Which integrations are available for this server?

Provides integration with local Ollama LLM and embedding APIs for extracting facts and generating embeddings from text. Provides integration with OpenAI's LLM and embedding APIs for extracting facts and generating embeddings from text.

How do I use memoryhub?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@memoryhub remember that I prefer dark mode" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

memoryhub

by taraksh01

Overview Schema Related Servers Score Discussions

JavaScript

Hybrid

memoryhub

MCP server for persistent memory using Qdrant vector store.

Stores text memories with LLM-generated embeddings and retrieves them via semantic search.

Install

npm install @taraksh011/memoryhub

Or run directly:

npx @taraksh011/memoryhub

Related MCP server: mcp-server-qdrant

Quick Start

# Start Qdrant (see docs/install-qdrant.md for help)
docker run -p 6333:6333 qdrant/qdrant

# Start memoryhub in stdio mode (for MCP clients)
memoryhub

# Or as a daemon
memoryhub start
memoryhub status
memoryhub stop

Prerequisites

Memory Hub needs three things:

Qdrant — vector database (install guide)
LLM API — extracts facts from text (e.g. OpenAI, Anthropic, local Ollama)
Embedding API — converts text to vectors (e.g. OpenAI text-embedding-3-small, local Ollama)

If your LLM and embedding APIs are the same provider, you can set just the LLM values and reuse them (see config example below).

Configuration

Configuration is checked in this order: config file → environment variable → default.

Config file

Create a memoryhub.json in your project root, or config.json in the memoryhub directory (~/.memoryhub/ by default):

{
  "qdrant": {
    "url": "http://localhost:6333"
  },
  "collection": "memories",
  "vector_size": 768,
  "llm": {
    "model": "gpt-4o-mini",
    "base_url": "https://api.openai.com/v1",
    "api_key": "sk-..."
  },
  "embedder": {
    "model": "text-embedding-3-small",
    "base_url": "https://api.openai.com/v1",
    "api_key": "sk-..."
  }
}

If your embedder matches your LLM provider, you can omit embedder — it falls back to the llm settings.

Environment variables

Env Var	Default	Description
`MEMORYHUB_DIR`	`~/.memoryhub`	Base directory for config and data files
`QDRANT_URL`	`http://localhost:6333`	Qdrant server URL
`MEMORYHUB_COLLECTION`	`memories`	Collection name
`MEMORYHUB_VECTOR_SIZE`	`768`	Vector dimension
`LLM_MODEL`	—	LLM model for extraction
`LLM_BASE_URL`	—	LLM API base URL
`LLM_API_KEY`	—	LLM API key
`EMBED_MODEL`	—	Embedding model (falls back to LLM_MODEL)
`EMBED_BASE_URL`	—	Embedding API base URL (falls back to LLM_BASE_URL)
`EMBED_API_KEY`	—	Embedding API key (falls back to LLM_API_KEY)
`MEMORYHUB_PORT`	`9876`	Port for HTTP/SSE mode

MCP Tools

Tool	Description
`add_memories`	Store text (LLM extracts facts, embeds them)
`search_memory`	Semantic search with optional limit
`list_memories`	List memories with pagination (`limit`, `offset`)
`get_memory`	Get a single memory by ID
`update_memory`	Update a memory's text (re-embeds)
`delete_memories`	Delete specific memories by IDs
`delete_all_memories`	Delete ALL memories
`memory_stats`	Collection statistics
`get_config`	Show current runtime configuration
`update_config`	Update a config value at runtime (not persisted)
`health_check`	Check connectivity to Qdrant

Config changes via update_config are in-memory only — lost on restart. Use config file or env vars for permanent changes.

Retry

LLM and embedding API calls retry up to 3 times on transient errors (rate limits, server errors) with exponential backoff.

CLI

Command	Description
`memoryhub`	Start MCP server in stdio mode
`memoryhub serve`	Start HTTP/SSE server
`memoryhub start`	Daemon mode (background)
`memoryhub stop`	Stop daemon
`memoryhub status`	Check daemon status
`memoryhub bootstrap`	Auto-start Qdrant if needed, then serve
`memoryhub install`	Install auto-start service (systemd/launchd/Windows)
`memoryhub uninstall`	Remove auto-start service
`memoryhub --help`	Show help
`memoryhub --version`	Show version

Transport Modes

stdio (default): Connect MCP clients via stdin/stdout
HTTP/SSE: memoryhub serve starts an HTTP server on port 9876

Build

pnpm build       # type-check + bundle
pnpm typecheck   # type-check only
pnpm dev         # run with tsx

License

MIT

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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