--- title: "pratical-llms vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/antoniogr7-pratical-llms-vs-shishirpatil-gorilla" tools: ["antoniogr7-pratical-llms", "shishirpatil-gorilla"] --- # pratical-llms vs gorilla *GraphCanon updated Aug 9, 2026* ## Verdict Pick pratical-llms if practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [pratical-llms](https://github.com/AntonioGr7/pratical-llms) reports 53 GitHub stars, 15 forks, and 0 open issues, last pushed Jan 13, 2025. [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 [pratical-llms's repository](https://github.com/AntonioGr7/pratical-llms) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [pratical-llms](/tools/antoniogr7-pratical-llms.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | A collection of hands-on notebooks for LLM practitioners | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 53 | 12,988 | | Forks | 15 | 1,397 | | Open issues | 0 | 272 | | Language | Jupyter Notebook | Python | | Adopt for | practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | - | 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._ | | [pratical-llms](/tools/antoniogr7-pratical-llms.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 572d | 117d | | Open issues (now) | 0 | 272 | | Full report | [trust report](/tools/antoniogr7-pratical-llms/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: pratical-llms - **Adopt for:** practical-llms is a collection of Jupyter Notebooks aimed at LLM practitioners with practical guidance on quantization, sharding, inference and evaluation techniques. ## 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 pratical-llms if… - pratical-llms is primarily Jupyter Notebook; gorilla is Python. - Tags unique to pratical-llms: genai, llm-evaluation, llm-inference, llm-serving. - Also covers Inference & Serving, LLM Frameworks. - If you want to explore specific quantization methods like BitandBytes, GPTQ, exllamav2, or Half-Quadratic Quantization (HQQ). ### Choose gorilla if… - gorilla is primarily Python; pratical-llms 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 pratical-llms - If you seek deep theoretical insights rather than practical implementation details. - For users looking for commercial support as this repository does not provide it, unlike some competitors. ## 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 pratical-llms and gorilla? pratical-llms: A collection of hands-on notebooks for LLM practitioners. 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 pratical-llms over gorilla? Choose pratical-llms over gorilla when pratical-llms is primarily Jupyter Notebook; gorilla is Python; Tags unique to pratical-llms: genai, llm-evaluation, llm-inference, llm-serving; Also covers Inference & Serving, LLM Frameworks; If you want to explore specific quantization methods like BitandBytes, GPTQ, exllamav2, or Half-Quadratic Quantization (HQQ). ### When should I choose gorilla over pratical-llms? Choose gorilla over pratical-llms when gorilla is primarily Python; pratical-llms 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 pratical-llms? If you seek deep theoretical insights rather than practical implementation details. For users looking for commercial support as this repository does not provide it, unlike some competitors. ### 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 pratical-llms or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 53). Stars measure visibility, not whether either tool fits your constraints. ### Are pratical-llms and gorilla open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to pratical-llms or gorilla? GraphCanon lists graph-backed alternatives at [pratical-llms alternatives](/tools/antoniogr7-pratical-llms/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([pratical-llms markdown twin](/tools/antoniogr7-pratical-llms/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/antoniogr7-pratical-llms-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, pratical-llms or gorilla? pratical-llms: Dormant. 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 pratical-llms and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pratical-llms trust report](/tools/antoniogr7-pratical-llms/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=antoniogr7-pratical-llms`](/api/graphcanon/graph?tool=antoniogr7-pratical-llms) - 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/_