad trace to a dataset, scorer, or regression test |
| Data control | Export, retention controls, and a credible self-hosting story where required |
The five tools, compared
| Tool | Best for | Strongest advantage | Main tradeoff |
|---|---|---|---|
| Braintrust | Teams connecting production debugging to evals | Trace-to-regression workflow | Closed source |
| Langfuse | Open-source, framework-neutral tracing | Deep traces and self-hosting | More eval wiring |
| LangSmith | LangChain and LangGraph applications | Near-automatic framework traces | Ecosystem coupling |
| Arize AI | OpenTelemetry and mixed ML/LLM estates | Phoenix plus production monitoring | Fragmented eval workflow |
| Comet Opik | OSS tracing with broad agent-framework support | Integrations and human review | Newer product |
The best tracing tool when the goal is to improve the application, not merely archive what happened. Braintrust captures nested spans with inputs, outputs, metadata, timing, token usage, and cost, then keeps traces connected to the rest of the evaluation system.
The key workflow starts after an engineer finds a bad run. Any trace or span can become a dataset case, scorers can be tested against it, and a proposed fix can run across the expanded regression suite before release. The same scoring logic can then monitor production. That makes tracing part of an improvement loop instead of a separate operations surface.
Braintrust also works well across provider SDKs and agent frameworks, which matters for applications that do not want their observability model tied to one orchestration library. Choose Langfuse instead when open source or self-hosting is the first requirement; choose Braintrust when the trace-to-eval handoff is the first requirement.
The strongest open-source tracing experience in the category. Langfuse's hierarchy, session grouping, metadata model, and filtering make it good at reconstructing real multi-step traffic. Instrumentation works across provider SDKs and frameworks, while OpenTelemetry gives teams a route that does not depend on one proprietary wrapper.
Self-hosting is first-class rather than an enterprise exception. That is decisive for teams with data-residency requirements or enough trace volume to justify operating their own storage. The hosted product offers the same conceptual model when the team does not want the operational burden.
Langfuse supports evaluation, datasets, prompts, and scores, but the route from a production failure to a CI-gated regression is less opinionated than Braintrust's. Expect to assemble more of that workflow yourself. For pure trace inspection and data control, however, Langfuse is difficult to beat.
The path of least resistance for LangChain and LangGraph. Because LangSmith comes from the same team, it understands chains, graph nodes, model calls, tools, and agent state without forcing developers to manually recreate that structure. A small amount of configuration can produce a trace that would take considerably more instrumentation elsewhere.
That framework awareness is especially useful for graph-based agents, where the sequence of nodes and state transitions is the debugging surface. LangSmith also connects traces to datasets, evaluators, prompt experiments, and review queues.
The advantage shrinks outside its home ecosystem. Provider-SDK-direct and mixed-framework applications need more manual instrumentation, while the pricing model deserves attention at production volume. Use LangSmith when the application architecture already makes LangChain the center of gravity.
The best fit for teams that want LLM traces inside a broader observability and ML-monitoring program. The open-source Phoenix project provides a credible trace viewer and evaluation toolkit, while Arize's commercial platform adds the production operations background the company built monitoring conventional ML systems.
OpenTelemetry support makes Phoenix attractive for teams standardizing instrumentation across application and AI workloads. Arize is also well suited to organizations that need to correlate LLM behavior with embedding drift, model performance, and other ML signals.
For an LLM-only product team, the workflow is less cohesive than the specialists. Traces, datasets, and evaluations feel like adjacent product areas, and a bad trace does not flow into a deployment-blocking test as cleanly as it does in Braintrust. Arize is a strong consolidated platform, not our first pick for the tightest eval-driven loop.
The promising open-source alternative for teams that care about agent-framework coverage. Opik traces model calls and multi-step workflows, supports experiments and model-based scoring, and provides human annotation in the same product. Its integrations with frameworks such as DSPy, AutoGen, and Google ADK stand out in a market where many vendors stop after LangChain.
Apache licensing and self-hosting make Opik credible for organizations that need control but want an established commercial sponsor behind the project. Comet's years operating MLOps infrastructure also reduce some of the platform-risk concern attached to young open-source tools.
Opik is newer than Langfuse and the product still has some documentation and identity seams between Comet and Opik. It belongs on the shortlist when its framework integrations match the stack or when a team wants an OSS tracing-and-eval platform with mature-company backing.
Tracing is not the finish line
A tracing deployment is successful when it shortens the distance between a user-visible failure and a verified fix. Collecting every prompt and tool call without a review workflow creates a more expensive log archive.
Whichever product you choose, the operating loop should be the same:
- Instrument model, retrieval, tool, and application steps as connected spans.
- Search production traffic by user, session, score, error, latency, cost, and release version.
- Find the first step where a failing run diverged from the intended behavior.
- Preserve that input and the relevant context as a regression case.
- Run the fix against the full dataset before releasing it.
Choose Braintrust when that complete loop matters most, Langfuse when open-source tracing and data control lead the decision, LangSmith for LangChain-native applications, Arize for a consolidated ML observability program, and Opik for an open platform with broad agent-framework support.