xsafeclaw 1.0.1

XSafeClaw - Keeping Your Claw Safe. Real-time monitoring and security for OpenClaw AI agents.

Build, Monitor, and Secure Your Agents

An Open-Source Agent Safety Platform

Real-time agent monitoring, security guardrails, and red-team testing for building reliable and safe AI agents.

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🎬 Introducing XSafeClaw

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📰 News

_{Release notes and project milestones.}

	Date	Update
🚀	2026-04-13	v 1.0.0 released — First public release of XSafeClaw with Claw Monitor, Safe Chat, Asset Shield, Guard, Agent Office, and Onboard Setup.

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🔍 What is XSafeClaw?

XSafeClaw is an open-source safety platform for AI agents, built to make agent behavior visible, controllable, and trustworthy. It turns complex agent execution into an intuitive visual “Safe Agent Valley,” providing real-time monitoring, risk interception, human-in-the-loop governance, and automated red-team testing — all accessible through a single xsafeclaw start command.

Module	Description
Claw Monitor	Real-time session timeline with event tracking, token usage, tool call inspection, skills & memory scanning
Safe Chat	Secure gateway to chat with your OpenClaw agent with built-in guard protection
Asset Shield	File system scanning with risk classification (L0–L3), software audit, hardware inventory
Guard (AgentDoG)	Trajectory-level & tool-call-level safety evaluation with human-in-the-loop approval
Agent Office	PixiJS-powered 2D visualization of all agents' status and activities
Onboard Setup	Interactive wizard to install and configure OpenClaw CLI

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🚀 Quick Start

pip install xsafeclaw
xsafeclaw start

Browser opens automatically at http://127.0.0.1:6874. If OpenClaw is not yet installed, the web UI will guide you through setup.

Common options:

xsafeclaw start --port 8080              # custom port
xsafeclaw start --host 0.0.0.0           # accessible from LAN
xsafeclaw start --no-browser --reload    # headless dev mode

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🛡️ Guard: How It Works

XSafeClaw's guard system protects users through a two-layer defense:

1. Trajectory-level evaluation — The full conversation history is sent to a guard model (AgentDoG) that evaluates the entire interaction sequence for emerging risks across multiple turns.

2. Tool-call interception — Every tool call passes through a before_tool_call hook. If the guard model deems it unsafe, the call is held in a pending queue for human review.

Agent wants to run a tool
        │
        ▼
  Guard Model evaluates
        │
   ┌────┴────┐
   │         │
  Safe     Unsafe
   │         │
   ▼         ▼
 Execute   Hold for human review
           ┌────┴────┐
           │         │
        Approve    Reject
           │         │
           ▼         ▼
        Execute   Block + notify agent

When rejected (or timed out after 5 min), the agent is instructed to stop all subsequent actions, inform the user about the risk, and wait for explicit confirmation.

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🏗️ Architecture

                     Browser (:6874)
                       │
           ┌───────────┴───────────┐
           │     FastAPI Server    │
           ├───────────────────────┤
           │   Guard Service       │◄── AgentDoG model
           │   File Watcher        │◄── ~/.openclaw/ JSONL sessions
           │   Asset Scanner       │◄── File/software/hardware scanning
           └───────────┬───────────┘
                       │
              ┌────────┴────────┐
              │                 │
         SQLite DB        OpenClaw Sessions
       ~/.xsafeclaw/       ~/.openclaw/

           OpenClaw Agent
               │ before_tool_call hook
               ▼
       safeclaw-guard plugin ──► POST /api/guard/tool-check

Layer	Technology
Backend	Python 3.11, FastAPI, SQLAlchemy (async), uvicorn
Frontend	React 19, TypeScript, Vite, Tailwind CSS 4
Database	SQLite (via aiosqlite)
Guard Model	AgentDoG (configurable base URL & model)

Full API docs available at http://localhost:6874/docs when running.

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📦 Installation

For detailed installation procedures, see the installation guide.

[!TIP] Requires Python 3.11+. The frontend is pre-built and bundled — no Node.js needed for production.

# From PyPI (recommended)
pip install xsafeclaw

# From GitHub
pip install git+https://github.com/XSafeAI/XSafeClaw.git

# From source
git clone https://github.com/XSafeAI/XSafeClaw.git
cd XSafeClaw && pip install .

# Development
git clone https://github.com/XSafeAI/XSafeClaw.git
cd XSafeClaw && pip install -e ".[dev]"

🔌 Install the Guard Plugin

To enable real-time tool-call interception in OpenClaw:

cp -r plugins/safeclaw-guard ~/.openclaw/extensions/safeclaw-guard

Then add to ~/.openclaw/openclaw.json:

{
  "plugins": {
    "entries": {
      "safeclaw-guard": {
        "path": "~/.openclaw/extensions/safeclaw-guard"
      }
    }
  }
}

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⚙️ Configuration

XSafeClaw works out of the box with sensible defaults. Copy .env.example to .env to customize:

Variable	Default	Description
API_PORT	6874	Server port
API_HOST	0.0.0.0	Bind address
OPENCLAW_SESSIONS_DIR	~/.openclaw/agents/main/sessions	OpenClaw session directory
GUARD_BASE_URL	(auto-detected)	Guard model API base URL
GUARD_BASE_MODEL	(auto-detected)	Guard model ID

If guard variables are not set, XSafeClaw reads model configuration from ~/.openclaw/openclaw.json automatically. See .env.example for the full list.

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🔧 Development

Prerequisites: Python 3.11+, Node.js 18+, uv (recommended)

git clone https://github.com/XSafeAI/XSafeClaw.git && cd XSafeClaw

# Backend
uv venv && uv pip install -e ".[dev]"
python run.py                    # http://localhost:6874, auto-reload

# Frontend (separate terminal)
cd frontend && npm install && npm run dev   # http://localhost:3000, HMR

# Build frontend for production
cd frontend && npm run build     # outputs to src/xsafeclaw/static/

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⭐ Star History

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🙏 Acknowledgements

• OpenClaw — The personal AI assistant platform that XSafeClaw is designed to protect. OpenClaw's open plugin architecture makes our guard integration possible.
• AgentDoG — The diagnostic guardrail framework for AI agent safety. XSafeClaw's guard module is powered by AgentDoG's trajectory-level risk assessment and fine-grained safety taxonomy.
• ISC-Bench — Research on Internal Safety Collapse in frontier LLMs. ISC-Bench's insights into task-completion-driven safety failures have informed our red team testing design.
• AgentHazard — A benchmark for evaluating harmful behavior in computer-use agents. AgentHazard's attack taxonomy and execution-level risk categories have shaped our threat modeling.

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⚠️ Disclaimer

[!CAUTION] XSafeClaw is a research tool intended for improving the safety of AI agent systems. The red team testing features are designed exclusively for defensive security research and evaluation purposes. Do not use this tool to cause harm or engage in any malicious activities.

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💼 Commercial Use

XSafeClaw is open-sourced under the MIT License for academic research and personal use. For commercial licensing, enterprise deployment, or collaboration, please contact:

Email: [e-mail]

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👥 Contributors

We welcome contributions of all kinds — bug reports, feature requests, documentation, and code.

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📄 License

MIT