CyberClaw offers an enterprise-grade transparent and controllable AI agent architecture, addressing the "black box" problem in AI systems. It provides full behavior auditing, zero-trust execution, and a dual-level memory system, enabling developers and users to build more trustworthy, reliable, and auditable AI agents. The architecture is designed for enhanced safety and predictability in complex AI operations.

How It Works

CyberClaw employs a white-box approach to AI decision-making, featuring five event types for real-time auditing, JSONL logging, and a rich monitoring terminal for full traceability. Its zero-trust execution model utilizes a two-stage call mechanism (help then run), ensuring actions are understood before execution, which significantly reduces critical P0 incidents. A dual-waterfall memory system, comprising long-term user profiles and short-term SQLite summaries, facilitates continuous learning and personalization. Complex tasks are managed via a heartbeat task engine for background execution, a pluggable skill system, and integration with MCP services.

Quick Start & Requirements

Installation involves cloning the repository and installing dependencies via pip install -e .. The project requires Python 3.10+ and access to LLM providers (OpenAI, Anthropic, Aliyun, Tencent, Z.AI, Ollama) with corresponding API keys. Configuration is streamlined through an interactive wizard (cyberclaw config) or manual editing of the .env file. Users can then launch the agent with cyberclaw run and monitor its activity using cyberclaw monitor.

Highlighted Details

• Achieves an 80% reduction in P0 incident rates (from 50% to 10%) through its two-stage, zero-trust execution model.
• Provides comprehensive auditing with 5 event types (input, tool call, tool result, AI message, system action) and a real-time monitoring terminal.
• Features a dual-waterfall memory system for persistent user profiling and short-term conversational summarization.
• Includes a heartbeat task engine for reliable background and scheduled task execution, with persistence across restarts.
• Offers cross-platform compatibility across Unix and Windows environments.
• Supports direct integration with OpenClaw and Claude Code skill ecosystems.

Maintenance & Community

The project is primarily maintained by the GitHub user ttguy0707. While specific community channels like Discord or Slack are not detailed, the GitHub repository serves as the central hub for contributions, issues, and pull requests.

Licensing & Compatibility

CyberClaw is released under the MIT License, which is permissive and generally allows for commercial use and integration into closed-source projects without significant restrictions.

Limitations & Caveats

The two-stage execution model, while enhancing safety, introduces a performance overhead, with tests indicating an approximate 23.5% increase in average decision-making time. The system's security relies heavily on the correct implementation of sandboxing and LLM prompt engineering to prevent potential exploits or unintended behaviors. Functionality is dependent on the availability and configuration of external LLM services and their respective API keys.