Lab 3: Chatbot vs ReAct Agent (Industry Edition)

Welcome to Phase 3 of the Agentic AI course! This lab focuses on moving from a simple LLM Chatbot to a sophisticated ReAct Agent with industry-standard monitoring.

🚀 Getting Started

1. Setup Environment

Copy the .env.example to .env and fill in your API keys:

cp .env.example .env

2. Install Dependencies

pip install -r requirements.txt

3. Directory Structure

• src/tools/: Extension point for your custom tools.

🏠 Running with Local Models (CPU)

If you don't want to use OpenAI or Gemini, you can run open-source models (like Phi-3) directly on your CPU using llama-cpp-python.

1. Download the Model

Download the Phi-3-mini-4k-instruct-q4.gguf (approx 2.2GB) from Hugging Face:

• Phi-3-mini-4k-instruct-GGUF
• Direct Download: phi-3-mini-4k-instruct-q4.gguf

2. Place Model in Project

Create a models/ folder in the root and move the downloaded .gguf file there.

3. Update .env

Change your DEFAULT_PROVIDER and set the path:

DEFAULT_PROVIDER=local
LOCAL_MODEL_PATH=./models/Phi-3-mini-4k-instruct-q4.gguf

🎯 Lab Objectives

1. Baseline Chatbot: Observe the limitations of a standard LLM when faced with multi-step reasoning.
2. ReAct Loop: Implement the Thought-Action-Observation cycle in src/agent/agent.py.
3. Provider Switching: Swap between OpenAI and Gemini seamlessly using the LLMProvider interface.
4. Failure Analysis: Use the structured logs in logs/ to identify why the agent fails (hallucinations, parsing errors).
5. Grading & Bonus: Follow the SCORING.md to maximize your points and explore bonus metrics.

🛠️ How to Use This Baseline

The code is designed as a Production Prototype. It includes:

• Telemetry: Every action is logged in JSON format for later analysis.
• Robust Provider Pattern: Easily extendable to any LLM API.
• Clean Skeletons: Focus on the logic that matters—the agent's reasoning process.

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Happy Coding! Let's build agents that actually work.