ttguy0707

ttguy0707 / CyberClaw

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👾 下一代透明智能体架构 | Next-Gen Transparent Agent Architecture 🔍 全行为审计 | 🛡️ 两段式安全调用 | 🧠 双水位记忆 | ⏰ 心跳任务 📊 P0 级事故率降低 80% | 兼容 OpenClaw + Claude Code 技能生态

agent agent-framework ai-agent ai-safety claude-code
14
1
100% credibility
Found Apr 07, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

CyberClaw is an open-source AI assistant that emphasizes transparency through detailed logging, safe sandboxed operations, user memory profiles, and automated task scheduling.

How It Works

1
👀 Discover CyberClaw

You hear about CyberClaw, a smart helper that watches everything it does to keep things safe and clear.

2
📥 Bring it home

Download it to your computer with a simple setup that feels easy and quick.

3
🔗 Connect the brain

Follow a friendly guide to link it to an AI thinking service, testing it right away to make sure it works.

4
🚀 Start chatting

Open the colorful chat window and talk naturally, like with a helpful friend who remembers your likes.

5
⏰ Set reminders and tasks

Ask it to remind you daily or handle files in a safe workspace, watching it plan and act step by step.

6
📊 Watch it work

Peek at a live view of every thought and action to feel in total control.

🎉 Your safe AI buddy

Now you have a trustworthy assistant that learns from you, stays secure, and handles tasks without surprises.

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Star Growth

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AI-Generated Review

What is CyberClaw?

CyberClaw is a Python-based next-gen transparent agent architecture that turns black-box AI bots into fully auditable systems. It solves the trust gap in AI agents by logging every decision in JSONL format, enforcing two-phase tool calls (help then run) to cut P0 incidents by 80%, and adding dual memory for user profiles plus heartbeat tasks for automation. Users get a CLI like `cyberclaw run` for chatting, `cyberclaw monitor` for real-time audits, and sandboxed office workspace for safe file ops and shell commands, all compatible with OpenClaw and Claude code skills.

Why is it gaining traction?

It stands out with zero-trust execution and full behavior auditing via Rich terminals, making agents predictable unlike opaque alternatives. The cross-platform support (Windows/Unix) and plug-and-play skills ecosystem hook devs building production bots, while heartbeat scheduling handles recurring tasks without babysitting. Early tests show 80% safer ops, drawing interest in next-gen GitHub agent projects.

Who should use this?

AI researchers debugging agent loops, enterprise security teams needing audit trails like CrowdStrike next-gen SIEM, and solo devs automating code workflows in sandboxes. Ideal for Python scripters handling file edits, reminders, or Claude-integrated tools without escape risks.

Verdict

Promising for transparent agents but at 14 stars and 1.0% credibility, it's raw—solid docs, tests passing, MIT license, yet needs more battle-testing. Try for prototypes if safety trumps polish; skip for mission-critical until adoption grows.

(198 words)

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