Comparison
promptsource vs Awesome-Prompt-Engineering
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.
Markdown twin · promptsource alternatives · Awesome-Prompt-Engineering alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | promptsource | Awesome-Prompt-Engineering |
|---|---|---|
| Maintenance | Dormant (1027d since push) As of 1w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- 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
Stars
- promptsource
- 3.0k
- Awesome-Prompt-Engineering
- 6.2k
Forks
- promptsource
- 375
- Awesome-Prompt-Engineering
- 734
Open issues
- promptsource
- 43
- Awesome-Prompt-Engineering
- 94
Language
- promptsource
- Python
- Awesome-Prompt-Engineering
- TypeScript
Adopt for
- promptsource
- PromptSource aids in creating, sharing, and using natural language prompts for large language models.
- Awesome-Prompt-Engineering
- Awesome-Prompt-Engineering curates resources tailored for GPT, ChatGPT, PaLM prompt engineering in TypeScript under Apache-2.0 license.
Persona
- promptsource
- -
- Awesome-Prompt-Engineering
- -
Runtime
- promptsource
- -
- Awesome-Prompt-Engineering
- -
License
- promptsource
- Apache-2.0
- Awesome-Prompt-Engineering
- Apache-2.0
Last pushed
- promptsource
- Oct 23, 2023
- Awesome-Prompt-Engineering
- Jul 27, 2026
Categories
- promptsource
- Developer Tools, Model Training
- Awesome-Prompt-Engineering
- Developer Tools, Model Training
Trust and health
Maintenance
- promptsource
- Dormant (18%)
- Awesome-Prompt-Engineering
- Very active (96%)
Days since push
- promptsource
- 1027d
- Awesome-Prompt-Engineering
- 0d
Open issues (now)
- promptsource
- 43
- Awesome-Prompt-Engineering
- 94
Stars delta
- promptsource
- +2 (30d)
- Awesome-Prompt-Engineering
- Unknown
Open issues delta
- promptsource
- -1 (30d)
- Awesome-Prompt-Engineering
- Unknown
Full report
- promptsource
- Trust report
- Awesome-Prompt-Engineering
- Trust report
Shared compatibility
- Python · promptsource: Python runtime · Awesome-Prompt-Engineering: Python runtime
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.
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.
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 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 (bigscience-workshop/promptsource) · observed Aug 15, 2026
- GitHub forks (bigscience-workshop/promptsource) · observed Aug 15, 2026
- Last push (bigscience-workshop/promptsource) · observed Oct 23, 2023
- License file (Apache-2.0) · 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: promptsource 3.0k · Awesome-Prompt-Engineering 6.2k (synced Aug 15, 2026).
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 and Awesome-Prompt-Engineering alternatives (promptsource 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, 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; Awesome-Prompt-Engineering trust report.