--- title: "DistiLlama vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/shreyaskarnik-distillama-vs-wangrongsheng-awesome-llm-resources" tools: ["shreyaskarnik-distillama", "wangrongsheng-awesome-llm-resources"] --- # DistiLlama vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick DistiLlama if distiLlama is a Chrome extension for summarizing and chatting with web pages and local documents using locally running language models to ensure data privacy; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [DistiLlama](https://github.com/shreyaskarnik/DistiLlama) reports 304 GitHub stars, 32 forks, and 9 open issues, last pushed Sep 2, 2024. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [DistiLlama's repository](https://github.com/shreyaskarnik/DistiLlama) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [DistiLlama](/tools/shreyaskarnik-distillama.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Chrome Extension for Summarizing and Chatting with Web Pages/Local Docs Using Local LLMs | Summary of the world's best LLM resources. | | Stars | 304 | 8,968 | | Forks | 32 | 993 | | Open issues | 9 | 40 | | Language | TypeScript | - | | Adopt for | DistiLlama is a Chrome extension for summarizing and chatting with web pages and local documents using locally running language models to ensure data privacy. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | MIT | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | AI Agents, LLM Frameworks | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DistiLlama](/tools/shreyaskarnik-distillama.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 747d | 3d | | Open issues (now) | 9 | 40 | | Stars delta | 0 (30d) | +123 (30d) | | Open issues delta | 0 (30d) | +17 (30d) | | Full report | [trust report](/tools/shreyaskarnik-distillama/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: DistiLlama - **Pricing:** freemium - DistiLlama is free and open source software under the MIT license, however, users are responsible for maintaining and running their own local language models. - **Adopt for:** DistiLlama is a Chrome extension for summarizing and chatting with web pages and local documents using locally running language models to ensure data privacy. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose DistiLlama if… - License: DistiLlama is MIT, awesome-LLM-resources is Apache-2.0. - Pricing: DistiLlama is free and open source software under the MIT license, however, users are responsible for maintaining and running their own local language models.. - Tags unique to DistiLlama: chrome-extension, langchain, llama2, local-llm. - When you prioritize data privacy and prefer not to send sensitive information over the internet for processing. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, DistiLlama is MIT. - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use DistiLlama - If your setup does not support the execution of local language models, as this is a prerequisite for using DistiLlama effectively. - When you require real-time interaction or access to cloud-based resources for more dynamic content generation since local LLMs may be limited by hardware performance. ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between DistiLlama and awesome-LLM-resources? DistiLlama: Chrome Extension for Summarizing and Chatting with Web Pages/Local Docs Using Local LLMs. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DistiLlama over awesome-LLM-resources? Choose DistiLlama over awesome-LLM-resources when License: DistiLlama is MIT, awesome-LLM-resources is Apache-2.0; Pricing: DistiLlama is free and open source software under the MIT license, however, users are responsible for maintaining and running their own local language models.; Tags unique to DistiLlama: chrome-extension, langchain, llama2, local-llm; When you prioritize data privacy and prefer not to send sensitive information over the internet for processing. ### When should I choose awesome-LLM-resources over DistiLlama? Choose awesome-LLM-resources over DistiLlama when License: awesome-LLM-resources is Apache-2.0, DistiLlama is MIT; Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid DistiLlama? If your setup does not support the execution of local language models, as this is a prerequisite for using DistiLlama effectively. When you require real-time interaction or access to cloud-based resources for more dynamic content generation since local LLMs may be limited by hardware performance. ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is DistiLlama or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 304). Stars measure visibility, not whether either tool fits your constraints. ### Are DistiLlama and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (DistiLlama: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to DistiLlama or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [DistiLlama alternatives](/tools/shreyaskarnik-distillama/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([DistiLlama markdown twin](/tools/shreyaskarnik-distillama/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/shreyaskarnik-distillama-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DistiLlama or awesome-LLM-resources? DistiLlama: Dormant. awesome-LLM-resources: 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 DistiLlama and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DistiLlama trust report](/tools/shreyaskarnik-distillama/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=shreyaskarnik-distillama`](/api/graphcanon/graph?tool=shreyaskarnik-distillama) - 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/_