Comparison
agentdojo vs embedguard
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.
Markdown twin · agentdojo alternatives · embedguard alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | embedguard |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Active (22d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- agentdojo
- 716
- embedguard
- 0
Forks
- agentdojo
- 188
- embedguard
- 0
Open issues
- agentdojo
- 41
- embedguard
- 0
Language
- agentdojo
- Python
- embedguard
- Python
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- embedguard
- EmbedGuard, a Python-based toolkit, ensures RAG systems are fortified against adversarial embedding attacks by providing robust detection and provenance attestation mechanisms.
Persona
- agentdojo
- -
- embedguard
- -
Runtime
- agentdojo
- -
- embedguard
- -
License
- agentdojo
- MIT
- embedguard
- MIT
Last pushed
- agentdojo
- Jun 2, 2026
- embedguard
- Jul 10, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- embedguard
- Evaluation & Observability, Vector Databases
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- embedguard
- Active (82%)
Days since push
- agentdojo
- 63d
- embedguard
- 22d
Open issues (now)
- agentdojo
- 41
- embedguard
- 0
Owner type
- agentdojo
- Organization
- embedguard
- User
OSV dependency advisories
- agentdojo
- No lockfile (source not queried)
- embedguard
- Published findings
Full report
- agentdojo
- Trust report
- embedguard
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · embedguard: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ethz-spylab/agentdojo) · observed Aug 5, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Aug 5, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (neerazz/embedguard) · observed Aug 1, 2026
- GitHub forks (neerazz/embedguard) · observed Aug 1, 2026
- Last push (neerazz/embedguard) · observed Jul 10, 2026
- License file (MIT) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · embedguard 0 (synced Aug 5, 2026).
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 and embedguard alternatives (agentdojo markdown twin, embedguard markdown twin), 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 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; embedguard trust report.