Open Source Elixir Business Software

SortedSet NIF is a performant and reliable sorted set data structure for Elixir, implemented in Rust using the Rustler crate to take advantage of native performance while maintaining seamless integration with the BEAM ecosystem. It provides ordering and uniqueness guarantees, with all terms stored according to Elixir’s built-in sorting rules. Internally, it uses a vector of vectors layout rather than a single vector to minimize costly reallocations, allowing efficient bucket pointer copying instead of expensive term copying during growth. This design achieves a balance between performance and simplicity, and developers can customize bucket sizes for specific workloads, with a default of 500 offering solid performance across common scenarios. SortedSet extends beyond traditional set semantics by providing indexing, random access, and slice operations thanks to its deterministic ordering.

Explorer brings series (one-dimensional) and data frames (two-dimensional) to Elixir for fast data exploration.

Cron-like job scheduler for Elixir. To use Quantum in your project, edit the mix.exs file and add Quantum to the list of dependencies. To see more transparently what quantum is doing, configure the logger to display :debug messages. If you encounter any problems with quantum, please search if there is already an open issue addressing the problem. Configure your cronjobs in your config/config.exs. Persistent storage can be used to track jobs and last execution times over restarts. If storage is present, the jobs from the configuration will not be loaded to prevent conflicts. Storage implementations must implement the Quantum.Storage behavior. The default job settings can be configured as shown in the example below. So if you have a lot of jobs and do not want to override the default setting in every job, you can set them globally.

An Elixir-based built-in error-tracking solution

TeslaMate is an open-source self-hosted data logger that collects and visualizes data from Tesla vehicles in real time. It provides detailed insights into driving, charging, efficiency, and battery health through intuitive dashboards powered by Grafana. TeslaMate is ideal for Tesla owners who want full control of their vehicle data, avoid cloud reliance, and access rich analytics for personal tracking or troubleshooting.

Keila is a fully open-source newsletter platform designed as a self-hosted alternative to services like Mailchimp or Sendinblue. It enables users to create and manage email campaigns, build customizable signup forms, and handle subscriber lists. Keila supports using a personal SMTP inbox for small-scale newsletters or integrating with major transactional email providers—AWS SES, Sendgrid, Mailgun, Postmark—for larger deployments. It offers a sleek WYSIWYG editor for campaign creation, version 0.17 adding features like mobile/desktop preview, preview emails, French localization, API enhancements, external contact IDs, and update notifications. Distributed under GNU AGPL‑3.0, and deployable via Docker or self-hosted using Elixir/Phoenix stack.

Kitto is a framework for interactive dashboards written in Elixir. The source for the demo dashboards can be found at: kittoframework/demo. Jobs are supervised processes running concurrently. Widgets are coded in the popular React library. Uses a modern asset tool-chain, Webpack. Allows streaming SSE to numerous clients concurrently with low memory/CPU footprint. Easy to deploy using the provided Docker images, Heroku (guide) or Distillery (guide). Can serve assets in production. Keeps stats about defined jobs and comes with a dashboard to monitor them (demo). Can apply exponential back-offs to failing jobs. Reloads code upon change in development. Kitto is a framework to help you create dashboards, written in Elixir / React. Widgets live in widgets/ are compiled using Webpack and are automatically loaded in the dashboards. Assets are rebuilt upon a change in development but have to be compiled for production.

Phoenix LiveDashboard is an interactive monitoring and debugging tool for Elixir applications, integrated directly into Phoenix. It offers real-time insights into system metrics such as memory, CPU, and process activity, as well as introspection of applications, supervision trees, and running processes. Built on top of Phoenix LiveView, it updates dashboards live without requiring page reloads. Developers can drill into request logs, Ecto queries, and telemetry events to diagnose performance issues. It also supports extensibility, allowing custom metrics and panels to be added for application-specific monitoring. LiveDashboard has become a staple for Elixir developers, giving them production-grade visibility into their systems without external monitoring services.

ExAws is a comprehensive Elixir client library for interfacing with AWS services. It provides low-level request builders for nearly all AWS APIs—like S3, EC2, Lambda, DynamoDB, SQS, SES, Route 53, and more—while supporting streaming, request configuration overrides, telemetry, flexible HTTP clients, and codecs. Its modular architecture enables importing only the services you need with separate packages (e.g., ex_aws_s3, ex_aws_ec2).

GenStage is a specification and set of behaviours for building demand-driven data pipelines on the BEAM. It formalizes the roles of producers, consumers, and producer-consumers, using back-pressure so that fast producers don’t overwhelm downstream stages. Developers implement callbacks like handle_demand and handle_events to control how items are emitted, transformed, and consumed across asynchronous boundaries. Because stages are OTP processes, you gain fault tolerance, supervised restarts, and concurrency tuned via configurable demand and partitioning. GenStage underpins higher-level libraries like Flow and Broadway, but it can also be used directly for custom pipelines where timing and throughput matter. Its clear separation of concerns encourages testable, composable stages that can be rearranged as requirements evolve. In production, this leads to predictable, resilient dataflows for event ingestion, batching, and parallel processing.