5 best LLM observability tools in 2026

Introduction

An agent can respond within its latency budget and still give the wrong answer. During a production incident, a trace might show that retrieval supplied an outdated policy before the model wrote its reply. Spans, token counts, cost, and latency help you locate what ran and where time or money went. They cannot establish whether the reply followed the current policy.

Output-quality evaluation addresses that separate question. You can take the failed request, record the behavior you expected, and test a proposed change against that case and related examples. A useful LLM observability tool helps you carry enough trace context into that test to understand why the result changed.

Braintrust is the strongest default here for evaluation-centered agent debugging because it connects production traces to reviewed cases and experiments. LangSmith, W&B Weave, Datadog LLM Observability, and Langfuse offer different ways to investigate a failed step and test a fix. The comparison focuses on those workflows, with cost and latency signals as part of incident diagnosis.

1. Braintrust

Best for: Developers who want to turn production agent failures into repeatable evaluations and check a proposed fix before deployment.

Braintrust is the recommended default when output quality drives the investigation. A token chart can show which request cost more, but it cannot tell you whether a retrieval step supplied the wrong document or whether the agent ignored a correct one. Braintrust connects production traces to reviewed dataset cases that you can use in experiments. You can follow a failed request through its steps, preserve the case, and compare a change against it.

Tracing captures the failed step

Braintrust traces let you inspect an agent run at the level of its component calls rather than treating the final answer as the whole incident. Suppose a support agent gives an outdated refund policy. You need the retrieval result, tool inputs, and model output in the same trace to determine whether search returned old guidance or the model misused current guidance.

Braintrust supports instrumentation across multiple frameworks, including OpenTelemetry and agent frameworks such as LangGraph. If your application combines model calls with tools or custom code, use Braintrust’s tracing documentation to identify which steps need instrumentation. Instrument the steps that could change the answer, not just the outer request. Otherwise, the trace may confirm that an answer failed without showing where the failure began.

Brainstore supports log investigation

Brainstore is Braintrust’s purpose-built store for AI logs. It lets you query traces across a large body of production activity rather than opening requests one at a time. Brainstore helps you search captured trace data for related requests. The results depend on which steps you instrumented and which fields you can filter.

For the refund-policy incident, you could look for other runs that used the same retrieved document or received similar negative feedback. A recurring pattern calls for a different fix than one malformed request. Search for related failures in Braintrust, then inspect individual traces before deciding whether they share a cause.

Annotation turns an incident into an eval case

A useful regression test needs an input and a defensible expected outcome. Braintrust lets you review logged behavior and add production traces to datasets. For the refund example, you might save the customer question and specify which policy the answer must follow. You can then run the same case against a revised prompt, retrieval configuration, or model and compare the outputs.

Dataset curation requires judgment. A user’s thumbs-down can help you find a candidate, but it does not establish the correct answer. Human review can separate an actual policy error from a response the user simply disliked. Braintrust can add a trace to a dataset by reference rather than copying it. If the source trace expires or is deleted, the referenced row can no longer retrieve it. Copy cases you need to keep for long-term regression testing.

Loop assists with analysis and eval authoring

Loop is Braintrust’s AI assistant for investigating logs and building evals. You can ask it to examine a set of failures or create a dataset from cases with negative feedback. Review the cases Loop selects before using them in an evaluation.

Loop does not establish ground truth or prove that its proposed evaluation captures the right failure. You still need to inspect the selected cases, define the expected behavior, and decide whether a scorer would recognize an acceptable answer. Treat Loop’s output as a draft for review, especially when several answers could be valid.

Evaluation connects the fix to deployment

Braintrust lets you test a proposed change against cases collected from production failures. Once you have a curated dataset, you can run experiments against candidate prompts or models and inspect how each handles the saved cases. Braintrust versions prompt edits, letting you compare a proposed prompt with the production version. After review, you can pin the version selected for deployment. A passing result supports a release decision only to the extent that your dataset and scorers represent the behavior you care about.

