Kalibr Documentation

Kalibr is the first autonomous routing system for AI agents — routing to the optimal execution path (model + tool + parameters) to prevent failures, degradations, and cost spikes before they impact users.

Why Kalibr?

Observability shows you what went wrong. Kalibr prevents it from happening. Most teams hardcode model choices and monitor dashboards. Kalibr captures step-level telemetry and success signals, canaries traffic across the paths you define, and actively adapts to changing conditions in production.

Routing is outcome-aware:

• Routes traffic across paths you define
• Learns from outcomes you report
• Shifts traffic to what works, away from what doesn't

No manual A/B tests. No spreadsheet tracking. No "we should try Claude for this."

What's a Path?

A path isn't just a model - it's a complete execution configuration:

model + tool + parameters = path

Examples:

• gpt-4o
• gpt-4o + calendar_api
• gpt-4o + calendar_api + {temperature: 0.3}
• claude-sonnet + search_tool + {max_tokens: 1000}

Kalibr learns which full configuration works best for each goal.

from kalibr import Router

router = Router(
    goal="book_meeting",
    paths=[
        {"model": "gpt-4o", "tools": ["calendar_api"]},
        {"model": "gpt-4o", "tools": ["google_calendar"]},
        {"model": "claude-sonnet-4-20250514", "tools": ["calendar_api"]}
    ]
)

response = router.completion(messages=[...])
router.report(success=True)

import { Router } from '@kalibr/sdk';

const router = new Router({
  goal: 'book_meeting',
  paths: [
    { model: 'gpt-4o', tools: ['calendar_api'] },
    { model: 'gpt-4o', tools: ['google_calendar'] },
    { model: 'claude-sonnet-4-20250514', tools: ['calendar_api'] }
  ],
});

const response = await router.completion(messages);
await router.report(true);

Kalibr picks the path, makes the call, and learns from the outcome.