Comparison
hello-agents vs dstack
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.
Markdown twin · hello-agents alternatives · dstack alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | dstack |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- 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.
Stars
- hello-agents
- 65k
- dstack
- 2.2k
Forks
- hello-agents
- 8.1k
- dstack
- 237
Open issues
- hello-agents
- 144
- dstack
- 62
Language
- hello-agents
- Python
- dstack
- Python
Adopt for
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
- dstack
- -
Persona
- hello-agents
- -
- dstack
- -
Runtime
- hello-agents
- -
- dstack
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- dstack
- MPL-2.0
Last pushed
- hello-agents
- Jul 10, 2026
- dstack
- Jul 10, 2026
Categories
- hello-agents
- AI Agents, LLM Frameworks
- dstack
- AI Agents, LLM Frameworks, Model Training
Trust and health
Open issues (now)
- hello-agents
- 144
- dstack
- 62
Full report
- hello-agents
- Trust report
- dstack
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datawhalechina/hello-agents) · observed Jul 11, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Jul 11, 2026
- Last push (datawhalechina/hello-agents) · observed Jul 10, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (dstackai/dstack) · observed Jul 11, 2026
- GitHub forks (dstackai/dstack) · observed Jul 11, 2026
- Last push (dstackai/dstack) · observed Jul 10, 2026
- License file (MPL-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hello-agents 65k · dstack 2.2k (synced Jul 11, 2026).
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 and dstack alternatives (hello-agents markdown twin, dstack markdown twin), 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 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; dstack trust report.