--- title: "awesome vs AI-Infra-Guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sindresorhus-awesome-vs-tencent-ai-infra-guard" tools: ["sindresorhus-awesome", "tencent-ai-infra-guard"] --- # awesome vs AI-Infra-Guard *GraphCanon updated Jul 12, 2026* ## Verdict Pick awesome when license: awesome is CC0-1.0, AI-Infra-Guard is Apache-2.0; pick AI-Infra-Guard when license: AI-Infra-Guard is Apache-2.0, awesome is CC0-1.0. [awesome](https://github.com/sindresorhus/awesome) reports 484k GitHub stars, 36k forks, and 92 open issues, last pushed Jun 30, 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 [awesome's repository](https://github.com/sindresorhus/awesome) and [AI-Infra-Guard's repository](https://github.com/Tencent/AI-Infra-Guard). | | [awesome](/tools/sindresorhus-awesome.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Tagline | 😎 Curated list of awesome topics including hardware resources | 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 | 484,026 | 4,091 | | Forks | 35,799 | 394 | | Open issues | 92 | 19 | | Language | - | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | Apache-2.0 | | Categories | LLM Frameworks | AI Agents, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome](/tools/sindresorhus-awesome.md) | [AI-Infra-Guard](/tools/tencent-ai-infra-guard.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 3d | | Open issues (now) | 92 | 19 | | Owner type | User | Organization | | Full report | [trust report](/tools/sindresorhus-awesome/trust.md) | [trust report](/tools/tencent-ai-infra-guard/trust.md) | ## Choose when ### Choose awesome if… - License: awesome is CC0-1.0, AI-Infra-Guard is Apache-2.0. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 4.1k) - visibility, not fit. ### Choose AI-Infra-Guard if… - License: AI-Infra-Guard is Apache-2.0, awesome is CC0-1.0. - Tags unique to AI-Infra-Guard: agent, agent-security, ai-infra, ai-red-teaming. - Also covers AI Agents, Vector Databases. ## 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. ## 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 awesome and AI-Infra-Guard? awesome: 😎 Curated list of awesome topics including hardware resources. 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 awesome over AI-Infra-Guard? Choose awesome over AI-Infra-Guard when License: awesome is CC0-1.0, AI-Infra-Guard is Apache-2.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 4.1k) - visibility, not fit. ### When should I choose AI-Infra-Guard over awesome? Choose AI-Infra-Guard over awesome when License: AI-Infra-Guard is Apache-2.0, awesome is CC0-1.0; Tags unique to AI-Infra-Guard: agent, agent-security, ai-infra, ai-red-teaming; Also covers AI Agents, Vector Databases. ### 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. ### 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 awesome or AI-Infra-Guard more popular on GitHub? awesome has more GitHub stars (484,026 vs 4,091). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome and AI-Infra-Guard open source? Yes - both are open-source projects on GitHub (awesome: CC0-1.0, AI-Infra-Guard: Apache-2.0). ### Where can I find alternatives to awesome or AI-Infra-Guard? GraphCanon lists graph-backed alternatives at [awesome alternatives](/tools/sindresorhus-awesome/alternatives) and [AI-Infra-Guard alternatives](/tools/tencent-ai-infra-guard/alternatives) ([awesome markdown twin](/tools/sindresorhus-awesome/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/sindresorhus-awesome-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, awesome or AI-Infra-Guard? awesome: 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 awesome and AI-Infra-Guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome trust report](/tools/sindresorhus-awesome/trust); [AI-Infra-Guard trust report](/tools/tencent-ai-infra-guard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=sindresorhus-awesome`](/api/graphcanon/graph?tool=sindresorhus-awesome) - 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/_