--- title: "trl vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-trl-vs-shishirpatil-gorilla" tools: ["huggingface-trl", "shishirpatil-gorilla"] --- # trl vs gorilla *GraphCanon updated Aug 8, 2026* ## Verdict Pick trl if tRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [trl](http://hf.co/docs/trl) reports 19k GitHub stars, 2.9k forks, and 250 open issues, last pushed Aug 6, 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 [trl's repository](https://github.com/huggingface/trl) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [trl](/tools/huggingface-trl.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Train transformer language models with reinforcement learning. | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 19,016 | 12,988 | | Forks | 2,891 | 1,397 | | Open issues | 250 | 272 | | Language | Python | Python | | Adopt for | TRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | TRL operates under the Apache-2.0 License, allowing for broad usage and modification under specific conditions including copyright preservation and license notices. | 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._ | | [trl](/tools/huggingface-trl.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 117d | | Open issues (now) | 250 | 272 | | Owner type | Organization | User | | Full report | [trust report](/tools/huggingface-trl/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: trl - **Requirements:** Min 8 GB RAM - **Adopt for:** TRL (Train Reinforcement Learning) by Hugging Face provides specialized trainer classes designed for fine-tuning or PEFT adapter post-training on custom datasets, including support for multiple distributed training modes - **License detail:** TRL operates under the Apache-2.0 License, allowing for broad usage and modification under specific conditions including copyright preservation and license notices. ## 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 trl if… - Requirements: Min 8 GB RAM. - Tags unique to trl: distributed-training, reinforcement-learning, transformers. - You need to fine-tune transformer language models with reinforcement learning using Python. ### 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 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 trl - If your task does not involve transformer language models or if you do not plan to use reinforcement learning for model fine-tuning. - When strict control over training parameters is less critical and a more streamlined framework suffices. - Your project's dataset size and computational requirements don't necessitate sophisticated distributed training mechanisms like DDP, DeepSpeed ZeRO, or FSDP. ## 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 trl and gorilla? trl: Train transformer language models with reinforcement learning.. 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 trl over gorilla? Choose trl over gorilla when Requirements: Min 8 GB RAM; Tags unique to trl: distributed-training, reinforcement-learning, transformers; You need to fine-tune transformer language models with reinforcement learning using Python. ### When should I choose gorilla over trl? Choose gorilla over trl 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 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 trl? If your task does not involve transformer language models or if you do not plan to use reinforcement learning for model fine-tuning. When strict control over training parameters is less critical and a more streamlined framework suffices. Your project's dataset size and computational requirements don't necessitate sophisticated distributed training mechanisms like DDP, DeepSpeed ZeRO, or FSDP. ### 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 trl or gorilla more popular on GitHub? trl has more GitHub stars (19,016 vs 12,988). Stars measure visibility, not whether either tool fits your constraints. ### Are trl and gorilla open source? Yes - both are open-source projects on GitHub (trl: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to trl or gorilla? GraphCanon lists graph-backed alternatives at [trl alternatives](/tools/huggingface-trl/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([trl markdown twin](/tools/huggingface-trl/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/huggingface-trl-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, trl or gorilla? trl: 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 trl and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [trl trust report](/tools/huggingface-trl/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-trl`](/api/graphcanon/graph?tool=huggingface-trl) - 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/_