dsh-okf-memory

When working with an Agent, the most frustrating moment is often this: the architecture decision you locked in last week, the preferences you confirmed repeatedly, the hard-won lessons from past failures, all gone the instant you open a new session. The Agent starts from zero every time, while your knowledge should compound and accumulate. dsh-okf-memory solves exactly this problem. It automatically distills high-value content from conversations into structured long-term memory following the OKF v0.1 spec, then intelligently recalls it in follow-up sessions, so the Agent genuinely gets better with every interaction.

It is far more than a keyword-matching note-taker. The plugin embeds a neuro-self-learning loop: predictive recall lets the Agent guess first and verify later, uncertainty quantification widens the retrieval scope when confidence is low, and every user choice, skip, or correction becomes a reinforcement signal that continuously adjusts memory weights and triggers consolidation or forgetting. Technology-choice scenarios (frontend, backend, language, architecture, configuration) are handled through a dedicated three-tier rule set so the Agent neither decides on your behalf nor loses historical iterations. A force-directed memory graph panel makes the memory base visible at a glance, where node size maps to weight, color encodes type, and search hits pulse with a conduction animation through the network.

If you rely heavily on the DSH Agent for project decisions, stack selection, or knowledge management, and you are tired of re-explaining the same context every session, this plugin can significantly reduce that repetition cost. It has zero runtime dependencies, works out of the box with no build step, and the Agent autonomously decides what to record and what to retrieve during conversation. You can also explicitly trigger a write or a lookup whenever you want.