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 statusscore 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.
📖 Read the full source: r/ClaudeAI
👀 See Also
Open-Source Ralph Loop Toolkit for Claude Code: Pickle Rick and Mr. Meeseeks Agents
An open-source extension for Claude Code implements the Ralph Loop technique with two autonomous agents: Pickle Rick for PRD-driven development and Mr. Meeseeks for code review. Both use tmux with live dashboards and macOS notifications.
Doc Harness: A Claude Code Skill for Maintaining Project State Across Sessions
Doc Harness is a Claude Code skill that creates a lightweight documentation system with five structured files to help AI agents maintain project context across sessions. It addresses issues like context resets, forgotten rules, and the need to re-explain projects to new agents.
Kanban CLI: A Local-First, Agent-First Task Manager for the Terminal
Kanban CLI is a Rust-based terminal tool that provides structured task tracking with full git integration, designed for AI agent-driven workflows.
LLM Cost Profiler: Open-source tool tracks API spending to make case for local models
LLM Cost Profiler is a Python tool that tracks every API call to OpenAI/Anthropic, showing exactly what you're spending and where. It exposes tasks that are overpriced relative to their complexity, providing concrete dollar amounts to justify moving to local models.