SIA: Self-Improving AI Framework
What it is
SIA is a Python framework that orchestrates a self-improving loop using three agents: a Meta-Agent to generate initial solutions, a Target-Agent to execute tasks, and a Feedback-Agent to iteratively refine both the code (harness) and weights. It supports multiple LLM providers and includes a CLI for running experiments and a web dashboard for visualizing performance across generations.
The problem it solves
Manual tuning of AI agents and models for specific benchmark tasks is time-consuming and lacks a systematic method for autonomous, iterative improvement of both code and weights.
What you could use it for
- Autonomously optimizing machine learning pipelines for Kaggle competitions
- Improving legal text classification accuracy (LawBench)
- Optimizing GPU kernels for latency targets
- Refining single-cell RNA sequencing denoising models
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.