--- title: "dingo vs gorilla" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/migoxlab-dingo-vs-shishirpatil-gorilla" tools: ["migoxlab-dingo", "shishirpatil-gorilla"] --- # dingo vs gorilla *GraphCanon updated Aug 8, 2026* ## Verdict Pick dingo if dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks; pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. [dingo](https://dingo.openxlab.org.cn/) reports 733 GitHub stars, 74 forks, and 4 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 [dingo's repository](https://github.com/MigoXLab/dingo) and [gorilla's repository](https://github.com/ShishirPatil/gorilla). | | [dingo](/tools/migoxlab-dingo.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Tagline | Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool | Training and Evaluating LLMs for Function Calls (Tool Calls) | | Stars | 733 | 12,988 | | Forks | 74 | 1,397 | | Open issues | 4 | 272 | | Language | Python | Python | | Adopt for | Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | | Persona | - | - | | Runtime | - | - | | License | Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | | Categories | Data & Retrieval, Evaluation & Observability | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dingo](/tools/migoxlab-dingo.md) | [gorilla](/tools/shishirpatil-gorilla.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 117d | | Open issues (now) | 4 | 272 | | Owner type | Organization | User | | Full report | [trust report](/tools/migoxlab-dingo/trust.md) | [trust report](/tools/shishirpatil-gorilla/trust.md) | ## Decision facts: dingo - **Pricing:** freemium - The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost. - **Adopt for:** Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. - **License detail:** Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. ## 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 dingo if… - Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.. - Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection. - Also covers Data & Retrieval. - When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ### 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. ## When NOT to use dingo - If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. - In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ## 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 dingo and gorilla? dingo: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. 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 dingo over gorilla? Choose dingo over gorilla when Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.; Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection; Also covers Data & Retrieval; When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ### When should I choose gorilla over dingo? Choose gorilla over dingo 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 avoid dingo? If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ### 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 dingo or gorilla more popular on GitHub? gorilla has more GitHub stars (12,988 vs 733). Stars measure visibility, not whether either tool fits your constraints. ### Are dingo and gorilla open source? Yes - both are open-source projects on GitHub (dingo: Apache-2.0, gorilla: Apache-2.0). ### Where can I find alternatives to dingo or gorilla? GraphCanon lists graph-backed alternatives at [dingo alternatives](/tools/migoxlab-dingo/alternatives) and [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) ([dingo markdown twin](/tools/migoxlab-dingo/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/migoxlab-dingo-vs-shishirpatil-gorilla.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dingo or gorilla? dingo: 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 dingo and gorilla? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dingo trust report](/tools/migoxlab-dingo/trust); [gorilla trust report](/tools/shishirpatil-gorilla/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=migoxlab-dingo`](/api/graphcanon/graph?tool=migoxlab-dingo) - 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/_