--- title: "ALERT vs agentdojo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/babelscape-alert-vs-ethz-spylab-agentdojo" tools: ["babelscape-alert", "ethz-spylab-agentdojo"] --- # ALERT vs agentdojo *GraphCanon updated Sep 20, 2026* ## Verdict Pick ALERT if aLERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. [ALERT](https://arxiv.org/abs/2404.08676) reports 59 GitHub stars, 8 forks, and 0 open issues, last pushed Sep 20, 2024. [agentdojo](https://agentdojo.spylab.ai/) has 802 stars, 205 forks, and 51 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [ALERT's repository](https://github.com/Babelscape/ALERT) and [agentdojo's repository](https://github.com/ethz-spylab/agentdojo). | | [ALERT](/tools/babelscape-alert.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Tagline | A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | | Stars | 59 | 802 | | Forks | 8 | 205 | | Open issues | 0 | 51 | | Language | Python | Python | | Adopt for | ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ALERT](/tools/babelscape-alert.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 719d | 94d | | Open issues (now) | 0 | 51 | | Stars delta | 0 (30d) | +86 (30d) | | Open issues delta | 0 (30d) | +10 (30d) | | Full report | [trust report](/tools/babelscape-alert/trust.md) | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | ## Decision facts: ALERT - **Adopt for:** ALERT is designed specifically for red-teaming based safety evaluation on large language models, using MIT licensed prompts and adversarial augmentation. ## 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 ALERT if… - License: ALERT is Other, agentdojo is MIT. - Tags unique to ALERT: ai, artificial-intelligence, bias-detection, llm-evaluation. - When evaluating safety metrics of large language models through red-teaming approaches ### Choose agentdojo if… - License: agentdojo is MIT, ALERT is Other. - 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: 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 ALERT - If your evaluation does not require bias detection or safety assessment under adversarial conditions - In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ## When NOT to use agentdojo - Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on 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 ALERT and agentdojo? ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety Through Red Teaming. 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 ALERT over agentdojo? Choose ALERT over agentdojo when License: ALERT is Other, agentdojo is MIT; Tags unique to ALERT: ai, artificial-intelligence, bias-detection, llm-evaluation; When evaluating safety metrics of large language models through red-teaming approaches. ### When should I choose agentdojo over ALERT? Choose agentdojo over ALERT when License: agentdojo is MIT, ALERT is Other; 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: 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 ALERT? If your evaluation does not require bias detection or safety assessment under adversarial conditions In scenarios where a broader range of model aspects beyond safety is needed, as ALERT focuses primarily on safety benchmarks ### When should I avoid agentdojo? Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on 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 ALERT or agentdojo more popular on GitHub? agentdojo has more GitHub stars (802 vs 59). Stars measure visibility, not whether either tool fits your constraints. ### Are ALERT and agentdojo open source? Yes - both are open-source projects on GitHub (ALERT: Other, agentdojo: MIT). ### Where can I find alternatives to ALERT or agentdojo? GraphCanon lists graph-backed alternatives at [ALERT alternatives](/tools/babelscape-alert/alternatives) and [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) ([ALERT markdown twin](/tools/babelscape-alert/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/babelscape-alert-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, ALERT or agentdojo? ALERT: Dormant. agentdojo: Slowing. 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 ALERT and agentdojo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ALERT trust report](/tools/babelscape-alert/trust); [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=babelscape-alert`](/api/graphcanon/graph?tool=babelscape-alert) - 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/_