--- title: "hello-agents vs dstack" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-dstackai-dstack" tools: ["datawhalechina-hello-agents", "dstackai-dstack"] --- # hello-agents vs dstack *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when license: hello-agents is Other, dstack is MPL-2.0; pick dstack when license: dstack is MPL-2.0, hello-agents is Other. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [dstack](https://dstack.ai/docs) has 2.2k stars, 237 forks, and 62 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [dstack's repository](https://github.com/dstackai/dstack). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal. | | Stars | 65,432 | 2,172 | | Forks | 8,109 | 237 | | Open issues | 144 | 62 | | 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 | hello-agents is covered under an unconventional license which may require further review before usage. | MPL-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [dstack](/tools/dstackai-dstack.md) | | --- | --- | --- | | Open issues (now) | 144 | 62 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/dstackai-dstack/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… - License: hello-agents is Other, dstack is MPL-2.0. - 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 dstack if… - License: dstack is MPL-2.0, hello-agents is Other. - Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud. - Also covers Model Training. ## 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 dstack - 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. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between hello-agents and dstack? hello-agents: Course on building intelligent agents from scratch. dstack: Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over dstack? Choose hello-agents over dstack when License: hello-agents is Other, dstack is MPL-2.0; 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 dstack over hello-agents? Choose dstack over hello-agents when License: dstack is MPL-2.0, hello-agents is Other; Tags unique to dstack: agent-skills, agentic-orchestration, amd, cloud; Also covers Model Training. ### 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 dstack? 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. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is hello-agents or dstack more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 2,172). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and dstack open source? Yes - both are open-source projects on GitHub (hello-agents: Other, dstack: MPL-2.0). ### Where can I find alternatives to hello-agents or dstack? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [dstack alternatives](/tools/dstackai-dstack/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [dstack markdown twin](/tools/dstackai-dstack/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-dstackai-dstack.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or dstack? hello-agents: Very active. dstack: 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 dstack? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [dstack trust report](/tools/dstackai-dstack/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/_