How to Build an LED Matrix Notification Display Agent with OpenClaw on Raspberry Pi

LED matrix panels are useful because they're visible from across a room, draw little power, and display information without requiring anyone to open an app or check a screen. A 64x64 HUB75 panel mounted on a wall can show server health, deployment status, weather alerts, or production metrics at a glance. The problem is feeding it. Most LED matrix projects end up showing a clock or a static scrolling message because nobody wants to write the logic that decides what's worth displaying at any given moment.

That's where OpenClaw comes in. OpenClaw is an open-source AI agent framework that runs on a Raspberry Pi, connects to cloud LLMs for reasoning, and executes tool calls through its skill system. Instead of hardcoding what the display shows, you give the agent a goal ("show me anything that needs my attention") and let it pull from email, calendars, monitoring APIs, or project management tools to compose a notification feed. The agent handles prioritization, formatting, and timing. The LED panel just renders whatever the agent sends.

One developer documented a similar setup using a Pixoo-64 LED display with OpenClaw on a Pi 4. The agent independently found the display library, installed it, and started pushing content to the screen without explicit instructions for each step. That's the appeal of an agentic approach: instead of scripting every display state, you describe the intent and the agent figures out the execution path.

HUB75 panels are cheaper and more flexible than the Pixoo. A 32x32 module with 3mm pitch starts around $15 to $20, and you can chain multiple panels together for larger displays. The tradeoff is that HUB75 panels need a driver library and GPIO wiring, while the Pixoo connects over USB or WiFi. This guide covers the HUB75 path because it gives you full control over resolution, layout, and pixel-level rendering.

You need three things: a Raspberry Pi, one or more HUB75 LED matrix panels, and a way to connect them.

Raspberry Pi

The Pi 5 with 8 GB RAM is the recommended board. OpenClaw's Node.js runtime and the LED matrix driver both need CPU time, and the Pi 5's ARM Cortex-A76 cores handle both comfortably. The Pi 4 with 4 GB RAM works too. The Cactus blog documented running OpenClaw on a Pi 4 with 4 GB handling Telegram connections, script execution, and hardware peripherals around the clock without issues.

LED Matrix Panels

HUB75 panels come in several sizes: 16x32, 32x32, 64x32, and 64x64 pixels. For a notification display, a single 64x64 panel (about 190mm square at 3mm pitch) gives you enough room for several lines of text, simple icons, and color-coded status indicators. If you want more space, chain two or three panels side by side for a 128x64 or 192x64 canvas.

The rpi-rgb-led-matrix library by Henner Zeller supports up to three parallel chains on any 40-pin Raspberry Pi. Each chain can hold multiple panels daisy-chained through the HUB75 connectors. On a Pi 3, 4, or 5, you can reasonably drive 12 panels per chain (36 panels total) while maintaining around 100 Hz refresh with full 24-bit color and CIE1931 luminance correction.

Adapter Board

An adapter board like the Adafruit RGB Matrix Bonnet or the Electrodragon RGB Matrix HAT sits on top of the Pi's GPIO header and provides clean HUB75 output with level shifters. You can wire directly to the GPIO pins, but an adapter board reduces noise and flicker. The Adafruit Triple LED Matrix Bonnet supports driving up to three chains from a single Pi.

Power Supply

LED panels draw significant current at full brightness. A 64x64 panel can pull 4 to 8 amps at 5V when all LEDs are white. Use a dedicated 5V power supply rated for your total panel count, not the Pi's USB-C port. A 10A supply handles one or two 64x64 panels with headroom. Power the Pi separately with its official 27W USB-C supply.

Wiring

Connect the adapter board's HUB75 output cable to the panel's input connector. If chaining multiple panels, run a ribbon cable from the first panel's output to the second panel's input. Share ground between the panel power supply and the Pi. The rpi-rgb-led-matrix documentation covers wiring diagrams for each adapter type in detail.

The software has two layers: the LED matrix driver that controls the hardware, and OpenClaw that decides what to display.

rpi-rgb-led-matrix

The rpi-rgb-led-matrix library provides C++ and Python bindings for controlling HUB75 panels through the Pi's GPIO. The Python bindings were overhauled in February 2026 with scikit-build-core and cmake, making installation simpler. You can now pip-install the library directly from the GitHub repository after installing build dependencies.

