Custom Search Agent – DeepAgents from Scratch

This repository contains a minimal implementation of a deep search agent that:

• Generates deterministic mock search results.
• Creates virtual files in memory during execution.
• Exports those virtual files to a specified directory on disk.

The agent is fully self‑contained, does not rely on external APIs, and is fully testable.

Project Structure

.
├── src
│   ├── agent.py      # Core agent implementation
│   └── run.py        # CLI entry point
├── tests
│   └── test_agent.py # Unit tests
├── requirements.txt
└── README.md

Installation

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate   # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

Command‑line

python -m src.run --query "python" --output "./search_results"

This will:

1. Search for "python" (mock results).
2. Create two virtual files (result_1.txt, result_2.txt) in memory.
3. Export those files to ./search_results.

Programmatic

from src.agent import CustomSearchAgent

agent = CustomSearchAgent(max_results=3)
results = agent.search("deep learning")
print(results)          # List of (title, snippet) tuples
agent.export_virtual_files("./output")

Testing

Run the unit tests with:

python -m unittest discover -s tests

All tests should pass, confirming that:

• The agent initializes correctly.
• Search results are deterministic.
• Virtual files are created during search.
• Export writes the correct files to disk.

Extending the Agent

The CustomSearchAgent inherits from DeepAgent. To add real search logic:

1. Override search to perform actual queries (e.g., to a local index).
2. Use create_virtual_file to store any generated data.
3. Call export_virtual_files when you need to persist the data.

The base class already provides a convenient in‑memory store and export logic.

License

This project is released under the MIT License.