nanobot is an ultra-lightweight, self-hosted personal AI agent designed to operate seamlessly within your workspace, communication channels, and automation workflows. It serves as a portable agent runtime kernel, allowing users to embed intelligence directly into their business processes or daily life. Developed to be steady, fast, and scalable, nanobot prioritizes efficient resource usage and predictable performance, ensuring it remains an effective assistant for both short-term tasks and complex, long-running automation sequences.

The project focuses on maintaining coherence across long-horizon tasks, allowing the agent to handle tens to hundreds of steps without losing context. By implementing strict token budgets and compact code, nanobot provides a cost-effective solution for users who require reliable AI assistance without the overhead of massive, cloud-dependent systems. Its architecture is built for portability, enabling it to run as a local service on a variety of Python-supported environments.

Some of the key features are:

• Lean: Utilizes compact code with sensible context management and defined token budgets to ensure predictable operational costs.
• Enduring: Supports long-horizon tasks spanning tens to hundreds of steps, maintaining state coherence throughout execution.
• Kernel: Provides a standard agent runtime kernel that is easily embeddable into various business or daily life applications.
• Scalable: Designed for steady and fast performance, ensuring reliability even as workflow requirements grow.

To begin using nanobot, users can install the package through Python-based tools such as pip or uv. Once installed, it operates as a command-line interface or an embedded module that interacts with the user's workspace or specified communication channels. Users define the agent's tasks, and the system executes them according to the configured token limits and step constraints. Because it is self-hosted, it offers a high degree of control over the agent's behavior and the data it accesses.

Some common use cases include:

• Automation: Executing complex, multi-step workflows that require long-term context retention without human intervention.
• Workspace Assistant: Providing an AI-driven interface for daily tasks, data retrieval, and workspace management through integrated communication channels.
• Long-running Task Management: Managing projects that require consistent tracking and execution over extended periods.
• Embedded Intelligence: Integrating an autonomous AI agent into existing software environments for specialized business logic processing.