--- title: "auto-evaluator vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rlancemartin-auto-evaluator-vs-sindresorhus-awesome" tools: ["rlancemartin-auto-evaluator", "sindresorhus-awesome"] --- # auto-evaluator vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick auto-evaluator when tags unique to auto-evaluator: python; pick awesome when tags unique to awesome: resources, awesome-list. [auto-evaluator](https://autoevaluator.langchain.com/) reports 1.1k GitHub stars, 92 forks, and 3 open issues, last pushed May 10, 2023. [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 [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Evaluation tool for LLM QA chains | 😎 Curated list of awesome topics including hardware resources | | Stars | 1,102 | 484,026 | | Forks | 92 | 35,799 | | Open issues | 3 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | - | CC0-1.0 | | Categories | LLM Frameworks, Data & Retrieval, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1158d | 11d | | Open issues (now) | 3 | 92 | | Security scan | 118 low (118 low) | No lockfile | | Full report | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose auto-evaluator if… - Tags unique to auto-evaluator: python. - Also covers Data & Retrieval, Vector Databases. - Leaner open-issue backlog (3). ### Choose awesome if… - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 1.1k) - visibility, not fit. ## When NOT to use auto-evaluator - Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - 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 auto-evaluator and awesome? auto-evaluator: Evaluation tool for LLM QA chains. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-evaluator over awesome? Choose auto-evaluator over awesome when Tags unique to auto-evaluator: python; Also covers Data & Retrieval, Vector Databases; Leaner open-issue backlog (3). ### When should I choose awesome over auto-evaluator? Choose awesome over auto-evaluator when Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 1.1k) - visibility, not fit. ### When should I avoid auto-evaluator? Last GitHub push was 1159 days ago (dormant maintenance, May 10, 2023). Validate activity before betting a new project on auto-evaluator. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. 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 auto-evaluator or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 1,102). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-evaluator and awesome open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to auto-evaluator or awesome? GraphCanon lists graph-backed alternatives at [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/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/rlancemartin-auto-evaluator-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-evaluator or awesome? auto-evaluator: Dormant. 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 auto-evaluator and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rlancemartin-auto-evaluator`](/api/graphcanon/graph?tool=rlancemartin-auto-evaluator) - 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/_