--- title: "agentdojo vs LLM-Agents-Ecosystem-Handbook" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-oxbshw-llm-agents-ecosystem-handbook" tools: ["ethz-spylab-agentdojo", "oxbshw-llm-agents-ecosystem-handbook"] --- # agentdojo vs LLM-Agents-Ecosystem-Handbook *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 LLM-Agents-Ecosystem-Handbook if lLM-Agents-Ecosystem-Handbook is a comprehensive resource for developers looking to build and deploy LLM agents. It includes 60+ agent skeletons, tutorials spanning from fine-tuning to local development, and evaluation工具. [agentdojo](https://agentdojo.spylab.ai/) reports 659 GitHub stars, 168 forks, and 33 open issues, last pushed Jun 2, 2026. [LLM-Agents-Ecosystem-Handbook](https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook) has 533 stars, 84 forks, and 0 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [LLM-Agents-Ecosystem-Handbook's repository](https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [LLM-Agents-Ecosystem-Handbook](/tools/oxbshw-llm-agents-ecosystem-handbook.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools. | | Stars | 659 | 533 | | Forks | 168 | 84 | | Open issues | 33 | 0 | | 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. | LLM-Agents-Ecosystem-Handbook is a comprehensive resource for developers looking to build and deploy LLM agents. It includes 60+ agent skeletons, tutorials spanning from fine-tuning to local development, and evaluation工具 | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | AI Agents, Evaluation & Observability | AI Agents, LLM Frameworks, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [LLM-Agents-Ecosystem-Handbook](/tools/oxbshw-llm-agents-ecosystem-handbook.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Active (82%) | | Days since push | 39d | 10d | | Open issues (now) | 33 | 0 | | Owner type | Organization | User | | Security scan | No lockfile | No MCP manifest | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/oxbshw-llm-agents-ecosystem-handbook/trust.md) | ## 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: LLM-Agents-Ecosystem-Handbook - **Requirements:** Min 2 GB RAM; Requires Python for full functionality.; Suitable for both local development and deployment. - **Adopt for:** LLM-Agents-Ecosystem-Handbook is a comprehensive resource for developers looking to build and deploy LLM agents. It includes 60+ agent skeletons, tutorials spanning from fine-tuning to local development, and evaluation工具 ## Choose when ### Choose agentdojo if… - 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. ### Choose LLM-Agents-Ecosystem-Handbook if… - Requirements: Min 2 GB RAM; Requires Python for full functionality.; Suitable for both local development and deployment.. - Tags unique to LLM-Agents-Ecosystem-Handbook: llmops, fine-tuning, llm, ai. - Also covers LLM Frameworks, Inference & Serving. - When you need detailed guides on the full lifecycle of developing a language model agent—from setup to deployment. ## 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 LLM-Agents-Ecosystem-Handbook - When you seek only theoretical knowledge without hands-on projects. This repository is heavily focused on practical aspects. - If your project strictly requires languages other than Python or frameworks not covered here—LLM-Agents-Ecosystem-Handbook focuses solely on Python tools and LLM ecosystem. - If you're aiming to work with a very niche aspect of LLMs that isn't yet covered by this extensive but still limited set of resources. ## Common questions ### What is the difference between agentdojo and LLM-Agents-Ecosystem-Handbook? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. LLM-Agents-Ecosystem-Handbook: One-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over LLM-Agents-Ecosystem-Handbook? Choose agentdojo over LLM-Agents-Ecosystem-Handbook when 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 choose LLM-Agents-Ecosystem-Handbook over agentdojo? Choose LLM-Agents-Ecosystem-Handbook over agentdojo when Requirements: Min 2 GB RAM; Requires Python for full functionality.; Suitable for both local development and deployment.; Tags unique to LLM-Agents-Ecosystem-Handbook: llmops, fine-tuning, llm, ai; Also covers LLM Frameworks, Inference & Serving; When you need detailed guides on the full lifecycle of developing a language model agent—from setup to deployment. ### 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 LLM-Agents-Ecosystem-Handbook? When you seek only theoretical knowledge without hands-on projects. This repository is heavily focused on practical aspects. If your project strictly requires languages other than Python or frameworks not covered here—LLM-Agents-Ecosystem-Handbook focuses solely on Python tools and LLM ecosystem. If you're aiming to work with a very niche aspect of LLMs that isn't yet covered by this extensive but still limited set of resources. ### Is agentdojo or LLM-Agents-Ecosystem-Handbook more popular on GitHub? agentdojo has more GitHub stars (659 vs 533). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and LLM-Agents-Ecosystem-Handbook open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, LLM-Agents-Ecosystem-Handbook: MIT). ### Where can I find alternatives to agentdojo or LLM-Agents-Ecosystem-Handbook? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [LLM-Agents-Ecosystem-Handbook alternatives](/tools/oxbshw-llm-agents-ecosystem-handbook/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [LLM-Agents-Ecosystem-Handbook markdown twin](/tools/oxbshw-llm-agents-ecosystem-handbook/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-oxbshw-llm-agents-ecosystem-handbook.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or LLM-Agents-Ecosystem-Handbook? agentdojo: Steady. LLM-Agents-Ecosystem-Handbook: 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 LLM-Agents-Ecosystem-Handbook? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [LLM-Agents-Ecosystem-Handbook trust report](/tools/oxbshw-llm-agents-ecosystem-handbook/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/_