--- title: "agentdojo vs BIG-bench" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/ethz-spylab-agentdojo-vs-google-big-bench" tools: ["ethz-spylab-agentdojo", "google-big-bench"] --- # agentdojo vs BIG-bench *GraphCanon updated Aug 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 BIG-bench if decision-critical facts for BIG-bench. [agentdojo](https://agentdojo.spylab.ai/) reports 716 GitHub stars, 188 forks, and 41 open issues, last pushed Jun 2, 2026. [BIG-bench](https://github.com/google/BIG-bench) has 3.2k stars, 617 forks, and 106 open issues, last pushed Jul 19, 2024. Figures are from public GitHub metadata via [agentdojo's repository](https://github.com/ethz-spylab/agentdojo) and [BIG-bench's repository](https://github.com/google/BIG-bench). | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [BIG-bench](/tools/google-big-bench.md) | | --- | --- | --- | | Tagline | A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents | Collaborative benchmark for language model capabilities | | Stars | 716 | 3,249 | | Forks | 188 | 617 | | Open issues | 41 | 106 | | 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. | Decision-critical facts for BIG-bench | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [agentdojo](/tools/ethz-spylab-agentdojo.md) | [BIG-bench](/tools/google-big-bench.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Archived (8%) | | Days since push | 63d | 748d | | Archived on GitHub | No | Yes | | Open issues (now) | 41 | 106 | | Full report | [trust report](/tools/ethz-spylab-agentdojo/trust.md) | [trust report](/tools/google-big-bench/trust.md) | ## Shared compatibility - **Python**: [agentdojo](/tools/ethz-spylab-agentdojo.md) - Python runtime; [BIG-bench](/tools/google-big-bench.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: BIG-bench - **Requirements:** Python 3.5-3.8 required.; `pytest` is necessary for running automated tests. - **Adopt for:** Decision-critical facts for BIG-bench ## Choose when ### Choose agentdojo if… - License: agentdojo is MIT, BIG-bench 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 AI Agents. - AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents. ### Choose BIG-bench if… - License: BIG-bench is Apache-2.0, agentdojo is MIT. - Requirements: Python 3.5-3.8 required.; `pytest` is necessary for running automated tests.. - Tags unique to BIG-bench: benchmarking, evaluation, language-models, seqio. - When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities. ## 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 BIG-bench - If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools. - As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential - If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice. ## Common questions ### What is the difference between agentdojo and BIG-bench? agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. BIG-bench: Collaborative benchmark for language model capabilities. See the comparison table for live GitHub stats and shared categories. ### When should I choose agentdojo over BIG-bench? Choose agentdojo over BIG-bench when License: agentdojo is MIT, BIG-bench 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 AI Agents; 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 BIG-bench over agentdojo? Choose BIG-bench over agentdojo when License: BIG-bench is Apache-2.0, agentdojo is MIT; Requirements: Python 3.5-3.8 required.; `pytest` is necessary for running automated tests.; Tags unique to BIG-bench: benchmarking, evaluation, language-models, seqio; When you need a comprehensive benchmark that evaluates language models across various tasks and includes methods for extrapolating model capabilities. ### 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 BIG-bench? If you are looking for a tool that simplifies benchmarking with minimal configuration, BIG-bench requires setting up an environment and can be more complex compared to streamlined benchmark tools. As BIG-bench relies on collaboration across various tasks and contributions from the community, it might not be ideal if you need benchmark tasks or evaluations immediately available without potential If your project does not require advanced extrapolation techniques for measuring model capabilities over a wide range of benchmarks, simpler evaluation tools may suffice. ### Is agentdojo or BIG-bench more popular on GitHub? BIG-bench has more GitHub stars (3,249 vs 716). Stars measure visibility, not whether either tool fits your constraints. ### Are agentdojo and BIG-bench open source? Yes - both are open-source projects on GitHub (agentdojo: MIT, BIG-bench: Apache-2.0). ### Where can I find alternatives to agentdojo or BIG-bench? GraphCanon lists graph-backed alternatives at [agentdojo alternatives](/tools/ethz-spylab-agentdojo/alternatives) and [BIG-bench alternatives](/tools/google-big-bench/alternatives) ([agentdojo markdown twin](/tools/ethz-spylab-agentdojo/alternatives.md), [BIG-bench markdown twin](/tools/google-big-bench/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-google-big-bench.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, agentdojo or BIG-bench? agentdojo: Steady. BIG-bench: Archived. 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 BIG-bench? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [agentdojo trust report](/tools/ethz-spylab-agentdojo/trust); [BIG-bench trust report](/tools/google-big-bench/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/_