engram: Claude memory plugin with salience-gated capture and dream cycles

What engram does

engram is a memory plugin for Claude that captures what matters and forgets what doesn't by filtering at capture time instead of logging everything and searching later. The developer built it out of frustration with existing memory plugins.

How it works

Every tool Claude uses gets scored on 5 salience dimensions: Surprise, Novelty, Arousal, Reward, and Conflict. Observations below threshold get evicted, while those above threshold persist to SQLite. The scoring system uses no LLM calls and takes <10ms per observation.

Key features

• Salience-gated capture: Routine actions like git status score low and evict, while significant events like a test failure after a refactor score high and persist.
• Automatic injection: 5 hooks handle memory operations automatically: SessionStart, UserPromptSubmit, PostToolUse, PostCompact, and Stop. Users never manually query it.
• Dream cycles: At session end, extracts recurring workflows, error→fix chains, and concept clusters. Optional "deep dream" asks Claude "what did this session mean?" for semantic consolidation.
• Confidence decay: Memories lose confidence daily and get pruned below 0.1, preventing old wrong patterns from distorting future sessions.
• Per-directory isolation: Each project gets its own database to prevent cross-project noise.
• Epistemic labeling: Observations tagged "observed", patterns tagged "inferred (may not be accurate)". The system knows the difference between what happened and what it thinks happened.

Technical details

The dream cycle is what the developer is most excited about — while other memory plugins remember what you did, engram "sleeps on it," consolidating what matters and forgetting the rest, similar to biological memory. The tool is a spinoff from SAGE, a cognition kernel for edge AI, and is currently running across a 6-machine fleet.

The developer is seeking feedback on whether the salience scoring thresholds feel right in practice. The project is available on GitHub under MIT license.