--- title: "agentic_security vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/msoedov-agentic-security-vs-sindresorhus-awesome" tools: ["msoedov-agentic-security", "sindresorhus-awesome"] --- # agentic_security vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick agentic_security when license: agentic_security is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, agentic_security is Apache-2.0. [agentic_security](https://agentic-security.vercel.app) reports 1.9k GitHub stars, 267 forks, and 70 open issues, last pushed Jun 23, 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 [agentic_security's repository](https://github.com/msoedov/agentic_security) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [agentic_security](/tools/msoedov-agentic-security.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Agentic LLM Vulnerability Scanner / AI red teaming kit 🧪 | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,923 | 484,026 | | Forks | 267 | 35,799 | | Open issues | 70 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentic_security](/tools/msoedov-agentic-security.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Days since push | 18d | 11d | | Open issues (now) | 70 | 92 | | Full report | [trust report](/tools/msoedov-agentic-security/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose agentic_security if… - License: agentic_security is Apache-2.0, awesome is CC0-1.0. - Tags unique to agentic_security: agent-framework, agent-security, ai-red-team, llm-evaluation. - Also covers AI Agents, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, agentic_security is Apache-2.0. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 1.9k) - visibility, not fit. ## When NOT to use agentic_security - 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. ## 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 agentic_security and awesome? agentic_security: Agentic LLM Vulnerability Scanner / AI red teaming kit 🧪. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentic_security over awesome? Choose agentic_security over awesome when License: agentic_security is Apache-2.0, awesome is CC0-1.0; Tags unique to agentic_security: agent-framework, agent-security, ai-red-team, llm-evaluation; Also covers AI Agents, Vector Databases. ### When should I choose awesome over agentic_security? Choose awesome over agentic_security when License: awesome is CC0-1.0, agentic_security is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 1.9k) - visibility, not fit. ### When should I avoid agentic_security? 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. ### 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 agentic_security or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,923). Stars measure visibility, not whether either tool fits your constraints. ### Are agentic_security and awesome open source? Yes - both are open-source projects on GitHub (agentic_security: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to agentic_security or awesome? GraphCanon lists graph-backed alternatives at [agentic_security alternatives](/tools/msoedov-agentic-security/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([agentic_security markdown twin](/tools/msoedov-agentic-security/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/msoedov-agentic-security-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentic_security or awesome? agentic_security: 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 agentic_security and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentic_security trust report](/tools/msoedov-agentic-security/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=msoedov-agentic-security`](/api/graphcanon/graph?tool=msoedov-agentic-security) - 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/_