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An Automatic Prompt Optimization Framework for Large Language Models
| Date | Stars |
|---|---|
| 2026-07-31 | 972 |
| 2026-08-02 | 973 |
| 2026-08-06 | 973 |
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<div align="center">
<img src="images/logo1.png" alt="Promptomatix Logo" width="400"/>
<h1>Promptomatix</h1>
<h3>A Powerful Framework for LLM Prompt Optimization</h3>
</div>
<div align="center">
<img src="https://img.shields.io/badge/Python-3.8%2B-brightgreen.svg" alt="Python 3.8+">
<img src="https://img.shields.io/badge/License-Apache-green.svg" alt="License">
<a href="https://arxiv.org/abs/2507.14241" target="_blank">
<img src="https://img.shields.io/badge/arXiv-2507.14241-b31b1b.svg" alt="arXiv">
</a>
<a href="docs/API_Reference.md" target="_blank">
<img src="https://img.shields.io/badge/Documentation-API-blue.svg" alt="API Documentation">
</a>
</div>
<p align="center">
<a href="#-overview">Overview</a> |
<a href="#-installation">Installation</a> |
<a href="#-example-usage">Examples</a> |
<a href="#-key-features">Features</a> |
<a href="docs/API_Reference.md" target="_blank">API Docs</a> |
<a href="#-cli-usage">CLI</a>
</p>
## 📋 Overview
Promptomatix is an AI-driven framework designed to automate and optimize large language model (LLM) prompts. It provides a structured approach to prompt optimization, ensuring consistency, cost-effectiveness, and high-quality outputs while reducing the trial-and-error typically associated with manual prompt engineering.
The framework leverages the power of DSPy and advanced optimization techniques to iteratively refine prompts based on task requirements, synthetic data, and user feedback. Whether you're a researcher exploring LLM capabilities or a developer building production applications, Promptomatix provides a comprehensive solution for prompt optimization.
**📚 API Documentation**: Comprehensive API documentation is available in the [`docs/`](docs/) directory, including detailed reference guides for all modules and functions.
## 🏗️ Architecture
<div align="center">
<a href="images/architecture1.pdf" target="_blank">
<img src="images/architecture1_quality.png" alt="Promptomatix Architecture" width="1200"/>
</a>
</div>
The Promptomatix architecture consists of several key components:
- **Input Processing**: Analyzes raw user input to determine task type and requirements
- **Synthetic Data Generation**: Creates training and testing datasets tailored to the specific task
- **Optimization Engine**: Uses DSPy or meta-prompt backends to iteratively improve prompts
- **Evaluation System**: Assesses prompt performance using task-specific metrics
- **Feedback Integration**: Incorporates human feedback for continuous improvement
- **Session Management**: Tracks optimization progress and maintains detailed logs
### 🌟 Key Features
- **Zero-Configuration Intelligence**: Automatically analyzes tasks, selects techniques, and configures prompts
- **Automated Dataset Generation**: Creates synthetic training and testing data tailored to your specific domain
- **Task-Specific Optimization**: Selects the appropriate DSPy module and metrics based on task type
- **Real-Time Human Feedback**: Incorporates user feedback for iterative prompt refinement
- **Comprehensive Session Management**: Tracks optimization progress and maintains detailed logs
- **Framework Agnostic Design**: Supports multiple LLM providers (OpenAI, Anthropic, Cohere)
- **CLI and API Interfaces**: Flexible usage through command-line or REST API
## ⚙️ Installation
### Quick Install (Recommended)
```bash
# Clone the repository
git clone https://github.com/airesearch-emu/promptomatix.git
cd promptomatix
# Install with one command
./install.sh
```
The installer will:
- ✅ Check Python 3 installation
- ✅ Create a virtual environment (`promptomatix_env`)
- ✅ Initialize git submodules (DSPy)
- ✅ Install all dependencies
### 🔧 Activate the Environment
**Important**: You need to activate the virtual environment each time you use Promptomatix:
```bash
# Activate the environment
source promptomatix_env/bin/activate
# You'll see (promptomatix_env) in your prompt when activated
```Excerpt of 9,395 characters
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