Comparison
trap vs promptfoo
Verdict
Pick trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques; pick promptfoo if promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support.
Markdown twin · trap alternatives · promptfoo alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | trap | promptfoo |
|---|---|---|
| Maintenance | Dormant (622d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- trap
- TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
- promptfoo
- Tool for evaluating prompts and AI agents by comparing performance across various models and red teaming.
Stars
- trap
- 15
- promptfoo
- 24k
Forks
- trap
- 1
- promptfoo
- 2.1k
Open issues
- trap
- 0
- promptfoo
- 481
Language
- trap
- Jupyter Notebook
- promptfoo
- TypeScript
Adopt for
- trap
- TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques.
- promptfoo
- promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support.
Persona
- trap
- -
- promptfoo
- -
Runtime
- trap
- -
- promptfoo
- -
License
- trap
- MIT License ensures permissive use and modification of TRAP under its terms.
- promptfoo
- MIT
Last pushed
- trap
- Nov 20, 2024
- promptfoo
- Aug 1, 2026
Categories
- trap
- Evaluation & Observability, LLM Frameworks
- promptfoo
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- trap
- Dormant (18%)
- promptfoo
- Very active (96%)
Days since push
- trap
- 622d
- promptfoo
- 0d
Open issues (now)
- trap
- 0
- promptfoo
- 481
OSV dependency advisories
- trap
- Published findings
- promptfoo
- No lockfile (source not queried)
Full report
- trap
- Trust report
- promptfoo
- Trust report
Shared compatibility
- Python · trap: Python runtime · promptfoo: Python runtime
Choose trap if…
- trap is primarily Jupyter Notebook; promptfoo is TypeScript.
- Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in `detect_llm/configs`..
- Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, large language models.
- When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings.
When NOT to use trap
- If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability.
- When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.
Choose promptfoo if…
- promptfoo is primarily TypeScript; trap is Jupyter Notebook.
- Tags unique to promptfoo: ci-cd, evaluation-framework, llm-evaluation, pentesting.
- promptfoo ships Docker support for self-hosted deployment.
- For comparing performance across GPT, Claude, Gemini, DeepSeek
When NOT to use promptfoo
- If you do not require comparative analysis among multiple LLM models
- If your project does not benefit from the specific red teaming capabilities offered by promptfoo
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (parameterlab/trap) · observed Aug 5, 2026
- GitHub forks (parameterlab/trap) · observed Aug 5, 2026
- Last push (parameterlab/trap) · observed Nov 20, 2024
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (promptfoo/promptfoo) · observed Aug 2, 2026
- GitHub forks (promptfoo/promptfoo) · observed Aug 2, 2026
- Last push (promptfoo/promptfoo) · observed Aug 1, 2026
- License file (MIT) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: trap 15 · promptfoo 24k (synced Aug 5, 2026).
Common questions
- What is the difference between trap and promptfoo?
- trap: TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. promptfoo: Tool for evaluating prompts and AI agents by comparing performance across various models and red teaming.. See the comparison table for live GitHub stats and shared categories.
- When should I choose trap over promptfoo?
- Choose trap over promptfoo when trap is primarily Jupyter Notebook; promptfoo is TypeScript; Requirements: Requires installation and use of HuggingFace transformers for downloading specific models.; Configuration files need to be adapted with the correct paths for model configurations as specified in
detect_llm/configs.; Tags unique to trap: acl2024, adversarial-attacks, fingerprinting, large language models; When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings. - When should I choose promptfoo over trap?
- Choose promptfoo over trap when promptfoo is primarily TypeScript; trap is Jupyter Notebook; Tags unique to promptfoo: ci-cd, evaluation-framework, llm-evaluation, pentesting; promptfoo ships Docker support for self-hosted deployment; For comparing performance across GPT, Claude, Gemini, DeepSeek.
- When should I avoid trap?
- If your objective is not specifically related to identifying or evaluating LLMs through adversarial attacks, and you require a more generalized framework for LLM evaluation or observability. When working with models that cannot be subjected to black-box testing due to their deployment environment or company policies.
- When should I avoid promptfoo?
- If you do not require comparative analysis among multiple LLM models If your project does not benefit from the specific red teaming capabilities offered by promptfoo
- Is trap or promptfoo more popular on GitHub?
- promptfoo has more GitHub stars (23,838 vs 15). Stars measure visibility, not whether either tool fits your constraints.
- Are trap and promptfoo open source?
- Yes - both are open-source projects on GitHub (trap: MIT, promptfoo: MIT).
- Where can I find alternatives to trap or promptfoo?
- GraphCanon lists graph-backed alternatives at trap alternatives and promptfoo alternatives (trap markdown twin, promptfoo 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, trap or promptfoo?
- trap: Dormant. promptfoo: 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 trap and promptfoo?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: trap trust report; promptfoo trust report.