I kept losing agent memory between sessions, so I built a memory broker that isolates per-agent and survives restarts

Every time I restarted my agent or spun up a new session, the context was gone. No recall of previous conversations, no learned preferences, no project state. Just... blank. So I built HeurChain — a memory broker that sits between your agents and long-term storage. What it does: Agent-isolated memory — each agent gets its own namespace. Agent A can't see Agent B's memory., Survives restarts — memory persists to disk/DB. Kill the process, restart, memory is still there., LLM-agnostic — works with Claude, GPT, local models. The broker handles the storage layer., Structured + semantic — stores both key-value facts and vector embeddings. Query by key or by meaning., The architecture is pretty simple: agents write to the broker via a thin client library. The broker handles deduplication, compression, and persistence. On restart, agents request their memory back. What I learned: Most memory systems are either per-session (context window) or global (shared RAG). The middle ground — agent-specific persistence — is underserved., Deduplication is tricky when agents rephrase the same fact differently. I use both hash-based matching and semantic similarity., Local model users want local-only. The broker can run entirely on-prem., Would love feedback from anyone who's built agent memory systems. What tradeoffs did you hit?