GraphCanon updated Sep 9, 2026 · GitHub synced Sep 9, 2026
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Decision brief
circle-guard-bench is a Python-based AI benchmark tool for evaluating large language model guard systems under various protection scenarios.
Good fit when
- 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.
- Consider using this tool if you are looking for detailed insights into how different guard systems perform under various potential security breaches or misuse attempts.
Avoid when
- 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.
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (185d since push)
- As of Sep 9, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 9, 2026
- Security (OSV)
- No lockfile
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install circle-guard-benchSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
whitecircle/circle-guard-bench is the first-of-its-kind AI benchmark focused on evaluating protection capabilities of large language model (LLM) guard systems including guardrails and safeguards, featuring extensive evaluation scenarios.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Sep 9, 2026
- Languages
- python
Source: github.language+pyproject.toml · Sep 9, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Installation using Poetry (basic installation) poetry install Installation with additional inference engines poetry install extras "vllm sglang transformers" Installation using pip (or uv) pip install e . Installation with additional inference engines using pip pip install e ".[v...
For agents
This page has a .md twin and JSON over the API.