A minimalist MVP demonstrating a simple yet profound insight: aligning AI memory with human episodic memory granularity. Shows how this single principle enables simple methods to rival complex memory frameworks for conversational tasks.
Local-first AI memory — runs offline on any machine with 8 GB+ RAM (SBC, mini PC, laptop, workstation). Zero-loss verbatim archive, knowledge graph, hybrid retrieval. Framework-agnostic, no cloud.
Open-source causal graph memory for AI agents. 89.9% on LoCoMo. MCP server with ACT-R scoring, spreading activation, and active forgetting.
Research benchmark: conversational memory learning on a corrected LoCoMo dataset
🚀 A modern, production-ready refactor of the LoCoMo long-term memory benchmark.
Per-speaker memory isolation with neural reranking for multi-party LLM agents. 56.3% on LoCoMo (+39.3 pp on temporal over RAG) at 1.9 LLM calls per query.
Simple CSS and animation focus project, clone from abhishekjha.me
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