From 49ba63214068e785f7adce75cf4e942a014a8faa 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: Fri, 5 Jun 2026 10:31:14 +0000
Subject: [PATCH] Update README.md

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 README.md | 76 ++++++++++++++++++++++++++++++++++++++++++-------------
 1 file changed, 58 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
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-# Deep Agent from Scratch
+# Deep Agent with StateGraph
 
-This repository contains a minimal implementation of a deep agent that can:
+This repository contains a minimal implementation of a **Deep Agent** based on the
+`deep-agents-from-scratch` notebook.  The agent uses a LangGraph
+`StateGraph` to orchestrate the following steps:
 
-1. Search the web using DuckDuckGo.
-2. Create virtual files in memory.
-3. Export the virtual files to the real file system.
+1. **Think** – decide what to search for.
+2. **Search** – perform a web search with Tavily.
+3. **Summarize** – summarize each search result and store the raw content in a
+   virtual file system.
+4. **Write File** – dump all virtual files to disk.
 
-The agent is built using the new LangChain 1.x and LangGraph 1.x APIs.
+The implementation follows the modern LangChain API guidelines:
 
-## Prerequisites
+* All imports use the current modular packages (`langchain_openai`,
+  `langgraph`, `langchain_core`, etc.).
+* No legacy `langchain.chat_models` or `langchain.llms` imports.
+* The agent can be run from the command line.
 
-- Python 3.11 or newer
-- Ollama running locally with a model such as `llama3.1`
-- `pip install -r requirements.txt`
-
-## Running the Agent
+## Setup
 
 ```bash
-python agent.py
+# Create a virtual environment (recommended)
+python -m venv .venv
+source .venv/bin/activate  # Windows: .venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the agent interactively
+python src/deep_agents_from_scratch/deep_agent.py
 ```
 
-The script will run the agent with a sample prompt, create a virtual file `report.txt`, and export it to the `exported_files` directory.
+## Usage
 
-## File Structure
+After starting the script you will be prompted to enter a research question.
+The agent will perform a web search, summarize the results, and write the
+raw content to the `output/` directory.
 
-- `agent.py` – Main implementation.
-- `requirements.txt` – Python dependencies.
-- `README.md` – This file.
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+## Project Structure
+
+```
+src/
+├── deep_agents_from_scratch/
+│   ├── __init__.py
+│   ├── deep_agent.py
+│   ├── research_tools.py
+│   └── state.py
+```
+
+- `state.py` – defines the `DeepAgentState` dataclass used by the graph.
+- `research_tools.py` – contains the Tavily search, think, and summarization
+  tools.
+- `deep_agent.py` – builds the `StateGraph`, defines node functions, and
+  provides a CLI entry point.
+
+## Extending the Agent
+
+You can add more tools or nodes by following the pattern used in
+`deep_agent.py`.  For example, to add a node that writes the summaries to a
+PDF you would:
+
+1. Create a new tool that formats the summaries.
+2. Add a node function that calls the tool.
+3. Connect the node in the graph.
+
+## License
+
+MIT License.
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