--- title: "hello-agents vs caveman" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-juliusbrussee-caveman" tools: ["datawhalechina-hello-agents", "juliusbrussee-caveman"] --- # hello-agents vs caveman *GraphCanon updated Sep 20, 2026* ## Verdict Pick hello-agents if hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python; pick caveman if caveman reduces token usage by 65% through minimalistic language for coding agents, supporting two sizes and primarily written in Go. [hello-agents](https://hello-agents.datawhale.cc) reports 80k GitHub stars, 9.9k forks, and 82 open issues, last pushed Sep 17, 2026. [caveman](https://docs.caveman.so/docs/quickstart) has 106k stars, 6.2k forks, and 127 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [caveman's repository](https://github.com/JuliusBrussee/caveman). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [caveman](/tools/juliusbrussee-caveman.md) | | --- | --- | --- | | Tagline | A comprehensive guide to building intelligent agents from scratch | Reduces token usage by 65% through minimalistic language for coding agents | | Stars | 79,727 | 106,401 | | Forks | 9,907 | 6,165 | | Open issues | 82 | 127 | | Language | Python | Go | | Adopt for | Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. | Caveman reduces token usage by 65% through minimalistic language for coding agents, supporting two sizes and primarily written in Go. | | Persona | - | - | | Runtime | - | - | | License | The license is not specified as open source, but the content is free to read and learn from. | 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. | | Categories | AI Agents, Model Training | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [caveman](/tools/juliusbrussee-caveman.md) | | --- | --- | --- | | Open issues (now) | 82 | 127 | | Stars delta | +6.6k (30d) | +8.0k (30d) | | Open issues delta | -73 (30d) | -358 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/juliusbrussee-caveman/trust.md) | ## Decision facts: hello-agents - **Pricing:** freemium - The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository. - **Requirements:** Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework. - **Adopt for:** Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python. - **License detail:** The license is not specified as open source, but the content is free to read and learn from. ## 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. ## Choose when ### Choose hello-agents if… - hello-agents is primarily Python; caveman is Go. - Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.. - Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.. - Tags unique to hello-agents: agent, rag, tutorial. - When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### Choose caveman if… - caveman is primarily Go; hello-agents is Python. - 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. - 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 NOT to use hello-agents - If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. - If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ## 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. ## Common questions ### What is the difference between hello-agents and caveman? hello-agents: A comprehensive guide to building intelligent agents from scratch. caveman: Reduces token usage by 65% through minimalistic language for coding agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over caveman? Choose hello-agents over caveman when hello-agents is primarily Python; caveman is Go; Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.; Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.; Tags unique to hello-agents: agent, rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation. ### When should I choose caveman over hello-agents? Choose caveman over hello-agents when caveman is primarily Go; hello-agents is Python; 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; 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 avoid hello-agents? If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API. ### 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. ### Is hello-agents or caveman more popular on GitHub? caveman has more GitHub stars (106,401 vs 79,727). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and caveman open source? Yes - both are open-source projects on GitHub (hello-agents: Other, caveman: Other). ### Where can I find alternatives to hello-agents or caveman? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [caveman alternatives](/tools/juliusbrussee-caveman/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [caveman markdown twin](/tools/juliusbrussee-caveman/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/datawhalechina-hello-agents-vs-juliusbrussee-caveman.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or caveman? hello-agents: Very active. caveman: Very active. 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 hello-agents and caveman? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [caveman trust report](/tools/juliusbrussee-caveman/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_