--- title: "core vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/cheshire-cat-ai-core-vs-sindresorhus-awesome" tools: ["cheshire-cat-ai-core", "sindresorhus-awesome"] --- # core vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick core when license: core is GPL-3.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, core is GPL-3.0. [core](https://cheshirecat.ai) reports 3.1k GitHub stars, 410 forks, and 4 open issues, last pushed Jul 8, 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 [core's repository](https://github.com/cheshire-cat-ai/core) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [core](/tools/cheshire-cat-ai-core.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | AI agent microservice | 😎 Curated list of awesome topics including hardware resources | | Stars | 3,072 | 484,026 | | Forks | 410 | 35,799 | | Open issues | 4 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | GPL-3.0 | CC0-1.0 | | Categories | AI Agents, LLM Frameworks, Vector Databases | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [core](/tools/cheshire-cat-ai-core.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 2d | 11d | | Open issues (now) | 4 | 92 | | Owner type | Organization | User | | Security scan | 2 low (2 low) | No lockfile | | Full report | [trust report](/tools/cheshire-cat-ai-core/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose core if… - License: core is GPL-3.0, awesome is CC0-1.0. - Tags unique to core: ag-ui-protocol, agent, ai, assistant. - Also covers AI Agents, Vector Databases. ### Choose awesome if… - License: awesome is CC0-1.0, core is GPL-3.0. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 3.1k) - visibility, not fit. ## When NOT to use core - 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 core and awesome? core: AI agent microservice. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose core over awesome? Choose core over awesome when License: core is GPL-3.0, awesome is CC0-1.0; Tags unique to core: ag-ui-protocol, agent, ai, assistant; Also covers AI Agents, Vector Databases. ### When should I choose awesome over core? Choose awesome over core when License: awesome is CC0-1.0, core is GPL-3.0; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 3.1k) - visibility, not fit. ### When should I avoid core? 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 core or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 3,072). Stars measure visibility, not whether either tool fits your constraints. ### Are core and awesome open source? Yes - both are open-source projects on GitHub (core: GPL-3.0, awesome: CC0-1.0). ### Where can I find alternatives to core or awesome? GraphCanon lists graph-backed alternatives at [core alternatives](/tools/cheshire-cat-ai-core/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([core markdown twin](/tools/cheshire-cat-ai-core/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/cheshire-cat-ai-core-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, core or awesome? core: 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 core and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [core trust report](/tools/cheshire-cat-ai-core/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=cheshire-cat-ai-core`](/api/graphcanon/graph?tool=cheshire-cat-ai-core) - 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/_