LLM-as-judge scores every response server-side. Agentic evaluation judges whole runs: tool choice, trajectory, and multi-agent coordination.
Total Evals
847
across 312 traces
Avg Score
0.91
threshold ≥ 0.7
Pass Rate
88.4%
749 / 847 passed
By Metric
Metrics
Hallucination, faithfulness, relevance, toxicity, coherence, and completeness, scored per response.
Thresholds
Set a threshold for each metric. Warn mode logs the score; block mode stops the response.
Agentic
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
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
Choose which model scores a run, and which models sit on the panel, per evaluation or per dataset batch.
Your keys
Save an OpenAI, Anthropic, or Google key and judge tokens bill to your account at the rate you already negotiated.
Auditable
Each result carries the exact judge prompt and version that produced it, so a shifting metric is traceable to a prompt change.
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
)Tracing, security, evaluation, datasets, and prompts all run on the free tier. Caching and Slack alerts unlock on a paid plan.