Comparison
agentdojo vs ReNeLLM
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 ReNeLLM if reNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2.
Markdown twin · agentdojo alternatives · ReNeLLM alternatives
GraphCanon updated Oct 6, 2026
7views this month
Trust & integrity
| Signal | agentdojo | ReNeLLM |
|---|---|---|
| Maintenance | Slowing (125d since push) As of Oct 6, 2026 · github_public_v1 | Dormant (397d since push) As of Oct 5, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Oct 6, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 5, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Jul 11, 2026 · 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
- ReNeLLM
- Implementation of generalized nested jailbreak prompts targeting large language models.
Stars
- agentdojo
- 893
- ReNeLLM
- 164
Forks
- agentdojo
- 235
- ReNeLLM
- 17
Open issues
- agentdojo
- 72
- ReNeLLM
- 0
Language
- agentdojo
- Python
- ReNeLLM
- 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.
- ReNeLLM
- ReNeLLM is an implementation of generalized nested jailbreak prompts targeting large language models such as gpt-3.5-turbo and claude-v2.
Persona
- agentdojo
- -
- ReNeLLM
- -
Runtime
- agentdojo
- -
- ReNeLLM
- -
License
- agentdojo
- MIT
- ReNeLLM
- MIT
Last pushed
- agentdojo
- Jun 2, 2026
- ReNeLLM
- Sep 2, 2025
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- ReNeLLM
- Evaluation & Observability, Inference & Serving
Trust and health
Maintenance
- agentdojo
- Slowing (36%)
- ReNeLLM
- Dormant (18%)
Days since push
- agentdojo
- 125d
- ReNeLLM
- 397d
Open issues (now)
- agentdojo
- 72
- ReNeLLM
- 0
Stars delta
- agentdojo
- +91 (30d)
- ReNeLLM
- 0 (30d)
Open issues delta
- agentdojo
- +21 (30d)
- ReNeLLM
- 0 (30d)
OSV dependency advisories
- agentdojo
- No lockfile (source not queried)
- ReNeLLM
- Published findings
Full report
- agentdojo
- Trust report
- ReNeLLM
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · ReNeLLM: 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, prompt-injection, 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
- Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on 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 ReNeLLM if…
- Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment.
- Also covers Inference & Serving.
- When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts.
When NOT to use ReNeLLM
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts.
- If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
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 Oct 6, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Oct 6, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Oct 6, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NJUNLP/ReNeLLM) · observed Oct 5, 2026
- GitHub forks (NJUNLP/ReNeLLM) · observed Oct 5, 2026
- Last push (NJUNLP/ReNeLLM) · observed Sep 2, 2025
- License file (MIT) · observed Oct 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 893 · ReNeLLM 164 (synced Oct 6, 2026).
Common questions
- What is the difference between agentdojo and ReNeLLM?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. ReNeLLM: Implementation of generalized nested jailbreak prompts targeting large language models.. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over ReNeLLM?
- Choose agentdojo over ReNeLLM 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, prompt-injection, 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 ReNeLLM over agentdojo?
- Choose ReNeLLM over agentdojo when Tags unique to ReNeLLM: api interaction, jailbreak prompts, language model evaluation, model reliability assessment; Also covers Inference & Serving; When you aim to evaluate the susceptibility of LLMs like gpt-3.5-turbo and claude-v2 to deception or jailbroken prompts.
- When should I avoid agentdojo?
- Last GitHub push was Jun 2, 2026 (slowing maintenance). Validate activity before betting a new project on 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 ReNeLLM?
- When you wish to develop applications that strictly adhere to ethical guidelines and do not involve the testing of harmful prompts. If your focus is on building production-ready LLM-based services without interest in evaluating security or adversarial aspects of these models.
- Is agentdojo or ReNeLLM more popular on GitHub?
- agentdojo has more GitHub stars (893 vs 164). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and ReNeLLM open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, ReNeLLM: MIT).
- Where can I find alternatives to agentdojo or ReNeLLM?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and ReNeLLM alternatives (agentdojo markdown twin, ReNeLLM 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 ReNeLLM?
- agentdojo: Slowing. ReNeLLM: Dormant. 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 ReNeLLM?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; ReNeLLM trust report.