Update README.md

cookbook/pocketflow-agent/README.md

@@ -1,54 +1,21 @@
 # PocketFlow Research Agent - Tutorial for Dummy
 
-This project demonstrates a simple yet powerful LLM-powered research agent built with PocketFlow, a minimalist LLM framework in just 100 lines of code! 
+This project demonstrates a simple yet powerful LLM-powered research agent built with PocketFlow, a minimalist LLM framework in just 100 lines of code! This implementation is based directly on the tutorial post [LLM Agents are simply Graph — Tutorial For Dummies](https://zacharyhuang.substack.com/p/llm-agent-internal-as-a-graph-tutorial).
 
-> 📝 **Note:** This implementation is based directly on the tutorial post [LLM Agents are simply Graph — Tutorial For Dummies](https://zacharyhuang.substack.com/p/llm-agent-internal-as-a-graph-tutorial). Check it out for a better understanding of the concepts!
+## Getting Started
-
-Want to learn more about PocketFlow and building cool LLM agents? Check out:
-- [PocketFlow GitHub](https://github.com/the-pocket/PocketFlow)
-- [PocketFlow Documentation](https://the-pocket.github.io/PocketFlow/)
-
-## ✨ What Can This Agent Do?
-
-This friendly little agent can:
-1. 🔎 Search the web for information when it needs more context
-2. 🧠 Decide intelligently when to search and when it has enough info to answer
-3. 📝 Generate helpful, informative responses based on its research
-
-## 🚀 Getting Started
-
-### What You'll Need
-- Python 3.8 or newer
-- An OpenAI API key (don't worry, we'll help you set this up!)
-
-### Easy Installation
 
 1. Install the packages you need with this simple command:
 ```bash
 pip install -r requirements.txt
 ```
 
-## 📂 Project Structure
+2. Let's get your OpenAI API key ready:
-
-Here's what's in each file:
-- [`main.py`](./main.py): The starting point - runs the whole show!
-- [`flow.py`](./flow.py): Connects everything together into a smart agent
-- [`nodes.py`](./nodes.py): The building blocks that make decisions and take actions
-- [`utils.py`](./utils.py): Helper functions for talking to the LLM and searching the web
-
-## 🏃‍♂️ Quick Start Guide
-
-### Step 1: Set Up Your API Key
-
-First, let's get your OpenAI API key ready:
 
 ```bash
 export OPENAI_API_KEY="your-api-key-here"
 ```
 
-### Step 2: Make Sure Everything Works
+3. Let's do a quick check to make sure your API key is working properly:
-
-Let's do a quick check to make sure your API key is working properly:
 
 ```bash
 python utils.py
@@ -56,23 +23,19 @@ python utils.py
 
 This will test both the LLM call and web search features. If you see responses, you're good to go!
 
-### Step 3: Run Your Agent
+4. Try out the agent with the default question (about Nobel Prize winners):
-
-Try out the agent with the default question (about Nobel Prize winners):
 
 ```bash
 python main.py
 ```
 
-### Step 4: Ask Your Own Questions
+5. Got a burning question? Ask anything you want by using the `--` prefix:
-
-Got a burning question? Ask anything you want by using the `--` prefix:
 
 ```bash
 python main.py --"What is quantum computing?"
 ```
 
-## 🧩 How It Works
+## How It Works?
 
 The magic happens through a simple but powerful graph structure with three main parts:
 
@@ -86,4 +49,10 @@ graph TD
 Here's what each part does:
 1. **DecideAction**: The brain that figures out whether to search or answer
 2. **SearchWeb**: The researcher that goes out and finds information
-3. **AnswerQuestion**: The writer that crafts the final answer
+3. **AnswerQuestion**: The writer that crafts the final answer
+
+Here's what's in each file:
+- [`main.py`](./main.py): The starting point - runs the whole show!
+- [`flow.py`](./flow.py): Connects everything together into a smart agent
+- [`nodes.py`](./nodes.py): The building blocks that make decisions and take actions
+- [`utils.py`](./utils.py): Helper functions for talking to the LLM and searching the web