Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"memlord-local": {
"env": {
"MEMLORD_DB_URL": "postgresql+asyncpg://postgres:postgres@localhost/memlord",
"MEMLORD_STDIO_USER_ID": "1"
},
"args": [
"memlord",
"--stdio"
],
"command": "python"
}
}
}Are you the author?
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- 🔍 Hybrid search — BM25 (full-text) + vector KNN (pgvector) fused via Reciprocal Rank Fusion - 📂 Multi-user — each user sees only their own memories; workspaces for shared team knowledge - 🛠️ 10 MCP tools — store, retrieve, recall, list, search by tag, get, update, delete, move, list workspaces - 🌐 Web UI — browse, search, edit and delete memories in the browser; export/import JSON - 🔒 OAuth 2.1 — full in-process authorization server, always enabled - 🐘 PostgreSQL — pgvector for embedding
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'memlord' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked memlord against OSV.dev.
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Quickstart • How It Works • MCP Tools • Configuration • Requirements • License
get_memory(id) only for what you
need, reducing token usage| Memlord | OpenMemory | mcp-memory-service | basic-memory | |
|---|---|---|---|---|
| Search | BM25 + vector + RRF | Vector only (Qdrant) | BM25 + vector + RRF | BM25 + vector |
| Embeddings | Local ONNX, zero config | OpenAI default; Ollama optional | Local ONNX, zero config | Local FastEmbed |
| Storage | PostgreSQL + pgvector | PostgreSQL + Qdrant | SQLite-vec / Cloudflare Vectorize | SQLite + Markdown files |
| Multi-user | ✅ | ❌ single-user in practice | ⚠️ agent-ID scoping, no isolation |