--- title: "agentset vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agentset-ai-agentset-vs-datawhalechina-hello-agents" tools: ["agentset-ai-agentset", "datawhalechina-hello-agents"] --- # agentset vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick agentset when agentset is primarily TypeScript; hello-agents is Python; pick hello-agents when hello-agents is primarily Python; agentset is TypeScript. [agentset](https://agentset.ai) reports 2.0k GitHub stars, 182 forks, and 12 open issues, last pushed Jul 6, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [agentset's repository](https://github.com/agentset-ai/agentset) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [agentset](/tools/agentset-ai-agentset.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more. | Course on building intelligent agents from scratch | | Stars | 2,027 | 65,432 | | Forks | 182 | 8,109 | | Open issues | 12 | 144 | | Language | TypeScript | 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 | MIT | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | LLM Frameworks, AI Agents, Vector Databases | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentset](/tools/agentset-ai-agentset.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 5d | 0d | | Open issues (now) | 12 | 144 | | Full report | [trust report](/tools/agentset-ai-agentset/trust.md) | [trust report](/tools/datawhalechina-hello-agents/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 agentset if… - agentset is primarily TypeScript; hello-agents is Python. - License: agentset is MIT, hello-agents is Other. - Tags unique to agentset: llms, ai-sdk, embeddings, genai. - Also covers Vector Databases. ### Choose hello-agents if… - hello-agents is primarily Python; agentset is TypeScript. - License: hello-agents is Other, agentset is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: llm, rag, tutorial, agent. - 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 agentset - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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. ## Common questions ### What is the difference between agentset and hello-agents? agentset: The open-source RAG platform: built-in citations, deep research, 22+ file formats, partitions, MCP server, and more.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentset over hello-agents? Choose agentset over hello-agents when agentset is primarily TypeScript; hello-agents is Python; License: agentset is MIT, hello-agents is Other; Tags unique to agentset: llms, ai-sdk, embeddings, genai; Also covers Vector Databases. ### When should I choose hello-agents over agentset? Choose hello-agents over agentset when hello-agents is primarily Python; agentset is TypeScript; License: hello-agents is Other, agentset is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; 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 avoid agentset? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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. ### Is agentset or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 2,027). Stars measure visibility, not whether either tool fits your constraints. ### Are agentset and hello-agents open source? Yes - both are open-source projects on GitHub (agentset: MIT, hello-agents: Other). ### Where can I find alternatives to agentset or hello-agents? GraphCanon lists graph-backed alternatives at [agentset alternatives](/tools/agentset-ai-agentset/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([agentset markdown twin](/tools/agentset-ai-agentset/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/agentset-ai-agentset-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentset or hello-agents? agentset: Very active. hello-agents: 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 agentset and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentset trust report](/tools/agentset-ai-agentset/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agentset-ai-agentset`](/api/graphcanon/graph?tool=agentset-ai-agentset) - 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/_