5 best AI agent evaluation platforms in 2026

Introduction

A production trace can show that an agent gave the wrong answer, but a trace alone cannot tell you whether the next change fixes it. You need to preserve the failed run, review the expected behavior, and test the revised agent against that case before release.

Braintrust is the default pick here because it connects production traces, reviewed datasets, experiments, and release checks. LangSmith, W&B Weave, Comet Opik, and Agenta offer different ways to evaluate agent behavior. The comparison follows a hypothetical support agent that incorrectly reports a refund as approved, then asks how each platform helps you review that run and test a fix.

1. Braintrust

Best for: Developers who want to turn production agent failures into reviewed test cases, compare fixes against those cases, and use the results in release checks.

Braintrust is the recommended default for an evaluation-centered workflow because its tracing, dataset, and experiment tools connect the steps that follow a failed run. You can inspect the run, preserve the relevant input, correct the expected result, and evaluate a proposed fix without first building a separate pipeline to move examples between tools. Braintrust's documentation covers the tracing, dataset, evaluation, and deployment steps. You still need to decide what counts as correct behavior and which failures should block a release.

Turn a production trace into a reviewed test case

An agent trace gives you more than its final answer. Instrumentation can capture the inputs and intermediate spans that show which tools the agent called and what those calls returned. When a run fails, you can select its trace in Braintrust Logs or Review and add it to a dataset. Braintrust can carry the source input into the dataset row and retain a link to the originating log, so a reviewer can return to the run that prompted the test.

A reviewer must check the expected result before using the row as a test. When you promote a logged span, its output can populate the dataset row’s expected field. If that output is the incorrect response, the reviewer must replace it with the intended response before scoring a new run against it. Braintrust’s dataset and annotation workflow lets a reviewer correct that expectation and keep the production case for future experiments. If a dataset row references a log instead of storing a copy of its data, keep the source log available so reviewers can inspect the original run.

Consider a support agent that tells a customer a refund was approved after its payment tool returned pending. The trace shows the customer request, the tool result, and the agent’s incorrect reply. You can add the run to a dataset, correct the expected behavior to say the refund is still pending, and record what the reviewer found wrong. That row checks whether the agent reports the payment status accurately.

Score the behavior you need to protect

Braintrust evaluators run a candidate task against dataset examples and apply scorers to its outputs. For the refund case, a custom scorer could check whether the agent’s claim matches the payment tool’s status. Another scorer could assess the reply’s wording, but wording alone would not catch the false approval. You can develop a check in a Braintrust Playground, run an Evaluator against the dataset, and inspect the outputs in an Experiment. Check the pending-status case separately from aggregate scores. An overall improvement would not justify releasing a version that still claims pending refunds are approved.

Scorer design remains your responsibility. An exact assertion may suit a tool status, while an open-ended answer may need a human reviewer or an LLM-based judge. An LLM judge can examine more varied replies, but you need to check its decisions against reviewed examples before using its score as a release condition. Human corrections and scorer logic serve different purposes. A reviewer records the intended behavior in the case, and the scorer checks a new run against that expectation.

Separate experiments from production monitoring

Use offline experiments to test a proposed change before release. You run a proposed prompt, model, or code change against a curated dataset and inspect how its outputs compare with a baseline. For the refund agent, the dataset should include the pending-status failure and other relevant cases, so a fix that blocks false approvals does not silently damage valid approval replies. Braintrust also versions prompts, which helps you identify the prompt used in an experiment and pin a production version. To compare runs when tool responses can change, supply the same tool inputs and responses to each candidate.

After deployment, online scoring evaluates production runs and helps identify cases the offline dataset missed. It can help you find failures that your curated dataset did not anticipate, but a production score does not substitute for running a candidate change against known cases before release. When online scoring surfaces another bad refund claim, a reviewer can inspect its trace and decide whether the case belongs in the regression dataset. Braintrust connects the two uses of evaluation without treating live traffic as automatically verified ground truth.

Use experiment results in CI

A CI job can run Braintrust evals against the reviewed dataset before a change ships. You define which scores and cases the job checks, compare the candidate with an appropriate baseline, and configure the pipeline to fail when a required condition is not met. For the refund example, a release check might require the agent to avoid claiming approval whenever the tool reports pending. A case-specific check makes the release decision clearer than an average score across unrelated support requests.

Braintrust’s Loop agent can assist with investigating logs and drafting datasets or evals, but a developer or reviewer should approve the proposed cases and scoring logic before using them in CI. The evaluator runs the approved tests, and your CI configuration determines whether a release passes.

Pros

• Braintrust provides a direct path from a logged run to a reviewed dataset example, with source-trace context available during investigation.
• Custom scorers, experiments, online scoring, and CI-run evals support distinct development and production decisions within the same evaluation workflow.

Cons

• You need to instrument the agent and preserve enough tool context for a failed run to become a useful test. A final answer alone may not explain the failure.
• You must maintain reviewed expectations and trustworthy scorers. A poorly chosen metric can pass the refund agent even when its underlying claim is wrong.

2. LangSmith

Best for: Developers who want production tracing, reviewed datasets, and evaluation across LLM frameworks without having to use LangChain.

LangSmith connects observed failures to repeatable tests. You can add production traces to datasets, review the cases, and run experiments against a changed agent. Code evaluators can check outcomes with explicit rules, while LLM judges can assess responses that need a more flexible rubric. Human reviewers can correct expected answers before a failed production response becomes part of a regression test.

