Weaviate - OpenClaw Skill — OpenClaw Tutorials

Install Command

npx clawhub@latest install weaviate

Installation Guide

Check Environment

Make sure Node.js 22+ and OpenClaw are installed. Run openclaw --version in your terminal to verify.

Run Installation

Run the install command above in your terminal. ClawHub will automatically download and install Weaviate to the ~/.openclaw/skills/ directory.

Verify Installation

Run openclaw skills list to check your installed skills and confirm Weaviate appears in the list.

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.

Multi-modal vector search GraphQL query interface Built-in vectorization modules

Detailed Description

Weaviate MCP server provides complete operational capabilities for the Weaviate AI-native vector database. Weaviate's built-in vectorization modules can automatically convert text, images, and other data into vectors without an external embedding service.

Core Features

Collection Management: Create and configure collections (Classes), define properties, vectorizers, and index parameters
Data Operations: Insert, update, and delete objects — Weaviate automatically calls the configured vectorizer to generate vectors
Semantic Search (nearText/nearVector): Perform semantic search via text descriptions or vectors, with hybrid search support (BM25 + vector)
GraphQL Query: Use Weaviate's GraphQL API for flexible data querying and aggregation
Cross-References: Establish references between objects in different collections, enabling graph-like data structures

Configuration

{
  "mcpServers": {
    "weaviate": {
      "command": "npx",
      "args": ["-y", "weaviate-mcp-server"],
      "env": {
        "WEAVIATE_URL": "http://localhost:8080",
        "WEAVIATE_API_KEY": "your-api-key",  // Required for cloud version
        "OPENAI_API_KEY": "sk-..."  // If using text2vec-openai module
      }
    }
  }
}

Use Cases

Multi-modal search: Combine text and image vectors for cross-modal retrieval
Knowledge graph: Build knowledge graphs with vector search using cross-references
Chat memory: Store conversation history and enable semantic-level memory recall
Content management: Add intelligent search and recommendation capabilities to CMS

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