openevolve

PythonApache-2.0★ 7.7k

Open-source AlphaEvolve implementation: an evolutionary coding agent that turns LLMs into autonomous code optimizers, discovering faster kernels, better

Agent

Run: python openevolve-run.py initial_program.py evaluator.py --config config.yaml --iterations 50
Interface: cli
Models: Gemini

From the project’s README

GitHub ↗

Written by the project's own authors, not by runchr. Snapshot taken 2026-10-31.

OpenEvolve

🧬 The most advanced open-source evolutionary coding agent

Turn your LLMs into autonomous code optimizers that discover breakthrough algorithms

🚀 Quick Start • Examples • System Messages • Discussions

From random search to state-of-the-art: Watch your code evolve in real-time

Why OpenEvolve?

Autonomous Discovery

LLMs don't just optimize—they discover entirely new algorithms. No human guidance needed.

Proven Results

2-3x speedups on real hardware. State-of-the-art circle packing. Breakthrough optimizations.

Research Grade

Full reproducibility, extensive evaluation pipelines, and scientific rigor built-in.

OpenEvolve vs Manual Optimization:

Aspect	Manual Optimization	OpenEvolve
Time to Solution	Days to weeks	Hours
Exploration Breadth	Limited by human creativity	Unlimited LLM creativity
Reproducibility	Hard to replicate	Fully deterministic
Multi-objective	Complex tradeoffs	Automatic Pareto optimization
Scaling	Doesn't scale	Parallel evolution across islands

Proven Achievements

Domain	Achievement	Example
GPU Optimization	Hardware-optimized kernel discovery	MLX Metal Kernels
Mathematical	State-of-the-art circle packing (n=26)	Circle Packing
Algorithm Design	Adaptive sorting algorithms	Rust Adaptive Sort
Scientific Computing	Automated filter design	Signal Processing
Multi-Language	Python, Rust, R, Metal shaders	All Examples

🚀 Quick Start

Get from zero to evolving code in 30 seconds:

# Install OpenEvolve
pip install openevolve

# The example uses Google Gemini by default (free tier available)
# Get your API key from: https://aistudio.google.com/apikey
export OPENAI_API_KEY="your-gemini-api-key"  # Yes, use OPENAI_API_KEY env var

# Run your first evolution!
python openevolve-run.py examples/function_minimization/initial_program.py \
  examples/function_minimization/evaluator.py \
  --config examples/function_minimization/config.yaml \
  --iterations 50

# Running several evolutions in parallel? Give each run a logger prefix so its
# log output is distinguishable (logs as "run1.openevolve.*")
python openevolve-run.py examples/function_minimization/initial_program.py \
  examples/function_minimization/evaluator.py \
  --logger-prefix run1

Note: The example config uses Gemini by default, but you can use any OpenAI-compatible provider by modifying the config.yaml. See the configs for full configuration options.

Library Usage

OpenEvolve can be used as a library without any external files:

from openevolve import run_evolution, evolve_function

# Evolution with inline code (no files needed!)
result = run_evolution(
    initial_program='''
    def fibonacci(n):
        if n <= 1: return n
        return fibonacci(n-1) + fibonacci(n-2)
    ''',
    evaluator=lambda path: {"score": benchmark_fib(path)},
    iterations=100
)

# Evolve Python functions directly
def bubble_sort(arr):
    for i in range(len(arr)):
        for j in range(len(arr)-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j] 
    return arr

if __name__ == '__main__':
    result = evolve_function(
        bubble_sort,
        test_cases=[([3,1,2], [1,2,3]), ([5,2,8], [2,5,8])],
        iterations=50
    )
    print(f"Evolved sorting algorithm: {result.best_code}")

Note: On macOS and Windows, Python uses spawn for multiprocessing. You must wrap evolution calls in if __name__ == '__main__': to avoid subprocess bootstrap errors. To experiment with island scheduling, pass an optional selector to run_evolution:

def select_island(context):
    # Schedule the next iteration on the island with the fewest pending tasks.
    return min(range(len(context.islands)), key=lambda i: context.pending_counts[i])

result = run_evolution(
    initial_program="program.py",
    evaluator="evaluator.py",
    island_selector=select_island,
)

The selector receives a read-only IslandSelectionContext with the iteration number, pending task counts, and each island's population size, best and average score, diversity, and generation. It must return an island ID from 0 to num_islands - 1. It runs in the main process before each iteration is submitted. Without a selector, OpenEvolve uses its existing balanced island scheduling.

