MemVault - Local-First Agent Memory

Name: MemVault - Local-First Agent Memory
Author: dreamor

by dreamorversion 0.7.0i★ 2iRuns on your computer

Registry nameio.github.dreamor/memvault

How to install

Overview

Description from the maker (original English)

Local-first memory for AI agents: save, recall, dedupe, and share across MCP clients.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Passed 2/2 checks · Runs on your computer

First: which AI do you use?

ClaudeWeb · app · phoneChatGPTWebClaude CodeTerminalCursorCode editorVS CodeCode editorOtherOther apps

Pick your app and the steps appear below.

Works with Claude, with some setup first

Install Docker on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone

Install Docker
One time only; it works for every server that needs Docker
Open the Docker download page ↗
Done
Open the Claude desktop app’s settings file
SettingsDeveloperEdit Config
Done

Paste these settings and save the file

{
  "mcpServers": {
    "memvault": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/dreamor/memvault:v0.7.0"
      ]
    }
  }
}

Done

Quit Claude and open it again
Done
Try it
Use MemVault - Local-First Agent Memory and tell me what you can do for me, then show me one thing
✓ If Claude says it is using MemVault - Local-First Agent Memory or shows a MemVault - Local-First Agent Memory tool in the answer, you are connected

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

ChatGPT cannot connect MemVault - Local-First Agent Memory yet

MemVault - Local-First Agent Memory runs on your computer, but ChatGPT can only connect servers that run on the web

What you can do instead

A · Use an app that can connect MemVault - Local-First Agent Memory

Steps for Claude →Install Docker on your computer first (one time) · Only the Claude desktop app (Windows or Mac), not the web or phone
Steps for Claude Code →Install Docker on your computer first (one time)
Steps for Cursor →Install Docker on your computer first (one time)
Steps for VS Code →Install Docker on your computer first (one time)

B · Others in Search & AI memory that work with ChatGPT

Picked from the most used in the same category, only ones that connect by address with no key

AIHOT✓ ChatGPT
Sui Knowledge Base (kapa.ai)✓ ChatGPT
Exa✓ ChatGPT

Works with Claude Code, with some setup first

Install Docker on your computer first (one time)

Install Docker
One time only; it works for every server that needs Docker
Open the Docker download page ↗
Done
Open Terminal
Mac: press Command+Space and type Terminal · Windows: open PowerShell
Done

Paste this command and press Enter

claude mcp add memvault -- docker run -i --rm ghcr.io/dreamor/memvault:v0.7.0

Done

Type /mcp in Claude Code
You should see MemVault - Local-First Agent Memory listed as connected
Done
Try it
Use MemVault - Local-First Agent Memory and tell me what you can do for me, then show me one thing
✓ If Claude Code says it is using MemVault - Local-First Agent Memory or shows a MemVault - Local-First Agent Memory tool in the answer, you are connected

Menu names checked against Anthropic’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with Cursor, with some setup first

Install Docker on your computer first (one time)

Install Docker
One time only; it works for every server that needs Docker
Open the Docker download page ↗
Done
Click this button to open Cursor
Open in Cursor
Cursor asks you to confirm; click Install
Done
Open the AI chat in Cursor in Agent mode
Done
Try it
Use MemVault - Local-First Agent Memory and tell me what you can do for me, then show me one thing
✓ If Cursor says it is using MemVault - Local-First Agent Memory or shows a MemVault - Local-First Agent Memory tool in the answer, you are connected

Menu names checked against Cursor’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Works with VS Code, with some setup first

Install Docker on your computer first (one time)

Install Docker
One time only; it works for every server that needs Docker
Open the Docker download page ↗
Done
Click this button to open VS Code
Open in VS Code
VS Code asks you to confirm; click Install
Done
Open the AI chat in VS Code in Agent mode
Done
Try it
Use MemVault - Local-First Agent Memory and tell me what you can do for me, then show me one thing
✓ If VS Code says it is using MemVault - Local-First Agent Memory or shows a MemVault - Local-First Agent Memory tool in the answer, you are connected

Menu names checked against VS Code’s help page on Nov 11, 2026 · your screen may differ slightly by app version

Other apps that use MCP, such as Windsurf, Zed or Gemini CLI, each add servers differently. See how in the app list.

