memory-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@memory-mcp 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.
@lakehouse/memory-mcp
Persistent memory for AI assistants via Model Context Protocol (MCP).
Give your AI assistant a memory that persists across conversations. Works with Claude Code, Claude Desktop, Cursor, Windsurf, and any MCP-compatible client.
Features
Remember - Store facts, preferences, tasks, and context
Recall - Semantic search to find relevant memories
Forget - Remove outdated information
Two modes:
Lakehouse42 (recommended) - Full semantic search, deduplication, knowledge graph
Local - Simple file-based storage with keyword search
Related MCP server: engram
Quick Start
Claude Code
Add to ~/.claude/claude_code_config.json:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["@lakehouse/memory-mcp"]
}
}
}That's it! Claude Code now has persistent memory using local storage.
With Lakehouse42 Backend (Recommended)
For full semantic search capabilities, connect to Lakehouse42:
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["@lakehouse/memory-mcp"],
"env": {
"LH42_URL": "https://api.lakehouse42.com",
"LH42_API_KEY": "lh42_your_api_key"
}
}
}
}Get your API key at lakehouse42.com.
Claude Desktop
Add to Claude Desktop's config (Settings → Developer → Edit Config):
{
"mcpServers": {
"memory": {
"command": "npx",
"args": ["@lakehouse/memory-mcp"],
"env": {
"LH42_URL": "https://api.lakehouse42.com",
"LH42_API_KEY": "lh42_your_api_key"
}
}
}
}Cursor / Windsurf
Follow the same pattern - add the MCP server to your client's configuration.
Tools
remember
Store a memory for later recall.
Remember that the user prefers dark modeParameters:
content(required) - The information to remembertype- fact, preference, task, event, context, reflectionimportance- 0.0 to 1.0 (default: 0.5)
recall
Search memories by semantic similarity.
What are the user's preferences?Parameters:
query(required) - What to search forlimit- Max results (default: 5)types- Filter by memory types
forget
Delete a memory by ID.
Parameters:
memoryId(required) - ID of memory to deletereason- Reason for deletion
list_memories
List recent memories.
Parameters:
limit- Max results (default: 10)
memory_status
Check memory system status and backend info.
Local vs Lakehouse42
Feature | Local | Lakehouse42 |
Persistence | ✅ JSON file | ✅ Cloud |
Search | Keyword matching | Semantic (AI-powered) |
Deduplication | ❌ | ✅ 3-tier |
Knowledge graph | ❌ | ✅ Entity relationships |
History tracking | ❌ | ✅ Full audit trail |
Multi-device sync | ❌ | ✅ |
Environment Variables
Variable | Description |
| Lakehouse42 API URL (enables LH42 backend) |
| API key for authentication |
| Enable debug logging ( |
Programmatic Usage
import { createMemoryServer, LH42Backend } from "@lakehouse/memory-mcp";
// Create server with custom config
const server = await createMemoryServer({
lh42Url: "https://api.lakehouse42.com",
apiKey: "lh42_xxx",
debug: true,
});
// Or use backends directly
const backend = new LH42Backend({
url: "https://api.lakehouse42.com",
apiKey: "lh42_xxx",
});
await backend.initialize();
await backend.remember({ content: "User likes TypeScript" });
const results = await backend.recall({ query: "programming preferences" });Privacy
Local mode: All data stored in
~/.lakehouse42/memory-mcp/memories.jsonLakehouse42 mode: Data stored securely in your Lakehouse42 account
No data is sent to third parties
You control your memories
License
MIT
Available Tools
5 toolsforgetA
Delete a specific memory by ID. Use when information is outdated or incorrect.
Inputs: memoryId (required), reason.
list_memoriesB
List recent memories. Useful for reviewing what has been remembered.
Inputs: limit.
memory_statusA
Get status of the memory system including backend type and features available.
Inputs: no named parameters.
recallA
Search memories by semantic similarity. Returns the most relevant memories matching the query.
Inputs: query (required), limit, types.
rememberA
Store a memory for later recall. Use this to remember important facts, user preferences, decisions, or any information that should persist across conversations.
Inputs: content (required), type, importance.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 5 tools
Each tool targets a distinct action: remember stores, recall searches, forget deletes, list_memories lists, and memory_status reports system status. There is no ambiguity or overlap between them.
Names are mostly verb-based and readable, but there is a slight inconsistency: remember, recall, and forget are bare verbs, while list_memories is verb_noun and memory_status is noun_noun. A more uniform pattern like get_memory_status would improve consistency.
With 5 tools, the server is well-scoped for a memory system. Each tool covers a necessary function without redundancy, making the set neither too thin nor overly heavy.
The tools cover the core CRUD operations (create, read, delete) plus status. The only minor gap is the lack of an explicit update tool, but users can forget and re-remember to modify entries, so the shortfall is easily worked around.
Maintenance
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