'persistent memory' 검색 결과

Set up, configure, update, and troubleshoot persistent memory for OpenClaw agents. The plugin stores conversations locally and supports automatic recall, task summaries, reusable skills derived from successful tasks, and a visual memory manager.

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• Setting up persistent memory for OpenClaw
• Updating or troubleshooting the memory plugin
• Choosing local or external memory search models

Design memory systems that help AI assistants retain conversation information and retrieve it when relevant. Explore short-term, long-term, and entity memory, along with memory consolidation, prompt integration, storage lifecycle management, and separation between users.

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• Designing an assistant that remembers across conversations
• Storing and updating facts about people or other entities
• Adding relevant memories to prompts

Gives AI agents persistent project memory for architecture, implementation patterns, and decisions. Supports saving, searching, and retrieving knowledge, automatically syncing it with project documentation, and viewing memory usage through a dashboard.

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• Save architecture decisions for later reference
• Search saved implementation patterns by type or tag
• Retrieve a specific design record by key

Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.

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Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.

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• Retain user preferences across conversations
• Track people and entities consistently across sessions
• Retrieve facts valid at a specific point in time

Supermemory is a state-of-the-art memory and context infrastructure for AI agents. Use this skill when building applications that need persistent memory, user personalization, long-term context retention, or semantic search across knowledge bases. It provides Memory API for learned user context, User Profiles for static/dynamic facts, and RAG for semantic search. Perfect for chatbots, assistants, and knowledge-intensive applications.

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Orchestrate persistent agent memory across coding sessions, repos, and tools. Load when the user asks to remember, recall context, save project memory, create a handoff, manage global memory, update memory, compact memory, audit memory, forget memory, continue from prior sessions, or before commit/push/git operations that checkpoint project state.

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Store project knowledge and design decisions in persistent memory that AI agents can search and read. New findings sync automatically to Markdown documentation, while usage statistics help you inspect the memory bank.

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• Save architecture decisions for later tasks
• Find previously recorded implementation patterns
• Keep agent memory synced with project documentation

Add persistent memory to NanoClaw agents using the default Claude provider. With mnemon installed in the container, agents retrieve relevant context before responding and save new insights after each turn, retaining memory across container restarts.

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• Recalling earlier conversations in a new session
• Keeping agent memory across container restarts
• Checking memory hooks and storage settings

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memoryUse when "conversation memory, remember, memory persistence, long-term memory, chat history, memory, conversation, persistence, context, llm" mentioned.

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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Configure and manage Claude Code persistent memory (CLAUDE.md, auto memory, rules) across sessions. Use when setting up project memory, organizing .claude/rules/, managing auto memory files, creating CLAUDE.md with imports, debugging memory loading, or advising on memory hierarchy and best practices.

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Organize, extract, prune, and verify Claude Code persistent memory files. Covers MEMORY.md as a concise index, topic extraction to dedicated files, staleness detection, accuracy verification against project state, the dual 200-line / 25KB load cap, and reachability of topic files from the index. Use when MEMORY.md is approaching either load cap, after a session produces durable insights worth preserving, when a topic section has grown beyond 10-15 lines and should be extracted, when project state has changed and memory entries may be stale, or before and after rewriting the index so that no topic file loses its only pointer.

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Persistent vector memory for Claude Code via LanceDB. Use when: remembering across sessions, storing lessons, recalling past decisions, maintaining context continuity. Tools: memory_store, memory_recall, memory_update, memory_forget, memory_merge.

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Provides persistent project memory for AI agents through a local MCP server, a tool interface for agents. Stores and searches architecture decisions and implementation patterns, syncs with project documentation, and includes a dashboard for viewing memory usage.

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• Save architecture decisions for later reference
• Search previously recorded implementation patterns
• Retrieve design notes by key

Retrieve earlier decisions, preferences, and saved resources from OpenViking across sessions. Store durable information, import documents or repositories, and manage deletion, with guidance on how project memory is shared between directories and repository clones.

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• Recall decisions from earlier sessions
• Save preferences and lasting decisions
• Import files, URLs, or Git repositories for later use

Teaches when to recall from long-term memory before acting and when to save durable decisions, corrections and failures afterwards. Use when a memory tool or MCP memory server is connected but the agent is not using it consistently, when the user complains that the assistant forgets preferences, conventions or past decisions between sessions, or when setting up persistent memory for a project. Works with any memory backend: a folder of Markdown files, a local MCP server, or a managed service.

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Agent Memory Skill workflow skill. Use this skill when the user needs A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions) and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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Audit this repo's auto-memory files and suggest promoting each one to a more persistent layer — user or repo CLAUDE.md, an existing or new skill, a settings.json hook, or a checked-in reference doc. Use when the user says "audit memory", "promote memory", "clean up memories", "memory-promote", or wants to move ephemeral memory into durable config.

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Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memory Use when: conversation memory, remember, memory persistence, long-term memory, chat history.

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Claude Code에서 전문 에이전트별로 작업을 배분하고 진행 상황과 결정을 파일에 저장합니다. 세션을 넘겨 작업을 이어 가고 반복 업무의 명령과 실행 일정을 설계합니다.

• 개발·글쓰기·조사 담당 간 협업
• 다음 세션으로 프로젝트 배경 전달
• 매일 아침 현황 확인 작업 예약

Build AI agents that retain conversation history, user preferences, and useful interaction patterns with AgentDB. The skill covers persistent storage, meaning memories survive between sessions, plus retrieval, context management, and ReasoningBank integration.

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• Add conversation history to a chat system
• Remember user preferences across sessions
• Reuse patterns from successful interactions

Manage Omni Skills, plugins, and persistent memory from the command line. Find and install skills, run them with supplied input, inspect execution history, and maintain saved information alongside plugin installations.

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• Finding and installing skills by category or tag
• Running skills and reviewing execution history
• Installing plugins and creating plugin scaffolds