See every app that uses MCP →

JSON settings are under “For developers” below

Stuck? Ask people who use MemVault - Local-First Agent Memory💬 Discussion →

For developers: Terminal commands and JSON settings

Installed on your computer

Claude Code

claude mcp add memvault -- docker run -i --rm ghcr.io/dreamor/memvault:v0.7.0

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "memvault": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/dreamor/memvault:v0.7.0"
      ]
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "memvault": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/dreamor/memvault:v0.7.0"
      ]
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "memvault": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/dreamor/memvault:v0.7.0"
      ]
    }
  }
}

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

At a glance

GitHub stars i: 2
Downloads per weeki: Not on npm/PyPI
Licence i: No licence stated
Connection: Runs on your computer
Transport: stdio
Latest versioni: 0.7.0
Categoryi: Search & AI memory
Published to registryi: Nov 7, 2026
Code last changedi: Nov 10, 2026
Maker: dreamor
Packages: oci: ghcr.io/dreamor/memvault:v0.7.0
Repo: dreamor/MemVault
Website: github.com
Registry status: Active

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

All apps, with pros and cons →

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dreamor, which owns the repo
✓ Pass
Still maintained
Code last changed yesterday
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Discussion

Ask, share a tip or report that it does not work. Sorted by what people marked helpful.

No posts yet. Be the first to ask or share.

Posts here are written by users, not the PopNx team.

User reviews

No reviews yet

Compared with others in Search & AI memoryi

More stars than 81% of the other 5880 servers in this category
Published to the registry more recently than 90% of the other 5880 servers in this category
Basic checks equal to or better than 100% of the other 5880 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MemVault - Local-First Agent Memory safe?

It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed yesterday, and the latest version (0.7.0) reached the registry 4 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Ruflo, Context7, Metabase

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

by ruvnetWidely used↓ 74.3K/wk★ 81.4KUpdated today

Runs on your computer

Install

Context7

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

by upstashWidely used↓ 1.2M/wk★ 64.4KUpdated yesterday

Computer or cloud

Install

Metabase

Lets AI clients search, explore, query, and visualize data in a Metabase instance.

by metabaseWidely used★ 50.3KUpdated 6 months ago

Runs in the cloud

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

MemVault

The Shared Memory Layer for Every AI Agent You Run

Not "agent learns to search memory" — memory finds the agent.

MCP Native · Hybrid Retrieval · Auto-Injection · Zero-Config Sync

Open Source · Self-Hosted · Private · MIT Licensed

English · 简体中文

If MemVault solves a real problem for you, a star helps others find it.

[⭐ Star on GitHub](https://github.com/dreamor/memvault) · [Report Bug](https://github.com/dreamor/memvault/issues/new)

curl -fsSL https://raw.githubusercontent.com/dreamor/memvault/master/scripts/install.sh | bash

Every AI agent session starts from scratch. Claude Desktop doesn't know what Cursor just learned. DeepSeek Harness (dsh) doesn't know what you told Claude Code yesterday. Whatever agent you're running — international or domestic, IDE plugin or CLI harness — it forgets your preferences every time you start a new conversation.

You've been manually repeating context — project conventions, personal preferences, past decisions — across agents that should already know. This isn't a limitation of the models. It's a missing infrastructure layer.

MemVault is that layer. A lightweight, self-hosted memory router that sits between your agents and their context. It speaks plain MCP — no MemVault-specific SDK, no per-agent API integration. Any MCP-compatible agent, from any vendor, automatically shares the same persistent memory the moment it connects.

Who it's for:

Anyone running an MCP-capable agent — Claude Code, Claude Desktop, Cursor, Cline, Continue, DeepSeek Harness (dsh), or any other MCP client, domestic or international — who wants preferences, project context, and past decisions to persist across sessions without repeating yourself
Multi-agent power users running several of the above side by side, on different models, from different vendors — all sharing the same memory without configuration
Platform teams deploying AI-assisted workflows where consistency matters across a mixed agent fleet: code review conventions, architecture decisions, project-specific preferences
Anyone tired of telling their AI the same thing twice — MemVault works the way your brain should: you say it once, it's there when you need it, no matter which agent is asking

[Quick Start](#quick-start) · [How It Works](#how-it-works) · [What MemVault Gives You](#what-memvault-gives-you) · [Why MemVault](#why-memvault) · [MCP Server](#mcp-server) · [CLI Reference](#cli-reference) · [Integrations](#integrations) · [Architecture](#architecture) · [Project Status](#project-status) · [Testing](#testing) · [Documentation](#documentation) · [Contributing](#contributing) · [License](#license)

Quick Start

Don't install MemVault by hand — paste this one prompt into Claude Code, Codex, Cursor, dsh, or any other MCP-capable agent. It points the agent at a machine-readable install guide, so the agent does the whole setup itself:

Install MemVault as your long-term memory and wire it into this agent. Fetch and follow https://raw.githubusercontent.com/dreamor/memvault/master/docs/AGENT-INSTALL.md — install the binaries, register MemVault as an MCP server for this host, and verify recall works. Then keep saving my preferences, project decisions, and lessons from each session so my next agent starts with them instead of from scratch.

