Clearwing is an autonomous offensive security tool built on LangGraph, designed to emulate advanced vulnerability scanning capabilities using accessible AI models. It functions as a dual-mode system featuring a network pentest agent for live target scanning and service detection, alongside a source-code hunter that utilizes agent-driven pipelines to identify, verify, and potentially patch vulnerabilities in codebases.
Key features include:
* Dual-mode operation covering both network penetration testing and source-code analysis.
* A ReAct-loop network agent equipped with 63 bind-tools for scanning and exploitation attempts.
* An automated source-code hunter that uses adversarial verification and sanitizer crashes as ground truth.
* Comprehensive reporting capabilities including SARIF, markdown, and JSON formats.
* Support for various AI providers such as Anthropic, OpenAI, and local LLM endpoints via OpenRouter or Ollama.
Key features include:
* Dual-mode operation covering both network penetration testing and source-code analysis.
* A ReAct-loop network agent equipped with 63 bind-tools for scanning and exploitation attempts.
* An automated source-code hunter that uses adversarial verification and sanitizer crashes as ground truth.
* Comprehensive reporting capabilities including SARIF, markdown, and JSON formats.
* Support for various AI providers such as Anthropic, OpenAI, and local LLM endpoints via OpenRouter or Ollama.
This article details how to use Ollama to run large language models locally, protecting sensitive data by keeping it on your machine. It covers installation, usage with Python, LangChain, and LangGraph, and provides a practical example with FinanceGPT, while also discussing the tradeoffs of using local LLMs.
Examples for common OpenSandbox use cases. Each subdirectory contains runnable code and documentation. Integrations and sandboxes are available for various tools and services like AI models, desktop environments, and web scraping.
This blog post details how to build a natural language Bash agent using NVIDIA Nemotron Nano v2, requiring roughly 200 lines of Python code. It covers the core components, safety considerations, and offers both a from-scratch implementation and a simplified approach using LangGraph.
This article details how to implement Generative User Interfaces (Generative UI) using LangGraph, specifically focusing on integrating React components with LangGraph graphs to create dynamic and interactive applications.
An analysis of the tradeoffs between building bespoke code-based agents and using major agent frameworks such as LangGraph and LlamaIndex Workflows for creating autonomous AI systems.
