--- title: "agentdojo vs embedguard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-neerazz-embedguard" tools: ["ethz-spylab-agentdojo", "neerazz-embedguard"] --- # agentdojo vs embedguard *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 embedguard if embedGuard, a Python-based toolkit, ensures RAG systems are fortified against adversarial embedding attacks by providing robust detection and provenance attestation mechanisms. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [embedguard](https://github.com/neerazz/embedguard) has 0 stars, 0 forks, and 0 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [embedguard's repository](https://github.com/neerazz/embedguard). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [embedguard](/tools/neerazz-embedguard.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems | | Stars | 716 | 0 | | Forks | 188 | 0 | | Open issues | 41 | 0 | | 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. | EmbedGuard, a Python-based toolkit, ensures RAG systems are fortified against adversarial embedding attacks by providing robust detection and provenance attestation mechanisms. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [embedguard](/tools/neerazz-embedguard.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 63d | 22d | | Open issues (now) | 41 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/neerazz-embedguard/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [embedguard](/tools/neerazz-embedguard.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: embedguard - **Adopt for:** EmbedGuard, a Python-based toolkit, ensures RAG systems are fortified against adversarial embedding attacks by providing robust detection and provenance attestation mechanisms. ## 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, large language models, 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 embedguard if… - Tags unique to embedguard: ai safety, embedding-attacks, llm security, provenance. - Also covers Vector Databases. - embedguard ships Docker support for self-hosted deployment. - When secure communication channels and provenance tracking of data embeddings in RAG (Retrieval-Augmented Generation) systems are critical to avoid security breaches or tampering by malicious actors. ## 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 embedguard - If your project does not involve RAG systems or you are working with simpler data structures that do not require embedding-level security mechanisms. - EmbedGuard may not be suitable if your primary focus is on general AI model performance optimization rather than specific defense against embedding attacks in complex RAG setups. ## Common questions ### What is the difference between agentdojo and embedguard? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. embedguard: Cross-Layer Detection and Provenance Attestation for Adversarial Embedding Attacks in RAG Systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over embedguard? Choose agentdojo over embedguard 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, large language models, 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 embedguard over agentdojo? Choose embedguard over agentdojo when Tags unique to embedguard: ai safety, embedding-attacks, llm security, provenance; Also covers Vector Databases; embedguard ships Docker support for self-hosted deployment; When secure communication channels and provenance tracking of data embeddings in RAG (Retrieval-Augmented Generation) systems are critical to avoid security breaches or tampering by malicious actors. ### 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 embedguard? If your project does not involve RAG systems or you are working with simpler data structures that do not require embedding-level security mechanisms. EmbedGuard may not be suitable if your primary focus is on general AI model performance optimization rather than specific defense against embedding attacks in complex RAG setups. ### Is agentdojo or embedguard more popular on GitHub? agentdojo has more GitHub stars (716 vs 0). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and embedguard open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, embedguard: MIT). ### Where can I find alternatives to agentdojo or embedguard? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [embedguard alternatives](/tools/neerazz-embedguard/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [embedguard markdown twin](/tools/neerazz-embedguard/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-neerazz-embedguard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or embedguard? agentdojo: Steady. embedguard: 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 agentdojo and embedguard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [embedguard trust report](/tools/neerazz-embedguard/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/_