Comparison
AutoPrompt vs Open-Prompt-Injection
Verdict
Pick AutoPrompt if autoPrompt provides a Python-based framework for refining prompts using Intent-based Prompt Calibration; pick Open-Prompt-Injection if open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
Markdown twin · AutoPrompt alternatives · Open-Prompt-Injection alternatives
GraphCanon updated Oct 4, 2026
8views this month
vs
Trust & integrity
| Signal | AutoPrompt | Open-Prompt-Injection |
|---|---|---|
| Maintenance | Slowing (291d since push) As of Sep 20, 2026 · github_public_v1 | Very active (6d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- AutoPrompt
- Framework for prompt tuning using Intent-based Prompt Calibration
- Open-Prompt-Injection
- Benchmark and toolkit for prompt injection attacks and defenses in LLMs
Stars
- AutoPrompt
- 3.0k
- Open-Prompt-Injection
- 503
Forks
- AutoPrompt
- 265
- Open-Prompt-Injection
- 81
Open issues
- AutoPrompt
- 23
- Open-Prompt-Injection
- 2
Language
- AutoPrompt
- Python
- Open-Prompt-Injection
- Python
Adopt for
- AutoPrompt
- AutoPrompt provides a Python-based framework for refining prompts using Intent-based Prompt Calibration.
- Open-Prompt-Injection
- Open-Prompt-Injection is a Python-based toolkit for benchmarking prompt injection attacks on LLMs, offering customization through config files and support for various LLM APIs.
Persona
- AutoPrompt
- -
- Open-Prompt-Injection
- -
Runtime
- AutoPrompt
- -
- Open-Prompt-Injection
- -
License
- AutoPrompt
- Apache-2.0
- Open-Prompt-Injection
- MIT
Last pushed
- AutoPrompt
- Dec 2, 2025
- Open-Prompt-Injection
- Sep 27, 2026
Categories
- AutoPrompt
- Data & Retrieval, LLM Frameworks
- Open-Prompt-Injection
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- AutoPrompt
- Slowing (36%)
- Open-Prompt-Injection
- Very active (96%)
Days since push
- AutoPrompt
- 291d
- Open-Prompt-Injection
- 6d
Open issues (now)
- AutoPrompt
- 23
- Open-Prompt-Injection
- 2
Stars delta
- AutoPrompt
- +26 (30d)
- Open-Prompt-Injection
- +33 (30d)
Open issues delta
- AutoPrompt
- 0 (30d)
- Open-Prompt-Injection
- -12 (30d)
Full report
- AutoPrompt
- Trust report
- Open-Prompt-Injection
- Trust report
Choose AutoPrompt if…
- License: AutoPrompt is Apache-2.0, Open-Prompt-Injection is MIT.
- Tags unique to AutoPrompt: prompt-engineering, prompt-tuning, synthetic-dataset-generation.
- Also covers Data & Retrieval.
- When you need to calibrate prompts specifically for enhancing intent clarity within the target language model.
When NOT to use AutoPrompt
- Avoid using AutoPrompt if your project requires a framework that supports multiple programming languages beyond Python.
- If you do not require or prefer Intent-based Prompt Calibration for tuning, look elsewhere as this feature could be less appealing and flexible compared to alternative methods in competing tools.
Choose Open-Prompt-Injection if…
- License: Open-Prompt-Injection is MIT, AutoPrompt is Apache-2.0.
- Tags unique to Open-Prompt-Injection: llm, llm-security, prompt-injection, security-and-privacy.
- Also covers Evaluation & Observability.
- You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
When NOT to use Open-Prompt-Injection
- You require broader, more generalized security features not centered on prompt injection attacks.
- Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
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 Sep 20, 2026
- GitHub forks (Eladlev/AutoPrompt) · observed Sep 20, 2026
- Last push (Eladlev/AutoPrompt) · observed Dec 2, 2025
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (liu00222/Open-Prompt-Injection) · observed Oct 4, 2026
- GitHub forks (liu00222/Open-Prompt-Injection) · observed Oct 4, 2026
- Last push (liu00222/Open-Prompt-Injection) · observed Sep 27, 2026
- License file (MIT) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: AutoPrompt 3.0k · Open-Prompt-Injection 503 (synced Sep 20, 2026).
Common questions
- What is the difference between AutoPrompt and Open-Prompt-Injection?
- AutoPrompt: Framework for prompt tuning using Intent-based Prompt Calibration. Open-Prompt-Injection: Benchmark and toolkit for prompt injection attacks and defenses in LLMs. See the comparison table for live GitHub stats and shared categories.
- When should I choose AutoPrompt over Open-Prompt-Injection?
- Choose AutoPrompt over Open-Prompt-Injection when License: AutoPrompt is Apache-2.0, Open-Prompt-Injection is MIT; Tags unique to AutoPrompt: prompt-engineering, prompt-tuning, synthetic-dataset-generation; Also covers Data & Retrieval; When you need to calibrate prompts specifically for enhancing intent clarity within the target language model.
- When should I choose Open-Prompt-Injection over AutoPrompt?
- Choose Open-Prompt-Injection over AutoPrompt when License: Open-Prompt-Injection is MIT, AutoPrompt is Apache-2.0; Tags unique to Open-Prompt-Injection: llm, llm-security, prompt-injection, security-and-privacy; Also covers Evaluation & Observability; You prioritize security testing specifically for prompt injection vulnerabilities in your LLM applications.
- When should I avoid AutoPrompt?
- Avoid using AutoPrompt if your project requires a framework that supports multiple programming languages beyond Python. If you do not require or prefer Intent-based Prompt Calibration for tuning, look elsewhere as this feature could be less appealing and flexible compared to alternative methods in competing tools.
- When should I avoid Open-Prompt-Injection?
- You require broader, more generalized security features not centered on prompt injection attacks. Your project does not involve working with Google PaLM2 or other specific models like Meta's Llama and OpenAI's GPT.
- Is AutoPrompt or Open-Prompt-Injection more popular on GitHub?
- AutoPrompt has more GitHub stars (3,019 vs 503). Stars measure visibility, not whether either tool fits your constraints.
- Are AutoPrompt and Open-Prompt-Injection open source?
- Yes - both are open-source projects on GitHub (AutoPrompt: Apache-2.0, Open-Prompt-Injection: MIT).
- Where can I find alternatives to AutoPrompt or Open-Prompt-Injection?
- GraphCanon lists graph-backed alternatives at AutoPrompt alternatives and Open-Prompt-Injection alternatives (AutoPrompt markdown twin, Open-Prompt-Injection 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 Open-Prompt-Injection?
- AutoPrompt: Slowing. Open-Prompt-Injection: 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 AutoPrompt and Open-Prompt-Injection?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AutoPrompt trust report; Open-Prompt-Injection trust report.