From 5f3f32613dd3dc30ea66f5a7d83e6b169a8f3e43 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=9A=D1=83=D1=82?=
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 <kirillkutlahmetov10@gmail.com>
Date: Tue, 26 May 2026 12:16:44 +0000
Subject: [PATCH] add README.md

---
 README.md | 58 ++++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 45 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index 9093c5e..48d6311 100644
--- a/README.md
+++ b/README.md
@@ -1,21 +1,53 @@
-# 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`).
-- **Real‑world 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 in‑memory 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 hard‑coded query.
+
+## License
+MIT License.