---
title: "Temporal Memory Workflows · Durable workflow templates that"
description: "Durable workflow templates that pair Temporal with persistent agent memory. Five Temporal templates (T01-T05): memory-aware agent, operator approval, saga"
canonical: "https://matthiasmeyer.tech/repos/temporal-memory-workflows"
language: "en"
last_updated: "2026-08-21"
markdown_versions: ["https://matthiasmeyer.tech/repos/temporal-memory-workflows.md", "https://matthiasmeyer.tech/de/repos/temporal-memory-workflows.md", "https://matthiasmeyer.tech/es/repos/temporal-memory-workflows.md"]
image: "https://matthiasmeyer.tech/api/og/repo/temporal-memory-workflows?lang=en"
publisher: "Matthias Meyer, https://matthiasmeyer.tech (llms.txt: https://matthiasmeyer.tech/llms.txt)"
---

workflow alpha TypeScript

# Temporal Memory Workflows

Durable workflow templates that pair Temporal with persistent agent memory

Five Temporal templates (T01-T05): memory-aware agent, operator approval, saga rollback, recurring synthesis, multi-agent coordination. All workflows write durable knowledge (learn), decisions (decide), and mistakes into Memory via Temporal activities. Bring-your-own backend via the MemoryClient adapter (HostedMemoryClient + InMemoryMemoryClient). 45 tests with time-skipping, MIT.

[View on GitHub →](https://github.com/studiomeyer-io/temporal-memory-workflows)

## What it solves

LangGraph is good at short chains driven by a model. n8n is good at visual deterministic flows. Neither handles the third case: a long-running workflow that is not about the model at all, where you need saga rollbacks, weeks of waiting on an external event, and retries that survive a process crash. That is what Temporal is for, and pairing it with agent memory is what these templates do.

## How it works

Five templates, each pairing a Temporal workflow with persistent memory so the workflow state and the agent knowledge stay consistent. The first shows the basic shape: read memory, reason, write memory, and survive a worker crash in the middle. All five were verified live against a self-hosted cluster and the test suite runs green.

## When to use it

Take them when the thing you are automating runs for days or weeks, has to be exactly-once, and has to pick up where it was after a restart. That is the narrow band where Temporal earns its operational cost, and the templates save you the memory integration on top.

## When not to use it

Temporal is a cluster you have to run, and that is a real cost for a short workflow. For a chain that finishes in a minute, LangGraph or n8n is the cheaper answer. Note also that this is pre-npm: you clone or fork it rather than installing it.

Stars

★ 1

Clones (14d)

⊟ 30

Language

TypeScript

Updated

2026-08-21

## Related repos

- Local Memory MCP

    → writes durable memory to

    →

    https://matthiasmeyer.tech/repos/local-memory-mcp

- StudioMeyer Memory

    → hosted backend variant for

    →

    https://matthiasmeyer.tech/repos/studiomeyer-memory

- Darwin LangGraph

    → durable layer for in-process graphs

    →

    https://matthiasmeyer.tech/repos/darwin-langgraph

## Sitemap

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