Can I migrate from Temporal to Trigger.dev?

Yes. The main work is removing the Workflow/Activity split and converting to Trigger.dev tasks. Since Trigger.dev has no determinism constraints, most Temporal Activities can be inlined directly into task code. The worker infrastructure can be decommissioned entirely.

Does Trigger.dev support multiple languages like Temporal?

Temporal has official SDKs for Go, Java, Python, TypeScript, .NET, PHP, and Ruby, with Rust in public preview. Trigger.dev is TypeScript-native, with a build extension for running Python scripts alongside your TypeScript tasks.

Is Temporal's deterministic replay actually a problem in practice?

Non-determinism errors are a common pain point in Temporal's community: calling Date.now(), using libraries with internal randomness, or making API calls without Activity wrappers. Once you learn the rules it's manageable, but the learning curve is real. Trigger.dev uses a different durability model, so your code runs as plain TypeScript with no replay constraints.

Can Trigger.dev handle Temporal-level scale?

Trigger.dev processes hundreds of millions of runs for thousands of engineering teams and scales automatically with no worker infrastructure to manage. Temporal Cloud has processed over 9.1 trillion lifetime actions. For most workloads, both platforms are more than capable.

Does Temporal have better observability?

Temporal's event history provides a replayable record of every workflow execution, which is powerful for debugging deterministic workflows. Trigger.dev provides OpenTelemetry tracing, a dashboard with trace viewer, run filtering, and custom views. Both have web dashboards for monitoring runs.

Which is better for AI agents specifically?

Depends on your language. Temporal has deep Python AI integration through the OpenAI Agents SDK. For TypeScript AI agents, Trigger.dev supports any framework (AI SDK, Mastra, OpenAI Agents SDK, etc.) without modification, with Realtime Streams, input streams, Waitpoints for human-in-the-loop, and no execution timeout.

Can I use Temporal's Signal/Query/Update pattern with Trigger.dev?

Partially. Trigger.dev's input streams and Waitpoints cover the Signal use case: send typed data into a running task from your backend or frontend. Realtime Streams cover reading task output. Queries (inspecting workflow state) and Updates (validated state mutations) don't have a direct equivalent.

Which platform is more future-proof?

Both are open source and actively developed. Temporal has deep enterprise traction with SDKs across eight languages. Trigger.dev has thousands of teams in production and is growing fast in the TypeScript and AI agent ecosystem. Both are self-hostable, so your code isn't locked in either way.

Can Trigger.dev replace Temporal for TypeScript background tasks?

For TypeScript teams running background tasks, data pipelines, or AI workflows, yes. Trigger.dev offers a simpler model with no determinism constraints, no worker infrastructure, and deploys in minutes. Temporal provides additional patterns like Signal/Query/Update and multi-language support that some architectures depend on.

Updated October 2026

Trigger.dev vs Temporal

Temporal and Trigger.dev both run code that survives crashes and long waits. They get there differently. Temporal replays deterministic workflow code across eight language SDKs, with battle-tested primitives like Signals, Queries, and Nexus proven at enterprise scale. Trigger.dev checkpoints and restores the real process, so you write normal TypeScript with no determinism rules and no workflow DSL to learn, on managed compute you never provision.

What is Trigger.dev?

Trigger.dev is the open source platform for building durable AI agents and workflows in TypeScript. Your code runs on managed compute as normal TypeScript, with no determinism constraints and no platform-imposed execution timeout. It includes built-in retries, queues, and scheduling, plus OpenTelemetry observability with custom dashboards and an SQL-style query language. Tasks can stream data to your frontend and backend in real-time, receive typed input while running, and pause for hours, days, or weeks for human approval or external events. Thousands of engineering teams run production workloads on it, processing hundreds of millions of runs.

What is Temporal?

Temporal is a durable execution platform that guarantees your code runs to completion through crashes and outages using event-sourced deterministic replay. It has official SDKs for Go, Java, Python, TypeScript, .NET, PHP, and Ruby (Rust in public preview), and is used in production by Netflix, Stripe, Datadog, and Snapchat . Temporal Cloud has processed over 9.1 trillion lifetime actions .

Feature deep-dive

How does developer experience compare?

Temporal gives you five primitives, Workflows, Activities, Workers, Task Queues, and Namespaces, each with its own role to learn. Trigger.dev starts with one: tasks. Queues, workers, and environments are managed for you.

