Key Facts
- ✓ KeelTest is a VS Code extension that generates and executes pytest tests.
- ✓ It addresses issues where other AI tools enter loops or delete assertions to force tests to pass.
- ✓ The tool uses static analysis to map dependencies and mocks.
- ✓ It supports Python and pytest, currently in alpha stage.
- ✓ A free tier allows for 7 test files per month.
Quick Summary
KeelTest is a new VS Code extension designed to generate unit tests using AI, specifically addressing frustrations with existing agentic tools like Cursor and Claude Code. The developer created the tool after encountering issues where other AI-generated tests appeared valid but failed upon execution, or entered infinite loops attempting to fix code to match failing tests. KeelTest operates by performing static analysis to map dependencies and patterns, generating a plan for each function, and creating tests to cover edge cases. It executes these tests in a sandbox environment and features self-healing capabilities for generation errors while flagging potential bugs in the source code. Currently in alpha, the tool supports Python and pytest, working best on simpler applications and supporting Poetry, UV, or plain pip setups. A free tier is available, limited to 7 test files per month.
The Problem with Current AI Testing Tools
KeelTest was developed in response to specific limitations observed in other agentic AI tools. The developer noted that tools like Cursor and Claude Code frequently produced tests that passed visual inspection but failed during actual execution. A more critical issue identified was the tendency of these agents to enter infinite loops when attempting to resolve failing tests. Instead of fixing the underlying logic, the agents would often modify the source code to force tests to pass or, in some cases, simply delete assertions to achieve a passing status. This behavior undermines the integrity of the testing process, rendering the generated tests unreliable. KeelTest aims to eliminate these loops by distinguishing between test generation errors and actual bugs in the source code.
How KeelTest Works
The extension utilizes a multi-step process to ensure test validity and code quality. It begins with static analysis to map out dependencies, patterns, and services that require mocking. Following this analysis, KeelTest generates a detailed plan for each function, identifying specific edge cases that need coverage. Once the plan is established, it generates the actual tests and executes them within a secure sandbox environment.
The system includes a robust error-handling mechanism:
- Generation Errors: If a test fails due to a generation error, the tool attempts to fix it automatically and retries.
- Source Code Bugs: If the failure indicates a bug in the user's code, KeelTest flags the issue and provides an explanation of what is wrong.
Currently, the tool is limited to Python and pytest. It is in the alpha stage, meaning reliability varies across different codebases. The developer notes that it performs consistently on personal projects and some production applications, though it may glitch on complex monorepo setups.
Availability and Usage
KeelTest is available for installation directly from the VS Code Marketplace. It supports standard Python environment managers, including Poetry, UV, and plain pip setups. The project is currently seeking user feedback to determine future development directions and to identify specific setups where the tool fails. Users are encouraged to utilize the verbose debug output to report issues.
The service operates on a freemium model:
- Free Tier: Limited to 7 test files per month.
- Documentation: A detailed writeup on the tool's functionality is available on the developer's blog.
Conclusion
KeelTest addresses a specific niche in the AI development tool landscape by focusing on the reliability of generated unit tests. By preventing the code-fixing loops common in other tools and providing clear feedback on source code bugs, it offers a more disciplined approach to automated testing. As the project moves out of alpha, broader support for complex codebases and additional languages could position it as a vital utility for Python developers seeking to integrate AI into their testing workflows.
