MCP server for enabling the named memory graphs to be persisted to a qdrant instance.
MCP server for enabling the named memory graphs to be persisted to a qdrant instance. That is what the mcp qdrant memory mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
This MCP server provides a knowledge graph implementation with semantic search capabilities powered by Qdrant vector database.
The server publishes 1 tool. What each one is for:
Synchronization — When entities or relations are modified: 1. Changes are written to memory.json 2. Embeddings are generated using OpenAI 3. Vectors are stored inConfiguration is passed through the environment: OPENAI_API_KEY, QDRANT_API_KEY, QDRANT_URL, QDRANT_COLLECTION_NAME. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
This sits in the knowledge and memory group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. MCP Qdrant Memory's toolset — Synchronization — is a fair guide to whether it matches your workflow. It is maintained by delorenj; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Synchronization | When entities or relations are modified: 1. Changes are written to memory.json 2. Embeddings are generated using OpenAI 3. Vectors are stored in Qdrant 4. Both storage systems remain consistent |
### Add to MCP settings:
```json
{
"mcpServers": {
"memory": {
"command": "/bin/zsh",
"args": ["-c", "cd /path/to/server && node dist/index.js"],
"env": {
"OPENAI_API_KEY": "your-openai-api-key",
"QDRANT_API_KEY": "your-qdrant-api-key",
"QDRANT_URL": "http://your-qdrant-server:6333",
"QDRANT_COLLECTION_NAME": "your-collection-name"
},
"alwaysAllow": [
"create_entities",
"create_relations",
"add_observations",
"delete_entities",
"delete_observations",
"delete_relations",
"read_graph",
"search_similar"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| QDRANT_API_KEY | Credential the server authenticates with. | Yes |
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| QDRANT_COLLECTION_NAME | Configuration value read at startup. | Optional |
A knowledge graph your assistant keeps between sessions — entities, relations and observations that persist.
Kill hallucinated APIs — version-accurate, up-to-date library documentation injected straight into context.
Your workspace, on speaking terms with AI — search, read and write Notion pages and databases.
A structured scratchpad for hard problems — stepwise reasoning with revisions, branches and visible logic.
Symbol-level code navigation, refactoring and memory for coding agents — the IDE brain your assistant has been missing.
Chat with your second brain — search, read and write vault notes through the Local REST API.