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APORIA
Monitor, guard, and improve your AI applications in production
ABOUT
LLM applications are unpredictable — prompts drift, models hallucinate, and subtle input changes cause wildly different outputs. Traditional monitoring tools can't catch semantic issues or enforce safety rules on language model outputs. Aporia provides real-time guardrails that block harmful or off-policy outputs, custom evaluation metrics that measure what matters for your use case, and full tracing across every LLM call. Teams get alerts when quality drops and can root-cause failures from the trace logs.
INSTALL
pip install aporiaINTEGRATION GUIDE
1. Deploy real-time guardrails that block toxic, biased, or off-topic LLM outputs before reaching users
2. Monitor LLM application quality with custom metrics for relevance, accuracy, and tone across all prompts
3. Trace every LLM call from user input to model response with full observability into latency and tokens
4. Set up automated regression detection that alerts on model output quality degradation after updates
5. Implement compliance monitoring for regulated industries requiring audit trails of AI decisions
TAGS
monitoringobservabilityguardrailsllmevaluationpythontracingsafety