I’m currently going through a tutorial series on LangSmith, and I found myself confused about the annotation queues section. This is the fifth out of seven parts in the series. I would really appreciate some guidance on how annotation queues function within LangSmith and the correct way to set them up. Can someone provide an explanation of the key concepts and perhaps a straightforward example on creating and managing annotation queues? I’ve been checking the documentation, but I’m struggling with the practical side of it. Any advice or code snippets would be fantastic to help me get it right.
Annotation queues let you queue up runs for human review. Your model outputs something, then someone checks it before it moves forward.
I use these constantly when testing new prompts or fine-tuning models. There are three parts: the queue, the runs you add, and the reviewers doing the work.
Creating one’s easy through the UI. Go to your project, hit annotation queues, make a new queue. Filter which runs go in by feedback scores, tags, or time ranges.
Here’s what I learned the hard way: start small with filtering. I dumped 10k runs into a queue once - total nightmare to manage.
Make sure your reviewers have proper access first. Nothing’s worse than setting everything up and your team can’t see the queue.
The API works if you want to get fancy later, but the UI handles most cases when you’re starting.
One tip: tag your runs consistently. Makes filtering for queues way easier later.
Been there with LangSmith annotation queues. The setup’s super manual and eats up tons of time through their interface.
I hit the same wall managing multiple annotation workflows. Constantly switching datasets, setting up reviewers, checking queue status - it killed productivity.
Automating the whole thing changed everything for me. Built flows that create queues automatically based on data criteria, assign reviewers dynamically, and ping you when reviews finish.
Now I focus on actual annotation instead of babysitting queues. Set triggers to create new queues when datasets hit thresholds or auto-archive completed ones.
Bonus: you get way better pipeline visibility with custom dashboards and reporting.
Check out Latenode for this workflow automation: https://latenode.com
hey there! annotation queues are super useful for managing data review. you can set 'em up via the ui or api, just make sure you add your datasets right. also, don’t forget to check the reviewer permissions, it can get a bit tricky!