Route agent tasks across model, tool, and parameter paths using production telemetry.
npx skills add https://github.com/kalibr-ai/kalibr-sdk-python --skill kalibr
Please help me install this Agent Skill. Skill: kalibr Source: https://github.com/kalibr-ai/kalibr-sdk-python/tree/main Command: npx skills add https://github.com/kalibr-ai/kalibr-sdk-python --skill kalibr
Kalibr enables autonomous, self-healing agent pipelines by continuously learning from real production telemetry and routing requests to the most reliable combination of model, tool, and parameters, reducing outages, degradation, and manual intervention.
Invoke the Router with a simple goal and observe automatic healing across paths.
High-intent search queries and answers about installing and using this skill.
Self-healing agent pipelines use production telemetry to automatically route requests to the most reliable combination of model, tool, and parameters. This provides automatic fallback and repair during provider outages or degradation.
Routing multimodal agent workflows across multiple providers requires learning from real production outcomes to dynamically select the optimal model and parameter path. This unified routing supports frameworks like LangChain, CrewAI, and OpenAI Agents SDK.
Thompson sampling enables LLM routing to continuously learn from production telemetry and balance exploration with exploitation. It dynamically routes tasks across model and tool paths to optimize for cost, reliability, and performance.
Yes, autonomous agent routing requires an active backend instance and credentials supplied via KALIBR_API_KEY and KALIBR_TENANT_ID. You must also configure a minimum of two routing paths to enable continuous learning and healing.
Yes, the routing mechanism supports multimodal workflows and framework integrations with LangChain, CrewAI, OpenAI Agents SDK, and HuggingFace. This allows unified routing across your existing agent frameworks.
Agent pipelines require a two-or-more-path configuration to enable learning and healing because the system needs multiple model, tool, and parameter combinations to compare production telemetry and route to the most reliable fallback.
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