Population management can also use decision hooks. For example, this policy lets a candidate replace an island's MAP-Elites cell occupant when their scores tie:

from openevolve import run_evolution
from openevolve.population import PopulationStrategy

strategy = PopulationStrategy(
    replace_cell=lambda state, candidate, incumbent, island: (
        candidate.metrics["combined_score"] >= incumbent.metrics["combined_score"]
    ),
)
result = run_evolution("program.py", "evaluator.py", population_strategy=strategy)

Other optional hooks are admit(state, candidate, island) -> bool, archive(state, candidate) -> ArchiveDecision, evict(state, count, protected_ids) -> sequence of program IDs, migration_due(state) -> bool, and migrate(state) -> sequence of MigrationMove. state is a detached PopulationSnapshot of programs, island memberships, cell owners, the archive, population limits, and migration generations. Unspecified hooks retain the existing rules. ProgramDatabase validates decisions and applies all changes; the strategy never receives the mutable database. The initial seed cannot be rejected.

Prefer Docker? See the Installation & Setup section for Docker options.

See It In Action

Circle Packing: From Random to State-of-the-Art

Watch OpenEvolve discover optimal circle packing in real-time:

Generation 1	Generation 190	Generation 460 (Final)

Random placement	Learning structure	State-of-the-art result

Result: Matches published benchmarks for n=26 circle packing problem.

GPU Kernel Evolution

Before (Baseline):

// Standard attention implementation
kernel void attention_baseline(/* ... */) {
    // Generic matrix multiplication
    float sum = 0.0;
    for (int i = 0; i < seq_len; i++) {
        sum += query[tid] * key[i];
    }
}

After Evolution (2.8x faster):

// OpenEvolve discovered optimization
kernel void attention_evolved(/* ... */) {
    // Hardware-aware tiling + unified memory optimization
    threadgroup float shared_mem[256];
    // ... evolved algorithm exploiting Apple Silicon architecture
}

Performance Impact: 2.8x speedup on Apple M1 Pro, maintaining numerical accuracy.

How OpenEvolve Works

OpenEvolve implements a sophisticated evolutionary coding pipeline that goes far beyond simple optimization:

Core Innovation: MAP-Elites + LLMs

Quality-Diversity Evolution: Maintains diverse populations across feature dimensions
Island-Based Architecture: Multiple populations prevent premature convergence
LLM Ensemble: Multiple models with intelligent fallback strategies
Artifact Side-Channel: Error feedback improves subsequent generations

Advanced Features

Scientific Reproducibility

Comprehensive Seeding: Every component (LLM, database, evaluation) is seeded
Default Seed=42: Immediate reproducible results out of the box
Deterministic Evolution: Exact reproduction of runs across machines
Component Isolation: Hash-based isolation prevents cross-contamination

Advanced LLM Integration

Universal API: Works with OpenAI, Google, Claude Code CLI, local models, and proxies
Intelligent Ensembles: Weighted combinations with sophisticated fallback
Test-Time Compute: Enhanced reasoning through proxy systems (see OptiLLM setup)
Plugin Ecosystem: Support for advanced reasoning plugins

Evolution Algorithm Innovations

Double Selection: Different programs for performance vs inspiration
Adaptive Feature Dimensions: Custom quality-diversity metrics
Migration Patterns: Ring topology with controlled gene flow
Multi-Strategy Sampling: Elite, diverse, and exploratory selection

Perfect For

Use Case	Why OpenEvolve Excels
Performance Optimization	Discovers hardware-specific optimizations humans miss
Algorithm Discovery	Finds novel approaches to classic problems
Scientific Computing	Automates tedious manual tuning processes
Competitive Programming	Generates multiple solution strategies
Multi-Objective Problems	Pareto-optimal solutions across dimensions

🛠 Installation & Setup

Requirements

Python: 3.10+
LLM Access: Any OpenAI-compatible API, or Claude Code CLI
Optional: Docker for containerized runs

