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samuelfajAutonomous AI agent for full-cycle software development
Top 73.0% on SourcePulse
Summary
Claudiomiro is an autonomous AI-powered development agent designed to automate complex software development tasks from prompt to production-ready commits. It targets developers seeking to overcome the "AI productivity paradox" where AI tools speed up initial coding but slow down the overall development lifecycle by requiring extensive manual review and debugging. Claudiomiro aims to deliver production-grade code quality through a fully automated process, breaking the cycle of AI-generated drafts needing significant human intervention.
How It Works
The agent tackles development tasks by decomposing them into smaller, parallelizable sub-tasks executed by AI agents. It simultaneously processes these tasks, reviews generated code, automatically runs tests, and fixes failures. This parallel, iterative approach aims to break the cycle of AI-generated drafts requiring significant human intervention, enabling a more streamlined and efficient development workflow.
Quick Start & Requirements
Installation is done via npm: npm install -g claudiomiro. Basic usage involves running claudiomiro --prompt="Your development task". For optimal results, projects should have linting (e.g., ESLint, Pylint) and unit tests to facilitate Claudiomiro's self-validation and fixing capabilities. Optional integration with local LLMs via Ollama (e.g., CLAUDIOMIRO_LOCAL_LLM=qwen2.5-coder:7b claudiomiro --prompt="Add authentication") can significantly reduce token costs and latency.
Highlighted Details
--loop-fixes).Maintenance & Community
Information regarding maintainers, community channels (Discord/Slack), or roadmap is not detailed in the provided README.
Licensing & Compatibility
The license type and compatibility notes for commercial use are not specified in the provided README.
Limitations & Caveats
Development cycles per task are capped at a maximum of 20 by default (customizable with --limit=N). Users can prevent automatic pushing to remote repositories using --push=false to allow for pre-commit review. The effectiveness relies heavily on the presence and quality of project linting and unit tests.
1 week ago
Inactive
merrymercy
Shengjia Zhao(Chief Scientist at Meta Superintelligence Lab),
google
grahamjenson
ThilinaRajapakse
google-research
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tensorflow
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