--- title: "awesome vs WeaveBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/sindresorhus-awesome-vs-weavebench-weavebench" tools: ["sindresorhus-awesome", "weavebench-weavebench"] --- # awesome vs WeaveBench *GraphCanon updated Jul 12, 2026* ## Verdict Pick awesome when license: awesome is CC0-1.0, WeaveBench is MIT; pick WeaveBench when license: WeaveBench is MIT, 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. [WeaveBench](https://weavebench.github.io) has 151 stars, 0 forks, and 3 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [awesome's repository](https://github.com/sindresorhus/awesome) and [WeaveBench's repository](https://github.com/weavebench/WeaveBench). | | [awesome](/tools/sindresorhus-awesome.md) | [WeaveBench](/tools/weavebench-weavebench.md) | | --- | --- | --- | | Tagline | 😎 Curated list of awesome topics including hardware resources | WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces | | Stars | 484,026 | 151 | | Forks | 35,799 | 0 | | Open issues | 92 | 3 | | Language | - | Python | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | MIT | | Categories | LLM Frameworks | Vector Databases, AI Agents, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome](/tools/sindresorhus-awesome.md) | [WeaveBench](/tools/weavebench-weavebench.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 11d | 3d | | Open issues (now) | 92 | 3 | | Owner type | User | Organization | | Security scan | No lockfile | 16 low (16 low) | | Full report | [trust report](/tools/sindresorhus-awesome/trust.md) | [trust report](/tools/weavebench-weavebench/trust.md) | ## Choose when ### Choose awesome if… - License: awesome is CC0-1.0, WeaveBench is MIT. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 151) - visibility, not fit. ### Choose WeaveBench if… - License: WeaveBench is MIT, awesome is CC0-1.0. - Tags unique to WeaveBench: computer-use-agent, benchmark, python, agent-as-judge. - Also covers Vector Databases, AI Agents. ## 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 WeaveBench - 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. ## Common questions ### What is the difference between awesome and WeaveBench? awesome: 😎 Curated list of awesome topics including hardware resources. WeaveBench: WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome over WeaveBench? Choose awesome over WeaveBench when License: awesome is CC0-1.0, WeaveBench is MIT; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 151) - visibility, not fit. ### When should I choose WeaveBench over awesome? Choose WeaveBench over awesome when License: WeaveBench is MIT, awesome is CC0-1.0; Tags unique to WeaveBench: computer-use-agent, benchmark, python, agent-as-judge; Also covers Vector Databases, AI Agents. ### 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 WeaveBench? 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. ### Is awesome or WeaveBench more popular on GitHub? awesome has more GitHub stars (484,026 vs 151). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome and WeaveBench open source? Yes - both are open-source projects on GitHub (awesome: CC0-1.0, WeaveBench: MIT). ### Where can I find alternatives to awesome or WeaveBench? GraphCanon lists graph-backed alternatives at [awesome alternatives](/tools/sindresorhus-awesome/alternatives) and [WeaveBench alternatives](/tools/weavebench-weavebench/alternatives) ([awesome markdown twin](/tools/sindresorhus-awesome/alternatives.md), [WeaveBench markdown twin](/tools/weavebench-weavebench/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-weavebench-weavebench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome or WeaveBench? awesome: Active. WeaveBench: 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 WeaveBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome trust report](/tools/sindresorhus-awesome/trust); [WeaveBench trust report](/tools/weavebench-weavebench/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/_