midos-memory-cascadeAuto-escalating multi-tier memory search that cascades from in-memory cache through SQLite, grep, and LanceDB vector search to find the best answer with mini...
Install via ClawdBot CLI:
clawdbot install msruruguay/midos-memory-cascadeGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 20, 2026
Enables AI agents to quickly retrieve accurate answers from knowledge bases, cascading from cached FAQs to detailed documentation. Reduces response latency and improves resolution rates by automatically escalating to semantic search for complex queries.
Assists legal professionals in searching through case files, contracts, and precedents across multiple storage tiers. Routes 'how/what/why' questions directly to semantic search for nuanced legal interpretations, ensuring comprehensive retrieval.
Helps medical staff access patient records, treatment guidelines, and research papers efficiently. Uses confidence thresholds to stop at reliable tiers, minimizing latency while ensuring accurate information retrieval for critical decisions.
Supports e-learning platforms by retrieving course materials, answers to student queries, and interactive content from various data sources. Self-learning evolves shortcuts to prioritize frequently accessed tiers, enhancing user experience.
Facilitates compliance and audit processes by searching structured databases and unstructured documents across an organization. Degrades gracefully to grep fallback if advanced tiers are unavailable, ensuring robust operation.
Offer the skill as a cloud-based API with tiered pricing based on query volume and storage tiers accessed. Generate recurring revenue from businesses needing scalable, low-latency memory search without infrastructure management.
Sell licenses for on-premise deployment in regulated industries like healthcare or finance, where data sovereignty is critical. Provide support and customization services for integration with existing systems.
Release core tiers (T0-T4) as open source to build community adoption, while monetizing advanced features like LanceDB semantic search and self-learning evolution. Upsell to enterprises requiring high-performance capabilities.
💬 Integration Tip
Start with stdlib tiers (T0-T4) for zero-dependency testing, then add LanceDB for semantic search if needed; use the evolve() function regularly to optimize performance based on query history.
Scored Apr 19, 2026
Search and analyze your own session logs (older/parent conversations) using jq.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...
Enable and configure Moltbot/Clawdbot memory search for persistent context. Use when setting up memory, fixing "goldfish brain," or helping users configure memorySearch in their config. Covers MEMORY.md, daily logs, and vector search setup.
Ultimate AI agent memory system for Cursor, Claude, ChatGPT & Copilot. WAL protocol + vector search + git-notes + cloud backup. Never lose context again. Vibe-coding ready.
Local memory management for agents. Compression detection, auto-snapshots, and semantic search. Use when agents need to detect compression risk before memory loss, save context snapshots, search historical memories, or track memory usage patterns. Never lose context again.
Audit, clean, and optimize Clawdbot's vector memory (LanceDB). Use when memory is bloated with junk, token usage is high from irrelevant auto-recalls, or setting up memory maintenance automation.