Home/Compare/agentdojo vs circle-guard-bench

Comparison

agentdojo vs circle-guard-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 circle-guard-bench if circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios.

Markdown twin · agentdojo alternatives · circle-guard-bench alternatives

GraphCanon updated Sep 20, 2026

14views this month

agentdojo logo

agentdojo

ethz-spylab/agentdojo

802pushed Jun 2, 2026
vs
circle-guard-bench logo

circle-guard-bench

whitecircle/circle-guard-bench

75pushed Mar 7, 2026

Trust & integrity

Signalagentdojocircle-guard-bench
Maintenance
Slowing (94d since push)
As of Sep 5, 2026 · github_public_v1
Slowing (185d since push)
As of Sep 9, 2026 · github_public_v1
Provenance
Not a fork · Organization account
As of Sep 5, 2026 · github_public_v1
Not a fork · Organization account
As of Sep 9, 2026 · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of Jul 11, 2026 · osv@v1
No lockfile (source not queried)
As of Jul 15, 2026 · 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
circle-guard-bench
AI benchmark for evaluating LLM guard systems

Stars

agentdojo
802
circle-guard-bench
75

Forks

agentdojo
205
circle-guard-bench
5

Open issues

agentdojo
51
circle-guard-bench
1

Language

agentdojo
Python
circle-guard-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.
circle-guard-bench
circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios.

Persona

agentdojo
-
circle-guard-bench
-

Runtime

agentdojo
-
circle-guard-bench
-

License

agentdojo
MIT
circle-guard-bench
Apache-2.0

Last pushed

agentdojo
Jun 2, 2026
circle-guard-bench
Mar 7, 2026

Categories

agentdojo
AI Agents, Evaluation & Observability
circle-guard-bench
Evaluation & Observability

Trust and health

Days since push

agentdojo
94d
circle-guard-bench
185d

Open issues (now)

agentdojo
51
circle-guard-bench
1

Stars delta

agentdojo
+86 (30d)
circle-guard-bench
+3 (30d)

Open issues delta

agentdojo
+10 (30d)
circle-guard-bench
+1 (30d)

Full report

agentdojo
Trust report
circle-guard-bench
Trust report

Shared compatibility

  • Python · agentdojo: Python runtime · circle-guard-bench: Python runtime

Choose agentdojo if…

  • License: agentdojo is MIT, circle-guard-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, 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 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.

Choose circle-guard-bench if…

  • License: circle-guard-bench is Apache-2.0, agentdojo is MIT.
  • Tags unique to circle-guard-bench: ai, benchmarking, guardrail, llm-evaluation.
  • Use circle-guard-bench when you need to evaluate the effectiveness of guardrails and safeguards in your LLM environment, as it offers an unparalleled set of scenarios specific to these protections.

When NOT to use circle-guard-bench

  • Avoid circle-guard-bench if your primary focus is on benchmarking the performance aspects like speed and latency of LLMs, as it specializes in evaluating protections rather than performance.
  • Do not use this tool when you intend to conduct general purpose evaluations or comparisons between different LLM models that do not specifically involve security-related guard systems.

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: agentdojo 802 · circle-guard-bench 75 (synced Sep 20, 2026).

Common questions

What is the difference between agentdojo and circle-guard-bench?
agentdojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents. circle-guard-bench: AI benchmark for evaluating LLM guard systems. See the comparison table for live GitHub stats and shared categories.
When should I choose agentdojo over circle-guard-bench?
Choose agentdojo over circle-guard-bench when License: agentdojo is MIT, circle-guard-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, 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 circle-guard-bench over agentdojo?
Choose circle-guard-bench over agentdojo when License: circle-guard-bench is Apache-2.0, agentdojo is MIT; Tags unique to circle-guard-bench: ai, benchmarking, guardrail, llm-evaluation; Use circle-guard-bench when you need to evaluate the effectiveness of guardrails and safeguards in your LLM environment, as it offers an unparalleled set of scenarios specific to these protections.
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 circle-guard-bench?
Avoid circle-guard-bench if your primary focus is on benchmarking the performance aspects like speed and latency of LLMs, as it specializes in evaluating protections rather than performance. Do not use this tool when you intend to conduct general purpose evaluations or comparisons between different LLM models that do not specifically involve security-related guard systems.
Is agentdojo or circle-guard-bench more popular on GitHub?
agentdojo has more GitHub stars (802 vs 75). Stars measure visibility, not whether either tool fits your constraints.
Are agentdojo and circle-guard-bench open source?
Yes - both are open-source projects on GitHub (agentdojo: MIT, circle-guard-bench: Apache-2.0).
Where can I find alternatives to agentdojo or circle-guard-bench?
GraphCanon lists graph-backed alternatives at agentdojo alternatives and circle-guard-bench alternatives (agentdojo markdown twin, circle-guard-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 circle-guard-bench?
agentdojo: Slowing. circle-guard-bench: Slowing. 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 circle-guard-bench?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: agentdojo trust report; circle-guard-bench trust report.

Was this helpful?

Anonymous feedback helps us improve pages and translations.