I Built a Safer OpenClaw Alternative Using Claude Code

OpenClaw Inspiration: Building a Personalized AI Assistant

Key Concepts:

• OpenClaw: A popular, fully open-source AI assistant known for its ability to build a memory of user preferences and work.
• SecBrain: A personal, secure second brain powered by AI, used for knowledge management and task automation.
• Agentic Workflow: Utilizing AI agents to proactively perform tasks and anticipate user needs.
• RAG (Retrieval-Augmented Generation): A technique used to enhance LLM responses with information retrieved from a knowledge base.
• Channel Adapters: Interfaces allowing interaction with the AI assistant through various platforms (Slack, Telegram, etc.).
• Skills Registry: A system for extending the AI assistant’s capabilities through user-defined or shared functionalities.
• Claude Code/Claude Agent SDK: Tools used for interacting with Anthropic’s Claude models.

Security Concerns with OpenClaw

The video begins by highlighting the rapid rise in popularity of OpenClaw, evidenced by its 185,000+ stars on GitHub, surpassing even N8N. However, it immediately pivots to significant security vulnerabilities. Two primary areas of concern are identified:

1. Architectural Vulnerabilities: A recent one-click remote code execution vulnerability allowed attackers to steal data and API keys. This exploit involved tricking users into clicking malicious links, which then sent an OAuth token back to the attacker, granting them full access to the OpenClaw instance and its connected services. Research demonstrated an attacker could hijack an instance in under two hours.
2. Claw Hub Risks: The Claw Hub, a marketplace for OpenClaw skills, contains hundreds of malicious packages designed to steal API keys, wallet private keys, and SSH credentials. The core issue is that all credentials are stored in plain text, making them easily accessible to compromised skills.

Despite these concerns, the creator of OpenClaw, Peter, is actively addressing security issues and adding new features. However, the sheer size and complexity of the codebase, combined with the extensive power granted to the agent, still pose inherent risks. As Cisco puts it, OpenClaw remains a “security nightmare.”

The Appeal of OpenClaw & Motivation for a Custom Solution

Despite the security risks, OpenClaw is lauded for its groundbreaking capabilities, representing a significant leap forward in personal AI assistant development. It achieves what developers have been striving for years to accomplish – a truly personalized and proactive agent.

The speaker’s motivation for building a custom solution stems from a desire for greater control, understanding, and customization. While acknowledging OpenClaw’s potential, they believe building a personal “secbrain” allows for a more secure and tailored experience. Existing lightweight alternatives like NanoClaw didn’t fully meet their requirements. The core philosophy is to learn from powerful open-source tools like OpenClaw, but ultimately build something custom to ensure complete control and understanding.

Replicating OpenClaw’s “Magic”: Four Core Components

The speaker successfully replicated the most powerful aspects of OpenClaw in approximately 2,000 lines of Python and Markdown code. These key components are:

1. Memory System: This is the foundation of the assistant’s personalization. It’s elegantly implemented using Markdown files:
   • soul.md: Evolves the agent’s identity over time, representing the user’s persona.
   • memory.mmd: Stores core memories and experiences.
   • agents.mmd: Defines the agent’s global rules and behavior.
   • Session Logs: Daily logs are maintained for tracking interactions.
   • SQLite Database: A lightweight RAG implementation is used for improved search capabilities.
2. Heartbeat: This feature enables proactive behavior. A heartbeat.md file instructs OpenClaw to autonomously identify and execute tasks beneficial to the user. The speaker notes OpenClaw excels at performing genuinely helpful actions, such as drafting emails or creating pull requests.
3. Channel Adapters: These allow interaction with the assistant through various platforms like WhatsApp, Telegram, Slack, and Discord. OpenClaw supports threads for concurrent conversations.
4. Skills Registry: This system allows users to add new capabilities to the assistant through single files, enabling both personal use and sharing with others.

Building a Custom SecBrain: Implementation Details

The speaker details their implementation process, emphasizing the ease with which OpenClaw’s concepts can be replicated.

• Tech Stack: The speaker’s stack includes Obsidian for storage, Markdown for the memory system, SQLite for the database (Postgres for remote deployment), and the Claude Agent SDK for proactive functionality. Claude Code is used as the primary driver.
• Code Generation: The speaker leveraged a coding agent (Claude Code) by cloning the OpenClaw repository and instructing it to analyze the memory system and build a similar system adapted to their tech stack. This process was largely automated, demonstrating the power of using AI to build AI.
• Heartbeat Implementation: A scheduled job runs every 30 minutes, sending a prompt to the Claude Agent SDK to analyze memory, emails, calendar events, and tasks, then provide relevant notifications.
• Adapters: A simplified adapter system was implemented, focusing on Slack for primary interaction and the terminal for local use. The architecture mirrors OpenClaw’s, allowing for easy addition of other adapters if needed.
• Skills: Utilizing the Claude Agent SDK and Claude Code, skills are created to define specific behaviors, such as generating diagrams or creating YouTube scripts. A skill for creating new skills further enhances the system’s adaptability.

Cost & Security Considerations

The speaker highlights the cost-effectiveness of building a custom solution, particularly when using the Claude Agent SDK and a personal Anthropic subscription, avoiding the high API credit costs associated with direct OpenClaw usage. They also emphasize the increased security of a self-built system, as it avoids the risks associated with downloading potentially malicious skills from public registries.

Conclusion & Call to Action

The speaker concludes that building a custom AI assistant, inspired by OpenClaw, is surprisingly accessible and offers significant benefits in terms of control, security, and personalization. They encourage viewers to follow a simple process: clone the OpenClaw repository, leverage a coding agent to replicate its core components, and customize the system to their specific needs. They plan to share more detailed content and resources on their channel and within the Dynamus community, offering workshops and courses on agentic engineering and second brain building. A like and subscribe are requested to support future content.

Notable Quote:

“Most people are not ready for their agent to wield all the power that OpenClaw gives it.” – The speaker, emphasizing the need for control and understanding in AI assistant development.