memory▌

Persistent memory systems for LLM conversations with tiered storage and intelligent retrieval. \n \n Implements three memory types: short-term (immediate context), long-term (historical facts), and entity-based (facts about specific entities) \n Provides memory retrieval and consolidation capabilities to surface relevant memories without overwhelming context windows \n Addresses critical concerns including unbounded memory growth, retrieval relevance, and strict user isolation to prevent cross-u

Configure persistent memory search for Moltbot/Clawdbot agents to retain context across sessions. \n \n Add memorySearch config block with provider (Voyage, OpenAI, or local), sources (memory files and/or sessions), and relevance thresholds \n Create a workspace structure with MEMORY.md for curated long-term facts and memory/logs/ for daily timestamped logs \n Supports three embedding providers; Voyage recommended but local option available without API keys \n Includes troubleshooting for common

Systematic memory management for agents through daily logging, session preservation, and knowledge extraction.

Comprehensive techniques for acquiring, analyzing, and extracting artifacts from memory dumps for incident response and malware analysis.

Multi-store memory with natural language triggers, knowledge graphs, decay-based forgetting, reflection consolidation, philosophical evolution, multi-agent support, and full audit trail.

Value: Feedback -- accumulated knowledge creates compound feedback loops across sessions. What you learn today should accelerate tomorrow's work.

Memory issues manifest as crashes after prolonged use. Core principle 90% of memory leaks follow 3 patterns (retain cycles, timer/observer leaks, collection growth). Diagnose systematically with Instruments, never guess.

You are a Memory Quality Auditor for NeuralMemory. You perform systematic, evidence-based reviews of brain health across multiple dimensions. You think like a data quality engineer — every finding must reference specific memories, every recommendation must be actionable.

Create evidence-based learning plans that maximize long-term retention through spaced repetition, retrieval practice, and interleaving.

Store and retrieve knowledge across sessions using semantic search.