Installation Options

📦 PyPI (Recommended)

pip install openevolve

🔧 Development Install

git clone https://github.com/algorithmicsuperintelligence/openevolve.git
cd openevolve
pip install -e ".[dev]"

🐳 Docker

# Pull the image
docker pull ghcr.io/algorithmicsuperintelligence/openevolve:latest

# Run an example
docker run --rm -v $(pwd):/app ghcr.io/algorithmicsuperintelligence/openevolve:latest \
  examples/function_minimization/initial_program.py \
  examples/function_minimization/evaluator.py --iterations 100

Cost Estimation

Cost depends on your LLM provider and iterations:

o3: ~$0.15-0.60 per iteration (depending on code size)
o3-mini: ~$0.03-0.12 per iteration (more cost-effective)
Gemini-2.5-Pro: ~$0.08-0.30 per iteration
Gemini-2.5-Flash: ~$0.01-0.05 per iteration (fastest and cheapest)
Local models: Nearly free after setup
OptiLLM: Use cheaper models with test-time compute for better results

Cost-saving tips:

Start with fewer iterations (100-200)
Use o3-mini, Gemini-2.5-Flash or local models for exploration
Use cascade evaluation to filter bad programs early
Configure smaller population sizes initially

LLM Provider Setup

OpenEvolve works with any OpenAI-compatible API:

🔥 OpenAI (Direct)

export OPENAI_API_KEY="sk-..."
# Uses OpenAI endpoints by default

🤖 Google Gemini

# config.yaml
llm:
  api_base: "https://generativelanguage.googleapis.com/v1beta/openai/"
  primary_model: "gemini-2.5-pro"

export OPENAI_API_KEY="your-gemini-api-key"

🏠 Local Models (Ollama/vLLM)

# config.yaml
llm:
  api_base: "http://localhost:11434/v1"  # Ollama
  primary_model: "codellama:7b"

⚡ OptiLLM (Advanced)

For maximum flexibility with rate limiting, model routing, and test-time compute:

# Install OptiLLM
pip install optillm

# Start OptiLLM proxy
optillm --port 8000

# Point OpenEvolve to OptiLLM
export OPENAI_API_KEY="your-actual-key"

llm:
  api_base: "http://localhost:8000/v1"
  primary_model: "moa&readurls-o3"  # Test-time compute + web access

🔮 Claude Code CLI (No API Key)

Use the Claude Code CLI as the LLM backend — no API keys needed, authentication uses the CLI's OAuth session.

# Install and authenticate
npm install -g @anthropic-ai/claude-code
claude login

# config.yaml
llm:
  provider: "claude_code"
  models:
    - name: "sonnet"
      weight: 0.8
      max_tokens: 16000
      max_budget_usd: 1.0
    - name: "haiku"
      weight: 0.2
      max_tokens: 8000

See the Claude Code quickstart example for a complete walkthrough.

🤖 GitHub Copilot CLI (No API Key)

Use the GitHub Copilot CLI as the LLM backend — no API keys needed, authentication uses your GitHub Copilot subscription.

# Install and authenticate
npm install -g @github/copilot
copilot login

# config.yaml
llm:
  provider: "copilot_cli"
  models:
    - name: "claude-sonnet-4.6"
      weight: 0.8
      reasoning_effort: "medium"
    - name: "gpt-5.4"
      weight: 0.2

See the Copilot CLI quickstart example for a complete walkthrough.

🧩 Codex App Server (No API Key)

Use the Codex CLI's app-server as the LLM backend — no API keys needed. Authentication uses your ChatGPT (Codex) subscription: the app-server owns the managed browser/device-code login and refreshes the tokens itself. OpenEvolve never reads the CLI's token files, and before each generation it verifies with the app-server that a ChatGPT-plan login is active (it fails with a clear message pointing at codex login otherwise). To bill an API key instead, keep the default OpenAI provider — both paths coexist.

# Install and authenticate
npm install -g @openai/codex
codex login          # browser flow, or: codex login --device-code

# config.yaml
llm:
  provider: "codex_app_server"
  models:
    - name: "gpt-5.3-codex"
      weight: 0.8
      reasoning_effort: "medium"
    - name: "gpt-5.3-codex-mini"
      weight: 0.2

Each call speaks newline-delimited JSON-RPC to a short-lived codex app-server process over stdio (unsupported on Windows). Threads run non-interactive: tool approvals are never and the sandbox is read-only, so responses are generated without mutating your workspace.

