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# AlphaEvolve on Google Cloud **Discover and optimize algorithms with [AlphaEvolve](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/overview) — a Gemini-powered evolutionary coding agent, available through Gemini Enterprise and running in your own Google Cloud project.** [](https://raw.githubusercontent.com/Google-Cloud-AI/alphaevolve-on-googlecloud/main/LICENSE) [](https://www.python.org/downloads/) [](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/overview) <p align="center"> <img src="assets/evolve-code.gif" alt="AlphaEvolve rewriting code while a candidate's metrics improve toward the 'Better' region across iterations" width="760"> <br><em>AlphaEvolve rewriting code as candidate solutions improve across iterations.</em> </p> AlphaEvolve is an evolutionary coding agent for **general-purpose algorithm discovery and optimization**. You provide a seed program and a scoring function; AlphaEvolve uses Gemini to propose code changes, evaluates each candidate, and evolves the population toward better solutions over many generations. This repository is the **client-side Python library** (`src/alpha_evolve/`) plus a set of **runnable examples** (`examples/`) that show how to wire AlphaEvolve into real Google Cloud environments — from a pure-Python loop with zero infrastructure to GPU training on GKE. --- ## Contents - [Quickstart (≈5 minutes)](#quickstart-5-minutes) - [What to expect during a run](#what-to-expect-during-a-run) - [Results](#results) - [How AlphaEvolve works](#how-alphaevolve-works) - [Examples](#examples) - [Using the `alpha_evolve` library](#using-the-alpha_evolve-library) - [Using the AlphaEvolve Skills](#using-the-alphaevolve-skills) - [Configuration](#configuration) - [Repository structure](#repository-structure) - [Cost](#cost) - [FAQ](#faq) - [Documentation & support](#documentation--support) - [License](#license) --- ## Quickstart (≈5 minutes) The **Circle Packing** example runs the full evolution loop with local Python evaluation and no cloud infrastructure beyond the AlphaEvolve API. It's the fastest way to see the loop work. ### Prerequisites - Python **3.9+**, [`uv`](https://docs.astral.sh/uv/), and the [`gcloud` CLI](https://cloud.google.com/sdk/docs/install) - A Google Cloud project with **AlphaEvolve provisioned** (this gives you a Gemini Enterprise **App / Engine ID**). Follow the [Install and configure](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/get-started) guide once, then reuse it for every example. ### Run it ```bash git clone https://github.com/Google-Cloud-AI/alphaevolve-on-googlecloud.git cd alphaevolve-on-googlecloud # Install the alpha_evolve package (from src/) with uv uv pip install -e ".[examples]" # Configure and run the Circle Packing example cd examples/circle_packing make setup # creates .env from the template (also ensures the package is installed) # then edit .env and set PROJECT_ID and GE_APP_ID make auth # gcloud auth application-default login make run # start the experiment ``` `make run` uploads the seed algorithm, then evolves and evaluates candidates. You'll see each generation's best score climb in the logs, and a `matplotlib` plot of the top packings at the end. Run `make help` in any example to see its targets. > Tip: set `PARALLEL_EVALUATION=True` (and `WORKER_CONCURRENCY`) in `.env` to evaluate candidates > concurrently. --- ## What to expect during a run Evolutionary search behaves differently from a single LLM call. Knowing this up front avoids false alarms: - **Progress is non-monotonic.** The best score can plateau for many generations and then jump. This is normal — **don't stop the run early** because the score looks stuck. - **Invalid candidates are expected.** Programs that break constraints or fail to run return a sentinel score (e.g. `-inf` for an overlapping circle packing, or `neg_eval_loss = -100.0` for a failed training job) **plus an insight message**. Those insights are fed back to Gemini to steer the next generation, so failures are part of how the search improves. - **You control the budget.