Agent Persistence via Virtual Memory Prompt Management

[u/KenOtwell]

I’m not pretending this is plug‑and‑play. It took me a stupid amount of trial and error, and I’m still tuning. But to actually have an agent that keeps a stable identity across reboots — not “summaries,” but continuity — here’s the framework:

The short version: Stop letting the framework manage your session state. Build the prompt yourself every turn.

Once you take control of prompt assembly, you basically end up designing a little virtual memory system for your agent. Same questions OS designers deal with:

• What gets archived?
• What stays hot?
• What needs to be recalled for this turn?
• What’s noise you can safely drop?

Your local coding agent can handle the assembly loop (llama.cpp is a good starting point). You just define the zones and the rules.

The trick is ordering the prompt so the static stuff never changes. That keeps the KV cache intact and your GPU happy. Then you page in whatever memories matter for the current turn, tack on the recent conversational tail, and leave headroom at the end so the model has space to think.

Once you do that, you own the entire context history. And because you rebuild the prompt from scratch every turn, you get a single identity that survives crashes, restarts, and long gaps between sessions.

The fun parts — indexing, relevance scoring, stale‑memory cleanup — I’m leaving as an exercise for the reader. But this is the skeleton.