CopilotKit CLiF: continual learning that turns agent trajectories into skills
CopilotKit launched CLiF, a continual-learning platform that captures agent traces plus user actions as full-stack trajectories over AG-UI and turns them into skills, production-based evals, and fine-tuning datasets — fully self-hostable.
Why it matters
Closes the loop shipping-agent teams struggle with: real user behavior becomes the eval and training data, with human review on every skill and no redeploy needed to publish.
What you could build with it
A SaaS team with an in-app AI agent could plug CLiF in to auto-generate evals from actual usage and continuously improve the agent from production trajectories, cutting the manual eval-writing grind.
Does it hold up?
Too early to judge — CLiF is in early access with no public deployments reported yet; the underlying CopilotKit framework is production-proven with 38k+ GitHub stars.
Built with CopilotKit CLiF
- Global 1 News: CopilotKit releases CLiF — full-stack trajectories as the missing training signalblog · Technical explainer of the four-stage learning loop: capture, analyze into insights, propose skills for human approval, then publish without redeploy.
- CopilotKit/CopilotKitgithub · 38,053 ★ · The open-source agent framework CLiF builds on — AG-UI event streams from these apps feed the learning loop.
Learn more
First spotted on article: source.
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