# Eventual / Daft

> Eventual is a data infrastructure company building Daft, an open-source, high-performance data engine for AI and multimodal workloads. Written in Rust with Python and SQL interfaces, Daft queries and processes images, audio, video, documents, embeddings, and structured data from a local laptop to petabyte-scale distributed clusters on Ray or Kubernetes. Daft runs in production at exabyte scale at Amazon, Mobileye, and Together AI. Eventual also builds MultiBase, a semantic data platform for Physical AI teams.

Generated by the API Evangelist enrichment pipeline from the Eventual/Daft public developer surface. No provider-published llms.txt was found at daft.ai or docs.daft.ai as of 2026-07-19.

## Docs

- [Daft Documentation](https://docs.daft.ai/en/stable/): User guide and reference home.
- [Quickstart](https://docs.daft.ai/en/stable/quickstart/): First DataFrame and query.
- [Install](https://docs.daft.ai/en/stable/install/): Installation and feature extras.
- [API Reference](https://docs.daft.ai/en/stable/api/): Public Python classes and functions.
- [Examples](https://docs.daft.ai/en/stable/examples/): Text, image, audio, video, and Physical AI use cases.

## Source

- [Daft on GitHub](https://github.com/Eventual-Inc/Daft): Source, issues, discussions, releases.
- [Releases / changelog](https://github.com/Eventual-Inc/Daft/releases): Dated semver release notes.
- [AGENTS.md](https://github.com/Eventual-Inc/Daft/blob/main/AGENTS.md): Provider agent/dev-workflow guide.

## Packages

- [daft on PyPI](https://pypi.org/project/daft/): `pip install daft` — official Python client.

## Company

- [Eventual](https://www.eventual.ai/): Company site (Daft + MultiBase).
- [Daft](https://www.daft.ai/): Product site.
- [Blog](https://www.daft.ai/blog): Daft engineering blog.
- [Community Slack](https://www.daft.ai/slack): Daft user community.