Comparison
hello-agents vs AdalFlow
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 AdalFlow if adalFlow is designed to streamline the development and automatic optimization of LLM applications.
Markdown twin · hello-agents alternatives · AdalFlow alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | AdalFlow |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Steady (43d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- hello-agents
- Course on building intelligent agents from scratch
- AdalFlow
- The library to build & auto-optimize LLM applications.
Stars
- hello-agents
- 65k
- AdalFlow
- 4.2k
Forks
- hello-agents
- 8.1k
- AdalFlow
- 378
Open issues
- hello-agents
- 144
- AdalFlow
- 65
Language
- hello-agents
- Python
- AdalFlow
- Python
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.
- AdalFlow
- AdalFlow is designed to streamline the development and automatic optimization of LLM applications.
Persona
- hello-agents
- -
- AdalFlow
- -
Runtime
- hello-agents
- -
- AdalFlow
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- AdalFlow
- MIT
Last pushed
- hello-agents
- Jul 10, 2026
- AdalFlow
- May 29, 2026
Categories
- hello-agents
- LLM Frameworks, AI Agents
- AdalFlow
- Model Training, LLM Frameworks, AI Agents, Data & Retrieval
Trust and health
Maintenance
- hello-agents
- Very active (96%)
- AdalFlow
- Steady (60%)
Days since push
- hello-agents
- 0d
- AdalFlow
- 43d
Open issues (now)
- hello-agents
- 144
- AdalFlow
- 65
Full report
- hello-agents
- Trust report
- AdalFlow
- Trust report
Choose hello-agents if…
- License: hello-agents is Other, AdalFlow is MIT.
- Requirements: Min 4 GB RAM; Python knowledge assumed.
- Tags unique to hello-agents: 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 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 AdalFlow if…
- License: AdalFlow is MIT, hello-agents is Other.
- Tags unique to AdalFlow: auto-prompting, ai, generative-ai, framework.
- Also covers Model Training, Data & Retrieval.
- When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively.
When NOT to use AdalFlow
- Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity.
- AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes.
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 (SylphAI-Inc/AdalFlow) · observed Jul 11, 2026
- GitHub forks (SylphAI-Inc/AdalFlow) · observed Jul 11, 2026
- Last push (SylphAI-Inc/AdalFlow) · observed May 29, 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 · AdalFlow 4.2k (synced Jul 11, 2026).
Common questions
- What is the difference between hello-agents and AdalFlow?
- hello-agents: Course on building intelligent agents from scratch. AdalFlow: The library to build & auto-optimize LLM applications.. See the comparison table for live GitHub stats and shared categories.
- When should I choose hello-agents over AdalFlow?
- Choose hello-agents over AdalFlow when License: hello-agents is Other, AdalFlow is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: 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 AdalFlow over hello-agents?
- Choose AdalFlow over hello-agents when License: AdalFlow is MIT, hello-agents is Other; Tags unique to AdalFlow: auto-prompting, ai, generative-ai, framework; Also covers Model Training, Data & Retrieval; When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively.
- 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 AdalFlow?
- Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity. AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes.
- Is hello-agents or AdalFlow more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 4,178). Stars measure visibility, not whether either tool fits your constraints.
- Are hello-agents and AdalFlow open source?
- Yes - both are open-source projects on GitHub (hello-agents: Other, AdalFlow: MIT).
- Where can I find alternatives to hello-agents or AdalFlow?
- GraphCanon lists graph-backed alternatives at hello-agents alternatives and AdalFlow alternatives (hello-agents markdown twin, AdalFlow 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 AdalFlow?
- hello-agents: Very active. AdalFlow: 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 AdalFlow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hello-agents trust report; AdalFlow trust report.