Examples Gallery

To use unchanged local evaluators from Google's AlphaEvolve examples, see the AlphaEvolve evaluator interoperability walkthrough.

Showcase Projects

Project	Domain	Achievement	Demo
Function Minimization	Optimization	Random → Simulated Annealing	View Results
MLX GPU Kernels	Hardware	Apple Silicon optimization	Benchmarks
Rust Adaptive Sort	Algorithms	Data-aware sorting	Code Evolution
Symbolic Regression	Science	Automated equation discovery	LLM-SRBench
Web Scraper + OptiLLM	AI Integration	Test-time compute optimization	Smart Scraping

Quick Example: Function Minimization

Watch OpenEvolve evolve from random search to sophisticated optimization:

# Initial Program (Random Search)
def minimize_function(func, bounds, max_evals=1000):
    best_x, best_val = None, float('inf')
    for _ in range(max_evals):
        x = random_point_in_bounds(bounds)
        val = func(x)
        if val < best_val:
            best_x, best_val = x, val
    return best_x, best_val

Evolution Process

# Evolved Program (Simulated Annealing + Adaptive Cooling)
def minimize_function(func, bounds, max_evals=1000):
    x = random_point_in_bounds(bounds)
    temp = adaptive_initial_temperature(func, bounds)
    
    for i in range(max_evals):
        neighbor = generate_neighbor(x, temp, bounds)
        delta = func(neighbor) - func(x)
        
        if delta < 0 or random.random() < exp(-delta/temp):
            x = neighbor
            
        temp *= adaptive_cooling_rate(i, max_evals)  # Dynamic cooling
    
    return x, func(x)

Performance: 100x improvement in convergence speed!

Advanced Examples

Prompt Evolution

Evolve prompts instead of code for better LLM performance. See the LLM Prompt Optimization example for a complete case study with HotpotQA achieving +23% accuracy improvement.

Full Example

🏁 Competitive Programming

Automatic solution generation for programming contests:

# Problem: Find maximum subarray sum
# OpenEvolve discovers multiple approaches:

# Evolution Path 1: Brute Force → Kadane's Algorithm
# Evolution Path 2: Divide & Conquer → Optimized Kadane's
# Evolution Path 3: Dynamic Programming → Space-Optimized DP

Online Judge Integration

Configuration

OpenEvolve offers extensive configuration for advanced users:

# Advanced Configuration Example
max_iterations: 1000
random_seed: 42  # Full reproducibility

# Optional prefix for all logger names, e.g. "run1" logs as "run1.openevolve.*";
# useful to tell apart log output from multiple concurrent runs
logger_prefix: "run1"

llm:
  # Ensemble configuration
  models:
    - name: "gemini-2.5-pro"
      weight: 0.6
    - name: "gemini-2.5-flash"
      weight: 0.4
  temperature: 0.7

database:
  # MAP-Elites quality-diversity
  population_size: 500
  num_islands: 5  # Parallel evolution
  # island_initial_programs: [null, "seeds/alt.py"]  # Optional per-island starting programs
  migration_interval: 20
  feature_dimensions: ["complexity", "diversity", "performance"]

  # Optional novelty filtering with Gemini embeddings
  embedding_model: "gemini-embedding-001"
  similarity_threshold: 0.99
  # Or any OpenAI-compatible embedding endpoint (OpenRouter, local servers):
  # embedding_api_base: "https://openrouter.ai/api/v1"  # or OPENAI_EMBEDDING_BASE_URL

evaluator:
  enable_artifacts: true      # Error feedback to LLM
  cascade_evaluation: true    # Multi-stage testing
  use_llm_feedback: true      # AI code quality assessment
  evaluator_args:             # Keyword arguments forwarded to evaluate()
    weight: 2.0

prompt:
  # Sophisticated inspiration system
  num_top_programs: 3         # Best performers
  num_diverse_programs: 2     # Creative exploration
  include_artifacts: true     # Execution feedback
  
  # Custom templates
  template_dir: "custom_prompts/"
  use_template_stochasticity: true  # Randomized prompts

For Gemini embeddings, set GEMINI_API_KEY. GOOGLE_API_KEY is also supported as a fallback. OpenEvolve uses Google's OpenAI-compatible endpoint automatically.

