diff --git a/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/__init__.py b/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/__init__.py
index 2f88e9c8eb..1d03297395 100644
--- a/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/__init__.py
+++ b/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/shared/__init__.py
@@ -232,7 +232,7 @@ def _set_response_attributes(span, response):
     prompt_tokens_details = dict(usage.get("prompt_tokens_details", {}))
     _set_span_attribute(
         span,
-        GenAIAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS,
+        SpanAttributes.LLM_USAGE_CACHE_READ_INPUT_TOKENS,
         prompt_tokens_details.get("cached_tokens", 0),
     )
     return
diff --git a/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/v1/responses_wrappers.py b/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/v1/responses_wrappers.py
index 7942e6b2c6..6fbea88168 100644
--- a/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/v1/responses_wrappers.py
+++ b/packages/opentelemetry-instrumentation-openai/opentelemetry/instrumentation/openai/v1/responses_wrappers.py
@@ -1,9 +1,11 @@
 import json
 import pydantic
 import re
+import threading
 import time
 
 from openai import AsyncStream, Stream
+from wrapt import ObjectProxy
 
 # Conditional imports for backward compatibility
 try:
@@ -192,13 +194,11 @@ def set_data_attributes(traced_response: TracedData, span: Span):
         if usage.input_tokens_details:
             _set_span_attribute(
                 span,
-                GenAIAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS,
+                SpanAttributes.LLM_USAGE_CACHE_READ_INPUT_TOKENS,
                 usage.input_tokens_details.cached_tokens,
             )
 
-        # Usage - count of reasoning tokens
         reasoning_tokens = None
-        # Support both dict-style and object-style `usage`
         tokens_details = (
             usage.get("output_tokens_details") if isinstance(usage, dict)
             else getattr(usage, "output_tokens_details", None)
@@ -216,20 +216,16 @@ def set_data_attributes(traced_response: TracedData, span: Span):
             reasoning_tokens or 0,
         )
 
-    # Reasoning attributes
-    # Request - reasoning summary
     _set_span_attribute(
         span,
         f"{SpanAttributes.LLM_REQUEST_REASONING_SUMMARY}",
         traced_response.request_reasoning_summary or (),
     )
-    # Request - reasoning effort
     _set_span_attribute(
         span,
         f"{SpanAttributes.LLM_REQUEST_REASONING_EFFORT}",
         traced_response.request_reasoning_effort or (),
     )
-    # Response - reasoning effort
     _set_span_attribute(
         span,
         f"{SpanAttributes.LLM_RESPONSE_REASONING_EFFORT}",
@@ -433,7 +429,19 @@ def responses_get_or_create_wrapper(tracer: Tracer, wrapped, instance, args, kwa
     try:
         response = wrapped(*args, **kwargs)
         if isinstance(response, Stream):
-            return response
+            span = tracer.start_span(
+                SPAN_NAME,
+                kind=SpanKind.CLIENT,
+                start_time=start_time,
+            )
+
+            return ResponseStream(
+                span=span,
+                response=response,
+                start_time=start_time,
+                request_kwargs=kwargs,
+                tracer=tracer,
+            )
     except Exception as e:
         response_id = kwargs.get("response_id")
         existing_data = {}
@@ -563,7 +571,19 @@ async def async_responses_get_or_create_wrapper(
     try:
         response = await wrapped(*args, **kwargs)
         if isinstance(response, (Stream, AsyncStream)):
-            return response
+            span = tracer.start_span(
+                SPAN_NAME,
+                kind=SpanKind.CLIENT,
+                start_time=start_time,
+            )
+
+            return ResponseStream(
+                span=span,
+                response=response,
+                start_time=start_time,
+                request_kwargs=kwargs,
+                tracer=tracer,
+            )
     except Exception as e:
         response_id = kwargs.get("response_id")
         existing_data = {}
@@ -728,4 +748,228 @@ async def async_responses_cancel_wrapper(
     return response
 
