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MCP server that provides cross-session persistent memory for AI coding assistants using local vector database and semantic search, enabling automatic recall of
MCP server that provides cross-session persistent memory for AI coding assistants using local vector database and semantic search, enabling automatic recall of project context, issues, and tasks.
🌐 简体中文 | 繁體中文 | English | Español | Deutsch | Français | 日本語
Give your AI coding assistant a memory — Cross-session persistent memory MCP Server
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.
| Feature | Description |
|---|---|
| 🧠 Cross-Session Memory | Your AI finally remembers your project — pitfalls, decisions, conventions all persist across sessions |
| 🔍 Hybrid Smart Search | FTS5 full-text + vector semantic dual-path search, RRF fusion ranking + composite scoring (recency × frequency × importance), far more precise than pure vector search |
| 🐛 Issue Tracking | Built-in Issue Tracker — discover → investigate → fix → archive, full lifecycle. AI manages bugs automatically |
| 📋 Task Management | Spec → task breakdown → nested subtasks → status sync → linked archival. AI drives the complete dev workflow |
| 🚦 Session State | Blocking management + breakpoint resume + progress tracking, seamless handoff across sessions and context compaction |
| 🪝 Hooks + Steering | Auto-inject workflow rules + behavior guard hooks, consistent AI behavior guaranteed — no need to repeat instructions |
| 🧬 Memory Evolution | Contradiction detection auto-supersedes stale knowledge + short-term → long-term auto-promotion + 90-day auto-archive, self-evolving memory |
| 📊 Desktop App + Web Dashboard | Native desktop app (macOS/Windows/Linux) + Web dashboard, 3D vector network reveals knowledge connections at a glance |
| 💰 Save 50%+ Tokens | Stop copy-pasting project context every conversation. Semantic retrieval on demand, no more bulk injection |
| 🏠 Fully Local | Zero cloud dependency. ONNX local inference, no API Key, data never leaves your machine |
| 🔌 11 IDEs Covered | Cursor / Kiro / Claude Code / Windsurf / VSCode / Copilot / OpenCode / Trae / Codex / Antigravity / OpenClaw — one-click install & uninstall |
| 📁 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 |
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Project Selection
Overview & Vector Network
┌─────────────────────────────────────────────────┐
│ AI IDE │
│ OpenCode / Codex / Claude Code / Cursor / ... │
└──────────────────────┬──────────────────────────┘
│ 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 │ │
│ └────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────┘
# 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-environmenterror, add--break-system-packages- If you get
enable_load_extensionerror, your Python doesn't support SQLite extension loading (macOS built-in Python and python.org installers don't support it). Use Homebrew Python instead:brew install python /opt/homebrew/bin/python3 -m pip install aivectormemory
No pip install needed, run directly:
cd /path/to/your/project
uvx aivectormemory install
Requires uv to be installed.
uvxauto-downloads and runs the package — no manual installation needed.
{
"mcpServers": {
"aivectormemory": {
"command": "run",
"args": ["--project-dir", "/path/to/your/project"]
}
}
}
| 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 |
| Codex | .codex/config.toml |
For Codex, use project-scoped TOML instead of JSON:
[mcp_servers.aivectormemory]
command = "run"
args = ["--project-dir", "/path/to/your/project"]
Codex only loads project-scoped
.codex/config.tomlafter the repository is marked as a trusted project.
remember — Store a memorycontent (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 searchquery (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 memoriesmemory_id (string) Single ID
memory_ids (string[]) Batch IDs
status — Session statestate (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 trackingaction (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 managementaction (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 generationaction (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 preferencespreferences (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.
graph — Code knowledge graphaction (string, required) "query" / "trace" / "batch" / "add_node" / "add_edge" / "remove" / "refresh"
name (string) Entity name (add_node/query)
entity_type (string) Entity type: function/class/module/api/table/config (add_node/query)
file_path (string) File path, auto-converts to relative (add_node/query/refresh)
source (string) Source node name or ID (add_edge)
target (string) Target node name or ID (add_edge)
relation (string) Relation type: calls/imports/inherits/uses/depends_on/contains (add_edge/trace)
start (string) Start node name or ID (trace)
direction (string) Traversal direction: "up" / "down" / "both" (trace)
max_depth (integer) Max traversal depth, default 3 (trace)
Manages function call chains, data flows, and dependency relationships. Trace upstream/downstream impact before code changes.
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).
Scan to join WeChat group | Scan to join QQ group
AIVectorMemory is the storage layer. Use Steering rules to tell AI when and how to call these tools.
