How to conditionally send traces to LangSmith after user feedback

[sarahj]

Hi everyone,

I’m working with a LangGraph agent in production and need some guidance on trace logging. We want to let users give feedback on failed runs, and I’m thinking about using LangSmith traces for this.

The challenge is that we can’t log all traces automatically because of sensitive data concerns. Is it possible to send a trace to LangSmith only after a user decides to provide feedback? This way we’d have their permission before logging anything.

Any suggestions on how to implement this conditional logging approach would be really helpful. Thanks in advance!

[FlyingStar]

The Problem:

You’re trying to integrate LangSmith tracing with your LangGraph agent in production, but you need to avoid logging sensitive data until a user explicitly provides feedback on a failed run. You want a solution that allows for conditional trace logging, only sending traces to LangSmith after obtaining user consent.

Understanding the “Why” (The Root Cause):

The challenge lies in balancing the need for detailed debugging information (provided by LangSmith traces) with the critical requirement of protecting sensitive user data. Directly logging all traces might violate privacy policies or security best practices. Therefore, a solution is needed that decouples trace capture from trace transmission, allowing you to gather the data and then decide whether to send it based on user actions.

Step-by-Step Guide:

1. Implement In-Memory Trace Buffering: Create a custom tracer (or modify LangSmith’s default tracing mechanism) to capture all trace events in memory instead of sending them immediately to LangSmith. This buffering can be implemented using Python’s built-in data structures like lists or dictionaries, or more sophisticated in-memory databases like Redis. Crucially, these trace events will be keyed by a unique session identifier (e.g., a user ID or a randomly generated UUID) to prevent accidental mixing of data from different runs.

2. Develop a User Feedback Mechanism: Create a mechanism that allows users to submit feedback, ideally connected directly to the failed runs. This might involve a feedback form, a button in a user interface, or a custom endpoint that accepts feedback data. This feedback should include a clear indication of the user’s consent to share trace details.

3. Conditional Trace Upload: When a user submits feedback and gives consent, retrieve the buffered traces associated with the relevant session ID from memory.

4. Data Sanitization (Optional but Highly Recommended): Before uploading traces, implement robust data sanitization to remove any Personally Identifiable Information (PII) that might still be present. This is a critical step to ensure compliance with data privacy regulations.

5. Upload Traces to LangSmith: Use the LangSmith API to upload the sanitized trace data. Include the user’s feedback as metadata with the upload. The LangSmith API will allow you to associate the feedback directly with the corresponding trace data.

6. Buffer Management: Implement a mechanism to manage the in-memory buffer. This might involve setting a time-to-live (TTL) for unclaimed traces to prevent memory leaks. For example, you could automatically discard traces after a certain time period (e.g., 30 minutes) if no feedback has been provided. Consider using background tasks or threads to handle buffer cleanup operations asynchronously.

Common Pitfalls & What to Check Next:

• Memory Leaks: Carefully monitor memory usage to avoid potential memory leaks caused by the in-memory buffer. Thorough testing is crucial to ensure the buffer management strategy is effective.
• Data Loss: Implement error handling and recovery mechanisms to prevent data loss in case of system failures or unexpected interruptions. Consider using persistent storage (database) for the buffer instead of purely in-memory storage for enhanced reliability.
• PII Leakage: Even with sanitization, review your data handling procedures frequently to ensure consistent protection of sensitive data. Regularly audit your data sanitization procedures to prevent any potential PII leaks.
• API Rate Limits: Be mindful of potential rate limits imposed by the LangSmith API. Implement appropriate retry mechanisms and batching strategies to prevent exceeding API limits.

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!

[miar]

We solved this with a deferred tracing pattern - buffer trace events locally instead of sending them to LangSmith right away. While the agent runs, we use a custom callback handler that writes everything to a local queue or database table (keyed by session ID). The traces just sit there until user feedback comes in, then we upload them. The trick is separating collection from transmission. You can keep using LangSmith’s normal tracing setup, just intercept at the transport layer. When users report failures, we batch-send those traces with the feedback metadata to LangSmith’s API. One thing that bit us - make sure your buffer handles partial traces properly. If your agent crashes mid-run, you’ll get incomplete trace trees that break LangSmith’s visualization. We added trace validation before upload to catch this.

[emparker]

We use a two-phase setup for this.

Phase one: capture traces locally with LangSmith’s callback handlers, but route them to temp storage instead of sending right away.

Phase two kicks in when users click feedback. We sanitize the temp traces to strip any PII we missed, then push everything to LangSmith with feedback as metadata.

The trick is configuring your LangChain callbacks properly. Override the LangSmithRunTree to hit local storage first:

from langsmith import Client

# Only create client when feedback comes in
def send_traces_with_feedback(temp_traces, feedback_data):
    client = Client()
    # Process and send your stored traces

Two things we learned the hard way - encrypt your trace buffers even if they’re temp (sensitive data doesn’t care if it’s temporary). And set up cleanup jobs or you’ll run out of disk space quick.