--- title: "500-AI-Agents-Projects vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ashishpatel26-500-ai-agents-projects-vs-datawhalechina-hello-agents" tools: ["ashishpatel26-500-ai-agents-projects", "datawhalechina-hello-agents"] --- # 500-AI-Agents-Projects vs hello-agents *GraphCanon updated Sep 20, 2026* ## Verdict Pick 500-AI-Agents-Projects if the 500-AI-Agents-Projects repository offers a diverse collection of practical AI agent use cases across multiple industries with links to open-source implementation projects; 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. [500-AI-Agents-Projects](https://ashishpatel26.github.io/500-AI-Agents-Projects/) reports 38k GitHub stars, 6.8k forks, and 63 open issues, last pushed Jul 27, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 80k stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [500-AI-Agents-Projects's repository](https://github.com/ashishpatel26/500-AI-Agents-Projects) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [500-AI-Agents-Projects](/tools/ashishpatel26-500-ai-agents-projects.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | A curated collection of AI agent use cases across various industries. | A comprehensive guide to building intelligent agents from scratch | | Stars | 37,872 | 79,727 | | Forks | 6,769 | 9,907 | | Open issues | 63 | 82 | | Language | Python | Python | | Adopt for | The 500-AI-Agents-Projects repository offers a diverse collection of practical AI agent use cases across multiple industries with links to open-source implementation projects. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | The license is not specified as open source, but the content is free to read and learn from. | | Categories | AI Agents | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [500-AI-Agents-Projects](/tools/ashishpatel26-500-ai-agents-projects.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 55d | 0d | | Open issues (now) | 63 | 82 | | Stars delta | +1.2k (30d) | +6.6k (30d) | | Open issues delta | -11 (30d) | -73 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/ashishpatel26-500-ai-agents-projects/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Decision facts: 500-AI-Agents-Projects - **Pricing:** freemium - The repository itself is freely available under the MIT License, allowing users to use, modify, and distribute the content. - **Requirements:** Min 4 GB RAM; - A basic understanding of Python will be advantageous as many projects are based on this language.; - Access to source code links for further exploration or integration. - **Adopt for:** The 500-AI-Agents-Projects repository offers a diverse collection of practical AI agent use cases across multiple industries with links to open-source implementation projects. ## 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. ## Choose when ### Choose 500-AI-Agents-Projects if… - License: 500-AI-Agents-Projects is MIT, hello-agents is Other. - Pricing: The repository itself is freely available under the MIT License, allowing users to use, modify, and distribute the content.. - Requirements: Min 4 GB RAM; - A basic understanding of Python will be advantageous as many projects are based on this language.; - Access to source code links for further exploration or integration.. - Tags unique to 500-AI-Agents-Projects: ai-agents, cross-industry, genai, implementation-links. - - When you need inspiration for implementing an AI agent in specific industry sectors such as healthcare, finance, education, or retail. ### Choose hello-agents if… - License: hello-agents is Other, 500-AI-Agents-Projects is MIT. - 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: agent, llm, rag, tutorial. - Also covers Model Training. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ## When NOT to use 500-AI-Agents-Projects - - Avoid if you require detailed technical documentation or implementation guides for each project; the repository primarily serves as a curated list of examples without deep dives into individual code - - Not suitable for teams looking for a single toolkit; instead, it provides multiple projects which vary in scope and complexity. ## 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. ## Common questions ### What is the difference between 500-AI-Agents-Projects and hello-agents? 500-AI-Agents-Projects: A curated collection of AI agent use cases across various industries.. hello-agents: A comprehensive guide to building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose 500-AI-Agents-Projects over hello-agents? Choose 500-AI-Agents-Projects over hello-agents when License: 500-AI-Agents-Projects is MIT, hello-agents is Other; Pricing: The repository itself is freely available under the MIT License, allowing users to use, modify, and distribute the content.; Requirements: Min 4 GB RAM; - A basic understanding of Python will be advantageous as many projects are based on this language.; - Access to source code links for further exploration or integration.; Tags unique to 500-AI-Agents-Projects: ai-agents, cross-industry, genai, implementation-links; - When you need inspiration for implementing an AI agent in specific industry sectors such as healthcare, finance, education, or retail. ### When should I choose hello-agents over 500-AI-Agents-Projects? Choose hello-agents over 500-AI-Agents-Projects when License: hello-agents is Other, 500-AI-Agents-Projects is MIT; 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: agent, llm, rag, tutorial; Also covers Model Training; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I avoid 500-AI-Agents-Projects? - Avoid if you require detailed technical documentation or implementation guides for each project; the repository primarily serves as a curated list of examples without deep dives into individual code - Not suitable for teams looking for a single toolkit; instead, it provides multiple projects which vary in scope and complexity. ### 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. ### Is 500-AI-Agents-Projects or hello-agents more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 37,872). Stars measure visibility, not whether either tool fits your constraints. ### Are 500-AI-Agents-Projects and hello-agents open source? Yes - both are open-source projects on GitHub (500-AI-Agents-Projects: MIT, hello-agents: Other). ### Where can I find alternatives to 500-AI-Agents-Projects or hello-agents? GraphCanon lists graph-backed alternatives at [500-AI-Agents-Projects alternatives](/tools/ashishpatel26-500-ai-agents-projects/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([500-AI-Agents-Projects markdown twin](/tools/ashishpatel26-500-ai-agents-projects/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/ashishpatel26-500-ai-agents-projects-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, 500-AI-Agents-Projects or hello-agents? 500-AI-Agents-Projects: Steady. hello-agents: 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 500-AI-Agents-Projects and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [500-AI-Agents-Projects trust report](/tools/ashishpatel26-500-ai-agents-projects/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ashishpatel26-500-ai-agents-projects`](/api/graphcanon/graph?tool=ashishpatel26-500-ai-agents-projects) - 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/_