🎯 Feature Engineering

Control how programs are organized in the quality-diversity grid:

database:
  feature_dimensions: 
    - "complexity"      # Built-in: code length
    - "diversity"       # Built-in: structural diversity
    - "performance"     # Custom: from your evaluator
    - "memory_usage"    # Custom: from your evaluator
    
  feature_bins:
    complexity: 10      # 10 complexity levels
    performance: 20     # 20 performance buckets
    memory_usage: 15    # 15 memory usage categories

Important: Return raw values from evaluator, OpenEvolve handles binning automatically.

📞 Passing Arguments to Your Evaluator

If your evaluate() function takes extra keyword arguments beyond the program path, pass them with the --evaluator-args CLI flag (a JSON object):

openevolve-run initial_program.py evaluator.py \
  --evaluator-args '{"weight": 2.0, "label": "experiment-1"}'

or configure them in YAML:

evaluator:
  evaluator_args:
    weight: 2.0
    label: "experiment-1"

OpenEvolve inspects the signature of your evaluate() function: arguments are forwarded only when the function accepts them (named parameters or **kwargs), and a startup error lists the offending keys and the actual signature if it does not — so typos fail fast instead of silently doing nothing. Evaluators with a single program_path argument are unaffected.

This replaces the earlier workaround of smuggling per-run settings to the evaluator through environment variables. (Note: custom top-level keys in config.yaml are dropped silently by the config parser — use evaluator.evaluator_args or the CLI flag instead.)

🎨 Custom Prompt Templates

Advanced prompt engineering with custom templates:

prompt:
  template_dir: "custom_templates/"
  use_template_stochasticity: true
  template_variations:
    greeting:
      - "Let's enhance this code:"
      - "Time to optimize:"
      - "Improving the algorithm:"
    improvement_suggestion:
      - "Here's how we could improve this code:"
      - "I suggest the following improvements:"
      - "We can enhance this code by:"

How it works: Place {greeting} or {improvement_suggestion} placeholders in your templates, and OpenEvolve will randomly choose from the variations for each generation, adding diversity to prompts.

See prompt examples for complete template customization.

Crafting Effective System Messages

System messages are the secret to successful evolution. They guide the LLM's understanding of your domain, constraints, and optimization goals. A well-crafted system message can be the difference between random mutations and targeted improvements.

Why System Messages Matter

The system message in your config.yaml is arguably the most important component for evolution success:

Domain Expertise: Provides LLM with specific knowledge about your problem space
Constraint Awareness: Defines what can and cannot be changed during evolution
Optimization Focus: Guides the LLM toward meaningful improvements
Error Prevention: Helps avoid common pitfalls and compilation errors

The Iterative Creation Process

Based on successful OpenEvolve implementations, system messages are best created through iteration:

🔄 Step-by-Step Process

Phase 1: Initial Draft

Start with a basic system message describing your goal
Run 20-50 evolution iterations to observe behavior
Note where the system gets "stuck" or makes poor choices

Phase 2: Refinement

Add specific guidance based on observed issues
Include domain-specific terminology and concepts
Define clear constraints and optimization targets
Run another batch of iterations

Phase 3: Specialization

Add detailed examples of good vs bad approaches
Include specific library/framework guidance
Add error avoidance patterns you've observed
Fine-tune based on artifact feedback

Phase 4: Optimization

Consider using OpenEvolve itself to optimize your prompt
Measure improvements using combined score metrics

Examples by Complexity

Simple: General Optimization

prompt:
  system_message: |
    You are an expert programmer specializing in optimization algorithms.
    Your task is to improve a function minimization algorithm to find the
    global minimum reliably, escaping local minima that might trap simple algorithms.

Intermediate: Domain-Specific Guidance

prompt:
  system_message: |
    You are an expert prompt engineer. Your task is to revise prompts for LLMs.

    Your improvements should:
    * Clarify vague instructions and eliminate ambiguity
    * Strengthen alignment between prompt and desired task outcome
    * Improve robustness against edge cases
    * Include formatting instructions and examples where helpful
    * Avoid unnecessary verbosity

    Return only the improved prompt text without explanations.