Running run install auto-generates Steering rules and Hooks config — no manual setup needed.
| IDE | Steering Location | Hooks |
|---|---|---|
| Kiro | .kiro/steering/aivectormemory.md |
.kiro/hooks/*.hook |
| Cursor | .cursor/rules/aivectormemory.md |
.cursor/hooks.json |
| Claude Code | CLAUDE.md (appended) |
.claude/settings.json |
| Windsurf | .windsurf/rules/aivectormemory.md |
.windsurf/hooks.json |
| VSCode | .github/copilot-instructions.md (appended) |
.claude/settings.json |
| Trae | .trae/rules/aivectormemory.md |
— |
| OpenCode | AGENTS.md (appended) |
.opencode/plugins/*.js |
| Codex | AGENTS.md (appended) |
— |
# AIVectorMemory - Workflow Rules
## 1. New Session Startup (execute in order)
1. `recall` (tags: ["project-knowledge"], scope: "project", top_k: 100) load project knowledge
2. `recall` (tags: ["preference"], scope: "user", top_k: 20) load user preferences
3. `status` (no state param) read session state
4. Blocked → report and wait; Not blocked → enter processing flow
## 2. Message Processing Flow
- Step A: `status` read state, wait if blocked
- Step B: Classify message type (chat/correction/preference/code issue)
- Step C: `track create` record issue
- Step D: Investigate (`recall` pitfalls + read code + find root cause)
- Step E: Present plan to user, set blocked awaiting confirmation
- Step F: Modify code (`recall` pitfalls before changes)
- Step G: Run tests to verify
- Step H: Set blocked awaiting user verification
- Step I: User confirms → `track archive` + clear block
## 3. Blocking Rules
Must `status({ is_blocked: true })` when proposing plans or awaiting verification.
Only clear after explicit user confirmation. Never self-clear.
## 4-9. Issue Tracking / Code Checks / Spec Task Mgmt / Memory Quality / Tool Reference / Dev Standards
(Full rules auto-generated by `run install`)
Auto-save on session end removed. Dev workflow check (.kiro/hooks/dev-workflow-check.kiro.hook):
{
"enabled": true,
"name": "Dev Workflow Check",
"version": "1",
"when": { "type": "promptSubmit" },
"then": {
"type": "askAgent",
"prompt": "Core principles: verify before acting, no blind testing, only mark done after tests pass"
}
}
The embedding model (~200MB) is auto-downloaded on first run. If slow:
export HF_ENDPOINT=https://hf-mirror.com
Or add env to MCP config:
{
"env": { "HF_ENDPOINT": "https://hf-mirror.com" }
}
| Component | Technology |
|---|---|
| Runtime | Python >= 3.10 |
| Vector DB | SQLite + sqlite-vec |
| Embedding | ONNX Runtime + intfloat/multilingual-e5-small |
| Tokenizer | HuggingFace Tokenizers |
| Protocol | Model Context Protocol (MCP) |
| Web | Native HTTPServer + Vanilla JS |
Patch: Hard Constraints Against Opus 4.7 Default Tendencies
Patch: Full A-I Message Processing Flow Alignment
Patch: Rule Enforcement & Graph Visualization
Patch: i18n Rules Sync
graph tool references to all steering rules (trace/batch/add_node/add_edge/remove)New: Code Knowledge Graph
graph tool — manage function call chains, data flows, and dependency relationships as a structured knowledge graphtrace action — traverse upstream/downstream call chains from any entity, assess impact scope before code changesgraph_nodes and graph_edges tables for graph storageEnhancement: Rule System Overhaul + OpenClaw Support
open command workaroundEnhancement: AI Rule System Upgrade
New: Smart Memory Engine + Uninstall
run uninstall — cleanly removes all IDE configurations (MCP, steering, hooks, permissions) while preserving memory dataEnhancement: Security & Rule Optimization
New: Playwright Browser Testing Built-in
run install now automatically configures Playwright browser testing — AI can open a real browser to verify frontend changes instead of guessingEnhancement: More IDE Support
run install now auto-configures tool permissions, reducing manual setupEnhancement: Faster Startup
Enhancement: Simpler Rules
Fix: Tool Reliability
Enhancement: Better Search & Safety
Enhancement: Rule Generalization & Desktop Version Fix
Fix: Hook Cross-Project Compatibility
check_track.sh now derives project path from script location instead of $(pwd), fixing track detection failure when Claude Code runs hooks from non-root working directorycompact-recovery.sh now uses relative path derivation instead of hardcoded absolute paths, ensuring correct behavior when installed to any projectinstall.py template synchronized with all hook fixesPerformance: ONNX INT8 Quantization
New: Remember Password
Enhancement: Steering Rules
New: Native Desktop App
New: CI/CD Auto Build
Fixes
Optimization: Token Usage Reduction
New: Full-Stack i18n (7 Languages)
New: Web Dashboard Settings Page
Optimization: Memory Search
recall search supports OR/AND tag matching modes, fixing missed results with multi-tag searchesApache-2.0
Выполни в терминале:
claude mcp add aivectormemory -- npx Безопасность
Низкий рискАвтоматическая эвристика по публичным данным — не гарантия безопасности.