--- title: "pentest-ai vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/0xsteph-pentest-ai-vs-sindresorhus-awesome" tools: ["0xsteph-pentest-ai", "sindresorhus-awesome"] --- # pentest-ai vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick pentest-ai when license: pentest-ai is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, 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. [awesome](https://github.com/sindresorhus/awesome) has 484k stars, 36k forks, and 92 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [pentest-ai's repository](https://github.com/0xSteph/pentest-ai) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [pentest-ai](/tools/0xsteph-pentest-ai.md) | [awesome](/tools/sindresorhus-awesome.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. | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,269 | 484,026 | | Forks | 249 | 35,799 | | Open issues | 2 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | LLM Frameworks, AI Agents, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [pentest-ai](/tools/0xsteph-pentest-ai.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 6d | 11d | | Open issues (now) | 2 | 92 | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/0xsteph-pentest-ai/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose pentest-ai if… - License: pentest-ai is MIT, awesome is CC0-1.0. - Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools. - Also covers AI Agents, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, pentest-ai is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 1.3k) - visibility, not fit. ## 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 awesome - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## Common questions ### What is the difference between pentest-ai and awesome? 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.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose pentest-ai over awesome? Choose pentest-ai over awesome when License: pentest-ai is MIT, awesome is CC0-1.0; Tags unique to pentest-ai: cybersecurity, exploit-chaining, ctf, hacking-tools; Also covers AI Agents, Vector Databases. ### When should I choose awesome over pentest-ai? Choose awesome over pentest-ai when License: awesome is CC0-1.0, pentest-ai is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.3k) - visibility, not fit. ### 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 awesome? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### Is pentest-ai or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,269). Stars measure visibility, not whether either tool fits your constraints. ### Are pentest-ai and awesome open source? Yes - both are open-source projects on GitHub (pentest-ai: MIT, awesome: CC0-1.0). ### Where can I find alternatives to pentest-ai or awesome? GraphCanon lists graph-backed alternatives at [pentest-ai alternatives](/tools/0xsteph-pentest-ai/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([pentest-ai markdown twin](/tools/0xsteph-pentest-ai/alternatives.md), [awesome markdown twin](/tools/sindresorhus-awesome/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-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, pentest-ai or awesome? pentest-ai: Very active. awesome: 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 awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [pentest-ai trust report](/tools/0xsteph-pentest-ai/trust); [awesome trust report](/tools/sindresorhus-awesome/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/_