---
name: claude-mem
version: 1.0.0
category: 开发编程
trigger_words:
tags:
  - ai
  - agent
platform: coze
source: DeepseekModel
source_url: https://deepseekmodel.com/skill?id=terminalskills-skills-skills-claude-mem-skill-md
---

name claude-mem description Add persistent memory to Claude Code that survives across sessions. Use when: maintaining continuity across Claude Code sessions, building agents with persistent project memory, avoiding repeated context setup. Covers claude-mem (AI-compressed session logs) and Claude Subconscious (Letta-based background agent). license Apache-2.0 compatibility Claude Code, Node.js 18+ metadata {"author":"terminal-skills","version":"2.0.0","category":"productivity","tags":["claude-code","memory","persistence","context","session"]} Claude Code Persistent Memory Overview Claude Code forgets everything between sessions. Two open-source tools solve this by automatically capturing context and injecting it into future sessions: claude-mem — captures session activity, compresses it with AI, injects relevant memories on next session. Lightweight, local-first. Claude Subconscious — a background Letta agent that watches sessions, builds up memory over time, and whispers guidance back. Cloud or self-hosted. Both eliminate the need to re-explain context when returning to a project. Instructions Option A: claude-mem (Local AI Compression) GitHub: thedotmack/claude-mem Setup npm install -g claude-mem cd your-project claude-mem init claude-mem setup-hooks This creates .claude-mem/ with config, compressed memories, and an index. Hooks auto-capture after each session and auto-inject before the next. How It Works Capture — hooks into Claude Code session, records interactions Compress — AI summarizes session into structured memory (decisions, code changes, learnings) Store — compressed memories saved to .claude-mem/ directory Retrieve — on new session, relevant memories injected into context Commands claude-mem capture # Capture current session claude-mem inject # Inject memories into context claude-mem search "auth flow" # Semantic search through memories claude-mem list # List all memories claude-mem stats # Show memory stats claude-mem compress # Reduce storage for old memories Configuration { "compression" : { "model" : "claude-sonnet-4-20250514" , "strategy" : "smart" } , "inject" : { "maxMemories" : 10 , "relevanceThreshold" : 0.7 , "strategy" : "semantic" } } Strategies: smart (AI picks what's important), full (captures everything), minimal (only decisions and errors). Option B: Claude Subconscious (Letta Background Agent) GitHub: letta-ai/claude-subconscious Setup /plugin marketplace add letta-ai/claude-subconscious /plugin install claude-subconscious@claude-subconscious export LETTA_API_KEY= "your-api-key" Get your API key from app.letta.com . Or self-host: pip install letta letta server --port 8283 export LETTA_BASE_URL= "http://localhost:8283" Modes Mode Behavior Token Cost whisper (default) Short guidance before each prompt Low full Full memory blocks + message history Higher off Disabled None Which to Choose claude-mem Claude Subconscious Storage Local files (.claude-mem/) Letta cloud or self-hosted Cost Uses your Claude API for compression Requires Letta API key (free tier) Latency Near-zero (local) ~1-2s per whisper Memory style Compressed session summaries Continuous learning agent Best for Local-first, privacy-sensitive Rich cross-session context Examples Example 1: Session Continuity with claude-mem # Session 1: Work on auth module $ claude-mem stats Memories: 12 | Storage: 45KB | Last capture: 2 hours ago # Session 2: Return to project — auto-injected context # Claude already knows: "You implemented JWT auth with RS256, refresh tokens in Redis" Example 2: Architecture Recall with Subconscious After discussing a REST-to-GraphQL migration, you start a new session: [subconscious] Last session you decided to switch from REST to GraphQL for the user service. Migration is 60% done — resolvers for User and Project are complete, Order and Payment still need conversion. You preferred code-first schema with TypeGraphQL. Guidelines Pair with CLAUDE.md — use CLAUDE.md for static project context, persistent memory for dynamic decisions One tool per project — don't run both claude-mem and Subconscious simultaneously For claude-mem: set relevanceThreshold higher (0.8+) if too much context is injected For Subconscious: whisper mode gives 90% of the value at lower token cost Add .claude-mem/memories/ to .gitignore for private projects Memory quality depends on session length — short sessions produce less useful memories