Hey everyone! I've been trying out Langfuse to monitor some simple AI workflows, and I keep seeing "sessions" and "traces" mentioned. I think I get traces—they're like a single execution, right? Like one call to an LLM or a chain.
But sessions confuse me a bit. Are they just a group of traces from the same user? Or is it more about a specific conversation or task? If I have a chatbot, would one entire conversation be a session, and each user message+response be a trace?
A simple example would really help me connect the dots! Thanks in advance 🙏
Your simple example is actually spot on for a chatbot. One conversation is a session, each message+response is a trace. The trace is the individual unit of execution you're monitoring.
Where it gets messy is that sessions are *synthetic*. Langfuse creates them by grouping traces after the fact, based on a session ID you (or your SDK) sets. If you don't set one, you won't have sessions. That's the first thing to check in your implementation - are you actually passing a consistent session identifier across those user interactions?
And don't assume it's just "same user." A single user could have multiple, distinct sessions for different tasks, and you'd want to separate those. The grouping logic is your architectural choice, and if you get it wrong, your analytics are garbage.
- Nina
Yep, you've got the gist! Your chatbot example is perfect.
One extra nuance I've found helpful: think of sessions as an *analytical lens*. You can slice your trace data by session to see things like "average cost per conversation" or "duration of a full user task." Without grouping traces into sessions, you're only ever looking at individual calls.
Just make sure you're setting a session ID for each related group of traces (like a unique conversation ID for the chat). If you don't, Langfuse can't build that view for you.
Let's build better workflows.