Judgeval is live · custom pricing · last verified 26 Oct 2026 · viability monitor
Judgeval
Slack-native continuous improvement for production AI agents: triage failures, isolate root causes, validate fixes before you ship.
Judgeval is worth a look if your agents already misbehave in production and you want the loop from detection to validated fix in one place — agent-swarm triage over real traces plus dollar-impact framing (142 occurrences, ~$6.8k in over-refunds in the demo) compresses the time between a complaint and a root cause. Compare Runs is the part that changes outcomes: testing a proposed fix against production replays before deploy is where most teams still fly blind. It's a narrow tool, though — Slack and LangSmith are the documented integrations, and you need enough failure volume and engineering bandwidth to act on findings.
Verified 2h ago · liveness 60/100 · cite: rightaichoice.com/tools/judgeval
- AI engineering teams debugging hard-to-reproduce production agent failures
- Agent ops teams triaging, prioritizing and tracking recurring issues
- Companies running customer-facing LLM agents where refunds, escalations or compliance failures carry dollar cost
- Platform teams owning agent reliability and guarding against model drift
- Teams without deployed production agents or meaningful trace data to investigate
- Pre-production projects still authoring their first eval suites from scratch
- Teams that want trace collection and observability from the same vendor
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Skip Judgeval if you have no live production agents generating traces yet, or if you want one vendor to both collect and improve on that data — Judgeval investigates traces you already have.
Judgeval reviews failures against traces you already collect, so you keep paying your existing observability or trace-storage vendor on top of it.
Judgment Labs has not published a pricing page, so there is no tier list to map against competitors. Budget the same way you would for any trace-and-eval platform bought through a sales conversation: expect per-seat plus usage, and weigh it against the cost of the failures it catches — the demo's ~$6.8k in over-refunds in a single week is the number to compare any quote against.
In short
Judgeval — Slack-native continuous improvement for production AI agents: triage failures, isolate root causes, validate fixes before you ship. Best for AI engineering teams debugging hard-to-reproduce production agent failures, Agent ops teams triaging, prioritizing and tracking recurring issues, Companies running customer-facing LLM agents where refunds, escalations or compliance failures carry dollar cost. Contact Sales pricing.
What is Judgeval?
Judgeval — Slack-native continuous improvement for production AI agents: triage failures, isolate root causes, validate fixes before you ship. Best for AI engineering teams debugging hard-to-reproduce production agent failures, Agent ops teams triaging, prioritizing and tracking recurring issues, Companies running customer-facing LLM agents where refunds, escalations or compliance failures carry dollar cost. Contact Sales pricing.
Judgeval is a continuous-improvement stack for teams already running AI agents in production. Instead of another observability dashboard, it deploys agent swarms over your real production traces to find similar failure cases, narrow down root causes, and check candidate matches for false positives before flagging anything. The product lives in Slack. You mention @Judgment in a channel and ask why an agent issued a full refund on a three-item order; it answers with the mechanism (the agent treated "the order is broken" as order-level and never called the damaged-items tool) and quantifies blast radius — 142 occurrences in a week, 3.9% of refund runs, roughly $6.8k in over-refunds, and 3 of your top 10 customers affected including Acme. From there the pattern is tracked as a behavior rather than a single trace, so recurrences surface on their own: the demo detection-rate view covers 1.3K traces over a 7-day window at a 5.4% missed-escalation detection rate. Triage, behavior tracking, Compare Runs (a Refund Guard judge scoring 96% passed, 48 true, 2 false on production replays), and the Agent Judge framework for long-horizon task evaluation anchor the product. Documented integrations are Slack and LangSmith. Judgment Labs raised $32M led by Lightspeed in May 2026. Judgeval sits closer to the fix-and-maintain loop than synthetic-eval tooling, and it presumes mature agent infrastructure plus existing production traces to investigate.
