Feature/OpenAI agents content capture

instrumentation-genai/opentelemetry-instrumentation-openai-agents/examples/content-capture/README.md

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+# OpenAI Agents Content Capture Demo
+
+This example exercises the `OpenAIAgentsInstrumentor` with message content
+capture enabled, illustrating how prompts, responses, and tool payloads are
+recorded on spans and span events.
+
+> The demo uses the local tracing utilities from the `openai-agents`
+> package—no outbound API calls are made.
+
+## Prerequisites
+
+1. Activate the repository virtual environment:
+
+   ```bash
+   source ../../../../.venv/bin/activate
+   ```
+
+2. Ensure `openai-agents` is installed in the environment (it is included in
+   the shared development venv for this repository).
+
+## Run the demo
+
+```bash
+python main.py
+```
+
+The script will:
+
+- Configure the OpenTelemetry SDK with a console span exporter.
+- Instrument the OpenAI Agents tracing hooks with content capture enabled.
+- Simulate an agent invocation that performs a generation and a tool call.
+- Print the resulting spans, attributes, and events (including JSON-encoded
+  prompts and responses) to stdout.
+
+## Customisation tips
+
+- Set `OTEL_SERVICE_NAME` before running to override the default service name.
+- Swap the `ConsoleSpanExporter` in `demo.py` for an OTLP exporter if you want
+  to ship spans to a collector.
+- Modify the prompts, tool payloads, or add additional spans in `run_workflow`
+  to explore different content capture scenarios.

instrumentation-genai/opentelemetry-instrumentation-openai-agents/examples/content-capture/main.py

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+"""
+Content capture demo for the OpenAI Agents instrumentation.
+
+This script spins up the instrumentation with message capture enabled and
+simulates an agent invocation plus a tool call using the tracing helpers from
+the ``openai-agents`` package. Spans are exported to the console so you can
+inspect captured prompts, responses, and tool payloads without making any
+OpenAI API calls.
+"""
+
+from __future__ import annotations
+
+import json
+import os
+from typing import Any
+
+from agents.tracing import agent_span, function_span, generation_span, trace
+
+from opentelemetry.instrumentation.openai_agents import (
+    OpenAIAgentsInstrumentor,
+)
+from opentelemetry.sdk.resources import Resource
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import (
+    ConsoleSpanExporter,
+    SimpleSpanProcessor,
+)
+
+
+def configure_tracing() -> None:
+    """Configure a tracer provider that writes spans to stdout."""
+    resource = Resource.create(
+        {
+            "service.name": os.environ.get(
+                "OTEL_SERVICE_NAME", "openai-agents-content-capture-demo"
+            )
+        }
+    )
+    provider = TracerProvider(resource=resource)
+    provider.add_span_processor(SimpleSpanProcessor(ConsoleSpanExporter()))
+
+    # Instrument with explicit content capture mode to ensure prompts/responses are recorded.
+    OpenAIAgentsInstrumentor().instrument(
+        tracer_provider=provider,
+        capture_message_content="span_and_event",
+        system="openai",
+        agent_name="Travel Concierge",
+        base_url="https://api.openai.com/v1",
+    )
+
+
+def dump(title: str, payload: Any) -> None:
+    """Pretty-print helper used to show intermediate context."""
+    print(f"\n=== {title} ===")
+    print(json.dumps(payload, indent=2))
+
+
+def run_workflow() -> None:
+    """Simulate an agent workflow with a generation and a tool invocation."""
+    itinerary_prompt = [
+        {"role": "system", "content": "Plan high level travel itineraries."},
+        {
+            "role": "user",
+            "content": "I'm visiting Paris for 3 days in November.",
+        },
+    ]
+
+    tool_args = {"city": "Paris", "date": "2025-11-12"}
+    tool_result = {
+        "forecast": "Mostly sunny, highs 15°C",
+        "packing_tips": ["light jacket", "comfortable shoes"],
+    }
+
+    with trace("travel-booking-workflow"):
+        with agent_span(operation="invoke", name="travel_planner") as agent:
+            dump(
+                "Agent span started",
+                {"span_id": agent.span_id, "trace_id": agent.trace_id},
+            )
+
+            with generation_span(
+                input=itinerary_prompt,
+                output=[
+                    {
+                        "role": "assistant",
+                        "content": (
+                            "Day 1 visit the Louvre, Day 2 tour Versailles, "
+                            "Day 3 explore Montmartre."
+                        ),
+                    }
+                ],
+                model="gpt-4o-mini",
+                usage={
+                    "input_tokens": 128,
+                    "output_tokens": 96,
+                    "total_tokens": 224,
+                },
+            ):
+                pass
+
+            with function_span(
+                name="fetch_weather",
+                input=json.dumps(tool_args),
+                output=tool_result,
+            ):
+                pass
+
+    print("\nWorkflow complete – spans exported to console above.")
+
+
+def main() -> None:
+    configure_tracing()
+    run_workflow()
+
+
+if __name__ == "__main__":
+    main()