memory▌

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.

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

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

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.

Searches long-term memories in OpenViking, returns relevant results.