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🤖 Base Agent Example

Sends an email when the Observer logo is on screen

System Prompt (uses $SCREEN for multimodal screen input)

You are an Observer agent, watch the screen and if you see the Observer logo say OBSERVER, if you don't, say CONTINUE. 
$SCREEN

Code using Email Tool if model identified an Observer logo

if(response.includes("OBSERVER")){
  sendEmail("your@email.com", response, screen); //sends the screen as an attached image
}

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🎯 What Observer AI Does Best

📊 Intelligent Logging 🧠 Text & Visual Memory 🎥 Smart Screen Recording

🚨 Powerful Notifications 📧 Email • 💬 Discord • 📱 Telegram 📞 SMS • 💚 WhatsApp • Pushover

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🏗️ Building Your Own Agent

Creating your own Observer AI consist of three things:

• SENSORS - input that your model will have
• MODELS - Small LLMs
• TOOLS - functions for your model to use

Quick Start

1. Navigate to the Agent Dashboard and click "Create New Agent"
2. Fill in the "Configuration" tab with basic details (name, description, model, loop interval)
3. Give your model a system prompt and Sensors! The current Sensors that exist are:
   • Screenshot ($SCREEN) Captures screen as an image for multimodal models.
   • Screen OCR ($SCREEN_OCR) Captures screen content as text via OCR.
   • CAMERA ($CAMERA) Captures the camera for multimodal models.
   • CAMERA OCR ($CAMERA_OCR) Captures the camera text via OCR.
   • Agent Memory ($MEMORY or$MEMORY@agent_id) Accesses agents' stored information (defaults to current agent)
   • Agent Image Memory ($IMEMORY or$IMEMORY@agent_id) Accesses agents' stored images (defaults to current agent)
   • Agent Image Memory OCR ($IMEMORY_OCR) Captures text on an image's memory via OCR.
   • Clipboard ($CLIPBOARD) It pastes the clipboard contents
   • Microphone* ($MICROPHONE) Captures the microphone and adds a transcription
   • Screen Audio* ($SCREEN_AUDIO) Captures the audio transcription of screen sharing a tab.
   • All audio* ($ALL_AUDIO) Mixes the microphone and screen audio and provides a complete transcription of both (used for meetings).

* Uses a whisper model with transformers.js

Agent Tools:

• getMemory(agentId?)* – Retrieve stored memory
• setMemory(agentId?, content)* – Replace stored memory
• appendMemory(agentId?, content)* – Add to existing memory
• getImageMemory(agentId?)* - Retrieve images stored in memory
• setImageMemory(agentId?, images)* - Set images to memory
• appendImageMemory(agentId?, images)* - Add images to memory
• startAgent(agentId?)* – Starts an agent
• stopAgent(agentId?)* – Stops an agent
• time() - Gets current time
• sleep(ms) - Waits that ammount of miliseconds

* agentId is optional, deaults to agent running code

Notification Tools:

• sendDiscord(discord_webhook, message, images?, videos?) - Directly sends a discord message to a server.
• sendTelegram(chat_id, message, images?, videos?) Sends a telegram message with the Observer bot. Get the chat_id messaging the bot @observer_notification_bot.
• sendEmail(email, message, images?, videos?) - Sends an email. Email must be the signed in email.
• sendPushover(user_token, message, images?, title?) - Sends a pushover notification.
• call(phone_number, message)* - Makes an automated phone call with text-to-speech message. Needs whitelisting.
• sendWhatsapp(phone_number, message, images?, videos?)* - Sends a whatsapp message with the Observer bot. Needs whitelisting.
• sendSms(phone_number, message, images?, videos?)* - Sends an SMS to a phone number. Due to A2P policy, blocked for US/Canada. Needs whitelisting.
• notify(title, options) – Send browser notification ⚠️IMPORTANT: Some browsers block notifications

* To whitelist, SMS or call +1 (863)208-5341 or whatsapp +1 (555)783-4727

Video Recording Tools:

• startClip() - Starts a recording of any video media and saves it to the recording Tab.
• stopClip() - Stops an active recording
• markClip(label) - Adds a label to any active recording that will be displayed in the recording Tab.
• getVideo() - Returns array of videos on buffer.

App Tools:

• ask(question, title="Confirmation") - Pops up a system confirmation dialog
• message(message, title="Agent Message") - Pops up a system message
• system_notify(body, title="Observer AI") - Sends a system notification
• overlay(body) - Pushes a message to the overlay
• click('left'|'right') - Triggers a mouse click at the current cursor position accepts either 'left' or 'right', defaults to left.
• celebrate() - Triggers a celebration animation in the Observer UI.

Code Tab

The "Code" tab receives the following variables as context before running:

• response - The model's response
• agentId - The id of the agent running the code
• screen - The screen if captured
• camera - The camera if captured
• imemory - The agent's current image in memory
• images - All images sent to the model
• prompt - The model's prompt
• microphone - Trascription from the microphone in this loop
• screenAudio - Transcription from screen audio in this loop
• allAudio - Transcription from microphone and screen audio mixed in this loop

JavaScript agents run in the browser sandbox, making them ideal for passive monitoring and notifications:

// Remove Think tags for deepseek model
const cleanedResponse = response.replace(/<think>[\s\S]*?<\/think>/g, '').trim();

// Get time
const time = time();

// Update memory with timestamp
appendMemory(`[${time}] ${cleanedResponse}`);

// Send to Telegram if the model mentions a word
if(response.includes("word")){
  sendTelegram(cleanedResponse, "12345678") // Example chat_id
}

🚀 Getting Started with Local Inference

There are a few ways to get Observer up and running with local inference. I recommend the Observer App.

Option 1 (Easiest): Pull models using Transformers.js on WebApp

If you go to the ModelHub you can download Gemma 4 e2b and Gemma4 e4b directly on the browser with no install. This is a bit unstable and crashes mobile devices, but no install is required at all!

Option 2 (Easy and Stable): Download the Observer App and use bundled llama.cpp models

Download the Official App:

The Observer App comes bundled with llama.cpp under the hood, so you can run any GGUF model! Be sure to load an mmproj file if using multimodality.

Option 3 (Most stable): Use Desktop App with any OpenAI compatible endpoint (Ollama, llama.cpp, vLLM)

Download Ollama for the best compatibility. Observer can connect directly to any server that provides a v1/chat/completions endpoint.

Set the Custom Model Server URL on the App to any OpenAI compatible endpoint.

NOTE: Your browser app sends the request to localhost:3838 which the ObserverApp proxies to your Custom Model Server URL, this is because of CORS.

Option 4: Full Docker Setup (Deprecated)

For Docker setup instructions, see docker/DOCKER.md.

Setting Up Python (Jupyter Server) (Deprecated)

For Jupyter server setup instructions, see app/JUPYTER.md.

Deploy & Share

Save your agent, test it from the dashboard, and upload to community to share with others!

🤝 Contributing

We welcome contributions from the community! Here's how you can help:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'feat: add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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Built with ❤️ by Roy Medina for the Observer AI Community Special thanks to the Ollama team for being an awesome backbone to this project!