--- title: "gorilla vs textgrad" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shishirpatil-gorilla-vs-zou-group-textgrad" tools: ["shishirpatil-gorilla", "zou-group-textgrad"] --- # gorilla vs textgrad *GraphCanon updated Aug 18, 2026* ## Verdict Pick gorilla if gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages; pick textgrad if textGrad optimizes prompts using large language models to backpropagate textual gradients. [gorilla](https://gorilla.cs.berkeley.edu/) reports 13k GitHub stars, 1.4k forks, and 272 open issues, last pushed Apr 13, 2026. [textgrad](http://textgrad.com/) has 3.7k stars, 294 forks, and 66 open issues, last pushed Jul 25, 2025. Figures are from public GitHub metadata via [gorilla's repository](https://github.com/ShishirPatil/gorilla) and [textgrad's repository](https://github.com/zou-group/textgrad). | | [gorilla](/tools/shishirpatil-gorilla.md) | [textgrad](/tools/zou-group-textgrad.md) | | --- | --- | --- | | Tagline | Training and Evaluating LLMs for Function Calls (Tool Calls) | Automatic 'Differentiation' via Text using Large Language Models to Backpropagate Textual Gradients | | Stars | 12,988 | 3,700 | | Forks | 1,397 | 294 | | Open issues | 272 | 66 | | Language | Python | Python | | Adopt for | Gorilla specializes in training and evaluating large language models (LLMs) to perform function calls or tool usages. | TextGrad optimizes prompts using large language models to backpropagate textual gradients. | | Persona | - | - | | Runtime | - | - | | License | Gorilla can be used freely under the Apache 2.0 license for both academic and commercial purposes. | MIT | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [gorilla](/tools/shishirpatil-gorilla.md) | [textgrad](/tools/zou-group-textgrad.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 117d | 388d | | Open issues (now) | 272 | 66 | | Stars delta | Unknown | +44 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/shishirpatil-gorilla/trust.md) | [trust report](/tools/zou-group-textgrad/trust.md) | ## Shared compatibility - **Python**: [gorilla](/tools/shishirpatil-gorilla.md) - Python runtime; [textgrad](/tools/zou-group-textgrad.md) - Python runtime ## 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. ## Decision facts: textgrad - **Adopt for:** TextGrad optimizes prompts using large language models to backpropagate textual gradients. ## Choose when ### Choose gorilla if… - License: gorilla is Apache-2.0, textgrad is MIT. - 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. ### Choose textgrad if… - License: textgrad is MIT, gorilla is Apache-2.0. - Tags unique to textgrad: ai_optimization, compound-systems, large language models, prompt-optimization. - When optimizing complex prompting for large language models in production due to its published effectiveness. ## 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. ## When NOT to use textgrad - If only basic and traditional manual tuning methods are needed for simpler use cases. - Avoid if strict version control is required since the bleeding edge installation points to GitHub directly. ## Common questions ### What is the difference between gorilla and textgrad? gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls). textgrad: Automatic 'Differentiation' via Text using Large Language Models to Backpropagate Textual Gradients. See the comparison table for live GitHub stats and shared categories. ### When should I choose gorilla over textgrad? Choose gorilla over textgrad when License: gorilla is Apache-2.0, textgrad is MIT; 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 choose textgrad over gorilla? Choose textgrad over gorilla when License: textgrad is MIT, gorilla is Apache-2.0; Tags unique to textgrad: ai_optimization, compound-systems, large language models, prompt-optimization; When optimizing complex prompting for large language models in production due to its published effectiveness. ### 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. ### When should I avoid textgrad? If only basic and traditional manual tuning methods are needed for simpler use cases. Avoid if strict version control is required since the bleeding edge installation points to GitHub directly. ### Is gorilla or textgrad more popular on GitHub? gorilla has more GitHub stars (12,988 vs 3,700). Stars measure visibility, not whether either tool fits your constraints. ### Are gorilla and textgrad open source? Yes - both are open-source projects on GitHub (gorilla: Apache-2.0, textgrad: MIT). ### Where can I find alternatives to gorilla or textgrad? GraphCanon lists graph-backed alternatives at [gorilla alternatives](/tools/shishirpatil-gorilla/alternatives) and [textgrad alternatives](/tools/zou-group-textgrad/alternatives) ([gorilla markdown twin](/tools/shishirpatil-gorilla/alternatives.md), [textgrad markdown twin](/tools/zou-group-textgrad/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/shishirpatil-gorilla-vs-zou-group-textgrad.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, gorilla or textgrad? gorilla: Slowing. textgrad: Dormant. 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 gorilla and textgrad? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [gorilla trust report](/tools/shishirpatil-gorilla/trust); [textgrad trust report](/tools/zou-group-textgrad/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=shishirpatil-gorilla`](/api/graphcanon/graph?tool=shishirpatil-gorilla) - 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/_