--- title: "hello-agents vs LLM-Agent-Paper-List" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-woooodyy-llm-agent-paper-list" tools: ["datawhalechina-hello-agents", "woooodyy-llm-agent-paper-list"] --- # hello-agents vs LLM-Agent-Paper-List *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 LLM-Agent-Paper-List if lLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [LLM-Agent-Paper-List](https://arxiv.org/abs/2309.07864) has 8.2k stars, 499 forks, and 32 open issues, last pushed Sep 12, 2025. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [LLM-Agent-Paper-List's repository](https://github.com/WooooDyy/LLM-Agent-Paper-List). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | Must-read papers for LLM-based agents. | | Stars | 79,727 | 8,222 | | Forks | 9,907 | 499 | | Open issues | 82 | 32 | | Language | 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. | LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. | | Persona | - | - | | Runtime | - | - | | License | The license is not specified as open source, but the content is free to read and learn from. | - | | Categories | AI Agents, Model Training | AI Agents, Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LLM-Agent-Paper-List](/tools/woooodyy-llm-agent-paper-list.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 371d | | Open issues (now) | 82 | 32 | | Stars delta | +6.6k (30d) | +50 (30d) | | Open issues delta | -73 (30d) | +1 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/woooodyy-llm-agent-paper-list/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: LLM-Agent-Paper-List - **Adopt for:** LLM-Agent-Paper-List is a curated repository of essential papers on large language model-based agents, focusing on AgentGym, a framework for developing and training LLM agents through reinforcement learning. ## Choose when ### Choose hello-agents if… - 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: rag, tutorial. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### Choose LLM-Agent-Paper-List if… - Tags unique to LLM-Agent-Paper-List: evaluation, large-language-models, nlp, reinforcement learning. - Also covers Evaluation & Observability. - When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through 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 LLM-Agent-Paper-List - If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents - When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications - If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ## Common questions ### What is the difference between hello-agents and LLM-Agent-Paper-List? hello-agents: A comprehensive guide to building intelligent agents from scratch. LLM-Agent-Paper-List: Must-read papers for LLM-based agents.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over LLM-Agent-Paper-List? Choose hello-agents over LLM-Agent-Paper-List when 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: rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I choose LLM-Agent-Paper-List over hello-agents? Choose LLM-Agent-Paper-List over hello-agents when Tags unique to LLM-Agent-Paper-List: evaluation, large-language-models, nlp, reinforcement learning; Also covers Evaluation & Observability; When you need a comprehensive list of must-read papers on LLM-based agents, including detailed insights into AgentGym, a framework for developing and training LLM agents through 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 LLM-Agent-Paper-List? If you are looking for a tool that focuses on a broader range of AI agent development beyond LLM-based agents, as LLM-Agent-Paper-List is specifically curated for LLM-based agents When you need real-time updates or a dynamic list of papers, as the repository is a static list and does not automatically update with new publications If you require a tool that includes practical code implementations for all listed papers, as the repository primarily serves as a list and does not provide comprehensive code for every paper ### Is hello-agents or LLM-Agent-Paper-List more popular on GitHub? hello-agents has more GitHub stars (79,727 vs 8,222). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and LLM-Agent-Paper-List open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to hello-agents or LLM-Agent-Paper-List? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [LLM-Agent-Paper-List alternatives](/tools/woooodyy-llm-agent-paper-list/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [LLM-Agent-Paper-List markdown twin](/tools/woooodyy-llm-agent-paper-list/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-woooodyy-llm-agent-paper-list.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or LLM-Agent-Paper-List? hello-agents: Very active. LLM-Agent-Paper-List: 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 LLM-Agent-Paper-List? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [LLM-Agent-Paper-List trust report](/tools/woooodyy-llm-agent-paper-list/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/_