Comparison
examor vs Awesome-Prompt-Engineering
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.
Markdown twin · examor alternatives · Awesome-Prompt-Engineering alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | examor | Awesome-Prompt-Engineering |
|---|---|---|
| Maintenance | Dormant (422d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- examor
- LLMs assist in learning for students, scholars, interviewees
- Awesome-Prompt-Engineering
- Hand-curated resources for Prompt Engineering focusing on Generative Pre-trained Transformers
Stars
- examor
- 1.1k
- Awesome-Prompt-Engineering
- 6.2k
Forks
- examor
- 64
- Awesome-Prompt-Engineering
- 734
Open issues
- examor
- 2
- Awesome-Prompt-Engineering
- 94
Language
- examor
- TypeScript
- Awesome-Prompt-Engineering
- TypeScript
Adopt for
- examor
- Examor uses LLMs such as Claude2 and GPT-4 within an app framework inspired by Ebbinghaus memory theories.
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
Persona
- examor
- -
- Awesome-Prompt-Engineering
- -
Runtime
- examor
- -
- Awesome-Prompt-Engineering
- -
License
- examor
- AGPL-3.0
- Awesome-Prompt-Engineering
- Apache-2.0
Last pushed
- examor
- Jun 18, 2025
- Awesome-Prompt-Engineering
- Jul 27, 2026
Categories
- examor
- Developer Tools, Evaluation & Observability
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
Trust and health
Maintenance
- examor
- Dormant (18%)
- Awesome-Prompt-Engineering
- Very active (96%)
Days since push
- examor
- 422d
- Awesome-Prompt-Engineering
- 0d
Open issues (now)
- examor
- 2
- Awesome-Prompt-Engineering
- 94
Stars delta
- examor
- +2 (30d)
- Awesome-Prompt-Engineering
- Unknown
Open issues delta
- examor
- 0 (30d)
- Awesome-Prompt-Engineering
- Unknown
Owner type
- examor
- User
- Awesome-Prompt-Engineering
- Organization
Full report
- examor
- Trust report
- Awesome-Prompt-Engineering
- Trust report
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.
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.
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 Awesome-Prompt-Engineering
- The project requires languages other than TypeScript
- Resource is about areas outside of GPT, ChatGPT, PaLM prompt engineering
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (codeacme17/examor) · observed Aug 15, 2026
- GitHub forks (codeacme17/examor) · observed Aug 15, 2026
- Last push (codeacme17/examor) · observed Jun 18, 2025
- License file (AGPL-3.0) · observed Aug 15, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- GitHub forks (promptslab/Awesome-Prompt-Engineering) · observed Jul 28, 2026
- Last push (promptslab/Awesome-Prompt-Engineering) · observed Jul 27, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: examor 1.1k · Awesome-Prompt-Engineering 6.2k (synced Aug 15, 2026).
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 and Awesome-Prompt-Engineering alternatives (examor markdown twin, Awesome-Prompt-Engineering markdown twin), 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 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; Awesome-Prompt-Engineering trust report.