--- title: "LangChain.js-LLM-Template vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ironcladdev-langchain-js-llm-template-vs-shishirpatil-gorilla" tools: ["ironcladdev-langchain-js-llm-template", "shishirpatil-gorilla"] --- # LangChain.js-LLM-Template vs gorilla *GraphCanon updated Aug 15, 2026* ## Verdict Pick LangChain.js-LLM-Template if langChain.js-LLM-Template is a JavaScript-based tool for training custom AI language models that stands out with its straightforward setup and vector store utilization; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [LangChain.js-LLM-Template](https://github.com/IroncladDev/LangChain.js-LLM-Template) reports 330 GitHub stars, 49 forks, and 2 open issues, last pushed Apr 1, 2023. [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 [LangChain.js-LLM-Template's repository](https://github.com/IroncladDev/LangChain.js-LLM-Template) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [LangChain.js-LLM-Template](/tools/ironcladdev-langchain-js-llm-template.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | LangChain LLM template to train custom AI models | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 330 | 12,988 | | Forks | 49 | 1,397 | | Open issues | 2 | 272 | | Language | JavaScript | Python | | Adopt for | LangChain.js-LLM-Template is a JavaScript-based tool for training custom AI language models that stands out with its straightforward setup and vector store utilization. | 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 | Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LangChain.js-LLM-Template](/tools/ironcladdev-langchain-js-llm-template.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Slowing (36%) | | Days since push | 1231d | 117d | | Archived on GitHub | Yes | No | | Open issues (now) | 2 | 272 | | Stars delta | -1 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/ironcladdev-langchain-js-llm-template/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: LangChain.js-LLM-Template - **Requirements:** Requires OpenAI API access for LLM training purposes via an API key.; Setup requires running yarn or npm commands for installation and training processes. - **Adopt for:** LangChain.js-LLM-Template is a JavaScript-based tool for training custom AI language models that stands out with its straightforward setup and vector store utilization. ## 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 LangChain.js-LLM-Template if… - LangChain.js-LLM-Template is primarily JavaScript; gorilla is Python. - Requirements: Requires OpenAI API access for LLM training purposes via an API key.; Setup requires running yarn or npm commands for installation and training processes.. - Tags unique to LangChain.js-LLM-Template: custom ai models, javascript, llm template. - When you prefer using JavaScript to develop your model training pipeline. ### Choose gorilla if… - gorilla is primarily Python; LangChain.js-LLM-Template is JavaScript. - 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. - Also covers Evaluation & Observability. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ## When NOT to use LangChain.js-LLM-Template - If you require a platform that supports multiple languages beyond JavaScript for flexibility. - In cases where complex preprocessing of train data cannot be easily managed through markdown files. - When dealing with very sensitive data and needing to keep API key management out-of-band, as the README suggests using environment variables. ## 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 LangChain.js-LLM-Template and gorilla? LangChain.js-LLM-Template: LangChain LLM template to train custom AI models. 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 LangChain.js-LLM-Template over gorilla? Choose LangChain.js-LLM-Template over gorilla when LangChain.js-LLM-Template is primarily JavaScript; gorilla is Python; Requirements: Requires OpenAI API access for LLM training purposes via an API key.; Setup requires running yarn or npm commands for installation and training processes.; Tags unique to LangChain.js-LLM-Template: custom ai models, javascript, llm template; When you prefer using JavaScript to develop your model training pipeline. ### When should I choose gorilla over LangChain.js-LLM-Template? Choose gorilla over LangChain.js-LLM-Template when gorilla is primarily Python; LangChain.js-LLM-Template is JavaScript; 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; Also covers Evaluation & Observability; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### When should I avoid LangChain.js-LLM-Template? If you require a platform that supports multiple languages beyond JavaScript for flexibility. In cases where complex preprocessing of train data cannot be easily managed through markdown files. When dealing with very sensitive data and needing to keep API key management out-of-band, as the README suggests using environment variables. ### 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 LangChain.js-LLM-Template or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 330). Stars measure visibility, not whether either tool fits your constraints. ### Are LangChain.js-LLM-Template and gorilla open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LangChain.js-LLM-Template or gorilla? GraphCanon lists graph-backed alternatives at [LangChain.js-LLM-Template alternatives](/tools/ironcladdev-langchain-js-llm-template/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([LangChain.js-LLM-Template markdown twin](/tools/ironcladdev-langchain-js-llm-template/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/ironcladdev-langchain-js-llm-template-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, LangChain.js-LLM-Template or gorilla? LangChain.js-LLM-Template: Archived. 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 LangChain.js-LLM-Template and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LangChain.js-LLM-Template trust report](/tools/ironcladdev-langchain-js-llm-template/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ironcladdev-langchain-js-llm-template`](/api/graphcanon/graph?tool=ironcladdev-langchain-js-llm-template) - 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/_