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LangWatch allows you to monitor your OpenAI Agents by integrating with their tracing capabilities. Since OpenAI Agents manage their own execution flow, including LLM calls and tool usage, the direct autotrack_openai_calls() method used for the standard OpenAI client is not applicable here. Instead, you can integrate LangWatch in one of two ways:
  1. Using OpenInference Instrumentation (Recommended): Leverage the openinference-instrumentation-openai-agents library, which provides OpenTelemetry-based instrumentation for OpenAI Agents. This is generally the simplest and most straightforward method.
  2. Alternative: Using OpenAI Agents’ Built-in Tracing with a Custom Processor: If you choose not to use OpenInference or have highly specific requirements, you can adapt the built-in tracing mechanism of the openai-agents SDK to forward trace data to LangWatch by implementing your own custom TracingProcessor.
This guide will walk you through both methods. The most straightforward way to integrate LangWatch with OpenAI Agents is by using the OpenInference instrumentation library specifically designed for it: openinference-instrumentation-openai-agents. This library is currently in an Alpha stage, so while ready for experimentation, it may undergo breaking changes. This approach uses OpenTelemetry-based instrumentation and is generally recommended for ease of setup.

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Installation

First, ensure you have the necessary packages installed: 
pip install langwatch openai-agents openinference-instrumentation-openai-agents

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Integration via langwatch.setup()

You can pass an instance of the OpenAIAgentsInstrumentor from openinference-instrumentation-openai-agents to the instrumentors list in the langwatch.setup() call. LangWatch will then manage the lifecycle of this instrumentor. 
import langwatch
from openai_agents.agents import Agent, Runner # Using openai_agents SDK
from openinference.instrumentation.openai_agents import OpenAIAgentsInstrumentor
import os
import asyncio

# Ensure LANGWATCH_API_KEY is set in your environment, or pass it to setup
# e.g., langwatch.setup(api_key="your_api_key", instrumentors=[OpenAIAgentsInstrumentor()])
# If LANGWATCH_API_KEY is in env, this is sufficient:
langwatch.setup(
    instrumentors=[OpenAIAgentsInstrumentor()]
)

# Initialize your agent
agent = Agent(name="ExampleAgent", instructions="You are a helpful assistant.")

@langwatch.trace(name="OpenAI Agent Run with OpenInference")
async def run_agent_with_openinference(prompt: str):
    # The OpenAIAgentsInstrumentor will automatically capture agent activities.
    result = await Runner.run(agent, prompt)
    return result.final_output

async def main():
    user_query = "Tell me a fun fact."
    response = await run_agent_with_openinference(user_query)
    print(f"User: {user_query}")
    print(f"AI: {response}")

if __name__ == "__main__":
    asyncio.run(main())
The OpenAIAgentsInstrumentor is part of the openinference-instrumentation-openai-agents package. Always refer to its official documentation for the latest updates, especially as it’s in Alpha.

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Direct Instrumentation

Alternatively, if you manage your OpenTelemetry TracerProvider more directly (e.g., if LangWatch is configured to use an existing global provider), you can use the instrumentor’s instrument() method. LangWatch will pick up the spans if its exporter is part of the active TracerProvider. 
import langwatch
from openai_agents.agents import Agent, Runner
from openinference.instrumentation.openai_agents import OpenAIAgentsInstrumentor
import os
import asyncio

# Initialize LangWatch (it will set up its OTel exporter)
langwatch.setup() # Ensure API key is available via env or parameters

# Instrument OpenAI Agents directly
OpenAIAgentsInstrumentor().instrument()

agent = Agent(name="ExampleAgentDirect", instructions="You are a helpful assistant.")

