riderprompt 0.1.0

Python client for RIDER — autonomous prompt optimization engine. 17 WIN / 0 LOSE across 6 benchmarks.

RIDER — Autonomous Prompt Optimization

Python client for RIDER (Reflective Iterative Diversity-Enhanced Reasoning) — a metaheuristic prompt optimization engine that evolves prompts through evolutionary algorithms, multi-armed bandits, and error-directed refinement.

17 WIN / 0 LOSE across 6 benchmarks (GSM8K, AG_News, SQuAD 2.0, CommonGen, XSum, CodeSearchNet) on 3 LLM models (Claude Haiku 4.5, GPT-4.1, DeepSeek V3.2). Published at FSE 2026 (CORE A*) and FRUCT 39 (IEEE).

Installation

pip install riderprompt

Quick Start

Get your API key at riderprompt.com, then:

from rider import Rider

client = Rider(api_key="rider_sk_...")

result = client.optimize(
    prompt="Write a summary of this article",
    mode="standard",
)

print(result.optimized_prompt)
print(f"Improvement: {result.improvement:.1f}%")

Optimization Modes

Mode	Time	API Calls	Best For
light	~15 sec	5	Quick rewrite, 2 strategies compete
blitz	~30 sec	11	4 strategies + merge + error refinement
standard	~1 min	19	Full 3-phase PHASE REACTOR (default)
ultra	~2 min	25	4 phases + adversarial review

Features

• 🧬 Evolutionary algorithms — 9 operators (EDA mutation, differential evolution, genetic crossover, semantic paraphrase, etc.)
• 🎰 Adaptive operator selection — Windowed Thompson Sampling with Bayesian elimination
• 🎯 Error-directed evolution — feeds misclassified examples back into generation
• ⚡ Evaluate-first (μ+λ) retention — diversity tiebreaking
• 🔄 8 exclusive protocols — PRISM, NEXUS, PHASE REACTOR, OPERATOR FORGE, ECHO, CHIMERA, VORTEX, MARS CONFIG

Examples

Optimize a prompt with custom validation data

result = client.optimize(
    prompt="Classify this text as positive or negative",
    mode="ultra",
    validation_examples=[
        {"input": "I love this!", "expected": "positive"},
        {"input": "This is terrible", "expected": "negative"},
    ],
)

Async usage with multiple prompts

import asyncio
from rider import AsyncRider

async def optimize_many(prompts):
    client = AsyncRider(api_key="rider_sk_...")
    tasks = [client.optimize(p, mode="light") for p in prompts]
    results = await asyncio.gather(*tasks)
    return results

Pricing

• Free — 3 optimizations/day (Light + Blitz modes)
• Pro ($9.99/mo) — 50/day, all 4 modes, REST API
• Ultra ($29.99/mo) — Unlimited, priority queue

See pricing

Citation

If you use RIDER in your research, please cite:

@inproceedings{dragomirov2026rider,
  title={{RIDER}: Evolutionary Prompt Optimization with Adaptive Operator Selection for Software Engineering},
  author={Dragomirov, Daglar and Kulin, Nikita and Muravyov, Sergey and Makarov, Ilya and Sukhorukov, Daniil and Mozikov, Mikhail},
  booktitle={Companion Proceedings of the 34th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (FSE Companion '26)},
  year={2026},
  doi={10.1145/3803437.3807393},
}

Links

• Website: riderprompt.com
• API Documentation: riderprompt.com/api-docs
• Developer Portal: riderprompt.com/developer
• Paper: FSE 2026 Companion Proceedings

License

MIT License — see LICENSE file.