Tools
Aivectormemory
By aivectormemory 是一款基于 Model Context Protocol (MCP) 开发的OpenClaw、OpenCode、ClaudeCodeAI记忆管理工具。它专门为 Claude、OpenCode、Cursor 和 主流IDE 编程工具设计，通过向量数据库技术解决 AI 在不同对话会话中「健忘」的问题。aivectormemory: A lightweight MCP Server enabling persistent, cross-session memory for AI-powered IDEs via vector search.
Install
pip install (Recommended)Configuration Example
{
"mcpServers": {
"aivectormemory": {
"command": "run",
"args": ["--project-dir", "/path/to/your/project"]
}
}
}README
🌐 [简体中文](docs/README.zh-CN.md) | [繁體中文](docs/README.zh-TW.md) | English | [Español](docs/README.es.md) | [Deutsch](docs/README.de.md) | [Français](docs/README.fr.md) | [日本語](docs/README.ja.md)
<p align="center">
<img src="docs/logo.png" alt="AIVectorMemory Logo" width="200">
</p>
<p align="center">
<img src="docs/image.png" alt="AI Vector Memory Architecture" width="100%">
</p>
<h1 align="center">AIVectorMemory</h1>
<p align="center">
<strong>Give your AI coding assistant a memory — Cross-session persistent memory MCP Server</strong>
</p>
<p align="center">
<a href="https://pypi.org/project/aivectormemory/"><img src="https://img.shields.io/pypi/v/aivectormemory?color=blue&label=PyPI" alt="PyPI"></a>
<a href="https://pypi.org/project/aivectormemory/"><img src="https://img.shields.io/pypi/pyversions/aivectormemory" alt="Python"></a>
<a href="https://github.com/Edlineas/aivectormemory/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache_2.0-green" alt="License"></a>
<a href="https://modelcontextprotocol.io"><img src="https://img.shields.io/badge/MCP-compatible-purple" alt="MCP"></a>
</p>
---
> **Still using CLAUDE.md / MEMORY.md as memory?** This Markdown-file memory approach has fatal flaws: the file keeps growing, injecting everything into every session and burning massive tokens; content only supports keyword matching — search "database timeout" and you won't find "MySQL connection pool pitfall"; sharing one file across projects causes cross-contamination; there's no task tracking, so dev progress lives entirely in your head; not to mention the 200-line truncation, manual maintenance, and inability to deduplicate or merge.
>
> **AIVectorMemory is a fundamentally different approach.** Local vector database storage with semantic search for precise recall (matches even when wording differs), on-demand retrieval that loads only relevant memories (token usage drops 50%+), automatic multi-project isolation with zero interference, and built-in issue tracking + task management that lets AI fully automate your dev workflow. All data is permanently stored on your machine — zero cloud dependency, never lost when switching sessions or IDEs.
## ✨ Core Features
| Feature | Description |
|---------|-------------|
| 🧠 **Cross-Session Memory** | Your AI finally remembers your project — pitfalls, decisions, conventions all persist across sessions |
| 🔍 **Semantic Search** | No need to recall exact wording — search "database timeout" and find "MySQL connection pool issue" |
| 💰 **Save 50%+ Tokens** | Stop copy-pasting project context every conversation. Semantic retrieval on demand, no more bulk injection |
| 🔗 **Task-Driven Dev** | Issue tracking → task breakdown → status sync → linked archival. AI manages the full dev workflow |
| 📊 **Desktop App + Web Dashboard** | Native desktop app (macOS/Windows/Linux) + Web dashboard, visual management for memories and tasks, 3D vector network reveals knowledge connections at a glance |
| 🏠 **Fully Local** | Zero cloud dependency. ONNX local inference, no API Key, data never leaves your machine |
| 🔌 **All IDEs** | Cursor / Kiro / Claude Code / Windsurf / VSCode / OpenCode / Trae — one-click install, works out of the box |
| 📁 **Multi-Project Isolation** | One DB for all projects, auto-isolated with zero interference, seamless project switching |
| 🔄 **Smart Dedup** | Similarity > 0.95 auto-merges updates, keeping your memory store clean — never gets messy over time |
| 🌐 **7 Languages** | 简体中文 / 繁體中文 / English / Español / Deutsch / Français / 日本語, full-stack i18n for dashboard + Steering rules |
<p align="center">
QQ群：1085682431 | 微信：changhuibiz<br>
共同参与项目开发加QQ群或微信交流
</p>
<p align="center">
<img src="docs/003.png" alt="Login" width="100%">
<br>
<em>Login</em>
</p>
<p align="center">
<img src="docs/001.png" alt="Project Selection" width="100%">
<br>
<em>Project Selection</em>
</p>
<p align="center">
<img src="docs/002.png" alt="Overview & Vector Network" width="100%">
<br>
<em>Overview & Vector Network</em>
</p>
## 🏗️ Architecture
```
┌─────────────────────────────────────────────────┐
│ AI IDE │
│ OpenCode / Claude Code / Cursor / Kiro / ... │
└──────────────────────┬──────────────────────────┘
│ MCP Protocol (stdio)
┌──────────────────────▼──────────────────────────┐
│ AIVectorMemory Server │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ remember │ │ recall │ │ auto_save │ │
│ │ forget │ │ task │ │ status/track │ │
│ └────┬─────┘ └────┬─────┘ └───────┬──────────┘ │
│ │ │ │ │
│ ┌────▼────────────▼───────────────▼──────────┐ │
│ │ Embedding Engine (ONNX) │ │
│ │ intfloat/multilingual-e5-small │ │
│ └────────────────────┬───────────────────────┘ │
│ │ │
│ ┌────────────────────▼───────────────────────┐ │
│ │ SQLite + sqlite-vec (Vector Index) │ │
│ │ ~/.aivectormemory/memory.db │ │
│ └────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────┘
```
## 🚀 Quick Start
### Option 1: pip install (Recommended)
```bash
# Install
pip install aivectormemory
# Upgrade to latest version
pip install --upgrade aivectormemory
# Navigate to your project directory, one-click IDE setup
cd /path/to/your/project
run install
```
`run install` interactively guides you to select your IDE, auto-generating MCP config, Steering rules, and Hooks — no manual setup needed.
