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nanobot

nanobot is a lightweight, open-source AI agent framework for integrating AI capabilities into tools, chats, and workflows.

Freemium · Web · API

What is nanobot?

nanobot is a personal AI agent runtime tool developed by HKUDS Data Intelligence Lab that enables developers and individuals to create and own ultra-lightweight AI agents. It functions as a self-hosted framework, emphasizing a small, readable core for auditability and efficient operation on various platforms, including edge devices. Its core capabilities include intent recognition, task execution, and contextual adaptation, supporting integration with numerous chat applications and tools like file operations and web search. The framework was initially released on February 2, 2026, and has since seen updates such as v0.2.2 (Durability Release) on July 21, 2026, which expanded provider coverage to AWS Bedrock Converse and NVIDIA NIM.

Key Features of nanobot

nanobot provides a set of features designed for building and deploying lightweight, auditable AI agents. Its architecture prioritizes efficiency and control, allowing users to integrate AI capabilities into diverse environments.

  • Lightweight and Open-source core (approx. 4,000 lines of Python code)
  • OpenAI-compatible API available for integration
  • Real-time weather updates and calendar event management
  • Context-aware responses for personalized interactions
  • Long-horizon execution and scheduled automations via cron tasks
  • Portable core for embedding into edge devices and microservices
  • Support for multiple chat applications (e.g., Telegram, Discord, Slack)
  • Tool utilization including file operations, shell commands, and web fetching
  • Segmented WebUI transcripts for improved conversation durability (v0.2.2)
  • Enhanced Python SDK runtime controls and automation management

Who Should Use nanobot?

nanobot is primarily targeted at individuals and organizations seeking a high degree of control and transparency over their AI agents, particularly those with technical expertise or specific resource constraints.

  • Developers and AI Researchers: For understanding AI agent internals, experimenting with custom LLM prompts, tools, and skills, and running experiments without heavy dependencies.
  • CTOs and Startup Founders: For building custom, efficient AI solutions with a small footprint and high auditability.
  • Automation Enthusiasts: For creating personal productivity bots, smart schedulers, and automated workflows with long-horizon goals.
  • Individuals Seeking Personal AI Control: For deploying self-hosted personal AI assistants that connect to various messaging platforms and provide real-time information.
  • Teams Requiring In-app Chatbots and Edge Assistants: For embedding AI capabilities into edge devices, mobile applications, and microservices.

How to Use nanobot

Getting started with nanobot involves setting up the open-source framework and configuring it to interact with desired LLM providers and communication channels. The process is designed for rapid deployment, often taking minutes to hours.

  1. Install the nanobot framework, typically via Python package managers or by cloning the GitHub repository.
  2. Configure API keys for preferred Large Language Model (LLM) providers (e.g., OpenAI, Anthropic) that nanobot will integrate with.
  3. Set up desired communication channels, such as Telegram, Discord, or Slack, by configuring gateway settings.
  4. Define custom tools and skills for the AI agent to utilize, such as web search or file operations.
  5. Utilize the built-in web interface (Workbench Release v0.2.1) for agent development, testing, and runtime adjustments.
  6. Deploy the nanobot agent for personal use, team collaboration, or workflow automation.

nanobot Pricing & Plans

nanobot operates on a freemium and open-source model, meaning the core framework is available at no cost. Users are responsible for any costs incurred from the underlying Large Language Model (LLM) providers that nanobot integrates with.

  • Freemium: Free access to the core nanobot framework.
  • Open-source: Free access to the source code for self-hosting and modification.

Pros

  • Ultra-lightweight core (approx. 4,000 lines of Python) for high auditability and efficiency.
  • Minimal memory footprint (<20MB median RSS on startup) suitable for edge devices.
  • Open-source and self-hostable, providing users with full ownership and control.
  • Rapid deployment and extensibility through a modular plugin system.
  • Comprehensive support for integration with numerous chat applications and LLM providers.
  • Features like long-horizon execution and scheduled automations for complex workflows.

Cons

  • Relies on external LLM providers, incurring their respective usage fees and rate limits.
  • May have a smaller skill ecosystem and fewer integrated platforms compared to more feature-rich, larger frameworks.
  • The /v1/chat/completions API endpoint currently lacks rate limiting, potentially leading to exhaustion of LLM provider credits.
  • Requires technical expertise for setup, configuration, and customization.

nanobot vs Competitors

nanobot is positioned as an ultra-lightweight, open-source AI agent framework, differentiating itself from more extensive alternatives primarily through its minimalist design and efficiency.

See every nanobot alternative, compared

nanobot

Freemium · Web · API

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