Can Raspberry Pi Run an AI Assistant? I Tested OpenClaw

Have you ever thought about how much easier life would be if AI assistants like Jarvis were real? With the recent progress in artificial intelligence (AI), this idea is becoming more realistic than ever. I recently tested an AI assistant on my Raspberry Pi, and the results were surprising.

A Raspberry Pi can run an AI assistant like OpenClaw, but it is only practical when paired with a cloud-based LLM. Running it fully locally is generally too slow for real-world use on typical Raspberry Pi hardware.

In this article, I will guide you through what an AI assistant is and the hardware requirements to run one. Furthermore, I will also share my experience on how the AI assistant performed on my Raspberry Pi.

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What Is an AI Assistant?

An AI assistant is software that takes commands in human-readable language and performs corresponding actions. For the most part, it is similar to talking to ChatGPT in plain English and getting a response.

However, AI assistants like OpenClaw, NanoClaw, NanoBot, and Agno go a step further. These programs run on your computer and connect to large language models (LLMs) to complete your tasks.

You can give them context-aware administrative instructions, e.g., to create a file or to schedule a task, in plain English. The whole process can be visualized as shown in this flowchart:

This is a simplified version of how the whole system works. The actual processing is obviously much more complicated, but we don’t need to get into the exact theory.

Can a Raspberry Pi Run an AI Assistant?

From our discussion above, we can see that there are two major components of an AI assistant:

• the AI Assistant software (such as OpenClaw) and,
• an LLM (the chatbot).

Running the AI assistant itself is not very demanding, and you can probably run it reasonably well on lower-end Raspberry Pis. However, running an LLM locally is very hardware-intensive.

I tried a bunch of LLMs on a Raspberry Pi 5 (8GB) and realized that even smaller models such as qwen2.5b:3b, qwen:1.8b, and llama3.1:8b can take up to 5 minutes to respond to simple chat queries.

However, AI Assistant tools have to do more than simple chat; they make context-aware queries and include the JSON structures of the tools that they have access to as well.

During my testing, even the lighter models, such as qwen:1.8b, failed to respond to simple chat queries via OpenClaw due to agent timeouts. I increased the default agent timeout to up to 30 minutes as well. However, the model’s response was still inconsistent.

Smaller models such as tinyllama, qwen:0.5b, and smollm2:135m are available and provide much quicker response times on a Raspberry Pi; however, these models do not support tool calling functionality and therefore can’t do what we need with OpenClaw.

After repeatedly hitting my head against one wall or another, I gave up trying to run an AI Assistant using a local LLM. Most of the models outright failed, and those that did work were practically unusable.

So, I went with a different approach instead.

Cloud-Based LLMs

Luckily, you can use cloud-based LLMs for this purpose instead. LLMs such as ChatGPT, Claude, Google Gemini, or Microsoft Copilot can be used for this purpose.

Since these models run from the cloud, they have minimal hardware requirements, making them a more practical route to use with AI Assistants running on your Raspberry Pi.

One drawback to be aware of is that AI Assistants often generate queries with high token counts, which can quickly increase costs.

Free alternatives, such as a free tier for Ollama cloud-based models or running a Local LLM on a separate powerful workstation/PC, are recommended for more technically-inclined readers.

The Setup That I Used

The stack I ultimately chose was OpenClaw, running on my Raspberry Pi 5 (8GB), along with OpenAI’s gpt-5.4-mini. I switched to gpt-5.4 as well; however, the cost difference and the high number of tokens required for OpenClaw made gpt-5.4 impractical for continuous usage.

Raspberry Pi 5 was a bit of an overkill. Using a cloud-based LLM leaves only the OpenClaw gateway running on your Raspberry Pi, and a less powerful model of Raspberry Pi would be sufficient for this use case as well.

Other AI Agents, such as NanoClaw, NanoBot, and Agno, can also be used in place of OpenClaw to achieve a similar experience.

Installing OpenClaw

Installing OpenClaw is relatively straightforward. Make sure you have the 64-bit version of Raspberry Pi OS installed for OpenClaw.

Once your Raspberry Pi is up and running (and updated), you can install OpenClaw using the installation script. You can run the installation script by executing this curl command:
curl -fsSL https://openclaw.ai/install.sh | bash

This script will automatically install all the prerequisites along with OpenClaw. Once the installation is complete, you will be taken to onboarding. During onboarding, you will get a few customization options to choose from.

The first choice you will have to make is which LLM you plan to use with OpenClaw:

Similarly, you will also get the choice of which messaging agent to connect to, and which skills and tools to install with OpenClaw.