Key features
- Slack-native agent failure investigation via @Judgment mentions
- Agent swarm triage to find similar failure cases across production traces
- Root cause analysis with dollar impact and occurrence counts
- Affected-customer identification including top-account hits
- Behavior tracking with automatic recurrence alerts
- Detection-rate views over a configurable window (7-day demo over 1.3K traces)
- Missed escalation detection and refund overrun monitoring
- Production trace replay to test proposed fixes
- Compare Runs to validate fixes against production cases before deployment
- Agent Judge framework for long-horizon task evaluation
- Agentic judges that search, verify, and adapt to evaluate agent output
- False-positive checking on candidate failure matches
- Custom eval criteria authoring via Agent Judge
- Trace filtering and drill-down across failed and passing sessions
- Blast-radius quantification by session count and percentage of runs
Behind the verdict
Judgeval's pitch is a fair criticism of the eval category: dashboards tell you something is wrong, they don't tell you whether it is worth fixing. The product answers that with numbers. Ask @Judgment in Slack and it returns the mechanism, the occurrence count (142 last week), the share of runs (3.9%), the dollar impact (~$6.8k in over-refunds), and the affected accounts — enough to make a prioritization call without an analyst digging through traces by hand. The strongest piece is the detection-to-verification loop. Behavior tracking converts a one-off trace into a tracked behavior with recurrence alerts, which is the right abstraction for model drift and regressions; the demo's 7-day missed-escalation view over 1.3K traces shows what that looks like in practice. Compare Runs then closes the loop by replaying production cases against a proposed fix — a Refund Guard judge scoring 96% passed with 48 true and 2 false gives you a pass rate with the false positives visible. The Agent Judge framework, launched May 2026, extends that to long-horizon tasks with agentic judges that search, verify, and adapt. Where it's a harder sell: it is not a tool you adopt before launch. It presumes production traces and the engineering setup to use them, and it needs enough failure volume for triage to be worth running. Its documented integrations are Slack and LangSmith, so it slots into an existing Slack incident culture rather than replacing your observability vendor — Judgeval assumes you have traces, not that it collects them. And because agentic judges are themselves models, the 2-false-out-of-50 result in the demo is a useful reminder that you should read judge output as evidence, not ground truth. Compared with synthetic-eval tooling, Judgeval is downstream: it assumes you have real failures to study. Compared with generic eval frameworks, the split is that Judgeval automates the investigation and the fix-validation cycle while a framework hands you primitives and a harness. Teams that already treat agent failures as incidents — with a Slack channel where they get discussed — will get value fastest.
Models under the hood
as of 2026-10-24
Limitations
- Judgeval assumes you already have production agent traces and the engineering setup to use them — it is not an eval tool you can spin up before your agents are live.
- It requires enough failure volume to justify the investment.
- Slack and LangSmith are the documented integrations, so it complements rather than replaces your trace store.
- Its agentic judges are models themselves: the demo's Compare Runs result (96% passed, 48 true, 2 false) is evidence to review, not ground truth, and you should expect to check judge output rather than accept it.
as of 2026-10-09
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Is Judgeval still active?
Judgeval is live · custom pricing · last verified 26 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Judgeval? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Judgeval
Checked yesterdayAcross the latest 1 update: 1 feature update.
What do users say about Judgeval?
What people actually say about Judgeval — is it worth it?
We scanned public community sources for Judgeval on Jul 31, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Judgeval— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 26 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Judgeval 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed domain expiry
- — changed limitations, who should skip it, setup time, hidden costs, pricing fit, switching out, who it suits, our verdict, switching in, description, summary, features
- — changed who it suits, who should skip it, summary, our verdict, features, description
- — changed use cases, who it suits, description, our verdict, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary
- — changed domain expiry, SSL certificate
- — changed our verdict, description, summary, who should skip it, features
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Judgeval cost in 2026?
Judgeval: Contact Sales pricing. Pricing last verified 26 Oct 2026.
Where the pricing makes sense
The company stage and team size where Judgeval's pricing actually pencils out — and where peers do it cheaper.
Judgment Labs has not published a pricing page, so there is no tier list to map against competitors. Budget the same way you would for any trace-and-eval platform bought through a sales conversation: expect per-seat plus usage, and weigh it against the cost of the failures it catches — the demo's ~$6.8k in over-refunds in a single week is the number to compare any quote against.
Has Judgeval's pricing changed?
No plan price for Judgeval has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Judgeval?
Pick Judgeval if you are AI engineering teams debugging hard-to-reproduce production agent failures, Agent ops teams triaging, prioritizing and tracking recurring issues, Companies running customer-facing LLM agents where refunds, escalations or compliance failures carry dollar cost, Platform teams owning agent reliability and guarding against model drift.
Look elsewhere if you are Teams without deployed production agents or meaningful trace data to investigate, Pre-production projects still authoring their first eval suites from scratch, Teams that want trace collection and observability from the same vendor, Simple chatbots that rarely fail and don't justify a procurement cycle.
Skip it if Skip Judgeval if you have no live production agents generating traces yet, or if you want one vendor to both collect and improve on that data — Judgeval investigates traces you already have.
Real-world workflow fit
Concrete scenarios for the personas Judgeval actually fits — and what changes day-one when you adopt it.
A support manager posts in Slack that a customer got a full refund on a three-item order where only one item was damaged. You mention @Judgment in the channel and ask why.
Outcome: You get the mechanism (the agent treated "the order is broken" as order-level and never called the damaged-items tool), the blast radius (142 occurrences, 3.9% of refund runs, ~$6.8k in over-refunds) and the affected top-10 accounts, in the same thread where the complaint arrived.
Before deploying the guard, you run Compare Runs against production replays with the Refund Guard judge.
Outcome: You see a 96% pass rate with 48 true and 2 false, review the two failures, and decide whether the fix is safe to ship rather than deploying into the dark.
You track the missed-escalation pattern as a behavior instead of a one-off trace, then watch the detection-rate view over a 7-day window.
