Comparison
hello-agents vs Made-With-ML
Verdict
Pick hello-agents when hello-agents is primarily Python; Made-With-ML is Jupyter Notebook; pick Made-With-ML when made-With-ML is primarily Jupyter Notebook; hello-agents is Python.
Markdown twin · hello-agents alternatives · Made-With-ML alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | hello-agents | Made-With-ML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 4d · github_public_v1 | Slowing (132d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 4d · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | Published findings As of today · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- hello-agents
- Course on building intelligent agents from scratch
- Made-With-ML
- Learn how to develop, deploy and iterate on production-grade ML applications.
Stars
- hello-agents
- 65k
- Made-With-ML
- 49k
Forks
- hello-agents
- 8.1k
- Made-With-ML
- 7.7k
Open issues
- hello-agents
- 144
- Made-With-ML
- 27
Language
- hello-agents
- Python
- Made-With-ML
- Jupyter Notebook
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.
- Made-With-ML
- -
Persona
- hello-agents
- -
- Made-With-ML
- -
Runtime
- hello-agents
- -
- Made-With-ML
- -
License
- hello-agents
- hello-agents is covered under an unconventional license which may require further review before usage.
- Made-With-ML
- MIT
Last pushed
- hello-agents
- Jul 10, 2026
- Made-With-ML
- Mar 4, 2026
Categories
- hello-agents
- AI Agents, LLM Frameworks
- Made-With-ML
- AI Agents, LLM Frameworks, Model Training
Trust and health
Maintenance
- hello-agents
- Very active (96%)
- Made-With-ML
- Slowing (36%)
Days since push
- hello-agents
- 0d
- Made-With-ML
- 132d
Open issues (now)
- hello-agents
- 144
- Made-With-ML
- 27
Owner type
- hello-agents
- Organization
- Made-With-ML
- User
OSV dependency advisories
- hello-agents
- No lockfile (source not queried)
- Made-With-ML
- Published findings
Full report
- hello-agents
- Trust report
- Made-With-ML
- Trust report
Choose hello-agents if…
- hello-agents is primarily Python; Made-With-ML is Jupyter Notebook.
- License: hello-agents is Other, Made-With-ML is MIT.
- Requirements: Min 4 GB RAM; Python knowledge assumed.
- Tags unique to hello-agents: agent, 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 Made-With-ML if…
- Made-With-ML is primarily Jupyter Notebook; hello-agents is Python.
- License: Made-With-ML is MIT, hello-agents is Other.
- Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning.
- Also covers Model Training.
When NOT to use Made-With-ML
- Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML.
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
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 (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- GitHub forks (GokuMohandas/Made-With-ML) · observed Jul 15, 2026
- Last push (GokuMohandas/Made-With-ML) · observed Mar 4, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: hello-agents 65k · Made-With-ML 49k (synced Jul 11, 2026).
Common questions
- What is the difference between hello-agents and Made-With-ML?
- hello-agents: Course on building intelligent agents from scratch. Made-With-ML: Learn how to develop, deploy and iterate on production-grade ML applications.. See the comparison table for live GitHub stats and shared categories.
- When should I choose hello-agents over Made-With-ML?
- Choose hello-agents over Made-With-ML when hello-agents is primarily Python; Made-With-ML is Jupyter Notebook; License: hello-agents is Other, Made-With-ML is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, 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 Made-With-ML over hello-agents?
- Choose Made-With-ML over hello-agents when Made-With-ML is primarily Jupyter Notebook; hello-agents is Python; License: Made-With-ML is MIT, hello-agents is Other; Tags unique to Made-With-ML: data-engineering, data-quality, data-science, deep-learning; Also covers Model Training.
- 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 Made-With-ML?
- Last GitHub push was 132 days ago (slowing maintenance, Mar 4, 2026). Validate activity before betting a new project on Made-With-ML. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is hello-agents or Made-With-ML more popular on GitHub?
- hello-agents has more GitHub stars (65,432 vs 48,703). Stars measure visibility, not whether either tool fits your constraints.
- Are hello-agents and Made-With-ML open source?
- Yes - both are open-source projects on GitHub (hello-agents: Other, Made-With-ML: MIT).
- Where can I find alternatives to hello-agents or Made-With-ML?
- GraphCanon lists graph-backed alternatives at hello-agents alternatives and Made-With-ML alternatives (hello-agents markdown twin, Made-With-ML 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 Made-With-ML?
- hello-agents: Very active. Made-With-ML: 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 Made-With-ML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hello-agents trust report; Made-With-ML trust report.