All Tools
P
MonitoringFreemium
PATRONUS AI
Enterprise LLM evaluation, guardrails, and observability platform
ABOUT
Evaluating LLM outputs at scale is fundamentally different from traditional software testing. LLMs produce variable, open-ended responses that can't be validated with simple assertions. Patronus AI provides a comprehensive platform for systematic LLM evaluation: automated evaluators that check for hallucination, relevance, toxicity, and custom criteria; experiment tracking to compare prompts, models, and parameters; guardrails to catch failures in production; and observability to trace and debug complex agent workflows. Teams get a single source of truth for AI quality without building evaluation infrastructure from scratch.
INSTALL
pip install patronusINTEGRATION GUIDE
1. Run automated evaluation suites on LLM outputs to detect hallucinations, off-topic responses, and safety violations before deployment
2. Track and compare experiments across different prompts, models, and hyperparameters to find the optimal configuration for your use case
3. Deploy production guardrails that intercept and flag problematic LLM responses in real-time with custom policy enforcement
4. Monitor and debug multi-step agent workflows with detailed traces showing tool calls, context retrieval, and decision points
TAGS
llm-observabilityevaluationexperimentationpythonguardrailstesting