--- title: "pentest-ai vs hello-agents" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xsteph-pentest-ai-vs-datawhalechina-hello-agents" tools: ["0xsteph-pentest-ai", "datawhalechina-hello-agents"] --- # pentest-ai vs hello-agents *GraphCanon updated Jul 11, 2026* ## Verdict Pick pentest-ai when license: pentest-ai is MIT, hello-agents is Other; pick hello-agents when license: hello-agents is Other, pentest-ai is MIT. [pentest-ai](https://pentestai.xyz) reports 1.3k GitHub stars, 249 forks, and 2 open issues, last pushed Jul 5, 2026. [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 [pentest-ai's repository](https://github.com/0xSteph/pentest-ai) and [hello-agents's repository](https://github.com/datawhalechina/hello-agents). | | [pentest-ai](/tools/0xsteph-pentest-ai.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Tagline | Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path. | Course on building intelligent agents from scratch | | Stars | 1,269 | 65,432 | | Forks | 249 | 8,109 | | Open issues | 2 | 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 | LLM Frameworks, AI Agents, Vector Databases | LLM Frameworks, AI Agents | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pentest-ai](/tools/0xsteph-pentest-ai.md) | [hello-agents](/tools/datawhalechina-hello-agents.md) | | --- | --- | --- | | Days since push | 6d | 0d | | Open issues (now) | 2 | 144 | | Owner type | User | Organization | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/0xsteph-pentest-ai/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 pentest-ai if… - License: pentest-ai is MIT, hello-agents is Other. - Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools. - Also covers Vector Databases. ### Choose hello-agents if… - License: hello-agents is Other, pentest-ai is MIT. - Requirements: Min 4 GB RAM; Python knowledge assumed. - Tags unique to hello-agents: llm, rag, tutorial, agent. - 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 pentest-ai - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## 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 pentest-ai and hello-agents? pentest-ai: Offensive-security MCP server with 205 wrapped tools, 17 specialist agents, and 60 SPA-aware probes for OWASP Top 10. CLI + MCP, BYO LLM. No API key needed on MCP path.. hello-agents: Course on building intelligent agents from scratch. See the comparison table for live GitHub stats and shared categories. ### When should I choose pentest-ai over hello-agents? Choose pentest-ai over hello-agents when License: pentest-ai is MIT, hello-agents is Other; Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools; Also covers Vector Databases. ### When should I choose hello-agents over pentest-ai? Choose hello-agents over pentest-ai when License: hello-agents is Other, pentest-ai is MIT; Requirements: Min 4 GB RAM; Python knowledge assumed; Tags unique to hello-agents: llm, rag, tutorial, agent; 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 pentest-ai? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### 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 pentest-ai or hello-agents more popular on GitHub? hello-agents has more GitHub stars (65,432 vs 1,269). Stars measure visibility, not whether either tool fits your constraints. ### Are pentest-ai and hello-agents open source? Yes - both are open-source projects on GitHub (pentest-ai: MIT, hello-agents: Other). ### Where can I find alternatives to pentest-ai or hello-agents? GraphCanon lists graph-backed alternatives at [pentest-ai alternatives](/tools/0xsteph-pentest-ai/alternatives) and [hello-agents alternatives](/tools/datawhalechina-hello-agents/alternatives) ([pentest-ai markdown twin](/tools/0xsteph-pentest-ai/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/0xsteph-pentest-ai-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, pentest-ai or hello-agents? pentest-ai: Very active. 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 pentest-ai and hello-agents? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pentest-ai trust report](/tools/0xsteph-pentest-ai/trust); [hello-agents trust report](/tools/datawhalechina-hello-agents/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=0xsteph-pentest-ai`](/api/graphcanon/graph?tool=0xsteph-pentest-ai) - 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/_