Decoupling ML memory from background loops: built a local memory daemon in Rust + Python to avoid C-linker deadlocks

hey guys, wanted to share an architecture choice i made for a local AI agent memory daemon (null-drift) and get some feedback.

​most agent memory systems (like mem0) log everything to a VectorDB, which causes massive context bloat and heavy overhead for long-running local tasks.

​i went a different route: a continuous state space with geometric decay (meaningless noise evaporates, high-salience milestones stay).

​to handle high-concurrency background streams without hitting the Python GIL or getting messy C-linker deadlocks when mixing async code with heavy ML libraries, i completely decoupled the stack:

​Rust (Axum/Tokio): runs bare-metal, handles the background loops, state decay math, and persistence.

​Python (FastAPI): strictly handles the embedding and ML inference layer.

​repo is fully open-source if u want to check the logic or give feedback on the architecture: null-drift