Jido AI Framework

Orchestrate multi-agent AI workflows and reasoning in Elixir.

1|1|Updated Jan 3, 2026
One-click install
npx skills add https://github.com/layeddie/ai-rules --skill jido-ai-framework
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: Jido AI Framework
Source: https://github.com/layeddie/ai-rules/tree/main/skills/jido_ai
Command: npx skills add https://github.com/layeddie/ai-rules --skill jido-ai-framework

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds and coordinates sophisticated AI agent workflows in Elixir, enabling multi-agent orchestration, advanced reasoning, and stateful conversations with local and cloud LLM providers.

Core Features & Use Cases

  • Multi-agent orchestration across specialized agents to tackle complex problems.
  • Advanced reasoning strategies (Chain of Thought, ReAct, Tree of Thoughts, Self-Consistency, GEPA) and structured prompt templates.
  • Stateful conversations and token-aware context management for long-running sessions.
  • Local LLM support (Ollama, LM Studio) and broad provider access for flexible deployment.
  • Integration patterns for OTP-based architectures, Domain Resource Action design, and test-driven workflows.

Quick Start

Add jido_ai to your Mix project and run a basic multi-agent workflow using Architect and Orchestrator roles.

Frequently Asked Questions about Jido AI Framework

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I orchestrate multiple AI agents in Elixir?

Multi-agent orchestration in Elixir coordinates specialized agents to tackle complex problems using structured reasoning strategies like Chain of Thought, ReAct, and Tree of Thoughts within an OTP-aligned architecture.

Can I use local LLMs like Ollama for AI agent workflows in Elixir?

Yes, you can use local LLMs like Ollama and LM Studio for AI agent workflows in Elixir. The framework supports local model runtimes alongside broad cloud provider access, enabling flexible deployment and stateful conversation management for your specific infrastructure needs.

What reasoning strategies are available for Elixir AI workflows?

Elixir AI workflows support advanced reasoning strategies including Chain of Thought, ReAct, Tree of Thoughts, Self-Consistency, and GEPA. These strategies are integrated with structured prompt templates to handle complex reasoning tasks within multi-agent orchestration systems.

How does Elixir handle stateful conversations and context management for LLMs?

Elixir manages stateful conversations and context for LLMs using token-aware context management designed for long-running sessions. This approach maintains conversation state within OTP-aligned architectures for resilient plan-build-review workflows.

Is an OTP-aligned architecture necessary for building scalable AI agents?

An OTP-aligned architecture is highly beneficial for building scalable AI agents in Elixir. It provides the necessary integration patterns for domain resource action design and test-driven workflows, ensuring your multi-agent orchestration remains resilient and robust.