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.

The Agent Supervisor Example repository demonstrates how to orchestrate several autonomous AI agents in a coordinated workflow. Built in Python, it defines a Supervisor class to dispatch tasks, monitor agent status, handle failures, and aggregate responses. You can extend base agent classes, plug in different model APIs, and configure scheduling policies. It logs activities for auditing, supports parallel execution, and offers a modular design for easy customization and integration into larger AI systems.