--- title: "hello-agents vs Awesome-LLM-in-Social-Science" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-valuebyte-ai-awesome-llm-in-social-science" tools: ["datawhalechina-hello-agents", "valuebyte-ai-awesome-llm-in-social-science"] --- # hello-agents vs Awesome-LLM-in-Social-Science *GraphCanon updated Jul 12, 2026* ## Verdict Pick hello-agents if hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods; pick Awesome-LLM-in-Social-Science if curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [Awesome-LLM-in-Social-Science](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science) has 635 stars, 49 forks, and 1 open issues, last pushed Jun 8, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [Awesome-LLM-in-Social-Science's repository](https://github.com/ValueByte-AI/Awesome-LLM-in-Social-Science). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Awesome papers involving LLMs in Social Science | | Stars | 65,432 | 635 | | Forks | 8,109 | 49 | | Open issues | 144 | 1 | | Language | 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. | Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | MIT | | Categories | AI Agents, LLM Frameworks | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [Awesome-LLM-in-Social-Science](/tools/valuebyte-ai-awesome-llm-in-social-science.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 32d | | Open issues (now) | 144 | 1 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/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. ## Decision facts: Awesome-LLM-in-Social-Science - **Adopt for:** Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, Awesome-LLM-in-Social-Science is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - Also covers AI Agents, LLM Frameworks. - 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-LLM-in-Social-Science if… - License: Awesome-LLM-in-Social-Science is MIT, hello-agents is Other. - Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large-language-models, llm-agent. - Also covers Evaluation & Observability, Model Training. - Need to explore academic insights into LLM impacts on specific social areas ## 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-LLM-in-Social-Science - Looking for a hands-on coding or practical implementation guide of LLMs - In need of real-time data analysis tools for immediate social science research outcomes ## Common questions ### What is the difference between hello-agents and Awesome-LLM-in-Social-Science? hello-agents: Course on building intelligent agents from scratch. Awesome-LLM-in-Social-Science: Awesome papers involving LLMs in Social Science. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over Awesome-LLM-in-Social-Science? Choose hello-agents over Awesome-LLM-in-Social-Science when License: hello-agents is Other, Awesome-LLM-in-Social-Science is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; Also covers AI Agents, LLM Frameworks; 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-LLM-in-Social-Science over hello-agents? Choose Awesome-LLM-in-Social-Science over hello-agents when License: Awesome-LLM-in-Social-Science is MIT, hello-agents is Other; Tags unique to Awesome-LLM-in-Social-Science: alignment, economics, large-language-models, llm-agent; Also covers Evaluation & Observability, Model Training; Need to explore academic insights into LLM impacts on specific social areas. ### 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-LLM-in-Social-Science? Looking for a hands-on coding or practical implementation guide of LLMs In need of real-time data analysis tools for immediate social science research outcomes ### Is hello-agents or Awesome-LLM-in-Social-Science more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 635). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and Awesome-LLM-in-Social-Science open source? Yes - both are open-source projects on GitHub (hello-agents: Other, Awesome-LLM-in-Social-Science: MIT). ### Where can I find alternatives to hello-agents or Awesome-LLM-in-Social-Science? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [Awesome-LLM-in-Social-Science alternatives](/tools/valuebyte-ai-awesome-llm-in-social-science/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [Awesome-LLM-in-Social-Science markdown twin](/tools/valuebyte-ai-awesome-llm-in-social-science/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-valuebyte-ai-awesome-llm-in-social-science.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-LLM-in-Social-Science? hello-agents: Very active. Awesome-LLM-in-Social-Science: Steady. 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-LLM-in-Social-Science? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [Awesome-LLM-in-Social-Science trust report](/tools/valuebyte-ai-awesome-llm-in-social-science/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/_