@langwatch.trace(name="OpenAI Agent Run with Direct OpenInference")
async def run_agent_direct_instrumentation(prompt: str):
    result = await Runner.run(agent, prompt)
    return result.final_output

async def main():
    user_query = "What's the weather like?"
    response = await run_agent_direct_instrumentation(user_query)
    print(f"User: {user_query}")
    print(f"AI: {response}")

if __name__ == "__main__":
    asyncio.run(main())
Key points for OpenInference instrumentation:
  • It patches openai-agents activities globally once instrumented.
  • Ensure langwatch.setup() is called so LangWatch’s OpenTelemetry exporter is active and configured.
  • The @langwatch.trace() decorator on your calling function helps create a parent span under which the agent’s detailed operations will be nested.

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2. Alternative: Using OpenAI Agents’ Built-in Tracing with a Custom Processor

If you prefer not to use the OpenInference instrumentor, or if you have highly specific tracing requirements not met by it, you can leverage the openai-agents SDK’s own built-in tracing system. This involves creating a custom TracingProcessor that intercepts trace data from the openai-agents SDK and then uses the standard OpenTelemetry Python API to create OpenTelemetry spans. LangWatch will then ingest these OpenTelemetry spans, provided langwatch.setup() has been called. Conceptual Outline for Your Custom Processor:
  1. Initialize LangWatch: Ensure langwatch.setup() is called in your application. This sets up LangWatch to receive OpenTelemetry data.
  2. Implement Your Custom TracingProcessor:
    • Following the openai-agents SDK documentation, create a class that implements their TracingProcessor interface (see their docs on Custom Tracing Processors and the API reference for TracingProcessor).
    • In your processor’s methods (e.g., on_span_start, on_span_end), you will receive Trace and Span objects from the openai-agents SDK.
    • You will then use the opentelemetry-api and opentelemetry-sdk (e.g., opentelemetry.trace.get_tracer(__name__).start_span()) to translate this information into OpenTelemetry spans, including their names, attributes, timings, and status. Consult the openai-agents documentation on Traces and spans for details on their data structures.
  3. Register Your Custom Processor: Use openai_agents.tracing.add_trace_processor(your_custom_processor) or openai_agents.tracing.set_trace_processors([your_custom_processor]) as per the openai-agents SDK documentation.
Implementation Guidance: LangWatch does not provide a pre-built custom TracingProcessor for this purpose. The implementation of such a processor is your responsibility and should be based on the official openai-agents SDK documentation. This ensures your processor correctly interprets the agent’s trace data and remains compatible with openai-agents SDK updates.
Implementing a custom TracingProcessor is an advanced task that requires:
  • A thorough understanding of both the openai-agents tracing internals and OpenTelemetry concepts and semantic conventions.
  • Careful mapping of openai-agents SpanData types to OpenTelemetry attributes.
  • Robust handling of span parenting, context propagation, and error states.
  • Diligent maintenance to keep your processor aligned with any changes in the openai-agents SDK. This approach offers maximum flexibility but comes with significant development and maintenance overhead.

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Which Approach to Choose?

  • OpenInference Instrumentation (Recommended):
    • Pros: Significantly simpler to set up and maintain. Relies on a community-supported library (openinference-instrumentation-openai-agents) designed for OpenTelemetry integration. Aligns well with standard OpenTelemetry practices.
    • Cons: As the openinference-instrumentation-openai-agents library is in Alpha, it may have breaking changes. You have less direct control over the exact span data compared to a fully custom processor.
  • Custom TracingProcessor (Alternative for advanced needs):
    • Pros: Offers complete control over the transformation of trace data from openai-agents to OpenTelemetry. Allows for highly customized span data and behaviors.
    • Cons: Far more complex to implement correctly and maintain. Requires deep expertise in both openai-agents tracing and OpenTelemetry. You are responsible for adapting your processor to any changes in the openai-agents SDK.
For most users, the OpenInference instrumentation is the recommended path due to its simplicity and lower maintenance burden. The custom TracingProcessor approach should generally be reserved for situations where the OpenInference instrumentor is unsuitable, or when you have highly specialized tracing requirements that demand direct manipulation of the agent’s trace data before converting it to OpenTelemetry spans.
Always refer to the latest documentation for langwatch, openai-agents, and openinference-instrumentation-openai-agents for the most up-to-date instructions and API details.