--- title: "agentfield vs agentdojo" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agent-field-agentfield-vs-ethz-spylab-agentdojo" tools: ["agent-field-agentfield", "ethz-spylab-agentdojo"] --- # agentfield vs agentdojo *GraphCanon updated Jul 12, 2026* ## Verdict Pick agentfield if agent-Field/agentfield is a comprehensive toolset built in Go under the Apache-2.0 license, aiming to streamline the development lifecycle of scalable AI agents that are observably secure; pick agentdojo if agentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. [agentfield](http://www.agentfield.ai) reports 2.3k GitHub stars, 371 forks, and 91 open issues, last pushed Jul 10, 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 [agentfield's repository](https://github.com/Agent-Field/agentfield) and [agentdojo's repository](https://github.com/ethz-spylab/agentdojo). | | [agentfield](/tools/agent-field-agentfield.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Tagline | Build, run and scale AI agents like API and microservices | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | | Stars | 2,339 | 659 | | Forks | 371 | 168 | | Open issues | 91 | 33 | | Language | Go | Python | | Adopt for | Agent-Field/agentfield is a comprehensive toolset built in Go under the Apache-2.0 license, aiming to streamline the development lifecycle of scalable AI agents that are observably secure. | AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | AI Agents | AI Agents, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentfield](/tools/agent-field-agentfield.md) | [agentdojo](/tools/ethz-spylab-agentdojo.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 39d | | Open issues (now) | 91 | 33 | | Full report | [trust report](/tools/agent-field-agentfield/trust.md) | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | ## Decision facts: agentfield - **Adopt for:** Agent-Field/agentfield is a comprehensive toolset built in Go under the Apache-2.0 license, aiming to streamline the development lifecycle of scalable AI agents that are observably secure. ## 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 agentfield if… - agentfield is primarily Go; agentdojo is Python. - License: agentfield is Apache-2.0, agentdojo is MIT. - Tags unique to agentfield: agent-auth, agent-authentication, agent-scaling, agentic-ai. - When you seek to manage and scale your AI agents with robust identity awareness and auditability features from inception. ### Choose agentdojo if… - agentdojo is primarily Python; agentfield is Go. - License: agentdojo is MIT, agentfield 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. - 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 agentfield - If your project requires heavy customization in languages other than Go as Agentfield is primarily built using Go which may limit its adaptability in polyglot environments. ## 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 agentfield and agentdojo? agentfield: Build, run and scale AI agents like API and microservices. 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 agentfield over agentdojo? Choose agentfield over agentdojo when agentfield is primarily Go; agentdojo is Python; License: agentfield is Apache-2.0, agentdojo is MIT; Tags unique to agentfield: agent-auth, agent-authentication, agent-scaling, agentic-ai; When you seek to manage and scale your AI agents with robust identity awareness and auditability features from inception. ### When should I choose agentdojo over agentfield? Choose agentdojo over agentfield when agentdojo is primarily Python; agentfield is Go; License: agentdojo is MIT, agentfield 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; 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 agentfield? If your project requires heavy customization in languages other than Go as Agentfield is primarily built using Go which may limit its adaptability in polyglot environments. ### 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 agentfield or agentdojo more popular on GitHub? agentfield has more GitHub stars (2,339 vs 659). Stars measure visibility, not whether either tool fits your constraints. ### Are agentfield and agentdojo open source? Yes - both are open-source projects on GitHub (agentfield: Apache-2.0, agentdojo: MIT). ### Where can I find alternatives to agentfield or agentdojo? GraphCanon lists graph-backed alternatives at [agentfield alternatives](/tools/agent-field-agentfield/alternatives) and [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) ([agentfield markdown twin](/tools/agent-field-agentfield/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/agent-field-agentfield-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, agentfield or agentdojo? agentfield: 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 agentfield and agentdojo? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentfield trust report](/tools/agent-field-agentfield/trust); [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agent-field-agentfield`](/api/graphcanon/graph?tool=agent-field-agentfield) - 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/_