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MemoVault

by Blvckjs96
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Python
Hybrid

MemoVault

A personal memory system for AI assistants — runs entirely on your machine, stores everything locally, and integrates with Claude Code, Cursor, Gemini CLI, and Codex via lifecycle hooks.

Privacy first. Nothing leaves your machine by default. Memories are stored as local files. The dashboard polls your own REST API. Plugin hooks call localhost only.

Features

  • STM / LTM architecture — short-term session memory with decay + long-term memory with 4-dimensional importance scoring

  • BM25 + vector search — keyword (simple) or semantic (Qdrant) retrieval

  • MCP server — first-class Claude Code integration with 15+ tools

  • Plugin hooks — lifecycle hooks for Claude Code, Cursor, Gemini CLI, Codex CLI

  • Dashboard UI — real-time web dashboard at http://localhost:8080/ui

  • Token economics — tracks discovery vs read tokens and efficiency ratio

  • Fully local — Ollama LLM + local embeddings + embedded Qdrant, zero cloud dependency

Installation

From source

git clone https://github.com/your-org/memovault
cd memovault
pip install -e .       # or: uv sync
cp .env.example .env

From PyPI

pip install memovault

Setup

Option A — Fully local (Ollama)

1. Install Ollama and pull models

# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

ollama pull llama3.1           # main LLM
ollama pull nomic-embed-text   # embeddings

2. Configure .env

MEMOVAULT_LLM_BACKEND=ollama
MEMOVAULT_OLLAMA_MODEL=llama3.1:latest
MEMOVAULT_OLLAMA_API_BASE=http://localhost:11434

MEMOVAULT_EMBEDDER_BACKEND=ollama
MEMOVAULT_EMBEDDER_OLLAMA_MODEL=nomic-embed-text:latest

# simple = BM25 (no vector DB), vector = Qdrant (semantic search)
MEMOVAULT_MEMORY_BACKEND=simple

MEMOVAULT_DATA_DIR=./memovault_data

3. Start

memovault service start
open http://localhost:8080/ui

Option B — OpenAI

MEMOVAULT_LLM_BACKEND=openai
MEMOVAULT_OPENAI_API_KEY=sk-...
MEMOVAULT_OPENAI_MODEL=gpt-4o-mini

MEMOVAULT_EMBEDDER_BACKEND=openai
MEMOVAULT_EMBEDDER_OPENAI_MODEL=text-embedding-3-small

MEMOVAULT_MEMORY_BACKEND=vector

Memory content is sent to OpenAI's API for scoring and embedding when using this backend.

Claude Code — MCP Integration

Add to ~/.claude/claude.json:

Local (Ollama)

{
  "mcpServers": {
    "memovault": {
      "command": "memovault",
      "args": ["mcp"],
      "env": {
        "MEMOVAULT_LLM_BACKEND": "ollama",
        "MEMOVAULT_OLLAMA_MODEL": "llama3.1:latest"
      }
    }
  }
}

OpenAI

{
  "mcpServers": {
    "memovault": {
      "command": "memovault",
      "args": ["mcp"],
      "env": {
        "MEMOVAULT_LLM_BACKEND": "openai",
        "MEMOVAULT_OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Plugin Hooks

Hooks automatically inject memory context before each prompt and save session summaries on exit. Requires the REST API to be running.

Quick start

memovault service start                        # start REST API
memovault plugins install claude-code          # install hooks

All platforms

memovault plugins list                         # show status for all platforms
memovault plugins install claude-code
memovault plugins install cursor
memovault plugins install gemini
memovault plugins install codex

memovault plugins uninstall claude-code        # remove hooks

What each hook does

Hook

Trigger

Action

UserPromptSubmit

Before every prompt

Fetches recent session summaries + relevant memories, prepends as context

Stop

When the tool exits

Summarizes the session and stores it to LTM

Platform details

Claude Code — writes hooks to ~/.claude/settings.json:

{
  "hooks": {
    "UserPromptSubmit": [{
      "matcher": ".*",
      "command": "memovault hook prompt-submit --api http://localhost:8080"
    }],
    "Stop": [{
      "command": "memovault hook session-end --api http://localhost:8080"
    }]
  }
}

Cursor — writes memovault.hooks config to Cursor's settings.json.

Gemini CLI / Codex CLI — adds a shell wrapper function to ~/.zshrc. Run source ~/.zshrc once after install to activate.

Auto-start the service on login

# Add to ~/.zshrc or ~/.bash_profile
memovault service start 2>/dev/null

Service Management

memovault service start           # start REST API in background
memovault service start --port 9090
memovault service status
memovault service stop

# Foreground (useful for debugging)
memovault api --host 127.0.0.1 --port 8080

License

MIT

Install Server
A
license - permissive license
A
quality
C
maintenance

How are these scores calculated?

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