Memory Vault

Name: Memory Vault
Author: MihaiBuilds

作者：MihaiBuilds版本 1.6.1i★ –i在你的电脑上运行

注册名称io.github.MihaiBuilds/memory-vault

安装

概览

作者提供的说明（英文原文）

Local-first AI memory layer with hybrid search. Postgres + pgvector. Self-hosted, MIT.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

连接方式: 在你的电脑上运行
传输方式: stdio
分类: 搜索与 AI 记忆
你需要填写：DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在你的电脑上运行，拥有你的用户账户的权限（文件、网络、应用）。
需要你自己填写： DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD

标为机密的值是你账户的钥匙。千万不要分享给别人，也不要粘贴到聊天里。

你需要

Docker已安装 Docker

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config，把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "memory-vault": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DB_HOST",
        "-e",
        "DB_PORT",
        "-e",
        "DB_NAME",
        "-e",
        "DB_USER",
        "-e",
        "DB_PASSWORD",
        "ghcr.io/mihaibuilds/memory-vault-mcp:1.6.1"
      ],
      "env": {
        "DB_HOST": "your-value",
        "DB_PORT": "your-value",
        "DB_NAME": "your-value",
        "DB_USER": "your-value",
        "DB_PASSWORD": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add memory-vault --env DB_HOST=your-value --env DB_PORT=your-value --env DB_NAME=your-value --env DB_USER=your-value --env DB_PASSWORD=your-value -- docker run -i --rm -e DB_HOST -e DB_PORT -e DB_NAME -e DB_USER -e DB_PASSWORD ghcr.io/mihaibuilds/memory-vault-mcp:1.6.1

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "memory-vault": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DB_HOST",
        "-e",
        "DB_PORT",
        "-e",
        "DB_NAME",
        "-e",
        "DB_USER",
        "-e",
        "DB_PASSWORD",
        "ghcr.io/mihaibuilds/memory-vault-mcp:1.6.1"
      ],
      "env": {
        "DB_HOST": "your-value",
        "DB_PORT": "your-value",
        "DB_NAME": "your-value",
        "DB_USER": "your-value",
        "DB_PASSWORD": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "memory-vault": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "DB_HOST",
        "-e",
        "DB_PORT",
        "-e",
        "DB_NAME",
        "-e",
        "DB_USER",
        "-e",
        "DB_PASSWORD",
        "ghcr.io/mihaibuilds/memory-vault-mcp:1.6.1"
      ],
      "env": {
        "DB_HOST": "your-value",
        "DB_PORT": "your-value",
        "DB_NAME": "your-value",
        "DB_USER": "your-value",
        "DB_PASSWORD": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

这个服务器在你的电脑上运行，而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上，无需账户。

需要你填写的值

DB_HOST 必填 env
Hostname of the Postgres+pgvector instance Memory Vault connects to (e.g. 'host.docker.internal' when Postgres runs on the host, 'localhost' when running natively, or the service name when both run i…
DB_PORT 必填 env
Postgres port
DB_NAME 必填 env
Postgres database name
DB_USER 必填 env
Postgres user with read/write access to the memory vault database
DB_PASSWORD 机密必填 env
Postgres password for DB_USER

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

它在你的电脑上运行；安装后，工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

✓ 通过
作者与仓库一致
注册名称已通过 GitHub 账户 MihaiBuilds 验证，该账户拥有此仓库
… 尚未检查
仍在维护
我们还没有从 GitHub 读取这个仓库
… 尚未检查
有许可证
我们还没有从 GitHub 读取这个仓库
– 不适用
端点有响应
它在你的电脑上运行，没有互联网端点
– 不适用
使用 OAuth 登录
在你的电脑上运行，改用本地设置
… 尚未检查
工具描述中没有隐藏指令
它在你的电脑上运行；我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

星标数多于此分类中其他 7245 个服务器的 0%
发布到注册中心的时间比此分类中其他 7245 个服务器的 57% 更新
基础检查结果等于或优于此分类中其他 7245 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

常见问题

Memory Vault 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

你需要填写：DB_HOST, DB_PORT, DB_NAME, DB_USER, DB_PASSWORD

它在我的电脑上运行还是在云端？

它在你的电脑上运行。你的 AI 应用会在需要时启动它，数据不会经过作者的服务器（除非它自己调用在线服务）。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.6.1）于28天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。

有什么可以替代它？

同一分类中有：Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者：upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新

安装

Codebase Memory

作者：DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新

安装

Exa

作者：exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

Memory Vault

The memory database for AI applications. Self-hosted Postgres + pgvector with hybrid search, MCP-native, and a knowledge graph baked in.

Every conversation with Claude or ChatGPT starts from zero. No memory of what you built last week, what decisions you made last month, what problems you've already solved. You either re-explain everything from scratch, or paste in a wall of context and hope it fits in the window.