LangSmith also supports pairwise comparisons when you need to choose between two agent versions rather than score either one in isolation. Online evaluators assess production runs, while dataset-based experiments let you test proposed fixes before release. LangSmith can trace and evaluate applications built outside LangChain, so you can use it without changing your agent’s orchestration framework.

Pros

• Production traces can become reviewed dataset cases for regression evaluation.
• Code evaluators, LLM judges, and human review support different kinds of quality checks.
• Pairwise comparisons and online evaluators extend evaluation beyond a single offline score.

Cons

• You still need to decide which failures deserve dataset entries and what a correct result looks like. An LLM judge cannot supply that product judgment.
• LangSmith offers several evaluation paths, so you must choose how datasets, reviewers, online checks, and release experiments fit your workflow.

3. W&B Weave

Best for: Developers already using Weights & Biases who want agent traces and evaluations alongside their existing experiment work.

W&B Weave can record agent calls and evaluate responses with custom scorers or LLM judges. You can instrument a tool call, inspect its inputs and output, then run an evaluation to check whether a change improves the behavior you care about. Weave’s RAG walkthrough gives a concrete example. It uses scorers and LLM judges to assess retrieval quality, rather than relying on the final answer alone.

Weave fits when you already use Weights & Biases for experiments and want to bring LLM traces into that work. CoreWeave documentation also describes CoreWeave Agent Lens as a successor for agent tracing. Check the current Weave and CoreWeave Agent Lens documentation before choosing where new tracing work should live.

Pros

• The tracing SDK can capture LLM calls and other functions, which helps you inspect the steps behind a failed agent response.
• Custom scorers and LLM judges let you evaluate task-specific behavior rather than depend on a single generic score.

Cons

• You still need to define useful test inputs and scoring criteria. A judge for retrieval quality, for example, may not catch a tool-selection error unless you add a separate check.
• Check how errors appear in Weave traces when instrumenting your application, especially if exception handling is part of the behavior you evaluate.

4. Comet Opik

Best for: Developers who want an open-source, self-hostable evaluation platform and several ways to turn agent traces into test suites.

Comet Opik connects production tracing with regression evaluation. Its documentation describes creating test suites from traces through the UI, SDK, or Ollie, then running those suites against a proposed fix. After selecting a failed run, you can review the expected behavior and use the resulting test case to evaluate a changed prompt, model, or tool configuration. A trace supplies the input and execution history, but you still need to review the expected result before treating the case as a reliable test.

Opik supports custom evaluation metrics, so you can score behavior that a generic answer-quality judge might miss, such as whether the agent chose the right tool. Online evaluation rules can also score production traces as new runs arrive. Those rules help you find cases worth investigating, while a reviewed test suite gives you a repeatable way to check a fix. Opik offers self-hosting for developers who need to operate the platform in their own environment.

Pros

• You can build test suites from traces using the UI, SDK, or Ollie, depending on how you prefer to work.
• Custom metrics and online rules support both repeatable tests and production scoring.
• The open-source, self-hostable option gives you control over deployment.

Cons

• Self-hosting makes you responsible for deployment, upgrades, and platform operations.
• Trace-derived cases still need reviewed expectations and appropriate metrics. Otherwise, a suite can reproduce a run without testing whether the agent behaved correctly.

5. Agenta

Best for: Developers who want an open-source workflow for prompt iteration and test cases drawn from production agent runs.

Agenta connects prompt iteration with evaluation of real production behavior. Its documented workflow lets you create test sets from production traces, so a failed run can become a case you revisit after changing a prompt. Before using that case as a regression test, you still need to review the expected result. Otherwise, a test could reward the same incorrect answer that prompted the investigation.

Agenta also supports online evaluation of production traffic. You can score live runs to find behavior worth examining, then add selected traces to a test set for repeatable evaluation. Online scores help you spot candidates for review, while the test set lets you check whether a later change handles those cases.

Pros

• Production traces can become test cases, connecting observed failures to repeatable evaluations.
• Online scoring gives you a way to examine production behavior while you iterate on prompts.
• Open-source availability suits you if you want control over how you deploy the platform.

Cons

• You must review selected traces and maintain expected results as agent behavior and requirements change.
• If you self-host Agenta, you take responsibility for operating and updating the deployment.

Comparison at a glance

Product	Best-fit user	Trace-to-dataset path	Notable strength
Braintrust	You want evals tied to release decisions	Annotate production logs into datasets	Custom scoring, experiments, and CI checks
LangSmith	You work across LLM frameworks	Add production traces to datasets	Human review and pairwise comparisons
W&B Weave	You already use Weights & Biases	Evaluate traced calls against test examples	Custom scorers and LLM judges
Comet Opik	You want a self-hostable option	Build reviewed test suites from traces	Suite creation through the UI, SDK, or Ollie
Agenta	You focus on prompt iteration	Create test sets from production traces	Online scoring of production traffic

Choosing for the regression-eval workflow

When comparing platforms, take one failed production run through review, dataset creation, an experiment, and a release check. Note which steps require manual exports or custom tooling, and whether the resulting test captures the failure you meant to prevent.

Braintrust is the default recommendation for this workflow because a production trace can become a reviewed dataset case that feeds custom scoring and experiments before a CI release check. You still decide which failures block a release and configure that check. Braintrust’s dataset guide is a starting point for testing the path with a failure from your own agent.