Summary: ARTEMIS (Automated Red Teaming Engine with Multi-agent Intelligent Supervision) is an autonomous agent for automated vulnerability discovery. Developed by the Stanford Trinity project, it targets security professionals and researchers seeking to streamline red teaming operations by leveraging multi-agent systems and large language models (LLMs) for intelligent supervision. The primary benefit is the automation of complex vulnerability discovery tasks.

How It Works: ARTEMIS employs a multi-agent architecture orchestrated by an LLM supervisor. It utilizes spawned "Codex" LLM instances to perform vulnerability discovery tasks. This approach enables autonomous exploration and exploitation of systems, mimicking human red team activities but at an accelerated pace. The intelligent supervision layer guides the agents, allowing for adaptive strategies and efficient identification of security weaknesses.

Quick Start & Requirements: Installation requires Rust (via rustup) and uv (Python package installer). After building the codex-rs binary (cargo build --release --manifest-path codex-rs/Cargo.toml), set up the Python environment (uv sync, source .venv/bin/activate). Configuration involves copying .env.example to .env and providing API keys for OpenAI or OpenRouter (OPENROUTER_API_KEY or OPENAI_API_KEY) and specifying a SUBAGENT_MODEL (e.g., anthropic/claude-sonnet-4). A quick test can be run using python -m supervisor.supervisor --config-file configs/tests/ctf_easy.yaml --benchmark-mode --duration 10 --skip-todos. Docker support is available via docker build -t artemis . followed by running ./run_docker.sh scripts, which handle environment variable mounting and optional ~/.codex/config.toml for OpenRouter.

Highlighted Details:

• Autonomous Red Teaming Engine leveraging multi-agent intelligent supervision.
• Integrates with OpenAI and OpenRouter LLM APIs for agent capabilities.
• Supports both direct Python execution and Docker deployment.
• Designed for automated vulnerability discovery and exploitation simulation.

Maintenance & Community: The provided README does not contain specific details regarding maintainers, community channels (like Discord or Slack), project roadmaps, or notable sponsorships.

Licensing & Compatibility: The project is licensed under the Apache-2.0 License. This license is permissive and generally compatible with commercial use and closed-source linking.

Limitations & Caveats: Setup complexity involves multiple prerequisites including Rust, uv, and LLM API key configuration. The system's effectiveness and cost are directly tied to the performance and pricing of the chosen external LLM APIs. The project is based on a specific commit of OpenAI Codex, which may imply dependencies or potential for divergence from newer Codex versions.