--- title: "trap vs promptfoo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/parameterlab-trap-vs-promptfoo-promptfoo" tools: ["parameterlab-trap", "promptfoo-promptfoo"] --- # trap vs promptfoo *GraphCanon updated Aug 5, 2026* ## 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. [trap](https://github.com/parameterlab/trap) reports 15 GitHub stars, 1 forks, and 0 open issues, last pushed Nov 20, 2024. [promptfoo](https://promptfoo.dev) has 24k stars, 2.1k forks, and 481 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [trap's repository](https://github.com/parameterlab/trap) and [promptfoo's repository](https://github.com/promptfoo/promptfoo). | | [trap](/tools/parameterlab-trap.md) | [promptfoo](/tools/promptfoo-promptfoo.md) | | --- | --- | --- | | Tagline | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification | Tool for evaluating prompts and AI agents by comparing performance across various models and red teaming. | | Stars | 15 | 23,838 | | Forks | 1 | 2,147 | | Open issues | 0 | 481 | | Language | Jupyter Notebook | TypeScript | | Adopt for | TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. | promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support. | | Persona | - | - | | Runtime | - | - | | License | MIT License ensures permissive use and modification of TRAP under its terms. | MIT | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [trap](/tools/parameterlab-trap.md) | [promptfoo](/tools/promptfoo-promptfoo.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 622d | 0d | | Open issues (now) | 0 | 481 | | Full report | [trust report](/tools/parameterlab-trap/trust.md) | [trust report](/tools/promptfoo-promptfoo/trust.md) | ## Shared compatibility - **Python**: [trap](/tools/parameterlab-trap.md) - Python runtime; [promptfoo](/tools/promptfoo-promptfoo.md) - Python runtime ## Decision facts: trap - **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`. - **Adopt for:** TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. - **License detail:** MIT License ensures permissive use and modification of TRAP under its terms. ## Decision facts: promptfoo - **Adopt for:** promptfoo aids in evaluating AI prompts, LLM agents, and RAG systems through declarative config testing with CI/CD support. ## Choose when ### 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. ### 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 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 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 ## 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](/tools/parameterlab-trap/alternatives) and [promptfoo alternatives](/tools/promptfoo-promptfoo/alternatives) ([trap markdown twin](/tools/parameterlab-trap/alternatives.md), [promptfoo markdown twin](/tools/promptfoo-promptfoo/alternatives.md)), 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](/compare/parameterlab-trap-vs-promptfoo-promptfoo.md) 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](/tools/parameterlab-trap/trust); [promptfoo trust report](/tools/promptfoo-promptfoo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=parameterlab-trap`](/api/graphcanon/graph?tool=parameterlab-trap) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_