Once you have configured everything, the OpenClaw will automatically be enabled and started. You can check its status using the command:
openclaw gateway status

Now you can access the dashboard by either visiting http://127.0.0.1:18789 in your web browser or by using the command:
openclaw dashboard

You can’t start messaging directly through your Telegram bot (or any other agent) because it requires some configuration. However, once the OpenClaw was up and running, I asked it to configure itself with my Telegram bot, and it did that automatically.

And that is it, your own AI Assistant is up and running on your Raspberry Pi.

Running OpenClaw on Raspberry Pi

When I first booted up OpenClaw, I initially thought it would operate similarly to Google Assistant and Alexa. However, running the AI Assistant on a Raspberry Pi rather than an Android device meant it had significantly more privileges, making it feel more capable than I expected.

Chat Bot

The default user interface OpenClaw provides is chatbot-style. You can access this interface from the OpenClaw dashboard on your Raspberry Pi or from another PC on the same network.

You can also talk to your AI Assistant through some messaging application like Telegram, WhatsApp, Slack, or Discord.

I configured OpenClaw to be accessible through Telegram. This provides a more convenient method to talk to your AI Assistant even when you are away from your computer.

The overall chatbot experience is primarily determined by the LLM you use. It is basically a context-aware chat with that specific LLM.

OpenClaw stores files in the ~/.openclaw/workspace directory that contain your AI assistant’s personality. Two particularly interesting files are SOUL.md and USER.md. The AI assistant periodically updates these files to modify its response behavior and personality.

Due to this constant sharing of detailed context and personality, the token count of simple chat queries gets significantly higher. For example, during the bootstrap process alone, it consumed approximately 14k tokens! That’s a lot!

Similarly, I frequently saw 1k to 10k tokens for a single chat response, especially when a lot of tool calling was involved. When considering cloud-based LLM, this can be a major bottleneck. This was the primary reason that I switched from gpt-5.4 to gpt-5.4-mini.

The overall response time of the chat was very quick. However, since I was using a cloud-based LLM, it mainly depended on my network connection speed.

System Administration

One neat use case for an AI Assistant like OpenClaw is remote system administration. Once it is connected to your Telegram account, you can ask it for system statistics such as CPU temperature or memory usage.

If you don’t like the traditional dashboard-based approach to system monitoring and administration, it’s a great alternative. It often feels like you are messaging your Raspberry Pi as if it is sentient, and it can tell you if it is hungry or overworked.

In addition to monitoring your system, OpenClaw has full administrative rights on your Raspberry Pi. Therefore, it can start or stop processes or programs or run any other command that you would traditionally need CLI access for.

However, you don’t need to remember any command; you can talk to it as if it were a human, and it will automatically figure out the command that it needs to run for that task and execute it.

Tip: Command lines can be a pain to memorize. I put the essential Linux commands on a printable cheat sheet so you don't have to keep googling them. You can grab the PDF here if you want to save some time.

File Management

Besides system administration, another cool result of giving an LLM system administrative powers is that it can be great at file management. You can use OpenClaw to create, delete, edit, or search for files.

Since OpenClaw can edit and create files on your system, it can also automatically configure itself. You don’t need to go through configuration menus or manually edit the configuration file. You can ask it to reconfigure it however you want.

For example, I frequently asked my OpenClaw to change the default LLM or fix Telegram configuration issues while setting it up. I did not have to configure anything manually; it automatically updated its configuration files to fix the bugs.

Task Scheduling

What good is an Assistant if it can’t maintain a schedule for you? Luckily, OpenClaw is very good at this. You can create cron jobs using OpenClaw that will automatically trigger at specific routine intervals, and the AI Assistant will perform the scheduled task for you.

And all this can be done by messaging your Raspberry Pi to do it. No need to manually create cron jobs or program anything.

This can be incredibly powerful because when the job is executed, it has all the administrative privileges of OpenClaw, along with the intelligence of your LLM.

This means you can confidently create complex jobs that require strong logical reasoning, and they will be carried out automatically as well.

The OpenClaw dashboard features a dedicated area to monitor all the jobs you have created, and you can manually edit or adjust these jobs from this section.

This was one of the most practical use cases that I found during my limited time with OpenClaw. It would be great if OpenClaw provided you with a summary of the day’s news and your scheduled tasks.

This is where an AI Assistant truly starts to feel different from a Chatbot and more like an agentic assistant that automatically handles mundane tasks for you.

Smart Home Manager

You can also use your AI Assistant as an intelligent interface terminal to turn your smart home truly smart. If you have Home Assistant and OpenClaw running on the same Raspberry Pi, this can serve as the central smart home interface.

To do this, you have to say yes to configure skills now during the onboarding process:

And then install the mcporter skill:

This tool allows your OpenClaw to communicate with the MCP server of your Home Assistant. You can ask OpenClaw to connect with your Home Assistant using the mcporter tool, and it will do this automatically. You need to provide the URL and the MCP access token.