** A run is bounded by `MAX_PROGRAMS_GENERATED` / `MAX_PROGRAMS_EVALUATED` and parallelized by `CONCURRENCY` / `WORKER_CONCURRENCY` (see [Configuration](#configuration)). --- ## Results A completed run gives you back the **evolved programs** themselves: their source code, per-candidate metric scores, and the insights that guided the search. Read the best ones back with `experiment.list_programs(...)`. Over successive generations the search usually improves the primary metric beyond the seed baseline; how much depends on the problem, your search budget (`MAX_PROGRAMS_*`), and the model mixture. Each example starts from a simple seed and evolves toward a specific target: - **`circle_packing`** — from a concentric-ring seed, maximizes `sum_of_radii`. - **`tsp`** — from a nearest-neighbor seed, maximizes `neg_tour_length` (shorter tours). - **`signal_processing`** — from a moving-average seed, maximizes a multi-objective `overall_score`. - **`llm_fine_tuning`** — from a default LoRA seed, maximizes `neg_eval_loss` (lower eval loss). Concretely, each run produces: - The **best evolved program** (its source code) plus per-metric scores and insights. - **Visualizations** — e.g. `circle_packing` plots its top packings, `tsp` emits summary charts, and `llm_fine_tuning` writes `report/evolution_progress.png` (best-so-far score per generation) and `report/score_distribution.png`, plus `evolved_program/program.py` and `result.json`. --- ## How AlphaEvolve works AlphaEvolve runs a closed evolutionary loop. The **generation** half is a managed service in Google Cloud; the **evaluation** half is your code, running wherever you choose. <p align="center"> <img src="assets/alphaevolve-architecture.png" alt="Logical flow of the Cloud AlphaEvolve service: a prompt sampler, an LLM ensemble, and a program database form the evolutionary loop, with a customer-supplied evaluator scoring candidates" width="820"> <br><em>Logical flow of the Cloud AlphaEvolve service. Source: <a href="https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/architecture-and-workflows">Architecture and workflows</a>, Google Cloud docs (CC BY 4.0).</em> </p> On the cloud side, the managed service runs the evolutionary heuristic through three components, plus the orchestration that connects them: - **Prompt sampler** — selects and formats the prompts that steer the LLM ensemble. - **LLM ensemble** — a configurable mixture of Gemini models that proposes new candidate programs. - **Program database** — stores and tracks the candidate programs and their solutions. You own the remaining piece: the **evaluator**. Scoring is domain-specific, so you write a function that runs each candidate and returns its metrics. The `alpha_evolve` client library runs the loop that joins the two halves: 1. **Seed program** — your starting code, with the region to evolve wrapped in `# EVOLVE-BLOCK-START` / `# EVOLVE-BLOCK-END` markers. 2. **Evaluator** — a deterministic function that returns one or more scores (higher = better). 3. **Controller** — `run_controller_loop()` acquires candidates from the service, runs your evaluator, and submits the scores and insights back. Those results land in the program database and shape the next generation. Because the evaluator is _your_ code, AlphaEvolve can optimize anything you can score: pure-Python heuristics, compiled Rust/C++, or a full model-training run. It can run locally (an `exec()` sandbox), in a Cloud Run function, or on a GKE + Ray GPU cluster — see [Examples](#examples). --- ## Examples Each example is self-contained; start with its `README.md`. They progress from zero infrastructure to production GPU training. | Example | What it teaches | Evaluation runs on | | --------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------ | | [`circle_packing`](examples/circle_packing) | The core loop end to end (seed program, `EVOLVE-BLOCK` markers, evaluator, controller). Pack N=26 circles in a unit square to maximize summed radii. | Local `exec()` | | [`tsp`](examples/tsp) | Evolve a tour-construction heuristic for the Travelling Salesman Problem (N=50), beyond nearest-neighbor. | Local `exec()` | | [`signal_processing`](examples/signal_processing) | **Multi-objective** evaluation: an adaptive time-series filter judged on 14 competing metrics across 5 non-stationary signals, with structured insights fed back to the LLM. | Local `exec()` | | [`adaptive_sort`](examples/adaptive_sort) / [`adaptive_sort_cpp`](examples/adaptive_sort_cpp) | Evolve a **Rust** / **C++** sorting routine that adapts to data patterns, compiled and benchmarked safely in a remote function. | Cloud Run | | [`llm_fine_tuning`](examples/llm_fine_tuning) | **Production GPU infra:** evolve LoRA hyperparameters for Gemma on autoscaling **L4 GPUs** via a persistent **RayCluster on GKE**, provisioned with Terraform and observed with Ray Dashboard + Prometheus/Grafana. | GKE + Ray | --- ## Using the `alpha_evolve` library The public API is small. A minimal experiment looks like this (see any example's `src/run_evolution.py` for the full version): ```python from alpha_evolve.client import AlphaEvolveClient from alpha_evolve.experiment import AlphaEvolveExperiment from alpha_evolve.controller import run_controller_loop import asyncio # 1. Connect to AlphaEvolve in your GCP project client = AlphaEvolveClient( project_id="my-project", location="global", collection="default_collection", engine="my-engine-id", # your Gemini Enterprise App / Engine ID assistant="default_assistant", base_url="discoveryengine.googleapis.com", ) # 2. Define the experiment (your evaluator returns {metric: score}) experiment = AlphaEvolveExperiment(client, my_evaluation_fn, max_programs_evaluated=10) experiment.create_experiment({ "title": "My Experiment", "problem_description": "Evolve <function> to maximize <metric>.", "program_language": "python", "run_settings": {"max_programs": 10, "concurrency": 4}, "generation_settings": {"models": [ {"name": "gemini-3.5-flash", "weight": 0.7}, {"name": "gemini-3.1-pro-preview", "weight": 0.3}, ]}, }) experiment.create_initial_program(seed_program) experiment.start_experiment() # 3. Run the loop, then read back the best programs asyncio.run(run_controller_loop(experiment)) best = experiment.list_programs(params={"order_by": "<metric> desc"}) ``` Mark the code you want AlphaEvolve to rewrite: ```python # EVOLVE-BLOCK-START def construct_packing(n, random_seed): ... # AlphaEvolve rewrites everything inside this block # EVOLVE-BLOCK-END ``` --- ## Using the AlphaEvolve Skills The AlphaEvolve skills allow you to run AlphaEvolve experiments directly from an agentic coding assistant (e.g. Antigravity). Using these skills, your coding assistant will guide you through the entire AlphaEvolve workflow, including configuring the experiment, running the evolutionary loop, monitoring progress, and integrating the best result back into your code. There are 6 AlphaEvolve skills included, with `README.md` and `SKILL.md` files provided for each skill. | Skill | Role | | --- | --- | | [`alpha_evolve_experiment_design`](skills/alpha_evolve_experiment_design) | Scaffolds new experiments via a test-driven workflow. | | [`alpha_evolve_runner`](skills/alpha_evolve_runner) | Configures backend requirements and launches the experiment. | | [`alpha_evolve_monitor`](skills/alpha_evolve_monitor) | Monitors running experiments and manages the local control loop. | | [`alpha_evolve_post_experiment`](skills/alpha_evolve_post_experiment) | Analyzes completed runs and integrates the best evolved code. | | [`alpha_evolve_orchestrator`](skills/alpha_evolve_orchestrator) | Master workflow skill that chains the core skills end-to-end. | | [`alpha_evolve_consultant`](skills/alpha_evolve_consultant) | Answers questions based on the expert reference guide. | Refer to the `README.md` file in the skills folder for instructions to get started with the skills. --- ## Configuration Every example reads settings from a `.env` file (copy from the provided `example.env`). | Variable | Description | Example | | --------------------------------------------------- | ------------------------------------------------- | -------------------------------- | | `PROJECT_ID` | Your Google Cloud project ID | `my-project` | | `LOCATION` | API location | `global` | | `COLLECTION` | Discovery Engine collection | `default_collection` | | `GE_APP_ID` | **Gemini Enterprise App / Engine ID** (see below) | `my-engine-id` | | `ASSISTANT` | Assistant ID | `default_assistant` | | `BASE_URL` | Discovery Engine endpoint | `discoveryengine.googleapis.com` | | `MODEL` / `MODEL_1`,`MODEL_2` (+ `_WEIGHT`) | Generation model(s) and mixture weights | `gemini-3.5-flash` | | `MAX_PROGRAMS_GENERATED` / `MAX_PROGRAMS_EVALUATED` | Search budget | `10` | | `CONCURRENCY` / `WORKER_CONCURRENCY` | Generation / evaluation parallelism | `4` | | `PARALLEL_EVALUATION` | Evaluate candidates concurrently | `True` | AlphaEvolve supports two generation models: `gemini-3.5-flash` and `gemini-3.1-pro-preview` (the latter is served from the `global` location only). You can blend them by assigning weights, as the Circle Packing example does. ### Finding your Gemini Enterprise App ID `GE_APP_ID` is the Engine ID of the Gemini Enterprise app created when you provision AlphaEvolve. Find it in the Google Cloud console under your Gemini Enterprise app, or via the [Install and configure](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/get-started) guide. --- ## Repository structure ``` src/alpha_evolve/ Client library: client, experiment, controller, workers, models, visualization examples/ circle_packing/ combinatorial optimization, local eval tsp/ TSP heuristic, local eval signal_processing/ multi-objective, local eval adaptive_sort/ evolve Rust, Cloud Run evaluator adaptive_sort_cpp/ evolve C++, Cloud Run evaluator llm_fine_tuning/ LoRA HPO on GKE + Ray (Terraform) tests/ Unit tests for the library bin/ Release tooling ``` Each example follows the same shape: `program.py` (seed + `EVOLVE-BLOCK`), `evaluate.py` (scoring), `run_evolution.py` (entry point), a `Makefile`, and an `example.env`. --- ## Cost - **Local examples** (`circle_packing`, `tsp`, `signal_processing`) incur only AlphaEvolve / Gemini API usage — evaluation runs on your machine. - **`adaptive_sort*`** adds a Cloud Run evaluator. - **`llm_fine_tuning`** provisions GKE + L4 GPUs. Its README gives a worked estimate (GPU nodes autoscale to zero between runs; a ~50-evaluation experiment lands in the low single-digit dollars of compute — see [that README](examples/llm_fine_tuning/README.md#cost-estimate)). AlphaEvolve and Gemini usage are billed to your Google Cloud project per your agreement. Use the search-budget knobs (`MAX_PROGRAMS_*`, `CONCURRENCY`) to bound cost. --- ## FAQ **Is this an open-source reimplementation of AlphaEvolve?** No. It's a client library and examples for the **managed AlphaEvolve service** on Google Cloud, served through Gemini Enterprise via the Discovery Engine API. Generation runs in the cloud; you supply the seed program and the evaluator. **How is AlphaEvolve different from a coding assistant?** AlphaEvolve optimizes functionally-correct code against measurable metrics using evolutionary search. It is not a general-purpose code generator and isn't meant for writing baseline code or linting — it needs a working seed program and a scoring function. **Which models does it use?** AlphaEvolve uses an LLM ensemble, that is a configurable mixture of LLMs available on the Gemini Enterprise platform. Currently available models for the mixture are Gemini — `gemini-3.5-flash` and `gemini-3.1-pro-preview` (`gemini-3.1-pro-preview` is served from the `global` location only). **Do I need a Gemini Enterprise license?** Yes. Any Gemini Enterprise tier, including a trial license, grants access. **What does it cost?** AlphaEvolve and Gemini usage is billed to your Google Cloud project. The local examples add nothing beyond API usage; `adaptive_sort*` adds a Cloud Run evaluator and `llm_fine_tuning` adds GKE + GPUs. Bound cost with the search-budget settings (`MAX_PROGRAMS_*`, `CONCURRENCY`). --- ## Documentation & support - **Overview & developer guide:** [Overview of AlphaEvolve](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/overview) - **Get started / provisioning:** [Install and configure AlphaEvolve](https://docs.cloud.google.com/gemini/enterprise/docs/alphaevolve/developer-guide/get-started) - **Support & feedback:** for technical issues, release questions, or feedback, contact your assigned Google Cloud account team. - **Contributing:** this project is **not currently accepting external contributions** (see [CONTRIBUTING.md](CONTRIBUTING.md)). ## License Licensed under the [Apache License 2.0](LICENSE). <!-- Suggested GitHub "About" for this repo: Description: "Client library and examples for running AlphaEvolve — a Gemini-powered evolutionary coding agent — on Google Cloud." Topics: alphaevolve, gemini, google-cloud, gemini-enterprise, evolutionary-algorithm, algorithm-discovery, llm, code-generation, discovery-engine, ray, gke, lora -->