Feature	Trigger.dev	Temporal
Minimum building blocks	Tasks	Workflows + Activities
Execution	Workers (managed or self-hosted)	Workers (self-hosted)
Routing	Queues	Task Queues
Isolation	Projects + Environments	Namespaces
Code constraints	None	Deterministic workflows required
Boilerplate	Minimal: just the task function	Activity stubs, worker setup, workflow registration
Error debugging	OpenTelemetry traces, TRQL dashboards, AI query assistant, Dashboard agent, alerting	Event history, replay debugging, web UI, Grafana integration
Type safety	Full TypeScript types	Full TypeScript types (with proxy indirection)
Primary language	TypeScript-native	Go, Java, Python, TypeScript, .NET, PHP, Ruby (+ Rust, preview)
Production CLI	`login`, `init`, `dev`, `deploy`	`workflow`, `batch`, `schedule`, `operator`, `task-queue`
Onboarding	`init` into your project or a blank folder	Tutorials + sample apps for each SDK
Versioning	Deploy new code, new runs use it. No patching needed	Workflow versioning / patching for in-flight deterministic code
Child tasks	`triggerAndWait` / `batchTriggerAndWait`	Child workflows with parent-close policies
AI coding tools	MCP server (docs, metrics, trigger, monitor, deploy), skills, llms.txt	MCP server (official docs assistant; ops tooling is community-built)
Local development	Hot reload, requires internet	Fully offline dev server

Temporal splits work across Workflows, Activities, Workers, Task Queues, and Namespaces, each unlocking patterns like Signals, Queries, and child workflows. Trigger.dev has one concept: tasks. Built-in retries, queues with concurrency keys, scheduled tasks, batch triggering, a Realtime API with React hooks, and OpenTelemetry tracing. Run npx trigger.dev@latest init and you're writing tasks in minutes. No determinism constraints means deploys are simple: new runs use the new version, in-progress runs finish on theirs. Temporal requires explicit versioning when workflow logic changes. Tasks can spawn other tasks and wait for results with triggerAndWait. Trigger.dev also ships an MCP server and skills for Claude Code, Cursor, Windsurf, and more. Temporal's official MCP server is a docs assistant; community MCP servers add workflow lifecycle, batch operations, schedules, and event history.

How does the build and deploy pipeline compare?

Trigger.dev deploys with a single command and manages build dependencies through config. Temporal leaves the build pipeline to you, so you pick the tooling and process.

Feature	Trigger.dev	Temporal
Build customization	Build extensions (Prisma, FFmpeg, Playwright, Python, more) or write your own	Manage your own Dockerfile
Deploy integrations	GitHub + Vercel: auto-deploy on push, env var sync	Manage your own CI/CD pipeline
Environments	Production, Staging, Preview (per-branch), Development	Namespaces (manual setup)

Build extensions add system-level dependencies (Prisma, FFmpeg, Playwright, Python, system packages) with a config line, and you can create custom extensions for anything not covered. The GitHub integration auto-deploys on every push, and the Vercel integration syncs environment variables and adds version skew protection. Trigger.dev includes built-in production, staging, and development environments, plus preview branches that create isolated environments per PR with their own API key, env vars, and schedules, auto-archived when the PR merges. Temporal uses namespaces for isolation, with environment setup handled manually.

How does durability work in each platform?

Both guarantee your code survives crashes and restarts. Temporal replays events from the start. Trigger.dev restores memory snapshots at waits; on retry, subtasks with idempotency keys return their saved results.

Feature	Trigger.dev	Temporal
Durability model	Checkpoint-resume snapshots	Event-sourcing with deterministic replay
Determinism required	No	Yes: no direct I/O; `Date.now()` and `Math.random()` are sandboxed
Recovery mechanism	Retry from the start (subtasks with idempotency keys return saved results); waits restore a process snapshot	Replay from event history
Long-running workflows	Checkpoints grow with process memory	`continue-as-new` resets event history

Temporal uses event-sourcing with deterministic replay. Every workflow action is recorded as an event, and on recovery the workflow replays from the start, skipping completed activities. This gives Temporal a complete, replayable audit trail and powers advanced patterns like continue-as-new for extremely long-running workflows. Trigger.dev uses checkpoint-resume instead. When your task reaches a wait point, the platform snapshots your task's state and restores it later. If a run fails, it retries from the start, and subtasks triggered with idempotency keys return their saved results. The tradeoff is losing the replayable event history, but your code runs as plain TypeScript with no determinism rules. There's no execution time limit, so you won't need continue-as-new to keep a task running.

How do AI agent capabilities compare?

Temporal has deep Python AI integration through the OpenAI Agents SDK and multi-language agent orchestration. Trigger.dev is purpose-built for TypeScript AI agents with real-time streaming, human-in-the-loop, and no timeout.

