--- title: "gorilla vs cupel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shishirpatil-gorilla-vs-tolitius-cupel" tools: ["shishirpatil-gorilla", "tolitius-cupel"] --- # gorilla vs cupel *GraphCanon updated Sep 20, 2026* ## Verdict Pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages; pick cupel if cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. [gorilla](https://gorilla.cs.berkeley.edu/) reports 13k GitHub stars, 1.4k forks, and 278 open issues, last pushed Apr 13, 2026. [cupel](https://cupel.run) has 64 stars, 0 forks, and 2 open issues, last pushed Aug 31, 2026. Figures are from public GitHub metadata via [gorilla's repository](https://github.com/ShishirPatil/gorilla) and [cupel's repository](https://github.com/tolitius/cupel). | | [gorilla](/tools/shishirpatil-gorilla.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Tagline | Training and Evaluating LLMs for Function Calls (Tool Calls) | discovery tool for evaluating LLM performance | | Stars | 13,017 | 64 | | Forks | 1,406 | 0 | | Open issues | 278 | 2 | | Language | Python | Python | | Adopt for | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. | | 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 | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gorilla](/tools/shishirpatil-gorilla.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 147d | 10d | | Open issues (now) | 278 | 2 | | Stars delta | +29 (30d) | +13 (30d) | | Open issues delta | +6 (30d) | 0 (30d) | | Full report | [trust report](/tools/shishirpatil-gorilla/trust.md) | [trust report](/tools/tolitius-cupel/trust.md) | ## Shared compatibility - **Python**: [gorilla](/tools/shishirpatil-gorilla.md) - Python runtime; [cupel](/tools/tolitius-cupel.md) - Python runtime ## 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: cupel - **Adopt for:** Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. ## 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. - Also covers Model Training. - You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### Choose cupel if… - Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue. - When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers - More recently updated (last pushed Aug 31, 2026). ## 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 cupel - If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive - When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ## Common questions ### What is the difference between gorilla and cupel? gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). cupel: discovery tool for evaluating LLM performance. See the comparison table for live GitHub stats and shared categories. ### When should I choose gorilla over cupel? Choose gorilla over cupel 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; Also covers Model Training; You should consider using Gorilla if you need a comprehensive framework for developing LLMs capable of leveraging external functions effectively. ### When should I choose cupel over gorilla? Choose cupel over gorilla when Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue; When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers; More recently updated (last pushed Aug 31, 2026). ### 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 cupel? If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ### Is gorilla or cupel more popular on GitHub? gorilla has more GitHub stars (13,017 vs 64). Stars measure visibility, not whether either tool fits your constraints. ### Are gorilla and cupel open source? Yes - both are open-source projects on GitHub (gorilla: Apache-2.0, cupel: Apache-2.0). ### Where can I find alternatives to gorilla or cupel? GraphCanon lists graph-backed alternatives at [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) and [cupel alternatives](/tools/tolitius-cupel/alternatives) ([gorilla markdown twin](/tools/shishirpatil-gorilla/alternatives.md), [cupel markdown twin](/tools/tolitius-cupel/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-tolitius-cupel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gorilla or cupel? gorilla: Slowing. cupel: 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 cupel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gorilla trust report](/tools/shishirpatil-gorilla/trust); [cupel trust report](/tools/tolitius-cupel/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/_