--- title: "LLMForEverybody vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/luhengshiwo-llmforeverybody-vs-shishirpatil-gorilla" tools: ["luhengshiwo-llmforeverybody", "shishirpatil-gorilla"] --- # LLMForEverybody vs gorilla *GraphCanon updated Aug 18, 2026* ## Verdict Pick LLMForEverybody if lLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [LLMForEverybody](https://www.learnllm.ai) reports 7.2k GitHub stars, 662 forks, and 0 open issues, last pushed Aug 17, 2026. [gorilla](https://gorilla.cs.berkeley.edu/) has 13k stars, 1.4k forks, and 272 open issues, last pushed Apr 13, 2026. Figures are from public GitHub metadata via [LLMForEverybody's repository](https://github.com/luhengshiwo/LLMForEverybody) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [LLMForEverybody](/tools/luhengshiwo-llmforeverybody.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | LLM knowledge sharing for everyone, essential reading before big model interviews | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 7,167 | 12,988 | | Forks | 662 | 1,397 | | Open issues | 0 | 272 | | Language | Jupyter Notebook | Python | | Adopt for | LLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t | 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, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMForEverybody](/tools/luhengshiwo-llmforeverybody.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 1d | 117d | | Open issues (now) | 0 | 272 | | Stars delta | +198 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/luhengshiwo-llmforeverybody/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: LLMForEverybody - **Adopt for:** LLMForEverybody is a repository primarily focused on sharing knowledge about large language models, with content that includes interview practice, research paper studies (from foundational Transformer papers to more up-t ## 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 LLMForEverybody if… - LLMForEverybody is primarily Jupyter Notebook; gorilla is Python. - Tags unique to LLMForEverybody: agent, interview-practice, learnllm, rag. - Also covers LLM Frameworks. - If you are preparing for job interviews in the field of LLMs or related technologies and want access to practical questions and answers. ### Choose gorilla if… - gorilla is primarily Python; LLMForEverybody is Jupyter Notebook. - 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 LLMForEverybody - If your learning preference leans towards a different language or if the Chinese-specific resources don't align with your needs. - For individuals looking for comprehensive open-source tools or frameworks to build upon directly; this is more about educational content than concrete implementations. ## 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 LLMForEverybody and gorilla? LLMForEverybody: LLM knowledge sharing for everyone, essential reading before big model interviews. 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 LLMForEverybody over gorilla? Choose LLMForEverybody over gorilla when LLMForEverybody is primarily Jupyter Notebook; gorilla is Python; Tags unique to LLMForEverybody: agent, interview-practice, learnllm, rag; Also covers LLM Frameworks; If you are preparing for job interviews in the field of LLMs or related technologies and want access to practical questions and answers. ### When should I choose gorilla over LLMForEverybody? Choose gorilla over LLMForEverybody when gorilla is primarily Python; LLMForEverybody is Jupyter Notebook; 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 LLMForEverybody? If your learning preference leans towards a different language or if the Chinese-specific resources don't align with your needs. For individuals looking for comprehensive open-source tools or frameworks to build upon directly; this is more about educational content than concrete implementations. ### 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 LLMForEverybody or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 7,167). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMForEverybody and gorilla open source? Yes - both are open-source projects on GitHub (LLMForEverybody: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to LLMForEverybody or gorilla? GraphCanon lists graph-backed alternatives at [LLMForEverybody alternatives](/tools/luhengshiwo-llmforeverybody/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([LLMForEverybody markdown twin](/tools/luhengshiwo-llmforeverybody/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/luhengshiwo-llmforeverybody-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LLMForEverybody or gorilla? LLMForEverybody: Very active. gorilla: 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 LLMForEverybody and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMForEverybody trust report](/tools/luhengshiwo-llmforeverybody/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=luhengshiwo-llmforeverybody`](/api/graphcanon/graph?tool=luhengshiwo-llmforeverybody) - 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/_