Once installed, test the display with one of the included example scripts. The library ships with demos for scrolling text, image display, and clock rendering. If your panel lights up with the demo, the hardware and wiring are correct.

Key configuration options include the number of rows and columns per panel, the number of chained panels, the number of parallel chains, the GPIO mapping (use "adafruit-hat" if you're using the Adafruit Bonnet), and the hardware PWM bit depth. These options are passed as command-line flags or set programmatically in Python.

OpenClaw

OpenClaw's official documentation covers Raspberry Pi installation. The process takes about five minutes: install Node.js, run the install script, and complete the onboarding wizard. The onboarding step configures your LLM provider (Anthropic, OpenAI, or Google), pairs a messaging channel (Telegram, Slack, Discord, or others), and installs the daemon for always-on operation.

The Pi doesn't run LLMs locally. It sends reasoning requests to cloud APIs and handles tool execution, skill management, and hardware interaction on-device. This keeps resource usage low enough that the LED matrix driver and OpenClaw can share the same Pi without competing for CPU or memory.

Connecting the Two

The bridge between OpenClaw and the LED panel is a Python script that exposes a simple interface: accept a notification payload (text, color, priority, optional icon), render it to the matrix, and manage a queue of active notifications. OpenClaw calls this script through a custom skill that wraps the rendering logic.

A practical architecture uses a lightweight HTTP server (Flask or FastAPI) running alongside the LED renderer. OpenClaw posts notification data to a local endpoint, and the renderer picks up new items and draws them to the panel. This keeps the agent and the display decoupled: you can test the renderer independently, and OpenClaw doesn't need to know the details of the LED hardware.

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The renderer is a Python process that owns the LED matrix hardware and draws notifications to the panel. It needs to handle three things: text rendering, layout management, and a notification queue.

Text and Graphics

The rpi-rgb-led-matrix library includes a graphics module with font rendering support. BDF fonts work well at low resolutions because they're pixel-aligned. The library ships with several BDF fonts in different sizes. For a 64x64 panel, a 6x10 font gives you about 10 characters per line and 6 lines of text.

For icons and color indicators, draw directly to the canvas using the library's graphics primitives: SetPixel for individual pixels, DrawLine, DrawCircle, and DrawText. Color-code notifications by type: red for errors, yellow for warnings, green for healthy status, blue for informational messages. Simple two-color icons (a checkmark, an X, an envelope, a bell) fit in an 8x8 pixel block and are readable from several meters away.

Layout Modes

Design two or three layout modes that the agent can switch between depending on what it needs to show:

• Single alert mode. One large notification fills the panel. Use a bigger font, a colored background, and an icon. Good for critical alerts that need immediate attention.
• Dashboard mode. Split the panel into zones: a status bar across the top (time, WiFi signal, agent status), and two to four notification slots below. Each slot shows a one-line summary with a priority color. Good for steady-state monitoring.
• Scrolling ticker mode. A single line of text scrolls horizontally across the panel. Useful for lower-priority updates like news headlines or build status messages.

The agent sends a layout hint along with each notification payload, or the renderer auto-selects based on the number and priority of queued items.

Notification Queue

Keep a priority queue in memory. Each notification has a text body, a color, a priority level (critical, warning, info), a timestamp, and an optional expiration time. The renderer cycles through active notifications on a timer, spending more screen time on higher-priority items. Expired notifications drop from the queue automatically.

For persistence across restarts, write the queue to a JSON file on disk. When the renderer starts, it reloads the queue and resumes display. This matters because the Pi might restart after a power outage, and you don't want to lose an active alert just because the process recycled.

OpenClaw uses skills to interact with tools and services. A skill is a folder containing a SKILL.md file with YAML frontmatter and natural-language instructions that teach the agent how to use a specific capability. You can write custom skills in the agent's workspace directory.

For the LED matrix, the skill needs to describe what the notification endpoint accepts and how the agent should decide what to display. The skill instructions explain the available layout modes, the priority levels, the color conventions, and any constraints (character limits per line, maximum queue depth).

