Tech Company Orchestrator - User Guide

https://github.com/kliewerdaniel/tech-company-orchestrator

Welcome to the Tech Company Orchestrator! This project is designed to simulate the workflow of a tech company by orchestrating various agents to collaboratively process prompts and generate comprehensive outputs such as code, design specifications, deployment scripts, and more. The program utilizes OpenAI models and a directed graph (via NetworkX) to model the interactions between different departments (agents).

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Table of Contents

1. Features
2. Requirements
3. Installation
4. Usage
5. Workflow
6. Customizing Agents
7. Troubleshooting
8. Future Improvements

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Features

• Agent-based Workflow: Simulates different tech company departments (e.g., Product Management, Design, Engineering).
• Directed Graph Processing: Uses NetworkX to define the flow of data between agents.
• OpenAI API Integration: Employs GPT models for generating agent-specific outputs.
• Iterative Processing: Refines outputs across iterations until the workflow is complete.
• Progress Persistence: Logs intermediate and final outputs to files.
• Custom Prompt Support: Accepts a structured prompt from an external file (initial_prompt.txt).

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Requirements

• Python: 3.8 or higher
• Dependencies:
  • openai
  • networkx
  • python-dotenv
  • json
• OpenAI API Key: You need an active OpenAI API key to use this program.

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Installation

1. Clone the Repository:

   git clone https://github.com/kliewerdaniel/tech-company-orchestrator.git
   cd tech-company-orchestrator
   

2. Install Dependencies: Use pip to install the required libraries:

   pip install -r requirements.txt
   

3. Set Up .env File: Create a .env file in the root directory and add your OpenAI API key:

   OPENAI_API_KEY=your-openai-api-key
   

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Usage

Step 1: Prepare Your Initial Prompt

Create an initial_prompt.txt file in the root directory. The prompt should be a JSON-formatted dictionary containing:

• message: The initial idea or requirements.
• code: Leave this as an empty string ("") initially.
• readme: Leave this as an empty string ("") initially.

Example initial_prompt.txt:

{
    "message": "Develop a platform that connects freelancers with clients using AI for project matching.",
    "code": "",
    "readme": ""
}

Step 2: Run the Program

Execute the main.py file:

python main.py

Step 3: Review the Outputs

The program generates the following files:

• output.txt: Contains the intermediate outputs after each iteration.
• final_output.txt: Contains the final output, including the message, code, and readme.

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Workflow

The program simulates the workflow of a tech company by processing the prompt through the following agents:

1. Product Management: Expands the initial idea into detailed product requirements.
2. Design: Creates UI/UX specifications, including wireframes and style guides.
3. Engineering: Develops the software application based on the specifications.
4. Testing: Generates comprehensive test cases for quality assurance.
5. Security: Analyzes and enhances the security of the application.
6. DevOps: Creates deployment scripts and CI/CD pipelines.
7. Final Agent: Verifies if the project is complete or requires further refinement.

The agents are connected in a directed graph, ensuring an organized flow of information between departments.

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Customizing Agents

Modify Agent Behavior

Each agent has its own Python file (e.g., engineering.py, design.py) where you can adjust:

• The prompts sent to the OpenAI API.
• How the agent processes the data (e.g., appending to code or readme).

Add a New Agent

1. Create a new Python file for the agent.
2. Define the agent's logic (similar to existing agents).
3. Add the new agent to the workflow graph in main.py:

   G.add_edges_from([
       ('PreviousAgent', 'NewAgent'),
       ('NewAgent', 'NextAgent')
   ])
   

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Troubleshooting

OpenAI API Key Not Found

Ensure the .env file is correctly configured with your API key:

OPENAI_API_KEY=your-openai-api-key

Invalid initial_prompt.txt Format

Validate the JSON structure using an online tool like jsonlint.com.

Empty or Incorrect Outputs

• Check the logs in output.txt for intermediate results.
• Ensure the OpenAI API is accessible and the specified model is available.

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Future Improvements

• Parallel Processing: Optimize the workflow to allow parallel execution of agents where applicable.
• Enhanced Error Handling: Improve robustness by adding retries and better error reporting.
• Interactive CLI: Provide a command-line interface for easier customization of inputs and parameters.
• Integration Testing: Add tests to validate the functionality of each agent and the overall workflow.

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Contributions

Feel free to fork the repository and submit pull requests for improvements. Feedback and suggestions are always welcome!

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With this guide, you should be able to set up, run, and customize the Tech Company Orchestrator to suit your needs. Happy orchestrating! 🎉