--- title: "lanarky vs llm-course" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ajndkr-lanarky-vs-mlabonne-llm-course" tools: ["ajndkr-lanarky", "mlabonne-llm-course"] --- # lanarky vs llm-course *GraphCanon updated Aug 21, 2026* ## Verdict Pick lanarky if lanarky, a deprecated Python-based framework for building LLM microservices with FastAPI, offers streamlined development but comes with caveats related to its deprecated status; 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. [lanarky](https://lanarky.ajndkr.com/) reports 990 GitHub stars, 76 forks, and 10 open issues, last pushed Jul 6, 2024. [llm-course](https://mlabonne.github.io/blog/) has 82k stars, 9.5k forks, and 86 open issues, last pushed Feb 5, 2026. Figures are from public GitHub metadata via [lanarky's repository](https://github.com/ajndkr/lanarky) and [llm-course's repository](https://github.com/mlabonne/llm-course). | | [lanarky](/tools/ajndkr-lanarky.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Tagline | A web framework for building LLM microservices (deprecated) | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | | Stars | 990 | 81,512 | | Forks | 76 | 9,490 | | Open issues | 10 | 86 | | Language | Python | - | | Adopt for | Lanarky, a deprecated Python-based framework for building LLM microservices with FastAPI, offers streamlined development but comes with caveats related to its deprecated status. | 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 | Lanarky is released under the MIT License, allowing free usage, modification, and distribution but with no warranty. | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lanarky](/tools/ajndkr-lanarky.md) | [llm-course](/tools/mlabonne-llm-course.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 775d | 183d | | Open issues (now) | 10 | 86 | | Stars delta | -2 (30d) | +771 (30d) | | Full report | [trust report](/tools/ajndkr-lanarky/trust.md) | [trust report](/tools/mlabonne-llm-course/trust.md) | ## Shared compatibility - **Python**: [lanarky](/tools/ajndkr-lanarky.md) - Python runtime; [llm-course](/tools/mlabonne-llm-course.md) - Python runtime ## Decision facts: lanarky - **Pricing:** freemium - The library itself is free to use due to its open-source licensing. However, any associated services like OpenAI's `ChatCompletion` may incur costs depending on the service provider’s pricing. - **Requirements:** Min 1 GB RAM; Ensure you have Python and Pip installed to utilize Lanarky.; No Docker installation is required; it works with standard Python environments. - **Adopt for:** Lanarky, a deprecated Python-based framework for building LLM microservices with FastAPI, offers streamlined development but comes with caveats related to its deprecated status. - **License detail:** Lanarky is released under the MIT License, allowing free usage, modification, and distribution but with no warranty. ## 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 lanarky if… - License: lanarky is MIT, llm-course is Apache-2.0. - Pricing: The library itself is free to use due to its open-source licensing. However, any associated services like OpenAI's `ChatCompletion` may incur costs depending on the service provider’s pricing.. - Requirements: Min 1 GB RAM; Ensure you have Python and Pip installed to utilize Lanarky.; No Docker installation is required; it works with standard Python environments.. - Tags unique to lanarky: fastapi, llmops, microservices, python3. - - Use if your project requires specific historical compatibility or knowledge of how Lanarky operated in the past. ### Choose llm-course if… - License: llm-course is Apache-2.0, lanarky is MIT. - Requirements: Course materials are available in Colab notebooks; access requires a Google account. - Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning. - Also covers Evaluation & Observability, Model Training. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ## When NOT to use lanarky - - Avoid new deployments that rely on active maintenance and updates; opt for actively maintained alternatives like FastAPI directly without Lanarky's now-deprecated layer. - - Do not use if your application needs modern security patches or features, as the deprecated status signifies no further development or support. ## 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 lanarky and llm-course? lanarky: A web framework for building LLM microservices (deprecated). 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 lanarky over llm-course? Choose lanarky over llm-course when License: lanarky is MIT, llm-course is Apache-2.0; Pricing: The library itself is free to use due to its open-source licensing. However, any associated services like OpenAI's `ChatCompletion` may incur costs depending on the service provider’s pricing.; Requirements: Min 1 GB RAM; Ensure you have Python and Pip installed to utilize Lanarky.; No Docker installation is required; it works with standard Python environments.; Tags unique to lanarky: fastapi, llmops, microservices, python3; - Use if your project requires specific historical compatibility or knowledge of how Lanarky operated in the past. ### When should I choose llm-course over lanarky? Choose llm-course over lanarky when License: llm-course is Apache-2.0, lanarky is MIT; Requirements: Course materials are available in Colab notebooks; access requires a Google account; Tags unique to llm-course: colab-notebooks, course, large language models, machine-learning; Also covers Evaluation & Observability, Model Training; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I avoid lanarky? - Avoid new deployments that rely on active maintenance and updates; opt for actively maintained alternatives like FastAPI directly without Lanarky's now-deprecated layer. - Do not use if your application needs modern security patches or features, as the deprecated status signifies no further development or support. ### 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 lanarky or llm-course more popular on GitHub? llm-course has more GitHub stars (81,512 vs 990). Stars measure visibility, not whether either tool fits your constraints. ### Are lanarky and llm-course open source? Yes - both are open-source projects on GitHub (lanarky: MIT, llm-course: Apache-2.0). ### Where can I find alternatives to lanarky or llm-course? GraphCanon lists graph-backed alternatives at [lanarky alternatives](/tools/ajndkr-lanarky/alternatives) and [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) ([lanarky markdown twin](/tools/ajndkr-lanarky/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/ajndkr-lanarky-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, lanarky or llm-course? lanarky: Dormant. 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 lanarky and llm-course? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [lanarky trust report](/tools/ajndkr-lanarky/trust); [llm-course trust report](/tools/mlabonne-llm-course/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ajndkr-lanarky`](/api/graphcanon/graph?tool=ajndkr-lanarky) - 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/_