--- title: "awesome-LLM-resources vs DS-1000" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/wangrongsheng-awesome-llm-resources-vs-xlang-ai-ds-1000" tools: ["wangrongsheng-awesome-llm-resources", "xlang-ai-ds-1000"] --- # awesome-LLM-resources vs DS-1000 *GraphCanon updated Oct 8, 2026* ## Verdict 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; pick DS-1000 if the DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) reports 9.0k GitHub stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. [DS-1000](https://ds1000-code-gen.github.io) has 278 stars, 31 forks, and 2 open issues, last pushed Oct 30, 2024. Figures are from public GitHub metadata via [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources) and [DS-1000's repository](https://github.com/xlang-ai/DS-1000). | | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Tagline | Summary of the world's best LLM resources. | Benchmark and code for evaluating large language models on data science tasks | | Stars | 8,968 | 278 | | Forks | 993 | 31 | | Open issues | 40 | 2 | | Language | - | Python | | 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. | The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. | | Persona | - | - | | Runtime | - | - | | License | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | CC-BY-SA-4.0 | | Categories | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | [DS-1000](/tools/xlang-ai-ds-1000.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 3d | 707d | | Open issues (now) | 40 | 2 | | Stars delta | +123 (30d) | +2 (30d) | | Open issues delta | +17 (30d) | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | [trust report](/tools/xlang-ai-ds-1000/trust.md) | ## 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. ## Decision facts: DS-1000 - **Adopt for:** The DS-1000 benchmark evaluates the code generation capabilities of large language models for data science tasks across Python libraries like Matplotlib, Numpy, Pandas, etc. ## Choose when ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, DS-1000 is CC-BY-SA-4.0. - 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, llama. - Also covers AI Agents, Computer Vision, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### Choose DS-1000 if… - License: DS-1000 is CC-BY-SA-4.0, awesome-LLM-resources is Apache-2.0. - Tags unique to DS-1000: benchmark, code-generation, data-science, semantic-parsing. - When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries. ## 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. ## When NOT to use DS-1000 - Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. - It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks. ## Common questions ### What is the difference between awesome-LLM-resources and DS-1000? awesome-LLM-resources: Summary of the world's best LLM resources.. DS-1000: Benchmark and code for evaluating large language models on data science tasks. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-LLM-resources over DS-1000? Choose awesome-LLM-resources over DS-1000 when License: awesome-LLM-resources is Apache-2.0, DS-1000 is CC-BY-SA-4.0; 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, llama; Also covers AI Agents, Computer Vision, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I choose DS-1000 over awesome-LLM-resources? Choose DS-1000 over awesome-LLM-resources when License: DS-1000 is CC-BY-SA-4.0, awesome-LLM-resources is Apache-2.0; Tags unique to DS-1000: benchmark, code-generation, data-science, semantic-parsing; When you want to assess how well a large language model can generate reliable and accurate code for data science projects involving popular Python libraries. ### 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. ### When should I avoid DS-1000? Avoid using DS-1000 if your project does not involve data science or if the models do not generate code in Python. It is unsuitable for evaluating text generation abilities unrelated to coding, such as natural language processing tasks. ### Is awesome-LLM-resources or DS-1000 more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 278). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-LLM-resources and DS-1000 open source? Yes - both are open-source projects on GitHub (awesome-LLM-resources: Apache-2.0, DS-1000: CC-BY-SA-4.0). ### Where can I find alternatives to awesome-LLM-resources or DS-1000? GraphCanon lists graph-backed alternatives at [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) and [DS-1000 alternatives](/tools/xlang-ai-ds-1000/alternatives) ([awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/alternatives.md), [DS-1000 markdown twin](/tools/xlang-ai-ds-1000/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/wangrongsheng-awesome-llm-resources-vs-xlang-ai-ds-1000.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-LLM-resources or DS-1000? awesome-LLM-resources: Very active. DS-1000: Dormant. 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 awesome-LLM-resources and DS-1000? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust); [DS-1000 trust report](/tools/xlang-ai-ds-1000/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=wangrongsheng-awesome-llm-resources`](/api/graphcanon/graph?tool=wangrongsheng-awesome-llm-resources) - 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/_