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ARLA: The Agent Simulation Framework

Build the Future of Agent-Based Modeling

ARLA combines cutting-edge cognitive architectures with high-performance simulation to create believable, intelligent agents that learn, adapt, and emerge complex behaviors.

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Why ARLA?

  • High-Performance Core

    Built on asynchronous Python with Entity-Component-System architecture. Scale to thousands of agents with concurrent execution and optimized memory management.

    Architecture Overview

  • Cognitively-Rich Agents

    Move beyond simple rules. Agents with memory, emotions, social awareness, and goal-driven behavior powered by Large Language Models.

    Cognitive Systems

  • Modular & Extensible

    Clean separation of data and logic through ECS. Add new behaviors, cognitive models, and environmental rules without touching the core engine.

    Developer Guide

  • Research-Ready

    Built-in experiment management, MLflow integration, and comprehensive logging. Perfect for ablation studies and reproducible research.

    Running Experiments

Quick Start

  • 1. Install

    git clone https://github.com/renbytes/arla.git
cd arla
make setup && make up

  • 2. Run

    make run-example

  • 3. Explore

    Open MLflow UI to view results and experiment tracking.

Use Cases

  • Social Dynamics

    Study how societies form, cooperate, and conflict. Model everything from small groups to large populations.

  • Economic Emergence

    Watch markets, trade, and currency systems emerge naturally from agent interactions and resource scarcity.

  • Learning & Adaptation

    Research how agents learn from experience, form memories, and adapt their strategies over time.

  • Moral Reasoning

    Explore how ethical systems develop through social feedback and cultural transmission.

Built for Researchers

Perfect for computational social science, AI research, and complex systems studies. Built-in support for:

  • Reproducible experiments with configuration management
  • Statistical analysis with automated data collection
  • Publication-ready visualizations and metrics

Prototype and test multi-agent systems for real-world applications:

  • Market simulation and economic modeling
  • Social network analysis and recommendation systems
  • Human-AI interaction studies

Teach complex systems, AI, and social dynamics with engaging simulations:

  • Pre-built scenarios for classroom use
  • Visual debugging and real-time monitoring
  • Comprehensive documentation and tutorials

Community & Support

  • Open Source

    MIT licensed with active development. Contribute features, report bugs, or extend the platform.

    Contributing Guide

  • Documentation

    Comprehensive guides, tutorials, and API reference. From first simulation to advanced cognitive architectures.

    Browse Docs

  • Research Blog

    Latest developments, research findings, and community showcases. Stay updated with the ARLA ecosystem.

    Read Blog

Ready to build intelligent agents?

Start Tutorial Install ARLA