Agent-Kernel

Why we included this project

Researchers working where LLMs meet social science keep asking the same question: how well can a crowd of language-model agents reproduce how real people behave as a group? Agent-Kernel is one framework built around that question. It follows a microkernel design, so instead of hand-wiring dozens of prompts you define environments, action rules, and a controller, then let the agent population grow, shrink, and change behavior while the run is in progress. Agent add/remove at runtime and per-action verification are built in, which makes it a practical fit for teams studying opinion dynamics, pedestrian flow, resource allocation, or clinical workflows. The project also publishes an arXiv paper describing the design and offers standalone and Ray-backed distributed installs plus a web panel, so you can size up the architecture before committing to a scenario.