5 OpenClaw Raspberry Pi Projects for Your First AI Agent

Raspberry Pi Foundation CEO Eben Upton confirmed two rounds of price increases in early 2026, driven by a 7x spike in LPDDR4 DRAM costs that pushed the 8GB Pi 5 from $80 to $125. Even so, the 4GB model at $85 remains one of the cheapest ways to run a dedicated AI agent. The Pi does not run the language model itself. It acts as a gateway: receiving messages, forwarding them to a cloud LLM like Claude or GPT-4, and executing the response. That relay architecture means even the Pi 4 with 4GB handles the workload without breaking a sweat.

OpenClaw is an open-source AI agent framework that turns a single-board computer into a persistent, always-on assistant. Unlike a chatbot running in a browser tab, an OpenClaw agent on a Pi stays active after you close your laptop. It can respond to Telegram messages at 3 a.m., read sensor data from GPIO pins, back up files on a schedule, or control smart home devices through Home Assistant.

Here is what makes the Pi a good fit for this kind of work:

• One-time hardware cost instead of monthly server bills. A Pi 5 (4GB) at $85 plus a power supply replaces a $5-10/month VPS
• Power draw under 5W at idle, translating to roughly $4 per year in electricity
• GPIO pins for connecting physical sensors, relays, cameras, and LED strips
• Runs headless with Raspberry Pi OS Lite, so no monitor or keyboard needed after initial setup
• Isolation from your main computer. Toby Roberts of the Raspberry Pi Foundation wrote that "running OpenClaw on a standalone device like a Raspberry Pi is a great way to mitigate security concerns. You gain isolation, control, and peace of mind."

The five projects below assume you have a working OpenClaw installation on your Pi. If you have not set that up yet, the official docs walk through the full process in eight steps: flash Raspberry Pi OS Lite (64-bit), install Node.js 24, run the one-line installer, and complete the onboarding wizard.

This is the simplest OpenClaw project and the one most tutorials recommend starting with. You will create a Telegram bot that forwards messages to a cloud LLM and returns the response, giving you a personal AI assistant that lives in your pocket.

What you need:

• A working OpenClaw installation on your Pi
• A Telegram account
• An API key from an LLM provider (Anthropic, OpenAI, or Google)

How it works:

Open Telegram and search for @BotFather. Send /newbot, pick a display name, and choose a username ending in "bot." BotFather hands you an API token.

On the Pi, add the Telegram channel to OpenClaw using the token. OpenClaw registers with the Telegram API and starts listening for messages. When someone sends your bot a message, the Pi receives it, forwards the text to your LLM provider, and pipes the response back to the Telegram chat.

The default setup requires you to approve new users before the bot responds to them, so you are not paying for strangers' API calls. You can also set the bot's privacy mode to control whether it responds to all messages in group chats or only direct mentions.

Why this project works for beginners: It requires zero hardware beyond the Pi itself. The feedback loop is immediate (send a message, get a response), so you can tell whether things are working within seconds. And the result is genuinely useful. A personal AI assistant on Telegram is available on any device, anywhere, without opening a browser.

Where to take it next: Add a system prompt that makes the bot behave like a specific assistant (a recipe helper, a language tutor, a coding buddy). Enable group chat mode so your whole family can use it.

Your second project puts the "agent" in AI agent by giving OpenClaw a recurring task that runs without prompting. Instead of waiting for a message, this agent wakes up on a schedule, backs up files from a directory on the Pi, and uploads them to cloud storage.

What you need:

• A working OpenClaw installation with Telegram channel (from Project 1)
• Files you want to back up (documents, photos, project files)
• A cloud storage destination

How it works:

OpenClaw skills are markdown files containing instructions that extend the agent's capabilities. The cron-backup skill, available through ClawHub, handles scheduled backups with version tracking and cleanup. Install it, point it at a source directory, and configure a schedule. The agent compresses the directory, timestamps the archive, and uploads it.

