Most "AI security" tools stop at scanning and flagging — they hand you a list of findings and leave the actual exploitation to a human. PentAGI closes that loop: it treats a pentest as an autonomous agent task, where separate agents reason over findings, write exploit code, run it against the target, and pick the next move from what came back.
What Sets It Apart
- Multi-agent division of labor: a Researcher gathers and reasons over intel, a Developer writes exploit and automation code, and an Executor runs the tooling — so each phase is specialized instead of one prompt doing everything.
- Memory built for long engagements: vector embeddings in PostgreSQL/pgvector plus a Graphiti and Neo4j knowledge graph let it recall earlier findings and track relationships across a multi-step attack chain rather than losing context mid-run.
- Provider-agnostic by design: runs on OpenAI, Anthropic, Gemini, AWS Bedrock, Ollama, and Chinese models (DeepSeek, GLM, Kimi, Qwen), so you are not tied to one LLM's pricing or refusal behavior.
Who It's For
Great fit if you are a security professional or researcher who wants to automate the repetitive recon-to-exploit chain in an authorized lab or engagement and can run Docker. Look elsewhere if you want a point-and-click scanner, cannot supply your own LLM keys, or — most importantly — lack written authorization for the target. This is offensive tooling: pointing it at systems you do not own or are not permitted to test is illegal, and all activity is meant to stay inside its sandboxed Docker environment.
