Langfuse Python SDK Bug Traces Break With FastAPI StreamingResponse And @observe

Introduction

Hey guys, let's dive into a tricky bug that some of us have been facing while using the Langfuse Python SDK with FastAPI StreamingResponses. This issue, which pops up when using the @observe decorator with streaming responses, can really throw a wrench in your tracing efforts. We'll break down the problem, look at how to reproduce it, and discuss the context around the SDK and hosting environment. If you're wrestling with this, you're in the right place! We'll make sure to cover all the important keywords and details so you can get a handle on what's going on.

The Bug: Traces Breaking with StreamingResponses

The core issue? When you're using @observe to trace functions that return a StreamingResponse in FastAPI, the traces don't quite work as expected. This problem mirrors the concerns raised in GitHub issues #3961 and #3922. It seems like version 3.2.1, and self-hosting setups are particularly affected. The key observation here is that traces function perfectly fine for regular endpoints (those not using StreamingResponse), indicating the bug is specifically tied to how streaming responses are handled within Langfuse's tracing mechanism. This is a crucial detail because it narrows down the scope of the problem and gives us a clearer direction for debugging. We need to understand why StreamingResponse behaves differently and how the @observe decorator interacts with it.

Think of it this way: you're trying to track the journey of a request through your application, but when the response is streamed, the tracing gets cut short or mangled. It's like trying to follow a river that suddenly disappears underground. You know the river (request) started and you know it's supposed to end somewhere, but you lose visibility in the middle. The @observe decorator is meant to provide that visibility, but with streaming responses, it's as if the decorator is missing key information or is unable to properly handle the asynchronous nature of the stream. This can lead to incomplete traces, making it difficult to pinpoint bottlenecks or understand the flow of data in your application.

How to Reproduce: Code Snippets and Function Structure

To really nail down the bug, let's look at a way to reproduce it. Imagine you have three functions: send_message, build_prompt, and send_message_to_service. The idea is that send_message acts as the parent function, orchestrating the calls to build_prompt and send_message_to_service. Here’s a breakdown of how these functions interact and what their roles are:

• send_message: This is the main function, decorated with @observe, which kicks off the process. It takes in parameters like assignment_id, helpfulness_level, a list of messages, and optional parameters such as essay_text and student_id. Its primary job is to build a prompt using build_prompt and then stream the response from send_message_to_service. Because it's the parent function, it’s crucial for setting the context of the trace. If the tracing breaks here, the entire operation's visibility is compromised.
• build_prompt: Also decorated with @observe, this function is responsible for constructing the prompt. It takes a PromptBlockInput object as input and returns a BuiltContext object. Think of it as the step where the instructions are prepared before sending them to the service. If tracing fails here, you lose insight into how the prompt is being constructed, which is vital for debugging prompt-related issues.
• send_message_to_service: This function, decorated with @observe(as_type="generation"), is where the actual message sending happens. It takes system prompts and user context and yields chunks of the response as an asynchronous generator. The as_type="generation" part is important because it signifies that this function is generating content, which Langfuse should track accordingly. This is often the most performance-sensitive part of the process, and if tracing breaks here, identifying performance bottlenecks becomes a major headache.

Here are some code snippets to illustrate this structure:

   @observe
    async def send_message(
        self,
        assignment_id: str,
        helpfulness_level: str,
        messages: List[Dict[str, str]],
        essay_text: Optional[str] = None,
        student_id: Optional[str] = None,
    ) -> AsyncGenerator[str, None]:
        prompt_input = PromptBlockInput(
            messages=messages,
        )
        built_context = await self.prompt_manager.build_prompt(prompt_input)

        async for chunk in self.send_message_to_service(
            built_context.system,
            built_context.user,
        ):
            yield chunk

   @observe
    async def build_prompt(self, ctx: PromptBlockInput) -> BuiltContext:

   @observe(as_type="generation")
    async def send_message_to_service(
        self,
        system_prompts: List[Dict[str, any]],
        user_context: List[Dict[str, str]],
    ) -> AsyncGenerator[str, None]:

By setting up these functions in this way, you can clearly see the parent-child relationship and how the streaming response is generated. If the traces break, you’ll notice that the connection between send_message and the other functions is either missing or incomplete. This structured approach is key to isolating the issue and providing a reproducible case for the Langfuse team or anyone trying to debug the problem.

