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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:

✨ What Can This Agent Do?

This friendly little agent can:

🔎 Search the web for information when it needs more context
🧠 Decide intelligently when to search and when it has enough info to answer
📝 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

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:

DecideAction: The brain that figures out whether to search or answer
SearchWeb: The researcher that goes out and finds information
AnswerQuestion: The writer that crafts the final answer

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