--- title: "agentdojo vs AssetOpsBench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-ibm-assetopsbench" tools: ["ethz-spylab-agentdojo", "ibm-assetopsbench"] --- # agentdojo vs AssetOpsBench *GraphCanon updated Oct 6, 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 AssetOpsBench if assetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management. [agentdojo](https://agentdojo.spylab.ai/) reports 893 GitHub stars, 235 forks, and 72 open issues, last pushed Jun 2, 2026. [AssetOpsBench](https://github.com/IBM/AssetOpsBench) has 2.3k stars, 325 forks, and 45 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [AssetOpsBench's repository](https://github.com/IBM/AssetOpsBench). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [AssetOpsBench](/tools/ibm-assetopsbench.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Framework for building and evaluating AI agents targeting Industry 4.0 asset operations | | Stars | 893 | 2,304 | | Forks | 235 | 325 | | Open issues | 72 | 45 | | 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. | AssetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | 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) | [AssetOpsBench](/tools/ibm-assetopsbench.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 125d | 0d | | Open issues (now) | 72 | 45 | | Stars delta | +91 (30d) | +235 (30d) | | Open issues delta | +21 (30d) | 0 (30d) | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/ibm-assetopsbench/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [AssetOpsBench](/tools/ibm-assetopsbench.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: AssetOpsBench - **Adopt for:** AssetOpsBench is a specialized framework for developing and evaluating AI agents in Industry 4.0 contexts, with an emphasis on operations and maintenance scenarios including HVAC systems and IoT management. ## Choose when ### Choose agentdojo if… - License: agentdojo is MIT, AssetOpsBench 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 AssetOpsBench if… - License: AssetOpsBench is Apache-2.0, agentdojo is MIT. - Tags unique to AssetOpsBench: ai-for-physical-assets, condition-based-maintenance, hvac-maintenance, iot. - When you need detailed evaluation frameworks for multiple types of AI agents operating in industry environments ## 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. ## When NOT to use AssetOpsBench - If your project focus is on general-purpose AI outside the domain-specific context of industrial operations - Do not use if you require real-time agent orchestration without any emphasis on condition-based or predictive maintenance in asset management ## Common questions ### What is the difference between agentdojo and AssetOpsBench? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. AssetOpsBench: Framework for building and evaluating AI agents targeting Industry 4.0 asset operations. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over AssetOpsBench? Choose agentdojo over AssetOpsBench when License: agentdojo is MIT, AssetOpsBench 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 AssetOpsBench over agentdojo? Choose AssetOpsBench over agentdojo when License: AssetOpsBench is Apache-2.0, agentdojo is MIT; Tags unique to AssetOpsBench: ai-for-physical-assets, condition-based-maintenance, hvac-maintenance, iot; When you need detailed evaluation frameworks for multiple types of AI agents operating in industry environments. ### 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 AssetOpsBench? If your project focus is on general-purpose AI outside the domain-specific context of industrial operations Do not use if you require real-time agent orchestration without any emphasis on condition-based or predictive maintenance in asset management ### Is agentdojo or AssetOpsBench more popular on GitHub? AssetOpsBench has more GitHub stars (2,304 vs 893). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and AssetOpsBench open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, AssetOpsBench: Apache-2.0). ### Where can I find alternatives to agentdojo or AssetOpsBench? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [AssetOpsBench alternatives](/tools/ibm-assetopsbench/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [AssetOpsBench markdown twin](/tools/ibm-assetopsbench/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-ibm-assetopsbench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or AssetOpsBench? agentdojo: Slowing. AssetOpsBench: 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 AssetOpsBench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [AssetOpsBench trust report](/tools/ibm-assetopsbench/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/_