Kalibr lets your agents optimize their own model and tool selection as they run. Any model, any modality. Based on their own outcome success.
Any model, any modality. Text · vision · audio · code · embeddings. Open source SDK.
Hardcoded agents break. Kalibr agents adapt.
Hardcoded agents kept calling the broken model. Kalibr knew it was failing and shifted to what was working.
Kalibr sees cost, latency, and outcome quality together, then routes to the cheapest option that's still returning great results.
Quality dropped during degradation. Kalibr detected it, rerouted, and recovered well above baseline. Automatically.
Tell Kalibr which model + tool combinations your agent is allowed to use. Any provider, any modality. Then define what a successful outcome looks like. You set the rules.
Kalibr canaries traffic across all your options, so it always knows how every one is performing right now. Each run captures cost, latency, and usage metrics alongside your outcome success scores.
Most traffic goes to the best-performing option. When something fails or degrades, Kalibr already knows which alternative is working and shifts there instantly.
The optimal model + tool combination for your agent changes constantly. Across providers, modalities, and costs. These architectures benefit most.
Search APIs fail. Providers rate-limit. Latency spikes. Your agent doesn't know. Kalibr does, and shifts to what's currently succeeding.
Frontier APIs, open-source models on Hugging Face, text, vision, audio. The option space is huge and shifting constantly. Kalibr scores every option and routes to the best one right now.
Your pipeline has steps scoring 0.96 and steps scoring 0.66 on the same model. Kalibr scores per step and can route each one independently.
What worked last week may not work now. Model updates change behavior. Kalibr keeps your agent aligned with what's actually working.