--- title: "hello-agents vs dingo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-migoxlab-dingo" tools: ["datawhalechina-hello-agents", "migoxlab-dingo"] --- # hello-agents vs dingo *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 dingo if dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [dingo](https://dingo.openxlab.org.cn/) has 722 stars, 74 forks, and 4 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 [dingo's repository](https://github.com/MigoXLab/dingo). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [dingo](/tools/migoxlab-dingo.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool | | Stars | 65,432 | 722 | | Forks | 8,109 | 74 | | Open issues | 144 | 4 | | 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. | Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. | | Categories | AI Agents, LLM Frameworks | Data & Retrieval, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [dingo](/tools/migoxlab-dingo.md) | | --- | --- | --- | | Open issues (now) | 144 | 4 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/migoxlab-dingo/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: dingo - **Pricing:** freemium - The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost. - **Adopt for:** Dingo includes a unique focus on multi-agent debate patterns ('Agent-as-a-Judge') for bias reduction and complex reasoning in evaluation tasks. - **License detail:** Licensed under the Apache-2.0 license, it includes fasttext functionality for language detection, which itself is licensed under the MIT License. ## Choose when ### Choose hello-agents if… - License: hello-agents is Other, dingo is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, rag, tutorial. - Also covers AI Agents, 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 dingo if… - License: dingo is Apache-2.0, hello-agents is Other. - Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.. - Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection. - Also covers Data & Retrieval, Evaluation & Observability. - When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ## 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 dingo - If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. - In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ## Common questions ### What is the difference between hello-agents and dingo? hello-agents: Course on building intelligent agents from scratch. dingo: Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over dingo? Choose hello-agents over dingo when License: hello-agents is Other, dingo is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, rag, tutorial; Also covers AI Agents, 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 dingo over hello-agents? Choose dingo over hello-agents when License: dingo is Apache-2.0, hello-agents is Other; Pricing: The tool currently offers free open-source options under an Apache 2.0 license with plans for future SaaS platform services that may come at a cost.; Tags unique to dingo: agent-as-a-judge, data-evaluation, data-quality, hallucination-detection; Also covers Data & Retrieval, Evaluation & Observability; When evaluating the quality of data, models, or applications that require insights from multiple perspectives to detect nuances such as bias or hallucination. ### 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 dingo? If your project does not benefit from a multi-agent approach for evaluation, and simpler single-model approaches suffice. In scenarios where immediate feedback is critical but Dingo's planned SaaS platform with API access and dashboard support are still under development. ### Is hello-agents or dingo more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 722). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and dingo open source? Yes - both are open-source projects on GitHub (hello-agents: Other, dingo: Apache-2.0). ### Where can I find alternatives to hello-agents or dingo? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [dingo alternatives](/tools/migoxlab-dingo/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [dingo markdown twin](/tools/migoxlab-dingo/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-migoxlab-dingo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or dingo? hello-agents: Very active. dingo: 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 dingo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [dingo trust report](/tools/migoxlab-dingo/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/_