--- title: "AdaRubrics vs agentdojo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alphadl-adarubrics-vs-ethz-spylab-agentdojo" tools: ["alphadl-adarubrics", "ethz-spylab-agentdojo"] --- # AdaRubrics vs agentdojo *GraphCanon updated Aug 5, 2026* ## Verdict Pick AdaRubrics if adaRubrics serves as an Adaptive Dynamic Rubric Evaluator specifically for assessing AI agent and language model performance based on evolving rubrics tailored to the agents' paths; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. [AdaRubrics](https://github.com/alphadl/AdaRubrics) reports 345 GitHub stars, 36 forks, and 0 open issues, last pushed Jun 7, 2026. [agentdojo](https://agentdojo.spylab.ai/) has 716 stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [AdaRubrics's repository](https://github.com/alphadl/AdaRubrics) and [agentdojo's repository](https://github.com/ethz-spylab/agentdojo). | | [AdaRubrics](/tools/alphadl-adarubrics.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Tagline | Adaptive Dynamic Rubric Evaluator for Agent Trajectories | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | | Stars | 345 | 716 | | Forks | 36 | 188 | | Open issues | 0 | 41 | | Language | Python | Python | | Adopt for | AdaRubrics serves as an Adaptive Dynamic Rubric Evaluator specifically for assessing AI agent and language model performance based on evolving rubrics tailored to the agents' paths. | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AdaRubrics](/tools/alphadl-adarubrics.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Days since push | 51d | 63d | | Open issues (now) | 0 | 41 | | Owner type | User | Organization | | Full report | [trust report](/tools/alphadl-adarubrics/trust.md) | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | ## Shared compatibility - **Python**: [AdaRubrics](/tools/alphadl-adarubrics.md) - Python runtime; [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime ## Decision facts: AdaRubrics - **Adopt for:** AdaRubrics serves as an Adaptive Dynamic Rubric Evaluator specifically for assessing AI agent and language model performance based on evolving rubrics tailored to the agents' paths. ## 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. ## Choose when ### Choose AdaRubrics if… - License: AdaRubrics is Apache-2.0, agentdojo is MIT. - Tags unique to AdaRubrics: agent-evaluation, llm-evaluation, reward-model, rlhf. - When you need dynamic evaluation criteria that adapt in real-time according to how your AI agents or language models are performing their tasks. ### Choose agentdojo if… - License: agentdojo is MIT, AdaRubrics is Apache-2.0. - 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, large language models, 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 NOT to use AdaRubrics - If fixed rubrics with static evaluation criteria suffice, AdaRubrics provides more complexity than needed. - For projects that do not require real-time adjustments in evaluation methods as the AI agents' or models' trajectories progress. ## 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. ## Common questions ### What is the difference between AdaRubrics and agentdojo? AdaRubrics: Adaptive Dynamic Rubric Evaluator for Agent Trajectories. agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. See the comparison table for live GitHub stats and shared categories. ### When should I choose AdaRubrics over agentdojo? Choose AdaRubrics over agentdojo when License: AdaRubrics is Apache-2.0, agentdojo is MIT; Tags unique to AdaRubrics: agent-evaluation, llm-evaluation, reward-model, rlhf; When you need dynamic evaluation criteria that adapt in real-time according to how your AI agents or language models are performing their tasks. ### When should I choose agentdojo over AdaRubrics? Choose agentdojo over AdaRubrics when License: agentdojo is MIT, AdaRubrics is Apache-2.0; 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, large language models, 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 avoid AdaRubrics? If fixed rubrics with static evaluation criteria suffice, AdaRubrics provides more complexity than needed. For projects that do not require real-time adjustments in evaluation methods as the AI agents' or models' trajectories progress. ### 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. ### Is AdaRubrics or agentdojo more popular on GitHub? agentdojo has more GitHub stars (716 vs 345). Stars measure visibility, not whether either tool fits your constraints. ### Are AdaRubrics and agentdojo open source? Yes - both are open-source projects on GitHub (AdaRubrics: Apache-2.0, agentdojo: MIT). ### Where can I find alternatives to AdaRubrics or agentdojo? GraphCanon lists graph-backed alternatives at [AdaRubrics alternatives](/tools/alphadl-adarubrics/alternatives) and [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) ([AdaRubrics markdown twin](/tools/alphadl-adarubrics/alternatives.md), [agentdojo markdown twin](/tools/ethz-spylab-agentdojo/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/alphadl-adarubrics-vs-ethz-spylab-agentdojo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AdaRubrics or agentdojo? AdaRubrics: Steady. agentdojo: Steady. 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 AdaRubrics and agentdojo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AdaRubrics trust report](/tools/alphadl-adarubrics/trust); [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alphadl-adarubrics`](/api/graphcanon/graph?tool=alphadl-adarubrics) - 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/_