Lightweight Durable Workflows Built on Postgres
Hi HN! This is Qian here with Peter (KraftyOne) and Jeremy (jedberg). We’re building DBOS, an open-source, lightweight durable workflows library that you can add to Python apps in just a few lines of code. It’s comparable to popular open-source workflow and queue libraries like Airflow and Celery, but more lightweight with a greater focus on reliability and automatically recovering from failures. Our goal in building DBOS is to make workflows lightweight and flexible so you can add them to your existing apps with minimal work. Everything you need to run durable workflows and queues is…
What it does
In the maker’s words, at launch
Hi HN! This is Qian here with Peter (KraftyOne) and Jeremy (jedberg). We’re building DBOS, an open-source, lightweight durable workflows library that you can add to Python apps in just a few lines of code. It’s comparable to popular open-source workflow and queue libraries like Airflow and Celery, but more lightweight with a greater focus on reliability and automatically recovering from failures. Our goal in building DBOS is to make workflows lightweight and flexible so you can add them to your existing apps with minimal work. Everything you need to run durable workflows and queues is contained in this Python library. You don’t need to manage a separate workflow server: just install the library, connect it to a Postgres database (to store workflow/queue state) and you’re good to go. DBOS workflows make your program durable by checkpointing its state in Postgres. If your program ever fails, when it restarts all your workflows will automatically resume from the last completed step. You add durable workflows to your existing program by annotating ordinary functions as workflows and steps: from dbos import DBOS @DBOS.step() def step_one(): ... @DBOS.step() def step_two(): ... @DBOS.workflow() def workflow(): step_one() step_two() The workflow is just an ordinary Python function. You can call it any way you like–from a FastAPI handler, in response to events, wherever you’d normally call a function. We’ve just released DBOS Python 1.0. This enhances workflows with many powerful features we’ve been building over the last few months, including: - Durable queues. Postgres-backed queues with all the queuing features of BullMQ/Celery (concurrency limits, rate limits, timeouts, priority, deduplication, etc.). Plus, they integrate with durable workflows, so you can write a workflow that enqueues 1K tasks, waits for and processes their results, and automatically recovers from any interruption. - Programmatic workflow management. Your workflows are stored as rows in a Postgres table, so you have full programmatic control over them. Write scripts to query workflow executions, batch pause or resume workflows, or even restart failed workflows from a specific step. This makes it much easier to diagnose and recover from bugs and failures that affect thousands of workflows. - Full support for both sync and async Python–write your workflows and steps as code either synchronously or asynchronously, it all works out of the box. - Improved tooling, including dashboards, workflow graph visualization, workflow management via web UI, and more. We’d love to hear your feedback and hope you can try DBOS out!
Does the same job
all alternatives →- DPDBOS Python 1.0 – Lightweight Durable Workflows Built on Postgres2025 · github.com · ▲6
Hi HN – this is Peter from DBOS here with Qian (qianli_cs) and Jeremy (jedberg). We’re building an open-source, lightweight durable workflows library on top of Postgres. Ever since we first launched on HN last year, we’ve been blown away by the support, feedback, and response we’ve received from the community. We've realized durable workflows are critical for everything from business processes to AI automation to data pipelines, but most existing durable orchestration tools are either too heavy or too complicated for most applications. Instead, we're building something lightweight, simple,…
- PBPostgres-Backed Durable Workflows in GoSep 2025 · github.com · ▲5
Hi HN - I’m Peter, here with Max (hmaxdml), and we’re building DBOS Go, an open-source Go library for durable workflows, backed by Postgres. https://github.com/dbos-inc/dbos-transact-golang DBOS workflows make your programs durable by automatically checkpointing their state to Postgres. If your program crashes or fails, all workflows seamlessly resume from their last completed step when your program restarts. This durability makes workflows useful for solving many different problems, including: - Operating an AI agent, or anything that connects to an unreliable or…
- AAAtlas – A deployment pipeline platform built on Argo CD2022 · greenops.io · ▲66
Atlas is an open-source deployment pipeline platform built for cloud-native applications. Atlas allows users to: - Create continuous pipelines across all their environments and clusters - Add custom tasks/tests plugins (Python scripts, K8S manifests, Argo Workflows, environment setup, etc.) - Automatically rollback applications in case of failure or degradation (Atlas watches the application past the scope of a pipeline run to ensure and enforce stability) - Use all existing Argo features Would love to hear all of your feedback and thoughts on this!
- RWRender Workflows – Durable task orchestration without queues or workersApr 2026 · render.com · ▲7
Hey HN, I'm Anurag, founder and CEO of Render, a cloud for application developers. We've just launched Render Workflows, a way to define durable tasks by decorating plain TypeScript or Python functions and running them on Render without operating queues, worker pools, retry logic, and state management. Code example below [1]. The use cases we have in mind include agent loops, ETL/data pipelines, billing flows, and other long-running jobs. The model is simple: mark an existing function as a Task, deploy the repo as a Workflow, then trigger runs from your application code or via our API.…
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