AutoGenAI
Adaptive Optimization of Prompts, Models and Generation Strategies
Provides provider-agnostic tools for jointly comparing and optimizing prompts, language-model providers, and generation strategies for generative artificial intelligence workflows. Candidate configurations can be evaluated using user-supplied scoring functions, cost and latency measurements, robustness perturbations, Pareto-front screening, budget and latency constraints, prompt evolution, adaptive routing, self-consistency, and text-output ensembles. The core workflow is designed to run offline with deterministic mock providers, while external model application programming interfaces can be connected through user-defined provider functions. Evolutionary search concepts are described by Goldberg (1989, ISBN:0201157675), and multi-objective optimization concepts are related to Deb, Pratap, Agarwal and Meyarivan (2002) <doi:10.1109/4235.996017>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | AutoGenAI_0.1.0.tar.gz |
102.8 KiB |
0.1.0 |
rolling linux/noble R-4.5 | AutoGenAI_0.1.0.tar.gz |
102.8 KiB |
0.1.0 |
rolling source/ R- | AutoGenAI_0.1.0.tar.gz |
22.7 KiB |
0.1.0 |
latest linux/jammy R-4.5 | AutoGenAI_0.1.0.tar.gz |
102.8 KiB |
0.1.0 |
latest linux/noble R-4.5 | AutoGenAI_0.1.0.tar.gz |
102.8 KiB |
0.1.0 |
latest source/ R- | AutoGenAI_0.1.0.tar.gz |
22.7 KiB |
0.1.0 |
2026-04-23 source/ R- | AutoGenAI_0.1.0.tar.gz |
0 B |