--- title: "hello-agents vs llm-leaderboard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-jonathanchaveztamales-llm-leaderboard" tools: ["datawhalechina-hello-agents", "jonathanchaveztamales-llm-leaderboard"] --- # hello-agents vs llm-leaderboard *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; llm-leaderboard is JavaScript; pick llm-leaderboard when llm-leaderboard is primarily JavaScript; 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-leaderboard](https://llm-stats.com) has 360 stars, 40 forks, and 14 open issues, last pushed Oct 24, 2025. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [llm-leaderboard's repository](https://github.com/JonathanChavezTamales/llm-leaderboard). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | A comprehensive set of LLM benchmark scores and provider prices. (deprecated, read more in README) | | Stars | 65,432 | 360 | | Forks | 8,109 | 40 | | Open issues | 144 | 14 | | Language | Python | JavaScript | | 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, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 259d | | Open issues (now) | 144 | 14 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/jonathanchaveztamales-llm-leaderboard/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-leaderboard is JavaScript. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, 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-leaderboard if… - llm-leaderboard is primarily JavaScript; hello-agents is Python. - Tags unique to llm-leaderboard: javascript, llm-agents, llm-evaluation, llmops. - Also covers Evaluation & Observability. ## 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-leaderboard - Last GitHub push was 260 days ago (slowing maintenance, Oct 24, 2025). Validate activity before betting a new project on llm-leaderboard. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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-leaderboard? hello-agents: Course on building intelligent agents from scratch. llm-leaderboard: A comprehensive set of LLM benchmark scores and provider prices. (deprecated, read more in README). See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over llm-leaderboard? Choose hello-agents over llm-leaderboard when hello-agents is primarily Python; llm-leaderboard is JavaScript; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, 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-leaderboard over hello-agents? Choose llm-leaderboard over hello-agents when llm-leaderboard is primarily JavaScript; hello-agents is Python; Tags unique to llm-leaderboard: javascript, llm-agents, llm-evaluation, llmops; Also covers Evaluation & Observability. ### 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-leaderboard? Last GitHub push was 260 days ago (slowing maintenance, Oct 24, 2025). Validate activity before betting a new project on llm-leaderboard. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. 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-leaderboard more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 360). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and llm-leaderboard open source? Yes - both are open-source projects on GitHub (hello-agents: Other, llm-leaderboard: Other). ### Where can I find alternatives to hello-agents or llm-leaderboard? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [llm-leaderboard alternatives](/tools/jonathanchaveztamales-llm-leaderboard/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [llm-leaderboard markdown twin](/tools/jonathanchaveztamales-llm-leaderboard/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-jonathanchaveztamales-llm-leaderboard.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-leaderboard? hello-agents: Very active. llm-leaderboard: Slowing. 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-leaderboard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [llm-leaderboard trust report](/tools/jonathanchaveztamales-llm-leaderboard/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/_