--- title: "llama-hub vs awesome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/run-llama-llama-hub-vs-sindresorhus-awesome" tools: ["run-llama-llama-hub", "sindresorhus-awesome"] --- # llama-hub vs awesome *GraphCanon updated Jul 12, 2026* ## Verdict Pick llama-hub when license: llama-hub is MIT, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, llama-hub is MIT. [llama-hub](https://llamahub.ai/) reports 3.5k GitHub stars, 719 forks, and 96 open issues, last pushed Mar 1, 2024. [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 [llama-hub's repository](https://github.com/run-llama/llama-hub) and [awesome's repository](https://github.com/sindresorhus/awesome). | | [llama-hub](/tools/run-llama-llama-hub.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Tagline | A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain | 😎 Curated list of awesome topics including hardware resources | | Stars | 3,473 | 484,026 | | Forks | 719 | 35,799 | | Open issues | 96 | 92 | | Language | Jupyter Notebook | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | MIT | CC0-1.0 | | Categories | Data & Retrieval, Evaluation & Observability, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [llama-hub](/tools/run-llama-llama-hub.md) | [awesome](/tools/sindresorhus-awesome.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Active (82%) | | Days since push | 861d | 11d | | Archived on GitHub | Yes | No | | Open issues (now) | 96 | 92 | | Owner type | Organization | User | | Security scan | 121 low (121 low) | No lockfile | | Full report | [trust report](/tools/run-llama-llama-hub/trust.md) | [trust report](/tools/sindresorhus-awesome/trust.md) | ## Choose when ### Choose llama-hub if… - License: llama-hub is MIT, awesome is CC0-1.0. - Tags unique to llama-hub: jupyter notebook. - Also covers Data & Retrieval, Evaluation & Observability. ### Choose awesome if… - License: awesome is CC0-1.0, llama-hub is MIT. - Tags unique to awesome: awesome-list, resources. - More GitHub stars (484k vs 3.5k) - visibility, not fit. ## When NOT to use llama-hub - llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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 llama-hub and awesome? llama-hub: A library of data loaders for LLMs made by the community -- to be used with LlamaIndex and/or LangChain. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose llama-hub over awesome? Choose llama-hub over awesome when License: llama-hub is MIT, awesome is CC0-1.0; Tags unique to llama-hub: jupyter notebook; Also covers Data & Retrieval, Evaluation & Observability. ### When should I choose awesome over llama-hub? Choose awesome over llama-hub when License: awesome is CC0-1.0, llama-hub is MIT; Tags unique to awesome: awesome-list, resources; More GitHub stars (484k vs 3.5k) - visibility, not fit. ### When should I avoid llama-hub? llama-hub is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. 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 llama-hub or awesome more popular on GitHub? awesome has more GitHub stars (484,026 vs 3,473). Stars measure visibility, not whether either tool fits your constraints. ### Are llama-hub and awesome open source? Yes - both are open-source projects on GitHub (llama-hub: MIT, awesome: CC0-1.0). ### Where can I find alternatives to llama-hub or awesome? GraphCanon lists graph-backed alternatives at [llama-hub alternatives](/tools/run-llama-llama-hub/alternatives) and [awesome alternatives](/tools/sindresorhus-awesome/alternatives) ([llama-hub markdown twin](/tools/run-llama-llama-hub/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/run-llama-llama-hub-vs-sindresorhus-awesome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, llama-hub or awesome? llama-hub: Archived. 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 llama-hub and awesome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [llama-hub trust report](/tools/run-llama-llama-hub/trust); [awesome trust report](/tools/sindresorhus-awesome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=run-llama-llama-hub`](/api/graphcanon/graph?tool=run-llama-llama-hub) - 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/_