Top 5 LLM-as-a-Judge Tools for Evaluating AI Agent Responses

Quick picks

Braintrust — Consider it for combining LLM judges with code-based checks across agent steps and end-to-end outcomes. Its published score allowances are separate from judge-model token costs.

LangSmith — A fit to investigate if you want offline dataset evaluation alongside online scoring of production outputs or traces. Its published plans include trace allowances, but do not specify a fixed per-judgment cost.

Langfuse — Useful to consider when you want to select production observations with filters and sampling before evaluating them. It also supports batch evaluation of selected historical observations; model and usage costs still depend on the workload.

Arize Phoenix — Worth a look for teams seeking self-hosted, open-source evaluation with agent metrics and evaluator traces that capture prompts, responses, scores, and timing. Self-hosting means accounting for infrastructure as well as model costs.

DeepEval — A local-first framework to consider for trajectory and component checks, including tool and argument correctness. Its judge model is configurable, while model-provider charges depend on the chosen setup.

Product shortlist

1. Braintrust

Braintrust

Braintrust is worth considering if you want to evaluate agent behavior at both the individual-step and end-to-end levels. I like that its evaluation approach brings together a dataset, a task, and scorers, including both LLM judges and code-based checks.

That gives teams room to combine qualitative judgments with deterministic rules rather than relying on a judge for every check.

What I like

• It supports LLM-as-a-judge scorers alongside code-based checks, so teams can match the evaluation method to the criterion.
• Evaluations can cover agent outcomes as well as individual steps, which is useful when you need to inspect more than the final response.
• Its tracing materials describe prompts, completions, tool calls, and spans, providing useful context for investigating agent behavior.

Limitations

• The published score allowance is not the full cost per judgment: judge-model token usage and plan overages can add charges.
• The available pricing information does not provide a comparable latency guarantee, so teams should measure evaluation speed in their own workflows.

My take

Braintrust’s combination of judge-based and code-based scoring makes sense for teams that want to assess both subjective qualities, such as relevance, and rules that can be checked deterministically. Its focus on outcomes and individual steps also fits multi-step agent evaluations.

Best for

Best for teams seeking a platform-based way to evaluate agent steps and final outcomes, with both LLM-judge and code-based scorers. Check expected model-token costs and benchmark latency with your own workloads before estimating operating costs.

Sources: How to evaluate LLMs and AI agents in production: The Braintrust way; Pricing - Braintrust.

2. LangSmith

LangSmith

LangSmith is a good fit if you want LLM-as-a-judge scoring alongside evaluation of production traces, rather than treating offline tests and live monitoring as separate jobs. I like that it also supports code, composite, summary, and pairwise evaluators, giving teams options beyond a single judge score.

The key trade-off is that the published pricing does not translate into a fixed cost per judgment.

What I like

• You can run evaluations offline on datasets and online against production outputs or traces, which helps connect test results with observed behavior.
• The choice of judge, code, composite, summary, and pairwise evaluators supports different evaluation approaches.
• The Developer plan is listed at $0 per seat per month, with up to 5,000 base traces monthly.

Limitations

• There is no documented benchmark here for evaluation latency, so you will need to measure it with your own agent and workload.
• Plan and trace pricing do not establish a standalone cost per judgment; judge-model charges and trace usage can add to the total.

My take

For teams already evaluating with datasets and monitoring production traces, LangSmith offers a coherent place to apply LLM-as-a-judge scoring across both contexts. That can make it easier to relate a quality signal to the trace it came from.

I would compare your expected trace volume and judge-model usage before estimating cost. The listed Plus plan is $39 per seat monthly and includes up to 10,000 base traces; additional trace usage may be billed, and Enterprise pricing is custom.

Since no latency figures or per-judgment rate are provided, validate both against your own workload before committing.

Best for

Teams that want both offline evaluation and LLM-as-a-judge scoring on production traces in one evaluation workflow. Budget-conscious teams should still estimate trace and judge-model charges against their expected volume.

Sources: Evaluation types - Docs by LangChain; LangSmith Plans and Pricing.

3. Langfuse

Langfuse

Langfuse is a good fit if you want LLM-as-a-judge scoring tied to production observations, with controls over what gets evaluated. You can test an evaluator on sample observations, then use rules, filters, and a sampling rate to select incoming traffic.

I like that this lets teams start with a narrower slice of production behavior rather than judging every observation by default.

What I like

• Rules can filter incoming observations and set a sampling rate, giving teams control over evaluation volume.
• Scores are written to matching observations, keeping judge results connected to the production data they assess.
• Online evaluations show an estimated cost, and selected historical observations can also be scored in batch.

Limitations

• The platform's estimated cost is not a universal per-judgment price; the selected judge model's token charges and workload also affect the total.
• Cloud plans have different usage-based unit allowances and overage rates, so the plan price alone does not describe the full operating cost.

My take

The workflow is practical for teams that want to sample live agent traffic and inspect judge scores alongside observations. Batch evaluation also gives you a way to assess selected historical data, which can help when investigating older runs.

Best for

Langfuse is best for teams that want configurable online judge evaluation connected to production observations, while controlling coverage through filters and sampling. Budget for both the platform's usage-based units and the chosen judge model's charges.

Sources: Evaluate Production Traffic - Langfuse; Pricing - Langfuse.

