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# Deep Agent Search + Virtual Files # Deep Agent from Scratch
This repository contains a minimal **deep agent** implementation based on the This repository contains a minimal implementation of a deep agent that can:
"Deep Agents from Scratch" course. The agent can:
1. Search the web (simple Bing wrapper). 1. Search the web using DuckDuckGo.
2. Create and modify files in an **inmemory virtual file system**. 2. Create virtual files in memory.
3. Read those virtual files. 3. Export the virtual files to the real file system.
4. Dump the virtual file system to the real disk at the end of the run.
## Setup The agent is built using the new LangChain 1.x and LangGraph 1.x APIs.
```bash ## Prerequisites
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies - Python 3.11 or newer
pip install -r requirements.txt - Ollama running locally with a model such as `llama3.1`
``` - `pip install -r requirements.txt`
> **Prerequisite** An Ollama server must be running locally with a model ## Running the Agent
> such as `llama3.1` available.
## Usage
```bash ```bash
python agent.py python agent.py
``` ```
You will be prompted to type questions. The agent will decide which tool to The script will run the agent with a sample prompt, create a virtual file `report.txt`, and export it to the `exported_files` directory.
use. When you are finished, type `exit`.
The virtual files are stored in memory. To persist them, use the `DumpVirtualFS` ## File Structure
tool or call `dump_virtual_fs_tool("output")` from within the agent.
## Files - `agent.py` Main implementation.
- `agent.py` Main agent implementation.
- `virtual_fs.py` Simple inmemory file system.
- `requirements.txt` Python dependencies. - `requirements.txt` Python dependencies.
- `README.md` This documentation. - `README.md` This file.