Bug: Litellm.responses Creates Separate Traces In Langfuse

Hey guys! Let's dive into this interesting bug report about litellm.responses messing up the tracing in Langfuse. The user, like many of us, expected a parent-child trace relationship but ended up with separate, independent traces. Let's break down the issue, explore the code, and understand why this might be happening.

Understanding the Bug: litellm.responses and Langfuse Tracing

The core of the problem lies in how litellm.responses interacts with Langfuse's @observe decorator. The user was aiming for a single trace, run_conversation, containing a child trace named litellm_request. This setup is crucial for understanding the flow of requests and responses in a conversational AI application. When traces are correctly linked, you can easily follow the path of a user's input, the LLM's response, and any intermediate steps.

However, the Langfuse dashboard showed two independent traces instead. This suggests that the @observe decorator, which is supposed to wrap the run_conversation function and capture its execution within a trace, isn't working as expected with litellm.responses. When separate traces are created, it becomes harder to piece together the entire conversation flow, making debugging and monitoring more complex. Imagine trying to debug a lengthy conversation where you can't easily link each turn to its parent context – a real headache, right?

The issue is that litellm.responses appears to be escaping the context set by @observe, leading to a new trace being initiated instead of a child trace being created within the existing one. This can happen if litellm.responses is inadvertently breaking the trace context propagation or if it's explicitly starting a new trace without the necessary linkage.

Code Snippet: Reproducing the Issue

Let's take a look at the code snippet provided by the user. It's a concise example that demonstrates the problem effectively:

# These two lines are for loading API keys, feel free to ignore 
from dotenv import load_dotenv
load_dotenv()

import litellm
import os
from langfuse import observe, get_client

litelm.callbacks = ["langfuse_otel"]
os.environ["LANGFUSE_TRACING_ENVIRONMENT"] = "251014-langfuse-test"
langfuse_context = get_client()

@observe()
def run_conversation(x: float):
    # langfuse_context.update_current_span(name="aresponses_test", input=str(10+x))
    response = litellm.responses(
        model="gpt-4o",
        input=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "1. Hello! What is 3+3?"}
        ],
        temperature=0.7,
    )
    print(response)
    return x

run_conversation(1.1)

Here's a breakdown:

• Environment Setup: The code starts by loading environment variables (likely containing API keys) and importing necessary libraries like litellm, langfuse, and os. Setting litellm.callbacks = ["langfuse_otel"] is crucial, as it tells LiteLLM to use the Langfuse OpenTelemetry (OTel) integration for tracing. This is the bridge that should connect LiteLLM calls to Langfuse traces.
• Langfuse Initialization: langfuse_context = get_client() initializes the Langfuse client, which is used for interacting with the Langfuse platform. The environment variable LANGFUSE_TRACING_ENVIRONMENT is set to a specific environment ID, ensuring that traces are logged to the correct place.
• @observe Decorator: This is where the magic should happen. The @observe() decorator from Langfuse wraps the run_conversation function. Ideally, this would mean that when run_conversation is called, Langfuse starts a trace, and any operations within the function (like the litellm.responses call) should be captured as child spans within that trace.
• run_conversation Function: This function simulates a simple conversation turn. It takes a float as input, calls litellm.responses to get a response from a language model (in this case, GPT-4o), prints the response, and returns the input. The commented-out line langfuse_context.update_current_span(...) suggests the user might have been experimenting with manual span updates, which can sometimes be necessary for fine-grained control over tracing.
• litellm.responses Call: This is the heart of the issue. The call to litellm.responses sends a request to the specified language model. Because litellm.callbacks is set to use the Langfuse OTel integration, this call should be automatically traced as a child span within the run_conversation trace.
• Execution: Finally, run_conversation(1.1) executes the function, triggering the Langfuse trace and the LiteLLM call.

Steps to Reproduce

The steps to reproduce the bug are straightforward: simply run the code snippet above with the necessary environment variables configured. The user reported that this consistently resulted in two independent traces in the Langfuse dashboard, rather than the expected parent-child relationship.

Environment Details

Knowing the environment details is crucial for debugging. The user provided the following information:

• Langfuse: Self-hosted, version 3.112.0
• SDK: langfuse-python 3.6.2

This tells us that the user is running a relatively recent version of Langfuse and the Python SDK. However, there could still be compatibility issues or specific configurations that are contributing to the problem.

Potential Causes and Solutions

So, what could be causing this issue? Let's brainstorm some potential causes and explore possible solutions.

1. Trace Context Propagation

The most likely culprit is an issue with trace context propagation. Trace context is the mechanism by which tracing systems like OpenTelemetry track the relationships between spans. When a function is called within a trace, the trace context needs to be passed along so that any new spans created are correctly linked as children of the parent span.

If litellm.responses isn't correctly propagating the trace context, it might be inadvertently starting a new trace instead of creating a child span. This could happen if:

• litellm.responses isn't using OpenTelemetry internally to manage spans.
• There's a bug in the Langfuse OTel integration that prevents context from being passed correctly.
• The context is being lost or overwritten somewhere within the LiteLLM call stack.

Possible Solutions:

• Verify LiteLLM's OTel Integration: Double-check that litellm.responses is indeed using OpenTelemetry for tracing and that it's correctly configured to propagate context. Look for any configuration options or settings related to tracing context propagation.
• Inspect Langfuse OTel Integration: Examine the Langfuse OTel integration code for any potential bugs or misconfigurations that might be preventing context propagation. Pay close attention to how spans are started and ended, and how context is passed between functions.
• Manual Context Propagation (Temporary Workaround): As a temporary workaround, you could try manually propagating the trace context. This involves explicitly getting the current span from Langfuse and passing it as an argument to litellm.responses. While this isn't ideal, it can help confirm whether context propagation is the issue.

