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engram-ai-memory
foramoment February 17, 2026
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🧠 Cognitive memory system for AI coding agents — persistent semantic search, knowledge graph, Ebbinghaus forgetting, zero API cost. Works with Claude, Cursor, Antigravity.
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<h1 align="center">🧠 Engram</h1>
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<strong>Cognitive Memory System for AI Agents</strong>
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Persistent semantic search · Knowledge graph · Sleep consolidation · Zero external APIs
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<a href="#quick-start">Quick Start</a> ·
<a href="#architecture">Architecture</a> ·
<a href="#features">Features</a> ·
<a href="#research-foundations">Research</a> ·
<a href="#cli-reference">CLI</a> ·
<a href="SKILLS/engram/SKILL.md">Agent Integration</a>
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<br/>Engram gives AI agents a brain — not a database. It remembers what matters, forgets what doesn't, and builds connections between ideas. All inference runs locally — no API keys, no cloud, no token cost per memory operation.
Why Engram?
Most AI memory solutions (like Mem0) use LLM calls for every add() — extracting facts, classifying operations, resolving conflicts. That's powerful, but expensive and opaque.
Engram takes a different approach:
| Mem0 | Engram | |
|---|---|---|
| Who decides what to save | LLM automatically | Agent explicitly |
| Cost per memory write | LLM call (extraction + update) | Local embedding only |
| Cost per memory read | Vector search + LLM | Hybrid search + local reranker |
| External dependencies | OpenAI API / vector DB service | None — fully local |
| Memory unit | Atomic fact ("User prefers X") | Typed record with full context |
| Forgetting | No built-in mechanism | Ebbinghaus-inspired decay + consolidation |
| **Knowledge graph |
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