A high-performance bridge for offloading BERTScore and embedding calculations to remote GPU infrastructure.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-gpu-bridge
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-gpu-bridge using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Openclaw GPU Bridge?
The Openclaw GPU Bridge is a robust technical solution designed to decouple resource-intensive machine learning tasks from your primary execution environment. By leveraging this plugin within your suite of Openclaw Skills, you can offload BERTScore evaluations and high-dimensional embedding generation to one or many remote GPU-enabled machines. This architecture ensures that your main agent remains highly responsive while specialized hardware handles the heavy lifting of transformer-based computations.
Equipped with advanced features like multi-host pooling and intelligent load balancing, the bridge is ideal for developers running complex RAG pipelines or large-scale NLP assessments. It transforms a single-node setup into a distributed system, allowing you to maximize the utility of your Openclaw Skills across heterogeneous hardware environments.
Openclaw GPU Bridge Use Cases
- Offloading high-latency BERTScore computations to remote servers to maintain local agent performance.
- Generating large batches of text embeddings using dedicated GPU hardware for vector database synchronization.
- Scaling AI workflows across multiple remote GPU hosts using least-busy load balancing.
- Implementing secure, remote ML processing via WireGuard or encrypted Nginx proxies for sensitive Openclaw Skills data.
How Openclaw GPU Bridge Works
- The OpenClaw agent invokes a specific tool, such as gpu_embed or gpu_bertscore, as part of its workflow.
- The bridge identifies the optimal remote host from the configured host pool based on the current load-balancing strategy.
- The request is securely transmitted to the remote Python-based GPU service via a REST API using pre-shared key authentication.
- The remote service processes the ML task using locally cached models (e.g., DeBERTa or MiniLM) to minimize latency.
- The bridge receives the computed results, handles any necessary error recovery, and returns the data to the agent within the Openclaw Skills ecosystem.
Openclaw GPU Bridge Setup
To integrate this bridge into your Openclaw Skills, first configure your agent settings:
{
"plugins": {
"@elvatis_com/openclaw-gpu-bridge": {
"hosts": [
{
"name": "primary-node",
"url": "http://your-gpu-host:8765",
"apiKey": "your-secure-api-key"
}
],
"loadBalancing": "least-busy"
}
}
}
Then, prepare your remote GPU server by installing the Python service:
cd gpu-service
pip install -r requirements.txt
uvicorn gpu_service:app --host 0.0.0.0 --port 8765
Openclaw GPU Bridge Data Schema & Taxonomy
The skill manages communication and configuration using the following schema structures:
| Configuration Key | Type | Description |
|---|---|---|
| hosts | Array | A list of GPU host objects containing names, URLs, and optional API keys. |
| loadBalancing | String | Strategy for request distribution: round-robin or least-busy. |
| timeout | Number | Request timeout in seconds for compute-heavy endpoints. |
| models | Object | Default model mapping for BERTScore and embedding tasks. |
The remote service provides metadata via gpu_health and gpu_info tools, offering real-time insights into queue status and hardware utilization for all Openclaw Skills operations.
Openclaw GPU Bridge Advanced Features
- Multi-GPU host pooling with automated health checks and periodic status polling.
- Intelligent load balancing using least-busy logic to prevent bottlenecks on individual nodes.
- On-demand model loading and memory caching for flexible model selection without service restarts.
- Operational hardening support for Nginx reverse proxies, TLS encryption, and WireGuard VPN integration to secure your Openclaw Skills environment.
SKILL.md
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