AIエージェント
vocode-core
Build voice-based LLM agents with a modular, open-source toolkit.
★ 3.8k スターMIT
ガイドを見る →mantisは、googleが公開しているオープンソースのAIエージェントプロジェクトです（GitHub: google/mantis）。GitHubスターは2,380、フォークは201で、Apache-2.0ライセンスの下で公開されています。GitHubでの最終更新は2026年10月7日です。
Google's modular toolkit that lets AI coding agents hunt, reproduce, and patch vulnerabilities autonomously.
Decide which codebase or file you want reviewed, and prepare an isolated, restricted environment with no production access, as the README strongly requires.
Clone the repository and move into the reference harness directory.
Run the install script, authenticate Google Cloud Application Default Credentials if you use Vertex AI, then let the configuration tool auto-detect capabilities or use its interactive wizard.
Run the preflight validation, then launch a review campaign against a file or repository, optionally focusing it with a plain-language hint.
Mantis reviews codebase history for past vulnerabilities, builds a semantic index or summaries for navigation, and generates a threat model before scanning.
Individual agents research hypotheses or scan files systematically, then deduplicate the findings they produce.
Findings are critiqued, statically checked for production viability, and calibrated with a rubric so severity is not inflated.
Suspected vulnerabilities are reproduced at whatever depth the environment allows, from a reasoned exploit sketch to a unit test or a mock server.
Mantis drafts patches and uses an adversarial loop to check the vulnerability is genuinely fixed.
You can steer a run with --focus, remove spend limits for long unattended runs with parallel workers, or have it design a new agent-graph topology from an objective.
Install the venv tooling first if needed, then run the bundled installer.
git clone https://github.com/google/mantis.git
cd mantis/reference && ./install.shAuthenticate ADC for Vertex AI, auto-configure, and run the fast preflight plus reachability probe.
gcloud auth application-default login
python3 scripts/configure.py --auto
python3 scripts/configure.py --test --probePoint run.sh at a file or repository, optionally with a focus hint.
./run.sh path/to/code
./run.sh path/to/code --focus "look for IDOR"It is developed by Google and published under the google GitHub organisation, though the README notes it is not an officially supported Google product.
Yes, the code is Apache-2.0 licensed and free to use and adapt. Running it still costs whatever the underlying AI models charge.
In practice yes: the reference harness is built around Google Cloud and Gemini/Vertex AI access, so you need Google Cloud credentials (ADC) and you pay for model usage, with budget controls in the tool.
Traditional SAST tools like CodeQL or Semgrep match deterministic rules; Mantis uses agents to form hypotheses, attempt reproductions, and draft patches, so it explores behaviour rules cannot express, but its findings need human verification.
Clone the repo, run reference/install.sh, configure with scripts/configure.py --auto, pass the preflight probe, then run ./run.sh on a target in an isolated environment.
Build voice-based LLM agents with a modular, open-source toolkit.
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