Comparison
agentdojo vs judgeval
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 judgeval if judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering.
Markdown twin · agentdojo alternatives · judgeval alternatives
GraphCanon updated Oct 6, 2026
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Trust & integrity
| Signal | agentdojo | judgeval |
|---|---|---|
| Maintenance | Slowing (125d since push) As of Oct 6, 2026 · github_public_v1 | Very active (4d since push) As of Sep 20, 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 Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) 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
- judgeval
- The Continuous-Improvement Stack for Agents
Stars
- agentdojo
- 893
- judgeval
- 1.1k
Forks
- agentdojo
- 235
- judgeval
- 99
Open issues
- agentdojo
- 72
- judgeval
- 38
Language
- agentdojo
- Python
- judgeval
- 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.
- judgeval
- Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering.
Persona
- agentdojo
- -
- judgeval
- -
Runtime
- agentdojo
- -
- judgeval
- -
License
- agentdojo
- MIT
- judgeval
- Apache-2.0
Last pushed
- agentdojo
- Jun 2, 2026
- judgeval
- Sep 16, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- judgeval
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Slowing (36%)
- judgeval
- Very active (96%)
Days since push
- agentdojo
- 125d
- judgeval
- 4d
Open issues (now)
- agentdojo
- 72
- judgeval
- 38
Stars delta
- agentdojo
- +91 (30d)
- judgeval
- +16 (30d)
Open issues delta
- agentdojo
- +21 (30d)
- judgeval
- +10 (30d)
Full report
- agentdojo
- Trust report
- judgeval
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · judgeval: Python runtime
Choose agentdojo if…
- License: agentdojo is MIT, judgeval 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.
- 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 judgeval if…
- License: judgeval is Apache-2.0, agentdojo is MIT.
- Tags unique to judgeval: agent, agentic-ai, agents, grpo.
- You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical.
When NOT to use judgeval
- If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies.
- You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach.
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 (JudgmentLabs/judgeval) · observed Sep 20, 2026
- GitHub forks (JudgmentLabs/judgeval) · observed Sep 20, 2026
- Last push (JudgmentLabs/judgeval) · observed Sep 16, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 893 · judgeval 1.1k (synced Oct 6, 2026).
Common questions
- What is the difference between agentdojo and judgeval?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. judgeval: The Continuous-Improvement Stack for Agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over judgeval?
- Choose agentdojo over judgeval when License: agentdojo is MIT, judgeval 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; 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 judgeval over agentdojo?
- Choose judgeval over agentdojo when License: judgeval is Apache-2.0, agentdojo is MIT; Tags unique to judgeval: agent, agentic-ai, agents, grpo; You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical.
- 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 judgeval?
- If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach.
- Is agentdojo or judgeval more popular on GitHub?
- judgeval has more GitHub stars (1,063 vs 893). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and judgeval open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, judgeval: Apache-2.0).
- Where can I find alternatives to agentdojo or judgeval?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and judgeval alternatives (agentdojo markdown twin, judgeval 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 judgeval?
- agentdojo: Slowing. judgeval: Very 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 judgeval?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; judgeval trust report.