--- title: "hello-agents vs aqueduct" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-runllm-aqueduct" tools: ["datawhalechina-hello-agents", "runllm-aqueduct"] --- # hello-agents vs aqueduct *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when hello-agents is primarily Python; aqueduct is Go; pick aqueduct when aqueduct is primarily Go; hello-agents is Python. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [aqueduct](https://aqueducthq.com) has 517 stars, 20 forks, and 11 open issues, last pushed Jun 7, 2023. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [aqueduct's repository](https://github.com/RunLLM/aqueduct). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure. | | Stars | 65,432 | 517 | | Forks | 8,109 | 20 | | Open issues | 144 | 11 | | Language | Python | Go | | 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 | 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, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [aqueduct](/tools/runllm-aqueduct.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 1130d | | Open issues (now) | 144 | 11 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/runllm-aqueduct/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 hello-agents if… - hello-agents is primarily Python; aqueduct is Go. - License: hello-agents is Other, aqueduct is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, 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. ### Choose aqueduct if… - aqueduct is primarily Go; hello-agents is Python. - License: aqueduct is Apache-2.0, hello-agents is Other. - Tags unique to aqueduct: ai, data, data-science, kubernetes. - Also covers Model Training. ## 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 aqueduct - Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. - 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. ## Common questions ### What is the difference between hello-agents and aqueduct? hello-agents: Course on building intelligent agents from scratch. aqueduct: Aqueduct is no longer being maintained. Aqueduct allows you to run LLM and ML workloads on any cloud infrastructure.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over aqueduct? Choose hello-agents over aqueduct when hello-agents is primarily Python; aqueduct is Go; License: hello-agents is Other, aqueduct is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, 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 aqueduct over hello-agents? Choose aqueduct over hello-agents when aqueduct is primarily Go; hello-agents is Python; License: aqueduct is Apache-2.0, hello-agents is Other; Tags unique to aqueduct: ai, data, data-science, kubernetes; 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 aqueduct? Last GitHub push was 1130 days ago (dormant maintenance, Jun 7, 2023). Validate activity before betting a new project on aqueduct. 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 aqueduct more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 517). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and aqueduct open source? Yes - both are open-source projects on GitHub (hello-agents: Other, aqueduct: Apache-2.0). ### Where can I find alternatives to hello-agents or aqueduct? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [aqueduct alternatives](/tools/runllm-aqueduct/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [aqueduct markdown twin](/tools/runllm-aqueduct/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-runllm-aqueduct.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or aqueduct? hello-agents: Very active. aqueduct: Dormant. 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 aqueduct? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [aqueduct trust report](/tools/runllm-aqueduct/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/_