4. Arize Phoenix

Arize Phoenix

Arize Phoenix is a good fit when you want LLM-as-a-judge evaluations alongside the traces from your agent runs. I like that it supports both SDK-driven evaluations and evaluators configured in its UI, so teams can choose a workflow that suits their setup.

Its tool-calling agent metrics also make it relevant for multi-step systems, not just isolated model responses.

What I like

• You can run evaluations from Python or TypeScript SDKs, or configure server-side evaluators in the UI, giving teams more than one way to fit evaluation into their workflow.
• Phoenix supports model-agnostic LLM judges and metrics for tool-calling agents, which can help assess agent behavior across tool use as well as final responses.
• OpenTelemetry evaluator traces include prompts, model responses, scores, and execution timing, making it easier to inspect how a judgment was produced.

Limitations

• Phoenix is self-hosted open source, so you take on the infrastructure and operating costs; the judge-model provider may also charge for evaluations.
• The cited documentation does not provide an independent latency benchmark or a fixed cost per judgment, so teams need to measure speed and budget with their own models and workloads.

My take

I’d consider Phoenix when trace visibility matters as much as the evaluation score. Its evaluator traces can help teams investigate what the judge saw and how long an evaluation took, while its SDK and UI options offer flexibility in how evaluations are run.

The trade-off is that free, self-hosted software is not the same as cost-free operation: you still manage infrastructure, and model-provider charges may apply. Phoenix documents concurrency and batching, but that is not a substitute for measuring evaluation latency on your own workload.

Best for

Best for teams that want an open-source, self-hosted evaluation workflow with agent metrics and trace-level visibility. It may be less suitable if you need a published latency guarantee or a predictable per-judgment price.

Sources: Evaluation - Phoenix; Pricing - Arize AI.

5. DeepEval

DeepEval

I’d shortlist DeepEval if you want to run LLM-as-a-judge evaluations within an open-source, local-first framework rather than start with a hosted evaluation service. Its documented metric library includes agent trajectory checks, which assess an ordered trace, and component-level checks such as tool and argument correctness.

That makes it relevant when you need to inspect both an agent’s overall path and specific steps along the way.

What I like

• More than 50 documented metrics give teams a range of ready-to-use evaluation checks.
• Trajectory and component-level metrics can help pinpoint whether a problem lies in the overall agent flow or a particular tool call.
• Configurable judge models let teams choose the model used for evaluation.

Limitations

• There is no documented fixed platform fee or cost per judgment; costs depend on the selected judge provider, model, calls, and token rates.
• The documentation does not publish a common latency benchmark, so teams need to measure evaluation speed in their own setup.

My take

DeepEval’s useful distinction is its evaluation scope: you can assess an agent’s trajectory as well as individual components, rather than treating the final response as the only result that matters. Its tracing documentation describes nested spans for plans, model calls, tools, and intermediate steps, with local tracing available without an account.

For teams that want hosted trace access, DeepEval documents a Confident AI integration that requires logging in. I’d consider it when control over the evaluation setup and detailed agent checks matter more than a bundled, fixed-cost service; budget and latency need to be checked against your chosen judge model and workflow.

Best for

Teams seeking an open-source, local-first evaluation framework with agent trajectory and component-level checks. It is less straightforward for buyers who need a published per-judgment price or comparable latency figures.

Sources: Introduction to LLM Evaluation Metrics - DeepEval; LLM Tracing - DeepEval.

How to choose

How to Choose the Right LLM-as-a-Judge Tool

Start with what you need to inspect: final answers, individual agent steps, or production traces. Then check whether the evaluation workflow fits your setup and budget for both judge-model calls and platform usage.

Measure latency with your own workload; the evidence here does not establish a common benchmark.

Common Mistakes to Avoid

Don’t treat a plan price or free self-hosted software as the full cost. Don’t judge an agent only by its final answer if tool use and intermediate steps matter.

Frequently Asked Questions

What does each judgment cost? There’s no comparable fixed rate in the available pricing information; model usage and platform charges can both contribute.

Final Verdict — Which One Would I Choose?

I’d lean toward Phoenix or DeepEval when self-hosting and detailed agent checks matter. For production-trace evaluation, I’d compare Langfuse and LangSmith based on how each fits your workflow and expected usage.

Sources: How to evaluate LLMs and AI agents in production: The Braintrust way; Pricing - Braintrust; Evaluation types - Docs by LangChain; LangSmith Plans and Pricing; Evaluate Production Traffic - Langfuse; Pricing - Langfuse; Evaluation - Phoenix; Pricing - Arize AI; Introduction to LLM Evaluation Metrics - DeepEval; LLM Tracing - DeepEval.

Which option fits?

There isn’t a single best choice for every agent-evaluation workflow. If you want an open-source, self-hosted route with evaluator-trace visibility, Phoenix is a strong candidate to assess; if you want local-first trajectory and component checks, compare DeepEval.

For scoring production observations, look closely at Langfuse’s filtering and sampling workflow or LangSmith’s online trace evaluation. Braintrust is another option when combining judge-based and deterministic checks across agent steps matters.

Before committing, run the same representative agent traces through your finalists. Check whether the scores help identify failures you care about, how long evaluation takes, and what the judge-model calls and platform or infrastructure usage cost at your expected volume.

The published information does not establish a shared latency benchmark or a fixed all-in price per judgment, so those comparisons need to come from your own workload.