--- title: "llm-engineer-toolkit vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kalyanks-nlp-llm-engineer-toolkit-vs-shishirpatil-gorilla" tools: ["kalyanks-nlp-llm-engineer-toolkit", "shishirpatil-gorilla"] --- # llm-engineer-toolkit vs gorilla *GraphCanon updated Aug 17, 2026* ## Verdict Pick llm-engineer-toolkit if a curated list of over 120 Large Language Model (LLM) libraries organized into categories essential for development and application creation, aimed at engineers working with generative AI technologies; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [llm-engineer-toolkit](https://www.linkedin.com/in/kalyanksnlp/) reports 11k GitHub stars, 1.7k forks, and 15 open issues, last pushed Aug 16, 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 [llm-engineer-toolkit's repository](https://github.com/KalyanKS-NLP/llm-engineer-toolkit) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [llm-engineer-toolkit](/tools/kalyanks-nlp-llm-engineer-toolkit.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | A curated list of over 120 LLM libraries categorized. | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 10,767 | 12,988 | | Forks | 1,682 | 1,397 | | Open issues | 15 | 272 | | Language | - | Python | | Adopt for | A curated list of over 120 Large Language Model (LLM) libraries organized into categories essential for development and application creation, aimed at engineers working with generative AI technologies. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License allows for free usage, modification, and distribution but requires appropriate attribution. | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llm-engineer-toolkit](/tools/kalyanks-nlp-llm-engineer-toolkit.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 117d | | Open issues (now) | 15 | 272 | | Stars delta | +106 (30d) | Unknown | | Open issues delta | -5 (30d) | Unknown | | Full report | [trust report](/tools/kalyanks-nlp-llm-engineer-toolkit/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: llm-engineer-toolkit - **Requirements:** - No specific programming language requirement noted in the repository content.; - Access to various LLM libraries listed within the repository. - **Adopt for:** A curated list of over 120 Large Language Model (LLM) libraries organized into categories essential for development and application creation, aimed at engineers working with generative AI technologies. - **License detail:** Apache-2.0 License allows for free usage, modification, and distribution but requires appropriate attribution. ## 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-engineer-toolkit if… - Requirements: - No specific programming language requirement noted in the repository content.; - Access to various LLM libraries listed within the repository.. - Tags unique to llm-engineer-toolkit: ai-engineer, generative-ai, large language models, llm-engineer. - Also covers Developer Tools, Inference & Serving. - - You need a wide range of categorized LLM libraries to explore various aspects of LLM engineering, including training, inference, application development, evaluation, and observability. ### 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-engineer-toolkit - - If you require real-time updates or active community support, this curated list might not provide real-time interactions compared to a more dynamic platform with an active developer community. - - You prefer specific use-case tutorials rather than a comprehensive, categorized library guide; other platforms may offer more detailed implementation guides and step-by-step instructions. ## 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-engineer-toolkit and gorilla? llm-engineer-toolkit: A curated list of over 120 LLM libraries categorized.. 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-engineer-toolkit over gorilla? Choose llm-engineer-toolkit over gorilla when Requirements: - No specific programming language requirement noted in the repository content.; - Access to various LLM libraries listed within the repository.; Tags unique to llm-engineer-toolkit: ai-engineer, generative-ai, large language models, llm-engineer; Also covers Developer Tools, Inference & Serving; - You need a wide range of categorized LLM libraries to explore various aspects of LLM engineering, including training, inference, application development, evaluation, and observability. ### When should I choose gorilla over llm-engineer-toolkit? Choose gorilla over llm-engineer-toolkit 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-engineer-toolkit? - If you require real-time updates or active community support, this curated list might not provide real-time interactions compared to a more dynamic platform with an active developer community. - You prefer specific use-case tutorials rather than a comprehensive, categorized library guide; other platforms may offer more detailed implementation guides and step-by-step instructions. ### 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-engineer-toolkit or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 10,767). Stars measure visibility, not whether either tool fits your constraints. ### Are llm-engineer-toolkit and gorilla open source? Yes - both are open-source projects on GitHub (llm-engineer-toolkit: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to llm-engineer-toolkit or gorilla? GraphCanon lists graph-backed alternatives at [llm-engineer-toolkit alternatives](/tools/kalyanks-nlp-llm-engineer-toolkit/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([llm-engineer-toolkit markdown twin](/tools/kalyanks-nlp-llm-engineer-toolkit/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/kalyanks-nlp-llm-engineer-toolkit-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-engineer-toolkit or gorilla? llm-engineer-toolkit: 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 llm-engineer-toolkit and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llm-engineer-toolkit trust report](/tools/kalyanks-nlp-llm-engineer-toolkit/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kalyanks-nlp-llm-engineer-toolkit`](/api/graphcanon/graph?tool=kalyanks-nlp-llm-engineer-toolkit) - 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/_