Jetson AGX Orin and LangChain: Remote Access and AI Agent Projects on the MIC-733

This Jetson AGX Orin LangChain tutorial demonstrates how to operate an Advantech MIC-733 remotely from a laptop and run Jupyter Notebook examples for Grove board control, voice interaction, news summaries, and product price searches. Writing and photography: 郭俊廷 (CAVEDU) Estimated time: 2 hours. Difficulty: 3/5. Hardware: MIC-733-AO6A1 with a 64 GB NVIDIA Jetson AGX Orin, and a USB webcam with a microphone. The materials list names a C710; the voice example below uses a C170. Hardware and workflow This tutorial uses the Advantech MIC-733-AO6A1. Manufacturer highlights cited in the original article include up to 275 TOPS, a compact fanless design, four GbE ports with optional PoE, four USB 3.2 Gen 2 ports (10 Gbit/s), two mPCIe slots, two Nano SIM slots, Allxon remote monitoring and OTA deployment, and Microsoft Azure certification. We will connect over SSH with MobaXterm, configure Wi-Fi, and use LangChain with the Arduino beginner kit to read sensors, control an LED, and interact through speech. We will also run environmental-news summarization and product-price lookup examples. Related reading: NemoClaw remote projects on the same MIC-733. 1. Connect to Jetson AGX Orin with MobaXterm Download MobaXterm A classroom or field setup may not have a separate monitor and keyboard. SSH lets you operate the MIC-733 from your laptop instead. The original tutorial uses the v26.0 Portable Edition, available from the MobaXterm download page. Extract the archive and run the executable. Connect the Ethernet cable Connect your laptop to the MIC-733's LAN2 port with an Ethernet cable. In this classroom setup, LAN2 has already been configured with the shared-network address 10.42.0.1. Use this address for the SSH and web connections below; it is a property of this prepared setup, not a universal device default. Start an SSH session Run MobaXterm_Personal_26.0.exe. If Windows displays the application's network-access prompt, the original walkthrough selects Allow access. Choose Session > SSH. Set Remote host to 10.42.0.1 and select OK. In the prepared classroom image, both the username and password are mic-733ao. Use the credentials configured for your own device. Password characters are not displayed while typing in the terminal; press Enter after entering the password. Configure Wi-Fi The examples need Internet access for online data and model resources. Open the network manager: sudo nmtui Choose Activate a connection, select your Wi-Fi network, and enter its password. Press q when finished to leave the interface. Test connectivity: ping google.com Receiving reply packets indicates that the connection is working. 2. Control a Grove board with LangChain Connect the hardware and upload the firmware Connect the Arduino beginner kit to a USB port on the MIC-733. If a teaching assistant has already uploaded the firmware, skip the following upload steps: Download llmcmd_blink.ino and the Hangeekduino graphical programming package. Open the sketch using the included Arduino IDE. Select Tools > Board: Arduino/Genuino Uno. Use Get Board Info to confirm the communication port, then click the upload arrow to flash the firmware. Activate the Conda environment and launch Jupyter Notebook In the remote terminal, enter the working directory and activate the prepared environment: cd ~/Downloads/GROVE_AGENT_LOCAL/ conda activate Grove-Agent For the remote setup in this tutorial, launch Jupyter with: jupyter notebook --ip='*' Open http://10.42.0.1:8888 in your laptop's browser. If the terminal displays a URL containing a token, use the complete access URL, including the token. The prepared classroom Jupyter setup may instead request its configured password, mic-733ao. These are classroom configuration details, not general Jupyter defaults. Keep this service on a trusted network and retain authentication. Run the Grove example Open Agent_Grove_Local_260907.ipynb. Hardware detection: Run the first code cell. It detects the Arduino's USB serial port, for example Device: /dev/ttyUSB0. Update the program to match the port reported by your device. Scenario analysis and control: Continue through the final cell. LangChain analyzes the environmental sensor readings, produces a risk assessment and care-related suggestions for the demonstration, and switches the board's LED on or off according to the result. Treat those suggestions as demonstration output rather than validated medical advice. Before moving to another example, run the final disconnect cell to release the board, interrupt the kernel, or close the notebook. Otherwise, the serial port may remain occupied. 3. Add voice interaction This example adds microphone input and synthesized speech so the agent can interact with the user through voice. Prepare the microphone Plug a USB webcam directly into the MIC-733, not into your laptop. The example uses the built-in microphone of a C170 webcam. Run the speech notebook Open Text_Speech_Local_260826.ipynb. Test TTS: Run the text-to-speech cell and click Convert and Play (轉換並播放) to test audio output. Record speech: Run through the fourth code cell and click Start Recording (開始錄音). In this setup, the webcam indicator lights while recording. Try a few times to become familiar with the timing. Identify USB hardware: Check the Arduino connection reported by the system, such as /dev/ttyUSB0. Model analysis and spoken response: The final cell takes the spoken request and uses the configured local model to analyze sensor data, assess the scenario, and generate suggestions followed by a spoken response. The original classroom example describes its model as “Gemma 26B”; check the actual installed model identifier and available memory in your environment. 4. Summarize environmental news and look up product prices These examples demonstrate LangChain workflows for crawling data, organizing information, and producing structured analysis. Activate Data-Agent and start Jupyter Open a new terminal and run: cd ~/Downloads/DataAgent conda activate Data-Agent Open the Jupyter interface at http://10.42.0.1:8888, using the address, port, and authentication information actually shown by your server. Environmental-news recap Open Agent_News_Recap_260828.ipynb. The aggregation and chart-output cells perform retrieval and summarization, so they can take longer to execute. Once completed, the notebook outputs charts and a written summary of the main points. Automated two-stage price search Open Agent_Price_List_260828.ipynb. When the last cell runs, the terminal displays a message equivalent to: The agent is starting a two-stage crawler for precise price lookup. Please wait... After crawling completes, the page presents a structured list of product-price results. Environment and software prerequisites The MIC-733 used in this class was configured in advance with the required Conda environments and packages, Ollama, AI models, and other software. That preparation allows participants to begin directly with Jupyter Notebook exercises. On a new device, first install and configure Conda, Jupyter Notebook, Ollama, the required AI models, and the remaining dependencies. You also need the example notebooks and classroom project files referenced above; this article describes how to operate that prepared environment rather than providing a complete fresh-install procedure. Some images and hardware specifications in the original article reference the Advantech MIC-733 product page. Translated into English from CAVEDU's original tutorial, originally published October 6, 2026. This link opens CAVEDU's website outside this platform. Author and photography credit: 郭俊廷. Minor clarifications identify classroom-specific settings and prerequisites; the example commands and filenames are preserved.