--- title: "agentdojo vs eval-view" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-hidai25-eval-view" tools: ["ethz-spylab-agentdojo", "hidai25-eval-view"] --- # agentdojo vs eval-view *GraphCanon updated Aug 5, 2026* ## 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. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [eval-view](https://evalview.com) has 126 stars, 21 forks, and 3 open issues, last pushed Jul 26, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [eval-view's repository](https://github.com/hidai25/eval-view). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Regression testing for AI agents | | Stars | 716 | 126 | | Forks | 188 | 21 | | Open issues | 41 | 3 | | Language | Python | Python | | Adopt for | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | Regression testing for AI agents to detect behavioral changes and output quality regressions over time. | | Persona | - | - | | Runtime | - | - | | License | MIT | The software uses the Apache-2.0 license, offering permissive terms for use and distribution. | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [eval-view](/tools/hidai25-eval-view.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 63d | 6d | | Open issues (now) | 41 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/hidai25-eval-view/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [eval-view](/tools/hidai25-eval-view.md) - Python runtime ## Decision facts: agentdojo - **Pricing:** freemium - Open-source under the MIT License. Some advanced features might require additional libraries or APIs. - **Requirements:** Min 8 GB RAM - **Adopt for:** AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ## Decision facts: eval-view - **Pricing:** freemium - 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. - **Adopt for:** Regression testing for AI agents to detect behavioral changes and output quality regressions over time. - **License detail:** The software uses the Apache-2.0 license, offering permissive terms for use and distribution. ## Choose when ### 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. ### 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 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 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. ## 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](/tools/ethz-spylab-agentdojo/alternatives) and [eval-view alternatives](/tools/hidai25-eval-view/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [eval-view markdown twin](/tools/hidai25-eval-view/alternatives.md)), 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](/compare/ethz-spylab-agentdojo-vs-hidai25-eval-view.md) 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](/tools/ethz-spylab-agentdojo/trust); [eval-view trust report](/tools/hidai25-eval-view/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=ethz-spylab-agentdojo`](/api/graphcanon/graph?tool=ethz-spylab-agentdojo) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_