For the storage destination, you can use any service the agent can reach via API. Fastio works well here because the free tier includes 50GB of storage and the agent can upload files through the MCP server without managing OAuth tokens or S3 credentials. The agent creates a workspace, uploads the archive, and you get a sharable link. If you prefer S3 or Google Drive, those work too.

The Telegram channel you set up in Project 1 doubles as a notification layer. Configure the agent to send you a message after each backup with the file size and a link. If a backup fails, you will know immediately instead of discovering the problem weeks later when you actually need the files.

Why this project works for beginners: It introduces two concepts that separate agents from chatbots: scheduled execution and file I/O. You learn how OpenClaw skills work, how to configure recurring tasks, and how to connect the agent to external services. The result is practical: automated backups that actually protect your data.

Store Your Agent's Output Where You Can Actually Find It

Fastio gives your OpenClaw agent 50GB of indexed, searchable cloud storage with no credit card and no expiration. Upload backups, research files, and sensor logs through the MCP server, then hand the workspace to a colleague when the project is done.

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This project adds physical hardware to the mix. You will connect a BME680 sensor to the Pi's GPIO pins and have the OpenClaw agent read temperature, humidity, and air pressure, then make decisions based on the readings.

What you need:

• BME680 sensor breakout board ($10-15)
• Jumper wires and a breadboard ($5)
• Python 3.9+ and the Adafruit Blinka library (CircuitPython on Pi)

How it works:

The BME680 connects to the Pi's I2C bus using four wires: power, ground, SDA, and SCL. A Python script polls the sensor at whatever interval you choose (every 30 seconds is a reasonable default) and passes the readings to OpenClaw as context.

Here is what makes this different from a standard IoT temperature logger: the agent does not just record numbers. It interprets them. Instead of writing if/else rules for every threshold, you give the agent instructions in plain English: "If the temperature exceeds 28C and the humidity is above 70%, send me a Telegram message suggesting I open a window. If the pressure is dropping rapidly, warn me about incoming weather changes."

The LLM handles the reasoning. You can change the behavior by editing the prompt, not the code. Want the agent to start logging readings to a CSV when the temperature is unusually high? Tell it. Want it to compare today's readings against yesterday's and flag anomalies? That is a prompt change, not a code change.

Why this project works for beginners: It is the first project where the Pi does something no phone app can do. The sensor data is local, the processing is real-time (by agent standards), and the physical wiring gives you tangible feedback that software alone does not provide. The total hardware cost is under $20 on top of the Pi.

Where to take it next: Add a PIR motion sensor for occupancy detection. Wire up a NeoPixel LED strip that changes color based on air quality. Point a USB camera at a plant and have the agent assess its health based on both sensor data and visual input. The Adafruit learning guide for OpenClaw on Raspberry Pi covers several of these extensions with wiring diagrams.

If you already run Home Assistant on a Pi (or a separate server), this project connects OpenClaw to your smart home. The result is natural-language control over every device Home Assistant knows about, routed through Telegram or any other messaging channel you have configured.

What you need:

• Home Assistant instance (on the same Pi or a separate device)
• The OpenClaw Home Assistant integration (available through HACS)
• A long-lived access token from Home Assistant

How it works:

The official OpenClaw Home Assistant integration shipped in January 2026 and is available through HACS (Home Assistant Community Store). Once installed, it exposes your full Home Assistant entity registry to OpenClaw automatically. No manual device mapping required.

With the integration active, you can message your Telegram bot "turn on the living room lights" and the agent translates that into a Home Assistant service call. It works with any protocol Home Assistant supports: Zigbee, Z-Wave, MQTT, Matter, Wi-Fi, and Bluetooth devices all become controllable through natural language.

The real power shows up in compound commands. Instead of opening three different apps to set up a movie night, send one message: "dim the living room to 20%, turn on the TV, and set the thermostat to 22." The agent breaks that into individual service calls and executes them in sequence.

