Wangdefa.Memory: Vector-Free, Local-First Five-Layer Memory System for AI Agents

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A developer has released Wangdefa.Memory, an open-source memory component for AI agents that mimics human memory rather than relying on conventional vector-based retrieval (RAG). The system is structured into five layers — cognition, feature inference, association, experience, and retrieval control — using feature tags instead of vector embeddings to match and recall memories. It runs entirely on local storage using SQLite and JSON, requires only .NET 10 and two basic packages, and has no cloud dependency. The project is in early development, licensed under Apache 2.0, and is available on GitHub with a README and console demo that can be tested within minutes. The developer invites those working on agent systems to try the component, file issues, and contribute feedback.

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