Comparison
AdaRubrics vs agentdojo
Verdict
Pick AdaRubrics if adaRubrics serves as an Adaptive Dynamic Rubric Evaluator specifically for assessing AI agent and language model performance based on evolving rubrics tailored to the agents' paths; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Markdown twin · AdaRubrics alternatives · agentdojo alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | AdaRubrics | agentdojo |
|---|---|---|
| Maintenance | Steady (51d since push) As of 4w · github_public_v1 | Steady (63d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 4w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- AdaRubrics
- Adaptive Dynamic Rubric Evaluator for Agent Trajectories
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
Stars
- AdaRubrics
- 345
- agentdojo
- 716
Forks
- AdaRubrics
- 36
- agentdojo
- 188
Open issues
- AdaRubrics
- 0
- agentdojo
- 41
Language
- AdaRubrics
- Python
- agentdojo
- Python
Adopt for
- AdaRubrics
- AdaRubrics serves as an Adaptive Dynamic Rubric Evaluator specifically for assessing AI agent and language model performance based on evolving rubrics tailored to the agents' paths.
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
Persona
- AdaRubrics
- -
- agentdojo
- -
Runtime
- AdaRubrics
- -
- agentdojo
- -
License
- AdaRubrics
- Apache-2.0
- agentdojo
- MIT
Last pushed
- AdaRubrics
- Jun 7, 2026
- agentdojo
- Jun 2, 2026
Categories
- AdaRubrics
- Evaluation & Observability
- agentdojo
- AI Agents, Evaluation & Observability
Trust and health
Days since push
- AdaRubrics
- 51d
- agentdojo
- 63d
Open issues (now)
- AdaRubrics
- 0
- agentdojo
- 41
Owner type
- AdaRubrics
- User
- agentdojo
- Organization
Full report
- AdaRubrics
- Trust report
- agentdojo
- Trust report
Shared compatibility
- Python · AdaRubrics: Python runtime · agentdojo: Python runtime
Choose AdaRubrics if…
- License: AdaRubrics is Apache-2.0, agentdojo is MIT.
- Tags unique to AdaRubrics: agent-evaluation, llm-evaluation, reward-model, rlhf.
- When you need dynamic evaluation criteria that adapt in real-time according to how your AI agents or language models are performing their tasks.
When NOT to use AdaRubrics
- If fixed rubrics with static evaluation criteria suffice, AdaRubrics provides more complexity than needed.
- For projects that do not require real-time adjustments in evaluation methods as the AI agents' or models' trajectories progress.
Choose agentdojo if…
- License: agentdojo is MIT, AdaRubrics is Apache-2.0.
- 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
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alphadl/AdaRubrics) · observed Jul 28, 2026
- GitHub forks (alphadl/AdaRubrics) · observed Jul 28, 2026
- Last push (alphadl/AdaRubrics) · observed Jun 7, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: AdaRubrics 345 · agentdojo 716 (synced Jul 28, 2026).
Common questions
- What is the difference between AdaRubrics and agentdojo?
- AdaRubrics: Adaptive Dynamic Rubric Evaluator for Agent Trajectories. agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose AdaRubrics over agentdojo?
- Choose AdaRubrics over agentdojo when License: AdaRubrics is Apache-2.0, agentdojo is MIT; Tags unique to AdaRubrics: agent-evaluation, llm-evaluation, reward-model, rlhf; When you need dynamic evaluation criteria that adapt in real-time according to how your AI agents or language models are performing their tasks.
- When should I choose agentdojo over AdaRubrics?
- Choose agentdojo over AdaRubrics when License: agentdojo is MIT, AdaRubrics is Apache-2.0; 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 avoid AdaRubrics?
- If fixed rubrics with static evaluation criteria suffice, AdaRubrics provides more complexity than needed. For projects that do not require real-time adjustments in evaluation methods as the AI agents' or models' trajectories progress.
- 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.
- Is AdaRubrics or agentdojo more popular on GitHub?
- agentdojo has more GitHub stars (716 vs 345). Stars measure visibility, not whether either tool fits your constraints.
- Are AdaRubrics and agentdojo open source?
- Yes - both are open-source projects on GitHub (AdaRubrics: Apache-2.0, agentdojo: MIT).
- Where can I find alternatives to AdaRubrics or agentdojo?
- GraphCanon lists graph-backed alternatives at AdaRubrics alternatives and agentdojo alternatives (AdaRubrics markdown twin, agentdojo 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, AdaRubrics or agentdojo?
- AdaRubrics: Steady. agentdojo: 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 AdaRubrics and agentdojo?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: AdaRubrics trust report; agentdojo trust report.