--- title: "hello-agents vs gpu-telemetry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-last9-gpu-telemetry" tools: ["datawhalechina-hello-agents", "last9-gpu-telemetry"] --- # hello-agents vs gpu-telemetry *GraphCanon updated Jul 15, 2026* ## Verdict Pick hello-agents when license: hello-agents is Other, gpu-telemetry is MIT; pick gpu-telemetry when license: gpu-telemetry is MIT, 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. [gpu-telemetry](https://last9.io/gpu-observability/) has 56 stars, 6 forks, and 5 open issues, last pushed Jul 7, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [gpu-telemetry's repository](https://github.com/last9/gpu-telemetry). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | GPU Observability with workload attribution. One OTLP agent per node ties hardware metrics (NVIDIA, AMD, Intel Gaudi) to the K8s pod or Slurm job burning the GPU. | | Stars | 65,432 | 56 | | Forks | 8,109 | 6 | | Open issues | 144 | 5 | | 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. | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [gpu-telemetry](/tools/last9-gpu-telemetry.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 8d | | Open issues (now) | 144 | 5 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/last9-gpu-telemetry/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, gpu-telemetry is MIT. - 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 gpu-telemetry if… - License: gpu-telemetry is MIT, hello-agents is Other. - Tags unique to gpu-telemetry: ai, amd, dcgm, gpu. - Also covers Evaluation & Observability. ## 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 gpu-telemetry - 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. ## Common questions ### What is the difference between hello-agents and gpu-telemetry? hello-agents: Course on building intelligent agents from scratch. gpu-telemetry: GPU Observability with workload attribution. One OTLP agent per node ties hardware metrics (NVIDIA, AMD, Intel Gaudi) to the K8s pod or Slurm job burning the GPU.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over gpu-telemetry? Choose hello-agents over gpu-telemetry when License: hello-agents is Other, gpu-telemetry is MIT; 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 gpu-telemetry over hello-agents? Choose gpu-telemetry over hello-agents when License: gpu-telemetry is MIT, hello-agents is Other; Tags unique to gpu-telemetry: ai, amd, dcgm, gpu; Also covers Evaluation & Observability. ### 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 gpu-telemetry? 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. ### Is hello-agents or gpu-telemetry more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 56). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and gpu-telemetry open source? Yes - both are open-source projects on GitHub (hello-agents: Other, gpu-telemetry: MIT). ### Where can I find alternatives to hello-agents or gpu-telemetry? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [gpu-telemetry alternatives](/tools/last9-gpu-telemetry/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [gpu-telemetry markdown twin](/tools/last9-gpu-telemetry/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-last9-gpu-telemetry.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or gpu-telemetry? hello-agents: Very active. gpu-telemetry: 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 gpu-telemetry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [gpu-telemetry trust report](/tools/last9-gpu-telemetry/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/_