Resources

A new Inngest API for launching typed, durable follow-up work from inside a function.

Official Inngest plugins and skills for Claude Code, Codex, Cursor, and whatever agent you already use.

As we scale our datacenter footprint, we consolidated Ansible, libvirt, and NetBox inventory sync into a single Go app—using Inngest events instead of scattered coordination logic.

If you're building a platform where every customer request will hit the same OpenAI bill, you need the ability to slice and shape concurrency at a per-tenant level.

Every six months, the "right" way to build an AI agent changes. How can you design for the next rewrite?

We surveyed 130 backend, full-stack, and AI engineers about what it takes to run reliable AI workflows in production. We wanted to know what's causing failures, and which infrastructure choices—across orchestration, observability, evals, and agent frameworks—actually reduce the burden of reliability.

I was looking for a side project to test some real concurrency. A Commander deck analyzer turned out to be the perfect excuse. Here's how I used Inngest's durable functions and Realtime to fan out parallel steps and stream results live to the UI.

CVE-2026-42047

Learn how to build a scalable, multi-step lead enrichment and qualification pipeline using Inngest's durable execution and concurrency controls. Handles tens of thousands of leads per day across multiple enrichment providers, LLM scoring, and CRM writes—without queue sprawl or provisioning overhead.

Orchestration has been the undefined pillar of Agent Experience. Stripe Projects just made it core infrastructure.

A full report on the incident that caused significant delays in function scheduling and execution for all customers.

I built a self-improving agent on Inngest, then learned the hard part wasn't retraining prompts, but stopping the system from gaming its own evaluation.

Inngest is an official launch partner for Stripe Projects—a new agent-first protocol for standing up production stacks without hopping between dashboards, digging around docs, or copying keys.

Support questions need evidence, not guesses. We built a bot that reads the live thread, searches the docs, finds similar historical tickets, and only then drafts a response.

Everyone needs retries. Not everyone needs priority queuing—yet. Here’s what 50,000 users taught me about when teams need to solve which problems, and why.

A promise that never resolves is a surprisingly clean way to interrupt an async function.

Queuing and durable execution are two layers of the same problem. In this post we’ll look at solutions dedicated to each, and when each are most appropriate for what you’re building.

fal.ai runs hundreds of models at scale. Inngest orchestrates the workflow around them — retries, async coordination, per-user fairness, and observability. Here's how the two work together to build a full media pipeline.

How we improved reliability of our cron scheduler by rebuilding it on top of our own durable queue primitives.

Worker threads solve real problems, but they come with constraints that Go, Rust, and Python developers would never expect. Here's what we learned moving Inngest Connect's internals off the main thread.

Use Inngest's step primitives to build resilient, observable AI agents without a heavyweight framework.

How Windmill replaced brittle n8n flows with a durable internal ops agent—Pim—powered by Inngest Connect, context-first design, and a focused toolset.

I thought I was good at TypeScript. Refactoring the Inngest SDK proved I was okay at best—and that changed how I think about types forever.

Every agentic system that actually ships ends up needing three delegation patterns: one that blocks, one that fires and forgets, and one that runs later.

We reduced pressure on our sharded Redis cluster by introducing a gRPC proxy that caches immutable run state and routes consistently by run ID.

Add step-level durability to your Next.js AI routes so failures don't restart from zero—each step is independently retryable and memoized.

We tried Event, Condition, and Queue. Each one gets closer but still breaks under real concurrency. Here's the observable pattern that finally works.

We're bringing richer execution data to the front end, making it easier to debug your jobs and workflows with enhanced Traces.

More type safety, less boilerplate, fewer round-trips.

Agent runtimes don't need yet another framework — they need a durable, event-driven harness that connects tools, memory, and models on production-grade infrastructure.

Last Friday, we ran a live session on durable endpoints and built a fully functional deep research agent from scratch in about 30 minutes. Two API endpoints, no job queue, no worker infrastructure, no state management code.

Every durable execution system adds some latency between steps. It's the tax we pay for reliability. Here's how to eliminate it.

How we extracted constraint enforcement into a dedicated service to unlock lower latency, greater scalability, and flow control for Durable Endpoints.

AI Agents introduce multiple points of failure that traditional retry logic cannot handle. Durable execution provides the automatic state persistence, retries, and workflow resumption that make agents production-ready.

When you're building developer tools, the line between building the product, using it and supporting it doesn't exist. The question isn't whether engineers should do support. It's how to make it work for you.

(you just didn't call it that)

It is time for a new approach. Durable Endpoints makes API endpoints unbreakable, with low latency, minimal API, and no infrastructure work.

We care a lot about developer experience. But it's hard to build beautiful experiences for customers, while grinding through 10-12 second page load times. Here's how—and why—our team migrated from Next.js to Tanstack Start.

