🔷
Qdrant
Vector Database Database & StorageInstall Command
npx clawhub@latest install qdrant
Installation Guide
1
Check Environment
Make sure Node.js 22+ and OpenClaw are installed. Run openclaw --version in your terminal to verify.
2
Run Installation
Run the install command above in your terminal. ClawHub will automatically download and install Qdrant to the ~/.openclaw/skills/ directory.
3
Verify Installation
Run openclaw skills list to check your installed skills and confirm Qdrant appears in the list.
4
Configure (Optional)
Follow the configuration instructions in the description below to add skill settings to ~/.config/openclaw/openclaw.json5.
Manual Installation: Copy the Skill folder to
~/.openclaw/skills/ or the skills/ directory in your project root. Make sure the folder contains a SKILL.md file.
Vector similarity search
Collection creation and index management
Payload filtering and hybrid search
Detailed Description
Qdrant MCP server provides complete management capabilities for the Qdrant vector database, supporting collection creation, vector insertion, semantic search, and more — an ideal choice for building RAG and recommendation systems.
Core Features
- Collection Management: Create, delete, and configure vector collections, set vector dimensions, distance metrics (cosine/Euclidean/dot product), and HNSW index parameters
- Vector Operations (upsert_points): Insert or update vector points with attached payload metadata
- Semantic Search (search_points): Search for nearest neighbors based on vector similarity, with payload filter condition support
- Hybrid Search: Combine vector similarity and payload filtering for precise search
- Scroll Browsing (scroll_points): Paginated browsing of all points in a collection
Configuration
{
"mcpServers": {
"qdrant": {
"command": "npx",
"args": ["-y", "@qdrant/mcp-server"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_API_KEY": "your-api-key" // Required for cloud version
}
}
}
}
Use Cases
- RAG knowledge base: Store document embeddings for semantic retrieval-augmented generation
- Similarity search: Product recommendations, image search, document deduplication
- Chat memory: Store conversation history vectors for long-term memory
- Anomaly detection: Identify anomalous data points through vector distances