--- title: "nanobot vs autogen" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hkuds-nanobot-vs-microsoft-autogen" tools: ["hkuds-nanobot", "microsoft-autogen"] --- # nanobot vs autogen Neutral, constraint-first comparison with live GitHub stats. | | [nanobot](/tools/hkuds-nanobot.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Tagline | Lightweight, open-source AI agent for your tools, chats, and workflows. | A framework for creating multi-agent AI applications | | Stars | 45,122 | 59,573 | | Forks | 7,967 | 8,967 | | Open issues | 912 | 930 | | Language | Python | Python | | Adopt for | nanobot is an open-source, lightweight AI agent that can be integrated into various applications to support tools, chats, and workflows. | AutoGen is a framework for developing multi-agent AI applications that can act autonomously or alongside humans. It's currently in maintenance mode with no additional features planned and users are encouraged to migrate. | | Persona | developer harness | - | | Runtime | - | - | | License | MIT License | CC-BY-4.0 | | Categories | AI Agents | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nanobot](/tools/hkuds-nanobot.md) | [autogen](/tools/microsoft-autogen.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 83d | | Open issues (now) | 912 | 930 | | Full report | [trust report](/tools/hkuds-nanobot/trust.md) | [trust report](/tools/microsoft-autogen/trust.md) | **Typed relationship:** nanobot _(alternative)_ autogen autogen and nanobot are both frameworks for creating multi-agent AI applications with differing methodologies and use cases. ## Shared compatibility - **Python**: [nanobot](/tools/hkuds-nanobot.md) - Python runtime; [autogen](/tools/microsoft-autogen.md) - Python runtime ## Decision facts: nanobot - **Pricing:** freemium - **Requirements:** Min 2 GB RAM; Works best on Python ≥3.11 - **Adopt for:** nanobot is an open-source, lightweight AI agent that can be integrated into various applications to support tools, chats, and workflows. - **License detail:** MIT License - **Persona:** developer harness - **Runtime:** unknown ## Decision facts: autogen - **Requirements:** AutoGen requires Python 3.10 or later. - **Adopt for:** AutoGen is a framework for developing multi-agent AI applications that can act autonomously or alongside humans. It's currently in maintenance mode with no additional features planned and users are encouraged to migrate. ## Choose when ### Choose nanobot if… - License: nanobot is MIT, autogen is CC-BY-4.0. - Requirements: Min 2 GB RAM; Works best on Python ≥3.11. - autogen and nanobot are both frameworks for creating multi-agent AI applications with differing methodologies and use cases. - Tags unique to nanobot: llm, codex, chatgpt, claude. - nanobot ships Docker support for self-hosted deployment. - When you need a lightweight solution for integrating with existing tools and workflows ### Choose autogen if… - License: autogen is CC-BY-4.0, nanobot is MIT. - Requirements: AutoGen requires Python 3.10 or later.. - autogen and nanobot are both frameworks for creating multi-agent AI applications with differing methodologies and use cases. - Tags unique to autogen: autogen, agents, agentic, framework. - Also covers LLM Frameworks. - You should use AutoGen if you have an existing project built on it and desire to maintain its current functionality without introducing advanced enterprise features or extensive new capabilities. ## When NOT to use nanobot - If your project requires complex integrations and features beyond basic tool support, chat channels, and memory management - For projects requiring heavyweight or specialized AI functionalities not covered by nanobot's core capabilities ## When NOT to use autogen - Do not use AutoGen if you are planning to build a production-ready application that requires long-term support, enterprise-grade orchestration features, or multi-provider model support. - Avoid using this tool if your project needs future-proof development with new and continuous enhancements as the framework is in maintenance mode. ## Common questions ### What is the difference between nanobot and autogen? nanobot: Lightweight, open-source AI agent for your tools, chats, and workflows.. autogen: A framework for creating multi-agent AI applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose nanobot over autogen? Choose nanobot over autogen when License: nanobot is MIT, autogen is CC-BY-4.0; Requirements: Min 2 GB RAM; Works best on Python ≥3.11; autogen and nanobot are both frameworks for creating multi-agent AI applications with differing methodologies and use cases; Tags unique to nanobot: llm, codex, chatgpt, claude; nanobot ships Docker support for self-hosted deployment; When you need a lightweight solution for integrating with existing tools and workflows. ### When should I choose autogen over nanobot? Choose autogen over nanobot when License: autogen is CC-BY-4.0, nanobot is MIT; Requirements: AutoGen requires Python 3.10 or later.; autogen and nanobot are both frameworks for creating multi-agent AI applications with differing methodologies and use cases; Tags unique to autogen: autogen, agents, agentic, framework; Also covers LLM Frameworks; You should use AutoGen if you have an existing project built on it and desire to maintain its current functionality without introducing advanced enterprise features or extensive new capabilities. ### When should I avoid nanobot? If your project requires complex integrations and features beyond basic tool support, chat channels, and memory management For projects requiring heavyweight or specialized AI functionalities not covered by nanobot's core capabilities ### When should I avoid autogen? Do not use AutoGen if you are planning to build a production-ready application that requires long-term support, enterprise-grade orchestration features, or multi-provider model support. Avoid using this tool if your project needs future-proof development with new and continuous enhancements as the framework is in maintenance mode. ### Is nanobot or autogen more popular on GitHub? autogen has more GitHub stars (59,573 vs 45,122). Stars measure visibility, not whether either tool fits your constraints. ### Are nanobot and autogen open source? Yes - both are open-source projects on GitHub (nanobot: MIT, autogen: CC-BY-4.0). ### Where can I find alternatives to nanobot or autogen? GraphCanon lists graph-backed alternatives at /tools/hkuds-nanobot/alternatives and /tools/microsoft-autogen/alternatives (/tools/hkuds-nanobot/alternatives.md, /tools/microsoft-autogen/alternatives.md), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at /compare/hkuds-nanobot-vs-microsoft-autogen.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, nanobot or autogen? nanobot: Very active. autogen: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for nanobot and autogen? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: nanobot: /tools/hkuds-nanobot/trust; autogen: /tools/microsoft-autogen/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hkuds-nanobot`](/api/graphcanon/graph?tool=hkuds-nanobot) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_