The Problem with "AI Memory" Today

Local AI agents forget context between sessions. RAG forces them to manually query a "library". Mnemostroma changes that: background memory layer that auto-captures, ranks, and injects relevant context. The agent focuses on the task, not "what to remember."

Automatic Capture: Zero Overhead

Key: Agent never writes memory. Observer (background layer) intercepts via proxy:

• 0.1ms filter: drops noise.
• Auto-classify: decision/fact/deadline.
• 20ms search: top-3 relevant + exact anchors (URLs, dates).

0 output tokens spent on summaries - full decoupling.

Continuity: Experience Builds Up

• Intuition signals: patterns mature (5>100 sessions), suggest "do this/avoid that."
• Dreamer: resolves conflicts during idle time.
• Temporal: infers "when it happened" without timestamps.

Critical items (principles, deadlines) never dissolve.

v1.11 Stats

• 485 sessions, 4.3 MB SQLite.
• 471 MB RAM baseline.
• Logs: 79% RAM stabilization, 10% intuitions.

Offline, local only.

Next: How Dissolver fits 1000s sessions in 600 MB without losing key experience. Questions?

Mnemostroma has reached version 1.11.0. We are moving away from the "chat history" model toward a professional-grade memory layer. The core philosophy has stabilized around a strict invariant: "Observer writes memory silently; Agent only reads and acts." This solves the 'memory pollution' problem where agents get stuck in recursive loops of their own previous mistakes.

HIGH-LEVEL STATE (APRIL 29, 2026):

• Total Memory Sessions: 485
• Knowledge Anchors: 481
• Experience Clusters: 71 tags / 307 sessions
• Storage: 4.3 MB total (SQLite-backed)

V1.11.0 AUTOMATION: The breakthrough in this version is "Content Branch" automation. The system now silently intercepts code, configs, and technical docs during live sessions. It classifies them using local ONNX pipelines and archives them without the agent ever being aware of the "saving" process. It's 100% passive capture.

API MINIMIZATION: We've stripped the MCP interface down to 12 core tools. By removing the agent's ability to manually 'save' or 'expire' context, we've forced a clean separation of concerns.

COMING UP NEXT: But storing 500 sessions is the easy part. How do you keep an AI's "brain" from eating 32GB of RAM? In Part 2, I'll break down the infrastructure we built to handle high-volume context on a strict consumer-grade budget.