--- title: "llm-course vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mlabonne-llm-course-vs-shishirpatil-gorilla" tools: ["mlabonne-llm-course", "shishirpatil-gorilla"] --- # llm-course vs gorilla *GraphCanon updated Jul 11, 2026* ## Verdict 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; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [llm-course](https://mlabonne.github.io/blog/) reports 81k GitHub stars, 9.4k forks, and 84 open issues, last pushed Feb 5, 2026. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 264 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [llm-course's repository](https://github.com/mlabonne/llm-course) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [llm-course](/tools/mlabonne-llm-course.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 80,839 | 12,940 | | Forks | 9,421 | 1,387 | | Open issues | 84 | 264 | | Language | - | Python | | 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 | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-course](/tools/mlabonne-llm-course.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Steady (60%) | | Days since push | 155d | 89d | | Open issues (now) | 84 | 264 | | Full report | [trust report](/tools/mlabonne-llm-course/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Shared compatibility - **Python**: [llm-course](/tools/mlabonne-llm-course.md) - Python runtime; [gorilla](/tools/shishirpatil-gorilla.md) - Python runtime ## 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 ## Decision facts: gorilla - **Pricing:** freemium - **Requirements:** Gorilla works best with Python environments and requires installation through pip or local repository cloning. - **Adopt for:** Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. - **License detail:** Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. ## Choose when ### Choose llm-course if… - 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 Inference & Serving, LLM Frameworks. - - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge ### Choose gorilla if… - Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.. - Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ## 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 ## When NOT to use gorilla - Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. - If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ## Common questions ### What is the difference between llm-course and gorilla? llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.. gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). See the comparison table for live GitHub stats and shared categories. ### When should I choose llm-course over gorilla? Choose llm-course over gorilla when 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 Inference & Serving, LLM Frameworks; - When you want a comprehensive roadmap for understanding large language models including fundamental knowledge. ### When should I choose gorilla over llm-course? Choose gorilla over llm-course when Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, chatgpt, claude-api, gpt-4-api; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### 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 ### When should I avoid gorilla? Avoid Gorilla if your primary focus is not on function calling or tool usage capabilities for LLMs; another model-specific framework may better fit your needs. If the lack of a direct comparison tool to other models' function-calling performance is critical in your decision process, and you find no suitable alternatives listed on their leaderboard. ### Is llm-course or gorilla more popular on GitHub? llm-course has more GitHub stars (80,839 vs 12,940). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-course and gorilla open source? Yes - both are open-source projects on GitHub (llm-course: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to llm-course or gorilla? GraphCanon lists graph-backed alternatives at [llm-course alternatives](/tools/mlabonne-llm-course/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([llm-course markdown twin](/tools/mlabonne-llm-course/alternatives.md), [gorilla markdown twin](/tools/shishirpatil-gorilla/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/mlabonne-llm-course-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llm-course or gorilla? llm-course: Slowing. gorilla: Steady. 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 llm-course and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-course trust report](/tools/mlabonne-llm-course/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mlabonne-llm-course`](/api/graphcanon/graph?tool=mlabonne-llm-course) - 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/_