Self-healing infrastructure for AI agents. Kalibr catches failures before they reach your users, heals them automatically, and learns which model works best for each task over time.
How you integrate Kalibr depends on who is making the routing decisions. Pick the one that matches how your system actually works.
Kalibr detects when an LLM call fails — structurally wrong output, empty responses, provider errors that return 200 — and heals automatically by rerouting to a model that works. No alert. No rollback. No human required. The dashboard calls this a heal.
Over time, Kalibr learns which model works best for each task based on real outcomes. It selects the cheapest model still succeeding, and shifts traffic away from models that start degrading. Model selection is the mechanism; failure detection and healing is the point.
This works for text LLMs, voice (TTS and STT), image generation, embeddings, classification, and any model on HuggingFace.
What Kalibr is not: a logging platform (Langfuse, Arize), a model gateway router (LiteLLM, OpenRouter), or a prompt optimizer. It never reads or modifies prompt content. Model calls go directly to the provider.