You can combine this with cron jobs to automate tasks and use your AI Assistant to analyze sensor data, make smart decisions based on sensor values, and execute necessary responses via Home Assistant.

Tools & Skills

AI Assistants can be equipped with a range of tools and skills. During onboarding, you can install several skills, like mcporter, as mentioned in the section above.

You can also install skills from the OpenClaw dashboard skills section:

The skills take the form of tools that your LLM can use to perform specialized functions like integrating with GitHub, Discord or other services.

Most of the skills are directed towards a specific service or API that needs to be integrated into OpenClaw. In fact, most of the skills are MD files containing human-language prompts and instructions for accessing a specific service.

In addition to the officially available skills, there are over 2000 community-built skills available on ClawHub.

There is a plethora of skills to choose from, and you can find numerous tutorials on installing and using them. Skills transform your AI assistant from a generic chatbot into a truly specialized butler that’s capable of performing specific tasks efficiently.

Tips for OpenClaw on Raspberry Pi

Here are a few tips that I learned while experimenting and trying to set up an AI Assistant on my Raspberry Pi:

Avoid Local LLMs (for now)

While the idea of using a Local LLM with OpenClaw on your Raspberry Pi sounds appealing, the practical implementation hits hardware bottlenecks that render the overall experience unviable at this time.

I realize that if I had experimented with a few more lightweight models, I might have discovered one or two that worked well. However, even then, the quality of the output tends to decline significantly with these lighter models.

Ultimately, the assistant feels less capable and requires constant guidance at every step, ironically transforming you into the assistant.

Choose the Right Model for Cost vs Performance

The AI Assistant applications make significantly token-heavy calls to your LLM. Since most of the LLM model subscriptions charge you based on the number of tokens, using a costly model can very quickly get expensive.

Therefore, carefully monitor the token count through your LLM service provider. Most LLM service providers like OpenAI allow you to set a budget as well, which can be used to avoid unexpected expenditure when trying things out.

Be Careful with System-Level Permissions

AI Assistants usually have full administrative privileges. While this makes your Assistant powerful, it also comes with significant security risks.

Be careful not to expose your AI Assistant API to an insecure network. Ensure proper authentication pipeline. Generally, I found that OpenClaw gives a fair amount of warnings and has good safety nets in place to avoid such security risks.

Keep an Eye on Context Bloat

Keeping all your conversations in one chat creates an extremely long context, which exponentially increases the token count of each call you make.

One neat trick I discovered was using the “/compress” command in chat. This command automatically summarizes your chat history to reduce token counts for further chat.

You can also monitor the context and token usage using the command “/status”.

Final Verdict: Is This Usable or Just a Tech Demo?

AI Assistants are powerful tools for managing daily tasks. I was pleasantly surprised by the simplicity of the complete setup and how, once it was set up, it actually felt like Jarvis, where you can talk to it, and it can get the job done for you.

Unfortunately, a fully local AI assistant on a Raspberry Pi feels more like a tech demo at this stage. However, with advancements in hardware and the increasing efficiency of LLMs, I believe we’ll be able to run a fully local AI assistant on the upcoming Raspberry Pi 6.

Currently, the most practical use of AI assistants is to serve as gateways to cloud-based large language models (LLMs) rather than local LLMs. Nevertheless, this approach has a downside. The significant number of token calls that AI agents utilize, along with the rising costs of cloud-based LLMs, makes it a very costly endeavor.

In my opinion, the cost-benefit ratio of using high-quality cloud-based LLMs makes me hesitant to permanently install OpenClaw on my Raspberry Pi. I would prefer to save this project for a more powerful mini PC that can also handle more demanding local LLMs.

Alternatively, I do plan to set up an AI Assistant on it and run a powerful Local LLM on my workstation. I think this project idea would lead to a much more practical solution.

Whenever you’re ready, here are other ways I can help you:

Test Your Raspberry Pi Level (Free): Not sure why everything takes so long on your Raspberry Pi? Take this free 3-minute assessment and see what’s causing the problems.

The RaspberryTips Community: Need help or want to discuss your Raspberry Pi projects with others who actually get it? Join the RaspberryTips Community and get access to private forums, exclusive lessons, and direct help.

Master your Raspberry Pi in 30 days: If you are looking for the best tips to become an expert on Raspberry Pi, this book is for you. Learn useful Linux skills and practice multiple projects with step-by-step guides.

Master Python on Raspberry Pi: Create, understand, and improve any Python script for your Raspberry Pi. Learn the essentials step-by-step without losing time understanding useless concepts.

Master Linux Commands: Overwhelmed with Linux commands? This book is your essential guide to mastering the terminal. It includes practical tips, real-world examples, and a bonus cheat sheet to keep by your side.

You can also find all my recommendations for tools and hardware on this page.