Temporal
Trigger.dev
n8n.io
Pipedream
Amazon AWS
Apache Airflow
Aditya Protocol
Molted
PromptLayer
Langfuse
Helicone AI
PromptHub
PromptForgeApp
xr2.uk
Opsmeter
WhichModel
Temporal PromptLayerNo features have been listed yet.
Based on our record, Temporal seems to be a lot more popular than PromptLayer. While we know about 17 links to Temporal, we've tracked only 1 mention of PromptLayer. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
When a team moves from a monolith into microservices and event-driven, asynchronous systems, it inherits a class of problems that used to be someone else's: work that fails halfway through, steps that must not run twice, calls that return before the work is done. Temporal is a durable execution engine that handles a lot of this - you define a multi-step process, and it guarantees the process runs to completion... - Source: dev.to / about 12 hours ago
Durable execution โ the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows โ treats waiting as a continuation rather than a loop:. - Source: dev.to / 6 days ago
Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal โ Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / about 1 month ago
We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of โ a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines โ an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate โ plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 2 months ago
The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago
Looks nice, and it's nice that it also supports function call simulation. I've been collecting a list of tools for prompt engineering, I've added Knit now. Newly added: https://promptknit.com/ Newly added: https://github.com/promptfoo/promptfoo https://promptable.ai/ https://github.com/ianarawjo/ChainForge https://promptknit.com/ https://promptrefine.com/ https://www.vellum.ai/ https://www.everyprompt.com/... - Source: Hacker News / almost 3 years ago
Trigger.dev - Trigger workflows from APIs, on a schedule, or on demand. API calls are easy with authentication handled for you. Add durable delays that survive server restarts.
Langfuse - Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Helicone AI - Open-source LLM Observability for Developers
Pipedream - Integration platform for developers
PromptHub - Test, deploy, and manage your prompts with PromptHub, a prompt management tool designed to be usable by your whole team, not just engineers.
