jkelly-dev1

Temporal Multi Agent — Productivity skill for Claude Code

Productivity community

How to install Temporal Multi Agent

This entry records only its repository, not the path inside it, so there is no exact command to give. Open jkelly-dev1/temporal-multi-agent and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Temporal Multi Agent does

Production-shaped multi-agent platform on Temporal: a main orchestrator plans a task, fans out to specialized agents that run concurrently and self-critique, then reviews and synthesizes, all as durable, replay-safe workflows with human-in-the-loop plan approval. Provider-agnostic (mock/Anthropic), runs fully offline; Python.

Alternatives in Productivity

Reviews Plans — Review all active architecture plans relevant to the current task 15.6k ★
Repurpose — Workflow recipe — turn one blog post, video, or idea into a full platform-native content pack (thread, LinkedI 1.3k ★
Browser Harness JS — Self-healing browser harness that enables LLMs to complete any task 480 ★

README

Temporal Multi-Agent Orchestration Demo

[![tests](https://github.com/jkelly-dev1/temporal-multi-agent/actions/workflows/ci.yml/badge.svg)](https://github.com/jkelly-dev1/temporal-multi-agent/actions/workflows/ci.yml)

A small but production-shaped **multi-agent platform** built on [Temporal](https://temporal.io): a **main orchestrator agent** plans a task, coordinates a fleet of **specialized agents** that run concurrently, each agent **self-critiques and refines** its own work, and the orchestrator **reviews and synthesizes** the result, all as **durable, replay-safe** workflows.

It runs **fully offline** with a deterministic mock "brain," so `python run_demo.py` works with no API key, no Docker, and no external Temporal server. Point it at a real Claude model by setting two env vars (see below); the orchestration code doesn't change.

Why Temporal

The hard part of a multi-agent platform isn't the prompts; it's the *systems*: coordinating long-running, failure-prone work across many concurrent agents and recovering cleanly when something dies mid-flight. Temporal gives us **durable execution**: workflow state is reconstructed by replaying an event history, so a crashed worker resumes exactly where it left off. That turns "orchestrate agents" into a tractable, testable engineering problem.

Architecture

                      +--------------------------------+
ResearchRequest ----->|      OrchestratorWorkflow      |   (the "main agent")
                      |                                |
                      |  1. plan_subtasks  ------------+--> Planner  (activity)
                      |  2. await approve_plan  <------+--- human signal (HITL)
                      |  3. fan-out (asyncio.gather)   |
                      |        |   |   |   |           |
                      |        v   v   v   v           |
                      |   ResearchAgentWorkflow xN     |   (specialized agents,
                      | research -> critique -> refine |