Comparison
agentdojo vs myclaw-bench
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 myclaw-bench if myclaw-bench is a benchmark suite comprising 45 tasks across four tiers designed for evaluating AI agents within the OpenClaw platform.
Markdown twin · agentdojo alternatives · myclaw-bench alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | agentdojo | myclaw-bench |
|---|---|---|
| Maintenance | Steady (63d since push) As of 2w · github_public_v1 | Active (8d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal 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
- myclaw-bench
- Benchmark for AI agents on OpenClaw
Stars
- agentdojo
- 716
- myclaw-bench
- 227
Forks
- agentdojo
- 188
- myclaw-bench
- 38
Open issues
- agentdojo
- 41
- myclaw-bench
- 2
Language
- agentdojo
- Python
- myclaw-bench
- 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.
- myclaw-bench
- myclaw-bench is a benchmark suite comprising 45 tasks across four tiers designed for evaluating AI agents within the OpenClaw platform.
Persona
- agentdojo
- -
- myclaw-bench
- -
Runtime
- agentdojo
- -
- myclaw-bench
- -
License
- agentdojo
- MIT
- myclaw-bench
- MIT
Last pushed
- agentdojo
- Jun 2, 2026
- myclaw-bench
- Jul 20, 2026
Categories
- agentdojo
- AI Agents, Evaluation & Observability
- myclaw-bench
- AI Agents, Evaluation & Observability
Trust and health
Maintenance
- agentdojo
- Steady (60%)
- myclaw-bench
- Active (82%)
Days since push
- agentdojo
- 63d
- myclaw-bench
- 8d
Open issues (now)
- agentdojo
- 41
- myclaw-bench
- 2
Owner type
- agentdojo
- Organization
- myclaw-bench
- User
Full report
- agentdojo
- Trust report
- myclaw-bench
- Trust report
Shared compatibility
- Python · agentdojo: Python runtime · myclaw-bench: Python runtime
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: 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 myclaw-bench if…
- Requirements: Requires Python version 3.10 or higher to execute the benchmark tasks.; Necessitates installation of the 'uv' package manager from Astral for dependencies management..
- Tags unique to myclaw-bench: ai-agent-evaluation, benchmarking-tools, openclaw.
- Use myclaw-bench if you are developing AI agents specifically for deployment on the OpenClaw platform, as it offers a precise evaluation tailored to this ecosystem.
When NOT to use myclaw-bench
- Avoid using myclaw-bench if your AI agents will not be deployed on the OpenClaw platform, as its benchmarks are specifically designed to test within this framework.
- Do not use if you require synthetic tests for controlling variables in a highly abstracted scenario, since myclaw-bench exclusively leverages real agent session data.
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 (LeoYeAI/myclaw-bench) · observed Jul 29, 2026
- GitHub forks (LeoYeAI/myclaw-bench) · observed Jul 29, 2026
- Last push (LeoYeAI/myclaw-bench) · observed Jul 20, 2026
- License file (MIT) · observed Jul 29, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: agentdojo 716 · myclaw-bench 227 (synced Aug 5, 2026).
Common questions
- What is the difference between agentdojo and myclaw-bench?
- agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. myclaw-bench: Benchmark for AI agents on OpenClaw. See the comparison table for live GitHub stats and shared categories.
- When should I choose agentdojo over myclaw-bench?
- Choose agentdojo over myclaw-bench 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: 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 myclaw-bench over agentdojo?
- Choose myclaw-bench over agentdojo when Requirements: Requires Python version 3.10 or higher to execute the benchmark tasks.; Necessitates installation of the 'uv' package manager from Astral for dependencies management.; Tags unique to myclaw-bench: ai-agent-evaluation, benchmarking-tools, openclaw; Use myclaw-bench if you are developing AI agents specifically for deployment on the OpenClaw platform, as it offers a precise evaluation tailored to this ecosystem.
- 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 myclaw-bench?
- Avoid using myclaw-bench if your AI agents will not be deployed on the OpenClaw platform, as its benchmarks are specifically designed to test within this framework. Do not use if you require synthetic tests for controlling variables in a highly abstracted scenario, since myclaw-bench exclusively leverages real agent session data.
- Is agentdojo or myclaw-bench more popular on GitHub?
- agentdojo has more GitHub stars (716 vs 227). Stars measure visibility, not whether either tool fits your constraints.
- Are agentdojo and myclaw-bench open source?
- Yes - both are open-source projects on GitHub (agentdojo: MIT, myclaw-bench: MIT).
- Where can I find alternatives to agentdojo or myclaw-bench?
- GraphCanon lists graph-backed alternatives at agentdojo alternatives and myclaw-bench alternatives (agentdojo markdown twin, myclaw-bench 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 myclaw-bench?
- agentdojo: Steady. myclaw-bench: 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 myclaw-bench?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; myclaw-bench trust report.