Comparison
hello-agents vs ART
Verdict
Pick hello-agents if hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python; pick ART if aRT is a specialized framework for training multi-step AI agents using GRPO, suitable for developers looking to train their agents with specific LLMs like Qwen3.6 and GPT-OSS.
Markdown twin · hello-agents alternatives · ART alternatives
GraphCanon updated Sep 20, 2026
9views this month
vs
Trust & integrity
| Signal | hello-agents | ART |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Very active (0d since push) As of Aug 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 19, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Sep 18, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- A comprehensive guide to building intelligent agents from scratch
- ART
- Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO.
Stars
- hello-agents
- 80k
- ART
- 11k
Forks
- hello-agents
- 9.9k
- ART
- 974
Open issues
- hello-agents
- 82
- ART
- 127
Language
- hello-agents
- Python
- ART
- Python
Adopt for
- hello-agents
- Hello-Agents is a systematic learning tutorial for building AI-native intelligent agents, offering hands-on experience with core principles and practical implementation in Python.
- ART
- ART is a specialized framework for training multi-step AI agents using GRPO, suitable for developers looking to train their agents with specific LLMs like Qwen3.6 and GPT-OSS.
Persona
- hello-agents
- -
- ART
- -
Runtime
- hello-agents
- -
- ART
- -
License
- hello-agents
- The license is not specified as open source, but the content is free to read and learn from.
- ART
- Apache-2.0
Last pushed
- hello-agents
- Sep 17, 2026
- ART
- Aug 19, 2026
Categories
- hello-agents
- AI Agents, Model Training
- ART
- AI Agents, Model Training
Trust and health
Open issues (now)
- hello-agents
- 82
- ART
- 127
Stars delta
- hello-agents
- +6.6k (30d)
- ART
- +109 (30d)
Open issues delta
- hello-agents
- -73 (30d)
- ART
- +6 (30d)
Full report
- hello-agents
- Trust report
- ART
- Trust report
Choose hello-agents if…
- License: hello-agents is Other, ART is Apache-2.0.
- Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository..
- Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework..
- Tags unique to hello-agents: llm, rag, tutorial.
- When you want to focus on building AI-native agents and understand their core principles and practical implementation.
When NOT to use hello-agents
- If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development.
- If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API.
Choose ART if…
- License: ART is Apache-2.0, hello-agents is Other.
- Tags unique to ART: agentic-ai, grpo, llms, lora.
- - When you need a framework that specifically supports the GRPO method for agent reinforcement learning.
When NOT to use ART
- - Avoid ART if your training needs do not align with the GRPO method and you require a different approach for reinforcement learning.
- - If your development revolves around non-supported Language Learning Models (LLMs), other than Qwen3.6, GPT-OSS, Llama etc., consider looking into alternative frameworks that offer broader model comp
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 Sep 20, 2026
- GitHub forks (datawhalechina/hello-agents) · observed Sep 20, 2026
- Last push (datawhalechina/hello-agents) · observed Sep 17, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
- GitHub stars (OpenPipe/ART) · observed Sep 20, 2026
- GitHub forks (OpenPipe/ART) · observed Sep 20, 2026
- Last push (OpenPipe/ART) · observed Aug 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: hello-agents 80k · ART 11k (synced Sep 20, 2026).
Common questions
- What is the difference between hello-agents and ART?
- hello-agents: A comprehensive guide to building intelligent agents from scratch. ART: Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO.. See the comparison table for live GitHub stats and shared categories.
- When should I choose hello-agents over ART?
- Choose hello-agents over ART when License: hello-agents is Other, ART is Apache-2.0; Pricing: The content is free to access and learn from, but there may be additional paid services or support options not detailed in the repository.; Requirements: Min 4 GB RAM; Python is required for practical implementation and hands-on experience with the HelloAgents framework.; Tags unique to hello-agents: llm, rag, tutorial; When you want to focus on building AI-native agents and understand their core principles and practical implementation.
- When should I choose ART over hello-agents?
- Choose ART over hello-agents when License: ART is Apache-2.0, hello-agents is Other; Tags unique to ART: agentic-ai, grpo, llms, lora; - When you need a framework that specifically supports the GRPO method for agent reinforcement learning.
- When should I avoid hello-agents?
- If you are looking for a tutorial that focuses on software engineering-based agents like Dify or Coze, which are more about process-driven software development. If you prefer a tool that does not emphasize the hands-on construction of an agent framework from the ground up using OpenAI's native API.
- When should I avoid ART?
- - Avoid ART if your training needs do not align with the GRPO method and you require a different approach for reinforcement learning. - If your development revolves around non-supported Language Learning Models (LLMs), other than Qwen3.6, GPT-OSS, Llama etc., consider looking into alternative frameworks that offer broader model comp
- Is hello-agents or ART more popular on GitHub?
- hello-agents has more GitHub stars (79,727 vs 10,603). Stars measure visibility, not whether either tool fits your constraints.
- Are hello-agents and ART open source?
- Yes - both are open-source projects on GitHub (hello-agents: Other, ART: Apache-2.0).
- Where can I find alternatives to hello-agents or ART?
- GraphCanon lists graph-backed alternatives at hello-agents alternatives and ART alternatives (hello-agents markdown twin, ART 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 ART?
- hello-agents: Very active. ART: 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 hello-agents and ART?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: hello-agents trust report; ART trust report.