Best LLM-as-a-judge tools for agents

the short answer

For production agents, Failproof AI is the strongest all-round option because it runs LLM and code-based evaluations, analyzes recurring failures across sessions, alerts owners and helps turn a tested fix into a behavioral policy. Use DeepEval for a lightweight CI library, Langfuse for open-source self-hosting, Braintrust for experiment-driven evaluation, or Galileo and Judgment Labs when judge tooling itself is the priority.

Most built-in metrics: Future AGI (50+), DeepEval (G-Eval, DAG, agentic metrics), Galileo (20+)
Free to self-host with judges: Langfuse and Latitude (MIT), Future AGI (Apache-2.0)
Judges in CI: DeepEval (pytest-style), Braintrust autoevals (Python and TypeScript)
Production failure intelligence: Failproof AI: evaluations, automated failure analysis, findings, alerts and tested behavioral policies
Runs the suite you already have: Failproof AI can run an existing evaluation suite in the cloud

How to Compare LLM-as-a-Judge Tools

Most tools can send a transcript and rubric to a model, then store the score. The product differences appear before and after that call: how you test the judge, where it runs, what happens when it fails and whether anyone can act on the result.

Where the judge runs. In CI against a frozen dataset, on production traces as they arrive, or both. A library gives you the first; a platform gives you the second.
Built-in or bring-your-own. Ready-made metrics get you a first score in minutes. Your own rubric fits your agent. Most teams end up with the second either way.
Who owns the judge model and its bill. Some platforms run judges through a model connection you configure; some run their own evaluation models.
Sampled or every session. Sampling keeps cost down and misses rare failures.
Calibration. Can you label sessions by hand and check the judge against them?
Versioning. Can you version a judge, see which version is live, and roll back when a new one misbehaves?
Failure analysis. Does the platform group related failures and surface patterns you did not already encode in a rubric?
What happens after a low score. Look for alerts, ownership, supporting evidence and a concrete route to a tested fix.

The Best Tools by Use Case

This ranking assumes you are evaluating production agents, want to start without a sales call and need more than a score stored in a dashboard. A CI-only project or a hard self-hosting requirement will produce a different shortlist.

1. Failproof AI

Failproof AI runs LLM judges and code-based evaluations on production agent sessions, with support for the evaluation suite your team already uses. The useful part starts after scoring. Automated audits look across sessions for recurring failure modes, group the supporting evidence into findings and recommend what to fix. Alerts reach email, Slack, webhooks or the dashboard, and a tested fix can become a behavioral policy for supported agent harnesses.

Limits: Failproof does not provide its own proprietary judge model, and self-hosted Cloud is an Enterprise deployment.
Best for: teams that want LLM judges connected to production failure analysis, ownership and a concrete fixing workflow.

2. Langfuse

Langfuse is an open-source LLM engineering platform, licensed under MIT outside its ee folders. It supports LLM judges, code evaluators, user feedback and manual labels. Judges can run on sampled production traces or datasets, using your own rubric or a template. The free self-hosted edition includes LLM-as-a-judge.

Limits: project-level RBAC, audit logs and retention management require an Enterprise license when self-hosted.
Best for: teams that want judges, traces and prompt management in one open-source platform.

3. Braintrust

Braintrust is built around experiments and reusable scorers. Those scorers can be LLM judges, custom code or metrics from its MIT-licensed autoevals library. Online scoring applies them asynchronously to production traces at a sampling rate you control.

Limits: self-hosting the data plane is Enterprise-only, and the free Starter tier keeps data for 14 days.
Best for: teams that run evaluation experiments in CI and want to reuse the same scorers in production.

4. DeepEval

DeepEval is an Apache-2.0 Python framework with a pytest-style runner. It includes G-Eval and DAG for custom criteria, plus agent metrics such as Task Completion, Tool Correctness, Plan Adherence and Step Efficiency.

Limits: DeepEval is a library. Production monitoring, dashboards and managed datasets belong to Confident AI, a separate platform.
Best for: engineering teams that want an open-source judge suite in CI with little setup.

5. Judgment Labs

Judgment Labs centers the product on judging. Agent Judge follows a natural-language rubric across long trajectories, while Code Judge runs Python checks. Both work online, offline or on demand. AutoRubrics and Behavior Discovery help teams decide what to measure.

Limits: pricing is not published, onboarding starts with a demo, production monitoring scores completed traces and platform self-hosting is listed as coming soon.
Best for: teams that need deep judge tooling, calibration and rubric development.

6. Galileo

Galileo, now part of Cisco and branded Splunk Agent Observability, provides more than 20 built-in evaluations for RAG, agents, safety and security. It also supports custom evaluators and CLHF, which tunes a judge metric from a small set of annotated records.

