--- title: "promptsource vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-promptsource-vs-wangrongsheng-awesome-llm-resources" tools: ["bigscience-workshop-promptsource", "wangrongsheng-awesome-llm-resources"] --- # promptsource vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick promptsource if promptSource aids in creating, sharing, and using natural language prompts for large language models; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [promptsource](https://github.com/bigscience-workshop/promptsource) reports 3.0k GitHub stars, 375 forks, and 43 open issues, last pushed Oct 23, 2023. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [promptsource's repository](https://github.com/bigscience-workshop/promptsource) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Toolkit for creating, sharing and using natural language prompts | Summary of the world's best LLM resources. | | Stars | 3,029 | 8,845 | | Forks | 375 | 950 | | Open issues | 43 | 23 | | Language | Python | - | | Adopt for | PromptSource aids in creating, sharing, and using natural language prompts for large language models. | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Developer Tools, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1027d | 2d | | Open issues (now) | 43 | 23 | | Stars delta | +2 (30d) | +142 (30d) | | Open issues delta | -1 (30d) | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/bigscience-workshop-promptsource/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: promptsource - **Adopt for:** PromptSource aids in creating, sharing, and using natural language prompts for large language models. ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose promptsource if… - Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning. - When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use promptsource - Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. - Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between promptsource and awesome-LLM-resources? promptsource: Toolkit for creating, sharing and using natural language prompts. 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 promptsource over awesome-LLM-resources? Choose promptsource over awesome-LLM-resources when Tags unique to promptsource: few-shot, fine-tuning, language-models, machine-learning; When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### When should I choose awesome-LLM-resources over promptsource? Choose awesome-LLM-resources over promptsource when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid promptsource? Avoid if you require complex prompt customization beyond what simple templating can offer, as PromptSource is not designed for intricate configurations. Not suitable for users focused on real-time interaction with prompts, since it lacks dynamic features for immediate adjustments. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is promptsource or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 3,029). Stars measure visibility, not whether either tool fits your constraints. ### Are promptsource and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (promptsource: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to promptsource or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [promptsource alternatives](/tools/bigscience-workshop-promptsource/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([promptsource markdown twin](/tools/bigscience-workshop-promptsource/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/bigscience-workshop-promptsource-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, promptsource or awesome-LLM-resources? promptsource: 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 promptsource and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [promptsource trust report](/tools/bigscience-workshop-promptsource/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigscience-workshop-promptsource`](/api/graphcanon/graph?tool=bigscience-workshop-promptsource) - 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/_