Overview
MLZero is a multi-agent system designed to automate end-to-end applied machine learning workflows—from raw dataset perception and library selection to code generation, sandboxed execution, LLM-based outcome evaluation, and error-recovery retry loops. The system addresses the brittleness of traditional AutoML pipelines by combining multimodal perception, semantic memory, episodic memory, an execution judge and retry loop, and modular ML adapters. It is a paper-aligned local B.Tech implementation inspired by the MLZero NeurIPS 2025 paper, reproducing the core architectural methodology but not claiming identical benchmark scale, GPU compute infrastructure, or raw numerical results. The system explicitly supports two operational modes: MockLLM (offline and deterministic) and RealLLM (online API mode).
Pricing
Free
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Use Cases
- Teams use MLZero to automate end-to-end applied machine learning workflows from raw dataset perception to code generation and execution.
- Developers use MLZero to run offline end-to-end execution with a mock LLM for reproducible local testing.
- Users use MLZero to run real LLM perception with online API mode.
- Teams use MLZero to run evaluation benchmark suites including smoke tests, formal evaluations, ablation studies, and robustness tests.
- Users use MLZero to launch a FastAPI backend and Gradio web UI for interactive use.
Features
End to end automated machine learning
Multi agent data perception
ML library selection
Semantic and episodic memory
Machine learning code generation
Code execution and iterative debugging
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