2. Asynchronous Operations

If litellm.responses involves asynchronous operations (e.g., making an HTTP request to a language model API), it's possible that the trace context is being lost across the asynchronous boundary. Asynchronous code can sometimes complicate tracing because the execution flow isn't always linear.

Possible Solutions:

• Ensure Asynchronous Context Management: If litellm.responses is using asynchronous code, ensure that it's using a mechanism to propagate trace context across asynchronous tasks. OpenTelemetry provides tools for this, such as contextvars and asynchronous context managers.
• Check for Context Switching: Look for any places where the context might be switched or cleared within the asynchronous code. This could happen if threads or processes are being used, as each thread or process has its own context.

3. Explicit Trace Creation

It's also possible that litellm.responses is explicitly starting a new trace, perhaps unintentionally. This could happen if the LiteLLM code is creating a new Tracer instance or calling a function that starts a new trace without checking for an existing one.

Possible Solutions:

• Review LiteLLM Code: Carefully review the source code of litellm.responses and any related functions to see if a new trace is being started explicitly. Look for calls to Tracer.start_as_current_span or similar methods.
• Prevent Duplicate Trace Creation: If a new trace is being started, modify the code to check for an existing trace and only start a new one if necessary. The goal is to ensure that LiteLLM respects the trace context established by Langfuse.

4. Version Incompatibilities

Although the user is running relatively recent versions of Langfuse and the Python SDK, there could still be version incompatibilities between these libraries and LiteLLM. Sometimes, changes in one library can break compatibility with others.

Possible Solutions:

• Check Compatibility Matrix: Consult the documentation for Langfuse and LiteLLM to see if there's a compatibility matrix that specifies which versions are known to work together.
• Experiment with Versions: Try downgrading or upgrading Langfuse, the Python SDK, or LiteLLM to see if a different combination resolves the issue. This can help isolate whether a specific version is causing the problem.

5. Configuration Errors

Finally, there's always the possibility of a configuration error. Perhaps there's a setting in Langfuse, LiteLLM, or the OTel integration that's not configured correctly.

Possible Solutions:

• Review Configuration: Carefully review all configuration settings related to tracing, context propagation, and the Langfuse OTel integration. Look for any typos, incorrect values, or missing settings.
• Simplify Configuration: Try simplifying the configuration as much as possible to rule out any complex interactions between settings. For example, you could try using the default settings for the OTel integration.

User's Environment: Self-Hosted Langfuse

The fact that the user is running a self-hosted instance of Langfuse is also relevant. Self-hosted deployments can sometimes have unique configuration challenges or network issues that might not be present in cloud-hosted environments.

Possible Considerations:

• Network Connectivity: Ensure that the Langfuse instance is reachable from the application and that there are no network firewalls or other restrictions that might be interfering with tracing data.
• Resource Limits: Check the resource limits (e.g., CPU, memory) of the Langfuse instance to ensure that it's not being overloaded. Overload can sometimes lead to tracing data being lost or corrupted.
• Configuration Files: Review the Langfuse configuration files for any settings that might be related to tracing or context propagation. Pay attention to settings that control the OTel integration or the behavior of spans.

Additional Information and Next Steps

The user didn't provide any additional information beyond the code snippet and environment details. To further diagnose the issue, it would be helpful to have:

• Langfuse Dashboard Screenshots: Screenshots of the Langfuse dashboard showing the two independent traces would provide visual confirmation of the problem.
• Logs: Logs from both the application and the Langfuse instance might contain clues about what's going wrong. Look for any error messages, warnings, or unusual activity related to tracing.
• Minimal Reproducible Example: While the provided code snippet is already quite minimal, it's always helpful to have a self-contained example that can be easily run and reproduced by others. This helps ensure that everyone is looking at the same problem.

Next Steps:

1. Reproduce the Issue: The first step is to try to reproduce the issue locally. This helps confirm that the problem is real and that you understand the steps to trigger it.
2. Gather More Information: Collect logs, screenshots, and any other relevant information that might help diagnose the problem.
3. Isolate the Cause: Systematically test potential causes (e.g., context propagation, asynchronous operations) by modifying the code or configuration and observing the results.
4. Implement a Solution: Once the cause is identified, implement a fix. This might involve modifying the code, changing the configuration, or updating libraries.
5. Test the Solution: Thoroughly test the solution to ensure that it resolves the issue and doesn't introduce any new problems.

Contributing a Fix

The user indicated that they're not interested in contributing a fix for this bug. However, if anyone else in the community is interested, this would be a great opportunity to contribute to open-source projects like Langfuse and LiteLLM.

Contributing a fix typically involves:

1. Forking the Repository: Create a fork of the relevant repository (e.g., Langfuse, LiteLLM) on GitHub.
2. Creating a Branch: Create a new branch in your fork for the bug fix.
3. Implementing the Fix: Make the necessary code changes to resolve the issue.
4. Testing the Fix: Add unit tests or integration tests to ensure that the fix works correctly.
5. Submitting a Pull Request: Submit a pull request to the original repository with your changes.

The maintainers of the repository will review your pull request and provide feedback. If everything looks good, they'll merge your changes into the main branch.

Conclusion

This bug report highlights an interesting issue with litellm.responses and Langfuse tracing. The problem appears to be related to trace context propagation, but there are several potential causes to investigate. By systematically exploring these causes and implementing a solution, we can ensure that tracing works correctly and provides valuable insights into the behavior of our applications. Remember, a well-traced application is a well-understood application, and that's crucial for building robust and reliable systems. Let's keep digging and get this sorted out, folks!