LangWatch provides seamless integration with DSPy, allowing you to automatically capture detailed information about your DSPy program executions, including module calls and language model interactions. The primary way to instrument your DSPy programs is by using autotrack_dspy() on the current LangWatch trace.

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Using autotrack_dspy()

The autotrack_dspy() function, when called on an active trace object, dynamically patches the DSPy framework to capture calls made during the execution of your DSPy programs within that trace. You typically call this method on the trace object obtained via langwatch.get_current_trace() inside a function decorated with @langwatch.trace(). This ensures that all DSPy operations within that traced function are monitored. 
import langwatch
import dspy
import os

# Ensure LANGWATCH_API_KEY is set, or set it in langwatch.setup()
langwatch.setup() # If not done elsewhere or API key not in env

# Initialize your DSPy LM (Language Model)
# This example uses OpenAI, ensure OPENAI_API_KEY is set
lm = dspy.LM("openai/gpt-5", api_key=os.environ.get("OPENAI_API_KEY"))
dspy.settings.configure(lm=lm)

class BasicRAG(dspy.Signature):
    """Answer questions with short factoid answers."""
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")

class SimpleModule(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_answer = dspy.Predict(BasicRAG)

    def forward(self, question):
        prediction = self.generate_answer(question=question)
        return dspy.Prediction(answer=prediction.answer)

@langwatch.trace(name="DSPy RAG Execution")
def run_dspy_program(user_query: str):
    # Get the current trace and enable autotracking for DSPy
    current_trace = langwatch.get_current_trace().autotrack_dspy()

    program = SimpleModule()
    prediction = program(question=user_query)
    return prediction.answer

def main():
    user_question = "What is the capital of France?"
    response = run_dspy_program(user_question)
    print(f"Question: {user_question}")
    print(f"Answer: {response}")

if __name__ == "__main__":
    main()

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Key points for autotrack_dspy():

  • It must be called on an active trace object (e.g., obtained via langwatch.get_current_trace()).
  • It instruments DSPy operations specifically for the duration and scope of the current trace.
  • LangWatch will capture interactions with DSPy modules (like dspy.Predict, dspy.ChainOfThought, dspy.Retrieve) and the underlying LM calls.

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Using Community OpenTelemetry Instrumentors

If you prefer to use broader OpenTelemetry-based instrumentation, or are already using libraries like OpenInference, LangWatch can seamlessly integrate with them. These libraries provide instrumentors that automatically capture data from various LLM frameworks, including DSPy. The OpenInference community provides an instrumentor for DSPy which can be used with LangWatch. There are two main ways to integrate these:

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1. Via langwatch.setup()

You can pass an instance of the instrumentor (e.g., OpenInferenceDSPyInstrumentor from OpenInference) to the instrumentors list in the langwatch.setup() call. LangWatch will then manage the lifecycle of this instrumentor. 
import langwatch
import dspy
import os

from openinference.instrumentation.dspy import DSPyInstrumentor

# Initialize LangWatch with the DSPyInstrumentor
langwatch.setup(
    instrumentors=[DSPyInstrumentor()]
)

# Configure DSPy LM
# This example uses OpenAI, ensure OPENAI_API_KEY is set
lm = dspy.LM("openai/gpt-5", api_key=os.environ.get("OPENAI_API_KEY"))
dspy.settings.configure(lm=lm)

class BasicRAG(dspy.Signature):
    question = dspy.InputField()
    answer = dspy.OutputField()

class SimpleModule(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_answer = dspy.Predict(BasicRAG)

    def forward(self, question):
        prediction = self.generate_answer(question=question)
        return dspy.Prediction(answer=prediction.answer)

@langwatch.trace(name="DSPy Call with Community Instrumentor")
def generate_with_community_instrumentor(prompt: str):
    # No need to call autotrack_dspy explicitly,
    # the community instrumentor handles DSPy calls globally.
    program = SimpleModule()
    prediction = program(question=prompt)
    return prediction.answer

if __name__ == "__main__":
    # from dotenv import load_dotenv # Make sure to load .env if you have one
    # load_dotenv()
    user_query = "What is DSPy?"
    response = generate_with_community_instrumentor(user_query)
    print(f"User: {user_query}")
    print(f"AI: {response}")
Ensure you have the respective community instrumentation library installed (e.g., pip install openinference-instrumentation-dspy).

