--- title: "ThoughtSource vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openbiolink-thoughtsource-vs-promptslab-awesome-prompt-engineering" tools: ["openbiolink-thoughtsource", "promptslab-awesome-prompt-engineering"] --- # ThoughtSource vs Awesome-Prompt-Engineering *GraphCanon updated Aug 15, 2026* ## Verdict Pick ThoughtSource if thoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools; pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. [ThoughtSource](https://github.com/OpenBioLink/ThoughtSource) reports 1.0k GitHub stars, 81 forks, and 15 open issues, last pushed Dec 16, 2024. [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 [ThoughtSource's repository](https://github.com/OpenBioLink/ThoughtSource) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [ThoughtSource](/tools/openbiolink-thoughtsource.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | Central resource for data and tools related to chain-of-thought reasoning in LLMs | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | | Stars | 1,015 | 6,197 | | Forks | 81 | 734 | | Open issues | 15 | 94 | | Language | Jupyter Notebook | TypeScript | | Adopt for | ThoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools. | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | MIT License allows free use, modification, and distribution of the project's source code under its terms and conditions without any cost. | Apache-2.0 | | Categories | Model Training | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ThoughtSource](/tools/openbiolink-thoughtsource.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 606d | 0d | | Open issues (now) | 15 | 94 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/openbiolink-thoughtsource/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Shared compatibility - **Python**: [ThoughtSource](/tools/openbiolink-thoughtsource.md) - Python runtime; [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) - Python runtime ## Decision facts: ThoughtSource - **Adopt for:** ThoughtSource is a curated, open repository maintained by the Samwald research group for enhancing chain-of-thought reasoning in large language models through data and tools. - **License detail:** MIT License allows free use, modification, and distribution of the project's source code under its terms and conditions without any cost. ## 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 ThoughtSource if… - ThoughtSource is primarily Jupyter Notebook; Awesome-Prompt-Engineering is TypeScript. - License: ThoughtSource is MIT, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to ThoughtSource: dataset, natural-language-processing, question-answering, reasoning. - You need focused resources on chain-of-thought reasoning techniques. ### Choose Awesome-Prompt-Engineering if… - Awesome-Prompt-Engineering is primarily TypeScript; ThoughtSource is Jupyter Notebook. - License: Awesome-Prompt-Engineering is Apache-2.0, ThoughtSource is MIT. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt. - Also covers Developer Tools. - You need focused materials on GPT and related models for prompt engineering ## When NOT to use ThoughtSource - Looking for a comprehensive general-purpose AI development environment. - Prefer tools with multi-language support beyond Jupyter Notebooks. ## 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 ThoughtSource and Awesome-Prompt-Engineering? ThoughtSource: Central resource for data and tools related to chain-of-thought reasoning in LLMs. 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 ThoughtSource over Awesome-Prompt-Engineering? Choose ThoughtSource over Awesome-Prompt-Engineering when ThoughtSource is primarily Jupyter Notebook; Awesome-Prompt-Engineering is TypeScript; License: ThoughtSource is MIT, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to ThoughtSource: dataset, natural-language-processing, question-answering, reasoning; You need focused resources on chain-of-thought reasoning techniques. ### When should I choose Awesome-Prompt-Engineering over ThoughtSource? Choose Awesome-Prompt-Engineering over ThoughtSource when Awesome-Prompt-Engineering is primarily TypeScript; ThoughtSource is Jupyter Notebook; License: Awesome-Prompt-Engineering is Apache-2.0, ThoughtSource is MIT; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; Also covers Developer Tools; You need focused materials on GPT and related models for prompt engineering. ### When should I avoid ThoughtSource? Looking for a comprehensive general-purpose AI development environment. Prefer tools with multi-language support beyond Jupyter Notebooks. ### 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 ThoughtSource or Awesome-Prompt-Engineering more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,197 vs 1,015). Stars measure visibility, not whether either tool fits your constraints. ### Are ThoughtSource and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (ThoughtSource: MIT, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to ThoughtSource or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [ThoughtSource alternatives](/tools/openbiolink-thoughtsource/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([ThoughtSource markdown twin](/tools/openbiolink-thoughtsource/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/openbiolink-thoughtsource-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, ThoughtSource or Awesome-Prompt-Engineering? ThoughtSource: 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 ThoughtSource and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ThoughtSource trust report](/tools/openbiolink-thoughtsource/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openbiolink-thoughtsource`](/api/graphcanon/graph?tool=openbiolink-thoughtsource) - 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/_