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temporal
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temporal

Active·★ 23.5k·MIT·Updated 2026-10-08
★ Most Popular★ Trending★ Essential

Temporal is a durable execution platform. You write workflows as ordinary code in one of Temporal's supported languages, and the Temporal server executes them resiliently: progress is persisted, intermittent failures are handled, failed operations are retried, and a workflow can resume exactly where it left off after a crash or restart. Temporal originated as a fork of Uber's Cadence and is developed by Temporal Technologies, founded by Cadence's creators.

For AI developers, durable execution solves a practical problem: agents that run for minutes or hours, call many tools and wait for humans should not lose their state when a process dies or an API times out. Agent frameworks are starting to build on it; for example, Pydantic AI can run agents inside Temporal workflows. Beyond AI, Temporal is widely used for order processing, payments, data pipelines and other business-critical workflows. This repository contains the server; workflows, activities and workers are written with the language SDKs.

Getting started: install the CLI with `brew install temporal`, start a local development server with `temporal server start-dev`, open the web UI at http://localhost:8233, and run one of the Go or Java samples.

#Durable Execution#Workflow Engine#Distributed Systems#Fault Tolerance#Scalable Applications
$ Install
$ brew install temporal && temporal server start-dev
↗ Visit site★ GitHub
01

Features

01Durable Execution Platform
02Scalable Application Development
03Automatic Failure Handling
04Resilient Workflow Execution
05Automated Operation Retries
02

Why choose it

+Mature, battle-tested durable execution with automatic retries and state persistence.
+Workflows are plain code in your language rather than a DSL or visual graph.
+Strong fit for long-running AI agents and human-in-the-loop steps; already integrated by frameworks such as Pydantic AI.
+MIT licensed with an easy local development server and web UI.
03

Trade-offs

!Running a production Temporal cluster (persistence, scaling, upgrades) is significant operational work; a managed cloud service exists for teams that want to avoid it.
!Workflow code must follow determinism rules, which takes time to learn.
!It is infrastructure, not an agent framework: you still need agent logic, prompts and tools.
04

Compatibility

Go
Supported
Verified via docs
Java
Supported
Verified via docs
05

Quick start

1
$ brew install temporal
2
$ temporal server start-dev
06

Use cases

↳Orchestrating long-running business processes
↳Building highly reliable microservices
↳Automating complex data processing pipelines
07

How it compares

≈vs Conductor: both provide durable workflow execution; Conductor defines workflows as JSON graphs with native LLM and MCP tasks, Temporal as code in your language.
≈vs Trigger.dev: Trigger.dev is a TypeScript-first platform for long-running background tasks and AI workflows with managed deployment; Temporal is a lower-level, multi-language engine.
≈vs Cadence: Temporal started as a fork of Cadence and has since evolved separately.
08

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On this page
01Features02Why choose it03Trade-offs04Compatibility05Quick start06Use cases07How it compares08Alternatives
Stats
GitHub Stars★ 23.5k
Last commit1d ago
StatusActive
LicenseMIT
CategoryWorkflow Automation
Trend (30d)
+0.9k↑ 4.7%
Links
Documentation↗Discussion↗Issues↗Releases↗

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