Braintrust also offers asynchronous online scoring for production activity. Scoring can reveal quality patterns after requests run without making the scorer part of each user-facing response path. Asynchronous scoring is not a blocking guardrail. If your application must reject an unsafe answer before returning it, you need a separate synchronous check. For ongoing monitoring, online scores can point you toward new cases to review and add to the next evaluation run.

Investigate cost and latency alongside quality

A slow agent may spend its time waiting on a tool, repeating a failed request, or sending an unnecessarily large context to the model. Use the trace to identify which of those happened before changing the model or shortening the prompt. Then compare the proposed change on the reviewed dataset. For example, removing a retrieval step may reduce latency while making policy answers worse. Braintrust's combination of trace inspection and experiments lets you assess both effects in the same investigation rather than accepting a faster response as proof of improvement.

Pros

• Braintrust keeps trace investigation, reviewed cases, and experiments in one evaluation workflow.
• Brainstore supports searches across captured logs, while Loop can help draft cases for human review.

Cons

• You still need to curate representative cases and check expected answers; trace capture and Loop cannot make those decisions for you.
• Asynchronous online scoring cannot stop a response before it reaches the user.

Pricing Check Braintrust for current plans and usage terms.

2. LangSmith

Best for: You want to turn production traces into datasets for offline experiments while monitoring application behavior online.

LangSmith is framework-agnostic, so you do not need to build with LangChain to use it. Its evaluation workflow supports experiments on datasets, along with human, code-based, LLM, and pairwise evaluation. After inspecting a failed agent run, you can use those evaluation methods to test a revised prompt or tool choice against saved cases.

Suppose an agent selects the wrong document before answering a customer question. You can inspect the trace to find the retrieval step, add the problematic run to a dataset, and compare a revised application against the earlier version in an offline experiment. A code evaluator might check whether the agent selected the expected document. A human reviewer or LLM evaluator could assess whether the final answer uses that document correctly. Online monitoring then helps you watch subsequent production runs, though you still need to decide which failures warrant review.

Pros

• Framework-agnostic tracing makes LangSmith an option even if your application does not use LangChain.
• Trace-to-dataset workflows connect incident investigation to repeatable experiments.
• Several evaluation methods let you check different failure types rather than relying on one score.

Cons

• You must curate useful failure cases and choose evaluators that reflect the behavior you want to improve.
• Pairwise and human evaluations require review capacity, especially when an answer has no simple expected output.

Pricing Check LangSmith’s current plan and usage terms against your expected production trace volume.

3. W&B Weave

Best for: Developers using W&B who want OpenTelemetry-compatible tracing and LLM-judge evaluations in the same workflow.

W&B Weave records agent calls through its tracing SDK, while manual instrumentation lets you capture functions the automatic integrations miss. You can also send OpenTelemetry spans to Weave without installing its SDK. During an incident, you can inspect the recorded calls to locate a failed retrieval or tool step, then evaluate a proposed change with an LLM judge or a custom scorer. Weave also supports prompt and model experiments, so the investigation can lead to a comparison rather than ending with a trace.

If you encounter CoreWeave Agent Lens in the documentation, check which interface your deployment uses before writing an incident runbook for W&B Weave.

Pros

• SDK tracing and direct OpenTelemetry span ingestion give you options when an agent uses several libraries.
• LLM judges and custom scorers let you test a fix against the behavior that failed, such as retrieval quality, instead of relying on latency alone.

Cons

• Check how Weave handles retries and recorded exceptions before using trace writes or .call() results in incident automation. The behavior of that automation may differ from what you expect from an ordinary function call.

Pricing: Confirm current access and usage terms with W&B or CoreWeave for your deployment.

4. Datadog LLM Observability (Agent Observability)

Best for: You already use Datadog for infrastructure observability and want to investigate agent traces and evaluate output quality in the same platform.

Datadog’s Agent Observability gives you a way to inspect an agent run alongside the infrastructure signals you use to investigate production incidents. A trace can show which step called a model or tool and where latency accumulated. Those signals help locate a failure, but they cannot tell you whether the final answer satisfied the user’s request.

