--- title: "hello-agents vs ART" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-openpipe-art" tools: ["datawhalechina-hello-agents", "openpipe-art"] --- # hello-agents vs ART *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 ART if aRT is a specialized framework for training multi-step AI agents using GRPO, suitable for developers looking to train their agents with specific LLMs like Qwen3.6 and GPT-OSS. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [ART](https://art.openpipe.ai) has 11k stars, 974 forks, and 127 open issues, last pushed Aug 19, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [ART's repository](https://github.com/OpenPipe/ART). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [ART](/tools/openpipe-art.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. | | Stars | 79,727 | 10,603 | | Forks | 9,907 | 974 | | Open issues | 82 | 127 | | 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. | ART is a specialized framework for training multi-step AI agents using GRPO, suitable for developers looking to train their agents with specific LLMs like Qwen3.6 and GPT-OSS. | | 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, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [ART](/tools/openpipe-art.md) | | --- | --- | --- | | Open issues (now) | 82 | 127 | | Stars delta | +6.6k (30d) | +109 (30d) | | Open issues delta | -73 (30d) | +6 (30d) | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/openpipe-art/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: ART - **Adopt for:** ART is a specialized framework for training multi-step AI agents using GRPO, suitable for developers looking to train their agents with specific LLMs like Qwen3.6 and GPT-OSS. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, ART 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 ART if… - License: ART is Apache-2.0, hello-agents is Other. - Tags unique to ART: agentic-ai, grpo, llms, lora. - - When you need a framework that specifically supports the GRPO method for agent reinforcement learning. ## 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 ART - - Avoid ART if your training needs do not align with the GRPO method and you require a different approach for reinforcement learning. - - If your development revolves around non-supported Language Learning Models (LLMs), other than Qwen3.6, GPT-OSS, Llama etc., consider looking into alternative frameworks that offer broader model comp ## Common questions ### What is the difference between hello-agents and ART? hello-agents: A comprehensive guide to building intelligent agents from scratch. ART: Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over ART? Choose hello-agents over ART when License: hello-agents is Other, ART 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 ART over hello-agents? Choose ART over hello-agents when License: ART is Apache-2.0, hello-agents is Other; Tags unique to ART: agentic-ai, grpo, llms, lora; - When you need a framework that specifically supports the GRPO method for agent reinforcement learning. ### 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 ART? - Avoid ART if your training needs do not align with the GRPO method and you require a different approach for reinforcement learning. - If your development revolves around non-supported Language Learning Models (LLMs), other than Qwen3.6, GPT-OSS, Llama etc., consider looking into alternative frameworks that offer broader model comp ### Is hello-agents or ART more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 10,603). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and ART open source? Yes - both are open-source projects on GitHub (hello-agents: Other, ART: Apache-2.0). ### Where can I find alternatives to hello-agents or ART? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [ART alternatives](/tools/openpipe-art/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [ART markdown twin](/tools/openpipe-art/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-openpipe-art.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or ART? hello-agents: Very active. ART: Very active. 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 ART? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [ART trust report](/tools/openpipe-art/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/_