Comparison
AutoPrompt vs awesome
Verdict
Pick AutoPrompt when license: AutoPrompt is Apache-2.0, awesome is CC0-1.0; pick awesome when license: awesome is CC0-1.0, AutoPrompt is Apache-2.0.
Markdown twin · AutoPrompt alternatives · awesome alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | AutoPrompt | awesome |
|---|---|---|
| Maintenance | Slowing (220d since push) As of today · github_public_v1 | Active (11d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of today · github_public_v1 | Not a fork · Personal account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- AutoPrompt
- A framework for prompt tuning using Intent-based Prompt Calibration
- awesome
- 😎 Curated list of awesome topics including hardware resources
Stars
- AutoPrompt
- 3.0k
- awesome
- 484k
Forks
- AutoPrompt
- 263
- awesome
- 36k
Open issues
- AutoPrompt
- 23
- awesome
- 92
Language
- AutoPrompt
- Python
- awesome
- -
Adopt for
- AutoPrompt
- -
- awesome
- -
Persona
- AutoPrompt
- -
- awesome
- -
Runtime
- AutoPrompt
- -
- awesome
- -
License
- AutoPrompt
- Apache-2.0
- awesome
- CC0-1.0
Last pushed
- AutoPrompt
- Dec 2, 2025
- awesome
- Jun 30, 2026
Categories
- AutoPrompt
- LLM Frameworks
- awesome
- LLM Frameworks
Trust and health
Maintenance
- AutoPrompt
- Slowing (36%)
- awesome
- Active (82%)
Days since push
- AutoPrompt
- 220d
- awesome
- 11d
Open issues (now)
- AutoPrompt
- 23
- awesome
- 92
Full report
- AutoPrompt
- Trust report
- awesome
- Trust report
Choose AutoPrompt if…
- License: AutoPrompt is Apache-2.0, awesome is CC0-1.0.
- Tags unique to AutoPrompt: synthetic-dataset-generation, python, prompt-tuning, prompt-engineering.
- Leaner open-issue backlog (23).
When NOT to use AutoPrompt
- Last GitHub push was 221 days ago (slowing maintenance, Dec 2, 2025). Validate activity before betting a new project on AutoPrompt.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Choose awesome if…
- License: awesome is CC0-1.0, AutoPrompt is Apache-2.0.
- Tags unique to awesome: resources, awesome-list.
- More GitHub stars (484k vs 3.0k) - visibility, not fit.
When NOT to use awesome
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Eladlev/AutoPrompt) · observed Jul 11, 2026
- GitHub forks (Eladlev/AutoPrompt) · observed Jul 11, 2026
- Last push (Eladlev/AutoPrompt) · observed Dec 2, 2025
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (sindresorhus/awesome) · observed Jul 11, 2026
- GitHub forks (sindresorhus/awesome) · observed Jul 11, 2026
- Last push (sindresorhus/awesome) · observed Jun 30, 2026
- License file (CC0-1.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AutoPrompt 3.0k · awesome 484k (synced Jul 11, 2026).
Common questions
- What is the difference between AutoPrompt and awesome?
- AutoPrompt: A framework for prompt tuning using Intent-based Prompt Calibration. awesome: 😎 Curated list of awesome topics including hardware resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose AutoPrompt over awesome?
- Choose AutoPrompt over awesome when License: AutoPrompt is Apache-2.0, awesome is CC0-1.0; Tags unique to AutoPrompt: synthetic-dataset-generation, python, prompt-tuning, prompt-engineering; Leaner open-issue backlog (23).
- When should I choose awesome over AutoPrompt?
- Choose awesome over AutoPrompt when License: awesome is CC0-1.0, AutoPrompt is Apache-2.0; Tags unique to awesome: resources, awesome-list; More GitHub stars (484k vs 3.0k) - visibility, not fit.
- When should I avoid AutoPrompt?
- Last GitHub push was 221 days ago (slowing maintenance, Dec 2, 2025). Validate activity before betting a new project on AutoPrompt. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- When should I avoid awesome?
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is AutoPrompt or awesome more popular on GitHub?
- awesome has more GitHub stars (484,026 vs 2,988). Stars measure visibility, not whether either tool fits your constraints.
- Are AutoPrompt and awesome open source?
- Yes - both are open-source projects on GitHub (AutoPrompt: Apache-2.0, awesome: CC0-1.0).
- Where can I find alternatives to AutoPrompt or awesome?
- GraphCanon lists graph-backed alternatives at AutoPrompt alternatives and awesome alternatives (AutoPrompt markdown twin, awesome 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, AutoPrompt or awesome?
- AutoPrompt: Slowing. awesome: 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 AutoPrompt and awesome?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AutoPrompt trust report; awesome trust report.