CopilotKit Releases CLiF: A Continual Learning Platform That Turns Agent Traces and User Actions into Skills, Evals, and Fine-Tuning Data

Introduction Teams shipping AI agents at scale keep running into the same pattern. Even agents built on top-tier models like Fable and Astra struggle to carry out complex, end-to-end tasks inside a specific product consistently enough to deploy. So teams grind out evals for the workflows that matter most, often hundreds or thousands of hand-written cases, and then improve the agent bit by bit through prompt edits, harness fixes, and special cases until it passes enough of them to go live.

Meanwhile, the product’s own users carry out those same tasks every day, and know which actions to take and in what order. CopilotKit, the company behind the open-source CopilotKit framework and the AG-UI protocol, released CLiF, a platform for continual learning from full-stack trajectories, in early access. CLiF securely captures what the agent does and what users do in the product, and turns that record into skills, production-based evals, and fine-tuning datasets, all exportable along with the raw trajectories.

CopilotKit is open source, with more than 38,000 stars on GitHub.