Absurd Workflows: Durable Execution with Just Postgres

Other question: why reimplementing your framework, rather than using an existing agent framework like Claude + MCP, or OpenAI + tool calling? Is it because you're using your own LM models, or just because you wanted more control on retries, etc?

There are not that many agent frameworks around at the moment. If you want to be provider independent you most likely either use pydantic AI or the vercel AI SDK would be my guess. Neither one have built-in solution for durable execution so you end up driving the loop yourself. So it's not that I don't use these SDKs, it's just that I need to drive the loop myself.

Okay very clear! I was saying that because your post example is just a kind of basic "tool use" example which is already implemented by MCP/OpenAI tool use, but obviously I guess your code can be suited to more complex scenarios

Two small questions:

1. in your README you give this example for durable execution:

const shipment = await ctx.awaitEvent(`shipment.packed:${params.orderId}`);

I was just wondering, how does it work? I was more expecting a generator with a `yield` statement to run "long-running tasks" in the background... otherwise is the node runtime keeping the thread running with the await? doesn't this "pile up"?

2. would your framework be suited to long-running jobs with multiple steps? I have sometimes big jobs running in the background on all of my IoT devices, eg:

for each d in devices: doSomeWork(d)

and I'd like to run the big outerloop each hour (say), but only if the previous one is complete (eg max num of workers per task = 1), and that the inner-loop be some "steps" that can be cached, but can be retried if they fail

would your framework be suited for that? or is that just a simpler use-case for pgmq and I don't need the Absurd framework?