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ArtiVC is a command-line tool for data versioning on cloud storage. It snapshots data and switches between versions, supporting commit history, commit messages, version tags, diffing commits, and pulling data from a specific version. Backends include local filesystem, remote filesystem over SSH, AWS S3, Google Cloud Storage, and Azure Blob Storage, with more through Rclone. No additional server is required.
Eidolon is an open source framework and agent server for building and deploying generative AI agent applications. It offers a pluggable SDK, declarative YAML agent definitions, and pre-built agents, and it supports adding RAG storage to applications. The server is described as a deployment server for agentic applications, and agents can be deployed to Kubernetes.
Chroma is open-source search infrastructure for AI under Apache 2.0. It supports vector, full-text, regex and metadata search, built on object storage. Chroma Cloud offers serverless, scalable deployment with usage-based pricing.
Feast is an open source feature store for machine learning. It manages an offline store for historical data, a low-latency online store, and a feature server. It generates point-in-time correct feature sets to avoid data leakage and provides a single data access layer abstracting feature storage from retrieval. Installable with pip and initialized through a CLI.
Git Large File Storage (Git LFS) is an open source Git extension for versioning large files. It replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git while storing the file contents on a remote server like GitHub.com or GitHub Enterprise. Installation is available for Windows, Mac, and Linux. Tracking file types does not convert pre-existing files, which requires the git lfs migrate command.
Apache Hadoop is an open-source project that develops software for reliable, scalable, distributed computing. Its library is a framework for distributed processing of large data sets across clusters of computers using simple programming models, scaling from single servers to thousands of machines with local computation and storage, and detecting failures at the application layer.
