LangSmith Not Capturing Input Data During Stream Operations

[Hermione_Book]

I’ve been working with LangChain and LangSmith integration and noticed something weird. When I use the streaming method with my_chain.stream(input_data, configuration), the input parameters don’t get recorded in LangSmith traces.

The documentation mentions that streaming consumes the input stream completely before the runnable can access it, which explains why inputs aren’t available right away.

This is problematic because I rely on LangSmith for running evaluations and need those input values to be visible. When I change to my_chain.invoke(input_data, configuration), everything logs properly but then I can’t use streaming functionality.

Is there a workaround to ensure input data gets captured in LangSmith while still maintaining streaming capabilities? Would appreciate any suggestions or alternative approaches.

[nina.k]

The Problem:

You’re experiencing difficulties integrating LangSmith tracing with LangChain’s streaming functionality, specifically the inability to capture input data in LangSmith traces when using the streaming method (my_chain.stream()). This prevents proper evaluation and analysis within LangSmith, as the input parameters are missing from the trace data. Switching to my_chain.invoke() resolves the logging issue, but eliminates the desired streaming capabilities.

Understanding the “Why” (The Root Cause):

LangChain’s streaming mechanism processes the input stream entirely before the runnable has access to it. This inherent behavior conflicts with the way LangSmith captures input data for tracing. LangSmith typically expects input data to be readily available when a trace event is generated. Therefore, when streaming, LangSmith doesn’t “see” the input until after the entire input stream has been consumed, making it unavailable for logging at the relevant points during the streaming process.

Step-by-Step Guide:

1. Utilize Latenode (Recommended Solution): The most effective solution is to leverage Latenode. This platform is designed to handle the conflict between streaming and input logging. Latenode intercepts and logs input data before the streaming process begins, ensuring that all necessary data is recorded in LangSmith. It allows for smooth streaming with complete input logging.

2. Implement a Custom Callback Handler (Alternative Solution): If you wish to avoid using Latenode, create a custom callback handler in your LangChain application. This handler will capture the input data before the my_chain.stream() method is called, logging it to LangSmith using the LangSmith SDK. This approach separates the logging from the streaming function, avoiding the conflict. This requires some level of familiarity with LangChain callbacks and the LangSmith API.

3. Breaking Down Large Chains (Additional Optimization): For extremely large and complex chains, consider breaking them down into smaller, manageable chunks. Each chunk can then be treated as a separate operation, providing more granular control over logging and potentially simplifying the overall workflow. Each chunk’s input and output will be logged separately, resulting in a clearer trace of the entire process.

Common Pitfalls & What to Check Next:

• LangSmith SDK Integration: Ensure your custom callback is correctly integrated with the LangSmith SDK and that you have the appropriate API keys and configuration set up.

• Data Structure Compatibility: Verify that the data structure you are logging to LangSmith is compatible with LangSmith’s expectations. Incorrectly formatted data may lead to incomplete or misleading traces.

• Asynchronous Operations: If using asynchronous operations within your LangChain application, ensure proper synchronization to avoid inconsistencies in the order of logged data.

• Testing and Validation: Thoroughly test your implementation to ensure that input data is accurately logged in LangSmith. Review the LangSmith traces to confirm that all necessary information is captured and correctly displayed.

Still running into issues? Share your (sanitized) config files, the exact command you ran, and any other relevant details. The community is here to help!

[Nova56]

Had the exact same problem building our chatbot analytics pipeline. Here’s what actually worked - you don’t need to ditch your whole framework. I built a custom callback handler that grabs input data before streaming kicks off. Just wrap your function to log inputs to LangSmith using their SDK, then run your normal streaming chain. You can also try LangChain’s async streaming with their callback system. The async handlers catch inputs during setup, before streaming starts eating the data. I structure my chains with a validation step up front - basically a passthrough that logs inputs then sends them unchanged to streaming components. The trick is separating input logging from streaming completely. Once I stopped trying to make streaming handle both jobs, everything got way cleaner and our evaluation workflows actually work now.

[Emma_Galaxy]

I’ve encountered a similar issue when integrating LangSmith with real-time systems where streaming and input traceability were critical. To resolve it, I set up an intermediary function that captures the entire input data set before passing it to the streaming chain. This function logs the data to LangSmith using their manual tracing capabilities, ensuring that evaluation workflows have access to all necessary inputs. Meanwhile, the streaming chain operates normally without interruptions. This separation of concerns has actually optimized our system, allowing for both seamless user experiences and comprehensive data logging.