--- title: "entroly vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/juyterman1000-entroly-vs-sindresorhus-awesome" tools: ["juyterman1000-entroly", "sindresorhus-awesome"] --- # entroly vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick entroly when license: entroly is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, entroly is Apache-2.0. [entroly](https://juyterman1000.github.io/entroly/docs/dashboard.html) reports 420 GitHub stars, 66 forks, and 2 open issues, last pushed Jul 11, 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 [entroly's repository](https://github.com/juyterman1000/entroly) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [entroly](/tools/juyterman1000-entroly.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | Local context-control plane for AI coding agents: select evidence, compress recoverably, keep caches hot, and verify answers. MCP/proxy/SDK for Cursor, Claude Code, Codex, and Aider. | 😎 Curated list of awesome topics including hardware resources | | Stars | 420 | 484,026 | | Forks | 66 | 35,799 | | Open issues | 2 | 92 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | LLM Frameworks, AI Agents, Computer Vision | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [entroly](/tools/juyterman1000-entroly.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Active (82%) | | Days since push | 0d | 11d | | Open issues (now) | 2 | 92 | | Security scan | 1 medium (1 medium) | No lockfile | | Full report | [trust report](/tools/juyterman1000-entroly/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose entroly if… - License: entroly is Apache-2.0, awesome is CC0-1.0. - Tags unique to entroly: ai-hallucination, ai, chatgpt, claude. - Also covers AI Agents, Computer Vision. - entroly ships Docker support for self-hosted deployment. ### Choose awesome if… - License: awesome is CC0-1.0, entroly is Apache-2.0. - Tags unique to awesome: resources, awesome-list. - More GitHub stars (484k vs 420) - visibility, not fit. ## When NOT to use entroly - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ## 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 entroly and awesome? entroly: Local context-control plane for AI coding agents: select evidence, compress recoverably, keep caches hot, and verify answers. MCP/proxy/SDK for Cursor, Claude Code, Codex, and Aider.. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose entroly over awesome? Choose entroly over awesome when License: entroly is Apache-2.0, awesome is CC0-1.0; Tags unique to entroly: ai-hallucination, ai, chatgpt, claude; Also covers AI Agents, Computer Vision; entroly ships Docker support for self-hosted deployment. ### When should I choose awesome over entroly? Choose awesome over entroly when License: awesome is CC0-1.0, entroly is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 420) - visibility, not fit. ### When should I avoid entroly? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. ### 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 entroly or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 420). Stars measure visibility, not whether either tool fits your constraints. ### Are entroly and awesome open source? Yes - both are open-source projects on GitHub (entroly: Apache-2.0, awesome: CC0-1.0). ### Where can I find alternatives to entroly or awesome? GraphCanon lists graph-backed alternatives at [entroly alternatives](/tools/juyterman1000-entroly/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([entroly markdown twin](/tools/juyterman1000-entroly/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/juyterman1000-entroly-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, entroly or awesome? entroly: 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 entroly and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [entroly trust report](/tools/juyterman1000-entroly/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=juyterman1000-entroly`](/api/graphcanon/graph?tool=juyterman1000-entroly) - 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/_