Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
A fully local MCP server for long-term memory with semantic search
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"longterm-memory": {
"command": "npx",
"args": [
"-y",
"longterm-memory-mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add longterm-memory -- npx -y longterm-memory-mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"longterm-memory": {
"command": "npx",
"args": [
"-y",
"longterm-memory-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"longterm-memory": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"longterm-memory-mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 MarcelRoozekrans 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.6.1)은 6일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
A fully local MCP server that gives AI agents persistent, semantic long-term memory — without any cloud dependencies.
Inspired by mcp-mem0, but runs 100% on your machine:
| Feature | mcp-mem0 | This project |
|---|---|---|
| Storage | PostgreSQL / Supabase | SQLite (Node's built-in node:sqlite) |
| Embeddings | OpenAI API | Local transformer (all-MiniLM-L6-v2) |
| Vector search | Cloud vector DB | In-process cosine similarity |
| LLM dependency | OpenAI / OpenRouter / Ollama | None |
| Setup | Database + API keys | `npx longterm-memory-mcp` |
| Tool | Description |
|---|---|
save_memory | Store text with auto-generated semantic embedding, tags, importance, and type |
search_memory | Find relevant memories using natural language queries (cosine similarity) |
update_memory | Modify an existing memory's content, metadata, tags, importance, or type |
delete_memory | Remove a specific memory by ID |
delete_all_memories | Wipe all memories (irreversible) |
get_all_memories | List all stored memories (paginated) |
memory_stats | Get count and database location |
| Tool | Description |
|---|---|
search_by_type | Filter memories by category (general, fact, preference, conversation, task, ephemeral) |
search_by_tags | Find memories matching any of the provided tags |
search_by_date_range | Find memories created within a specific date range (ISO format) |
| Tool | Description |
|---|---|
create_backup | Manually trigger a database backup with JSON export |
Install via the Claude Code marketplace — this sets up both the MCP server and a companion skill that teaches Claude how to use memory effectively:
/plugin marketplace add MarcelRoozekrans/LongtermMemory-MCP
/plugin install longterm-memory@longterm-memory-marketplace
This automatically:
long-term-memory skill (Claude learns to recall context at session start, save insights after tasks, and deduplicate memories)npx longterm-memory-mcp
npx keeps running the version it cached first and does not look for updates. To move to a new release, name the version, as in npx -y longterm-memory-mcp@1.6.0, or use @latest, which checks the registry on every start.
npm install -g longterm-memory-mcp
longterm-memory-mcp
git clone https://github.com/MarcelRoozekrans/LongtermMemory-MCP.git
cd LongtermMemory-MCP npm install && npm run build npm start
If you installed via the plugin marketplace, the MCP server is already configured. For manual setup, add to your MCP settings (~/.claude/settings.json or project .claude/settings.json):
{
"mcpServers": { "longterm-memory": { "command": "npx", "args": ["-y", "longterm-memory-mcp"] } } }
Add to claude_desktop_config.json:
{
"mcpServers": { "longterm-memory": { "command": "npx", "args": ["-y", "longterm-memory-mcp"] } } }
The repo includes agent instructions that teach AI agents how to use memory effectively (automatic recall, save patterns, deduplication):
| File | Purpose |
|---|---|
skills/long-term-memory/SKILL.md | Self-contained skill — auto-loaded by Claude Code plugin, can be referenced from any MCP client |
By default, memories are stored in a shared, user-scoped location:
~/.longterm-memory-mcp/memories.db
This means every project and every MCP client shares the same memory pool — you save a memory in one project and it's available everywhere.
Several sessions can use the same database at once. Each server process reads and writes the shared file directly (SQLite in WAL mode), so a memory saved in one session is visible to every other session right away, and no session overwrites what another saved. Requires Node.js 22.13 or later.
Databases from older releases open unchanged. A database from v1.0–1.3, which has no schema_meta table, is migrated in place with all its memories kept; a copy named memories.db.pre-migration- .bak is written next to it first.
Upgrading from 1.4 or earlier: stop every running session that uses the old version before starting one with the new version. Older releases keep the whole database in memory and rewrite the file on each save, so an old process left running would overwrite what new sessions saved.
