Adaptive Recall

Adaptive Recall is an adaptive memory system designed for AI applications, offering a platform for storing, recalling, and forgetting information in a way that evolves with use. The system distinguishes itself from traditional memory APIs by incorporating multiple retrieval strategies and learning mechanisms inspired by cognitive science and machine learning. Unlike standard approaches that rely solely on vector embeddings and cosine similarity searches, Adaptive Recall operates with four parallel retrieval strategies: vector similarity, temporal recency, full-text keyword search, and knowledge graph traversal. The platform automatically learns which retrieval strategy to prioritize based on the nature of each query. A key feature of Adaptive Recall is its use of ACT-R cognitive scoring, which draws on decades of cognitive science research to rank retrieval results. The system also supports automatic knowledge graph construction, allowing for more complex and interconnected memory representations. As users interact with the memory system, it monitors and evolves the lifecycle of stored information, adjusting confidence levels and learning parameters based on evidence from usage. This includes self-verification of retrieval quality and the detection of knowledge gaps driven by curiosity-based mechanisms. An integrated machine learning pipeline continually trains on usage patterns, enabling the system to improve retrieval quality automatically over time. Adaptive Recall is positioned for developers building AI applications that require dynamic, evolving memory capabilities. The service is accessible through a memory API, and it offers a free tier for users to get started. The system is described as patent-pending and emphasizes features not commonly found in other memory APIs, such as evidence-gated parameter learning and multi-strategy retrieval in every query. Pricing and further details about licensing are not specified in the available information.