--- title: "caveman vs Keras-TextClassification" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/juliusbrussee-caveman-vs-yongzhuo-keras-textclassification" tools: ["juliusbrussee-caveman", "yongzhuo-keras-textclassification"] --- # caveman vs Keras-TextClassification *GraphCanon updated Sep 20, 2026* ## Verdict Pick caveman if caveman reduces token usage by 65% through minimalistic language for coding agents, supporting two sizes and primarily written in Go; pick Keras-TextClassification if chinese-focused text classification models using Keras. [caveman](https://docs.caveman.so/docs/quickstart) reports 106k GitHub stars, 6.2k forks, and 127 open issues, last pushed Sep 18, 2026. [Keras-TextClassification](https://blog.csdn.net/rensihui) has 1.8k stars, 397 forks, and 4 open issues, last pushed Jun 17, 2024. Figures are from public GitHub metadata via [caveman's repository](https://github.com/JuliusBrussee/caveman) and [Keras-TextClassification's repository](https://github.com/yongzhuo/Keras-TextClassification). | | [caveman](/tools/juliusbrussee-caveman.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Tagline | Reduces token usage by 65% through minimalistic language for coding agents | Chinese text classification models based on Keras | | Stars | 106,401 | 1,808 | | Forks | 6,165 | 397 | | Open issues | 127 | 4 | | Language | Go | Python | | Adopt for | Caveman reduces token usage by 65% through minimalistic language for coding agents, supporting two sizes and primarily written in Go. | Chinese-focused text classification models using Keras. | | Persona | - | - | | Runtime | - | - | | License | Caveman operates under a split license: MIT for the skill, Agent SDK, and initializer, and BSL-1.1 for the engine-linked runtime, which converts to Apache-2.0 by 2030 or four years after shipping. | MIT License - allows free use, modification, distribution with attribution required but no guarantee or liability from contributors. | | Categories | AI Agents, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caveman](/tools/juliusbrussee-caveman.md) | [Keras-TextClassification](/tools/yongzhuo-keras-textclassification.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 824d | | Open issues (now) | 127 | 4 | | Stars delta | +8.0k (30d) | -4 (30d) | | Open issues delta | -358 (30d) | 0 (30d) | | Full report | [trust report](/tools/juliusbrussee-caveman/trust.md) | [trust report](/tools/yongzhuo-keras-textclassification/trust.md) | ## Decision facts: caveman - **Pricing:** freemium - The skill, Agent SDK, and initializer are free under MIT license. Hosting Caveman for third parties requires a commercial license. - **Requirements:** Min 1 GB RAM; Caveman is primarily written in Go and supports two sizes, allowing for flexible deployment. - **Adopt for:** Caveman reduces token usage by 65% through minimalistic language for coding agents, supporting two sizes and primarily written in Go. - **License detail:** Caveman operates under a split license: MIT for the skill, Agent SDK, and initializer, and BSL-1.1 for the engine-linked runtime, which converts to Apache-2.0 by 2030 or four years after shipping. ## Decision facts: Keras-TextClassification - **Requirements:** Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources. - **Adopt for:** Chinese-focused text classification models using Keras. - **License detail:** MIT License - allows free use, modification, distribution with attribution required but no guarantee or liability from contributors. ## Choose when ### Choose caveman if… - caveman is primarily Go; Keras-TextClassification is Python. - License: caveman is Other, Keras-TextClassification is MIT. - Pricing: The skill, Agent SDK, and initializer are free under MIT license. Hosting Caveman for third parties requires a commercial license.. - Requirements: Min 1 GB RAM; Caveman is primarily written in Go and supports two sizes, allowing for flexible deployment.. - Tags unique to caveman: ai, anthropic, caveman, claude. - Also covers AI Agents. - caveman ships Docker support for self-hosted deployment. - When you need to significantly reduce token usage in coding agents, as Caveman can cut usage by 65% through its simplified language style. ### Choose Keras-TextClassification if… - Keras-TextClassification is primarily Python; caveman is Go. - License: Keras-TextClassification is MIT, caveman is Other. - Requirements: Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources.. - Tags unique to