--- title: "ruby_llm vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/crmne-ruby-llm-vs-datawhalechina-hello-agents" tools: ["crmne-ruby-llm", "datawhalechina-hello-agents"] --- # ruby_llm vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick ruby_llm when ruby_llm is primarily Ruby; hello-agents is Python; pick hello-agents when hello-agents is primarily Python; ruby_llm is Ruby. [ruby_llm](https://rubyllm.com/) reports 4.2k GitHub stars, 474 forks, and 36 open issues, last pushed Jul 7, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [ruby_llm's repository](https://github.com/crmne/ruby_llm) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [ruby_llm](/tools/crmne-ruby-llm.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code. | Course on building intelligent agents from scratch | | Stars | 4,235 | 65,432 | | Forks | 474 | 8,109 | | Open issues | 36 | 144 | | Language | Ruby | Python | | Adopt for | - | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | | Persona | - | - | | Runtime | - | - | | License | MIT | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | Vector Databases, AI Agents, LLM Frameworks | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ruby_llm](/tools/crmne-ruby-llm.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 36 | 144 | | Owner type | User | Organization | | Full report | [trust report](/tools/crmne-ruby-llm/trust.md) | [trust report](/tools/datawhalechina-hello-agents/trust.md) | ## Decision facts: hello-agents - **Requirements:** Min 4 GB RAM; Python knowledge assumed - **Adopt for:** hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. - **License detail:** hello-agents is covered under an unconventional license which may require further review before usage. ## Choose when ### Choose ruby_llm if… - ruby_llm is primarily Ruby; hello-agents is Python. - License: ruby_llm is MIT, hello-agents is Other. - Tags unique to ruby_llm: embeddings, deepseek, agents, ai. - Also covers Vector Databases. ### Choose hello-agents if… - hello-agents is primarily Python; ruby_llm is Ruby. - License: hello-agents is Other, ruby_llm is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: llm, rag, tutorial, agent. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ## When NOT to use ruby_llm - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## When NOT to use hello-agents - Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. - Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ## Common questions ### What is the difference between ruby_llm and hello-agents? ruby_llm: One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose ruby_llm over hello-agents? Choose ruby_llm over hello-agents when ruby_llm is primarily Ruby; hello-agents is Python; License: ruby_llm is MIT, hello-agents is Other; Tags unique to ruby_llm: embeddings, deepseek, agents, ai; Also covers Vector Databases. ### When should I choose hello-agents over ruby_llm? Choose hello-agents over ruby_llm when hello-agents is primarily Python; ruby_llm is Ruby; License: hello-agents is Other, ruby_llm is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### When should I avoid ruby_llm? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### When should I avoid hello-agents? Avoid using hello-agents if you are looking for a quick, superficial introduction to AI agents; this tool focuses heavily on in-depth learning and practical application. Do not opt for hello-agents if you want a more general AI development resource; unlike some competitors, it has a narrower focus specifically on agent creation with advanced methods like LLMs and RAG. ### Is ruby_llm or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 4,235). Stars measure visibility, not whether either tool fits your constraints. ### Are ruby_llm and hello-agents open source? Yes - both are open-source projects on GitHub (ruby_llm: MIT, hello-agents: Other). ### Where can I find alternatives to ruby_llm or hello-agents? GraphCanon lists graph-backed alternatives at [ruby_llm alternatives](/tools/crmne-ruby-llm/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([ruby_llm markdown twin](/tools/crmne-ruby-llm/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/crmne-ruby-llm-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ruby_llm or hello-agents? ruby_llm: Very active. hello-agents: 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 ruby_llm and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ruby_llm trust report](/tools/crmne-ruby-llm/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=crmne-ruby-llm`](/api/graphcanon/graph?tool=crmne-ruby-llm) - 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/_