Skip to main content

A curated list of Machine Learning libraries and resources for the Elixir programming language.

296
GitHub Stars
158
Curated Resources
5
Categories
7 hours ago
Last Refreshed
Core ToolsMachine LearningGenerative AILivebooks & ExamplesResources

Use this list with your AI agent

Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:

"Show me videos resources from awesome-ml-gen-ai-elixir"

Installation instructions →

What's inside

Generative AI

  • A2AAgent Frameworks

    Elixir implementation of the Agent-to-Agent (A2A) protocol with GenServer-based agents, JSON-RPC 2.0 dispatch, SSE streaming, skill-based discovery, and fleet supervision.

  • A2UIAgent Frameworks

    Phoenix LiveView renderer for the Agent-to-UI (A2UI) protocol, converting declarative JSONL agent messages into native LiveView components with two-way data binding.

  • AgentObsDevelopment Tools

    LLM agent observability with telemetry, token tracking, and OpenTelemetry spans following OpenInference conventions.

  • AlikeDevelopment Tools

    Semantic similarity testing library using a wave operator (

  • AlloyAgent Frameworks

    Minimal, OTP-native completion and tool-call loop. Provider-agnostic, with cost guards, and deliberately not a framework.

  • AludelLLM Tools

    Phoenix-native LLM evaluation workbench: prompt tests, model comparison, rubric-based LLM judges, red-team suites, ExUnit assertions and CI gates.

Livebooks & Examples

Machine Learning

  • AxonDeep Learning

    Neural Networks for Elixir. Built with Nx.

  • BumblebeeDeep Learning

    Pre-trained neural network models on top of Axon. Provides integration with

  • EdificeDeep Learning

    Catalog of ready-to-use neural network architectures for Nx/Axon, from MLPs and transformers to Mamba, GNNs and VAEs, behind a uniform build API.

  • EvisionComputer Vision

    OpenCV bindings for Elixir/Erlang.

  • EXGBoostTraditional Machine Learning

    Decision Trees implemented using the

  • eXMCTraditional Machine Learning

    Probabilistic programming on the BEAM inspired by PyMC: declarative models, NUTS sampling and Bayesian diagnostics on Nx tensors.

Core Tools

  • Emily

    Alternative MLX-based Nx backend with fused transformer kernels, running Nx.Serving and Bumblebee on Metal.

  • EMLX

    Nx backend and compiler for Apple MLX. Runs Nx, Axon and Bumblebee on Apple Silicon GPUs.

  • Explorer

    Series and dataframes for data exploration in Elixir.

  • Handoff

    Nx-compatible library for building and executing distributed DAGs of dependent functions across BEAM nodes, with per-node cost and allocation requirements.

  • Kino

    Render rich and interactive output. Used in Livebook.

  • Livebook

    Write interactive and collaborative notebooks, with integrations to databases, messaging, visualization and more.

Showing a sample of 158 resources. View the full list on GitHub →