Keras-TextClassification: albert, bert, capsulenetwork, charcnn. - Requires Chinese text classification for tasks like multi-label, sentence similarity analysis. ## When NOT to use caveman - If your project requires a fully OSI-approved open-source license, as Caveman's engine-linked runtime is BSL-1.1, which is not OSI-approved until a future date. - When you need a tool that does not involve a trademarked name and logo, as Caveman is trademarked and has specific guidelines for its use. ## When NOT to use Keras-TextClassification - Does not cater to non-Chinese language datasets effectively due to its Chinese-specific models. - Avoid if you seek a tool with extensive support beyond text classification like NER or POS tagging. ## Common questions ### What is the difference between caveman and Keras-TextClassification? caveman: Reduces token usage by 65% through minimalistic language for coding agents. Keras-TextClassification: Chinese text classification models based on Keras. See the comparison table for live GitHub stats and shared categories. ### When should I choose caveman over Keras-TextClassification? Choose caveman over Keras-TextClassification when caveman is primarily Go; Keras-TextClassification is Python; License: caveman is Other, Keras-TextClassification is MIT; Pricing: The skill, Agent SDK, and initializer are free under MIT license. Hosting Caveman for third parties requires a commercial license.; Requirements: Min 1 GB RAM; Caveman is primarily written in Go and supports two sizes, allowing for flexible deployment.; Tags unique to caveman: ai, anthropic, caveman, claude; Also covers AI Agents; caveman ships Docker support for self-hosted deployment; When you need to significantly reduce token usage in coding agents, as Caveman can cut usage by 65% through its simplified language style. ### When should I choose Keras-TextClassification over caveman? Choose Keras-TextClassification over caveman when Keras-TextClassification is primarily Python; caveman is Go; License: Keras-TextClassification is MIT, caveman is Other; Requirements: Min 4 GB RAM; Python environment must be prepared for running Keras.; Supports multiple model types requiring different levels of computation resources.; Tags unique to Keras-TextClassification: albert, bert, capsulenetwork, charcnn; Requires Chinese text classification for tasks like multi-label, sentence similarity analysis. ### When should I avoid caveman? If your project requires a fully OSI-approved open-source license, as Caveman's engine-linked runtime is BSL-1.1, which is not OSI-approved until a future date. When you need a tool that does not involve a trademarked name and logo, as Caveman is trademarked and has specific guidelines for its use. ### When should I avoid Keras-TextClassification? Does not cater to non-Chinese language datasets effectively due to its Chinese-specific models. Avoid if you seek a tool with extensive support beyond text classification like NER or POS tagging. ### Is caveman or Keras-TextClassification more popular on GitHub? caveman has more GitHub stars (106,401 vs 1,808). Stars measure visibility, not whether either tool fits your constraints. ### Are caveman and Keras-TextClassification open source? Yes - both are open-source projects on GitHub (caveman: Other, Keras-TextClassification: MIT). ### Where can I find alternatives to caveman or Keras-TextClassification? GraphCanon lists graph-backed alternatives at [caveman alternatives](/tools/juliusbrussee-caveman/alternatives) and [Keras-TextClassification alternatives](/tools/yongzhuo-keras-textclassification/alternatives) ([caveman markdown twin](/tools/juliusbrussee-caveman/alternatives.md), [Keras-TextClassification markdown twin](/tools/yongzhuo-keras-textclassification/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/juliusbrussee-caveman-vs-yongzhuo-keras-textclassification.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caveman or Keras-TextClassification? caveman: Very active. Keras-TextClassification: 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 caveman and Keras-TextClassification? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caveman trust report](/tools/juliusbrussee-caveman/trust); [Keras-TextClassification trust report](/tools/yongzhuo-keras-textclassification/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=juliusbrussee-caveman`](/api/graphcanon/graph?tool=juliusbrussee-caveman) - 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/_