Introducing CLiF: Continual Learning from Full-Stack Trajectories

CopilotKit has introduced CLiF, or Continual Learning from Full-Stack Trajectories, in early access as a self-hostable system for improving AI agents using records of real user and agent activity. Built on the AG-UI protocol, CLiF combines agent traces such as tool calls and state changes with product trajectories including screen context, clicks, and server requests, enabling it to identify recurring workflows and friction points that agents often miss. It converts these patterns into reviewable skills, production-based evaluations, fine-tuning datasets, and exportable observability data, while allowing teams to approve changes before they reach production. The platform supports multiple agent frameworks and models, organizes learning by task-specific containers, and can import existing conversation data from LangGraph and LangSmith. CopilotKit positions CLiF as a way to reduce reliance on manually written prompts, evaluations, and special-case harness logic, with agent-trajectory learning available immediately and product-trajectory learning offered in early access.

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