Or install manually:

# Install (Linux / macOS): official script, auto-verifies SHA-256

curl -fsSL https://raw.githubusercontent.com/dreamor/memvault/master/scripts/install.sh | bash export PATH="$HOME/.memvault/bin:$PATH"

Windows (PowerShell):

powershell -ExecutionPolicy Bypass -File scripts\install.ps1

Homebrew (Apple Silicon):

brew install dreamor/tap/memvault

Or from crates.io:

cargo install memvault-cli memvault-mcp

The installers auto-start the local REST backend (host of the Web

Dashboard). Open it, or start it on demand:

memvault-cli dashboard

Save a MUST-level preference (injected as instruction, agent must follow)

memvault-cli save --content "User prefers Python" --priority MUST --type preference \ --instruction "Use Python for code, not Java" --tags "coding,python"

Search across all memory (hybrid: keyword + semantic when embedding is enabled)

memvault-cli search --query "Python"

See what context gets injected when a specific agent connects

memvault-cli session-start --agent-id claude-desktop --context "Help me write code"

Extract structured memories from free text

memvault-cli extract --text "I prefer dark mode. Our project uses Rust." --save

Auto-generate agent instruction files from memory

memvault-cli sync

All in one: dedup, decay, archive stale memories

memvault-cli dedup && memvault-cli decay

System requirements:

- Prebuilt Linux binaries (install script / GitHub release): glibc ≥ 2.39 + GLIBCXX_3.4.31 (GCC 13-era runtime — Ubuntu 24.04+ / Fedora 40+ / Arch). Release builds are pinned to Ubuntu 24.04 runners, and the release workflow gates the highest glibc/GLIBCXX symbol version every binary may require plus a startup smoke test, so this floor cannot silently drift upward (0.6.9 shipped x86_64 binaries requiring glibc 2.43 that way). TLS is rustls, so no OpenSSL dependency. Ubuntu 22.04 / RHEL or EL8/EL9 users: use the Docker image (dreamor/memvault) or build from source.

- Building from source (cargo install / cargo build): links fastembed's prebuilt ONNX Runtime static library and needs a GCC 13-class toolchain. Older toolchains (e.g. GCC 8, CentOS 7/8-era libstdc++) fail at link time with missing C++20/23 stdlib symbols (std::format, std::to_chars). macOS / Windows (MSVC) / Homebrew / Docker are unaffected.

Verify your install in 5 seconds:

memvault-cli --version

memvault 0.6.12

sanity check: list saved memories (verifies DB is healthy)

memvault-cli list

Local Ollama Demo (Zero Cost, Stays on Your Machine)

MemVault is plug-and-play with a local Ollama: unconfigured LLM extraction (full-text understanding / failure reflection / relation extraction) auto-detects a local Ollama; embeddings use the local model with the ollama or auto provider.

# 1. Install and start Ollama

brew install ollama && brew services start ollama # or the official installer

2. Pull models

ollama pull nomic-embed-text # embeddings, 768-dim (default for ollama/auto) ollama pull qwen2.5:3b-instruct # chat: LLM extraction/reflection (default qwen2.5:7b, use 3b on small machines)

3. (Optional) Pin the providers explicitly — persist them in ~/.memvault/.env

(shell exports also work — env vars take precedence over the file — but the file survives reboots)

mkdir -p ~/.memvault cat >> ~/.memvault/.env <<'EOF' MEMVAULT_EMBEDDING_PROVIDER=ollama MEMVAULT_LLM_EXTRACTION_PROVIDER=ollama MEMVAULT_LLM_EXTRACTION_MODEL=qwen2.5:3b-instruct EOF

4. Verify

memvault-cli status # Embedding provider: configured and reachable memvault-cli save --content "Build server IP is 10.20.30.40" # output (embedded int8) memvault-cli outcome --task "Deploy trading service" --status failure --cause "Disk space insufficient" --task-type deploy

→ Lesson (Llm): ... means failure reflection ran through the local LLM (not the rule-based fallback)

With no configuration at all (neither env vars nor ~/.memvault/.env), LLM extraction auto-detects a local Ollama and enables itself (default model qwen2.5:7b; pull it in advance with ollama pull qwen2.5:7b, or point MEMVAULT_LLM_EXTRACTION_MODEL at an installed model). Embeddings still default to the in-process native embedder; set MEMVAULT_EMBEDDING_PROVIDER=auto in ~/.memvault/.env to prefer Ollama and fall back to native when it isn't running. The native model's first launch downloads the weights from HuggingFace silently in the background — the server/dashboard come up immediately and hot-swap to semantic search once the download finishes, so a fresh install never blocks on it (set MEMVAULT_EMBEDDING_PROVIDER=none to run pure keyword and skip the download).

