LangGraph-Swift provides a graph-based DSL for constructing AI workflows by chaining nodes representing actions such as LLM queries, retrieval operations, tool calls, and memory management. Each node is type-safe and can be connected to define execution order. The framework supports adapters for popular LLM services like OpenAI, Azure, and Anthropic, as well as custom tool integrations for calling APIs or functions. It includes built-in memory modules to retain context across sessions, debugging and visualization tools, and cross-platform support for iOS, macOS, and Linux. Developers can extend nodes with custom logic, enabling rapid prototyping of chatbots, document processors, and autonomous agents within native Swift.

Xemantic AI Tool Schema is a set of JSON Schema and TypeScript type definitions designed to standardize the way AI agent tools are described, validated, and invoked. Developers can define tool metadata such as name, description, and parameters, then validate instances against the schema or use generated TypeScript interfaces during development. The schema supports parameter types, nested structures, default values, and version control, ensuring robust validation and compatibility. By following a consistent schema, AI Agents can discover and call tools reliably at runtime, improving maintainability and reducing integration errors. The package integrates seamlessly with Xemantic AI Agents and can be extended for custom use cases.

The Automated Vulnerability Scanning with Agentic AI project leverages large language models to autonomously plan, execute, and report on security assessments. By integrating tools like Bandit for static analysis, Nmap for network enumeration, and CVE databases for vulnerability matching, the agent creates a step-by-step scanning workflow. It analyzes code repositories for insecure patterns, probes network ports for exposed services, correlates findings with known vulnerabilities, and generates an actionable report with risk ratings and remediation guidance. Users can customize scanning pipelines, define target scopes, and integrate results into existing CI/CD pipelines. This LLM-driven agentic framework reduces manual effort and accelerates the discovery and mitigation of security risks across applications and infrastructure.

Smaty is a powerful tool designed for developers to effortlessly code secure smart contracts. It features advanced vulnerability detection, unit test generation, and customizable contract creation, ensuring robust and secure blockchain applications. Whether you're a beginner or an experienced developer, Smaty streamlines the coding process, making it easier and faster to create secure smart contracts that meet industry standards and best practices. Stay ahead in the blockchain technology by leveraging Smaty's comprehensive suite of tools.