> **macOS users note**:
> - If you get `externally-managed-environment` error, add `--break-system-packages`
> - If you get `enable_load_extension` error, your Python doesn't support SQLite extension loading (macOS built-in Python and python.org installers don't support it). Use Homebrew Python instead:
> ```bash
> brew install python
> /opt/homebrew/bin/python3 -m pip install aivectormemory
> ```
### Option 2: uvx (zero install)
No `pip install` needed, run directly:
```bash
cd /path/to/your/project
uvx aivectormemory install
```
> Requires [uv](https://docs.astral.sh/uv/getting-started/installation/) to be installed. `uvx` auto-downloads and runs the package — no manual installation needed.
### Option 3: Manual configuration
```json
{
"mcpServers": {
"aivectormemory": {
"command": "run",
"args": ["--project-dir", "/path/to/your/project"]
}
}
}
```
<details>
<summary>📍 IDE Configuration File Locations</summary>
| IDE | Config Path |
|-----|------------|
| Kiro | `.kiro/settings/mcp.json` |
| Cursor | `.cursor/mcp.json` |
| Claude Code | `.mcp.json` |
| Windsurf | `.windsurf/mcp.json` |
| VSCode | `.vscode/mcp.json` |
| Trae | `.trae/mcp.json` |
| OpenCode | `opencode.json` |
</details>
## 🛠️ 8 MCP Tools
### `remember` — Store a memory
```
content (string, required) Memory content in Markdown format
tags (string[], required) Tags, e.g. ["pitfall", "python"]
scope (string) "project" (default) / "user" (cross-project)
```
Similarity > 0.95 auto-updates existing memory, no duplicates.
### `recall` — Semantic search
```
query (string) Semantic search keywords
tags (string[]) Exact tag filter
scope (string) "project" / "user" / "all"
top_k (integer) Number of results, default 5
```
Vector similarity matching — finds related memories even with different wording.
### `forget` — Delete memories
```
memory_id (string) Single ID
memory_ids (string[]) Batch IDs
```
### `status` — Session state
```
state (object, optional) Omit to read, pass to update
is_blocked, block_reason, current_task,
next_step, progress[], recent_changes[], pending[]
```
Maintains work progress across sessions, auto-restores context in new sessions.
### `track` — Issue tracking
```
action (string) "create" / "update" / "archive" / "list"
title (string) Issue title
issue_id (integer) Issue ID
status (string) "pending" / "in_progress" / "completed"
content (string) Investigation content
```
### `task` — Task management
```
action (string, required) "batch_create" / "update" / "list" / "delete" / "archive"
feature_id (string) Linked feature identifier (required for list)
tasks (array) Task list (batch_create, supports subtasks)
task_id (integer) Task ID (update)
status (string) "pending" / "in_progress" / "completed" / "skipped"
```
Links to spec docs via feature_id. Update auto-syncs tasks.md checkboxes and linked issue status.
### `readme` — README generation
```
action (string) "generate" (default) / "diff" (compare differences)
lang (string) Language: en / zh-TW / ja / de / fr / es
sections (string[]) Specify sections: header / tools / deps
```
Auto-generates README content from TOOL_DEFINITIONS / pyproject.toml, multi-language support.
### `auto_save` — Auto save preferences
```
preferences (string[]) User-expressed technical preferences (fixed scope=user, cross-project)
extra_tags (string[]) Additional tags
```
Auto-extracts and stores user preferences at end of each conversation, smart dedup.
## 📊 Web Dashboard
```bash
run web --port 9080
run web --port 9080 --quiet # Suppress request logs
run web --port 9080 --quiet --daemon # Run in background (macOS/Linux)
```
Visit `http://localhost:9080` in your browser. Default username `admin`, password `admin123` (can be changed in settings after first login).
- Multi-project switching, memory browse/search/edit/delete/export/import
- Semantic search (vector similarity matching)
- One-click project data deletion
- Session status, issue tracking
- Tag management (rename, merge, batch delete)
- Token authentication protection
- 3D vector memo
... (truncated)
tools
Comments
Sign in to leave a comment