--- title: "hello-agents vs continuum" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-shyftlabs-continuum" tools: ["datawhalechina-hello-agents", "shyftlabs-continuum"] --- # hello-agents vs continuum *GraphCanon updated Jul 15, 2026* ## Verdict Pick hello-agents when license: hello-agents is Other, continuum is Apache-2.0; pick continuum when license: continuum is Apache-2.0, hello-agents is Other. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [continuum](https://docs.continuum.shyftlabs.io/) has 75 stars, 8 forks, and 16 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [continuum's repository](https://github.com/shyftlabs/continuum). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [continuum](/tools/shyftlabs-continuum.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Continuum, the agent runtime by ShyftLabs. Build, orchestrate, ship. | | Stars | 65,432 | 75 | | Forks | 8,109 | 8 | | Open issues | 144 | 16 | | 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 | 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, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [continuum](/tools/shyftlabs-continuum.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 144 | 16 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/shyftlabs-continuum/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… - License: hello-agents is Other, continuum is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, 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 continuum if… - License: continuum is Apache-2.0, hello-agents is Other. - Tags unique to continuum: agent-framework, agentic-ai, ai-agents, ai-orchestration. - Also covers Vector Databases. - continuum ships Docker support for self-hosted deployment. ## 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 continuum - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between hello-agents and continuum? hello-agents: Course on building intelligent agents from scratch. continuum: Continuum, the agent runtime by ShyftLabs. Build, orchestrate, ship.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over continuum? Choose hello-agents over continuum when License: hello-agents is Other, continuum is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, 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 continuum over hello-agents? Choose continuum over hello-agents when License: continuum is Apache-2.0, hello-agents is Other; Tags unique to continuum: agent-framework, agentic-ai, ai-agents, ai-orchestration; Also covers Vector Databases; continuum ships Docker support for self-hosted deployment. ### 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 continuum? 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is hello-agents or continuum more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 75). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and continuum open source? Yes - both are open-source projects on GitHub (hello-agents: Other, continuum: Apache-2.0). ### Where can I find alternatives to hello-agents or continuum? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [continuum alternatives](/tools/shyftlabs-continuum/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [continuum markdown twin](/tools/shyftlabs-continuum/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-shyftlabs-continuum.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or continuum? hello-agents: Very active. continuum: 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 continuum? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [continuum trust report](/tools/shyftlabs-continuum/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/_