The agent reads the skill instructions, and when it determines that a notification is worth showing on the physical display, it makes an HTTP request to the local renderer endpoint with the appropriate payload. You don't script every notification scenario. Instead, you tell the agent what kinds of events matter (build failures, calendar reminders, email from specific senders, monitoring threshold breaches) and let it use judgment about formatting and priority.

What to Feed the Agent

The agent's value comes from the data sources it can access. Connect it to the services that generate notifications worth showing on a physical display:

• CI/CD pipelines. Pull build and deployment status from GitHub Actions, GitLab CI, or Jenkins through their APIs.
• Monitoring tools. Query Prometheus, Grafana, or Datadog for metrics that cross alert thresholds.
• Email and messaging. Summarize unread emails or Slack messages from specific channels.
• Calendar. Show upcoming meetings with a countdown timer.
• Custom webhooks. Accept POST requests from any service that supports webhooks and let the agent decide whether the event warrants a display notification.

ClawHub, OpenClaw's public skill registry, has published skills for many of these integrations. Search for existing skills before writing your own, since community-maintained skills often handle edge cases and authentication patterns that take time to get right.

Storing Notification History

Raw notification data is ephemeral on the LED panel, but keeping a history is valuable for debugging and trend analysis. Local storage on the Pi's SD card works for short-term logging, but SD cards wear out under constant writes and the data is stranded on a single device.

Fastio workspaces give the agent a cloud-backed store for notification logs. The agent can upload daily summaries, alert counts, and raw event data to a Fastio workspace using the MCP server. With Intelligence Mode enabled, those logs become searchable through natural language, so you can later ask "how many critical alerts fired last week?" and get an answer with citations pointing to the specific log files.

The Business Trial includes 50 GB of storage, included credits, and 5 workspaces with no credit card required. For a notification agent generating text logs, that's more capacity than you'll use. Other options like S3 or Google Drive handle bulk storage, but they don't offer the combination of MCP access, automatic indexing, and agent-to-human handoff that makes the agent workflow feel connected.

Flicker and Ghosting

If the display flickers or shows ghost images, the most common cause is insufficient PWM bit depth or a clock rate that's too high for the wiring. Reduce the PWM bits from 11 to 7 or 8, and lower the GPIO slowdown factor. The rpi-rgb-led-matrix README documents these tuning parameters and their tradeoffs between color depth and stability.

Long ribbon cables between chained panels also introduce noise. Keep cables as short as possible, and use the shielded cables that come with most panels rather than generic IDC ribbon.

CPU Contention The LED matrix driver needs a dedicated CPU core for consistent refresh timing. On a Pi 4 or 5, pin the matrix process to one core and let OpenClaw and the OS share the remaining cores. The rpi-rgb-led-matrix library supports core isolation through its configuration options. Without isolation, you'll see occasional frame drops when OpenClaw executes a CPU-intensive skill.

Power Issues

Under-voltage causes color distortion and dim LEDs. If your display looks washed out at full brightness, measure the 5V rail at the panel connector with a multimeter. Voltage should stay above 4.7V under load. Use thick gauge wires (18 AWG or lower) between the power supply and the panel, and keep cable runs short. Don't daisy-chain power through multiple panels. Instead, run separate power leads to each panel from a common supply.

Outdoor and Bright-Room Use

Standard indoor HUB75 panels are readable in typical office or home lighting. Direct sunlight washes them out. For outdoor or bright environments, look for outdoor-rated panels with higher brightness ratings (above 2000 nits) and conformal coating. These cost more but survive rain, temperature swings, and UV exposure.

Security

Running an AI agent with tool access on a networked device needs thought. OpenClaw's official documentation recommends locking the gateway to localhost, limiting channel access, and using allowlists for command execution. The notification renderer's HTTP endpoint should bind to localhost only, not to your network interface. If you need remote access, tunnel through SSH or a VPN rather than exposing the endpoint directly.

Scaling to Multiple Displays

If you want LED matrix displays in multiple locations, each Pi runs its own OpenClaw instance and renderer. Coordinate them through a shared Fastio workspace where a central agent posts notifications and each display agent pulls the items relevant to its location or role. This way, the server room display shows infrastructure alerts while the office lobby display shows team announcements, all fed from the same data pipeline.