Comparison
agentdojo vs ClawBench
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 ClawBench if clawBench offers an open-source benchmark framework for evaluating browser-based AI agents on real-world online tasks.
Markdown twin · agentdojo alternatives · ClawBench alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | ClawBench |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- agentdojo
- A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- ClawBench
- Open-source benchmark for browser AI agents on daily tasks
Stars
- agentdojo
- 716
- ClawBench
- 532
Forks
- agentdojo
- 188
- ClawBench
- 30
Open issues
- agentdojo
- 41
- ClawBench
- 47
Language
- agentdojo
- Python
- ClawBench
- Python
Adopt for
- agentdojo
- AgentDojo serves as a benchmarking environment to evaluate security attacks, like prompt injection, and defenses for Large Language Model (LLM) agents.
- ClawBench
- ClawBench offers an open-source benchmark framework for evaluating browser-based AI agents on real-world online tasks.
Persona
- agentdojo
- -
- ClawBench
- -
Runtime
- agentdojo
- -
- ClawBench
- -
License
- agentdojo
- MIT
- ClawBench
- ClawBench operates under the Apache-2.0 license, offering a permissive free software license that encourages software reuse and interoperability.
Last pushed
- agentdojo
- Jun 2, 2026
- ClawBench
- Jul 28, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- ClawBench
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- ClawBench
- Very active (96%)
Days since push
- agentdojo
- 63d
- ClawBench
- 0d
Open issues (now)
- agentdojo
- 41
- ClawBench
- 47
Full report
- agentdojo
- Trust report
- ClawBench
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · ClawBench: Python runtime
Choose agentdojo if…
- License: agentdojo is MIT, ClawBench 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.
- 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 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.
Choose ClawBench if…
- License: ClawBench is Apache-2.0, agentdojo is MIT.
- Requirements: Requires setup for browser automation tasks within the Chrome environment..
- Tags unique to ClawBench: agent-evaluation, agentic-ai, browser-agent, llm-evaluation.
- You are developing a browser AI agent and wish to measure its performance against everyday online activities, as ClawBench specifically simulates these scenarios.
When NOT to use ClawBench
- If your focus is solely on backend server-based AI evaluations without a browser interface involvement, ClawBench will not be the appropriate choice.
- For those developing standalone applications or mobile agents, ClawBench’s browser-centric tasks will not reflect their operational capabilities accurately.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (ethz-spylab/agentdojo) · observed Aug 5, 2026
- GitHub forks (ethz-spylab/agentdojo) · observed Aug 5, 2026
- Last push (ethz-spylab/agentdojo) · observed Jun 2, 2026
- License file (MIT) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (TIGER-AI-Lab/ClawBench) · observed Jul 28, 2026
- GitHub forks (TIGER-AI-Lab/ClawBench) · observed Jul 28, 2026
- Last push (TIGER-AI-Lab/ClawBench) · observed Jul 28, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · ClawBench 532 (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and ClawBench?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. ClawBench: Open-source benchmark for browser AI agents on daily tasks. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over ClawBench?
- Choose agentdojo over ClawBench when License: agentdojo is MIT, ClawBench 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; 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 ClawBench over agentdojo?
- Choose ClawBench over agentdojo when License: ClawBench is Apache-2.0, agentdojo is MIT; Requirements: Requires setup for browser automation tasks within the Chrome environment.; Tags unique to ClawBench: agent-evaluation, agentic-ai, browser-agent, llm-evaluation; You are developing a browser AI agent and wish to measure its performance against everyday online activities, as ClawBench specifically simulates these scenarios.
- 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 ClawBench?
- If your focus is solely on backend server-based AI evaluations without a browser interface involvement, ClawBench will not be the appropriate choice. For those developing standalone applications or mobile agents, ClawBench’s browser-centric tasks will not reflect their operational capabilities accurately.
- Is agentdojo or ClawBench more popular on GitHub?
- agentdojo has more GitHub stars (716 vs 532). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and ClawBench open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, ClawBench: Apache-2.0).
- Where can I find alternatives to agentdojo or ClawBench?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and ClawBench alternatives (agentdojo markdown twin, ClawBench markdown twin), 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 mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, agentdojo or ClawBench?
- agentdojo: Steady. ClawBench: 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 ClawBench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; ClawBench trust report.