Datadog supports that quality check through evaluations. You can attach evaluations to spans, traces, or sessions, depending on whether you need to assess one model response or a complete interaction. Trace-level LLM-as-judge evaluations run after a trace completes, so you can assess the whole agent run without treating the score as a blocking guardrail.

For a fix you want to test before release, Datadog’s experiments support versioned datasets and result comparisons. You could save cases resembling a failed production run, change the agent, and compare its outputs against the earlier version. You still need to choose cases and evaluation criteria that represent the failure you found.

Pros

• Agent traces connect LLM behavior with the broader Datadog environment you may already use for incident investigation.
• Evaluations cover individual steps and complete runs, while experiments provide a way to compare proposed fixes.

Cons

• If you only need an LLM evaluation workspace, Datadog’s broader observability scope may add more platform than your workflow requires.
• Trace-level judge scores arrive after completion. You need a separate control if a request must be stopped before its answer reaches a user.

5. Langfuse

Best for: Developers who want open-source-friendly, self-hostable LLM tracing and regression checks in CI.

Langfuse lets you turn a production trace into a dataset case and test a proposed agent change. If an agent selects the wrong tool, you can inspect the trace, add the failure to a dataset, and run an experiment against a revised agent. Its evaluation workflow supports live scoring, trace-to-dataset creation, experiments, and human annotation. You can use annotations to record what the agent should have done before comparing a candidate change with the earlier behavior.

Langfuse also supports a CI regression gate through langfuse/experiment-action. You define the checks and thresholds in an experiment script. If that script raises RegressionError, the GitHub Action fails the job. The gate therefore depends on the cases and thresholds you choose, rather than treating every score change as a failure.

Pros

• Self-hosting gives you control over where traces and evaluation data run.
• Datasets and experiments let you test a production failure before shipping a fix.
• Human annotation helps you review outputs that an automated score cannot settle.

Cons

• Self-hosting requires you to operate more than PostgreSQL. Langfuse also uses ClickHouse, Redis, and object storage.
• You must maintain useful test cases and decide which score changes should fail CI.

Pricing: Langfuse offers self-hosted and managed cloud options. Compare current plan terms alongside the infrastructure work your deployment would require.

How the five compare

Tool	Best-fit user	Trace and instrumentation approach	Path from incident to verified fix
Braintrust	You want evaluation at the center of agent debugging	Instrument agent steps and inspect production traces	Add a failed case to a dataset, then evaluate a proposed change
LangSmith	You want a framework-agnostic trace-to-dataset workflow	Trace application runs across frameworks	Turn a trace into a dataset example and compare offline experiments
W&B Weave	You already use the W&B/CoreWeave ecosystem	Use OpenTelemetry-compatible tracing or instrument functions manually	Reproduce a failed call and evaluate changes with judges or custom scorers
Datadog Agent Observability	You investigate AI incidents alongside infrastructure telemetry	Inspect agent traces in Datadog	Add cases to versioned datasets and compare experiments
Langfuse	You want self-hostable tracing and CI-integrated checks	Capture application traces and annotate failures	Create dataset cases from traces, run experiments, and check regressions in CI

Choosing the right tool for your debugging loop

Choose an LLM observability tool by testing what happens after you find a failed step. A trace can show which retrieval result or tool call preceded a bad answer. An evaluation lets you check whether a proposed fix improves that case without breaking others. Braintrust’s workflow connects traces, reviewed failures, and experiments, which makes it the strongest default here for agent debugging centered on verifying fixes.

Your existing tooling may point you elsewhere. LangSmith fits a framework-agnostic trace-to-dataset workflow, while W&B Weave fits work already tracked in the W&B ecosystem. Datadog suits users who want LLM investigation alongside infrastructure monitoring, and Langfuse suits users who want a self-hostable option. To test Braintrust against your own requirements, take one reviewed production failure through its trace-to-dataset workflow, then evaluate a proposed fix against that case.