Rajappa R

Agent Recall is a persistent memory layer for AI agents that helps them retain, retrieve, and reuse knowledge from past experiences across tasks and sessions. Today, AI agents often repeat investigations and rediscover solutions because valuable context and lessons are lost between sessions. Agent Recall addresses this challenge through hybrid search, semantic retrieval, evidence-backed recommendations, and outcome-aware memory retrieval. When an agent encounters a new problem, Agent Recall finds relevant past experiences, surfaces useful solutions with supporting evidence, and enables outcomes to be recorded for future use. This creates a feedback loop that helps agents build on previous work rather than starting from scratch. Our vision is to make AI agents more consistent, context-aware, and effective by turning past experiences into reusable intelligence.