How we shipped Insights AI, what we learned about building agentic features, and why dogfooding your own infrastructure actually works.

Ask Insights any question about your production events and workflow runs.

By enabling checkpointing, Inngest workflows keep durability while achieving near-zero inter-step latency.

Add end-to-end visibility and supercharge your debugging with distributed tracing for Express APIs and background workflows.

Insights' schema explorer lets you discover all the available data to write queries that are now shareable with your team.

Inngest Traces now captures every nested API requests, database query, and 3rd party libraries.

A lot has been written about Context Engineering theory and its principles, with few examples. This blog post serves as a practical reference for Context Engineering using Inngest.

An interview with Paul Sanglé-Ferrière, Founder of cubic, on how context engineering informs AI product success

How to stop your AI agents from breaking in production — and start making them debuggable, deterministic, and durable.

Summary of the incident that caused function execution to fail on October 24, 2025

AI customer support agent need more than just routing—you need cost control, durable execution, and streaming infrastructure for real-time responses. Let's build.

Comparing explicit APIs vs magic directives for workflows, queues, and durable execution. Or, directives as an antipattern.

Developers shouldn't be slowed down by infrastructure. By integrating with DigitalOcean, developers can now go from prototype to millions of users without any infrastructure work.

We've released realtime support for Python, enabling developers to build interactive applications that push updates from durable workflows to the browser.

Replit’s agent builder is powered by Inngest. Novice builders can now vibe code durable, complex agents quickly. Not a vibe coder? Read on to learn exactly how Replit built on top of Inngest, so you can do the same.

Multi-agent setups are hard to build. So we built useAgent: a one-line code for streaming real-time, multi-step backend workflows to the frontend.

We were tired of writing custom metrics and grepping logs every time we wanted to know what happened in a run. So, we built Insights, now in beta for all Inngest users.

We're bringing one-step durability to APIs. Add a single line of code to any REST API to immediately add durability, automatic retries, and observability to your applications, without ever having to think about queues, workers, or extra infra.

We raised a $21M Series A to help companies ship and iterate faster. Inngest's step-function architecture and built-in observability let any engineer quickly productionize workflows and agents—without touching infrastructure.

A technical deep dive interview into 10 years of solving orchestration challenges at Hubspot, Netflix and now Day AI.

The keystone of Context Engineering is a flexible and robust orchestration layer that connects LLM apps to various data sources to provide the richest context to your Agents.

Kafka is effective for data-streaming and often used for application queuing. This blog explores its shortcomings by implementing Flow Control like concurrency.

Leveraging Inngest AgentKit to build an AI assistant that turns natural language into structured database queries

Learn how to implement background jobs in Next.js using Inngest.

Integrate Mem0 with AgentKit

Making it easier than ever to get started with Inngest for all Vercel developers

The latest version of the Inngest Python SDK is now stable and ready for production use.

Our brand transformation

Connect is a new API to connect your application to Inngest over a outbound persistent connection. This brings better support for servers (Render, Fly.io, etc.) and lower latency.

Leverage our new Datadog and Prometheus integrations to set up custom metrics and alerting.

Stream updates from your Inngest functions to your users.

Offload HTTP requests to the Inngest Platform to save compute and improve reliability.

Dark mode is here. Customize your Inngest experience your way.

Learn how to build the famous Cursor Agent mode with our AI Agent framework

A deep dive into DeepSeek-R1's Multi-Lingual and Agentic RAG Capabilities

Build an AI Agent with Inngest and Render

Combine Weaviate and Inngest step.ai API to build AI workflows

Learn what Durable Execution is, how it works, and why it's beneficial to your system.

Combining step.run() and step.ai.infer() is the best toolset to build reliable AI applications on Serverless while saving on compute.

Build reliable event-driven workflows with a streamlined developer experience

The easiest way to build production-ready AI workflows with AgentKit and step.ai

Summary of the queue related issues in October 2024

Instantly search across all your Function runs with advanced queries filtering on events and function runs properties.

Solving Next.js timeout issues isn't only about increasing the timeout but also using the right tools for the job.

How LLM evaluations, guardrails, and orchestration shape safe and reliable AI experiences.

Common pitfalls of traditional queues and how Inngest can help

A reimagined contacts importer leveraging the power of reasoning models with Inngest

Joe Adams from MEGA SEO shares how Inngest enabled them to build AI workflows that would have been difficult or impossible to achieve with SQS.

Handle incidents

A new era for monitoring durable functions

The fastest way to build workflow UIs

A new integration for Postgres database events

The easiest way to self-host durable execution.

Lydia Hallie's powerful animated illustrations cover the inner workings of Durable Functions.

Explore three different ways to control your Inngest Function's runs.

A full report on the incident that caused function execution to fail on August 16, 2024 UTC.

Learn how Durable Functions remove the need of a separate server to handle long-running workflows or to power queues.

