--- title: "agenta vs agentdojo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agenta-ai-agenta-vs-ethz-spylab-agentdojo" tools: ["agenta-ai-agenta", "ethz-spylab-agentdojo"] --- # agenta vs agentdojo *GraphCanon updated Jul 12, 2026* ## Verdict Pick agenta if agenta is an open-source LLMOps platform that supports prompt engineering, evaluation of language models, and monitoring their performance. It can be self-hosted and comes with a comprehensive set of tools for managing L; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. [agenta](http://www.agenta.ai) reports 4.3k GitHub stars, 565 forks, and 184 open issues, last pushed Jul 11, 2026. [agentdojo](https://agentdojo.spylab.ai/) has 659 stars, 168 forks, and 33 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [agenta's repository](https://github.com/Agenta-AI/agenta) and [agentdojo's repository](https://github.com/ethz-spylab/agentdojo). | | [agenta](/tools/agenta-ai-agenta.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Tagline | The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place. | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | | Stars | 4,283 | 659 | | Forks | 565 | 168 | | Open issues | 184 | 33 | | Language | TypeScript | Python | | Adopt for | Agenta is an open-source LLMOps platform that supports prompt engineering, evaluation of language models, and monitoring their performance. It can be self-hosted and comes with a comprehensive set of tools for managing L | 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, LLM Frameworks, Inference & Serving | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agenta](/tools/agenta-ai-agenta.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 39d | | Open issues (now) | 184 | 33 | | Full report | [trust report](/tools/agenta-ai-agenta/trust.md) | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | ## Decision facts: agenta - **Adopt for:** Agenta is an open-source LLMOps platform that supports prompt engineering, evaluation of language models, and monitoring their performance. It can be self-hosted and comes with a comprehensive set of tools for managing L ## 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 agenta if… - agenta is primarily TypeScript; agentdojo is Python. - License: agenta is Other, agentdojo is MIT. - Tags unique to agenta: llm-framework, llm-monitoring, evaluation, agents. - Also covers LLM Frameworks, Inference & Serving. - You should use Agenta if you're working on managing prompts and evaluating the performance of your language models while needing observability features in an open-source environment. ### Choose agentdojo if… - agentdojo is primarily Python; agenta is TypeScript. - License: agentdojo is MIT, agenta 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: prompt-injection, benchmark, large-language-models, security. - Also covers Evaluation & Observability. - 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 agenta - Avoid Agenta if you prefer pre-packaged SaaS solutions over DIY open-source deployments; setting up and maintaining can be complex. - Agenta may not be suitable if your project or organization does not have the technical know-how to handle self-hosted environments, as configuration and deployment require specific Docker setup. ## 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 agenta and agentdojo? agenta: The open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place.. 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 agenta over agentdojo? Choose agenta over agentdojo when agenta is primarily TypeScript; agentdojo is Python; License: agenta is Other, agentdojo is MIT; Tags unique to agenta: llm-framework, llm-monitoring, evaluation, agents; Also covers LLM Frameworks, Inference & Serving; You should use Agenta if you're working on managing prompts and evaluating the performance of your language models while needing observability features in an open-source environment. ### When should I choose agentdojo over agenta? Choose agentdojo over agenta when agentdojo is primarily Python; agenta is TypeScript; License: agentdojo is MIT, agenta 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: prompt-injection, benchmark, large-language-models, security; Also covers Evaluation & Observability; 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 agenta? Avoid Agenta if you prefer pre-packaged SaaS solutions over DIY open-source deployments; setting up and maintaining can be complex. Agenta may not be suitable if your project or organization does not have the technical know-how to handle self-hosted environments, as configuration and deployment require specific Docker setup. ### 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 agenta or agentdojo more popular on GitHub? agenta has more GitHub stars (4,283 vs 659). Stars measure visibility, not whether either tool fits your constraints. ### Are agenta and agentdojo open source? Yes - both are open-source projects on GitHub (agenta: Other, agentdojo: MIT). ### Where can I find alternatives to agenta or agentdojo? GraphCanon lists graph-backed alternatives at [agenta alternatives](/tools/agenta-ai-agenta/alternatives) and [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) ([agenta markdown twin](/tools/agenta-ai-agenta/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/agenta-ai-agenta-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, agenta or agentdojo? agenta: Very 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 agenta and agentdojo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agenta trust report](/tools/agenta-ai-agenta/trust); [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agenta-ai-agenta`](/api/graphcanon/graph?tool=agenta-ai-agenta) - 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/_