This project explore how to create autonomous agents capable of interacting with websites, from navigating the DOM to filling forms and summarizing content — using powerful decision-making frameworks like Monte Carlo Tree Search (MCTS) and AgentQ.
- Understand Web Agents: Learn the fundamentals of browser-based AI agents, including what they are, how they work, and their architectural components.
- Build Smart Agents: Create agents that can navigate, extract data, fill forms, and interact with real websites.
- Explore AgentQ: Learn how to build self-correcting agents using:
- Monte Carlo Tree Search (MCTS)
- Self-critique mechanism
- Direct Preference Optimization (DPO)
- Develop End-to-End Solutions: Build agents that handle tasks such as:
- Finding and summarizing webpages
- Scraping project data from sites like DeepLearning.AI
- Automating form-filling and sign-up processes
- Understand Real-World Constraints: Dive into the challenges and future of AI agents, including action explosion, error handling, and long-term memory planning.
| File | Description |
|---|---|
📘 Building_AI_Browser_Agents_Notes.docx |
Full theory notes covering agent design, planning, rewards, DOM exploration, and the AgentQ framework |
📓 MCTS_and_AgentQ.ipynb |
Implementation of browser agents using MCTS and AgentQ; includes DOM tree traversal and simulated environments |
📓 Building_a_Simple_Web_Agent.ipynb |
A beginner-friendly notebook to build an agent that interacts with a real webpage (e.g., scraping or form filling) |
- Python (Jupyter Notebooks)
- Playwright / Puppeteer – for browser automation
- BeautifulSoup4 – for parsing HTML/DOM
- NetworkX – for tree/graph structures in MCTS
- LangChain (optional) – for LLM-style reasoning
- AgentQ Framework – combining MCTS + DPO + Self-critique
git clone https://github.com/your-username/ai-browser-agents.git
cd ai-browser-agentspip install -r requirements.txtjupyter notebook- 🔍 Scraping Project Info: Scrape and structure project recommendations from DeepLearning.AI
- 📝 Form Submission: Automate newsletter sign-ups or contact forms
- 🧠 Autonomous Browsing: Use MCTS to explore websites intelligently and take goal-directed actions
AgentQ is a modular framework enabling AI agents to self-correct and learn preferences using:
🧭 MCTS (Monte Carlo Tree Search): Simulate and explore possible future actions 🪞 Self-Critique: Reflect on previous mistakes and improve behavior 🎯 DPO (Direct Preference Optimization): Optimize outcomes using feedback-based learning ➡️ The MCTS + DPO combo helps agents intelligently choose the best action among thousands of dynamic web elements.
This project represents an early step toward building AI that can navigate the web like humans. Future directions:
🧠 Integrate with LLMs for better natural language reasoning 🧾 Add memory modules for multi-session task handling 👥 Train using RLHF (Reinforcement Learning with Human Feedback)
The AGI Inc. team – For pioneering work in browser automation and intelligent web agents
For discussions, feedback, or collaboration:
- Open an issue on this repo
- Or connect via LinkedIn- https://www.linkedin.com/in/sagar-shahari-703b84257/
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