 
-# TODO: build streaming responses
+class ResponseStream(ObjectProxy):
+    """Proxy class for streaming responses to capture telemetry data"""
+
+    _span = None
+    _start_time = None
+    _request_kwargs = None
+    _tracer = None
+    _traced_data = None
+
+    def __init__(
+        self,
+        span,
+        response,
+        start_time=None,
+        request_kwargs=None,
+        tracer=None,
+        traced_data=None,
+    ):
+        super().__init__(response)
+        self._span = span
+        self._start_time = start_time
+        self._request_kwargs = request_kwargs or {}
+        self._tracer = tracer
+        self._traced_data = traced_data or TracedData(
+            start_time=start_time,
+            response_id="",
+            input=process_input(self._request_kwargs.get("input", [])),
+            instructions=self._request_kwargs.get("instructions"),
+            tools=get_tools_from_kwargs(self._request_kwargs),
+            output_blocks={},
+            usage=None,
+            output_text="",
+            request_model=self._request_kwargs.get("model", ""),
+            response_model="",
+            request_reasoning_summary=self._request_kwargs.get("reasoning", {}).get(
+                "summary"
+            ),
+            request_reasoning_effort=self._request_kwargs.get("reasoning", {}).get("effort"),
+            response_reasoning_effort=None,
+        )
+
+        self._complete_response_data = None
+        self._output_text = ""
+
+        self._cleanup_completed = False
+        self._cleanup_lock = threading.Lock()
+
+    def __del__(self):
+        """Cleanup when object is garbage collected"""
+        if hasattr(self, "_cleanup_completed") and not self._cleanup_completed:
+            self._ensure_cleanup()
+
+    def __enter__(self):
+        """Context manager entry"""
+        if hasattr(self.__wrapped__, "__enter__"):
+            self.__wrapped__.__enter__()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """Context manager exit"""
+        suppress = False
+        try:
+            if exc_type is not None:
+                self._handle_exception(exc_val)
+            else:
+                self._process_complete_response()
+        finally:
+            if hasattr(self.__wrapped__, "__exit__"):
+                suppress = bool(self.__wrapped__.__exit__(exc_type, exc_val, exc_tb))
+        return suppress
+
+    async def __aenter__(self):
+        """Async context manager entry"""
+        if hasattr(self.__wrapped__, "__aenter__"):
+            await self.__wrapped__.__aenter__()
+        return self
+
+    async def __aexit__(self, exc_type, exc_val, exc_tb):
+        """Async context manager exit"""
+        suppress = False
+        try:
+            if exc_type is not None:
+                self._handle_exception(exc_val)
+            else:
+                self._process_complete_response()
+        finally:
+            if hasattr(self.__wrapped__, "__aexit__"):
+                suppress = bool(await self.__wrapped__.__aexit__(exc_type, exc_val, exc_tb))
+        return suppress
+
+    def close(self):
+        try:
+            self._ensure_cleanup()
+        finally:
+            if hasattr(self.__wrapped__, "close"):
+                return self.__wrapped__.close()
+
+    async def aclose(self):
+        try:
+            self._ensure_cleanup()
+        finally:
+            if hasattr(self.__wrapped__, "aclose"):
+                return await self.__wrapped__.aclose()
+
+    def __iter__(self):
+        """Synchronous iterator"""
+        return self
+
+    def __next__(self):
+        """Synchronous iteration"""
+        try:
+            chunk = self.__wrapped__.__next__()
+        except StopIteration:
+            self._process_complete_response()
+            raise
+        except Exception as e:
+            self._handle_exception(e)
+            raise
+        else:
+            self._process_chunk(chunk)
+            return chunk
+
+    def __aiter__(self):
+        """Async iterator"""
+        return self
+
+    async def __anext__(self):
+        """Async iteration"""
+        try:
+            chunk = await self.__wrapped__.__anext__()
+        except StopAsyncIteration:
+            self._process_complete_response()
+            raise
+        except Exception as e:
+            self._handle_exception(e)
+            raise
+        else:
+            self._process_chunk(chunk)
+            return chunk
+
+    def _process_chunk(self, chunk):
+        """Process a streaming chunk"""
+        if hasattr(chunk, "type"):
+            if chunk.type == "response.output_text.delta":
+                if hasattr(chunk, "delta") and chunk.delta:
