Inngest 1.0 – Open-source durable workflows on every platform
Hi HN! I’m Tony, one of the co-founders of Inngest (https://inngest.com/) Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows. Technical details: unlike other solutions,…
In plain words
Inngest is an open-source platform for building durable workflows—stateful, long-running functions that automatically retry on failure—across any cloud. It handles queues, event streams, and state management, allowing developers to write workflow logic directly in code. The platform supports use cases including AI step functions, data pipelines, integrations, webhooks, and payment flows. It emphasizes developer experience through straightforward APIs and includes built-in multi-tenancy, batching, and debouncing capabilities.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! I’m Tony, one of the co-founders of Inngest (https://inngest.com/) Inngest is an open-source durable workflow platform that works on any cloud. Durable workflows are stateful, long running step functions written in code, which automatically retry on failure. It abstracts everything about queues, event streams and state for you, letting you focus on code. Some examples of uses: managing stateful AI chained step functions; managing search/rag indexes and data pipelines; integrations and webhooks; billing and payment flows. Technical details: unlike other solutions, we put lots of effort into designing our SDK’s step.run APIs to make them extremely easy to use — developer experience is the most important thing for us. We had to design and build our own queueing system to work with multi-tenancy, batching, and debouncing, and we’re iterating on this as we move to FoundationDB. It’s largely all Go in the backend, with a bunch of caching, clickhouse, event streams, and coordination on our behalf. Workers are shared nothing, and run based off of the queue and execution state. We did a post last year as we iterated on our TS SDK. The product has changed a lot since then and wanted to show the community what’s changed as we reach 1.0: * Golang, Java, and Python SDKs with cross-language function invocation (across clouds, too) * Multi-tenant aware flow control (concurrency, throttling, debounce) * Batching, grouping many events into a single function call * Much improved dashboard, with tracing and metrics built in * Advanced recovery tools like function replay, temporary pausing, bulk cancellation (with optional expressions). No more dead letter queues! * Branch deploys built in, with staging env support out of the box * Full local testing with production parity There's a ton on the roadmap, with more launching next week. We’re hiring systems & infra engineers, too — it’s a fun job with lots of challenges! Wanted to say thank you to the HN community for feedback so far! Happy Friday :)
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, September 2024
the whole month →
BeforeSunset AI 2.0▲1,267Personalized AI daily planning that suits your life
AI · 2024 · beforesunset.ai
