deja-vu

Deja-vu is a zero-dependency, local-first binary that indexes and makes searchable the historical sessions of various AI coding agents. It acts as a universal memory layer, allowing agents to recall past solutions and context efficiently, even from before its installation.

deja-vu is currently grouped under Memory & Context, which makes it easier to evaluate through workflow fit instead of isolated features alone. Based on the available data, it leans most heavily toward Fast, Retroactive Session Search and Reusing solutions from past agent conversations to avoid re-debugging or re-implementing.. The listed license is MIT, which is useful when adoption constraints matter. It also shows measurable community traction with 420 GitHub stars.