Openclaw Mesh

🌐 OpenClawMesh — Decentralized P2P AI Mesh Skill

openclaw-mesh enables your OpenClaw agent to seamlessly discover, collaborate with, and delegate tasks to other AI agent nodes on your local network (LAN) and peer-to-peer clusters across all hardware architectures, fully utilizing the JarvisMesh 1.0 P2P Protocol.

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🎯 When to Use This Skill (Triggers)

Activate this skill when:

1. Multi-Hardware AI Inference & Task Delegation: The user wants to run inference across any hardware accelerator:
   • 🟢 NVIDIA GPUs (GeForce RTX, A100, H100 via CUDA / TensorRT / PyTorch)
   • 🔴 AMD GPUs (Radeon RX, Instinct via ROCm / DirectML / HIP)
   • 🔵 Intel Core Ultra & Arc (Intel NPU, OpenVINO, oneAPI, AVX-512)
   • 🟣 Apple Silicon (M1/M2/M3/M4 Metal GPU via MLX)
   • ⚪ Universal CPU (Ollama, llama.cpp, vLLM, CPU fallback)
2. Streaming AI Responses: Real-time token streaming is required for interactive dialogue or large code generation (llm_stream).
3. Local Vector Memory & RAG: Querying or updating persistent episodic memory stored on SQLite vector store nodes (memory_search, memory_store, memory_recall, rag_query).
4. Multimodal Vision & Audio STT: Transcribing audio via Whisper (transcribe_audio) or analyzing images with Vision models (vlm_analyze).
5. Cluster Peer Discovery: Checking what AI nodes, machines, and tools are available across your machines on the local Wi-Fi/LAN (discover).
6. Hardware Acceleration Diagnostic: Inspecting local AI hardware accelerators (hardware).
7. Exposing OpenClaw Tools: Publishing OpenClaw tools so other JarvisMesh / OpenClaw agents can call them remotely.

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🚀 Quick Execution Guide

All commands can be executed via the unified Python CLI or standalone helper scripts located in scripts/.

1. Diagnose AI Hardware

Identify available GPU / NPU accelerators on the local machine:

python3 scripts/mesh_cli.py hardware

2. Discover Active Mesh Peers

Find all online JarvisMesh & OpenClaw nodes, their addresses, latencies, and advertised skills:

python3 scripts/mesh_cli.py discover --inspect

Or structured JSON output:

python3 scripts/mesh_discover.py

3. Delegate a Task (Auto-Routed or Targeted)

Send a task request to the network. If --peer is omitted, OpenClawMesh automatically picks the best and least-loaded node:

# Auto-routed LLM prompt to the best available GPU/NPU node
python3 scripts/mesh_cli.py call --skill llm --payload '{"prompt": "Write a Python FastAPI health check endpoint."}'

# Target a specific peer node (e.g. NVIDIA GPU server or Intel Ultra laptop)
python3 scripts/mesh_cli.py call --peer gpu-server --skill memory_search --payload '{"query": "P2P protocol memory", "top_k": 3}'

4. Stream LLM Generation Token-by-Token

Stream responses directly to stdout in real time:

python3 scripts/mesh_cli.py stream --skill llm_stream --payload '{"prompt": "Explain quantum computing in 3 bullet points."}'

Or via script:

python3 scripts/mesh_stream.py llm_stream '{"prompt": "Summarize today tasks."}'

5. Check Health & Latency

Probe a specific peer node:

python3 scripts/mesh_cli.py ping --peer mac-m3

6. Run OpenClaw as a Mesh Node

Expose local OpenClaw capabilities as a discoverable P2P service:

python3 scripts/mesh_cli.py serve --name openclaw-worker --port 8770

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🛠️ Available Mesh Skills Reference

When interacting with a standard JarvisMesh / OpenClawMesh cluster, the following skills are commonly available:

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🔐 Security & Zero-Trust Mesh

OpenClawMesh supports two authentication modes:

A. Pre-Shared Key (HMAC-SHA256)

Set --psk on both server and client:

python3 scripts/mesh_cli.py call --skill llm --payload '{"prompt": "Hello"}' --psk "my_secret_token"

B. Asymmetric Ed25519 Signatures

1. Generate an identity keypair:

   python3 scripts/mesh_cli.py keygen --out ~/.openclaw/identity.key
   

2. Call nodes using the private key:

   python3 scripts/mesh_cli.py call --skill llm --payload '{"prompt": "Hello"}' --keyfile ~/.openclaw/identity.key
   

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📋 Python API (for custom OpenClaw Plugins / Tools)

import asyncio
from openclaw_mesh import MeshClient

async def main():
    # Initialize client
    client = MeshClient(name="openclaw-agent")
    await client.start()
    
    # Discover peers
    await asyncio.sleep(1.5)
    print("Peers detected on LAN:", client.list_peers())

    # Call remote GPU / NPU inference node
    response = await client.delegate(
        skill="llm",
        payload={"prompt": "Explain quantum computing in one sentence."}
    )
    print("Result:", response.result)

    await client.stop()

if __name__ == "__main__":
    asyncio.run(main())