--- title: "agentdojo vs llm-guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-protectai-llm-guard" tools: ["ethz-spylab-agentdojo", "protectai-llm-guard"] --- # agentdojo vs llm-guard *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 llm-guard if lLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [llm-guard](https://protectai.github.io/llm-guard/) has 3.2k stars, 435 forks, and 40 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [llm-guard's repository](https://github.com/protectai/llm-guard). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [llm-guard](/tools/protectai-llm-guard.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | The Security Toolkit for LLM Interactions | | Stars | 716 | 3,202 | | Forks | 188 | 435 | | Open issues | 41 | 40 | | Language | Python | Python | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Evaluation & Observability | Developer Tools, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [llm-guard](/tools/protectai-llm-guard.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Archived (8%) | | Days since push | 63d | 27d | | Archived on GitHub | No | Yes | | Open issues (now) | 41 | 40 | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/protectai-llm-guard/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [llm-guard](/tools/protectai-llm-guard.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: llm-guard - **Requirements:** Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed. - **Adopt for:** LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. ## Choose when ### Choose agentdojo if… - 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, 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 llm-guard if… - Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.. - Tags unique to llm-guard: adversarial-machine-learning, chatgpt, llm security, prompt-engineering. - Also covers Developer Tools. - - You need to secure your application from sophisticated prompt injection techniques. ## 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 llm-guard - - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ## Common questions ### What is the difference between agentdojo and llm-guard? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. llm-guard: The Security Toolkit for LLM Interactions. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over llm-guard? Choose agentdojo over llm-guard when 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, 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 llm-guard over agentdojo? Choose llm-guard over agentdojo when Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.; Tags unique to llm-guard: adversarial-machine-learning, chatgpt, llm security, prompt-engineering; Also covers Developer Tools; - You need to secure your application from sophisticated prompt injection techniques. ### 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 llm-guard? - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ### Is agentdojo or llm-guard more popular on GitHub? llm-guard has more GitHub stars (3,202 vs 716). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and llm-guard open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, llm-guard: MIT). ### Where can I find alternatives to agentdojo or llm-guard? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [llm-guard alternatives](/tools/protectai-llm-guard/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [llm-guard markdown twin](/tools/protectai-llm-guard/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-protectai-llm-guard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or llm-guard? agentdojo: Steady. llm-guard: Archived. 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 llm-guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [llm-guard trust report](/tools/protectai-llm-guard/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/_