Memory Vault is the persistent layer underneath. It stores what you want your AI to remember — decisions, conversations, notes, project context — in a single Postgres database with hybrid semantic + keyword search. Claude can recall and store memories during any session via MCP, you can chat with your own memories through a local LLM, or you can build your own AI tool on top of the REST API.

_Chat with your vault using a local LLM. Every answer shows the exact memories it was grounded in — click any source to verify._

Status

v1.0 — released 2026-05-07. First stable release of Memory Vault. M1-M7 (hybrid search, Docker, MCP, REST API, dashboard, knowledge graph, local LLM chat) all shipped and stable.

See CHANGELOG.md for the full per-release history, or GitHub Releases for the platform-native view.

Semver from here forward — the public surface (REST API endpoints, MCP tool signatures, DB schema) is stable. Breaking changes only on a major version bump.

Quick Start (Docker)

git clone https://github.com/MihaiBuilds/memory-vault.git

cd memory-vault docker compose up -d

That's it. PostgreSQL + pgvector + Memory Vault, running and ready. Migrations run automatically on first start.

# Check it's working

docker compose exec app memory-vault status

Ingest a file

docker compose exec app memory-vault ingest /path/to/file.md --space default

Search

docker compose exec app memory-vault search "your query here"

Data persists in a Docker volume — docker compose down and up again, your memories are still there.

Open http://localhost:8000 in your browser to use the dashboard (Chat, Search, Browse, Graph, Ingest, Stats).

Windows users: clone into WSL2, not a Windows path, and read docs/windows.md if you hit a line-ending error.

No-Docker quick start

If you prefer running without Docker:

Prerequisites

Python 3.11+
PostgreSQL 16 with pgvector extension
uv (recommended) or pip + venv

Setup with uv (recommended)

# Clone

git clone https://github.com/MihaiBuilds/memory-vault.git cd memory-vault

Create virtual environment and install dependencies

uv sync

Install the spaCy language model

uv run python -m spacy download en_core_web_sm

Configure

cp .env.example .env

Edit .env with your PostgreSQL credentials

Run migrations

uv run memory-vault migrate

Verify

uv run memory-vault status

Setup with pip + venv (fallback)

If you don't want to install uv, plain pip + venv works too:

# Clone

git clone https://github.com/MihaiBuilds/memory-vault.git cd memory-vault

Create virtual environment

python -m venv .venv source .venv/bin/activate

Install dependencies

pip install -e .

Install the spaCy language model

python -m spacy download en_core_web_sm

Configure

cp .env.example .env

Edit .env with your PostgreSQL credentials

Run migrations

memory-vault migrate

Verify

memory-vault status

Usage

If you set up with uv, prefix commands with uv run (e.g. uv run memory-vault search ...). If you used pip + venv, activate the venv first (source .venv/bin/activate) and run commands directly:

# Ingest a file

memory-vault ingest notes.md --space default

Search memories

memory-vault search "hybrid search architecture" --limit 5

Check status

memory-vault status

Features

Hybrid search — semantic similarity + keyword matching combined, so you find the right memory even when you don't remember the exact words
MCP integration — four tools (recall, remember, forget, memory_status) that Claude can use natively during any session
Local LLM chat — query your own memories through LM Studio without sending anything to the cloud, with sources shown for every answer
Knowledge graph — entities and relationships extracted automatically, connections between things emerge over time
Memory spaces — separate namespaces for different projects or domains
REST API — integrate AI memory into any application
One-command setup — docker compose up and it's running
Self-hosted — your data stays on your machine, always

Architecture

_Postgres + pgvector at the core. The same memory layer is reachable from MCP (Claude), the dashboard chat page, the REST API, and any app you build on top._

Three things are deliberate about this stack:

One database, not two. Vector embeddings, full-text indexes, and relational data all live in Postgres. No separate vector DB to keep in sync.
Frontend-agnostic. The dashboard is one consumer of the API, not the API itself. MCP, REST, CLI, and your own apps are equal first-class clients.
CPU-only by default. No GPU required. Embeddings (sentence-transformers) and entity extraction (spaCy) both run on a normal laptop.

Tech Stack

PostgreSQL 16 + pgvector — vector storage and hybrid search in one database
Python 3.11+ — async backend with psycopg 3
sentence-transformers — all-MiniLM-L6-v2 embeddings (384-d, runs on CPU)
spaCy — en_core_web_sm for entity extraction (CPU-only, no LLM calls)
FastAPI — REST API with bearer auth, rate limiting, and OpenAPI docs
React 19 + Vite + TanStack Query — web dashboard, baked into the main Docker image
Cytoscape.js + cose-bilkent — force-directed knowledge graph rendering on the dashboard
Docker — one-command deployment with docker compose up
MCP — Claude integration via FastMCP (stdio transport)

MCP Integration (Claude Desktop & Claude Code)

Memory Vault exposes six tools via the Model Context Protocol so Claude can read and write memories during any conversation.

