The Multi-LLM Dynamic Agent Router is an open-architecture framework for building AI agent collaborations. It features a dynamic router that directs sub-requests to the optimal language model, and a GraphQL interface to define composite prompts, query results, and merge responses. This enables developers to break complex tasks into micro-prompts, route them to specialized LLMs, and recombine outputs programmatically, yielding higher relevance, efficiency, and maintainability.

ToolFuzz provides a comprehensive fuzz testing framework specifically tailored for tool-using AI agents. It systematically generates randomized tool invocation sequences, malformed API inputs, and unexpected parameter combinations to stress-test the agent’s tool-calling modules. Users can define custom fuzz strategies using a modular plugin interface, integrate third-party tools or APIs, and adjust mutation rules to target specific failure modes. The framework collects execution traces, measures code coverage for each component, and highlights unhandled exceptions or logic flaws. With built-in result aggregation and reporting, ToolFuzz accelerates the identification of edge cases, regression issues, and security vulnerabilities, ultimately strengthening the robustness and reliability of AI-driven workflows.