micro agent

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Basic Information

Micro Agent is a small, focused CLI AI agent that helps developers generate and fix code by writing a definitive test and iterating on code until the test passes. It is intended for use in a single-file, test-driven workflow rather than as an end-to-end developer agent. Users run the CLI against a target file and supply a test command; the agent uses large language models to produce code, run the supplied tests, observe failures, and produce new code until tests succeed or a run limit is reached. The project intentionally avoids risky operations such as installing modules or editing many files. It supports interactive mode, manual runs, and experimental visual matching where the agent compares rendered output to screenshots. The CLI is distributed as an npm package and requires Node.js v18 or later. Configuration is done via the micro-agent config command or environment variables and it supports multiple LLM providers rather than a single built-in model.

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App Details

Features
Command-line interface for iterative test-driven code generation and repair. Interactive mode that prompts the user for guidance and feedback. Unit test matching mode where the user supplies a test script to run after each code generation attempt. Visual matching experimental mode that compares a local URL rendering to a screenshot and iterates to improve pixel or component fidelity. Integration with multiple LLM providers including OpenAI, Anthropic Claude, Ollama or OpenAI-compatible endpoints like Groq. Simple configuration via micro-agent config, environment variables, or a small interactive config UI. Options and flags include max runs, prompt file, test script, test file, visual URL and thread resume. Integrates with Visual Copilot and Figma for higher-fidelity design-to-code workflows. Distributed via npm install -g @builder.io/micro-agent.
Use Cases
Micro Agent automates repetitive developer tasks by combining test-driven development with LLM-driven code generation and iteration, reducing the manual loop of writing, running tests, and fixing broken AI-produced code. It produces a clear feedback loop: generate a test, generate code, run tests, and repeat until success, which helps catch and correct compounding errors common in general-purpose coding agents. The visual matching feature helps align rendered UI to a target screenshot or Figma design, useful for component and page-level adjustments. Support for multiple LLM providers and configurable run limits gives teams flexibility to use preferred models and control cost and runtime. The CLI and focused scope make it useful for developers who want a safe, constrained assistant to help implement small functions or UI components without changing lots of files or performing risky system actions.

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