mnemos

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Task-scoped memory lifecycle — typed MnemoGraph prevents lossy context compaction by treating facts/decisions/code-refs/handoffs as distinct node types with per-type eviction policies

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# Mnemos — Task-Scoped Memory Lifecycle ## What It Does Mnemos prevents lossy context compaction from destroying the structured knowledge you need most. It treats your working memory as a **typed graph** (MnemoGraph) where different types of knowledge have different eviction policies: - **GoalNodes** and **ConstraintNodes** are NEVER evicted — they survive all compaction - **ResultNodes** are compressed (summary kept) before eviction - **ContextNodes** are evictable when their activation weight drops - **CheckpointNodes** persist to disk for session resume ## Fatigue Model Mnemos monitors 4 dimensions of "agent fatigue" — all passively observed from hook data, no manual input needed: | Dimension | Weight | Signal Source | What It Measures | |-----------|--------|--------------|-----------------| | Token utilization | 0.40 | Statusline JSON | How full the context window is | | Scope scatter | 0.25 | PreToolUse file paths | How many directories the agent is bouncing between | | Re-read ratio | 0.20 | PreToolUse Read calls | How often the agent re-reads files it already read (context loss) | | Error density | 0.15 | PostToolUse outcomes | What fraction of tool calls are failing (agent struggling) | Fatigue states and actions: | State | Score | Action | |-------|-------|--------| | FLOW | 0.0–0.4 | Normal operation | | COMPRESS | 0.4–0.6 | Micro-consolidation runs (compress 3 ResultNodes, evict 1 cold ContextNode) | | PRE-SLEEP | 0.6–0.75 | Checkpoint written, consolidati...

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Author
alinaqi
Repository
alinaqi/maggy
Created
5 months ago
Last Updated
today
Language
Python
License
MIT

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