To isolate memories for a specific project, set the MEMORY_DB_PATH environment variable:
{
"mcpServers": { "longterm-memory": { "command": "npx", "args": ["-y", "longterm-memory-mcp"], "env": { "MEMORY_DB_PATH": "/path/to/project/memories.db" } } } }
Each memory has a memory_type that determines how it's categorized and how quickly it decays:
| Type | Description | Decay half-life |
|---|---|---|
general | Default catch-all | 60 days |
fact | Verified information | 120 days |
preference | User/project preferences | 90 days |
conversation | Conversation context | 45 days |
task | Task-related notes | 30 days |
ephemeral | Short-lived context | 10 days |
["auth", "backend"]). Search with search_by_tags.core, identity, or pinned skip decay entirely.Memories decay over time to keep the store relevant:
Memory content is hashed (SHA-256) on save. If identical content already exists, the save is rejected with a reference to the existing memory ID. This prevents duplicate entries automatically.
Memories end up in the database and in its JSON backups, so content is filtered before it is stored:
… is never stored, embedded or hashed. An unclosed removes everything after it. Content that is empty once the spans are removed is rejected.save_memory and update_memory refuse content, metadata values and tags that look like a secret. The error names the pattern that matched and never the value.| Pattern | Matches |
|---|---|
private-key | PEM / OpenSSH / PGP private key headers (certificates are allowed) |
github-token | ghp_, gho_, ghu_, ghs_, ghr_ and github_pat_ tokens |
api-key | sk-… style API keys |
aws-access-key | AKIA… / ASIA… access key ids |
jwt | JSON Web Tokens |
slack-token | xoxb-, xoxp- and similar Slack tokens |
secret-assignment | random-looking values after password=, token:, secret=, api_key=, KEY= and similar. References ($VAR, ${{ … }}), placeholders (<…>), calls and plain words are allowed |
Configure it with environment variables:
{
"mcpServers": { "longterm-memory": { "command": "npx", "args": ["longterm-memory-mcp"], "env": { "MEMORY_GUARD_DISABLE": "jwt,slack-token", "MEMORY_GUARD_EXTRA_PATTERNS": "{\"internal-host\": \"\\\\b[a-z0-9-]+\\\\.corp\\\\.example\\\\b\"}" } } } }
MEMORY_GUARD_DISABLE: a comma-separated list of pattern names to turn off, or all for every built-in pattern. spans are removed either way. An unknown name stops the server at startup.MEMORY_GUARD_EXTRA_PATTERNS: a JSON object of extra patterns to reject, as name → regular expression.The guard applies to new saves and updates only. Memories stored before it existed are not rescanned.
The Claude Code plugin ships a UserPromptSubmit hook that recalls memories without the agent having to ask. On the first prompt of each session it searches with the prompt text and adds the best matches to the context as a short list:
Long-term memories that may be relevant (longterm-memory; search_memory gives the full text):
recall-sessions/ next to the database. Later prompts in that session cost one node start, about 30 ms, and no search.npx longterm-memory-mcp@ recall --hook, so a cached older copy is never used. The plugin's MCP server is pinned the same way.Configure it with environment variables in your Claude Code settings (env in ~/.claude/settings.json). The hook runs in Claude Code's environment, not the MCP server's, so a custom MEMORY_DB_PATH has to be set there too.
| Variable | Default | Meaning |
|---|---|---|
MEMORY_RECALL_ENABLED | true | false turns the hook off |
MEMORY_RECALL_K | 5 | at most this many memories (1–50) |
MEMORY_RECALL_THRESHOLD | 0.5 | minimum cosine similarity (-1 to 1) |
MEMORY_RECALL_MAX_TOKENS | 400 | cap on the injected block, estimated at 4 characters per token |
MEMORY_RECALL_STATE_DIR | /recall-sessions | where the once-per-session markers live; markers older than 30 days are removed |
The hook calls the recall subcommand, which also works on its own:
npx longterm-memory-mcp recall "how do we deploy the stack"
npx longterm-memory-mcp recall --k 3 --threshold 0.4 --project docker "deploy"
It prints the same list the hook injects, or No relevant memories found. on stderr. Options: --k, --threshold, --max-tokens and --project (default: the current directory's name, "" for none). It does not create a database that does not exist yet. With --hook it reads a Claude Code UserPromptSubmit event from stdin and answers with additionalContext.
Backups are managed automatically and can also be triggered manually via the create_backup tool.
앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요