Comparison
agentdojo vs eval-view
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 eval-view if regression testing for AI agents to detect behavioral changes and output quality regressions over time.
Markdown twin · agentdojo alternatives · eval-view alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | eval-view |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Very active (6d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal 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
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- eval-view
- Regression testing for AI agents
Stars
- agentdojo
- 716
- eval-view
- 126
Forks
- agentdojo
- 188
- eval-view
- 21
Open issues
- agentdojo
- 41
- eval-view
- 3
Language
- agentdojo
- Python
- eval-view
- 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.
- eval-view
- Regression testing for AI agents to detect behavioral changes and output quality regressions over time.
Persona
- agentdojo
- -
- eval-view
- -
Runtime
- agentdojo
- -
- eval-view
- -
License
- agentdojo
- MIT
- eval-view
- The software uses the Apache-2.0 license, offering permissive terms for use and distribution.
Last pushed
- agentdojo
- Jun 2, 2026
- eval-view
- Jul 26, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- eval-view
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- eval-view
- Very active (96%)
Days since push
- agentdojo
- 63d
- eval-view
- 6d
Open issues (now)
- agentdojo
- 41
- eval-view
- 3
Owner type
- agentdojo
- Organization
- eval-view
- User
Full report
- agentdojo
- Trust report
- eval-view
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · eval-view: Python runtime
Choose agentdojo if…
- License: agentdojo is MIT, eval-view 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
- 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 eval-view if…
- License: eval-view is Apache-2.0, agentdojo is MIT.
- Pricing: Free to use under the terms of the Apache License, Version 2.0..
- Requirements: Python environment is required for installation and usage.; Installation with pip: `pip install evalview`; Offline support means no live API keys necessary for the basic diff functionality..
- Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing.
- eval-view ships Docker support for self-hosted deployment.
- When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls.
When NOT to use eval-view
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time.
- When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
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 (hidai25/eval-view) · observed Aug 2, 2026
- GitHub forks (hidai25/eval-view) · observed Aug 2, 2026
- Last push (hidai25/eval-view) · observed Jul 26, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · eval-view 126 (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and eval-view?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. eval-view: Regression testing for AI agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over eval-view?
- Choose agentdojo over eval-view when License: agentdojo is MIT, eval-view 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 eval-view over agentdojo?
- Choose eval-view over agentdojo when License: eval-view is Apache-2.0, agentdojo is MIT; Pricing: Free to use under the terms of the Apache License, Version 2.0.; Requirements: Python environment is required for installation and usage.; Installation with pip:
pip install evalview; Offline support means no live API keys necessary for the basic diff functionality.; Tags unique to eval-view: agent-benchmark, agent-evaluation, ai-agents, regression-testing; eval-view ships Docker support for self-hosted deployment; When you need to track and assess the behavior consistency of your AI agent across versions without involving live API calls. - 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 eval-view?
- If you do not need to monitor specific behavioral characteristics such as tool call sequences and parameter consistency over time. When real-time output quality evaluation is critical, as eval-view's offline diffing does not provide immediate feedback on output changes without an LLM judge.
- Is agentdojo or eval-view more popular on GitHub?
- agentdojo has more GitHub stars (716 vs 126). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and eval-view open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, eval-view: Apache-2.0).
- Where can I find alternatives to agentdojo or eval-view?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and eval-view alternatives (agentdojo markdown twin, eval-view 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 eval-view?
- agentdojo: Steady. eval-view: 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 eval-view?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; eval-view trust report.