How It Works

MemVault is a pipeline, not a single script. Every stage below is a shipping module:

Agent connects (MCP stdio/SSE)

│ ▼ ┌───────────────────┐ │ Agent Router │ ← match agent type/tag → filter relevant memory │ (Agent Registry) │ └────────┬──────────┘ │ ▼ ┌───────────────────┐ │ Memory Retrieval │ ← keyword (BM25) + vector (embedding) + RRF fusion │ (3 search modes) │ synonym expansion · scoring · soft filtering └────────┬──────────┘ │ ▼ ┌───────────────────┐ │ Auto-Injection │ ← MUST-level → instruction prompt │ │ REFERENCE → context resource │ │ NORMAL → search result └────────┬──────────┘ │ ▼ Agent receives context ──→ makes better decisions

Storage: SQLite with bundled FTS5 (full-text search); embeddings stored int8-quantized (~1/4 the size of f32 at near-identical ranking quality, legacy f32 rows still readable)
Retrieval: BM25 keyword search over FTS5 with CJK bigram tokenization (Chinese two-character words match correctly) and tiered match fallback (strict → relaxed unigram → synonym OR; relaxations are reported, never silent), local-first embedding (in-process native by default — switch to local Ollama or any OpenAI-compatible model), RRF fusion with per-result recall provenance (kw#2/vec#5), synonym expansion, relevance scoring, soft intent filtering
Pipeline: Automatic entity extraction, delta-write on save (near-duplicates skipped, similar memories absorb only the residual), semantic deduplication, time-based decay, archive of stale memories
Sync: Zero-invasion file generation — memvault-cli sync produces CLAUDE.md, AGENTS.md, etc. directly from database contents

What MemVault Gives You

Auto-Injected Context: Session start automatically pulls relevant memory by agent identity — MUST-level rules land as instructions, not just chat history
Hybrid Retrieval: BM25 + vector + RRF fusion with synonym expansion, relevance scoring, and per-result provenance (which path recalled each memory, at what rank) — available via CLI, MCP tool, and REST API
Explainable Injection: every candidate dropped on the way into an agent's context is recorded with a reason (budget, caps, intent/type penalties) — "why didn't the agent get this memory?" always has an answer
MUST Enforcement: MUST-priority memories are never filtered or truncated. Always in context, always obeyed — trust comes from provenance (human-authored/r

Only the beginning is shown; read the rest in the repo

MemVault - Local-First Agent Memory

Overview

What you can do with it

Install

Install Docker

Open the Claude desktop app’s settings file

Paste these settings and save the file

Quit Claude and open it again

Try it

What you can do instead

Install Docker

Open Terminal

Paste this command and press Enter

Type /mcp in Claude Code

Try it

Install Docker

Click this button to open Cursor

Open the AI chat in Cursor in Agent mode

Try it

Install Docker

Click this button to open VS Code

Open the AI chat in VS Code in Agent mode

Try it

Installed on your computer

At a glance

Which apps can use it

Related guides

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Discussion

User reviews

Compared with others in Search & AI memoryi

Common questions

Alternatives

Ruflo

Context7

Metabase

README from the maker

MemVault

The Shared Memory Layer for Every AI Agent You Run

Quick Start

Windows (PowerShell):

powershell -ExecutionPolicy Bypass -File scripts\install.ps1

Homebrew (Apple Silicon):

brew install dreamor/tap/memvault

Or from crates.io:

cargo install memvault-cli memvault-mcp

The installers auto-start the local REST backend (host of the Web

Dashboard). Open it, or start it on demand:

Save a MUST-level preference (injected as instruction, agent must follow)

Search across all memory (hybrid: keyword + semantic when embedding is enabled)

See what context gets injected when a specific agent connects

Extract structured memories from free text

Auto-generate agent instruction files from memory

All in one: dedup, decay, archive stale memories

memvault 0.6.12

sanity check: list saved memories (verifies DB is healthy)

Local Ollama Demo (Zero Cost, Stays on Your Machine)

2. Pull models

3. (Optional) Pin the providers explicitly — persist them in ~/.memvault/.env

(shell exports also work — env vars take precedence over the file — but the file survives reboots)

4. Verify

→ Lesson (Llm): ... means failure reflection ran through the local LLM (not the rule-based fallback)

How It Works

What MemVault Gives You