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AIOS

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Basic Information

AIOS is an operating-system-like kernel that embeds large language models (LLMs) and provides infrastructure to develop, run and deploy LLM-based AI agents. The repository implements the AIOS kernel which acts as an abstraction layer over host systems and manages agent-facing resources such as LLM access, memory management, storage, tool management, scheduling and SDK integration. It is intended to be used together with the AIOS SDK (Cerebrum) so agent developers and users can build, onboard and execute agents via Web UI or a terminal UI. The README documents supported deployment modes including local and remote kernels, configuration details for API keys and model backends, installation requirements (Python 3.10–3.11), and runtime launch commands. The project also includes an experimental Rust scaffold for performance-focused components.

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App URL
https://github.com/agiresearch/AIOS
Github Repository
https://github.com/agiresearch/AIOS/blob/main/README.md

App Details

Features
AIOS provides a modular kernel with explicit managers for LLM cores, memory, storage, tools and schedulers. It supports multiple deployment modes: local kernel, remote kernel, remote dev, personal remote and virtual kernels with features for packaging, synchronization and user persistence. The repo documents support for many LLM backends and providers and enables function/tool calling for open and hosted models. It integrates with multiple agent creation frameworks and lists supported frameworks. There is a terminal-based semantic file system UI and a Web UI option. For computer-use agents it adds a VM controller and MCP server to sandbox interactions. The codebase includes an experimental Rust rewrite with trait scaffolding and simple examples.
Use Cases
AIOS reduces operational and engineering friction when building and deploying LLM agents by centralizing resource and lifecycle management. Developers get abstractions for scheduling, context switching, memory and tool invocation so agents can be developed against a consistent kernel API. Users can run agents locally or remotely which helps resource-limited devices use powerful remote models. The system supports agent distribution via an agent hub and aims to provide persistent personal kernels and synchronization for multi-device workflows. Sandboxed VM and MCP server support helps safer computer-use agents. Broad model backend compatibility and the Cerebrum SDK simplify connecting models and tools, accelerating prototyping, testing and production deployment of diverse agent workflows.

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