I Tested Clawdbot and Built My Own Local AI Agent

Most AI assistants still stop at conversation. They answer questions, forget everything afterward, and never actually do anything for you.

Clawdbot changes that.

Instead of living inside a chat window, Clawdbot runs on your own machine, stays online, remembers past interactions, and executes real tasks. It connects directly to messaging platforms like WhatsApp and Telegram, plans actions, runs commands, and follows through like a digital operator rather than a chatbot.

In this article, we take a deep dive into Clawdbot, now called Moltbot. We explore how it works under the hood, how to install and use it, its architecture, real-world use cases, and the risks of running a powerful self-hosted AI agent.

What is Clawdbot (or Moltbot)?

Clawdbot is an open-source AI assistant that runs locally as a persistent personal agent. While the project originally launched under the name Clawdbot, Moltbot is now its official name. It operates at the intersection of three domains:

• AI agents
• Local automation tools
• Messaging-based interfaces

Because Clawdbot runs entirely on user-owned systems, developers retain full control over data, execution, and model selection without relying on cloud-based platforms.

What does Clawdbot or Moltbot do?

• Self-hosted and local-first: Runs entirely on user-controlled infrastructure, giving full ownership over data, execution, and configuration with no cloud dependency.
• Persistent and always-on: Operates continuously in the background, tracking ongoing tasks and maintaining context across multiple conversations and sessions.
• Messaging-based interaction: Integrates directly with platforms like WhatsApp, Telegram, and Discord, enabling natural communication without a separate UI.
• Long-term memory: Retains user context and preferences over time, allowing for personalized, context-aware responses.
• Local task execution: Executes shell commands, manages files, automates scripts, and performs web actions directly on the local system via the execution layer.
• Model-agnostic design: Supports multiple AI models such as Claude, GPT, and Gemini, allowing users to choose based on cost, performance, and privacy needs.
• Extensible and modular: Uses a modular architecture that makes it easy to build custom skills, tools, and integrations.

Architecture of Clawdbot

1. Messaging Gateway: Discusses communication, acting as an interface between various platforms for communication and authentication. 
2. Agent Core: Interprets intent, plans actions in a modulated way, remembers past events, and orchestrates reasoning toward execution. 
3. Memory System: An enduring, structured memory is maintained as sequences and distributed memory vectors. 
4. Execution Layer: Provides the interfacing to perform the task with the operating system. 

Also Read: Complete Guide to Building Scalable Multi-Agent Systems with AgentScope 

Getting Started with Clawdbot 

Clawdbot is designed for technical users who are comfortable with command-line tools.  

1. The prerequisites for running Clawdbot are: 

• Node.js (v22+) 
• Terminal access 
• API key for an LLM provider 
• Messaging platform account 

2. You can install Clawdbot by using the following command:  

npm install -g clawdbot@latest 

3. For the initial setup of your environment, use the below command: 

clawdbot onboard --install-daemon 

This command guides you through configuring the model provider, workspace, Gateway service, and messaging integrations. You will be prompted with a series of setup options. Review each step carefully and enable only what matches your requirements.

Building a Personal AI Research Assistant using Clawdbot

In this task, we use Clawdbot to generate and track daily AI research summaries.

Task Workflow

User message:

Every morning, please provide me with a summary of the latest AI research news and updates.

Clawdbot actions:

• Identifies the intent for daily summaries
• Stores the request in persistent memory
• Creates a scheduled task
• Retrieves, summarizes, and delivers AI research updates
• Sends a daily message with the summarized content

What This Demonstrates

• Persistent memory
• Scheduled task execution
• Clawdbot’s usefulness beyond simple messaging

Output:

Risks of using Clawdbot

The primary risks of using Clawdbot stem from its powerful capabilities and the level of access it requires.

• Security exposure: Granting broad system access without proper controls can create serious security risks.
• Prompt injection attacks: Malicious inputs can trigger unintended actions and compromise system behavior.
• Operational overhead: Running a persistent agent requires ongoing monitoring, maintenance, and system management.
• Complexity for non-technical users: Clawdbot currently assumes familiarity with terminal commands, APIs, and system configuration.

To reduce these risks, organizations should implement sandboxing, allowlisting, and strict access control mechanisms.

Benefits of Using Clawdbot

• Full data control: Clawdbot keeps data entirely under user control, enabling a privacy-first AI workflow.
• True AI agency: It can reason, retain memory, and take action, which are core traits of agentic AI systems.
• Highly extensible: Its modular architecture makes it easy to build custom tools and integrate with existing workflows.
• Cost flexibility: Users can choose between cloud-based or on-premise models based on performance, cost, and privacy needs.
• Real automation: Clawdbot bridges the gap between AI intelligence and real-world execution.

Real-world Use Cases

• Personal productivity automation: Automates task tracking, follow-ups, daily reminders, and real-time updates through messaging platforms.
• Automated AI research assistant: Monitors multiple information sources, summarizes new research findings, and delivers customized updates based on user preferences.
• Automation tools for software developers: Automates local tasks, assists with routine development workflows, and enables quick file analysis and summarization to save time.
• AI assistants for developer organizations: Enables teams to deploy internal AI assistants on private infrastructure, providing relevant insights without exposing sensitive data externally.
• AI agent experimentation platform: Offers a hands-on environment for developers and researchers to build, test, and refine agentic AI systems with memory and execution capabilities.

Conclusion

Clawdbot stands out as a real-world example of agentic AI. With persistent memory, local execution, and a messaging-based interface, it moves artificial intelligence beyond simple conversation and into real action.

While configuring Clawdbot requires technical familiarity, it offers developers, researchers, and AI enthusiasts a forward-looking view of how self-contained autonomous agents will operate in the future. It serves both as a practical tool and a learning platform for building the next generation of agentic AI systems.