AI memory with biological decay

Implements spaced repetition and memory decay using biological forgetting curves (Ebbinghaus-inspired) rather than simple TTL or LRU eviction. Memories degrade probabilistically over time based on access frequency and recency, with recall probability decreasing according to a decay function. The system tracks memory age, access count, and last-accessed timestamp to compute dynamic decay rates, enabling memories to fade naturally while high-value memories remain retrievable longer.

Maintains a time-indexed memory store where each memory record includes creation timestamp, last-access timestamp, and access frequency counters. Retrieval queries compute decay scores on-the-fly by evaluating the memory's age against a decay function, then filter/rank results by decay probability. The system supports both semantic similarity search (via embeddings) and temporal filtering, allowing queries like 'retrieve memories from the last week' or 'find facts I've accessed frequently'.

Implements a confidence-based filtering mechanism where memories are included in the agent's context window only if their decay probability exceeds a configurable threshold. The system computes decay probability as a function of memory age, access frequency, and a parameterized decay curve (e.g., exponential, power-law). Memories below the threshold are excluded from LLM prompts, effectively implementing 'soft forgetting' where low-confidence memories don't influence reasoning but remain in storage for potential recovery.

Tracks how many times each memory has been retrieved or referenced by the agent, using access count as a signal of memory importance. Frequently accessed memories decay more slowly (higher half-life) than rarely accessed ones, implementing a reinforcement mechanism where 'using' a memory strengthens it. The system updates access counts on every retrieval and incorporates them into the decay function, so memories that are repeatedly useful resist forgetting longer.

Converts memory text to dense vector embeddings (via OpenAI, Anthropic, or local embedding model) and stores them in a vector index. Retrieval queries are also embedded and matched against the index using cosine similarity or other distance metrics, enabling semantic search where 'what did we discuss about budgets' retrieves memories about 'financial planning' even without exact keyword match. The system integrates embedding generation with the decay filtering pipeline, so retrieved memories are ranked by both semantic relevance and decay probability.

Allows users to specify decay function parameters (half-life, shape, minimum confidence floor) that control how quickly memories fade. The system supports multiple decay models (exponential, power-law, or custom functions) and applies them uniformly across all memories. Parameters can be adjusted globally or per-memory-type, enabling domain-specific tuning (e.g., facts decay slower than opinions). The decay function is evaluated at query time using memory age and access frequency to compute current confidence probability.

Based on the 52% recall metric and biological memory inspiration, the system likely implements or supports memory consolidation where related memories are periodically merged or summarized to reduce storage and improve retrieval efficiency. This would involve identifying semantically similar memories, generating summaries, and replacing clusters with consolidated records. The consolidation process would preserve high-level information while discarding redundant details, mimicking biological memory consolidation during sleep.