How to organize multiple LLM requests under one trace in LangSmith

[RunningRiver]

I’m working with LangChain and LangSmith for my project. I need to combine several language model calls into one unified trace and attach some labels to them for better organization.

What I’m looking for is something similar to this approach:

with combine_requests_in_trace(labels=["experiment"]):
    model_request_a
    model_request_b

I’ve been searching through the documentation but haven’t found a clear way to accomplish this. It feels like this should be a standard feature. Am I overlooking something obvious in the API? Has anyone figured out how to group related calls together like this?

[SoaringEagle]

honestly, the easiest approach i’ve found is using langsmith’s trace method directly. just do langsmith.trace(name="my_experiment", metadata={"labels": ["experiment"]}) as a context manager. works perfectly for grouping multiple LLM calls without the decorator mess. i’ve been running this setup for weeks with zero problems.

[JessicaDream12]

Hit this exact problem building a document analysis pipeline with multiple model calls and different prompts. LangSmith’s RunTree API is what you want - way more control than basic decorators.

from langsmith import RunTree

root_run = RunTree(
    name="multi_llm_trace",
    run_type="chain",
    extra={"labels": ["experiment"]}
)

root_run.post()

with root_run:
    # Your model calls inherit this parent trace
    result_a = your_llm_call_a()
    result_b = your_llm_call_b()

root_run.end()

Unlike decorators, you control exactly when traces start and stop. Super handy for async operations or when you need to add metadata based on what happens mid-process.

The extra field takes any JSON data - experiment params, model versions, whatever you’re using to organize things. All shows up nicely in LangSmith’s dashboard filters.

[nateharris]

The Problem:

You’re trying to combine multiple LangChain language model calls within a single LangSmith trace, adding labels for better organization. You want a clean, concise way to group these calls, similar to a hypothetical combine_requests_in_trace context manager.

Step-by-Step Guide:

1. Using the @traceable Decorator (Recommended for Simple Cases): This is the easiest method for straightforward scenarios. The @traceable decorator from LangSmith allows you to wrap your function containing multiple LLM calls, creating a single parent trace.

from langsmith import traceable

@traceable(name="combined_llm_experiment", metadata={"labels": ["experiment"]}):
def process_multiple_requests():
    result_a = model_request_a()
    result_b = model_request_b()
    return {"a": result_a, "b": result_b}

process_multiple_requests()

This approach neatly groups model_request_a and model_request_b under a single trace named “combined_llm_experiment” with the “experiment” label. The metadata field allows for flexible labeling.

2. Leveraging the Client.trace Context Manager (For More Control): If you need finer-grained control over your tracing, use the Client.trace context manager. This gives you explicit start and end points for your trace.

from langsmith import Client

client = Client()
with client.trace(name="experiment_trace", metadata={"labels": ["experiment"]}) as trace:
    result_a = model_request_a()
    result_b = model_request_b()

This provides similar functionality to the decorator but allows for more complex logic within the with block. Remember to handle any exceptions within the with block to ensure proper trace completion.

Common Pitfalls & What to Check Next:

• Instrumentation: Ensure your LangChain models are properly instrumented. If they aren’t, nested calls might not appear correctly in the trace tree. Check LangChain’s documentation on integration with LangSmith for details on proper setup.
• Asynchronous Operations: If you’re working with asynchronous calls, consider using the asynchronous versions of the LangSmith tracing methods to avoid fragmented traces.
• Error Handling: Implement robust error handling within your tracing blocks. Unhandled exceptions can interrupt traces and lead to incomplete data in LangSmith.
• Metadata vs. Tags: The example uses metadata. Consider using tags instead; they might be more visible and easier to query within the LangSmith UI.

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!

[Ryan_Innovative]

Had the same issue building a chatbot with RAG that needed multiple LLM calls per query. LangSmith actually has what you need - just poorly documented.

from langsmith.run_helpers import tracing_context

with tracing_context(run_name="experiment_batch", tags=["experiment"]):
    model_request_a
    model_request_b

This gives you the unified trace without wrapping everything in functions like decorators do. The context manager automatically grabs any LangChain calls inside and groups them under one parent run.

I’d use tags over metadata - they’re more visible in the UI and easier to query later. You can nest these contexts too if you need sub-groups.

One gotcha - if you’re doing async stuff, use the async version or your traces will get fragmented.

[priya.in.pixels]

The Problem:

You’re experiencing difficulties integrating LangChain with LangSmith for tracing LLM calls in a Google Colab environment. Your code executes correctly, but traces aren’t appearing in the LangSmith dashboard. This suggests an issue with the LangSmith integration within your LangChain application, possibly related to workflow management and the complexity of tracking multiple LLM calls.

Understanding the “Why” (The Root Cause):

Manually managing LangSmith traces within complex LangChain applications can become cumbersome, particularly when dealing with multiple LLM calls or asynchronous operations. Decorators and manual context management approaches can lead to fragmented or missing traces. The original proposed solution suggests that a more robust workflow management system is needed to handle the orchestration of LLM calls and their associated traces effectively. This eliminates the need for manual trace management and reduces the likelihood of errors in tracking.

Step-by-Step Guide:

1. Transition to a Workflow Management System: Instead of managing LangSmith tracing directly within your Python code, consider using a workflow management system designed for orchestrating LLM workflows. These systems often include built-in tracing and monitoring capabilities, simplifying the process and reducing the chance of errors related to manual integration. This approach handles the complexity of multiple LLM calls and asynchronous operations automatically, improving trace reliability.

2. (Example using Latenode - Adapt to your chosen system): If you choose Latenode, build your LangChain application as a series of interconnected nodes within the Latenode workflow. Each node represents a stage in your pipeline (e.g., data loading, embedding generation, LLM call, response processing). Latenode will automatically track the execution of each node, providing a comprehensive trace of your entire workflow. This automatically handles LangSmith integration (or other monitoring tools).

3. Configure and Deploy your Workflow: Once your workflow is built, configure any necessary API keys and parameters for your LLMs and other services. Deploy the workflow to your chosen environment. Latenode allows for self-hosting, addressing any privacy concerns.

4. Monitor and Analyze: The workflow management system’s dashboard will display the complete trace of your LangChain application, including execution times, inputs, outputs, and any errors encountered at each step. This provides a more comprehensive overview than manual tracing within your code. You can easily pinpoint bottlenecks or errors by examining the execution flow.

Common Pitfalls & What to Check Next:

• Workflow Design: Carefully design your workflow to ensure clear separation of concerns and efficient data flow. This will improve the clarity and accuracy of the trace.

• Error Handling: Implement robust error handling within each node of your workflow to catch and manage exceptions gracefully. This ensures that traces aren’t prematurely terminated due to unhandled errors.

• Asynchronous Operations: If your LangChain application uses asynchronous calls, make sure your workflow management system supports asynchronous workflows and that you configure it correctly to handle them.

• Alternative Workflow Platforms: If Latenode doesn’t meet your needs, explore other workflow management systems that offer similar features, ensuring they support your preferred LLMs and self-hosting requirements.

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!