Feature	Trigger.dev	Temporal
Durable chat agents	`chat.agent`: a durable machine per conversation, no platform timeout, memory across turns	No dedicated chat-agent primitive
AI framework support	Any TS framework (AI SDK, Mastra, OpenAI Agents SDK, etc.)	OpenAI Agents SDK (Python)
Tasks as AI tools	`ai.toolExecute()` exposes tasks to the Vercel AI SDK	`activity_as_tool` exposes activities to OpenAI Agents SDK
Real-time streaming	Realtime Streams (to frontend and backend)	No dedicated streaming primitive
Frontend hooks	`useRealtimeRun`, `useRealtimeStream`, `useWaitToken`, `useInputStreamSend`	No frontend SDK
Human-in-the-loop	Waitpoints (pause for approval, webhooks, events)	Signals (external data into workflows)
Bidirectional communication	Input streams (typed data into running tasks from backend or frontend)	Signal/Query/Update
Max execution time	No limit	No limit
Multi-language agents	TypeScript (Python scripts via build extension)	Go, Java, Python, TypeScript, .NET, PHP, Ruby (+ Rust, preview)

Temporal's AI story is strongest in Python, where the OpenAI Agents SDK wraps agent interactions as durable workflow steps with activity_as_tool, and Signal/Query/Update lets external systems push data into running agent workflows or query their state. If your AI agents are in Python or span multiple languages, Temporal is the natural fit. Trigger.dev is TypeScript-native: any AI framework (AI SDK, Mastra, OpenAI Agents SDK, etc.) works without wrappers, Realtime Streams push tokens to your frontend and backend, and Waitpoints pause tasks indefinitely for human approval or external events, and input streams let you send typed data into running tasks from your backend or frontend (cancel signals, approvals, or any structured payload).

What infrastructure do you need to manage?

Temporal gives you full control over your worker infrastructure, even on Cloud. Trigger.dev runs your code on managed compute with nothing to provision.

Feature	Trigger.dev	Temporal
Managed option	Trigger.dev Cloud (fully managed)	Temporal Cloud (server managed, workers are yours)
Self-hosted minimum	Docker Compose	Database + Server + Workers (Elasticsearch optional)
Workers to deploy	None	At least one per task queue
Compute isolation	Each run gets its own container with configurable CPU/RAM	Activities share worker process resources
Regions	Per-run region option, plus a target region for bulk replay	Multi-region (Cloud, branded High Availability)
Time to production	~5 minutes	Days to weeks (self-hosted) or hours (Cloud)
License	Apache 2.0	MIT (server)

Temporal's architecture separates the server (orchestration) from workers (your code), giving you full control over where and how your code runs. Teams with strict compliance, data residency, or custom runtime needs require this level of control. Temporal Cloud manages the server side; you manage workers. With Trigger.dev, you write your task, run npx trigger.dev@latest deploy, and your code runs on managed compute with nothing to provision or scale. Each run gets its own container with a configurable machine preset (up to 8 vCPU, 16 GB RAM), so a memory spike in one task can't affect others. Temporal workers share resources across activities on the same task queue, isolation requires dedicating separate task queues and worker pools. Both offer self-hosting: Trigger.dev via Docker Compose or Kubernetes (Apache 2.0), Temporal via its MIT-licensed server. Temporal Nexus connects workflows across namespace and region boundaries with typed service contracts for cross-team orchestration. Trigger.dev has no equivalent.

How does pricing compare at scale?

Temporal bills per action across every workflow event. Trigger.dev bills per compute-second plus a per-run fee. The models favor different workload shapes.

Feature	Trigger.dev	Temporal
Pricing model	Compute-seconds + per-run fee	Per-action (every timer, signal, activity is billable)
Free tier	Free plan with compute credit included	Trial credits included
Self-hosted cost	Free (Apache 2.0) + your infra	Free (MIT) + significant infra
Essentials	N/A	Essentials tier with bundled actions
Business	N/A	Business tier with more bundled actions

Temporal's per-action model charges for every timer, signal, and activity across a workflow's lifecycle. A workflow with 5 activities may consume roughly 15-20 actions (estimate). Trigger.dev's compute-second model charges for actual execution time regardless of internal operations. Temporal's model favors many short workflows with few steps. Trigger.dev's model favors longer-running tasks like AI summarization. Note that Temporal Cloud pricing covers orchestration only, so you pay separately for the compute to run your workers. Self-hosting either platform eliminates cloud fees entirely.

