Comparison
agentdojo vs PentestGPT
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 PentestGPT if pentestGPT specializes in automating parts of the penetration testing process through large language models, offering a unique approach to AI-assisted security assessments.
Markdown twin · agentdojo alternatives · PentestGPT alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | agentdojo | PentestGPT |
|---|---|---|
| Maintenance | Steady (63d since push) As of 1w · github_public_v1 | Steady (33d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Personal account As of 1d · 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
- PentestGPT
- Automated Penetration Testing Agentic Framework Powered by Large Language Models
Stars
- agentdojo
- 716
- PentestGPT
- 15k
Forks
- agentdojo
- 188
- PentestGPT
- 2.6k
Open issues
- agentdojo
- 41
- PentestGPT
- 65
Language
- agentdojo
- Python
- PentestGPT
- 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.
- PentestGPT
- PentestGPT specializes in automating parts of the penetration testing process through large language models, offering a unique approach to AI-assisted security assessments.
Persona
- agentdojo
- -
- PentestGPT
- -
Runtime
- agentdojo
- -
- PentestGPT
- -
License
- agentdojo
- MIT
- PentestGPT
- MIT License, which allows for free use, modification, and distribution provided that attribution is maintained and any warranties or liabilities are disclaimed.
Last pushed
- agentdojo
- Jun 2, 2026
- PentestGPT
- Jul 14, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- PentestGPT
- AI Agents, LLM Frameworks
Trust and health
Days since push
- agentdojo
- 63d
- PentestGPT
- 33d
Open issues (now)
- agentdojo
- 41
- PentestGPT
- 65
Stars delta
- agentdojo
- Unknown
- PentestGPT
- +597 (30d)
Open issues delta
- agentdojo
- Unknown
- PentestGPT
- +3 (30d)
Owner type
- agentdojo
- Organization
- PentestGPT
- User
Full report
- agentdojo
- Trust report
- PentestGPT
- Trust report
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, prompt-injection, security.
- Also covers Evaluation & Observability.
- 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 PentestGPT if…
- Requirements: - Python environment; - Access to large language models as stipulated by PentestGPT's operational requirements.
- Tags unique to PentestGPT: llm, penetration-testing, python.
- Also covers LLM Frameworks.
- PentestGPT ships Docker support for self-hosted deployment.
- - When you need an automated framework for certain tasks within penetration testing that can be handled by large language models.
When NOT to use PentestGPT
- - Avoid using PentestGPT if manual, nuanced analysis is required, as its reliance on LLM might not cover all complexities of a security assessment.
- - If your organization does not have the legal authority to conduct penetration testing on specific targets, as indicated by its disclaimer for 'educational purposes and authorized security testing'.
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 (GreyDGL/PentestGPT) · observed Aug 17, 2026
- GitHub forks (GreyDGL/PentestGPT) · observed Aug 17, 2026
- Last push (GreyDGL/PentestGPT) · observed Jul 14, 2026
- License file (MIT) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · PentestGPT 15k (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and PentestGPT?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. PentestGPT: Automated Penetration Testing Agentic Framework Powered by Large Language Models. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over PentestGPT?
- Choose agentdojo over PentestGPT 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, prompt-injection, security; Also covers Evaluation & Observability; 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 PentestGPT over agentdojo?
- Choose PentestGPT over agentdojo when Requirements: - Python environment; - Access to large language models as stipulated by PentestGPT's operational requirements; Tags unique to PentestGPT: llm, penetration-testing, python; Also covers LLM Frameworks; PentestGPT ships Docker support for self-hosted deployment; - When you need an automated framework for certain tasks within penetration testing that can be handled by large language models.
- 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 PentestGPT?
- - Avoid using PentestGPT if manual, nuanced analysis is required, as its reliance on LLM might not cover all complexities of a security assessment. - If your organization does not have the legal authority to conduct penetration testing on specific targets, as indicated by its disclaimer for 'educational purposes and authorized security testing'.
- Is agentdojo or PentestGPT more popular on GitHub?
- PentestGPT has more GitHub stars (14,900 vs 716). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and PentestGPT open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, PentestGPT: MIT).
- Where can I find alternatives to agentdojo or PentestGPT?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and PentestGPT alternatives (agentdojo markdown twin, PentestGPT 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 PentestGPT?
- agentdojo: Steady. PentestGPT: Steady. 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 PentestGPT?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; PentestGPT trust report.