Comparison
agentdojo vs llm-guard
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.
Markdown twin · agentdojo alternatives · llm-guard alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | llm-guard |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Archived (27d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) 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
- llm-guard
- The Security Toolkit for LLM Interactions
Stars
- agentdojo
- 716
- llm-guard
- 3.2k
Forks
- agentdojo
- 188
- llm-guard
- 435
Open issues
- agentdojo
- 41
- llm-guard
- 40
Language
- agentdojo
- Python
- llm-guard
- 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.
- llm-guard
- LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks.
Persona
- agentdojo
- -
- llm-guard
- -
Runtime
- agentdojo
- -
- llm-guard
- -
License
- agentdojo
- MIT
- llm-guard
- MIT
Last pushed
- agentdojo
- Jun 2, 2026
- llm-guard
- Jul 8, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- llm-guard
- Developer Tools, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- llm-guard
- Archived (8%)
Days since push
- agentdojo
- 63d
- llm-guard
- 27d
Archived on GitHub
- agentdojo
- No
- llm-guard
- Yes
Open issues (now)
- agentdojo
- 41
- llm-guard
- 40
Full report
- agentdojo
- Trust report
- llm-guard
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · llm-guard: 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, 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 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 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.
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 (protectai/llm-guard) · observed Aug 5, 2026
- GitHub forks (protectai/llm-guard) · observed Aug 5, 2026
- Last push (protectai/llm-guard) · observed Jul 8, 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 on cards: agentdojo 716 · llm-guard 3.2k (synced Aug 5, 2026).
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 and llm-guard alternatives (agentdojo markdown twin, llm-guard 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 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; llm-guard trust report.