--- title: "agentdojo vs RagaAI-Catalyst" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-raga-ai-hub-ragaai-catalyst" tools: ["ethz-spylab-agentdojo", "raga-ai-hub-ragaai-catalyst"] --- # agentdojo vs RagaAI-Catalyst *GraphCanon updated Jul 12, 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 RagaAI-Catalyst if ragaAI-Catalyst emerges as a specialized Python framework designed for monitoring and evaluating AI agents, with unique features around self-hosted dashboards, advanced analytics, and support for tracing and debugging LL. [agentdojo](https://agentdojo.spylab.ai/) reports 659 GitHub stars, 168 forks, and 33 open issues, last pushed Jun 2, 2026. [RagaAI-Catalyst](https://catalyst.raga.ai/) has 16k stars, 3.6k forks, and 34 open issues, last pushed Feb 11, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [RagaAI-Catalyst's repository](https://github.com/raga-ai-hub/RagaAI-Catalyst). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [RagaAI-Catalyst](/tools/raga-ai-hub-ragaai-catalyst.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Python SDK for AI agent observability and evaluation | | Stars | 659 | 16,145 | | Forks | 168 | 3,576 | | Open issues | 33 | 34 | | 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. | RagaAI-Catalyst emerges as a specialized Python framework designed for monitoring and evaluating AI agents, with unique features around self-hosted dashboards, advanced analytics, and support for tracing and debugging LL | | 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) | [RagaAI-Catalyst](/tools/raga-ai-hub-ragaai-catalyst.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 39d | 149d | | Open issues (now) | 33 | 34 | | Security scan | No lockfile | 159 low (159 low) | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/raga-ai-hub-ragaai-catalyst/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [RagaAI-Catalyst](/tools/raga-ai-hub-ragaai-catalyst.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: RagaAI-Catalyst - **Adopt for:** RagaAI-Catalyst emerges as a specialized Python framework designed for monitoring and evaluating AI agents, with unique features around self-hosted dashboards, advanced analytics, and support for tracing and debugging LL ## Choose when ### Choose agentdojo if… - License: agentdojo is MIT, RagaAI-Catalyst 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: prompt-injection, benchmark, large-language-models, security. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### Choose RagaAI-Catalyst if… - License: RagaAI-Catalyst is Apache-2.0, agentdojo is MIT. - Tags unique to RagaAI-Catalyst: ai-performance-optimization, ai-application-debugging, ai-agent-monitoring, agents. - When you need comprehensive tools for the observability of complex multi-agentic systems. ## 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 RagaAI-Catalyst - When you prefer a language-agnostic solution or require support outside of the Python ecosystem. - If your primary need is focused solely on basic monitoring without advanced debugging and evaluation features. - For projects that do not utilize multi-agentic systems or do not benefit from timeline and execution graph visualizations. - In scenarios where a fully managed service with no self-hosting requirements is preferred. ## Common questions ### What is the difference between agentdojo and RagaAI-Catalyst? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. RagaAI-Catalyst: Python SDK for AI agent observability and evaluation. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over RagaAI-Catalyst? Choose agentdojo over RagaAI-Catalyst when License: agentdojo is MIT, RagaAI-Catalyst 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: prompt-injection, benchmark, large-language-models, 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 RagaAI-Catalyst over agentdojo? Choose RagaAI-Catalyst over agentdojo when License: RagaAI-Catalyst is Apache-2.0, agentdojo is MIT; Tags unique to RagaAI-Catalyst: ai-performance-optimization, ai-application-debugging, ai-agent-monitoring, agents; When you need comprehensive tools for the observability of complex multi-agentic systems. ### 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 RagaAI-Catalyst? When you prefer a language-agnostic solution or require support outside of the Python ecosystem. If your primary need is focused solely on basic monitoring without advanced debugging and evaluation features. For projects that do not utilize multi-agentic systems or do not benefit from timeline and execution graph visualizations. In scenarios where a fully managed service with no self-hosting requirements is preferred. ### Is agentdojo or RagaAI-Catalyst more popular on GitHub? RagaAI-Catalyst has more GitHub stars (16,145 vs 659). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and RagaAI-Catalyst open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, RagaAI-Catalyst: Apache-2.0). ### Where can I find alternatives to agentdojo or RagaAI-Catalyst? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [RagaAI-Catalyst alternatives](/tools/raga-ai-hub-ragaai-catalyst/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [RagaAI-Catalyst markdown twin](/tools/raga-ai-hub-ragaai-catalyst/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-raga-ai-hub-ragaai-catalyst.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or RagaAI-Catalyst? agentdojo: Steady. RagaAI-Catalyst: Slowing. 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 RagaAI-Catalyst? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [RagaAI-Catalyst trust report](/tools/raga-ai-hub-ragaai-catalyst/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/_