⚡ Advanced: Hardware-Specific Optimization

prompt:
  system_message: |
    You are an expert Metal GPU programmer specializing in custom attention
    kernels for Apple Silicon.

    # TARGET: Optimize Metal Kernel for Grouped Query Attention (GQA)
    # HARDWARE: Apple M-series GPUs with unified memory architecture
    # GOAL: 5-15% performance improvement

    # OPTIMIZATION OPPORTUNITIES:
    **1. Memory Access Pattern Optimization:**
    - Coalesced access patterns for Apple Silicon
    - Vectorized loading using SIMD
    - Pre-compute frequently used indices

    **2. Algorithm Fusion:**
    - Combine max finding with score computation
    - Reduce number of passes through data

    # CONSTRAINTS - CRITICAL SAFETY RULES:
    **MUST NOT CHANGE:**
    ❌ Kernel function signature
    ❌ Template parameter names or types
    ❌ Overall algorithm correctness

    **ALLOWED TO OPTIMIZE:**
    ✅ Memory access patterns and indexing
    ✅ Computation order and efficiency
    ✅ Vectorization and SIMD utilization
    ✅ Apple Silicon specific optimizations

Best Practices

🎨 Prompt Engineering Patterns

Structure Your Message: Start with role definition → Define task/context → List optimization opportunities → Set constraints → Success criteria

Use Specific Examples:

# Good: "Focus on reducing memory allocations. Example: Replace `new Vector()` with pre-allocated arrays."
# Avoid: "Make the code faster"

Include Domain Knowledge:

# Good: "For GPU kernels: 1) Memory coalescing 2) Occupancy 3) Shared memory usage"
# Avoid: "Optimize the algorithm"

Set Clear Boundaries:

system_message: |
  MUST NOT CHANGE: ❌ Function signatures ❌ Algorithm correctness ❌ External API
  ALLOWED: ✅ Internal implementation ✅ Data structures ✅ Performance optimizations

🔬 Advanced Techniques

Artifact-Driven Iteration: Enable artifacts in config → Include common error patterns in system message → Add guidance based on stderr/warning patterns

Multi-Phase Evolution: Start broad ("Explore different algorithmic approaches"), then focus ("Given successful simulated annealing, focus on parameter tuning")

Template Stochasticity: See the Configuration section for complete template variation examples.

Meta-Evolution: Using OpenEvolve to Optimize Prompts

You can use OpenEvolve to evolve your system messages themselves! This powerful technique lets you optimize prompts for better LLM performance automatically.

See the LLM Prompt Optimization example for a complete implementation, including the HotpotQA case study with +23% accuracy improvement.

Common Pitfalls to Avoid

Too Vague: "Make the code better" → Specify exactly what "better" means
Too Restrictive: Over-constraining can prevent useful optimizations
Missing Context: Include relevant domain knowledge and terminology
No Examples: Concrete examples guide LLM better than abstract descriptions
Ignoring Artifacts: Don't refine prompts based on error feedback

Artifacts & Debugging

Artifacts side-channel provides rich feedback to accelerate evolution:

# Evaluator can return execution context
from openevolve.evaluation_result import EvaluationResult

return EvaluationResult(
    metrics={"performance": 0.85, "correctness": 1.0},
    artifacts={
        "stderr": "Warning: suboptimal memory access pattern",
        "profiling_data": {...},
        "llm_feedback": "Code is correct but could use better variable names",
        "build_warnings": ["unused variable x"]
    }
)

Next generation prompt automatically includes:

## Previous Execution Feedback
⚠️ Warning: suboptimal memory access pattern
💡 LLM Feedback: Code is correct but could use better variable names
🔧 Build Warnings: unused variable x

This creates a feedback loop where each generation learns from previous mistakes!

