Kalytera

Kalytera is an evaluation and observability platform for AI agents running in production, aimed at catching failures that only show up mid-workflow rather than in a sampled final output. Instead of scoring a subset of interactions, it traces every step of every agent run and scores each step across four dimensions: accuracy, goal alignment, decision quality, and completeness, then groups recurring failures into named patterns ranked by business impact with plain-English explanations of the root cause. It's built for teams running autonomous agents with LangChain, CrewAI, AutoGen, or custom frameworks who need governance and quality assurance beyond spot-checking. Integration is a Python SDK: a @kalytera.watch decorator gives zero-config instrumentation, and kalytera.trace() supports manual step logging, with a REST API for exporting evaluations on the Growth plan. A three-view dashboard covers agent overviews, a failure feed, and detailed interaction traces, with a seven-type failure taxonomy and industry-specific quality weightings for domains like healthcare or retail. Pricing starts free for 10,000 sessions a month and scales to a custom Enterprise tier with VPC deployment and SSO. One honest limitation: full evaluation coverage of every step is inherently more compute-intensive than sampled scoring, which is part of why higher session volumes require a paid plan.