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AutoGenAI

AutoGenAI is a provider-agnostic R framework for joint optimization of prompts, model providers, and generation strategies.

Main ideas

• compare prompt-model-strategy configurations;
• optimize quality, robustness, cost, and latency;
• identify Pareto-efficient configurations;
• evolve prompts with mutation and crossover;
• stress-test prompts with reproducible perturbations;
• learn a lightweight adaptive model router;
• build majority or weighted text ensembles;
• enforce budget, latency, and abstention constraints;
• run all package examples offline with deterministic mock providers.

Minimal offline example

library(AutoGenAI)

ex <- autogenai_example()
fit <- optimize_ai(
  task = ex$task,
  data = ex$data,
  providers = ex$providers,
  prompts = ex$prompts,
  temperatures = c(0, 0.2),
  strategies = c("single", "self_consistency")
)

fit
pareto_ai(fit)
select_configuration(fit, preference = "balanced")

Connecting a real model

my_provider <- ai_provider(
  name = "my-model",
  generate = function(prompt, input, params) {
    # Call any external model here and return text.
    paste(prompt, input)
  },
  input_cost_per_1k = 0,
  output_cost_per_1k = 0
)

The package itself does not require API keys and does not contact external services during checks.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.