--- title: "Wax vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/christopherkarani-wax-vs-sindresorhus-awesome" tools: ["christopherkarani-wax", "sindresorhus-awesome"] --- # Wax vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick Wax when license: Wax is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, Wax is Apache-2.0. [Wax](https://christopherkarani.github.io/Wax/) reports 773 GitHub stars, 46 forks, and 0 open issues, last pushed Jul 6, 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 [Wax's repository](https://github.com/christopherkarani/Wax) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [Wax](/tools/christopherkarani-wax.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift | 😎 Curated list of awesome topics including hardware resources | | Stars | 773 | 484,026 | | Forks | 46 | 35,799 | | Open issues | 0 | 92 | | Language | Swift | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Vector Databases, AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Wax](/tools/christopherkarani-wax.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 4d | 11d | | Open issues (now) | 0 | 92 | | Security scan | No MCP manifest | No lockfile | | Full report | [trust report](/tools/christopherkarani-wax/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose Wax if… - License: Wax is Apache-2.0, awesome is CC0-1.0. - Tags unique to Wax: data-science, coreml-framework, mcp-server, machine-learning. - Also covers Vector Databases, AI Agents. ### Choose awesome if… - License: awesome is CC0-1.0, Wax is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 773) - visibility, not fit. ## When NOT to use Wax - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - 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. ## 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 Wax and awesome? Wax: Single-file memory layer for AI agents, sub mili-second RAG on Apple Silicon. Metal Optimized On-Device. No Server. No API. One File. Pure Swift. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose Wax over awesome? Choose Wax over awesome when License: Wax is Apache-2.0, awesome is CC0-1.0; Tags unique to Wax: data-science, coreml-framework, mcp-server, machine-learning; Also covers Vector Databases, AI Agents. ### When should I choose awesome over Wax? Choose awesome over Wax when License: awesome is CC0-1.0, Wax is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 773) - visibility, not fit. ### When should I avoid Wax? Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. 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. ### 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 Wax or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 773). Stars measure visibility, not whether either tool fits your constraints. ### Are Wax and awesome open source? Yes - both are open-source projects on GitHub (Wax: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to Wax or awesome? GraphCanon lists graph-backed alternatives at [Wax alternatives](/tools/christopherkarani-wax/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([Wax markdown twin](/tools/christopherkarani-wax/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/christopherkarani-wax-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Wax or awesome? Wax: 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 Wax and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Wax trust report](/tools/christopherkarani-wax/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=christopherkarani-wax`](/api/graphcanon/graph?tool=christopherkarani-wax) - 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/_