--- title: "hello-agents vs OpenSwarm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-unohee-openswarm" tools: ["datawhalechina-hello-agents", "unohee-openswarm"] --- # hello-agents vs OpenSwarm *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 OpenSwarm if autonomous AI team orchestrator integrating Discord and Linear; vector database for memory enhancement. [hello-agents](https://hello-agents.datawhale.cc) reports 65k GitHub stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. [OpenSwarm](https://github.com/unohee/OpenSwarm) has 817 stars, 141 forks, and 0 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [OpenSwarm's repository](https://github.com/unohee/OpenSwarm). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [OpenSwarm](/tools/unohee-openswarm.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | Autonomous AI dev team orchestrator powered by Claude Code CLI. | | Stars | 65,432 | 817 | | Forks | 8,109 | 141 | | Open issues | 144 | 0 | | Language | Python | TypeScript | | Adopt for | hello-agents is a comprehensive guide and hands-on tutorial for developing AI agents using LLMs (Large Language Models) and RAG methods. | Autonomous AI team orchestrator integrating Discord and Linear; vector database for memory enhancement. | | Persona | - | - | | Runtime | - | - | | License | hello-agents is covered under an unconventional license which may require further review before usage. | MIT | | Categories | AI Agents, LLM Frameworks | AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [OpenSwarm](/tools/unohee-openswarm.md) | | --- | --- | --- | | Open issues (now) | 144 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/unohee-openswarm/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: OpenSwarm - **Adopt for:** Autonomous AI team orchestrator integrating Discord and Linear; vector database for memory enhancement. ## Choose when ### Choose hello-agents if… - hello-agents is primarily Python; OpenSwarm is TypeScript. - License: hello-agents is Other, OpenSwarm is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: agent, llm, 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 OpenSwarm if… - OpenSwarm is primarily TypeScript; hello-agents is Python. - License: OpenSwarm is MIT, hello-agents is Other. - Tags unique to OpenSwarm: ai-agents, autonomous-agents, claude, claude-code. - Also covers Vector Databases. - OpenSwarm ships Docker support for self-hosted deployment. - OpenSwarm ships an MCP server manifest. - When you need an autonomous tool that leverages Claude Code CLI to organize your development tasks. ## 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 OpenSwarm - Avoid if you are not currently utilizing Discord and Linear within your workflow since the integrations might not provide value without these tools. - Do not use if cognitive memory via vector databases is unnecessary or unpreferred for task management in development teams. ## Common questions ### What is the difference between hello-agents and OpenSwarm? hello-agents: Course on building intelligent agents from scratch. OpenSwarm: Autonomous AI dev team orchestrator powered by Claude Code CLI.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over OpenSwarm? Choose hello-agents over OpenSwarm when hello-agents is primarily Python; OpenSwarm is TypeScript; License: hello-agents is Other, OpenSwarm is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: agent, llm, 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 OpenSwarm over hello-agents? Choose OpenSwarm over hello-agents when OpenSwarm is primarily TypeScript; hello-agents is Python; License: OpenSwarm is MIT, hello-agents is Other; Tags unique to OpenSwarm: ai-agents, autonomous-agents, claude, claude-code; Also covers Vector Databases; OpenSwarm ships Docker support for self-hosted deployment; OpenSwarm ships an MCP server manifest; When you need an autonomous tool that leverages Claude Code CLI to organize your development tasks. ### 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 OpenSwarm? Avoid if you are not currently utilizing Discord and Linear within your workflow since the integrations might not provide value without these tools. Do not use if cognitive memory via vector databases is unnecessary or unpreferred for task management in development teams. ### Is hello-agents or OpenSwarm more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 817). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and OpenSwarm open source? Yes - both are open-source projects on GitHub (hello-agents: Other, OpenSwarm: MIT). ### Where can I find alternatives to hello-agents or OpenSwarm? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [OpenSwarm alternatives](/tools/unohee-openswarm/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [OpenSwarm markdown twin](/tools/unohee-openswarm/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-unohee-openswarm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or OpenSwarm? hello-agents: Very active. OpenSwarm: 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 OpenSwarm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [OpenSwarm trust report](/tools/unohee-openswarm/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/_