Outcome: Recurrences surface automatically and you catch drift or regression before it shows up as another customer complaint.
Use cases
- Investigate why an agent gave a full refund when only one of three items was damaged.
- Deploy agent swarms to find similar failure cases across thousands of production traces.
- Sanity-check a proposed fix by comparing judge pass rates on production replays before deploying.
- Monitor agent behavior in Slack and receive alerts on recurring failures.
- Track a root cause as a behavior instead of a single trace so recurrences surface automatically.
- Quantify the dollar impact and affected customers of a failure to decide whether it's worth fixing.
Setup time & first value
How long it actually takes to get something useful out of Judgeval — broken out by persona, not the marketing-page minute.
Slack-native teams: connecting Slack and pointing Judgeval at existing traces is the first step, and time to first value depends mostly on how clean and accessible your trace data already is. Agent ops leads who already discuss incidents in a Slack channel get to a first triaged failure fastest. Engineers planning a fix should budget a Compare Runs cycle on top of that before treating a guard as
Switching to or from Judgeval
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual trace spelunking: point Judgeval at your existing production traces and ask @Judgment in Slack instead of reading sessions by hand.
- →From LangSmith alone: keep LangSmith as the trace store and let Judgeval investigate the failures it records.
- →From a synthetic eval suite: keep the suite for pre-launch checks and use Judgeval for production failure triage and fix validation.
- →From a generic observability dashboard: leave the dashboard in place for collection and run the triage, tracking and Compare Runs loop in Judgeval.
- ↗To a generic eval framework: export the eval criteria you authored in Agent Judge into your own harness, accepting you lose the Slack triage and behavior tracking.
- ↗To a full observability platform: keep that platform for traces and drop Judgeval, accepting you return to hand-investigating individual failures.
- ↗To an in-house triage script: reproduce the swarm search and false-positive checking yourself, accepting the maintenance burden of the judge logic.
- ↗To your trace store's native tooling: stay with LangSmith or equivalent and investigate inside it, accepting you lose the dollar-impact and blast-radius framing.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Judgeval”, and we withheld 6: 6 could not be judged, because “Judgeval” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Judgeval.
Official links
What are the best Judgeval alternatives?
The closest alternatives to Judgeval we list are Raindrop, Braintrust and Dash0. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Raindrop | Freemium | Free plan | — |
| Braintrust | Freemium | Free plan | — |
| Dash0 | Freemium | Free plan | — |
Alternatives to Judgeval
View all
Raindrop
Raindrop traces, detects, and simulates fixes for silent AI agent failures in production before they reach your users.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Tools that pair well with Judgeval
Common stack mates teams adopt alongside Judgeval, with the specific reason each pairing earns its keep.
Raindrop
Raindrop traces, detects, and simulates fixes for silent AI agent failures in production before they reach your users.
Braintrust
Braintrust traces agent runs, scores them with evals, and surfaces production failure patterns before they ship.
Dash0
Dash0 is OpenTelemetry-native observability with Agent0 doing autonomous root cause analysis and fix PRs.
Judgeval vs its closest rival
Judgeval's closest head-to-head is with Spider Cloud — Spider Cloud and Judgeval solve entirely different problems. Choose Spider Cloud if you need fast, cheap web data extraction for your AI agents or RAG pipelines — it excels at crawling and scraping with a Rust engine, AI Studio, and 1,000+ ready-made scraper examples. Choose Judgeval if your agents are already in production and you need to monitor, triage, and fix their behavior at scale — it offers Slack-native investigation, agent swarm triage, and automated recurrence detection. They are complementary tools, not competitors.
Head-to-head comparisons
Judgeval vs Spider Cloud
Spider Cloud and Judgeval solve entirely different problems. Choose Spider Cloud if you need fast, cheap web data extraction for your AI agents or RAG pipelines — it excels at crawling and scraping with a Rust engine, AI Studio, and 1,000+ ready-made scraper examples. Choose Judgeval if your agents are already in production and you need to monitor, triage, and fix their behavior at scale — it offers Slack-native investigation, agent swarm triage, and automated recurrence detection. They are complementary tools, not competitors.
Judgeval vs Temporal Ai
Choose Temporal if you need a durable execution engine to build reliable agents and workflows that survive failures—it's proven by companies like OpenAI. Choose Judgeval if your agents are already in production and you need a continuous improvement loop to detect, triage, and fix issues at scale with minimal overhead. They complement each other: Temporal builds reliability in; Judgeval keeps it there.
Judgeval vs Presto Voice
If you run QSR drive-thrus and want to automate orders with upselling, Presto Voice is your choice—proven with chains like Dairy Queen. If you're an AI engineering team debugging production agents, Judgeval offers a continuous improvement stack with Slack-native triage and recent $32M backing. They serve completely different use cases; choose based on whether your bottleneck is drive-thru labor or agent reliability.
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