--- title: "agentdojo vs agent-learning-kit" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-future-agi-agent-learning-kit" tools: ["ethz-spylab-agentdojo", "future-agi-agent-learning-kit"] --- # agentdojo vs agent-learning-kit *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 agent-learning-kit if agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language. [agentdojo](https://agentdojo.spylab.ai/) reports 893 GitHub stars, 235 forks, and 72 open issues, last pushed Jun 2, 2026. [agent-learning-kit](https://futureagi.com) has 119 stars, 44 forks, and 19 open issues, last pushed Sep 18, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [agent-learning-kit's repository](https://github.com/future-agi/agent-learning-kit). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | General Purpose Evaluation and Simulation Environment for all your AI related Workflows | | Stars | 893 | 119 | | Forks | 235 | 44 | | Open issues | 72 | 19 | | 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. | agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | 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) | [agent-learning-kit](/tools/future-agi-agent-learning-kit.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 125d | 0d | | Open issues (now) | 72 | 19 | | Stars delta | +91 (30d) | +1 (30d) | | Open issues delta | +21 (30d) | +13 (30d) | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/future-agi-agent-learning-kit/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [agent-learning-kit](/tools/future-agi-agent-learning-kit.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: agent-learning-kit - **Adopt for:** agent-learning-kit is a Python-based toolkit for evaluating and simulating AI workflows, particularly suited for AI agents. It offers optional extras for specific functionalities and a TypeScript SDK for broader language ## Choose when ### Choose agentdojo if… - License: agentdojo is MIT, agent-learning-kit 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. ### Choose agent-learning-kit if… - License: agent-learning-kit is Other, agentdojo is MIT. - Tags unique to agent-learning-kit: agentic-ai, ai-agents, cicd, evaluation. - When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents ## 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 agent-learning-kit - If your project strictly requires a different programming language other than Python or TypeScript - When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras ## Common questions ### What is the difference between agentdojo and agent-learning-kit? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. agent-learning-kit: General Purpose Evaluation and Simulation Environment for all your AI related Workflows. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over agent-learning-kit? Choose agentdojo over agent-learning-kit when License: agentdojo is MIT, agent-learning-kit 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 choose agent-learning-kit over agentdojo? Choose agent-learning-kit over agentdojo when License: agent-learning-kit is Other, agentdojo is MIT; Tags unique to agent-learning-kit: agentic-ai, ai-agents, cicd, evaluation; When you need a comprehensive environment for evaluating and simulating AI workflows, especially for AI agents. ### 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 agent-learning-kit? If your project strictly requires a different programming language other than Python or TypeScript When you need a tool that is already at a mature v1 release, as agent-learning-kit is still developing its TypeScript SDK and extras ### Is agentdojo or agent-learning-kit more popular on GitHub? agentdojo has more GitHub stars (893 vs 119). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and agent-learning-kit open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, agent-learning-kit: Other). ### Where can I find alternatives to agentdojo or agent-learning-kit? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [agent-learning-kit alternatives](/tools/future-agi-agent-learning-kit/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [agent-learning-kit markdown twin](/tools/future-agi-agent-learning-kit/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-future-agi-agent-learning-kit.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or agent-learning-kit? agentdojo: Slowing. agent-learning-kit: 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 agent-learning-kit? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [agent-learning-kit trust report](/tools/future-agi-agent-learning-kit/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/_