cuba_zafra
Perform memory maintenance on a long-term knowledge graph by decaying, pruning, merging, summarizing, and re-encoding observations to maintain relevance and reduce redundancy.
Instructions
Memory maintenance: decay (stratified exponential by type), prune (remove low-importance), merge (deduplicate), summarize (compress observations), pagerank (personalized importance), find_duplicates, export, stats, reembed (re-encode with current model).
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| c | No | Power-law c parameter for decay_episodes (default 0.1) | |
| beta | No | Power-law β exponent for decay_episodes (default 0.5) | |
| action | Yes | Consolidation action. decay_episodes applies power-law decay to brain_episodes. | |
| threshold | No | Importance threshold for prune (default 0.1) | |
| batch_size | No | Max observations to re-encode in reembed (default 500) | |
| entity_name | No | Entity to summarize (for summarize action) | |
| halflife_days | No | Global halflife override for decay (overrides per-type stratification) | |
| compressed_summary | No | Compressed text replacing observations (for summarize) | |
| similarity_threshold | No | Similarity threshold for merge (default 0.8) |