The Inngest Cloud and Dev Server got a brand new design. This post digs into the process behind this new Information Architecture.

Batch keys allows developers to group work units by leveraging Inngest's efficient event-matching engine.

Read about how we rolled our new sharded infrastructure out to production without a millisecond of downtime and how it improved Inngest's overall performance.

Win $2,500 in a thrilling two-week hackathon using the PCXI stack: Prisma, Xata, Clerk, Inngest 🎉

Ensuring fairness and consistent performance for all users with concurrency controls

Our commitment to security and privacy for our company and platform

Dave Kiss shares his takeaways from building Truckload, a project which simplifies heavy video migration between hosting platforms.

What is it, when to use it, and when not to use it

Building the expression engine that powers ephemeral event matching.

Banger.Show is a video app maker that heavily relies on background data processing

What do you need to take your LLM based product from demo to production?

Why you shouldn't directly use message queues in 2024

Learn how Finta builds and optimizes data pipelines.

Explore backend message queuing systems, implementing debouncing in Postgres, and simplifying the process with Inngest.

Accelerating development of the reliability layer for modern applications

Targeting sub 100ms response times from anywhere in the world

A look at all the releases of the past week: from Replay and per-step error handling to new SKDs and integrations.

Using native language primitives to handle failed steps

How to trigger Inngest functions with Clerk events in the new integration

Svix customers can now quickly integrate with Inngest

The Inngest SDKs provide a language- and cloud-agnostic way to create fault-tolerant, long-running functions with built-in flow control.

How the Inngest system is designed to help you migrate across clouds with minimal effort.

The death of the dead-letter queue.

Building the Inngest queue - Part I

Cancel a time range of functions using the REST API.

Learn how to extend the range of your Astro app with long-running processes, and when to do so.

Over the past twelve months, we've shipped a lot and improved the DX across the board, our team has grown three-fold, and we were able to raise a seed round.

How we built better observability into Inngest

Safer error handling, inspired by Go and Rust

Better observability into function runs

Get your workflows up and running quickly with Sanity and Inngest in Next.js

Learn about the exciting new features in v3.0 and how to upgrade.

Breaking down how a durable workflow engine works, and how event-driven workflow engines improve DX.

Use Inngest and PartyKit to Power Up Your OpenAI Chatbots.

How to Send Effective and Reliable Emails in your Next.js Applications with Resend & Inngest

How Badass Courses built a self-service video publishing workflow for Kent C. Dodds with AI generated transcripts and subtitles.

A how-to guide

New round led by Glenn Solomon of GGV Capital, including Guillermo Rauch and Tom Preston-Werner

Providing a way to handle high load events, and processing them in bulk

Learn about the exciting new features in v2.0 and how to upgrade.

Modern workflow for your business-critical code

Build production-ready zero-infra LLM backends using TypeScript in minutes

What did we learn from building and shipping our new app with the Next.js 13 App Router?

Introducing the next version of Inngest!

How to run business critical jobs for minutes, hours or days

Import data from Shopify, WooCommerce or BigCommerce APIs reliably and quickly

Providing developer-specific examples to help developers learn how to use the Inngest SDK. The beginning of AI-personalized learning flows for users.

Announcing our new RedwoodJS handler.

Run critical code with guarantees and observability

Where the Jamstack is today and what is left to complete the vision.

Announcing our new Vercel integration.

Learn how to use Next.js api functions and run them as you would a message queue or a cron job.

Create inituitive TypeScript libraries; don't make your user open the docs.

Almost all developers use job schedulers stuck in the past. This post explores our modern system that improves dev UX with better tooling

Skip the queue and workers.

How we built a reliable webhook based PR collaboration tool in less time your average company's sprint planning meeting

We're excited to release support for step functions which can run any language, all locally testable.

Our next release improving rollbacks and developer UX

Battle-test your local code with real production events.

How to quickly run load tests on event-driven queues via K6

Lessons learned scaling webhooks to millions of requests a day

This release contains exciting new functionality, including replay and our self-hosting services

We explore the difference between queueing systems and message busses

The design decisions and architecture for a next-gen queuing platform

The first tool purposely designed for event-driven asynchronous system local development

The open source, serverless event-driven platform for developers.

Our product is just different enough to make our CLI require really good interactivity. We bundle an interactive event browser in our CLI. Here's how it's built.

Our experience building https://typedwebhook.tools in 2 days using SvelteKit.

What's fresh out of the oven recently, and what's cooking? Here's our bi-weekly product deep dive.

What's fresh out of the oven recently, and what's cooking? Here's our bi-weekly product deep dive.

Programmable event platforms allow you to build serverless event-driven systems in minutes. Here's an introduction to them.

We’re launching Inngest, a platform designed to make building event-driven systems fast and easy.