--- title: "AgentGuide vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/adongwanai-agentguide-vs-datawhalechina-hello-agents" tools: ["adongwanai-agentguide", "datawhalechina-hello-agents"] --- # AgentGuide vs hello-agents *GraphCanon updated Sep 30, 2026* ## Verdict Pick AgentGuide if agentGuide is a comprehensive guide for AI Agent development and job interview preparation, focusing on topics like LangGraph, RAG, and reinforcement learning, with a strong emphasis on practical projects and job-readyno; 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. [AgentGuide](https://github.com/adongwanai/AgentGuide) reports 8.5k GitHub stars, 835 forks, and 36 open issues, last pushed Aug 4, 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 [AgentGuide's repository](https://github.com/adongwanai/AgentGuide) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [AgentGuide](/tools/adongwanai-agentguide.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | AI Agent development guide with job interview preparation | A comprehensive guide to building intelligent agents from scratch | | Stars | 8,484 | 79,727 | | Forks | 835 | 9,907 | | Open issues | 36 | 82 | | Language | HTML | Python | | Adopt for | AgentGuide is a comprehensive guide for AI Agent development and job interview preparation, focusing on topics like LangGraph, RAG, and reinforcement learning, with a strong emphasis on practical projects and job-readyno | 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 | The license information for AgentGuide is unknown | The license is not specified as open source, but the content is free to read and learn from. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AgentGuide](/tools/adongwanai-agentguide.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 14d | 0d | | Open issues (now) | 36 | 82 | | Stars delta | +1.4k (30d) | +6.6k (30d) | | Open issues delta | -77 (30d) | -73 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/adongwanai-agentguide/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Shared compatibility - **LangGraph**: [AgentGuide](/tools/adongwanai-agentguide.md) - LangGraph integration; [hello-agents](/tools/datawhalechina-hello-agents.md) - LangGraph integration ## Decision facts: AgentGuide - **Pricing:** freemium - AgentGuide is free to use, but it is not clear if there are premium features or services associated with it - **Requirements:** AgentGuide is primarily a guide and does not have specific system requirements - **Adopt for:** AgentGuide is a comprehensive guide for AI Agent development and job interview preparation, focusing on topics like LangGraph, RAG, and reinforcement learning, with a strong emphasis on practical projects and job-readyno - **License detail:** The license information for AgentGuide is unknown ## 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 AgentGuide if… - AgentGuide is primarily HTML; hello-agents is Python. - Pricing: AgentGuide is free to use, but it is not clear if there are premium features or services associated with it. - Requirements: AgentGuide is primarily a guide and does not have specific system requirements. - Tags unique to AgentGuide: agenticrag, ai-agent, crewai, graphrag. - Also covers Evaluation & Observability. - When you need a structured learning path for AI Agent development that includes practical projects and job interview preparation ### Choose hello-agents if… - hello-agents is primarily Python; AgentGuide is HTML. - 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 AgentGuide - If you are looking for a tool that focuses solely on theoretical knowledge without practical application or job-readyno - When you need a resource that covers only a specific aspect of AI Agent development, such as just reinforcement learning or just LangGraph, without the broader context and job preparation ## 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 AgentGuide and hello-agents? AgentGuide: AI Agent development guide with job interview preparation. 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 AgentGuide over hello-agents? Choose AgentGuide over hello-agents when AgentGuide is primarily HTML; hello-agents is Python; Pricing: AgentGuide is free to use, but it is not clear if there are premium features or services associated with it; Requirements: AgentGuide is primarily a guide and does not have specific system requirements; Tags unique to AgentGuide: agenticrag, ai-agent, crewai, graphrag; Also covers Evaluation & Observability; When you need a structured learning path for AI Agent development that includes practical projects and job interview preparation. ### When should I choose hello-agents over AgentGuide? Choose hello-agents over AgentGuide when hello-agents is primarily Python; AgentGuide is HTML; 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 AgentGuide? If you are looking for a tool that focuses solely on theoretical knowledge without practical application or job-readyno When you need a resource that covers only a specific aspect of AI Agent development, such as just reinforcement learning or just LangGraph, without the broader context and job preparation ### 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 AgentGuide or hello-agents more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 8,484). Stars measure visibility, not whether either tool fits your constraints. ### Are AgentGuide and hello-agents open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to AgentGuide or hello-agents? GraphCanon lists graph-backed alternatives at [AgentGuide alternatives](/tools/adongwanai-agentguide/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([AgentGuide markdown twin](/tools/adongwanai-agentguide/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/adongwanai-agentguide-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, AgentGuide or hello-agents? AgentGuide: Active. 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 AgentGuide and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AgentGuide trust report](/tools/adongwanai-agentguide/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=adongwanai-agentguide`](/api/graphcanon/graph?tool=adongwanai-agentguide) - 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/_