
aisdk-prompt-optimizer
A tool kit for generating high quality prompts for AISDK using DSPy GEPA optimizer
Stars: 83

AISDK Prompt Optimizer is an open-source tool designed to transform AI interactions by optimizing prompts. It utilizes the GEPA reflective optimizer to evolve textual components of AI systems, providing features such as reflective prompt mutation, rich textual feedback, and Pareto-based selection. Users can teach their AI desired behaviors, collect ideal samples, run optimization to generate optimized prompts, and deploy the results in their applications. The tool leverages advanced optimization algorithms to guide AI through interactive conversations and refine prompt candidates for improved performance.
README:
Transform your AI interactions with intelligent prompt optimization. Teach your AI, collect ideal samples, and generate optimized prompts using the powerful AISDK Prompt Optimizer.
Fully Open Source - Built by the team that created Langtrace AI and Zest
GEPA (Genetic-Pareto) is a reflective optimizer that adaptively evolves textual components (such as prompts) of AI systems. Unlike traditional optimization methods that only use scalar scores, GEPA leverages rich textual feedback to guide the optimization process, allowing it to propose high-performing prompts in very few rollouts.
Key features of GEPA:
- Reflective Prompt Mutation: Uses LLMs to reflect on execution traces and propose new instructions
- Rich Textual Feedback: Leverages any textual feedback beyond just scalar rewards
- Pareto-based Selection: Maintains a frontier of candidates that excel in different scenarios
Learn more: GEPA Documentation
- Start Conversation: Begin chatting with the AI and teach it desired behaviors through examples
- Mark Examples: Save ideal conversation samples that represent perfect responses
- Run Optimization: Let AISDK Prompt Optimizer analyze patterns and generate optimized prompts
- Deploy Results: Use the optimized prompts in your applications
- Teach Your AI: Guide your AI through interactive conversations and demonstrate the ideal responses you want to achieve
- Collect Ideal Samples: Gather high-quality conversation examples that represent perfect AI behavior for your use case
- AISDK Prompt Optimizer: Leverage advanced optimization algorithms to automatically generate and refine prompt candidates
- Node.js (18+ recommended)
-
uv
package manager for Python - OpenAI API key
- AI Gateway API key
Before running the application, you need to set up your environment variables:
-
Copy the example environment file:
cp .env.example .env
-
Edit the
.env
file and add your API keys:# Required: OpenAI API key for AI model access OPENAI_API_KEY=your_actual_openai_api_key_here # Required: AI Gateway API key for prompt optimization AI_GATEWAY_API_KEY=your_actual_ai_gateway_api_key_here
Important: Never commit your actual API keys to version control. The .env
file is already included in .gitignore
.
# Clone the repository
git clone https://github.com/Scale3-Labs/aisdk-prompt-optimizer
cd aisdk-prompt-optimizer
# Install dependencies
npm install
# Start both services (recommended)
npm run dev:all
# Terminal 1: Start the Python optimizer
cd python_optimizer
uv run app.py
# Terminal 2: Start the web app
npm run dev
The web app will be available at http://localhost:3000
and the Python optimizer at http://localhost:8000
. Both services need to be running for the optimization features to work.
-
npm run dev
- Start the Next.js development server -
npm run dev:py
- Start the Python optimizer server -
npm run dev:all
- Start both services concurrently
The repository includes a lightweight Flask server exposing the dspy.GEPA
optimizer, managed with uv
. The Next.js /api/optimize
route calls this server and writes optimization artifacts to:
-
data/prompt.md
- Generated optimized prompts -
data/complete-optimization.json
- Complete optimization results and metadata
Built with Next.js and shadcn/ui components, the web interface provides:
- Interactive chat interface for teaching the AI
- Sample collection and management
- Optimization trigger and results visualization
- Modern, responsive UI with dark/light mode support
- Frontend: Next.js, TypeScript, Tailwind CSS, shadcn/ui
- Backend: Python Flask server with dspy.GEPA optimizer
- Package Management: npm (frontend), uv (Python)
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
For the Python optimizer, you'll need to deploy it to a Python-compatible hosting service and update the API endpoints accordingly.
Check out the Next.js deployment documentation for more details.
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
We welcome contributions! This is a fully open source project. Please feel free to submit issues and pull requests.
Built with ❤️ by the team that created Langtrace AI and Zest.
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