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2. Direct Instrumentation

If you have an existing OpenTelemetry TracerProvider configured in your application (or if LangWatch is configured to use the global provider), you can use the community instrumentor’s instrument() method directly. LangWatch will automatically pick up the spans generated by these instrumentors as long as its exporter is part of the active TracerProvider. 
import langwatch
import dspy
import os

from openinference.instrumentation.dspy import DSPyInstrumentor

langwatch.setup() # Initialize LangWatch

# Configure DSPy LM
lm = dspy.LM("openai/gpt-5", api_key=os.environ.get("OPENAI_API_KEY"))
dspy.settings.configure(lm=lm)

# Instrument DSPy directly using the community library
DSPyInstrumentor().instrument()

class BasicRAG(dspy.Signature):
    question = dspy.InputField()
    answer = dspy.OutputField()

class SimpleModule(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_answer = dspy.Predict(BasicRAG)

    def forward(self, question):
        prediction = self.generate_answer(question=question)
        return dspy.Prediction(answer=prediction.answer)

@langwatch.trace(name="DSPy Call with Direct Community Instrumentation")
def ask_dspy_directly_instrumented(original_question: str):
    program = SimpleModule()
    prediction = program(question=original_question)
    return prediction.answer

if __name__ == "__main__":
    query = "Explain the concept of few-shot learning in LLMs."
    response = ask_dspy_directly_instrumented(query)
    print(f"Query: {query}")
    print(f"Response: {response}")

​
Key points for community instrumentors:

  • These instrumentors often patch DSPy at a global level, meaning all DSPy calls will be captured once instrumented.
  • If using langwatch.setup(instrumentors=[...]), LangWatch handles the setup.
  • If instrumenting directly (e.g., DSPyInstrumentor().instrument()), ensure that the TracerProvider used by the instrumentor is the same one LangWatch is exporting from. This usually means LangWatch is configured to use an existing global provider or one you explicitly pass to langwatch.setup().

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Which Approach to Choose?

  • autotrack_dspy() is ideal for targeted instrumentation within specific traces or when you want fine-grained control over which DSPy program executions are tracked. It’s simpler if you’re not deeply invested in a separate OpenTelemetry setup.
  • Community Instrumentors (like OpenInference’s DSPyInstrumentor) are powerful if you’re already using OpenTelemetry, want to capture DSPy calls globally across your application, or need to instrument other libraries alongside DSPy with a consistent OpenTelemetry approach. They provide a more holistic observability solution if you have multiple OpenTelemetry-instrumented components.
Choose the method that best fits your existing setup and instrumentation needs. Both approaches effectively send DSPy call data to LangWatch for monitoring and analysis.

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Example: Chainlit Bot with DSPy and LangWatch

The following is a more complete example demonstrating autotrack_dspy() in a Chainlit application, similar to the dspy_bot.py found in the SDK examples. 
import os

import chainlit as cl
import langwatch
import dspy

langwatch.setup()

# Configure DSPy LM and RM (Retriever Model)
# Ensure OPENAI_API_KEY is in your environment for the LM
lm = dspy.LM("openai/gpt-5", api_key=os.environ["OPENAI_API_KEY"])
# This example uses a public ColBERTv2 instance for retrieval
colbertv2_wiki17_abstracts = dspy.ColBERTv2(
    url="http://20.102.90.50:2017/wiki17_abstracts"
)
dspy.settings.configure(lm=lm, rm=colbertv2_wiki17_abstracts)


class GenerateAnswer(dspy.Signature):
    """Answer questions with short factoid answers."""

    context = dspy.InputField(desc="may contain relevant facts")
    question = dspy.InputField()
    answer = dspy.OutputField(desc="often between 1 and 5 words")


class RAG(dspy.Module):
    def __init__(self, num_passages=3):
        super().__init__()
        self.retrieve = dspy.Retrieve(k=num_passages)
        self.generate_answer = dspy.ChainOfThought(GenerateAnswer)

    def forward(self, question):
        context = self.retrieve(question).passages  # type: ignore
        prediction = self.generate_answer(question=question, context=context)
        return dspy.Prediction(answer=prediction.answer)


@cl.on_message # Decorator for Chainlit message handling
@langwatch.trace() # Decorator to trace this function with LangWatch
async def main_chat_handler(message: cl.Message):
    # Get the current LangWatch trace and enable DSPy autotracking
    current_trace = langwatch.get_current_trace()
    if current_trace:
        current_trace.autotrack_dspy()

    msg_ui = cl.Message(content="") # Chainlit UI message

    # Initialize and run the DSPy RAG program
    program = RAG()
    prediction = program(question=message.content)

    # Stream the answer to the Chainlit UI
    # Note: For simplicity, this example doesn't stream token by token
    # from the DSPy prediction if it were a streaming response.
    # DSPy's prediction.answer is typically the full string.
    final_answer = prediction.answer
    if isinstance(final_answer, str):
        await msg_ui.stream_token(final_answer)
    else:
        # Handle cases where answer might not be a direct string (e.g. structured output)
        await msg_ui.stream_token(str(final_answer))

    await msg_ui.update()

# To run this example:
# 1. Ensure you have .env file with OPENAI_API_KEY and LANGWATCH_API_KEY.
# 2. Install dependencies: pip install langwatch dspy-ai openai chainlit python-dotenv
# 3. Run with Chainlit: chainlit run your_script_name.py -w
By calling autotrack_dspy() within your LangWatch-traced functions, you gain valuable insights into your DSPy program’s behavior, including latencies, token counts, and the flow of data through your defined modules and signatures. This is essential for debugging, optimizing, and monitoring your DSPy-powered AI applications.