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LiteLLM provides a unified interface to various Large Language Models. LangWatch integrates with LiteLLM by capturing OpenTelemetry traces, enabling detailed observability into your LLM calls made through LiteLLM. This guide outlines three primary approaches for instrumenting LiteLLM with LangWatch:
  1. Using autotrack_litellm_calls(): This method, part of the LangWatch SDK, dynamically patches your LiteLLM module instance for the current trace to capture its calls.
  2. Using LiteLLM’s Native OpenTelemetry Tracing with Global Setup: LiteLLM can automatically generate OpenTelemetry traces for its operations when a global OpenTelemetry environment (established by langwatch.setup()) is active.
  3. Using Community OpenTelemetry Instrumentors (for Underlying SDKs): If LiteLLM internally uses other instrumented SDKs (like the openai SDK for OpenAI models), you can leverage community instrumentors for those specific underlying SDKs.

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Using autotrack_litellm_calls()

The autotrack_litellm_calls() function, called on a trace object, provides a straightforward way to capture all LiteLLM calls for the duration of the current trace. This is often the most direct way to ensure LiteLLM operations are captured by LangWatch within a specific traced function. You typically call this method on the trace object obtained via langwatch.get_current_trace() inside a function decorated with @langwatch.trace(). 
import langwatch
import litellm
import os
import asyncio
from typing import cast
from litellm import CustomStreamWrapper # For streaming example
from litellm.types.utils import StreamingChoices # For streaming example

langwatch.setup()

@langwatch.trace(name="LiteLLM Autotrack Example")
async def get_litellm_response_autotrack(user_prompt: str):
    # Get the current trace and enable autotracking for the litellm module
    langwatch.get_current_trace().autotrack_litellm_calls(litellm) # Pass the imported litellm module

    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": user_prompt}
    ]

    # This call will now be automatically captured as a span by LangWatch
    response = await litellm.acompletion(
        model="groq/llama3-70b-8192",
        messages=messages,
    )
    return response.choices[0].message.content

async def main():
    reply = await get_litellm_response_autotrack("Tell me about LiteLLM.")
    print("AI Response:", reply)

if __name__ == "__main__":
    asyncio.run(main())
Key points for autotrack_litellm_calls():
  • It must be called on an active trace object (e.g., obtained via langwatch.get_current_trace()).
  • It instruments the passed litellm module instance specifically for the current trace.

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Using Community OpenTelemetry Instrumentors

If you are already using a dedicated community instrumentor for LiteLLM, such as the one provided by OpenInference, you can pass an instance of LiteLLMInstrumentor from openinference.instrumentation.litellm to the instrumentors list in langwatch.setup().

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1. Via langwatch.setup()

You can pass an instance of LiteLLMInstrumentor from openinference.instrumentation.litellm to the instrumentors list in langwatch.setup(). 
import langwatch
import litellm
import os
import asyncio

# Example using OpenInference's LiteLLMInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor

# 1. Initialize LangWatch with the LiteLLMInstrumentor
langwatch.setup(
    instrumentors=[LiteLLMInstrumentor()] # Instruments LiteLLM directly
)

@langwatch.trace(name="LiteLLM via OpenInference Instrumentor Setup")
async def get_response_via_litellm_instrumentor_setup(user_prompt: str):
    messages = [
        {"role": "user", "content": user_prompt}
    ]

    # This LiteLLM call will be captured by the LiteLLMInstrumentor
    response = await litellm.acompletion(
        model="groq/llama3-70b-8192", # Example model
        messages=messages
    )
    return response.choices[0].message.content

async def main_community_litellm_instrumentor_setup():
    reply = await get_response_via_litellm_instrumentor_setup("Explain OpenInference for LiteLLM.")
    print(f"AI Response (OpenInference via setup): {reply}")

if __name__ == "__main__":
    asyncio.run(main_community_litellm_instrumentor_setup())
Ensure you have the openinference-instrumentation-litellm library installed.

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2. Direct Instrumentation with LiteLLMInstrumentor

If you are managing your OpenTelemetry setup more directly, you can call instrument() on an instance of LiteLLMInstrumentor. 
import langwatch
import litellm
import os
import asyncio

from openinference.instrumentation.litellm import LiteLLMInstrumentor

# 1. Initialize LangWatch (sets up global OTel provider for LangWatch exporter)
langwatch.setup()

# 2. Instrument LiteLLM directly using its OpenInference instrumentor
# This should be done once, early in your application lifecycle.
LiteLLMInstrumentor().instrument()

@langwatch.trace(name="LiteLLM via Directly Instrumented OpenInference")
async def get_response_direct_litellm_instrumentation(user_prompt: str):
    messages = [
        {"role": "user", "content": user_prompt}
    ]
    response = await litellm.acompletion(model="groq/llama3-70b-8192", messages=messages)
    return response.choices[0].message.content

async def main_direct_litellm_instrumentation():
    reply = await get_response_direct_litellm_instrumentation("How does direct OTel instrumentation work for LiteLLM?")
    print(f"AI Response (direct OpenInference): {reply}")

if __name__ == "__main__":
    asyncio.run(main_direct_litellm_instrumentation())
Key points for using OpenInference LiteLLMInstrumentor:
  • This instrumentor specifically targets LiteLLM calls.
  • It provides an alternative to autotrack_litellm_calls if you prefer an explicit instrumentor pattern or are using OpenInference across your stack.

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Which Approach to Choose?

  • autotrack_litellm_calls(): Best for explicit, trace-specific instrumentation of LiteLLM. Offers clear control over when LiteLLM calls are tracked by LangWatch within a given trace.
  • OpenInference LiteLLMInstrumentor: Use if you are standardizing on OpenInference instrumentors or prefer this explicit way of instrumenting LiteLLM itself (rather than its underlying SDKs). It provides traces directly from LiteLLM’s perspective.
Choose the method that best fits your instrumentation strategy and the level of detail required.