--- title: "hello-agents vs Awesome-LLMSecOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-wearetyomsmnv-awesome-llmsecops" tools: ["datawhalechina-hello-agents", "wearetyomsmnv-awesome-llmsecops"] --- # hello-agents vs Awesome-LLMSecOps *GraphCanon updated Jul 15, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; Awesome-LLMSecOps is HTML; pick Awesome-LLMSecOps when awesome-LLMSecOps is primarily HTML; hello-agents is Python. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [Awesome-LLMSecOps](https://github.com/wearetyomsmnv/Awesome-LLMSecOps) has 144 stars, 51 forks, and 8 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [Awesome-LLMSecOps's repository](https://github.com/wearetyomsmnv/Awesome-LLMSecOps). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | LLM | Agentic | Security | Operations in one github repo with good links and pictures. | | Stars | 65,432 | 144 | | Forks | 8,109 | 51 | | Open issues | 144 | 8 | | Language | Python | HTML | | 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. | - | | 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) | [Awesome-LLMSecOps](/tools/wearetyomsmnv-awesome-llmsecops.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 144 | 8 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/wearetyomsmnv-awesome-llmsecops/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… - hello-agents is primarily Python; Awesome-LLMSecOps is HTML. - 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 Awesome-LLMSecOps if… - Awesome-LLMSecOps is primarily HTML; hello-agents is Python. - Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security. - 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 Awesome-LLMSecOps - 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 Awesome-LLMSecOps? hello-agents: Course on building intelligent agents from scratch. Awesome-LLMSecOps: LLM | Agentic | Security | Operations in one github repo with good links and pictures.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over Awesome-LLMSecOps? Choose hello-agents over Awesome-LLMSecOps when hello-agents is primarily Python; Awesome-LLMSecOps is HTML; 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 Awesome-LLMSecOps over hello-agents? Choose Awesome-LLMSecOps over hello-agents when Awesome-LLMSecOps is primarily HTML; hello-agents is Python; Tags unique to Awesome-LLMSecOps: adversarial-ml-threat-modeling, ai-agents-security, ai-red-team, ai-safety-supply-chain-security; 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 Awesome-LLMSecOps? 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 Awesome-LLMSecOps more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 144). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and Awesome-LLMSecOps open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to hello-agents or Awesome-LLMSecOps? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [Awesome-LLMSecOps alternatives](/tools/wearetyomsmnv-awesome-llmsecops/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [Awesome-LLMSecOps markdown twin](/tools/wearetyomsmnv-awesome-llmsecops/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-wearetyomsmnv-awesome-llmsecops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or Awesome-LLMSecOps? hello-agents: Very active. Awesome-LLMSecOps: 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 Awesome-LLMSecOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [Awesome-LLMSecOps trust report](/tools/wearetyomsmnv-awesome-llmsecops/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/_