Limits: Luna-2 evaluation models are Enterprise-only. Only the Free tier is self-serve, while Pro and Enterprise start with a demo.
Best for: larger organizations that want ready-made metrics and enterprise procurement support.

7. Future AGI

Future AGI is an Apache-2.0 platform with more than 50 metrics and its own Turing judge models. The same platform also covers tracing, simulation, prompt optimization and an agent gateway.

Limits: self-hosting requires a large stack that includes Postgres, ClickHouse, Redis, RabbitMQ and Temporal.
Best for: teams that want a broad library of ready-made metrics and can operate the infrastructure.

8. Latitude

Latitude is an MIT-licensed agent observability platform. Evaluations can use rule conditions, an LLM judge or custom JavaScript, and an eval-to-human alignment check measures agreement with human labels.

Limits: evaluations run after a session completes and do not change the response or action.
Best for: teams that want open-source observability and judges calibrated against human review.

Side by Side

Tool	Evaluation options	Production scoring	Open source	Self-serve start
Failproof AI	LLM judges, code checks and existing evaluation suites	Yes	MIT CLI	Yes
Langfuse	Templates	Yes, with sampling	MIT (except `ee`)	Yes
Braintrust	autoevals	Yes, online scoring	autoevals (MIT)	Yes
DeepEval	G-Eval, DAG, agentic metrics	Via Confident AI	Apache-2.0	Yes
Judgment Labs	Agent Judge, Code Judge	Yes, sampled	judgeval SDK (Apache-2.0)	Demo
Galileo	20+ evals, Luna-2 (Enterprise)	Yes	Agent Control (Apache-2.0)	Free tier only
Future AGI	50+ metrics, TURING models	Yes	Apache-2.0	Yes
Latitude	LLM judge, rules, JS	Yes, after completion	MIT	Yes

Based on each vendor's published product information.

Picking One in an Afternoon

Take ten sessions you already know are bad and ten you know are good.
Write one rubric for the failure you care about most. One criterion, pass or fail.
Run it in the two tools that fit your constraints: DeepEval if you only need CI, Langfuse or Latitude if you must self-host, Braintrust or Galileo if judge tooling is the priority, and Failproof AI if you need production failure analysis and fixing around the scores.
Count how many of the twenty each judge gets right, and how long it took to get a first score.

The judge model and the rubric decide accuracy far more than the tool does, so the tool that makes it easiest to read the judge's reasoning next to the trace usually wins. See how to set up LLM judges and how to calibrate one.

When a Script Is Enough

If you have one agent, a few hundred sessions a week and nobody on call for its behavior, you do not need a platform. A Python script that replays saved cases through a judge in CI, plus a spreadsheet of human labels, is honest and cheap. Many failures do not need a judge at all: a tool error, a repeated call or a missing file is a code check over the event stream. Buy a tool when you have to read production at a volume you cannot read by hand. Do you need an eval platform? walks through the signs.

FAQ

Which model should I use as the judge?

None of these tools removes that decision. Pick a model that follows instructions well, pin its version, and measure its agreement with twenty to fifty human labels before trusting it. Re-check after every model upgrade, because a new version can move scores without your agent changing. The trade-offs are in the guide on choosing a judge model.

Are open-source judge libraries good enough for production?

For scoring, yes: DeepEval, autoevals and Ragas run the same kind of judge call a platform does. What a library does not give you is storage, a UI for reading traces next to scores, scheduled scoring of production traffic, retention and alerting. Teams usually pair a library in CI with a platform, open source or hosted, for production.

Does Failproof AI include LLM judges?

Yes. Failproof AI runs LLM judges and code-based evaluations in the cloud and can also run your existing evaluation suite. Results appear beside the session trace and feed automated failure analysis, findings and alerts. Failproof does not provide a proprietary judge model, so the model connection remains part of your evaluation setup.

Can an LLM judge block a bad agent action?

Not on its own. A judge reads a finished run, so its score arrives after the action happened. To stop the action, something has to sit before the tool executes: a hook policy at PreToolUse, or a guardrail in the request path. The usual loop is to let the judge find a repeated failure and then write a policy for it, as in turning judge findings into runtime policies.

Get Started

Failproof AI is free to start. It finds recurring failure modes across agent sessions using code-based and LLM-based evaluations, groups the evidence into findings, and recommends fixes. Bring the eval suite you already have, alert the right owner when behavior drifts, and turn a tested fix into a policy that prevents the failure from recurring. See pricing for the tiers.

start free get a demo →

Sources

Checked against each vendor's own site and docs on 2026-09-14. Products change; if a detail here is out of date, tell us at support@befailproof.ai.