A Pi 4 with 4GB runs both Home Assistant and OpenClaw simultaneously with room to spare, though a Pi 5 with 8GB gives more headroom if you have 50+ devices or want to run automations alongside the agent.

Why this project works for beginners: If you have an existing Home Assistant setup, this project gives you a visible, satisfying result with minimal new hardware. It also demonstrates how OpenClaw agents connect to existing ecosystems rather than replacing them. You are not rebuilding your smart home; you are adding a natural-language layer on top of what already works.

The final project combines everything you have learned into a general-purpose research assistant that runs on your Pi 24/7. It reads documents, searches the web, summarizes findings, and stores results in organized workspaces.

What you need:

• A working OpenClaw installation with Telegram (from Project 1)
• Cloud storage for research outputs
• Two or three ClawHub skills installed

How it works:

OpenClaw's skill system is what turns a basic chat relay into a capable agent. ClawHub hosts over 2,800 community-built skills covering categories from coding and data analysis to file management and web scraping. For a research assistant, you want skills that handle web search, document parsing, and file organization.

Start by installing skills for web search and PDF extraction. Feed the agent a topic, and it searches the web, pulls relevant pages, extracts key information, and compiles a summary. Configure it to save outputs to a Fastio workspace and you get organized research files with version history, shareable links, and built-in search through Intelligence Mode. When the research is done, you can transfer ownership of the workspace to a colleague or client who needs the results.

The Telegram interface from Project 1 means you can kick off research from your phone. "Research the current state of RISC-V adoption in consumer devices and save a summary" is a valid prompt. The agent works while you do other things and sends you a notification when it finishes.

Why this project works for beginners: It ties together messaging (Project 1), file management (Project 2), and external service integration (Project 4) into a single useful workflow. You learn how skills compose, how agents handle multi-step tasks, and how cloud storage turns ephemeral agent output into something you can reference later.

The progression from Project 1 to Project 5 mirrors how most people actually use OpenClaw: start with a simple chat relay, add scheduled tasks, connect hardware, integrate with existing systems, and eventually build a multi-skill agent that handles real work.

Here is the complete shopping list for all five projects, with approximate costs as of May 2026. Prices reflect the DRAM-driven increases announced by Raspberry Pi Foundation earlier this year.

Core hardware (required for all projects):

• Raspberry Pi 5 (4GB): $85. The Pi 4 (4GB) also works but is roughly 2-2.5x slower in CPU tasks
• USB-C power supply (27W official): $12
• MicroSD card (32GB, Class 10): $8. Or a USB SSD ($25-40) for better reliability on always-on setups
• Ethernet cable or Wi-Fi connection

Additional hardware for Project 3:

• BME680 sensor breakout: $10-15
• Jumper wires and breadboard: $5
• Optional: PIR motion sensor ($3), NeoPixel LED strip ($8), USB camera ($15)

Software (free):

• Raspberry Pi OS Lite (64-bit)
• Node.js 24
• OpenClaw (open source)

Ongoing costs:

• Electricity: roughly $4-5 per year
• LLM API usage: varies by provider and volume. Claude Sonnet and GPT-4o-mini cost fractions of a cent per message for short conversations

Total cost for Projects 1-2: under $110. Adding the sensor hardware for Project 3 brings it to about $130. Projects 4 and 5 require no additional hardware beyond what you already have.

Installation in brief:

1. Flash Raspberry Pi OS Lite (64-bit) using Raspberry Pi Imager. Pre-configure hostname, SSH credentials, and Wi-Fi during imaging
2. SSH into the Pi and update system packages
3. Install Node.js 24 from NodeSource
4. Run the OpenClaw one-line installer
5. Complete the onboarding wizard, which sets up the daemon and configures your first LLM provider

The official OpenClaw docs cover each step in detail, including swap configuration for boards with 2GB RAM or less and performance tuning for headless operation.