confident-ai/deepteam is an open source framework for red teaming LLMs and AI agents. The GitHub description summarizes the purpose of this project as red teaming for LLMs and AI agents. Red teaming means simulating attacks, as in a penetration test, to find weaknesses instead of carrying out real attacks. The latest release of this repository is v1.0.9. The implementation language is Python and the license is Apache-2.0.
The targets are chatbots, RAG pipelines, and AI agents. The README states that it detects vulnerabilities such as bias, privacy leakage, and SQL injection by combining vulnerability types like jailbreak, prompt injection, and multi-turn attacks. The same document also says it provides production guardrails. It runs locally and is built on top of DeepEval. It has 3032 GitHub stars.
That said, among the collected scores only momentum remains, at 2.3 on a 10-point scale, and no novelty, technology, or quality scores were given. The release notes for v1.0.9 are also not in the material, so what changed in this version cannot be confirmed. No performance comparisons or benchmarks are presented either. Considering also that the repository was created on March 5, 2025, all that can be seen right now is the goal and the scope of the publicly disclosed features. Without comparative material to decide which is better against competing tools, it is too early to judge. Rather than adopting it right away, it is closer to something to look at first to see whether its vulnerability detection coverage and the Apache-2.0 license fit in-house conditions.