--- title: "gorilla vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shishirpatil-gorilla-vs-wangrongsheng-awesome-llm-resources" tools: ["shishirpatil-gorilla", "wangrongsheng-awesome-llm-resources"] --- # gorilla vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [gorilla](https://gorilla.cs.berkeley.edu/) reports 13k GitHub stars, 1.4k forks, and 272 open issues, last pushed Apr 13, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [gorilla's repository](https://github.com/ShishirPatil/gorilla) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [gorilla](/tools/shishirpatil-gorilla.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Training and Evaluating LLMs for Function Calls (Tool Calls) | Summary of the world's best LLM resources. | | Stars | 12,988 | 8,845 | | Forks | 1,397 | 950 | | Open issues | 272 | 23 | | Language | Python | - | | Adopt for | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | Apache-2.0 | | Categories | Evaluation & Observability, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gorilla](/tools/shishirpatil-gorilla.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 117d | 2d | | Open issues (now) | 272 | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Full report | [trust report](/tools/shishirpatil-gorilla/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## 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. ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### 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. ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## 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. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between gorilla and awesome-LLM-resources? gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose gorilla over awesome-LLM-resources? Choose gorilla over awesome-LLM-resources 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 choose awesome-LLM-resources over gorilla? Choose awesome-LLM-resources over gorilla when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### 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. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is gorilla or awesome-LLM-resources more popular on GitHub? gorilla has more GitHub stars (12,988 vs 8,845). Stars measure visibility, not whether either tool fits your constraints. ### Are gorilla and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (gorilla: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to gorilla or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([gorilla markdown twin](/tools/shishirpatil-gorilla/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/shishirpatil-gorilla-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gorilla or awesome-LLM-resources? gorilla: Slowing. awesome-LLM-resources: 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 gorilla and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gorilla trust report](/tools/shishirpatil-gorilla/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=shishirpatil-gorilla`](/api/graphcanon/graph?tool=shishirpatil-gorilla) - 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/_