claude-mem
- Repo stars 62
- Author updated Live
- Author repo skills
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @TerminalSkills · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Required · Vendor-specific
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: claude-mem
description: >- Claude Code forgets everything between sessions. Two open-source tools solve this by automati…
category: other
runtime: no special runtime
---
# claude-mem output preview
## PART A: Task fit
- Use case: >- Claude Code forgets everything between sessions. Two open-source tools solve this by automatically capturing context and injecting it into future sessions: Both eliminate the need to re-explain context when returning to a project. requires Vendor-specific API key. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / Instructions / Option A: claude-mem (Local AI Compression)” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “>- Claude Code forgets everything between sessions. Two open-source tools solve this by automatically capturing context and injecting it into future sessions: Both eliminate the need to re-explain context when returning to a project. requires Vendor-specific API key. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “Overview / Instructions / Option A: claude-mem (Local AI Compression)” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source mentions slash commands such as `/plugin`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Overview / Instructions / Option A: claude-mem (Local AI Compression)”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: claude-mem
description: >- Claude Code forgets everything between sessions. Two open-source tools solve this by automati…
category: other
source: TerminalSkills/skills
---
# claude-mem
## When to use
- >- Claude Code forgets everything between sessions. Two open-source tools solve this by automatically capturing contex…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / Instructions / Option A: claude-mem (Local AI Compression)” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "claude-mem" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / Instructions / Option A: claude-mem (Local AI Compression)
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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
relevanceThresholdhigher (0.8+) if too much context is injected - For Subconscious:
whispermode gives 90% of the value at lower token cost - Add
.claude-mem/memories/to.gitignorefor private projects - Memory quality depends on session length — short sessions produce less useful memories
Decide Fit First
Design Intent
How To Use It
Boundaries And Review