--- title: "agentdojo vs trap" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-parameterlab-trap" tools: ["ethz-spylab-agentdojo", "parameterlab-trap"] --- # agentdojo vs trap *GraphCanon updated Aug 5, 2026* ## Verdict Pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents; pick trap if tRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [trap](https://github.com/parameterlab/trap) has 15 stars, 1 forks, and 0 open issues, last pushed Nov 20, 2024. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [trap's repository](https://github.com/parameterlab/trap). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification | | Stars | 716 | 15 | | Forks | 188 | 1 | | Open issues | 41 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | TRAP is specialized for identifying large language models through adversarial attacks and fingerprinting techniques. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License ensures permissive use and modification of TRAP under its terms. | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [trap](/tools/parameterlab-trap.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 63d | 622d | | Open issues (now) | 41 | 0 | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/parameterlab-trap/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [trap](/tools/parameterlab-trap.md) - Python runtime ## Decision facts: agentdojo - **Pricing:** freemium - Open-source under the MIT License. Some advanced features might require additional libraries or APIs. - **Requirements:** Min 8 GB RAM - **Adopt for:** AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ## 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. ## Choose when ### Choose agentdojo if… - agentdojo is primarily Python; trap is Jupyter Notebook. - Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.. - Requirements: Min 8 GB RAM. - Tags unique to agentdojo: benchmark, prompt-injection, security. - Also covers AI Agents. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### Choose trap if… - trap is primarily Jupyter Notebook; agentdojo is Python. - 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, research. - Also covers LLM Frameworks. - When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings. ## When NOT to use agentdojo - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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. ## Common questions ### What is the difference between agentdojo and trap? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. trap: TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over trap? Choose agentdojo over trap when agentdojo is primarily Python; trap is Jupyter Notebook; Pricing: Open-source under the MIT License. Some advanced features might require additional libraries or APIs.; Requirements: Min 8 GB RAM; Tags unique to agentdojo: benchmark, prompt-injection, security; Also covers AI Agents; AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### When should I choose trap over agentdojo? Choose trap over agentdojo when trap is primarily Jupyter Notebook; agentdojo is Python; 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, research; Also covers LLM Frameworks; When you need to perform black-box identification of large language models using adversarial prompt techniques in research settings. ### When should I avoid agentdojo? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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. ### Is agentdojo or trap more popular on GitHub? agentdojo has more GitHub stars (716 vs 15). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and trap open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, trap: MIT). ### Where can I find alternatives to agentdojo or trap? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [trap alternatives](/tools/parameterlab-trap/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [trap markdown twin](/tools/parameterlab-trap/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/ethz-spylab-agentdojo-vs-parameterlab-trap.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or trap? agentdojo: Steady. trap: Dormant. 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 agentdojo and trap? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [trap trust report](/tools/parameterlab-trap/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethz-spylab-agentdojo`](/api/graphcanon/graph?tool=ethz-spylab-agentdojo) - 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/_