--- title: "aikit vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/kaito-project-aikit-vs-shishirpatil-gorilla" tools: ["kaito-project-aikit", "shishirpatil-gorilla"] --- # aikit vs gorilla *GraphCanon updated Aug 24, 2026* ## Verdict Pick aikit if aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [aikit](https://kaito-project.github.io/aikit/) reports 537 GitHub stars, 57 forks, and 40 open issues, last pushed Aug 24, 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 [aikit's repository](https://github.com/kaito-project/aikit) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [aikit](/tools/kaito-project-aikit.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Fine-tune, build, and deploy open-source LLMs easily! | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 537 | 12,988 | | Forks | 57 | 1,397 | | Open issues | 40 | 272 | | Language | Go | Python | | Adopt for | Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | MIT | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [aikit](/tools/kaito-project-aikit.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 117d | | Open issues (now) | 40 | 272 | | Stars delta | +3 (30d) | Unknown | | Open issues delta | -3 (30d) | Unknown | | Owner type | Organization | User | | Full report | [trust report](/tools/kaito-project-aikit/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: aikit - **Adopt for:** Aikit is a toolkit designed for fine-tuning, building and deploying large language models (LLMs) with an emphasis on open-source technologies. ## 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 aikit if… - aikit is primarily Go; gorilla is Python. - License: aikit is MIT, gorilla is Apache-2.0. - Tags unique to aikit: ai, buildkit, docker, fine-tuning. - Also covers Inference & Serving, LLM Frameworks. - aikit ships Docker support for self-hosted deployment. - - You need a flexible solution specifically built using Go and prefer its concurrency model. ### Choose gorilla if… - gorilla is primarily Python; aikit is Go. - License: gorilla is Apache-2.0, aikit is MIT. - Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.. - Tags unique to gorilla: api, claude-api, gpt-4-api, llm. - 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 aikit - - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ## 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 aikit and gorilla? aikit: Fine-tune, build, and deploy open-source LLMs easily!. 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 aikit over gorilla? Choose aikit over gorilla when aikit is primarily Go; gorilla is Python; License: aikit is MIT, gorilla is Apache-2.0; Tags unique to aikit: ai, buildkit, docker, fine-tuning; Also covers Inference & Serving, LLM Frameworks; aikit ships Docker support for self-hosted deployment; - You need a flexible solution specifically built using Go and prefer its concurrency model. ### When should I choose gorilla over aikit? Choose gorilla over aikit when gorilla is primarily Python; aikit is Go; License: gorilla is Apache-2.0, aikit is MIT; Requirements: Gorilla works best with Python environments and requires installation through pip or local repository cloning.; Tags unique to gorilla: api, claude-api, gpt-4-api, llm; 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 aikit? - You have a preference or requirement for Python-based tools due to the lack of native support in Aikit. - If your deployment setup strictly uses cloud-specific platforms and you do not use Kubernetes or Docker, as Aikit heavily integrates with containerized environments like these. ### 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 aikit or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 537). Stars measure visibility, not whether either tool fits your constraints. ### Are aikit and gorilla open source? Yes - both are open-source projects on GitHub (aikit: MIT, gorilla: Apache-2.0). ### Where can I find alternatives to aikit or gorilla? GraphCanon lists graph-backed alternatives at [aikit alternatives](/tools/kaito-project-aikit/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([aikit markdown twin](/tools/kaito-project-aikit/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/kaito-project-aikit-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, aikit or gorilla? aikit: 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 aikit and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [aikit trust report](/tools/kaito-project-aikit/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=kaito-project-aikit`](/api/graphcanon/graph?tool=kaito-project-aikit) - 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/_