Tools

Tool	Description
`recall`	Search memories with hybrid search (vector + full-text + RRF)
`remember`	Store a new memory — auto-classified and embedded
`forget`	Soft-delete a memory by chunk ID
`purge_forgotten`	Permanently delete memories forgotten more than N days ago
`move_memory`	Move a memory to another space, rebuilding its graph entries
`memory_status`	Database health, chunk counts, embedding model info

Resources

Resource	Description
`memory://spaces`	List all memory spaces with chunk counts
`memory://stats`	Current system statistics

Setup — Claude Code

Make sure you've completed the No-Docker quick start above (uv sync or pip install, plus the spaCy model download) so the memory_vault package is installed.

Project scope — add to your project's .mcp.json:

"mcpServers": { "memory-vault": { "command": "/path/to/memory-vault/.venv/bin/python", "args": ["-m", "memory_vault.mcp"], "env": { "DB_HOST": "localhost", "DB_PORT": "5432", "DB_NAME": "memory_vault", "DB_USER": "memory_vault", "DB_PASSWORD": "memory_vault" } } } }

Global scope — to make memory-vault available in every Claude Code session, add the same server block to ~/.claude/.mcp.json, then add memory-vault to enabledMcpjsonServers in ~/.claude/settings.json:

"enabledMcpjsonServers": ["memory-vault"] }

Verify with claude mcp list — memory-vault should show connected.

Setup — Claude Desktop

Add the same server block to Claude Desktop's config (Settings → Developer → Edit Config), then restart Claude Desktop.

Docker Users

If you're running Memory Vault via Docker, use DB_HOST: "127.0.0.1" and make sure port 5432 is exposed in your docker-compose.yml:

"mcpServers": { "memory-vault": { "command": "/path/to/memory-vault/.venv/bin/python", "args": ["-m", "memory_vault.mcp"], "env": { "DB_HOST": "127.0.0.1", "DB_PORT": "5432", "DB_NAME": "memory_vault", "DB_USER": "memory_vault", "DB_PASSWORD": "memory_vault" } } } }

The MCP server runs on the host (not inside Docker) and connects to the PostgreSQL container over the exposed port.

Verify It Works

Once configured, Claude will have access to the memory tools. Try:

"Use memory_status to check the memory system."

"Remember that we chose Redis for the session cache."

"Recall everything about hybrid search."

Troubleshooting

`ModuleNotFoundError` on startup — the virtual environment isn't installed; re-run uv sync (or pip install -e .) in the repo directory.
`OSError: [E050]` on startup — the spaCy language model isn't installed; re-run python -m spacy download en_core_web_sm.
Server shows `failed` in Claude Code — run claude --debug mcp to see the server's error output.
Tools not available in Claude Code despite server connecting — confirm memory-vault is listed in enabledMcpjsonServers in ~/.claude/settings.json.
Connection refused with Docker running — use DB_HOST: "127.0.0.1" instead of "localhost".

Remote access over HTTP

Everything above runs MCP over stdio, which assumes the client and the server are on the same machine. If your client is somewhere else — Claude Desktop on another machine, an agent harness in a different container — Memory Vault can also serve MCP over HTTP using SSE.

It is off by default. A memory store is not something to start listening on a port without being asked:

MCP_HTTP_ENABLED=true

The transport then mounts at /api/mcp on the same port as the REST API, so a client connects to http:// :8000/api/mcp/sse.

Every request needs a token, created the same way as for the REST API:

memory-vault token create my-remote-client

Clients differ in how they express a remote MCP server, but they all need the same three things — the SSE transport, the URL, and the token. In the shape most of them use:

"mcpServers": { "memory-vault": { "type": "sse", "url": "http://192.168.1.50:8000/api/mcp/sse", "headers": { "Authorization": "Bearer mv_your-token-here" } } } }

Say which hostnames are yours

Memory Vault answers only to hostnames you have declared, and

只显示了开头部分；其余内容请在仓库中阅读

Memory Vault

概览

PopNx MCP 的三大支柱

它能帮你做什么

安装

安装之前

需要你填写的值

它能做什么

基础检查i

作者与仓库一致

仍在维护

有许可证

端点有响应

使用 OAuth 登录

工具描述中没有隐藏指令

与“搜索与 AI 记忆”中的其他服务器比较i

常见问题

替代选择

Context7

Codebase Memory

Exa

作者的 README

Memory Vault

Status

Quick Start (Docker)

Ingest a file

Search

No-Docker quick start

Prerequisites

Setup with uv (recommended)

Create virtual environment and install dependencies

Install the spaCy language model

Configure

Edit .env with your PostgreSQL credentials

Run migrations

Verify

Setup with pip + venv (fallback)

Create virtual environment

Install dependencies

Install the spaCy language model

Configure

Edit .env with your PostgreSQL credentials

Run migrations

Verify

Usage

Search memories

Check status

Features

Architecture

Tech Stack

MCP Integration (Claude Desktop & Claude Code)

Tools

Resources

Setup — Claude Code

Setup — Claude Desktop

Docker Users

Verify It Works

Troubleshooting

Remote access over HTTP

Say which hostnames are yours