🤖 AGI Web Agent

A modular web agent designed for the AGI SDK REAL benchmark, featuring dynamic prompt routing and Chain-of-Thought planning for autonomous web navigation.

📐 System Architecture Diagram (Miro)

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✨ Key Features

• 🧠 Modular Architecture — High-level Orchestrator (project manager) + focused Agent (LLM specialist)
• 🔀 Dynamic Prompt Routing — Automatically selects task-specific prompts based on the goal
• 💭 Chain-of-Thought Planning — Self-verification step reviews plans for logical flaws before execution
• 🔄 Advanced Self-Correction — Detects stuck states and changes strategy to recover
• 📋 Granular Planning — Breaks down complex goals into single-action steps for reliability

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📂 Project Structure

agiinc/
├── README.md
├── requirements.txt              # Root dependencies
├── Dockerfile                    # Docker support
├── docker-compose.yml            # Easy orchestration
│
├── agiwebagent/                  # Web agent implementation
│   ├── main.py                   # Entry point
│   ├── requirements.txt          # Agent-specific dependencies
│   └── agent_src/
│       ├── agent.py              # LLM communication layer
│       ├── config.py             # Configuration dataclass
│       ├── memory.py             # Action history tracking
│       ├── orchestrator.py       # Task lifecycle manager
│       ├── prompt_selector.py    # Dynamic prompt routing
│       ├── llm_utils.py          # LLM utilities with retry logic
│       ├── vision_tools.py       # Visual OCR extraction
│       ├── utils.py              # Helper functions
│       └── prompts/              # Task-specific prompt files
│           ├── omnizon_prompts.py
│           ├── dashdish_prompts.py
│           └── ...
│
└── agisdk/                       # AGI SDK (submodule/dependency)

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🚀 Quick Start

Option 1: Local Installation

1. Create a virtual environment

   python -m venv agienv
   source agienv/bin/activate  # On Windows: agienv\Scripts\activate

2. Install dependencies

   pip install -r requirements.txt
   pip install -r agiwebagent/requirements.txt

3. Configure your API key

   Copy the example environment file and add your API key:

   cp .env.example .env
   # Edit .env and replace 'sk-your-api-key-here' with your actual OpenAI API key

4. Run the agent

   python agiwebagent/main.py --task_name webclones.omnizon-1 --headless true

Option 2: Docker (Recommended)

1. Configure your API key

   cp .env.example .env
   # Edit .env and add your OpenAI API key

2. Build and run

   docker compose build
   docker compose run --rm agiwebagent \
     --task_name webclones.omnizon-1 \
     --headless true

---

📖 Usage

Running a Single Task

python agiwebagent/main.py --task_name webclones.omnizon-1 --no-cache --headless true

Running a Full Task Suite

# Run all Omnizon (e-commerce) tasks
python agiwebagent/main.py --task_type omnizon --headless true

# Run all DashDish (food delivery) tasks
python agiwebagent/main.py --task_type dashdish --headless true

# Run all NetworkIn (professional networking) tasks
python agiwebagent/main.py --task_type networkin --headless true

Command-Line Arguments

Argument	Description	Example
--task_name	Run a single task by ID	webclones.omnizon-1
--task_type	Run all tasks of a type	omnizon, dashdish
--headless	Run browser in background	true / false
--no-cache	Force re-run without cache	Flag
--model	OpenAI model for main agent	gpt-4o, gpt-4o-mini
--vision_model	Model for OCR/vision	gpt-4o
--use_ocr	Enable visual OCR	true / false

---

⚙️ Configuration

Environment Variables

Variable	Description	Required
OPENAI_API_KEY	Your OpenAI API key	✅ Yes

Agent Configuration

Edit agiwebagent/agent_src/config.py to customize:

@dataclass
class AgentConfig:
    model_name: str = "gpt-4o"          # Main execution model
    plan_model_name: str = "gpt-4o"     # Planning model
    parser_model_name: str = "gpt-4o-mini"  # Prompt routing model
    vision_model_name: str = "gpt-4o"   # OCR/vision model
    max_steps: int = 25                 # Max steps per task
    max_retries: int = 3                # Max plan generation retries
    use_screenshot: bool = True         # Include screenshots
    use_axtree: bool = True             # Include accessibility tree
    use_ocr: bool = False               # Enable visual OCR

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🔧 Troubleshooting

Common Issues

1. Playwright browsers not installed

playwright install chromium
playwright install-deps

2. Rate limit errors The agent has built-in exponential backoff retry logic. If you're hitting limits frequently, consider:

• Using a model with higher rate limits
• Reducing parallel execution

3. Docker display issues (headed mode) For headed mode in Docker on Linux:

xhost +local:docker
docker-compose run --rm agiwebagent --headless false

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐
│                        main.py                              │
│                    (Entry Point)                            │
└──────────────────────────┬──────────────────────────────────┘
                           │
                           ▼
┌─────────────────────────────────────────────────────────────┐
│                    TaskOrchestrator                         │
│              (Manages task lifecycle)                       │
│  • Creates plans  • Handles errors  • Tracks progress       │
└──────────────────────────┬──────────────────────────────────┘
                           │
              ┌────────────┴────────────┐
              ▼                         ▼
┌──────────────────────┐    ┌──────────────────────┐
│   PromptSelector     │    │  HighPerformanceAgent │
│ (Routes to prompts)  │    │    (LLM Interface)    │
└──────────────────────┘    └──────────────────────┘
              │                         │
              ▼                         ▼
┌──────────────────────┐    ┌──────────────────────┐
│  prompts/*.py        │    │    AgentMemory       │
│ (Task-specific)      │    │  (History tracking)  │
└──────────────────────┘    └──────────────────────┘

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🔮 Future Improvements

• Integrate DSPy for better prompt optimization
• Add RL post-training (GRPO, PPO)
• Test with different LLMs for role-based cost optimization
• Build on Nova-act and browser-use frameworks
• Fine-tune multimodal LLM components

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📝 License

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

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🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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