--- title: "MetaClaw vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aiming-lab-metaclaw-vs-datawhalechina-hello-agents" tools: ["aiming-lab-metaclaw", "datawhalechina-hello-agents"] --- # MetaClaw vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick MetaClaw when license: MetaClaw is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, MetaClaw is MIT. [MetaClaw](https://arxiv.org/abs/2603.17187) reports 3.5k GitHub stars, 445 forks, and 16 open issues, last pushed Jun 7, 2026. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [MetaClaw's repository](https://github.com/aiming-lab/MetaClaw) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [MetaClaw](/tools/aiming-lab-metaclaw.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | 🦞 Just talk to your agent — it learns and EVOLVES 🧬. | Course on building intelligent agents from scratch | | Stars | 3,466 | 65,432 | | Forks | 445 | 8,109 | | Open issues | 16 | 144 | | Language | Python | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | LLM Frameworks, Model Training, AI Agents | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MetaClaw](/tools/aiming-lab-metaclaw.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 34d | 0d | | Open issues (now) | 16 | 144 | | Full report | [trust report](/tools/aiming-lab-metaclaw/trust.md) | [trust report](/tools/datawhalechina-hello-agents/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. ## Choose when ### Choose MetaClaw if… - License: MetaClaw is MIT, hello-agents is Other. - Tags unique to MetaClaw: meta-learning, metaclaw, fine-tuning, lora. - Also covers Model Training. ### Choose hello-agents if… - License: hello-agents is Other, MetaClaw is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: 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 MetaClaw - 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. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## 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. ## Common questions ### What is the difference between MetaClaw and hello-agents? MetaClaw: 🦞 Just talk to your agent — it learns and EVOLVES 🧬.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose MetaClaw over hello-agents? Choose MetaClaw over hello-agents when License: MetaClaw is MIT, hello-agents is Other; Tags unique to MetaClaw: meta-learning, metaclaw, fine-tuning, lora; Also covers Model Training. ### When should I choose hello-agents over MetaClaw? Choose hello-agents over MetaClaw when License: hello-agents is Other, MetaClaw is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: 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 avoid MetaClaw? 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. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### 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. ### Is MetaClaw or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 3,466). Stars measure visibility, not whether either tool fits your constraints. ### Are MetaClaw and hello-agents open source? Yes - both are open-source projects on GitHub (MetaClaw: MIT, hello-agents: Other). ### Where can I find alternatives to MetaClaw or hello-agents? GraphCanon lists graph-backed alternatives at [MetaClaw alternatives](/tools/aiming-lab-metaclaw/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([MetaClaw markdown twin](/tools/aiming-lab-metaclaw/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/aiming-lab-metaclaw-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MetaClaw or hello-agents? MetaClaw: Steady. hello-agents: 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 MetaClaw and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MetaClaw trust report](/tools/aiming-lab-metaclaw/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aiming-lab-metaclaw`](/api/graphcanon/graph?tool=aiming-lab-metaclaw) - 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/_