KLYC-PMM Kunlun Yaochi Precision Memory Management

Problem

AI agent memory often lives in local files, context windows, or temporary notes, making it easy to lose, duplicate, or contradict during reinstall, migration, or collaboration. KLYC-PMM targets that need by storing key conclusions, rules, hooks, and distilled results in a recoverable memory store, rather than relying only on session context. It fits agents that need precise long-term memory, not one-off notes or millisecond-level read/write workloads.

How It Works

• Init and recovery: onboarding creates a 'Kunlun key' used to restore memory from the service.
• Memory domains: writes explicitly target domains such as public, shared, essence, private, or disaster, which helps keep irrelevant context out.
• Sync and watch: file changes can trigger incremental pushes, while distilled hooks are periodically pulled into a marked section of MEMORY.md.
• Search and distillation: local index and cloud search are supported, and a multi-stage pipeline handles deduplication, merging, contradiction checks, and semantic fidelity.
• Operability: it depends on curl and jq, and uses consistent exit codes for parameter, encryption, upload, and network failures.

Boundaries

It requires stable HTTPS egress and stores text knowledge only, not large binaries such as images, audio, or video. MEMORY.md contains recovery credentials and should not be committed to public repositories. Watch mode continuously observes file changes, so data flow and write scope should be understood before enabling it. Paid services need an additional payment path, often completed by the owner when a payment plugin is unavailable.