Adaptive Recall MCP Server

Adaptive memory system for AI applications. Multi-strategy retrieval, cognitive scoring, knowledge graph, and self-improving ML. Connects via MCP or REST API.

What the server does

Adaptive Recall is a hosted memory server that stores, retrieves, and manages long-term memory for AI applications. It connects via MCP or REST API.

• Multi-strategy retrieval — four search strategies run in parallel (vector similarity, temporal recency, full-text keyword, knowledge graph traversal) and the system learns which to prioritize for each type of query
• Cognitive scoring — results ranked using ACT-R activation modeling from cognitive science, factoring in recency, access frequency, entity connections, and validated confidence
• Knowledge graph — entities and relationships extracted automatically from stored memories, used as a retrieval pathway alongside text similarity
• Memory lifecycle — memories progress through stages, gain or lose confidence based on corroborating evidence, and fade naturally when unused
• Self-improving — ML models train on your usage patterns, every parameter change must pass statistical validation against real query history before being adopted
• Retrieval quality monitoring — the system verifies its own retrieval consistency and identifies knowledge gaps

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Maintainer setup documentation.