I am struggling to see the nested steps in my multi-stage RAG pipeline. Has anyone successfully used Langfuse to trace individual tool calls within a LangChain Agent? I need to identify exactly which retrieval step is causing the most latency, but the default logs are too messy to read without a proper trace UI.
3 answers
The integration with LangChain is actually quite seamless if you use the CallbackHandler. You simply need to initialize the Langfuse handler with your public and secret keys and then pass it into the run or invoke method of your chain. This will automatically capture every nested span, including the prompts sent to the LLM and the raw documents retrieved from your vector store. In the Langfuse dashboard, you’ll see a hierarchical tree view that breaks down the execution time for each component. This makes it incredibly easy to spot if your embedding model is taking 2 seconds or if the actual generation is the bottleneck.
Are you running this in a synchronous environment or are you using the async ainvoke methods? Sometimes the callback tracking can get tricky with concurrent async calls.
Make sure you use the latest version of the SDK. The recent updates significantly improved how the nested metadata is displayed in the trace UI.
Danielle is right. Upgrading to the latest SDK solved a major issue I had where my custom metadata tags weren't showing up correctly in the sidebar.
We are using the async version primarily. I noticed that if we don't properly await the trace flush, some logs get dropped when the Lambda function terminates. I had to add a manual flush call at the end of my handler to ensure every trace reached the server before the environment shut down.