pocketflow/cookbook/pocketflow-agent

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!

📝 Note: This implementation is based directly on the tutorial post LLM Agents are simply Graph — Tutorial For Dummies. Check it out for a better understanding of the concepts!

Want to learn more about PocketFlow and building cool LLM agents? Check out:

• PocketFlow GitHub
• PocketFlow Documentation

✨ 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:

pip install -r requirements.txt

📂 Project Structure

Here's what's in each file:

• main.py: The starting point - runs the whole show!
• flow.py: Connects everything together into a smart agent
• nodes.py: The building blocks that make decisions and take actions
• 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:

export OPENAI_API_KEY="your-api-key-here"

Step 2: Make Sure Everything Works

Let's do a quick check to make sure your API key is working properly:

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

Try out the agent with the default question (about Nobel Prize winners):

python main.py

Step 4: Ask Your Own Questions

Got a burning question? Ask anything you want by using the -- prefix:

python main.py --"What is quantum computing?"

🧩 How It Works

The magic happens through a simple but powerful graph structure with three main parts:

graph TD
    A[DecideAction] -->|"search"| B[SearchWeb]
    A -->|"answer"| C[AnswerQuestion]
    B -->|"decide"| A

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