A MicroKernel Multi-Agent System Framework for Adaptive Social Simulation Powered by LLMs

Agent-Kernel is a user-friendly, microkernel-based framework designed for developing large-scale, adaptive social simulations powered by LLMs. It enables researchers and developers to explore collective intelligence scaling laws through dynamic, configurable, and verifiable agent-based models, offering a powerful tool for complex sociological and behavioral studies.

How It Works

The framework employs a modular microkernel architecture, comprising core system modules (Agent, Environment, Action, Controller, System) and specialized plugins. This design facilitates dynamic agent management, allowing LLM-based agents to be added or removed at runtime, ensuring unlimited scalability and real-time simulation intervention. A key advantage is its plugin-based, standardized structure, promoting code reusability across diverse simulation scenarios and accelerating research iteration.

Quick Start & Requirements

• Requirements: Python version 3.11 or higher.
• Installation: Install via pip: pip install agentkernel-standalone for local use or pip install agentkernel-distributed for distributed setups (which automatically installs Ray). Optional features like [web] or [storages] can be included during installation.
• Society-Panel: An optional web-based control panel is available for visual configuration, deployment, and monitoring. It can be launched using provided scripts (scripts/start_society_panel.sh or scripts\start_society_panel.bat) and accessed at http://localhost:5174.

Highlighted Details

• Supports dynamic addition and removal of LLM-based agents during simulation runtime.
• Offers unlimited scalability for agent populations.
• Enables real-time intervention and parameter adjustments during simulations.
• Features a strict system-level verification mechanism for agent behaviors and LLM outputs, ensuring scientific rigor.
• Demonstrated applications include simulating the "Universe 25 Experiment" and "ZJU Campus Life" dynamics.

Maintenance & Community

The project actively welcomes contributions via Pull Requests. Specific community channels or notable contributors are not detailed in the provided README excerpt.

Licensing & Compatibility

The project is licensed under the Apache 2.0 license, which permits commercial use and integration into closed-source projects.

Limitations & Caveats

No explicit limitations or known caveats are detailed in the provided README content. The framework emphasizes adaptability, configurability, reliability, and reusability as core strengths.