--- title: "awesome-evals vs agentdojo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-ethz-spylab-agentdojo" tools: ["benchflow-ai-awesome-evals", "ethz-spylab-agentdojo"] --- # awesome-evals vs agentdojo *GraphCanon updated Aug 5, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [agentdojo](https://agentdojo.spylab.ai/) has 716 stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [agentdojo's repository](https://github.com/ethz-spylab/agentdojo). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | | Stars | 761 | 716 | | Forks | 71 | 188 | | Open issues | 21 | 41 | | Language | - | Python | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | AI Agents, Evaluation & Observability | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Maintenance | Active (82%) | Steady (60%) | | Days since push | 26d | 63d | | Open issues (now) | 21 | 41 | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## 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. ## Choose when ### Choose awesome-evals if… - License: awesome-evals is Other, agentdojo is MIT. - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose agentdojo if… - License: agentdojo is MIT, awesome-evals is Other. - 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 awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## 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. ## Common questions ### What is the difference between awesome-evals and agentdojo? awesome-evals: A curated library of resources for building and evaluating AI agents. 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 awesome-evals over agentdojo? Choose awesome-evals over agentdojo when License: awesome-evals is Other, agentdojo is MIT; Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose agentdojo over awesome-evals? Choose agentdojo over awesome-evals when License: agentdojo is MIT, awesome-evals is Other; 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 avoid awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### 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 awesome-evals or agentdojo more popular on GitHub? awesome-evals has more GitHub stars (761 vs 716). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and agentdojo open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, agentdojo: MIT). ### Where can I find alternatives to awesome-evals or agentdojo? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [agentdojo markdown twin](/tools/ethz-spylab-agentdojo/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/benchflow-ai-awesome-evals-vs-ethz-spylab-agentdojo.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-evals or agentdojo? awesome-evals: Active. 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 awesome-evals and agentdojo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - 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/_