--- title: "hello-agents vs AI-Infra-Guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/datawhalechina-hello-agents-vs-tencent-ai-infra-guard" tools: ["datawhalechina-hello-agents", "tencent-ai-infra-guard"] --- # hello-agents vs AI-Infra-Guard *GraphCanon updated Jul 11, 2026* ## Verdict Pick hello-agents when license: hello-agents is Other, AI-Infra-Guard is Apache-2.0; pick AI-Infra-Guard when license: AI-Infra-Guard 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. [AI-Infra-Guard](https://tencent.github.io/AI-Infra-Guard/) has 4.1k stars, 394 forks, and 19 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [hello-agents's repository](https://github.com/datawhalechina/hello-agents) and [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard). | | [hello-agents](/tools/datawhalechina-hello-agents.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Tagline | Course on building intelligent agents from scratch | A full-stack AI Red Teaming platform securing AI ecosystems via OpenClaw Security Scan, Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation. | | Stars | 65,432 | 4,091 | | Forks | 8,109 | 394 | | Open issues | 144 | 19 | | 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) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Days since push | 0d | 3d | | Open issues (now) | 144 | 19 | | Full report | [trust report](/tools/datawhalechina-hello-agents/trust.md) | [trust report](/tools/tencent-ai-infra-guard/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, AI-Infra-Guard is Apache-2.0. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: 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 AI-Infra-Guard if… - License: AI-Infra-Guard is Apache-2.0, hello-agents is Other. - Tags unique to AI-Infra-Guard: agent-security, ai-infra, ai-red-teaming, ai-security. - Also covers Vector Databases. ## 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 AI-Infra-Guard - 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 AI-Infra-Guard? hello-agents: Course on building intelligent agents from scratch. AI-Infra-Guard: A full-stack AI Red Teaming platform securing AI ecosystems via OpenClaw Security Scan, Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hello-agents over AI-Infra-Guard? Choose hello-agents over AI-Infra-Guard when License: hello-agents is Other, AI-Infra-Guard is Apache-2.0; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: 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 AI-Infra-Guard over hello-agents? Choose AI-Infra-Guard over hello-agents when License: AI-Infra-Guard is Apache-2.0, hello-agents is Other; Tags unique to AI-Infra-Guard: agent-security, ai-infra, ai-red-teaming, ai-security; Also covers Vector Databases. ### 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 AI-Infra-Guard? 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 AI-Infra-Guard more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 4,091). Stars measure visibility, not whether either tool fits your constraints. ### Are hello-agents and AI-Infra-Guard open source? Yes - both are open-source projects on GitHub (hello-agents: Other, AI-Infra-Guard: Apache-2.0). ### Where can I find alternatives to hello-agents or AI-Infra-Guard? GraphCanon lists graph-backed alternatives at [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) and [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) ([hello-agents markdown twin](/tools/datawhalechina-hello-agents/alternatives.md), [AI-Infra-Guard markdown twin](/tools/tencent-ai-infra-guard/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-tencent-ai-infra-guard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hello-agents or AI-Infra-Guard? hello-agents: Very active. AI-Infra-Guard: 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 AI-Infra-Guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hello-agents trust report](/tools/datawhalechina-hello-agents/trust); [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/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/_