Visualization

Real-time evolution tracking with interactive web interface:

# Install visualization dependencies
pip install -r scripts/requirements.txt

# Launch interactive visualizer
python scripts/visualizer.py

# Or visualize specific checkpoint
python scripts/visualizer.py --path examples/function_minimization/openevolve_output/checkpoints/checkpoint_100/

Features:

🌳 Evolution tree with parent-child relationships
📈 Performance tracking across generations
🔍 Code diff viewer showing mutations
📊 MAP-Elites grid visualization
🎯 Multi-metric analysis with custom dimensions

Roadmap

🔥 Upcoming Features

Multi-Modal Evolution: Images, audio, and text simultaneously
Federated Learning: Distributed evolution across multiple machines
AutoML Integration: Hyperparameter and architecture evolution
Benchmark Suite: Standardized evaluation across domains

🌟 Research Directions

Self-Modifying Prompts: Evolution modifies its own prompting strategy
Cross-Language Evolution: Python → Rust → C++ optimization chains
Neurosymbolic Reasoning: Combine neural and symbolic approaches
Human-AI Collaboration: Interactive evolution with human feedback

Want to contribute? Check out our roadmap discussions!

FAQ

💰 How much does it cost to run?

See the Cost Estimation section in Installation & Setup for detailed pricing information and cost-saving tips.

🆚 How does this compare to manual optimization?

Aspect	Manual	OpenEvolve
Initial Learning	Weeks to understand domain	Minutes to start
Solution Quality	Depends on expertise	Consistently explores novel approaches
Time Investment	Days-weeks per optimization	Hours for complete evolution
Reproducibility	Hard to replicate exact process	Perfect reproduction with seeds
Scaling	Doesn't scale beyond human capacity	Parallel evolution across islands

OpenEvolve shines when you need to explore large solution spaces or optimize for multiple objectives simultaneously.

🔧 Can I use my own LLM?

Yes! OpenEvolve supports any OpenAI-compatible API:

Commercial: OpenAI, Google, Cohere
Local: Ollama, vLLM, LM Studio, text-generation-webui
Advanced: OptiLLM for routing and test-time compute

Just set the api_base in your config to point to your endpoint.

🚨 What if evolution gets stuck?

Built-in mechanisms prevent stagnation:

Island migration: Fresh genes from other populations
Temperature control: Exploration vs exploitation balance
Diversity maintenance: MAP-Elites prevents convergence
Artifact feedback: Error messages guide improvements
Template stochasticity: Randomized prompts break patterns

Manual interventions:

Increase num_diverse_programs for more exploration
Add custom feature dimensions to diversify search
Use template variations to randomize prompts
Adjust migration intervals for more cross-pollination

📈 How do I measure success?

Multiple success metrics:

Primary Metric: Your evaluator's combined_score or metric average
Convergence: Best score improvement over time
Diversity: MAP-Elites grid coverage
Efficiency: Iterations to reach target performance
Robustness: Performance across different test cases

Use the visualizer to track all metrics in real-time and identify when evolution has converged.

Contributors

Thanks to all our amazing contributors who make OpenEvolve possible!

Contributing

We welcome contributions! Here's how to get started:

🍴 Fork the repository
🌿 Create your feature branch: git checkout -b feat-amazing-feature
✨ Add your changes and tests
✅ Test everything: python -m unittest discover tests
📝 Commit with a clear message
🚀 Push and create a Pull Request

New to open source? Check out our Contributing Guide and look for good-first-issue labels!

Academic & Research

Articles & Blog Posts About OpenEvolve:

Towards Open Evolutionary Agents - Evolution of coding agents and the open-source movement
OpenEvolve: GPU Kernel Discovery - Automated discovery of optimized GPU kernels
OpenEvolve: Evolutionary Coding with LLMs - Introduction to evolutionary algorithm discovery using large language models

Citation

If you use OpenEvolve in your research, please cite:

@software{openevolve,
  title = {OpenEvolve: an open-source evolutionary coding agent},
  author = {Asankhaya Sharma},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/algorithmicsuperintelligence/openevolve}
}

🚀 Ready to evolve your code?

Maintained by the OpenEvolve community

If OpenEvolve helps you discover breakthrough algorithms, please consider starring this repository.

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Agents

Coding Browser Research Multi-agent

Python27.6k10 days ago

Fully local autonomous agent that browses the web, writes code, and plans tasks.

ai-agent-book GitHub ↗

Frameworks

Coding Multi-agent Knowledge Memory Research

Python56.9k25 days ago

Open-source book on AI agent design with 109 hands-on experiment projects.

archify GitHub ↗

Skills

Coding Research Writing

JavaScript92.6ktoday

Agent skill that turns ideas, plans, and codebases into interactive diagrams.