Daft dataframes can load any data such as PDF documents, images, protobufs, csv, parquet and audio files into a table dataframe structure for easy querying

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Website • Docs • Installation • Daft Quickstart • Community and Support

Daft: High-Performance Data Engine for AI and Multimodal Workloads

Eventual-Inc/Daft | Trendshift

Daft is a high-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale.

  • Native multimodal processing: Process images, audio, video, and embeddings alongside structured data in a single framework

  • Built-in AI operations: Run LLM prompts, generate embeddings, and classify data at scale using OpenAI, Transformers, or custom models

  • Python-native, Rust-powered: Skip the JVM complexity with Python at its core and Rust under the hood for blazing performance

  • Seamless scaling: Start local, scale to distributed clusters on Ray, Kubernetes

  • Universal connectivity: Access data anywhere (S3, GCS, Iceberg, Delta Lake, Hugging Face, Unity Catalog)

  • Out-of-box reliability: Intelligent memory management and sensible defaults eliminate configuration headaches

Getting Started

Installation

Install Daft with pip install daft. Requires Python 3.10 or higher.

For more advanced installations (e.g. installing from source or with extra dependencies such as Ray and AWS utilities), please see our Installation Guide

Quickstart

Get started in minutes with our Quickstart - load a real-world e-commerce dataset, process product images, and run AI inference at scale.

More Resources

  • Examples - see Daft in action with use cases across text, images, audio, and more

  • User Guide - take a deep-dive into each topic within Daft

  • API Reference - API reference for public classes/functions of Daft

Benchmarks

AI Benchmarks

To see the full benchmarks, detailed setup, and logs, check out our benchmarking page.

Contributing

We ❤️ developers! To start contributing to Daft, please read CONTRIBUTING.md. This document describes the development lifecycle and toolchain for working on Daft. It also details how to add new functionality to the core engine and expose it through a Python API.

Here’s a list of good first issues to get yourself warmed up with Daft. Comment in the issue to pick it up, and feel free to ask any questions!

Telemetry

To help improve Daft, we collect non-identifiable data via Scarf (https://scarf.sh).

To disable this behavior, set the environment variable DO_NOT_TRACK=true.

The data that we collect is:

  1. Non-identifiable: No session IDs or user identifiers are collected

  2. Metadata-only: We do not collect any of our users’ proprietary code or data

  3. For development only: We do not buy or sell any user data

Please see our documentation for more details.

https://static.scarf.sh/a.png?x-pxid=31f8d5ba-7e09-4d75-8895-5252bbf06cf6

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License

Daft has an Apache 2.0 license - please see the LICENSE file.

Project links

Data verified by PyPI on Jul 10, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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Latest release

3 maintainers

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Credits

Author: Eventual Inc

Maintainer: Sammy Sidhu

GitHub Statistics

Data verified by PyPI on Jul 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

Requires

Python >=3.10

Provides Extra

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