--- title: "examor vs Awesome-Prompt-Engineering" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/codeacme17-examor-vs-promptslab-awesome-prompt-engineering" tools: ["codeacme17-examor", "promptslab-awesome-prompt-engineering"] --- # examor vs Awesome-Prompt-Engineering *GraphCanon updated Aug 15, 2026* ## Verdict Pick examor if examor uses LLMs such as Claude2 and GPT-4 within an app framework inspired by Ebbinghaus memory theories; pick Awesome-Prompt-Engineering if awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. [examor](https://github.com/codeacme17/examor) reports 1.1k GitHub stars, 64 forks, and 2 open issues, last pushed Jun 18, 2025. [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 [examor's repository](https://github.com/codeacme17/examor) and [Awesome-Prompt-Engineering's repository](https://github.com/promptslab/Awesome-Prompt-Engineering). | | [examor](/tools/codeacme17-examor.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Tagline | LLMs assist in learning for students, scholars, interviewees | Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers | | Stars | 1,070 | 6,197 | | Forks | 64 | 734 | | Open issues | 2 | 94 | | Language | TypeScript | TypeScript | | Adopt for | Examor uses LLMs such as Claude2 and GPT-4 within an app framework inspired by Ebbinghaus memory theories. | Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 | Apache-2.0 | | Categories | Developer Tools, Evaluation & Observability | Developer Tools, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [examor](/tools/codeacme17-examor.md) | [Awesome-Prompt-Engineering](/tools/promptslab-awesome-prompt-engineering.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 422d | 0d | | Open issues (now) | 2 | 94 | | Stars delta | +2 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/codeacme17-examor/trust.md) | [trust report](/tools/promptslab-awesome-prompt-engineering/trust.md) | ## Decision facts: examor - **Adopt for:** Examor uses LLMs such as Claude2 and GPT-4 within an app framework inspired by Ebbinghaus memory theories. ## 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 examor if… - License: examor is AGPL-3.0, Awesome-Prompt-Engineering is Apache-2.0. - Tags unique to examor: azure, claude2, ebbinghaus-memory, gpt-4. - Also covers Evaluation & Observability. - When aiming to optimize learning with artificial memory retention strategies for students, scholars, or interview preparation. ### Choose Awesome-Prompt-Engineering if… - License: Awesome-Prompt-Engineering is Apache-2.0, examor is AGPL-3.0. - Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt. - Also covers Model Training. - You need focused materials on GPT and related models for prompt engineering ## When NOT to use examor - If you require direct integration with non-supported platforms like AWS Bedrock or Anthropic models not including Claude2. - When looking for a more generalized tool without specific learning and memory application features, such as pure code debugging assistance. ## 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 examor and Awesome-Prompt-Engineering? examor: LLMs assist in learning for students, scholars, interviewees. 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 examor over Awesome-Prompt-Engineering? Choose examor over Awesome-Prompt-Engineering when License: examor is AGPL-3.0, Awesome-Prompt-Engineering is Apache-2.0; Tags unique to examor: azure, claude2, ebbinghaus-memory, gpt-4; Also covers Evaluation & Observability; When aiming to optimize learning with artificial memory retention strategies for students, scholars, or interview preparation. ### When should I choose Awesome-Prompt-Engineering over examor? Choose Awesome-Prompt-Engineering over examor when License: Awesome-Prompt-Engineering is Apache-2.0, examor is AGPL-3.0; Tags unique to Awesome-Prompt-Engineering: chatgpt, deep-learning, few-shot-learning, gpt; Also covers Model Training; You need focused materials on GPT and related models for prompt engineering. ### When should I avoid examor? If you require direct integration with non-supported platforms like AWS Bedrock or Anthropic models not including Claude2. When looking for a more generalized tool without specific learning and memory application features, such as pure code debugging assistance. ### 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 examor or Awesome-Prompt-Engineering more popular on GitHub? Awesome-Prompt-Engineering has more GitHub stars (6,197 vs 1,070). Stars measure visibility, not whether either tool fits your constraints. ### Are examor and Awesome-Prompt-Engineering open source? Yes - both are open-source projects on GitHub (examor: AGPL-3.0, Awesome-Prompt-Engineering: Apache-2.0). ### Where can I find alternatives to examor or Awesome-Prompt-Engineering? GraphCanon lists graph-backed alternatives at [examor alternatives](/tools/codeacme17-examor/alternatives) and [Awesome-Prompt-Engineering alternatives](/tools/promptslab-awesome-prompt-engineering/alternatives) ([examor markdown twin](/tools/codeacme17-examor/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/codeacme17-examor-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, examor or Awesome-Prompt-Engineering? examor: 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 examor and Awesome-Prompt-Engineering? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [examor trust report](/tools/codeacme17-examor/trust); [Awesome-Prompt-Engineering trust report](/tools/promptslab-awesome-prompt-engineering/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=codeacme17-examor`](/api/graphcanon/graph?tool=codeacme17-examor) - 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/_