diff --git a/README.md b/README.md
index 49ec66b..d496ddf 100644
--- a/README.md
+++ b/README.md
@@ -1,54 +1,45 @@
-# Deep Agent based on LangGraph & LangChain
+# Deep Agent
 
-This repository contains a minimal implementation of a **Deep Agent** that can:
+This repository contains a minimal implementation of a **Deep Agent** inspired by the *Deep Agents from Scratch* course. The agent can:
 
-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.
+1. Search the web (DuckDuckGo).
+2. Create, read and combine virtual files.
+3. Export the virtual files to the real filesystem.
+4. Use LangChain + Ollama for LLM inference.
 
-The agent is built on top of the *LangGraph* framework and uses the *LangChain* tools API.
-
-## Installation
+## Setup
 
 ```bash
-# Create a virtual environment (recommended)
-python -m venv .venv
-source .venv/bin/activate
-
 # Install dependencies
 pip install -r requirements.txt
 ```
 
-> **Note**: The Tavily API key is required for the web search tool. Set it via the
-> environment variable `TAVILY_API_KEY`.
-
 ## Usage
 
-```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
+python deep_agent.py "Write a summary of the latest Python release and store it in summary.txt"
 ```
 
-## License
+The agent will perform the task, create virtual files, and export them to `output_files/`.
 
-MIT
+## Architecture
+
+- `DeepAgent` class implements the agent logic.
+- `search` uses DuckDuckGo HTML API.
+- `write_file`, `read_file`, `combine` manage a simple in‑memory virtual filesystem.
+- `export_files` writes the virtual files to disk.
+- The agent loop uses a JSON‑based action protocol.
+
+## Requirements
+
+- Python 3.10+
+- LangChain >= 1.0.0
+- LangGraph >= 1.0.0
+- langchain_ollama
+- langchain_text_splitters
+- langchain_chroma
+- requests
+
+---
+
+Feel free to extend the agent with more actions or integrate other LLM providers.
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