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# DeepAgent Implementation
# Deep Agent with Web Search and Virtual File Creation
This repository contains a minimal **DeepAgent** that can search the web and create virtual files. The agent uses OpenRouter for LLM calls, DuckDuckGo for web searches, and `deepagents` to manage a hybrid filesystem + shell backend.
## Overview
This repository contains a minimal implementation of a **Deep Agent** built from scratch using the `deepagents` framework. The agent can:
## Features
- **Web search**: Uses DuckDuckGo API to fetch up to 5 results per query.
- **Virtual file creation**: The agent writes files into a temporary workspace (`./workspace`).
- **Realworld file export**: After completing the task, the virtual files are copied to the real filesystem under `./workspace`.
1. **Search the web** for information using DuckDuckGo.
2. **Create virtual files** during its execution.
3. **Export** those virtual files to the real filesystem after the agent finishes.
## How it works
1. The agent is instantiated with an OpenRouter LLM and a single web search tool.
2. A backend combines a local shell environment (for executing commands) and a simple inmemory filesystem.
3. When the user asks for information, the agent calls `web_search`, receives the results, and stores them in `results.txt` inside the virtual workspace.
4. The script then copies that file to the real working directory so it can be committed to Git later.
The implementation follows the guidelines from the *Deep Agents from Scratch* course and uses the BroJS LLM endpoint.
## Running locally
## Prerequisites
- Python 3.10 or newer
- A valid BroJS API key set in the environment variable `JOURNAL_MCP_PAT`.
- (Optional) A virtual environment to isolate dependencies.
## Installation
```bash
# Clone the repository
git clone https://git.brojs.ru/KirillKutlakhmetov/task-69de7223f309a98be0007e09.git
cd task-69de7223f309a98be0007e09
# Create and activate a virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate # On Windows use .venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
## Usage
```bash
# Ensure the environment variable is set
export JOURNAL_MCP_PAT=your_brojs_api_key
# Run the agent
python main.py
```
The program will print search results and create a `results.txt` file in the current directory.
The agent will perform a sample query (`"Python async programming"`). After completion, the virtual files created during the run will be exported to the `./exported_files` directory.
## Project Structure
- `main.py` Entry point and agent definition.
- `requirements.txt` Python dependencies.
- `README.md` Project documentation.
## Customization
- To change the query, modify the `user_query` variable in `main.py`.
- To add more tools, define additional functions decorated with `@tool` and include them in the `tools` list when creating the agent.
- The agent can be extended to accept user input at runtime by replacing the hardcoded query.
## License
MIT License.