--- title: "AutoRAG vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/marker-inc-korea-autorag-vs-shishirpatil-gorilla" tools: ["marker-inc-korea-autorag", "shishirpatil-gorilla"] --- # AutoRAG vs gorilla *GraphCanon updated Aug 8, 2026* ## Verdict Pick AutoRAG if autoRAG: Automate RAG task evaluation and optimization using AutoML techniques; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [AutoRAG](https://marker-inc-korea.github.io/AutoRAG/) reports 5.0k GitHub stars, 419 forks, and 123 open issues, last pushed Aug 5, 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 [AutoRAG's repository](https://github.com/Marker-Inc-Korea/AutoRAG) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Open-source framework for RAG evaluation and optimization via AutoML | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 4,968 | 12,988 | | Forks | 419 | 1,397 | | Open issues | 123 | 272 | | Language | TypeScript | Python | | Adopt for | AutoRAG: Automate RAG task evaluation and optimization using AutoML techniques. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 licensed, allowing free use in commercial projects while retaining copyright notices. | 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._ | | [AutoRAG](/tools/marker-inc-korea-autorag.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 2d | 117d | | Open issues (now) | 123 | 272 | | Owner type | Organization | User | | Full report | [trust report](/tools/marker-inc-korea-autorag/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: AutoRAG - **Adopt for:** AutoRAG: Automate RAG task evaluation and optimization using AutoML techniques. - **License detail:** Apache-2.0 licensed, allowing free use in commercial projects while retaining copyright 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 AutoRAG if… - AutoRAG is primarily TypeScript; gorilla is Python. - Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser. - Automated benchmarking is needed for retrieval-augmented generation tasks ### Choose gorilla if… - gorilla is primarily Python; AutoRAG is TypeScript. - 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 AutoRAG - Requirements exceed capabilities of open-source tools - No need for RAG-specific optimization and evaluation features ## 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 AutoRAG and gorilla? AutoRAG: Open-source framework for RAG evaluation and optimization via AutoML. 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 AutoRAG over gorilla? Choose AutoRAG over gorilla when AutoRAG is primarily TypeScript; gorilla is Python; Tags unique to AutoRAG: analysis, automl, benchmarking, document-parser; Automated benchmarking is needed for retrieval-augmented generation tasks. ### When should I choose gorilla over AutoRAG? Choose gorilla over AutoRAG when gorilla is primarily Python; AutoRAG is TypeScript; 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 AutoRAG? Requirements exceed capabilities of open-source tools No need for RAG-specific optimization and evaluation features ### 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 AutoRAG or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 4,968). Stars measure visibility, not whether either tool fits your constraints. ### Are AutoRAG and gorilla open source? Yes - both are open-source projects on GitHub (AutoRAG: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to AutoRAG or gorilla? GraphCanon lists graph-backed alternatives at [AutoRAG alternatives](/tools/marker-inc-korea-autorag/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([AutoRAG markdown twin](/tools/marker-inc-korea-autorag/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/marker-inc-korea-autorag-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AutoRAG or gorilla? AutoRAG: 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 AutoRAG and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AutoRAG trust report](/tools/marker-inc-korea-autorag/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=marker-inc-korea-autorag`](/api/graphcanon/graph?tool=marker-inc-korea-autorag) - 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/_