Hey everyone! I’m working through a LangSmith tutorial series and I’m stuck on the automation and online evaluation part. This is the sixth section out of seven total parts.
I need help understanding how to properly configure automated workflows and implement real-time evaluation features. The documentation seems a bit confusing to me and I’m not sure if I’m setting things up correctly.
Has anyone worked with LangSmith’s automation tools before? I’m particularly interested in:
Setting up automated test runs
Configuring online evaluation metrics
Best practices for monitoring performance in real-time
Any tips or examples would be really helpful. I want to make sure I understand this properly before moving on to the final part of the tutorial series.
LangSmith automation is tricky - you’re stuck with tons of manual setup that doesn’t need to be so complicated.
I’ve hit the same wall. The native tools lock you into their specific workflow patterns without much flexibility.
I built my own automation layer using their API instead. Now I can trigger tests based on whatever conditions I want, pull evaluation data in real time, and send results anywhere.
For online evaluation, you need something that monitors multiple metrics at once and alerts you when things drift. LangSmith’s built-in monitoring is pretty basic.
I use Latenode to handle everything. It connects directly to LangSmith’s API, so I can set up automated test runs triggered by code deployments, schedule regular evaluation cycles, and pull performance data into other tools.
Best part? You can build custom evaluation logic beyond what LangSmith offers. Want to compare results across model versions? Easy. Need alerts when accuracy drops below a threshold? Done.
the langsmith automation finally made sense when i quit overcomplicating it. run their basic setup wizard first - don’t touch any custom settings yet. I kept trying to get fancy with configs and everything broke. their real-time evaluation works great with default metrics. follow their examples exactly first, then tweak once it’s running. trust me, you’ll save hours of frustration.
I had the exact same struggle with that section a few months ago. Start with simple automated runs first - don’t jump straight into complex evaluation metrics. Get your dataset formatted correctly before anything else. I messed this up initially and it broke my entire automation pipeline with weird errors. For online evaluation, stick to basic accuracy measurements at first. Skip the custom metrics until later. Real-time monitoring makes way more sense once you’ve got a few successful runs working. Here’s what the docs don’t tell you: there’s a delay between setting up automation and getting real-time data. It’s not instant. Spend extra time on configuration because fixing pipeline errors later is a pain. Much easier to get it right upfront.
Everyone’s overcomplicating this. I’ve deployed LangSmith automation on three major projects - here’s what actually works.
Run everything in test mode for a few days first. Don’t go live immediately. The automation engine has weird quirks that only show up under load.
Focus on latency metrics before anything else. Yeah, accuracy matters, but slow systems kill user engagement. Learned this when our chatbot was spot-on accurate but took 8 seconds per response - users just left.
Set up monitoring alerts BEFORE enabling automation. I had a pipeline running bad evaluations for 6 hours with no alerts. Burned through API credits and completely messed up our metrics.
Batch your requests instead of processing one at a time. Group test runs into 50-100 request chunks. Way more efficient and gives you cleaner performance data.
Biggest tip: use their preset evaluation templates first. Build custom metrics later once you get how their data flow works.
Working through that same tutorial and caught something others missed - environment variables matter way more than you’d think. My automated runs kept failing randomly until I figured out LangSmith was pulling different configs between local and prod. Set your API keys and dataset references properly in environment configs before you test anything. Also, their webhook integration crushes their API if you’re doing real-time assessment. Skip the constant polling - set up webhooks to push evaluation results to your monitoring system instead. Cut our monitoring overhead by 70% and we get way faster alerts when performance shifts. One gotcha - their evaluation caching will mess with you during development. Clear the cache between test runs or you’ll think your changes aren’t working when they are.
The automation section was a nightmare until I figured out that workflow sequencing beats individual configs every time. You can’t run online evaluations without baseline metrics from your training data - period. LangSmith needs historical performance data to compare against real-time results. Most people skip this and then complain about incomplete dashboards. Run manual evaluations for at least a week before turning on automation. You need that baseline data for meaningful comparisons. Here’s the kicker: automated test scheduling conflicts with manual runs. Schedule your automation windows carefully or you’ll get timeout errors from resource conflicts. Once you understand this dependency chain, performance monitoring actually makes sense.