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pentagi
by vxcontrol
Autonomous multi-agent penetration testing with structured, auditable attack runs
Go
Updated Apr 3, 2026
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What It Does
Implements fully autonomous multi-agent workflows to perform complex penetration testing tasks. Coordinates specialized offensive-security agents that discover, exploit, and report vulnerabilities using plugins and LLM backends. Notable for its end-to-end pentest focus, Go runtime, and integrations with OpenAI/Anthropic for agent reasoning. LLM backends.
The Value Proposition
As autonomous agents take on higher-risk tasks like security testing, knowing their behavior and failures becomes critical for trust. Pentagi surfaces reproducible attack workflows and structured results that make agent actions auditable and easier to evaluate. This matters because it turns opaque multi-agent red-team activity into traceable runs you can benchmark, analyze, and improve. traceable runs.
When to Use
Red teams and security engineers who want to prototype autonomous penetration tests and capture reproducible agent-driven attack traces. Red teams.
Use Cases
- Automating red-team scenarios and capturing step-by-step agent attack traces
- Pre-production stress testing of infrastructure with autonomous adversary agents
- Reproducing and auditing exploit chains produced by LLM-driven agents
- Integrating LLM backends (OpenAI/Anthropic) for simulated offensive workflows
Works With
openaianthropic
Topics
ai-agentsai-security-toolanthropicautonomous-agentsgolanggptgraphqlmulti-agent-systemoffensive-securityopen-source+8 more
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Keywords
multi-agent trustpenetration-testingautonomous-agentsagent track record