Enables AI agents to store, retrieve, and self-improve procedural memories (lessons learned) based on relevance to the current task, pruning unused memories to reduce context load and prevent repetition of past mistakes.
Provides persistent, cross-session memory for AI agents, allowing them to store and automatically retrieve information across different conversations and sessions without repeating context.
Enables AI agents to offload and recall working memory as indexed episodes with LLM-written digests, reducing token usage and preserving detail across sessions.
Enables AI agents to store, search, and recall semantic memories with three memory types (semantic, episodic, procedural) and auto-consolidation, compounding intelligence over time.
