Tech Company Orchestrator: Simulate Full-Stack Development Workflow with AI Agents
Author: Daniel Kliewer
Date: 2024-11-29
Tags: AI Agents, Tech Workflow, NetworkX, OpenAI, SDLC Automation
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# Tech Company Orchestrator - User Guide

[https://github.com/kliewerdaniel/tech-company-orchestrator](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](#features)
2. [Requirements](#requirements)
3. [Installation](#installation)
4. [Usage](#usage)
5. [Workflow](#workflow)
6. [Customizing Agents](#customizing-agents)
7. [Troubleshooting](#troubleshooting)
8. [Future Improvements](#future-improvements)

---

## 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.

---

## Installation

1. **Clone the Repository**:
   ```bash
   git clone https://github.com/kliewerdaniel/tech-company-orchestrator.git
   cd tech-company-orchestrator
   ```

2. **Install Dependencies**:
   Use `pip` to install the required libraries:
   ```bash
   pip install -r requirements.txt
   ```

3. **Set Up `.env` File**:
   Create a `.env` file in the root directory and add your OpenAI API key:
   ```bash
   OPENAI_API_KEY=your-openai-api-key
   ```

---

## 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`:**
```json
{
    "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:
```bash
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`.

---

## 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.

---

## 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`:
   ```python
   G.add_edges_from([
       ('PreviousAgent', 'NewAgent'),
       ('NewAgent', 'NextAgent')
   ])
   ```

---

## Troubleshooting

### OpenAI API Key Not Found
Ensure the `.env` file is correctly configured with your API key:
```bash
OPENAI_API_KEY=your-openai-api-key
```

### Invalid `initial_prompt.txt` Format
Validate the JSON structure using an online tool like [jsonlint.com](https://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.

---

## 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.

---

## 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! 🎉