ToolFuzz is an open-source framework designed to automatically generate diverse fuzzing scenarios that probe the tool-calling logic of AI agents. By injecting malformed inputs and varying tool invocation sequences, it identifies edge cases and failure modes. Developers can customize fuzzing strategies, track coverage metrics, and visualize results in real time, enabling efficient debugging and reliability improvement of agent-driven applications.
AI Platforms & Frameworks
Software Testing
Tool Libraries
#fuzz testing
#AI Testing
#automated testing tools
#software quality assurance
#API Testing
#error detection
#security vulnerabilities
#Performance Testing
#edge case identification
#debugging tools
#Data protection
#custom testing strategies
#Open-source tools
#runtime analysis
#modular plugin architecture
#input validation
#test automation
#code coverage
#fault tolerance
...
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ToolFuzz
ToolFuzz is an open-source framework designed to automatically generate diverse fuzzing scenarios that probe the tool-calling logic of AI agents. By injecting malformed inputs and varying tool invocation sequences, it identifies edge cases and failure modes. Developers can customize fuzzing strategies, track coverage metrics, and visualize results in real time, enabling efficient debugging and reliability improvement of agent-driven applications.
AI Platforms & Frameworks
Software Testing
Tool Libraries
#fuzz testing
#AI Testing
#automated testing tools
#software quality assurance
#API Testing
#error detection
#security vulnerabilities
#Performance Testing
#edge case identification
#debugging tools
#Data protection
#custom testing strategies
#Open-source tools
#runtime analysis
#modular plugin architecture
#input validation
#test automation
#code coverage
#fault tolerance
...
Featured
What is ToolFuzz?
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.
Who will use ToolFuzz?
- AI researchers
- LLM developers
- QA engineers
- AI safety auditors
- Tool integration specialists
How to use the ToolFuzz?
- Step1: Install ToolFuzz via pip.
- Step2: Configure your AI agent environment and define tool interfaces.
- Step3: Create a fuzzing profile specifying mutation rules and target tool modules.
- Step4: Run the ToolFuzz test suite to generate and execute fuzz cases.
- Step5: Review coverage reports and error logs.
- Step6: Refine fuzz strategies and rerun tests to validate fixes.
Platform
- mac
- windows
- linux
ToolFuzz's Core Features & Benefits
The Core Features
- Automated fuzz case generation
- Malformed input injection
- Tool invocation sequence exploration
- Customizable fuzz strategies
- Coverage tracking and metrics
- Real-time result visualization
- Modular plugin interface
The Benefits
- Detects edge cases and failure modes early
- Enhances tool-calling reliability
- Accelerates debugging and QA
- Improves AI agent robustness
- Customizable to diverse tool APIs
- Open-source and extensible
ToolFuzz's Main Use Cases & Applications
- Testing LLM-based agents with external tool plugins
- Benchmarking AI agent tool integration
- Automated QA for agent-driven applications
- Security and stability evaluation of tool calls
- Regression testing after agent updates
FAQs of ToolFuzz
ToolFuzz Company Information
ToolFuzz Reviews
5/5
ToolFuzz's Main Competitors and alternatives?
- American Fuzzy Lop (AFL)
- Hypothesis
- QuickFuzz
- LangFuzz
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