# Ondine

**Run LLMs on tabular data: 100x fewer API calls, 40-50% lower cost.**

Ondine is an open-source Python SDK for data engineers and ML practitioners who need to process large CSV/DataFrame datasets with LLMs. Multi-row batching, prefix caching, and budget controls are built in from day one, not bolted on later.

## Quick Start

```python
from ondine import QuickPipeline

result = QuickPipeline.create(
    data="products.csv",
    prompt="Classify this product into a category: {name} - {description}",
    model="gpt-4o-mini"
)
```

## Why Ondine?

Most teams processing tabular data with LLMs hit the same three walls: **API cost spirals**, **brittle pipelines that crash halfway through**, and **hallucinated outputs that are hard to catch at scale**. Ondine is built to solve exactly those three problems.

| Problem                        | Ondine's answer                                                      |
| ------------------------------ | -------------------------------------------------------------------- |
| API bills too high             | Multi-row batching (100 rows/call) + prefix caching → 40-50% savings |
| Pipeline crashes lose progress | Checkpointing -- resume from last saved row, not row 0               |
| LLM makes things up            | Context Store -- ground each response against your source data       |
| Too many providers to juggle   | 100+ providers via LiteLLM, single unified API                       |

## Key Features

* **Quick API** -- 3-line hello world with smart defaults
* **Builder API** -- Full control over pipeline configuration
* **100+ LLM providers** via LiteLLM (OpenAI, Anthropic, Groq, Azure, local MLX)
* **Multi-row batching** -- Process 100 rows per API call
* **Prefix caching** -- 40-50% cost reduction on repeated prompts
* **Cost estimation** -- Know the cost before you run
* **Budget limits** -- Hard caps on spending
* **Checkpointing** -- Resume failed pipelines from where they stopped
* **Structured output** -- Pydantic models, JSON, regex parsing
* **Observability** -- OpenTelemetry, Langfuse, structured logging

## Installation

```bash
pip install ondine
```

## Links

* [GitHub Repository](https://github.com/ptimizeroracle/ondine)
* [PyPI Package](https://pypi.org/project/ondine/)
* [Contributing Guide](/contributing.md)


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