Kiln is a workbench for building, evaluating, and optimizing AI systems that combines evals, prompt optimization, RAG, fine-tuning, synthetic data generation, and agent development in a single integrated environment.
The tool addresses the fragmentation of AI development workflows by providing a unified platform where evaluation, optimization, and iteration happen together. It works by offering both a desktop application for team collaboration and a Python library for production deployment. The desktop app enables non-technical team members such as product managers, subject matter experts, and QA staff to contribute by rating outputs and adding training data without writing code. The Python library ships the same capabilities to production environments, allowing workflows developed in the app to run at scale. The system runs locally with user-supplied API keys or can operate fully offline using Ollama.
Teams should adopt this tool if they need to coordinate AI development across both technical and non-technical contributors, or if they want to avoid switching between separate tools for evaluation, optimization, and data generation. It suits projects where iteration speed and team collaboration matter, particularly those building with large language models where prompt engineering and synthetic data generation are central to the development process. The tool's local-first architecture makes it appropriate for teams with data privacy requirements or those preferring to avoid cloud dependencies.
The project maintains active build and test automation. The codebase is distributed through standard Python package channels. The tool supports multiple model providers and local inference options, indicating ongoing work to maintain compatibility across the AI ecosystem.
