--- title: "curator vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bespokelabsai-curator-vs-datawhalechina-hello-agents" tools: ["bespokelabsai-curator", "datawhalechina-hello-agents"] --- # curator vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick curator when license: curator is Apache-2.0, hello-agents is Other; pick hello-agents when license: hello-agents is Other, curator is Apache-2.0. [curator](https://docs.bespokelabs.ai/bespoke-curator) reports 1.7k GitHub stars, 142 forks, and 69 open issues, last pushed Jul 8, 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 [curator's repository](https://github.com/bespokelabsai/curator) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [curator](/tools/bespokelabsai-curator.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | Synthetic data curation for post-training and structured data extraction | Course on building intelligent agents from scratch | | Stars | 1,700 | 65,432 | | Forks | 142 | 8,109 | | Open issues | 69 | 144 | | Language | Python | 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 | Apache-2.0 | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | LLM Frameworks, Model Training, AI Agents | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [curator](/tools/bespokelabsai-curator.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 69 | 144 | | Full report | [trust report](/tools/bespokelabsai-curator/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 curator if… - License: curator is Apache-2.0, hello-agents is Other. - Tags unique to curator: deep-learning, fine-tuning, agents, instruction-tuning. - Also covers Model Training. ### Choose hello-agents if… - License: hello-agents is Other, curator is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: 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 curator - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## 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 curator and hello-agents? curator: Synthetic data curation for post-training and structured data extraction. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose curator over hello-agents? Choose curator over hello-agents when License: curator is Apache-2.0, hello-agents is Other; Tags unique to curator: deep-learning, fine-tuning, agents, instruction-tuning; Also covers Model Training. ### When should I choose hello-agents over curator? Choose hello-agents over curator when License: hello-agents is Other, curator is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: 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 curator? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### 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 curator or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 1,700). Stars measure visibility, not whether either tool fits your constraints. ### Are curator and hello-agents open source? Yes - both are open-source projects on GitHub (curator: Apache-2.0, hello-agents: Other). ### Where can I find alternatives to curator or hello-agents? GraphCanon lists graph-backed alternatives at [curator alternatives](/tools/bespokelabsai-curator/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([curator markdown twin](/tools/bespokelabsai-curator/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/bespokelabsai-curator-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, curator or hello-agents? curator: 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 curator and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [curator trust report](/tools/bespokelabsai-curator/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bespokelabsai-curator`](/api/graphcanon/graph?tool=bespokelabsai-curator) - 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/_