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  • ObservabilityTrace every call, cost, and latency
  • SecurityBlock attacks, redact PII and secrets
  • OptimizationCache repeated prompts automatically
  • EvaluationScore responses and whole agent runs
  • DatasetsGolden sets that capture whole agent runs
  • Prompt ManagementVersion and deploy prompt templates
  • AlertsPush eval and security events to Slack

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Pricing
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Evaluation

Gate responses that fail quality thresholds

LLM-as-judge scores every response server-side. Agentic evaluation judges whole runs: tool choice, trajectory, and multi-agent coordination.

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Fluiq/ tests

Total Evals

847

across 312 traces

Avg Score

0.91

threshold ≥ 0.7

Pass Rate

88.4%

749 / 847 passed

By Metric

hallucination247
avg 0.9294% pass
relevance247
avg 0.8988% pass
faithfulness130
avg 0.8582% pass
toxicity89
avg 0.9799% pass

What you get

Metrics

Six judge metrics

Hallucination, faithfulness, relevance, toxicity, coherence, and completeness, scored per response.

Thresholds

Per-metric gates

Set a threshold for each metric. Warn mode logs the score; block mode stops the response.

Agentic

Whole-run evaluation

Layered judging of a full agent run: deterministic checks, tool-selection quality, trajectory against the goal, and multi-agent coordination across fan-outs and joins.

Jury

Multi-model judge panel

Borderline verdicts convene a jury of different judge models and aggregate their votes, with every member's score and reasoning kept for audit.

Your call

Pick the judge, or the whole jury

Choose which model scores a run, and which models sit on the panel, per evaluation or per dataset batch.

Your keys

Bring your own provider key

Save an OpenAI, Anthropic, or Google key and judge tokens bill to your account at the rate you already negotiated.

Auditable

Every score shows its prompt

Each result carries the exact judge prompt and version that produced it, so a shifting metric is traceable to a prompt change.

The judge runs server-side. Opt in with one call.

  • instrument() only traces; scoring starts when you call fluiq.eval()
  • Warn mode logs scores; block mode raises FluiqEvalError below threshold
  • Run Agentic Eval on any root trace to judge the whole run: tools, trajectory, coordination
  • CI gates run the same checks in GitHub Actions
fluiq.eval(thresholds={...})All plans
app.py
import fluiq

fluiq.instrument(api_key="fl_...")

fluiq.eval(
    metrics=["hallucination", "relevance", "toxicity"],
    thresholds={"hallucination": 0.8, "relevance": 0.75},
    mode="warn",   # "block" raises FluiqEvalError
)

Part of the Fluiq platform

Compare plans
ObservabilityTrace every call, cost, and latency.ExploreSecurityBlock attacks, redact PII and secrets.ExploreOptimizationCache repeated prompts automatically.ExploreDatasetsGolden sets that capture whole agent runs.ExplorePrompt ManagementVersion and deploy prompt templates.ExploreAlertsPush eval and security events to Slack.Explore

Unlimited traces, always free.

Tracing, security, evaluation, datasets, and prompts all run on the free tier. Caching and Slack alerts unlock on a paid plan.

Start freeRead the docs
FluiqFluiq

Observe, protect, optimize, evaluate.

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