AI Ops Stack · LLM Applications
FluiqAI is the unified ops layer for LLM applications: security scanning, intelligent caching, deep observability, and automated evaluation on every request.
Most platforms stop at tracing. Fluiq adds a security layer, a caching layer, and a quality gate, so you catch problems before your users do.
Every token, latency, and cost attributed to the exact agent node that spent it. Streaming traces, cost anomaly alerts, and per-model breakdowns, without changing how you write code.
fluiq.instrument(api_key="fl_...")
Pre-call scanning catches jailbreaks, prompt injections, and skeleton-key attacks before the LLM call is made. Post-call scanning redacts PII and secrets from stored traces.
fluiq.secure(mode="block")
Fluiq analyses your actual trace history to find which prompts repeat, then provisions a dedicated cache instance for your account. Repeated calls are served from cache automatically.
Server-side caching, zero infra to manage
Profile built from your real traffic patterns
Configurable TTL and model scope
fluiq.optimize() # "cache" | "observe"
LLM-as-judge runs server-side after each call. Set per-metric thresholds. Warn mode logs quality scores to the dashboard; block mode raises FluiqEvalError before the response reaches your app.
fluiq.eval(thresholds={'hallucination': 0.8})
A dedicated IDE-style editor for your prompt templates, with {{variable}} injection, full version history, and per-environment deployment. Iterate directly on real production traces, compare model outputs side-by-side, and ship with confidence.
fluiq.get_prompt("customer-support", env="production")
How it works
Framework-agnostic
Fluiq patches at the function-call level, not the framework level. Any Python function that hits an LLM or vector database becomes a traced span with one decorator.
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Start with observability on the free tier. Add security, optimization, and evaluation as your pipeline grows. No code changes required.
No credit card required. pip install fluiq, instrument in 60 seconds.
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