--- title: "hello-agents vs LazyLLM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-lazyagi-lazyllm" tools: ["datawhalechina-hello-agents", "lazyagi-lazyllm"] --- # hello-agents vs LazyLLM *GraphCanon updated Jul 12, 2026* ## 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 LazyLLM if critical facts for LazyLLM. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [LazyLLM](https://docs.lazyllm.ai/) has 3.9k stars, 396 forks, and 46 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [LazyLLM's repository](https://github.com/LazyAGI/LazyLLM). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LazyLLM](/tools/lazyagi-lazyllm.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Easiest and laziest way for building multi-agent LLMs applications. | | Stars | 65,432 | 3,856 | | Forks | 8,109 | 396 | | Open issues | 144 | 46 | | 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. | Critical facts for LazyLLM | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Apache-2.0 | | Categories | AI Agents, LLM Frameworks | AI Agents, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [LazyLLM](/tools/lazyagi-lazyllm.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 144 | 46 | | Security scan | No lockfile | 31 low (31 low) | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/lazyagi-lazyllm/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. ## Decision facts: LazyLLM - **Pricing:** freemium - LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects. - **Requirements:** Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary. - **Adopt for:** Critical facts for LazyLLM ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, LazyLLM is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, rag, tutorial. - Also covers 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. ### Choose LazyLLM if… - License: LazyLLM is Apache-2.0, hello-agents is Other. - Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects.. - Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary.. - Tags unique to LazyLLM: agents, ai-agent, deep-learning, framework. - Also covers Model Training. - - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ## 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. ## When NOT to use LazyLLM - - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools. - - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable. ## Common questions ### What is the difference between hello-agents and LazyLLM? hello-agents: Course on building intelligent agents from scratch. LazyLLM: Easiest and laziest way for building multi-agent LLMs applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over LazyLLM? Choose hello-agents over LazyLLM when License: hello-agents is Other, LazyLLM is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, rag, tutorial; Also covers 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 LazyLLM over hello-agents? Choose LazyLLM over hello-agents when License: LazyLLM is Apache-2.0, hello-agents is Other; Pricing: LazyLLM is open-source under the Apache-2.0 license, making it free to use for both personal and commercial projects.; Requirements: Min 8 GB RAM; Installation can be done via pip or from source. No Docker required, but a Python environment is necessary.; Tags unique to LazyLLM: agents, ai-agent, deep-learning, framework; Also covers Model Training; - When you need a highly user-friendly framework specifically designed for building multi-agent LLM applications, emphasizing simplicity and streamlined installation. ### 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 LazyLLM? - Avoid if you require extensive customization options or a more complex framework; LazyLLM's focus on being the 'laziest' way may mean it lacks advanced or specialized features found in other tools. - If you are working with non-Python environments, as LazyLLM is specifically language-oriented towards Python. Users needing cross-language support might not find LazyLLM suitable. ### Is hello-agents or LazyLLM more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 3,856). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and LazyLLM open source? Yes - both are open-source projects on GitHub (hello-agents: Other, LazyLLM: Apache-2.0). ### Where can I find alternatives to hello-agents or LazyLLM? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [LazyLLM alternatives](/tools/lazyagi-lazyllm/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [LazyLLM markdown twin](/tools/lazyagi-lazyllm/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/datawhalechina-hello-agents-vs-lazyagi-lazyllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or LazyLLM? hello-agents: Very active. LazyLLM: 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 LazyLLM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [LazyLLM trust report](/tools/lazyagi-lazyllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=datawhalechina-hello-agents`](/api/graphcanon/graph?tool=datawhalechina-hello-agents) - 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/_