How do self-hosting and licensing compare?

Trigger.dev licenses its entire platform under Apache 2.0, while Temporal uses an MIT license for its server.

Feature	Trigger.dev	Temporal
License	Apache 2.0	MIT (server)
Minimum setup	Docker Compose	Database + Server + Workers (Elasticsearch optional)
Production complexity	Docker Compose or Kubernetes	Database cluster + multiple services (Elasticsearch optional)
Managed alternative	Trigger.dev Cloud	Temporal Cloud (paid tiers)

This server runs the exact full-featured codebase that powers Temporal Cloud, letting teams with dedicated platform engineering run it at massive scale. The setup involves a database cluster (PostgreSQL or Cassandra), optional Elasticsearch, and multiple server services. Trigger.dev self-hosts with Docker Compose for small deployments or Kubernetes for production, with the entire platform under Apache 2.0. Your choice between them comes down to how much operational surface area your team wants to manage.

Code comparison

Normal TypeScript vs deterministic workflows

Trigger.dev (1 file)

import { task } from "@trigger.dev/sdk";

export const myTask = task({
  id: "my-task",
  run: async (payload: { url: string }) => {
    const now = Date.now(); // fine
    const id = crypto.randomUUID(); // fine
    const data = await fetch(payload.url); // fine
    return { now, id, data: await data.json() };
  },
});

Temporal (2 files)

// workflow.ts
import { proxyActivities } from '@temporalio/workflow';
import type * as activities from './activities';

const { fetchData } = proxyActivities<typeof activities>({
  startToCloseTimeout: '5 minutes',
});

export async function myWorkflow(url: string) {
  const data = await fetchData(url);
  return { data };
}

// activities.ts
export async function fetchData(url: string) {
  const response = await fetch(url);
  const now = Date.now();
  const id = crypto.randomUUID();
  return { now, id, data: await response.json() };
}

Normal TypeScript vs deterministic workflows

import { task } from "@trigger.dev/sdk";

export const myTask = task({
  id: "my-task",
  run: async (payload: { url: string }) => {
    const now = Date.now(); // fine
    const id = crypto.randomUUID(); // fine
    const data = await fetch(payload.url); // fine
    return { now, id, data: await data.json() };
  },
});

AI document summarizer

Trigger.dev (1 file)

import { task } from "@trigger.dev/sdk";
import { generateText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

export const summarizeDoc = task({
  id: "summarize-doc",
  run: async (payload: { documentUrl: string }) => {
    const doc = await fetch(payload.documentUrl).then(r => r.text());

    const { text } = await generateText({
      model: anthropic("claude-sonnet-4-6"),
      prompt: `Summarize this document:\n\n${doc}`,
    });

    return { summary: text };
  },
});

Temporal (3 files + worker)

// workflow.ts: must be deterministic (no fetch, no LLM calls here)
import { proxyActivities } from '@temporalio/workflow';
import type * as activities from './activities';

const { fetchDocument, summarizeWithLLM } = proxyActivities<typeof activities>({
  startToCloseTimeout: '10 minutes',
});

export async function summarizeDocWorkflow(
  documentUrl: string
): Promise<{ summary: string }> {
  const doc = await fetchDocument(documentUrl);
  const summary = await summarizeWithLLM(doc);
  return { summary };
}

// activities.ts: side effects go here
import { generateText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

export async function fetchDocument(url: string): Promise<string> {
  const response = await fetch(url);
  return response.text();
}

export async function summarizeWithLLM(content: string): Promise<string> {
  const { text } = await generateText({
    model: anthropic('claude-sonnet-4-6'),
    prompt: `Summarize this document:\n\n${content}`,
  });
  return text;
}

// worker.ts: you deploy and operate this separately
import { NativeConnection, Worker } from '@temporalio/worker';
import * as activities from './activities';

async function run() {
  const connection = await NativeConnection.connect({
    address: process.env.TEMPORAL_ADDRESS ?? 'localhost:7233',
  });
  const worker = await Worker.create({
    connection,
    workflowsPath: require.resolve('./workflow'),
    activities,
    taskQueue: 'summarize-queue',
  });
  await worker.run();
}

run().catch(console.error);

AI document summarizer

import { task } from "@trigger.dev/sdk";
import { generateText } from "ai";
import { anthropic } from "@ai-sdk/anthropic";

export const summarizeDoc = task({
  id: "summarize-doc",
  run: async (payload: { documentUrl: string }) => {
    const doc = await fetch(payload.documentUrl).then(r => r.text());

    const { text } = await generateText({
      model: anthropic("claude-sonnet-4-6"),
      prompt: `Summarize this document:\n\n${doc}`,
    });

    return { summary: text };
  },
});

