Master Inngest with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Sign up for free account at inngest.com and verify your email address Install Inngest SDK via npm: 'npm install inngest' and initialize with your signing key Create your first function using createFunction() with event trigger and step
based handler Test locally using 'npx inngest
cli dev' to start the development server with live debugging Deploy to production by connecting your preferred hosting platform (Vercel, AWS, etc.)
💡 Quick Start: Follow these 3 steps in order to get up and running with Inngest quickly.
Explore the key features that make Inngest powerful for ai workflow infrastructure workflows.
Inngest is a fully managed cloud service with a code-first SDK approach — you wrap functions in `step.run` and ship, with no cluster to manage. Temporal is more powerful for highly customized workflow orchestration but requires running and operating Temporal Server (or paying for Temporal Cloud) and learning its workflow/activity programming model. Inngest's AgentKit also adds AI-specific features like step.ai prompt/response tracing that Temporal lacks natively. For most teams building AI agents or background jobs, Inngest ships faster; for teams with dedicated platform engineers needing fine-grained orchestration control, Temporal can be a better fit.
Yes — Inngest's AgentKit is purpose-built for AI agent workloads, and many teams adopt Inngest exclusively for agent pipelines. AgentKit handles multi-step LLM orchestration, automatic retries on model failures, prompt/response tracing via step.ai, and durable state between tool calls. Aomni's founder publicly recommends Inngest for multi-step AI agents specifically because of the free traceability, timeouts, and retries. You can start with just agents and expand to background jobs, webhooks, and scheduled tasks later if needed.
Yes, significantly — especially for AI workloads where LLM calls dominate costs. In a 10-step agent workflow, if step 8 (an LLM call) fails with traditional queue systems, you restart from step 1 and pay for steps 1–7 again. With Inngest, only step 8 retries because the prior steps' outputs are persisted as durable state. For multi-step AI pipelines this can reduce wasted LLM spend by 70–90% during transient failures. The savings compound when you add retry policies with exponential backoff.
Yes, Inngest offers a self-hosted option suitable for enterprise deployments and air-gapped environments. The core engine is open source (inngest/inngest on GitHub with 6.6K+ stars), so you can run it on your own infrastructure with full feature parity for execution. Cloud-managed features like the hosted dashboard, multi-region scaling, and Inngest's SOC 2 audit boundary apply only to the managed service. Most teams start with Inngest Cloud and migrate to self-hosting only if they have strict data residency or compliance needs.
Inngest provides official SDKs for TypeScript/JavaScript, Python, Go, and Kotlin, plus a dedicated AgentKit SDK for AI agents. It runs on any infrastructure — edge functions (Vercel, Cloudflare Workers), serverless platforms (AWS Lambda, Google Cloud Functions), and traditional long-running servers (Node, Express, Fastify, FastAPI, Django, Gin). Triggers include HTTP webhooks, scheduled cron jobs, event payloads, and direct API calls. This runtime-agnostic design means you can deploy Inngest functions alongside your existing stack with no infrastructure refactoring.
Now that you know how to use Inngest, it's time to put this knowledge into practice.
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Tutorial updated March 2026