Show HN: Adaptive Recall, persistent memory for AI assistants over MCP
Adaptive Recall is a new memory API for AI assistants that uses cognitive science and multi-strategy retrieval to improve information recall. It integrates with CLI tools via MCP and offers features like temporal recency, knowledge graph traversal, and automated entity extraction.
Why it matters
It addresses the 'context window' limitation of current LLMs by providing a persistent, evolving memory layer that mimics human cognitive processes.
Store, recall, and forget with a memory system that learns from every interaction. Retrieval quality improves automatically over time, powered by cognitive science and machine learning.
Most memory APIs store embeddings and search by cosine similarity. Adaptive Recall does that and five layers more.
Six capabilities that no other memory API offers, working together in every query.
Four search strategies run in parallel: vector similarity, temporal recency, full-text keyword, and knowledge graph traversal. The system learns which strategies to prioritize for each type of query.
Results are ranked using ACT-R activation modeling from cognitive science. Recency, access frequency, entity connections, and validated confidence all factor into which memories surface first.
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