Google DeepMind Opens AlphaEvolve to Everyone After Doubling Klarna's AI Throughput

AlphaEvolve, Google DeepMind's Gemini-powered algorithm optimization agent, is now generally available on Google Cloud. What started as an internal research tool quietly improving Google's own data centers and chip designs has graduated into a full cloud product, accessible to any organization willing to point it at their hardest engineering problems.

Not your average coding assistant

AlphaEvolve is not a code completion tool. It is an evolutionary coding agent -- a system that autonomously generates, tests, and iteratively refines entire algorithms through a loop of mutation and selection, guided by a scoring function you define. Think of it as a search engine for better code, one that explores a combinatorially vast design space no human team could comb through manually.

AlphaEvolve pairs the creative problem-solving capabilities of Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas. It leverages an ensemble of models: Gemini Flash maximizes the breadth of ideas explored, while Gemini Pro provides critical depth with insightful suggestions.

The key mechanic that separates this from a standard LLM coding agent is the evaluator. AlphaEvolve combines creative, server-side LLM exploration with secure client-side code execution to autonomously discover new, optimized solutions that surpass human-designed baselines. Your evaluator runs locally, on your own infrastructure, scoring each candidate the system proposes.

The evolutionary loop, in practice

Getting started requires just two inputs:

1. A seed program: Your existing algorithm, written as code. You mark which sections are open for optimization.
2. An evaluator: A deterministic script that compiles, runs, and scores each candidate solution, returning scalar metrics for AlphaEvolve to maximize.

From there, AlphaEvolve runs an autonomous loop. Gemini Flash handles speed, generating mutated and optimized versions of the code added to the population space, while Gemini Pro provides higher-quality suggestions. Evolution algorithms then select which mutations to combine and further mutate, prioritizing them as the starting point for the next generation.