Comparison
hello-agents vs Awesome-LLM-in-Social-Science
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.
Markdown twin · hello-agents alternatives · Awesome-LLM-in-Social-Science alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | Awesome-LLM-in-Social-Science |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1d · github_public_v1 | Steady (32d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of 1d · none | No lockfile As of today · none |
Tagline
- hello-agents
- Course on building intelligent agents from scratch
- Awesome-LLM-in-Social-Science
- Awesome papers involving LLMs in Social Science
Stars
- hello-agents
- 65k
- Awesome-LLM-in-Social-Science
- 635
Forks
- hello-agents
- 8.1k
- Awesome-LLM-in-Social-Science
- 49
Open issues
- hello-agents
- 144
- Awesome-LLM-in-Social-Science
- 1
Language
- hello-agents
- Python
- Awesome-LLM-in-Social-Science
- -
Adopt for
- hello-agents
- hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods.
- Awesome-LLM-in-Social-Science
- Curate research papers on LLM applications in social science, covering topics like alignment, economics, policy, psychology, and more.
Persona
- hello-agents
- -
- Awesome-LLM-in-Social-Science
- -
Runtime
- hello-agents
- -
- Awesome-LLM-in-Social-Science
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- Awesome-LLM-in-Social-Science
- MIT
Last pushed
- hello-agents
- Jul 10, 2026
- Awesome-LLM-in-Social-Science
- Jun 8, 2026
Categories
- hello-agents
- AI Agents, LLM Frameworks
- Awesome-LLM-in-Social-Science
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- hello-agents
- Very active (96%)
- Awesome-LLM-in-Social-Science
- Steady (60%)
Days since push
- hello-agents
- 0d
- Awesome-LLM-in-Social-Science
- 32d
Open issues (now)
- hello-agents
- 144
- Awesome-LLM-in-Social-Science
- 1
Full report
- hello-agents
- Trust report
- Awesome-LLM-in-Social-Science
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (datawhalechina/hello-agents) · observed Jul 11, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Jul 11, 2026
- Last push (datawhalechina/hello-agents) · observed Jul 10, 2026
- License file (Other) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ValueByte-AI/Awesome-LLM-in-Social-Science) · observed Jul 11, 2026
- GitHub forks (ValueByte-AI/Awesome-LLM-in-Social-Science) · observed Jul 11, 2026
- Last push (ValueByte-AI/Awesome-LLM-in-Social-Science) · observed Jun 8, 2026
- License file (MIT) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hello-agents 65k · Awesome-LLM-in-Social-Science 635 (synced Jul 11, 2026).
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 and Awesome-LLM-in-Social-Science alternatives (hello-agents markdown twin, Awesome-LLM-in-Social-Science markdown twin), 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 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; Awesome-LLM-in-Social-Science trust report.