OpenClaw Memory Qdrant
Local semantic memory with Qdrant and Transformers.js. Store, search, and recall conversation context using vector embeddings (fully local, no API keys).
zuiho
@zuiho-kai
What This Skill Does
Local semantic memory plugin for OpenClaw agents that stores, searches, and recalls conversation context using vector embeddings powered by Qdrant and Transformers.js. Runs fully locally with no API keys required, offering in-memory or persistent Qdrant storage.
Replaces cloud-based memory services and API-key-dependent semantic search by providing a zero-configuration, fully local vector memory system for AI agents.
When to Use It
- Store user preferences or facts for later recall across conversations
- Search past conversations for semantically relevant context
- Automatically inject relevant memories into ongoing agent responses
- Delete outdated or incorrect memories by ID or semantic query
- Enable persistent memory across agent restarts using an external Qdrant server
Install
$ openclaw skills install @zuiho-kai/memory-qdrantmemory-qdrant
Use when you need your OpenClaw agent to remember and recall information across conversations using semantic search.
Local semantic memory plugin powered by Qdrant vector database and Transformers.js embeddings. Zero configuration, fully local, no API keys required.
Features
- Semantic search with local Transformers.js embeddings
- In-memory mode (zero config) or persistent Qdrant storage
- Optional auto-capture of conversation context (opt-in, disabled by default)
- Context-aware memory recall
- Fully local, no API keys or external services required
Installation
clawhub install memory-qdrant
First-time setup: This plugin downloads a 25MB embedding model from Hugging Face on first run and may require build tools for native dependencies (sharp, onnxruntime). See README for detailed installation requirements.
Configuration
Enable in your OpenClaw config:
{
"plugins": {
"memory-qdrant": {
"enabled": true
}
}
}
Options:
autoCapture(default: false) - Auto-record conversations. Note: trigger patterns include email/phone regex, so enabling this may capture PII.autoRecall(default: true) - Auto-inject relevant memoriesqdrantUrl(optional) - External Qdrant server (leave empty for in-memory)
Usage
Three tools available:
memory_store - Save information
memory_store({
text: "User prefers Opus for complex tasks",
category: "preference"
})
memory_search - Find relevant memories
memory_search({
query: "workflow preferences",
limit: 5
})
memory_forget - Delete memories
memory_forget({ memoryId: "uuid" })
// or
memory_forget({ query: "text to forget" })
Privacy & Security
- In-memory mode (default): Data cleared on restart
- Qdrant mode: Data sent to configured server (use trusted servers only)
- Network: Downloads ~25MB model from Hugging Face on first run
- autoCapture: Disabled by default for privacy. Trigger patterns match emails and phone-like numbers, so enabling autoCapture can capture PII.
Technical Details
- Vector DB: Qdrant (in-memory or external)
- Embeddings: Xenova/all-MiniLM-L6-v2 (local)
- Module: ES6 with factory function pattern
Links
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