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More about Shodh Memory
shodh-memory is a neuroscience-inspired memory engine that gives AI agents a persistent, adaptive "brain" running entirely on your own hardware. Instead of static vector search, it combines Hebbian learning, temporal indexing, and a rich knowledge graph to create memories that strengthen with use, decay realistically, and surface blind spots before they break your workflows.
Designed for edge deployments and privacy-critical environments, shodh-memory ships as a single ~30MB binary with no external dependencies, no Docker, and no cloud calls. It delivers sub-millisecond graph lookups and fast semantic retrieval, even on Raspberry Pi and air-gapped industrial systems, making it ideal for robotics, IoT, and offline assistants.
Key capabilities include:
- Hebbian learning and synaptic-style potentiation for durable, frequently accessed associations
- Hybrid decay models based on cognitive science, so unused memories fade instead of bloating context
- Knowledge graph with spreading activation for richer reasoning than raw embeddings alone
- Blind spot and stale zone detection to reveal forgotten or shallow knowledge regions
- First-class MCP integration for Claude, Cursor, and other modern AI agent frameworks
With open-source code, rigorous testing, and context durability grounded in research, shodh-memory turns basic retrieval into true cognitive memory for autonomous agents.
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Scale globally with less complexity
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