--- title: "awesome-evals vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-datawhalechina-hello-agents" tools: ["benchflow-ai-awesome-evals", "datawhalechina-hello-agents"] --- # awesome-evals vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick awesome-evals when tags unique to awesome-evals: agent-evaluation, ai-agents, awesome, awesome-list; pick hello-agents when requirements: Min 4 GB RAM; Python knowledge assumed. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 706 GitHub stars, 55 forks, and 8 open issues, last pushed Jul 1, 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 [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow. | Course on building intelligent agents from scratch | | Stars | 706 | 65,432 | | Forks | 55 | 8,109 | | Open issues | 8 | 144 | | Language | - | 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 | Other | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | AI Agents, Evaluation & Observability, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 9d | 0d | | Open issues (now) | 8 | 144 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/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 awesome-evals if… - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome, awesome-list. - Also covers Evaluation & Observability. - Leaner open-issue backlog (8). ### Choose hello-agents if… - 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 awesome-evals - 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. ## 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 awesome-evals and hello-agents? awesome-evals: A curated, non-BS library of the best resources for building and evaluating AI agents — papers, blogs, talks, tools, benchmarks. Maintained by BenchFlow.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over hello-agents? Choose awesome-evals over hello-agents when Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome, awesome-list; Also covers Evaluation & Observability; Leaner open-issue backlog (8). ### When should I choose hello-agents over awesome-evals? Choose hello-agents over awesome-evals when 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 awesome-evals? 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. ### 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 awesome-evals or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 706). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and hello-agents open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, hello-agents: Other). ### Where can I find alternatives to awesome-evals or hello-agents? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/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/benchflow-ai-awesome-evals-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, awesome-evals or hello-agents? awesome-evals: 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 awesome-evals and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - 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/_