From 4b13f965bbf88a5097f1f23f3250c8c4bf042d60 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=A0=D0=BE=D0=BC?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BE=D0=B2?= <kirilinka320@gmail.com>
Date: Thu, 4 Jun 2026 16:15:40 +0000
Subject: [PATCH] Update README.md

---
 README.md | 63 ++++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 44 insertions(+), 19 deletions(-)

diff --git a/README.md b/README.md
index 737edbe..49ec66b 100644
--- a/README.md
+++ b/README.md
@@ -1,29 +1,54 @@
-# Deep Agent from Scratch
+# Deep Agent based on LangGraph & LangChain
 
-This repository contains a minimal implementation of a deep agent that can:
+This repository contains a minimal implementation of a **Deep Agent** that can:
 
-1. Search the web using DuckDuckGo.
-2. Create virtual files in memory.
-3. Export the virtual files to the real file system.
+1. Search the web using the Tavily search API.
+2. Create virtual files in an in‑memory file system.
+3. Export all virtual files to the real filesystem.
 
-The agent is built using the new LangChain 1.x and LangGraph 1.x APIs.
+The agent is built on top of the *LangGraph* framework and uses the *LangChain* tools API.
 
-## Prerequisites
-
-- Python 3.11 or newer
-- Ollama running locally with a model such as `llama3.1`
-- `pip install -r requirements.txt`
-
-## Running the Agent
+## Installation
 
 ```bash
-python agent.py
+# Create a virtual environment (recommended)
+python -m venv .venv
+source .venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
 ```
 
-The script will run the agent with a sample prompt, create a virtual file `report.txt`, and export it to the `exported_files` directory.
+> **Note**: The Tavily API key is required for the web search tool. Set it via the
+> environment variable `TAVILY_API_KEY`.
 
-## File Structure
+## Usage
 
-- `agent.py` – Main implementation.
-- `requirements.txt` – Python dependencies.
-- `README.md` – This file.
\ No newline at end of file
+```python
+from agent import create_agent_executor, vfs
+
+# Create an executor
+executor = create_agent_executor()
+
+# Run the agent with a simple prompt
+result = executor.invoke({"input": "Find the latest news about Python and create a file called news.txt with the summary."})
+print(result)
+
+# After the agent finishes, export the virtual files
+vfs.export_to_disk("output")
+```
+
+The `output` directory will contain `news.txt` with the content produced by the agent.
+
+## Testing
+
+The project includes a simple test that verifies the existence of the two
+required functions:
+
+```bash
+python -m unittest discover -s tests
+```
+
+## License
+
+MIT