--- title: "ROLL vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alibaba-roll-vs-shishirpatil-gorilla" tools: ["alibaba-roll", "shishirpatil-gorilla"] --- # ROLL vs gorilla *GraphCanon updated Aug 8, 2026* ## Verdict Pick ROLL if efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [ROLL](https://alibaba.github.io/ROLL/) reports 3.4k GitHub stars, 304 forks, and 120 open issues, last pushed Aug 7, 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 [ROLL's repository](https://github.com/alibaba/ROLL) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [ROLL](/tools/alibaba-roll.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Scaling Library for Reinforcement Learning with Large Language Models | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 3,354 | 12,988 | | Forks | 304 | 1,397 | | Open issues | 120 | 272 | | Language | Python | Python | | Adopt for | Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. | 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, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ROLL](/tools/alibaba-roll.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 117d | | Open issues (now) | 120 | 272 | | Owner type | Organization | User | | Full report | [trust report](/tools/alibaba-roll/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: ROLL - **Adopt for:** Efficient library for scaling reinforcement learning tasks with large language models; user-friendly setup and debugging tools provided. ## 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 ROLL if… - Tags unique to ROLL: agentic, rlhf, rlvr. - When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions. - More recently updated (last pushed Aug 7, 2026). ### 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 ROLL - Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. - Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ## 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 ROLL and gorilla? ROLL: Scaling Library for Reinforcement Learning with Large Language 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 ROLL over gorilla? Choose ROLL over gorilla when Tags unique to ROLL: agentic, rlhf, rlvr; When developing reinforcement learning applications requiring integration of large language models, offering efficient scalability solutions; More recently updated (last pushed Aug 7, 2026). ### When should I choose gorilla over ROLL? Choose gorilla over ROLL 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 ROLL? Avoid for tasks that prioritize minimalist setups over advanced feature integrations like Alibaba Cloud Function Compute DevPods. Not suitable if you prefer tools without built-in support for converting models between MCoreAdapter and Hugging Face formats. ### 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 ROLL or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 3,354). Stars measure visibility, not whether either tool fits your constraints. ### Are ROLL and gorilla open source? Yes - both are open-source projects on GitHub (ROLL: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to ROLL or gorilla? GraphCanon lists graph-backed alternatives at [ROLL alternatives](/tools/alibaba-roll/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([ROLL markdown twin](/tools/alibaba-roll/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/alibaba-roll-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ROLL or gorilla? ROLL: 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 ROLL and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ROLL trust report](/tools/alibaba-roll/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alibaba-roll`](/api/graphcanon/graph?tool=alibaba-roll) - 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/_