--- title: "promptsource vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigscience-workshop-promptsource-vs-promptslab-awesome-prompt-engineering" tools: ["bigscience-workshop-promptsource", "promptslab-awesome-prompt-engineering"] --- # promptsource vs Awesome-Prompt-Engineering *GraphCanon updated Aug 15, 2026* ## Verdict Pick promptsource if promptSource aids in creating, sharing, and using natural language prompts for large language models; pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. [promptsource](https://github.com/bigscience-workshop/promptsource) reports 3.0k GitHub stars, 375 forks, and 43 open issues, last pushed Oct 23, 2023. [Awesome-Prompt-Engineering](https://discord.gg/m88xfYMbK6) has 6.2k stars, 734 forks, and 94 open issues, last pushed Jul 27, 2026. Figures are from public GitHub metadata via [promptsource's repository](https://github.com/bigscience-workshop/promptsource) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | Toolkit for creating, sharing and using natural language prompts | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | | Stars | 3,029 | 6,197 | | Forks | 375 | 734 | | Open issues | 43 | 94 | | Language | Python | TypeScript | | Adopt for | PromptSource aids in creating, sharing, and using natural language prompts for large language models. | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Developer Tools, Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [promptsource](/tools/bigscience-workshop-promptsource.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1027d | 0d | | Open issues (now) | 43 | 94 | | Stars delta | +2 (30d) | Unknown | | Open issues delta | -1 (30d) | Unknown | | Full report | [trust report](/tools/bigscience-workshop-promptsource/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Shared compatibility - **Python**: [promptsource](/tools/bigscience-workshop-promptsource.md) - Python runtime; [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) - Python runtime ## Decision facts: promptsource - **Adopt for:** PromptSource aids in creating, sharing, and using natural language prompts for large language models. ## Decision facts: Awesome-Prompt-Engineering - **Adopt for:** Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. ## Choose when ### Choose promptsource if… - promptsource is primarily Python; Awesome-Prompt-Engineering is TypeScript. - Tags unique to promptsource: few-shot, fine-tuning, language-models, natural-language-processing. - When you need to create reusable prompts for multiple datasets with a focus on simplicity through a templating language called Jinja. ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; promptsource is Python. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt. - You need focused materials on GPT and related models for prompt engineering ## 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-Prompt-Engineering - The project requires languages other than TypeScript - Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ## Common questions ### What is the difference between promptsource and Awesome-Prompt-Engineering? promptsource: Toolkit for creating, sharing and using natural language prompts. Awesome-Prompt-Engineering: Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers. See the comparison table for live GitHub stats and shared categories. ### When should I choose promptsource over Awesome-Prompt-Engineering? Choose promptsource over Awesome-Prompt-Engineering when promptsource is primarily Python; Awesome-Prompt-Engineering is TypeScript; Tags unique to promptsource: few-shot, fine-tuning, language-models, natural-language-processing; 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-Prompt-Engineering over promptsource? Choose Awesome-Prompt-Engineering over promptsource when Awesome-Prompt-Engineering is primarily TypeScript; promptsource is Python; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; You need focused materials on GPT and related models for prompt engineering. ### 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-Prompt-Engineering? The project requires languages other than TypeScript Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering ### Is promptsource or Awesome-Prompt-Engineering more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,197 vs 3,029). Stars measure visibility, not whether either tool fits your constraints. ### Are promptsource and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (promptsource: Apache-2.0, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to promptsource or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [promptsource alternatives](/tools/bigscience-workshop-promptsource/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([promptsource markdown twin](/tools/bigscience-workshop-promptsource/alternatives.md), [Awesome-Prompt-Engineering markdown twin](/tools/promptslab-awesome-prompt-engineering/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-promptslab-awesome-prompt-engineering.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, promptsource or Awesome-Prompt-Engineering? promptsource: Dormant. Awesome-Prompt-Engineering: 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-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [promptsource trust report](/tools/bigscience-workshop-promptsource/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/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/_