--- title: "hello-agents vs superagent" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-superagent-ai-superagent" tools: ["datawhalechina-hello-agents", "superagent-ai-superagent"] --- # hello-agents vs superagent *GraphCanon updated Jul 12, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; superagent is TypeScript; pick superagent when superagent is primarily TypeScript; 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. [superagent](https://superagent.sh) has 6.7k stars, 963 forks, and 9 open issues, last pushed Apr 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [superagent's repository](https://github.com/superagent-ai/superagent). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [superagent](/tools/superagent-ai-superagent.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers. | | Stars | 65,432 | 6,669 | | Forks | 8,109 | 963 | | Open issues | 144 | 9 | | Language | Python | TypeScript | | 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, Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [superagent](/tools/superagent-ai-superagent.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 144 | 9 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/superagent-ai-superagent/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; superagent is TypeScript. - License: hello-agents is Other, superagent is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, 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 superagent if… - superagent is primarily TypeScript; hello-agents is Python. - License: superagent is MIT, hello-agents is Other. - Tags unique to superagent: ai, anthropic, guardrails, openai. - Also covers Inference & Serving. ## 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 superagent - Last GitHub push was 92 days ago (slowing maintenance, Apr 11, 2026). Validate activity before betting a new project on superagent. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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 superagent? hello-agents: Course on building intelligent agents from scratch. superagent: Superagent protects your AI applications against prompt injections, data leaks, and harmful outputs. Embed safety directly into your app and prove compliance to your customers.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over superagent? Choose hello-agents over superagent when hello-agents is primarily Python; superagent is TypeScript; License: hello-agents is Other, superagent is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, 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 superagent over hello-agents? Choose superagent over hello-agents when superagent is primarily TypeScript; hello-agents is Python; License: superagent is MIT, hello-agents is Other; Tags unique to superagent: ai, anthropic, guardrails, openai; Also covers Inference & Serving. ### 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 superagent? Last GitHub push was 92 days ago (slowing maintenance, Apr 11, 2026). Validate activity before betting a new project on superagent. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is hello-agents or superagent more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 6,669). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and superagent open source? Yes - both are open-source projects on GitHub (hello-agents: Other, superagent: MIT). ### Where can I find alternatives to hello-agents or superagent? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [superagent alternatives](/tools/superagent-ai-superagent/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [superagent markdown twin](/tools/superagent-ai-superagent/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-superagent-ai-superagent.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or superagent? hello-agents: Very active. superagent: Slowing. 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 superagent? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [superagent trust report](/tools/superagent-ai-superagent/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/_