lancedb-memoryManage and retrieve long-term memories with LanceDB using semantic vector search, category filtering, and detailed metadata storage.
Install via ClawdBot CLI:
clawdbot install pntrivedy/lancedb-memoryGrade Good — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated Mar 1, 2026
Agents use the skill to store and retrieve past customer interactions, enabling quick access to resolved issues and personalized responses. It improves support efficiency by leveraging semantic search to find similar cases.
Educational platforms integrate the skill to track user progress and preferences, storing learning materials and interactions. This allows for adaptive content recommendations and memory-based tutoring sessions.
Medical AI agents utilize the skill to maintain and search patient records, symptoms, and treatment plans. It ensures quick retrieval of relevant information for diagnosis and continuity of care.
Online retailers employ the skill to store user browsing history and purchase data, enabling personalized product suggestions through vector similarity searches. It enhances user engagement and sales conversion.
Law firms use the skill to archive and search legal documents, case notes, and precedents. It aids in fast retrieval of relevant information for case preparation and research.
Offer the skill as a cloud-based service with tiered pricing based on storage capacity and API calls. It targets businesses needing scalable memory management without infrastructure overhead.
Sell on-premise licenses to large organizations requiring data privacy and customization. This includes support, training, and integration services for seamless deployment.
Provide a free basic version with limited features to attract individual developers and small teams. Monetize through premium upgrades offering advanced analytics and higher limits.
💬 Integration Tip
Ensure LanceDB is installed and accessible; use the provided global instance for quick setup without managing database connections manually.
Scored Apr 19, 2026
Search and analyze your own session logs (older/parent conversations) using jq.
Maintain Clawdbot's compounding knowledge graph under life/areas/** by adding/superseding atomic facts (items.json), regenerating entity summaries (summary.md), and keeping IDs consistent. Use when you need deterministic updates to the knowledge graph rather than manual JSON edits.
Fast semantic memory system with JSON indexing, auto-consolidation, and <20ms search. Capture learnings, decisions, insights, events. Use when you need persistent memory across sessions or want to recall prior work/decisions.
Search and retrieve relevant information from your indexed memory files using semantic queries and direct file reads for context.
Fast semantic search for AI agent memory files using TF-IDF and SQLite. Enables instant context retrieval from MEMORY.md or any markdown documentation. Use when the agent needs to (1) Find relevant context before starting a task, (2) Search through large memory files efficiently, (3) Retrieve specific rules or decisions without reading entire files, (4) Enable semantic similarity search instead of keyword matching. Lightweight alternative to heavy embedding models - zero external dependencies, <10ms search time.
Hybrid memory strategy combining OpenClaw's built-in vector memory with Graphiti temporal knowledge graph. Use when you need to recall past context, answer temporal questions ("when did X happen?"), or search memory files. Provides decision framework for when to use memory_search vs Graphiti.