--- title: "hello-agents vs nextpy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-dot-agent-nextpy" tools: ["datawhalechina-hello-agents", "dot-agent-nextpy"] --- # hello-agents vs nextpy *GraphCanon updated Sep 20, 2026* ## Verdict Pick hello-agents if hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python; pick nextpy if nextpy is a framework developed for building self-modifying software with advanced prompt engineering and session state management specifically targeted at large language models. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [nextpy](https://dotagent.ai) has 2.3k stars, 179 forks, and 23 open issues, last pushed May 1, 2024. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [nextpy's repository](https://github.com/dot-agent/nextpy). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [nextpy](/tools/dot-agent-nextpy.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | Self-Modifying Framework from the Future | | Stars | 79,727 | 2,348 | | Forks | 9,907 | 179 | | Open issues | 82 | 23 | | Language | Python | Python | | Adopt for | Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. | Nextpy is a framework developed for building self-modifying software with advanced prompt engineering and session state management specifically targeted at large language models. | | Persona | - | - | | Runtime | - | - | | License | The license is not specified as open source, but the content is free to read and learn from. | Apache-2.0 | | Categories | AI Agents, Model Training | AI Agents, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [nextpy](/tools/dot-agent-nextpy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 871d | | Open issues (now) | 82 | 23 | | Stars delta | +6.6k (30d) | 0 (30d) | | Open issues delta | -73 (30d) | 0 (30d) | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/dot-agent-nextpy/trust.md) | ## Decision facts: hello-agents - **Pricing:** freemium - The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository. - **Requirements:** Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework. - **Adopt for:** Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. - **License detail:** The license is not specified as open source, but the content is free to read and learn from. ## Decision facts: nextpy - **Adopt for:** Nextpy is a framework developed for building self-modifying software with advanced prompt engineering and session state management specifically targeted at large language models. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, nextpy is Apache-2.0. - Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.. - Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.. - Tags unique to hello-agents: llm, rag, tutorial. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### Choose nextpy if… - License: nextpy is Apache-2.0, hello-agents is Other. - Tags unique to nextpy: agi, ai-agents, autogpt, llmops. - Also covers Inference & Serving. - When you require precise control over what the AI system can do by setting clear boundaries, ensuring it does not overstep defined limits while remaining dynamic and self-improving. ## When NOT to use hello-agents - If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. - If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ## When NOT to use nextpy - If your project does not need precise boundary controls for AI systems or if full session state management with LLMs is not required. - When working with proprietary models that do not support maintaining state with LLMs or reusing KV caches, since some of Nextpy's optimizations are only available for open-source models. ## Common questions ### What is the difference between hello-agents and nextpy? hello-agents: A comprehensive guide to building intelligent agents from scratch. nextpy: Self-Modifying Framework from the Future. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over nextpy? Choose hello-agents over nextpy when License: hello-agents is Other, nextpy is Apache-2.0; Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.; Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.; Tags unique to hello-agents: llm, rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I choose nextpy over hello-agents? Choose nextpy over hello-agents when License: nextpy is Apache-2.0, hello-agents is Other; Tags unique to nextpy: agi, ai-agents, autogpt, llmops; Also covers Inference & Serving; When you require precise control over what the AI system can do by setting clear boundaries, ensuring it does not overstep defined limits while remaining dynamic and self-improving. ### When should I avoid hello-agents? If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ### When should I avoid nextpy? If your project does not need precise boundary controls for AI systems or if full session state management with LLMs is not required. When working with proprietary models that do not support maintaining state with LLMs or reusing KV caches, since some of Nextpy's optimizations are only available for open-source models. ### Is hello-agents or nextpy more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 2,348). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and nextpy open source? Yes - both are open-source projects on GitHub (hello-agents: Other, nextpy: Apache-2.0). ### Where can I find alternatives to hello-agents or nextpy? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [nextpy alternatives](/tools/dot-agent-nextpy/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [nextpy markdown twin](/tools/dot-agent-nextpy/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 [this comparison](/compare/datawhalechina-hello-agents-vs-dot-agent-nextpy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or nextpy? hello-agents: Very active. nextpy: Dormant. 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 hello-agents and nextpy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [nextpy trust report](/tools/dot-agent-nextpy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_