Manage and query your agent's distilled long-term memory with lightweight, fast SQLite-powered tools and automated daily extraction.
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2mo agoAdd persistent memory, vector search, and retrieval-augmented generation (RAG) to your AI agents.
Go beyond one-shot conversations. These skills add long-term memory, knowledge base search, RAG pipelines, and context persistence to your agents — connecting to vector databases like Pinecone and Chroma, file-based memory stores, and relational databases for structured recall.
Store and retrieve memories using vector embeddings and semantic similarity search.
Manage and query your agent's distilled long-term memory with lightweight, fast SQLite-powered tools and automated daily extraction.
Lightweight structured memory system for OpenClaw - inspired by memU, zero external dependencies. Provides atomic memory storage with categories (preferences...
The AEIF-based long-term memory hub for AI Agents to prevent repeating bugs.
基于 LanceDB 与纯 JavaScript Embedding 的本地向量记忆系统，无需外部 API 或原生模块. 通过 Ollama 本地运行 nomic-embed-text 模型生成向量，所有数据完全留在本地，隐私至上. 提供五层记忆架构：热内存（SESSION-STATE.md）、温存储（Lance...
SOTA Memory Suite — auto-recall, cross-encoder reranking, multi-hop deep search, causal knowledge graph, episodic memory, and nightly sleep-cycle consolidation.
三层记忆系统自动管理长对话记忆，支持跨会话检索、结构化存储和智能压缩，提升记忆连续性与上下文管理效率。
Quick install — most popular agent memory & rag skill:
clawdbot install Trypto1019/arc-memory-pruner841 skills found
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Automatically prune and compact agent memory files to prevent unbounded growth. Circular buffer for logs, importance-based retention for state, and configura...
Founder onboarding interview that captures company context across 7 dimensions. Invoke with /cs:setup for initial interview or /cs:update for quarterly refre...
Auto context management with seamless session switching. Monitors usage, triggers at 85% threshold, automatically creates new session with loaded memory. Zer...
Session-Memory Enhanced v4.0 - 统一增强版。融合 session-memory + memu-engine 核心功能。特性：结构化提取 + 向量检索 + 不可变分片 + 三位一体自动化 + 多代理隔离 + AI 摘要 + 零配置启动。
QAOA-powered memory optimization for AI agents. Three quantum layers: clustering, compaction, and recall. Integrates with QMG v1.2 for chunked gte-large retr...
Persist agent state across crashes, context deaths, and restarts. Use when you need to save current context, restore after a crash, maintain a memory file ac...
四层渐进式长时记忆系统，让 AI 跨会话保持上下文、沉淀知识、持续进化。 【核心设计：静默自动】AI 自动识别、记录、整理记忆，无需用户确认。 【凭据要求】需要 LLM/Embedding API Key（llm_config.json 或 secrets.env） 【网络调用】调用外部 LLM API 进行向量...
Manage and query your agent's distilled long-term memory with lightweight, fast SQLite-powered tools and automated daily extraction.
Enables agents to externalize memory into shared fields as evolving relational traces for safe, non-coercive coordination, care, and collective intelligence.
Local-first research orchestration engine. Manages state, synthesis, and optional background services (MCP/Watchdog).
九章法律AI帝国建设日记 - 记录帝国建设的点点滴滴。 这是一个记忆存档技能，包含从2026年3月13日至今的完整建设记录： - 每日工作日志和任务完成情况 - 关键决策和重要对话记录 - 29个技能包的开发和发布历程 - ClawHub和GitHub发布记录 - 九章币(JZB)经济模型规划 - 九章城元宇宙建...
Persistent semantic memory layer for AI agents. Local-first storage (SQLite+LanceDB) with Ollama embeddings. Store and recall facts, decisions, preferences, events, relationships across sessions. Supports memory decay, deduplication, typed memories (5 types), memory relationships (7 graph relation types), agent/user scoping, semantic search, context-aware recall, auto-extraction from text (rules/LLM/hybrid), import/export, REST API, MCP protocol. Solves context window and compaction amnesia. Server at localhost:3400, dashboard at /dashboard. Install via npm (engram-memory), requires Ollama with nomic-embed-text model.
Automatically audits and compresses memory and context files to reduce token usage and save weekly API costs without data loss.
Advanced security protocol for LLM agents focusing on Prompt Injection mitigation, code auditing, and data exfiltration prevention.
Help users decide when Tmall is the best choice for official-brand shopping, then assist with browser automation for search, flagship-store verification, rev...
EvoClaw Tiered Memory Architecture v2.1.0 - LLM-powered three-tier memory system with structured metadata extraction, URL preservation, validation, and cloud...
Gives your OpenClaw a powerful dashboard-ready memory system with milestones, prompt engineering, and Obsidian compatibility. Creates structured projects wit...
Implement the 5-file agent memory architecture for durable continuity across sessions. Covers SOUL.md (identity), IDENTITY.md, USER.md, AGENTS.md (operating...
Complete zero-dependency memory system for AI agents — file-based architecture, daily notes, long-term curation, context management, heartbeat integration, a...
AI 协作助手，以「错题本」机制帮助 AI 学习改进。支持人为干预、自动记录摩擦点、协作偏好记忆，让 AI 越用越顺手。
Analyze and archive low-value memory notes in OpenClaw workspace to reduce context bloat and improve responsiveness. 适用于上下文冗余、维护整理场景；默认流程为 analyze → plan → c...
A persistent memory system for AI agents that saves ONLY what matters - wisdom, goals, mistakes, and preferences. Quality over quantity. Supports automatic l...
Convert Elite Longterm Memory data into a structured ExpertPack. Migrates the 5-layer memory system (SESSION-STATE hot RAM, LanceDB warm store, Git-Notes col...
Pure-Python recursive memory recall for persistent AI agents. Manager→workers→synthesis RLM loop — no Deno, no fast-rlm, just HTTP calls to any OpenAI-compat...
Memory skills connect to Pinecone, Chroma, Weaviate, Qdrant, pgvector, and Supabase Vector. Some use local file-based storage for simpler deployments without external dependencies.
RAG retrieves only the most relevant chunks from a large knowledge base at query time — keeping context focused and within token limits while allowing agents to reason over thousands of documents.