ai intermediate

Jido.AI Actions Runtime Demos

Deterministic `Jido.Exec.run/3` walkthrough for LLM envelopes, tool execution, planning, reasoning, retrieval, and quota flows.

primary showcase ai l2 ai-tool-use jido_ai actions runtime

Related guides and notebooks

What you’ll learn

  • When to use direct action runtime calls instead of long-lived agent loops
  • How the core action families map to practical workflows
  • How to keep a runtime demo deterministic without external API keys or network access
  • How to swap fixture-backed families to Jido.AI.Actions.* modules in your own app

Covered action families

  • LLM envelopes: fixture-backed chat, complete, and generate_object
  • Tool calling: shipped list_tools / execute_tool plus one deterministic call_with_tools companion
  • Planning: deterministic plan, decompose, and prioritize
  • Reasoning: deterministic analyze, infer, explain, and run_strategy
  • Retrieval: shipped upsert_memory, recall_memory, and clear_memory
  • Quota: shipped get_status and reset

How this demo stays deterministic

This page runs real Jido.Exec.run/3 calls on every button press.

  • Retrieval and quota use the shipped Jido.AI.Actions.* modules directly.
  • Tool discovery and direct tool execution use the shipped tool-calling actions directly.
  • The LLM-backed families use local fixture actions in this repo so the site demo never depends on provider credentials.

That keeps the public example truthful: the runtime surface is real, the code is local, and the outputs stay repeatable.

Pull the pattern into your own app

In your own project, keep the same Jido.Exec.run/3 shape and swap the fixture-backed families to the production modules from Jido.AI.Actions.*.

  • Keep retrieval and quota as-is if the in-process stores fit your needs.
  • Replace the fixture tool with your own tool Action modules.
  • Replace the fixture LLM/planning/reasoning actions with the shipped Jido.AI.Actions.* modules once provider credentials are configured.