SDK and Hosting Environment: Version and Setup Details

Understanding the environment in which this bug occurs is crucial for troubleshooting. The specific setup involves:

• Python SDK Version: The SDK version in use is v3.2.1. This is important because bugs can be version-specific. A bug present in one version might be fixed in a later version, or it might be a regression introduced in this version. Knowing the exact version helps narrow down the possibilities and allows for targeted testing and debugging.
• Self-Hosting in GCP: The Langfuse instance is self-hosted on Google Cloud Platform (GCP). Self-hosting adds a layer of complexity compared to using a managed service because the user is responsible for the infrastructure and configuration. The setup was done using the langfuse-terraform-gcp configuration found in this GitHub repository: https://github.com/langfuse/langfuse-terraform-gcp/tree/main. This Terraform configuration automates the deployment of Langfuse on GCP, setting up the necessary resources and configurations. However, the specific configuration and versions of the underlying services (like databases, message queues, etc.) can influence the behavior of Langfuse. Therefore, having this level of detail is essential for replicating the environment and identifying potential conflicts or misconfigurations.

Knowing these details helps in several ways. It allows others to reproduce the environment as closely as possible, ensuring that the bug can be reliably triggered. It also allows for comparisons with other setups. For example, if the bug doesn’t occur in a different hosting environment or with a different SDK version, it suggests that the issue is specific to this combination. Furthermore, the Terraform configuration provides a detailed blueprint of the infrastructure, which can be inspected for potential issues, such as networking configurations, resource constraints, or version incompatibilities. This holistic view of the environment is vital for effective debugging and resolution.

Contributing a Fix: Interest and Community Involvement

While the reporter of the bug isn't able to contribute a fix directly at this time, it's important to highlight the value of community involvement in addressing issues like this. Open-source projects thrive on contributions from their users, and bug reports like this are the first step in that process. By providing detailed information and a way to reproduce the bug, the reporter has already made a significant contribution.

The question of contributing a fix is crucial because it speaks to the sustainability and robustness of the project. When users are willing to contribute fixes, it not only resolves the immediate issue but also strengthens the project's resilience and responsiveness to future problems. In this case, even though the reporter can't contribute code, the information provided can enable others in the community to step in and help.

For example, someone familiar with the Langfuse SDK and FastAPI might take the provided code snippets and environment details to reproduce the bug locally. They could then use debugging tools to trace the execution flow, identify the root cause of the issue, and develop a fix. This fix could then be submitted as a pull request, benefiting all users of the SDK. Additionally, the discussion around the bug and potential fixes can lead to a deeper understanding of the interaction between Langfuse, FastAPI, and streaming responses, which can inform future development and prevent similar issues from arising.

Conclusion: Addressing the StreamingResponse Trace Bug

So, there you have it, guys – a deep dive into the bug affecting tracing with Langfuse's Python SDK when using FastAPI StreamingResponses. We've covered the core problem, walked through a way to reproduce it with code snippets, and looked at the specific SDK version and hosting environment where it occurs. The key takeaway here is that the @observe decorator, which is crucial for tracing, doesn't play nicely with streaming responses, leading to broken traces. This is a significant issue because it hampers the ability to monitor and debug applications that rely on streaming.

The next steps in addressing this bug involve further investigation by the Langfuse team and potentially the community. Understanding why the traces break with streaming responses will require looking into the internal workings of the @observe decorator and how it interacts with FastAPI's StreamingResponse. It's possible that the asynchronous nature of streaming responses introduces complexities that the tracing mechanism isn't currently handling correctly. Alternatively, there might be an issue with how the trace context is propagated across asynchronous calls or how the streaming response is finalized within the tracing framework.

Ultimately, resolving this bug will enhance the usability of Langfuse, particularly for applications that leverage streaming for performance and real-time capabilities. A fix will not only restore the tracing functionality but also provide valuable insights into the best practices for integrating tracing with asynchronous streaming frameworks. Keep an eye on the Langfuse GitHub repository for updates and potential solutions. Your contributions, whether in the form of code, feedback, or further debugging efforts, can help make Langfuse an even more powerful tool for monitoring and understanding your applications.