LLM_Engineering_OLD/community-contributions/muhammad_qasim_sheikh/Week 2/day1/readme.md

Multi-Agent Conversation Simulator (OpenAI + Ollama)

Project Overview

This project is an experimental multi-agent conversational simulation built with OpenAI GPT models and a locally-hosted Ollama LLM (Llama 3.2). It demonstrates how multiple AI personas can participate in a shared conversation, each with distinct roles, perspectives, and behaviors — producing a dynamic, evolving debate from different angles.

The script orchestrates a three-way dialogue around a single topic (“Why did the chicken cross the road?”) between three agents, each powered by a different model and persona definition:

• Athena (OpenAI GPT-4o): A strategic thinker who looks for deeper meaning, long-term consequences, and practical wisdom.
• Loki (Ollama Llama 3.2): A sarcastic trickster who mocks, questions, and challenges the others with wit and irony.
• Orion (OpenAI GPT-4o): A data-driven realist who grounds the discussion in facts, statistics, or logical deductions.

What’s Happening in the Code

1. Environment Setup

   • Loads the OpenAI API key from a .env file.
   • Initializes OpenAI’s Python client and configures a local Ollama endpoint.

2. Persona System Prompts

   • Defines system prompts for each agent to give them unique personalities and communication styles.
   • These prompts act as the “character definitions” for Athena, Loki, and Orion.

3. Conversation Initialization

   • Starts with a single conversation topic provided by the user.
   • All three agents are aware of the discussion context and prior messages.

4. Conversation Loop

   • The conversation runs in multiple rounds (default: 5).
   • In each round:
     • Athena (GPT) responds first with a strategic viewpoint.
     • Loki (Ollama) replies next, injecting sarcasm and skepticism.
     • Orion (GPT) follows with a fact-based or analytical perspective.
   • Each response is appended to the conversation history so future replies build on previous statements.

5. Dynamic Context Sharing

   • Each agent receives the entire conversation so far as context before generating a response.
   • This ensures their replies are relevant, coherent, and responsive to what the others have said.

6. Output Rendering

   • Responses are displayed as Markdown in a readable, chat-like format for each speaker, round by round.

Key Highlights

• Demonstrates multi-agent orchestration with different models working together in a single script.
• Uses OpenAI GPT models for reasoning and Ollama (Llama 3.2) for local, cost-free inference.
• Shows how system prompts and context-aware message passing can simulate realistic dialogues.
• Provides a template for experimenting with AI characters, debate simulations, or collaborative agent systems.