--- title: "LLMEvaluation vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alopatenko-llmevaluation-vs-datawhalechina-hello-agents" tools: ["alopatenko-llmevaluation", "datawhalechina-hello-agents"] --- # LLMEvaluation vs hello-agents *GraphCanon updated Jul 12, 2026* ## Verdict Pick LLMEvaluation when lLMEvaluation is primarily HTML; hello-agents is Python; pick hello-agents when hello-agents is primarily Python; LLMEvaluation is HTML. [LLMEvaluation](https://alopatenko.github.io/LLMEvaluation/) reports 197 GitHub stars, 20 forks, and 1 open issues, last pushed Jul 6, 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 [LLMEvaluation's repository](https://github.com/alopatenko/LLMEvaluation) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [LLMEvaluation](/tools/alopatenko-llmevaluation.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | A comprehensive guide to LLM evaluation methods designed to assist in identifying the most suitable evaluation techniques for various use cases, promote the adoption of best practices in LLM assessmen | Course on building intelligent agents from scratch | | Stars | 197 | 65,432 | | Forks | 20 | 8,109 | | Open issues | 1 | 144 | | Language | HTML | 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 | - | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | AI Agents, LLM Frameworks, Vector Databases | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [LLMEvaluation](/tools/alopatenko-llmevaluation.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 5d | 0d | | Open issues (now) | 1 | 144 | | Owner type | User | Organization | | Full report | [trust report](/tools/alopatenko-llmevaluation/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 LLMEvaluation if… - LLMEvaluation is primarily HTML; hello-agents is Python. - Tags unique to LLMEvaluation: evaluation, generative-ai-benchmarking, html, llm-benchmarking. - Also covers Vector Databases. ### Choose hello-agents if… - hello-agents is primarily Python; LLMEvaluation is HTML. - 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 NOT to use LLMEvaluation - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 LLMEvaluation and hello-agents? LLMEvaluation: A comprehensive guide to LLM evaluation methods designed to assist in identifying the most suitable evaluation techniques for various use cases, promote the adoption of best practices in LLM assessmen. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose LLMEvaluation over hello-agents? Choose LLMEvaluation over hello-agents when LLMEvaluation is primarily HTML; hello-agents is Python; Tags unique to LLMEvaluation: evaluation, generative-ai-benchmarking, html, llm-benchmarking; Also covers Vector Databases. ### When should I choose hello-agents over LLMEvaluation? Choose hello-agents over LLMEvaluation when hello-agents is primarily Python; LLMEvaluation is HTML; 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 avoid LLMEvaluation? 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 LLMEvaluation or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 197). Stars measure visibility, not whether either tool fits your constraints. ### Are LLMEvaluation and hello-agents open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to LLMEvaluation or hello-agents? GraphCanon lists graph-backed alternatives at [LLMEvaluation alternatives](/tools/alopatenko-llmevaluation/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([LLMEvaluation markdown twin](/tools/alopatenko-llmevaluation/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/alopatenko-llmevaluation-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, LLMEvaluation or hello-agents? LLMEvaluation: 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 LLMEvaluation and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [LLMEvaluation trust report](/tools/alopatenko-llmevaluation/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alopatenko-llmevaluation`](/api/graphcanon/graph?tool=alopatenko-llmevaluation) - 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/_