What developers say about Trigger.dev

Frequently asked questions

Can I migrate from Temporal to Trigger.dev?: Yes. The main work is removing the Workflow/Activity split and converting to Trigger.dev tasks. Since Trigger.dev has no determinism constraints, most Temporal Activities can be inlined directly into task code. The worker infrastructure can be decommissioned entirely.
Does Trigger.dev support multiple languages like Temporal?: Temporal has official SDKs for Go, Java, Python, TypeScript, .NET, PHP, and Ruby, with Rust in public preview. Trigger.dev is TypeScript-native, with a build extension for running Python scripts alongside your TypeScript tasks.
Is Temporal's deterministic replay actually a problem in practice?: Non-determinism errors are a common pain point in Temporal's community: calling Date.now(), using libraries with internal randomness, or making API calls without Activity wrappers. Once you learn the rules it's manageable, but the learning curve is real. Trigger.dev uses a different durability model, so your code runs as plain TypeScript with no replay constraints.
Can Trigger.dev handle Temporal-level scale?: Trigger.dev processes hundreds of millions of runs for thousands of engineering teams and scales automatically with no worker infrastructure to manage. Temporal Cloud has processed over 9.1 trillion lifetime actions . For most workloads, both platforms are more than capable.
Does Temporal have better observability?: Temporal's event history provides a replayable record of every workflow execution, which is powerful for debugging deterministic workflows. Trigger.dev provides OpenTelemetry tracing, a dashboard with trace viewer, run filtering, and custom views. Both have web dashboards for monitoring runs.
Which is better for AI agents specifically?: Depends on your language. Temporal has deep Python AI integration through the OpenAI Agents SDK. For TypeScript AI agents, Trigger.dev supports any framework (AI SDK, Mastra, OpenAI Agents SDK, etc.) without modification, with Realtime Streams, input streams , Waitpoints for human-in-the-loop, and no execution timeout.
Can I use Temporal's Signal/Query/Update pattern with Trigger.dev?: Partially. Trigger.dev's input streams and Waitpoints cover the Signal use case: send typed data into a running task from your backend or frontend. Realtime Streams cover reading task output. Queries (inspecting workflow state) and Updates (validated state mutations) don't have a direct equivalent.
Which platform is more future-proof?: Both are open source and actively developed. Temporal has deep enterprise traction with SDKs across eight languages. Trigger.dev has thousands of teams in production and is growing fast in the TypeScript and AI agent ecosystem. Both are self-hostable, so your code isn't locked in either way.
Can Trigger.dev replace Temporal for TypeScript background tasks?: For TypeScript teams running background tasks, data pipelines, or AI workflows, yes. Trigger.dev offers a simpler model with no determinism constraints, no worker infrastructure, and deploys in minutes. Temporal provides additional patterns like Signal/Query/Update and multi-language support that some architectures depend on.

Next comparison

Product

AI Agents

Trigger.dev Realtime

Concurrency & queues

Scheduled tasks

Observability & monitoring

Roadmap

Latest changelogs

Trigger.dev v4.7.4

Introducing Ask Trigger to your dashboard

Preview branches now archive themselves

Latest blog posts

7 things we wish we'd known before building an AI chat agent

Zod 4 got rid of a bunch of CPUs for us

Upgrading a 56TB database that's growing at 250GB per day with (nearly) no downtime

Documentation

Guides

Trigger.dev vs Temporal

What is Trigger.dev?

What is Temporal?

Feature deep-dive

How does developer experience compare?

How does the build and deploy pipeline compare?

How does durability work in each platform?

How do AI agent capabilities compare?

What infrastructure do you need to manage?

How does pricing compare at scale?

How do self-hosting and licensing compare?

Code comparison

Normal TypeScript vs deterministic workflows

Normal TypeScript vs deterministic workflows

AI document summarizer

AI document summarizer

What developers say about Trigger.dev

Frequently asked questions

Can I migrate from Temporal to Trigger.dev?

Does Trigger.dev support multiple languages like Temporal?

Is Temporal's deterministic replay actually a problem in practice?

Can Trigger.dev handle Temporal-level scale?

Does Temporal have better observability?

Which is better for AI agents specifically?

Can I use Temporal's Signal/Query/Update pattern with Trigger.dev?

Which platform is more future-proof?

Can Trigger.dev replace Temporal for TypeScript background tasks?

Trigger.dev vs BullMQ