--- title: "agent-protocol vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-inc-agent-protocol-vs-datawhalechina-hello-agents" tools: ["agi-inc-agent-protocol", "datawhalechina-hello-agents"] --- # agent-protocol vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick agent-protocol when license: agent-protocol is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, agent-protocol is MIT. [agent-protocol](https://agentprotocol.ai) reports 1.5k GitHub stars, 185 forks, and 47 open issues, last pushed Apr 8, 2025. [hello-agents](https://hello-agents.datawhale.cc) has 65k stars, 8.1k forks, and 144 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [agent-protocol's repository](https://github.com/agi-inc/agent-protocol) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [agent-protocol](/tools/agi-inc-agent-protocol.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | Common interface for interacting with AI agents. The protocol is tech stack agnostic - you can use it with any framework for building agents. | Course on building intelligent agents from scratch | | Stars | 1,462 | 65,432 | | Forks | 185 | 8,109 | | Open issues | 47 | 144 | | 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 | MIT | hello-agents is covered under an unconventional license which may require further review before usage. | | Categories | AI Agents, Inference & Serving, LLM Frameworks | AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agent-protocol](/tools/agi-inc-agent-protocol.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 459d | 0d | | Open issues (now) | 47 | 144 | | Security scan | 35 low (35 low) | No lockfile | | Full report | [trust report](/tools/agi-inc-agent-protocol/trust.md) | [trust report](/tools/datawhalechina-hello-agents/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 agent-protocol if… - License: agent-protocol is MIT, hello-agents is Other. - Tags unique to agent-protocol: agents, ai, ai-agent, api. - Also covers Inference & Serving. ### Choose hello-agents if… - License: hello-agents is Other, agent-protocol is MIT. - 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 NOT to use agent-protocol - Last GitHub push was 460 days ago (dormant maintenance, Apr 8, 2025). Validate activity before betting a new project on agent-protocol. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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. ## Common questions ### What is the difference between agent-protocol and hello-agents? agent-protocol: Common interface for interacting with AI agents. The protocol is tech stack agnostic - you can use it with any framework for building agents.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose agent-protocol over hello-agents? Choose agent-protocol over hello-agents when License: agent-protocol is MIT, hello-agents is Other; Tags unique to agent-protocol: agents, ai, ai-agent, api; Also covers Inference & Serving. ### When should I choose hello-agents over agent-protocol? Choose hello-agents over agent-protocol when License: hello-agents is Other, agent-protocol is MIT; 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 avoid agent-protocol? Last GitHub push was 460 days ago (dormant maintenance, Apr 8, 2025). Validate activity before betting a new project on agent-protocol. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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. ### Is agent-protocol or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 1,462). Stars measure visibility, not whether either tool fits your constraints. ### Are agent-protocol and hello-agents open source? Yes - both are open-source projects on GitHub (agent-protocol: MIT, hello-agents: Other). ### Where can I find alternatives to agent-protocol or hello-agents? GraphCanon lists graph-backed alternatives at [agent-protocol alternatives](/tools/agi-inc-agent-protocol/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([agent-protocol markdown twin](/tools/agi-inc-agent-protocol/alternatives.md), [hello-agents markdown twin](/tools/datawhalechina-hello-agents/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/agi-inc-agent-protocol-vs-datawhalechina-hello-agents.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agent-protocol or hello-agents? agent-protocol: Dormant. hello-agents: 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 agent-protocol and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agent-protocol trust report](/tools/agi-inc-agent-protocol/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-inc-agent-protocol`](/api/graphcanon/graph?tool=agi-inc-agent-protocol) - 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/_