--- title: "caveman vs contextualized-topic-models" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/juliusbrussee-caveman-vs-milanlproc-contextualized-topic-models" tools: ["juliusbrussee-caveman", "milanlproc-contextualized-topic-models"] --- # caveman vs contextualized-topic-models *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 contextualized-topic-models if contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. [caveman](https://docs.caveman.so/docs/quickstart) reports 106k GitHub stars, 6.2k forks, and 127 open issues, last pushed Sep 18, 2026. [contextualized-topic-models](https://github.com/MilaNLProc/contextualized-topic-models) has 1.3k stars, 155 forks, and 11 open issues, last pushed Jul 24, 2025. Figures are from public GitHub metadata via [caveman's repository](https://github.com/JuliusBrussee/caveman) and [contextualized-topic-models's repository](https://github.com/MilaNLProc/contextualized-topic-models). | | [caveman](/tools/juliusbrussee-caveman.md) | [contextualized-topic-models](/tools/milanlproc-contextualized-topic-models.md) | | --- | --- | --- | | Tagline | Reduces token usage by 65% through minimalistic language for coding agents | A python package for contextualized topic modeling using BERT and other embeddings. | | Stars | 106,401 | 1,272 | | Forks | 6,165 | 155 | | Open issues | 127 | 11 | | 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. | Contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. | | 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 | | Categories | AI Agents, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [caveman](/tools/juliusbrussee-caveman.md) | [contextualized-topic-models](/tools/milanlproc-contextualized-topic-models.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 423d | | Open issues (now) | 127 | 11 | | Stars delta | +8.0k (30d) | 0 (30d) | | Open issues delta | -358 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/juliusbrussee-caveman/trust.md) | [trust report](/tools/milanlproc-contextualized-topic-models/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: contextualized-topic-models - **Adopt for:** Contextualized-topic-models is a Python package that enhances traditional topic modeling by integrating contextualized embeddings like BERT. ## Choose when ### Choose caveman if… - caveman is primarily Go; contextualized-topic-models is Python. - License: caveman is Other, contextualized-topic-models 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 contextualized-topic-models if… - contextualized-topic-models is primarily Python; caveman is Go. - License: contextualized-topic-models is MIT, caveman is Other. - Tags unique to contextualized-topic-models: bert, embeddings, multilingual-models, neural-topic-models. - - When you need to analyze text data with enriched topic coherence provided by models utilizing BERT-like embeddings. ## 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 contextualized-topic-models - - If your project does not require advanced contextual embedding integration and more conventional topic modeling techniques suffice. - - In scenarios where model complexity can be a bottleneck for real-time processing or when working with hardware limitations that cannot efficiently process BERT embeddings. ## Common questions ### What is the difference between caveman and contextualized-topic-models? caveman: Reduces token usage by 65% through minimalistic language for coding agents. contextualized-topic-models: A python package for contextualized topic modeling using BERT and other embeddings.. See the comparison table for live GitHub stats and shared categories. ### When should I choose caveman over contextualized-topic-models? Choose caveman over contextualized-topic-models when caveman is primarily Go; contextualized-topic-models is Python; License: caveman is Other, contextualized-topic-models 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 contextualized-topic-models over caveman? Choose contextualized-topic-models over caveman when contextualized-topic-models is primarily Python; caveman is Go; License: contextualized-topic-models is MIT, caveman is Other; Tags unique to contextualized-topic-models: bert, embeddings, multilingual-models, neural-topic-models; - When you need to analyze text data with enriched topic coherence provided by models utilizing BERT-like embeddings. ### 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 contextualized-topic-models? - If your project does not require advanced contextual embedding integration and more conventional topic modeling techniques suffice. - In scenarios where model complexity can be a bottleneck for real-time processing or when working with hardware limitations that cannot efficiently process BERT embeddings. ### Is caveman or contextualized-topic-models more popular on GitHub? caveman has more GitHub stars (106,401 vs 1,272). Stars measure visibility, not whether either tool fits your constraints. ### Are caveman and contextualized-topic-models open source? Yes - both are open-source projects on GitHub (caveman: Other, contextualized-topic-models: MIT). ### Where can I find alternatives to caveman or contextualized-topic-models? GraphCanon lists graph-backed alternatives at [caveman alternatives](/tools/juliusbrussee-caveman/alternatives) and [contextualized-topic-models alternatives](/tools/milanlproc-contextualized-topic-models/alternatives) ([caveman markdown twin](/tools/juliusbrussee-caveman/alternatives.md), [contextualized-topic-models markdown twin](/tools/milanlproc-contextualized-topic-models/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-milanlproc-contextualized-topic-models.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, caveman or contextualized-topic-models? caveman: Very active. contextualized-topic-models: 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 contextualized-topic-models? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [caveman trust report](/tools/juliusbrussee-caveman/trust); [contextualized-topic-models trust report](/tools/milanlproc-contextualized-topic-models/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/_