--- title: "hello-agents vs LLM-Hub" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-timmyy123-llm-hub" tools: ["datawhalechina-hello-agents", "timmyy123-llm-hub"] --- # hello-agents vs LLM-Hub *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; LLM-Hub is C++; pick LLM-Hub when lLM-Hub is primarily C++; hello-agents is Python. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [LLM-Hub](https://llm-hub.app) has 491 stars, 103 forks, and 31 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [LLM-Hub's repository](https://github.com/timmyy123/LLM-Hub). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LLM-Hub](/tools/timmyy123-llm-hub.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Local AI Assistant on your phone | | Stars | 65,432 | 491 | | Forks | 8,109 | 103 | | Open issues | 144 | 31 | | Language | Python | C++ | | 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 | hello-agents is covered under an unconventional license which may require further review before usage. | Other | | Categories | AI Agents, LLM Frameworks | AI Agents, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LLM-Hub](/tools/timmyy123-llm-hub.md) | | --- | --- | --- | | Open issues (now) | 144 | 31 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/timmyy123-llm-hub/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 hello-agents if… - hello-agents is primarily Python; LLM-Hub is C++. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - You should use hello-agents if you are interested in practical, step-by-step instructions on building intelligent agents from the ground up. ### Choose LLM-Hub if… - LLM-Hub is primarily C++; hello-agents is Python. - Tags unique to LLM-Hub: ai, gemma3, gemma3n, gemma4. - Also covers Inference & Serving. ## 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. ## When NOT to use LLM-Hub - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between hello-agents and LLM-Hub? hello-agents: Course on building intelligent agents from scratch. LLM-Hub: Local AI Assistant on your phone. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over LLM-Hub? Choose hello-agents over LLM-Hub when hello-agents is primarily Python; LLM-Hub is C++; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; 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 choose LLM-Hub over hello-agents? Choose LLM-Hub over hello-agents when LLM-Hub is primarily C++; hello-agents is Python; Tags unique to LLM-Hub: ai, gemma3, gemma3n, gemma4; Also covers Inference & Serving. ### 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. ### When should I avoid LLM-Hub? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is hello-agents or LLM-Hub more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 491). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and LLM-Hub open source? Yes - both are open-source projects on GitHub (hello-agents: Other, LLM-Hub: Other). ### Where can I find alternatives to hello-agents or LLM-Hub? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [LLM-Hub alternatives](/tools/timmyy123-llm-hub/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [LLM-Hub markdown twin](/tools/timmyy123-llm-hub/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-timmyy123-llm-hub.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or LLM-Hub? hello-agents: Very active. LLM-Hub: 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 LLM-Hub? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [LLM-Hub trust report](/tools/timmyy123-llm-hub/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/_