darwinian-evolver

You can automate prompt engineering by running an LLM-driven evolution loop that iteratively mutates and evaluates prompts against defined criteria to surface robust improvements automatically.

An evolution loop for code and regex optimization uses LLMs to apply mutator and evaluator workflows, repeatedly refining artifacts like SQL queries or small code blocks until optimal performance is achieved.

Yes, running evolutionary mutator workflows requires Python, an active LLM provider API key, and the darwinian_evolver tools to execute the defined Problem with Organism, Evaluator, and Mutator components.

To set up custom prompt mutation, you configure a Problem definition using provided templates, specify your Organism, Evaluator, and Mutator logic, then execute the EvolveProblemLoop with your LLM driver.

Yes, built-in OpenRouter-style drivers support the automated evolution of SQL queries by passing them through the iterative LLM mutation and evaluation workflow alongside your custom evaluation data.

A key limitation of LLM-driven code evolution is the risk of overfitting artifacts to specific evaluation data, requiring careful hyperparameter experimentation and failure pattern analysis to guard against degraded generalization.