--- title: "hello-agents vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-mlabonne-llm-course" tools: ["datawhalechina-hello-agents", "mlabonne-llm-course"] --- # hello-agents vs llm-course *GraphCanon updated Jul 17, 2026* ## Verdict Pick hello-agents if hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods; pick llm-course if the llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to. [hello-agents](https://hello-agents.datawhale.cc) reports 67k GitHub stars, 8.3k forks, and 147 open issues, last pushed Jul 10, 2026. [llm-course](https://mlabonne.github.io/blog/) has 81k stars, 9.4k forks, and 85 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 66,690 | 80,904 | | Forks | 8,277 | 9,424 | | Open issues | 147 | 85 | | Language | Python | - | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 159d | | Open issues (now) | 147 | 85 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | **Typed relationship:** hello-agents _(alternative)_ llm-course Both Hello-Agents and mlabonne's LLM course offer educational content on building large language models and intelligent agents, but they may have different approaches or focuses. ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Decision facts: llm-course - **Requirements:** Course materials are available in Colab notebooks; access requires a Google account - **Adopt for:** The llm-course provides a comprehensive guided course on Large Language Models (LLMs), divided into three parts: LLM Fundamentals, The LLM Scientist, and The LLM Engineer. It includes resources such as Colab notebooks to - **License detail:** Apache-2.0 ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, llm-course is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Both Hello-Agents and mlabonne's LLM course offer educational content on building large language models and intelligent agents, but they may have different approaches or focuses. - Tags unique to hello-agents: agent, llm, rag, tutorial. - Also covers AI Agents. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose llm-course if… - License: llm-course is Apache-2.0, hello-agents is Other. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Both Hello-Agents and mlabonne's LLM course offer educational content on building large language models and intelligent agents, but they may have different approaches or focuses. - Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## When NOT to use llm-course - - If you only require a quick introduction to LLMs without deep dive into core components - - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ## Common questions ### What is the difference between hello-agents and llm-course? hello-agents: Course on building intelligent agents from scratch. llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over llm-course? Choose hello-agents over llm-course when License: hello-agents is Other, llm-course is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Both Hello-Agents and mlabonne's LLM course offer educational content on building large language models and intelligent agents, but they may have different approaches or focuses; Tags unique to hello-agents: agent, llm, rag, tutorial; Also covers AI Agents; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I choose llm-course over hello-agents? Choose llm-course over hello-agents when License: llm-course is Apache-2.0, hello-agents is Other; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Both Hello-Agents and mlabonne's LLM course offer educational content on building large language models and intelligent agents, but they may have different approaches or focuses; Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning; Also covers Evaluation & Observability, Inference & Serving, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### When should I avoid llm-course? - If you only require a quick introduction to LLMs without deep dive into core components - When you prefer working directly with commercial platforms that provide complete services rather than following detailed steps on building and deploying models yourself through this course's open,DI ### Is hello-agents or llm-course more popular on GitHub? llm-course has more GitHub stars (80,904 vs 66,690). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and llm-course open source? Yes - both are open-source projects on GitHub (hello-agents: Other, llm-course: Apache-2.0). ### Where can I find alternatives to hello-agents or llm-course? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [llm-course markdown twin](/tools/mlabonne-llm-course/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-mlabonne-llm-course.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-course? hello-agents: Very active. llm-course: Slowing. 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-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [llm-course trust report](/tools/mlabonne-llm-course/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/_