+                    self._output_text += chunk.delta
+            elif chunk.type == "response.completed" and hasattr(chunk, "response"):
+                self._complete_response_data = chunk.response
+
+        if hasattr(chunk, "delta"):
+            if hasattr(chunk.delta, "text") and chunk.delta.text:
+                self._output_text += chunk.delta.text
+
+        if hasattr(chunk, "response") and chunk.response:
+            self._complete_response_data = chunk.response
+
+    @dont_throw
+    def _process_complete_response(self):
+        """Process the complete response and emit span"""
+        with self._cleanup_lock:
+            if self._cleanup_completed:
+                return
+
+            try:
+                if self._complete_response_data:
+                    parsed_response = parse_response(self._complete_response_data)
+
+                    self._traced_data.response_id = parsed_response.id
+                    self._traced_data.response_model = parsed_response.model
+                    self._traced_data.output_text = self._output_text
+
+                    if parsed_response.usage:
+                        self._traced_data.usage = parsed_response.usage
+
+                    if parsed_response.output:
+                        self._traced_data.output_blocks = {
+                            block.id: block for block in parsed_response.output
+                        }
+
+                    responses[parsed_response.id] = self._traced_data
+
+                set_data_attributes(self._traced_data, self._span)
+                self._span.set_status(StatusCode.OK)
+                self._span.end()
+                self._cleanup_completed = True
+
+            except Exception as e:
+                if self._span and self._span.is_recording():
+                    self._span.set_attribute(ERROR_TYPE, e.__class__.__name__)
+                    self._span.set_status(StatusCode.ERROR, str(e))
+                    self._span.end()
+                self._cleanup_completed = True
+
+    @dont_throw
+    def _handle_exception(self, exception):
+        """Handle exceptions during streaming"""
+        with self._cleanup_lock:
+            if self._cleanup_completed:
+                return
+
+            if self._span and self._span.is_recording():
+                self._span.set_attribute(ERROR_TYPE, exception.__class__.__name__)
+                self._span.record_exception(exception)
+                self._span.set_status(StatusCode.ERROR, str(exception))
+                self._span.end()
+
+            self._cleanup_completed = True
+
+    @dont_throw
+    def _ensure_cleanup(self):
+        """Ensure cleanup happens even if stream is not fully consumed"""
+        with self._cleanup_lock:
+            if self._cleanup_completed:
+                return
+
+            try:
+                if self._span and self._span.is_recording():
+                    set_data_attributes(self._traced_data, self._span)
+                    self._span.set_status(StatusCode.OK)
+                    self._span.end()
+
+                self._cleanup_completed = True
+
+            except Exception:
+                self._cleanup_completed = True
diff --git a/packages/opentelemetry-instrumentation-openai/tests/traces/cassettes/test_responses/test_responses_streaming.yaml b/packages/opentelemetry-instrumentation-openai/tests/traces/cassettes/test_responses/test_responses_streaming.yaml
new file mode 100644
index 0000000000..98ad9da66f
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-openai/tests/traces/cassettes/test_responses/test_responses_streaming.yaml
@@ -0,0 +1,255 @@
+interactions:
+- request:
+    body: '{"input":"Tell me a three sentence bedtime story about a unicorn.","model":"gpt-4.1-nano","stream":true}'
+    headers:
+      accept:
+      - application/json
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '104'
+      content-type:
+      - application/json
+      host:
+      - api.openai.com
+      user-agent:
+      - OpenAI/Python 1.97.1
+      x-stainless-arch:
+      - arm64
+      x-stainless-async:
+      - 'false'
+      x-stainless-lang:
+      - python
+      x-stainless-os:
+      - MacOS
+      x-stainless-package-version:
+      - 1.97.1
+      x-stainless-read-timeout:
+      - '600'
+      x-stainless-retry-count:
+      - '0'
+      x-stainless-runtime:
+      - CPython
+      x-stainless-runtime-version:
+      - 3.10.16
+    method: POST
+    uri: https://api.openai.com/v1/responses
+  response:
+    body:
+      string: "event: response.created\ndata: {\"type\":\"response.created\",\"sequence_number\":0,\"response\":{\"id\":\"resp_0fef0f8a68937870006911e9ecf124819491634b434678464a\",\"object\":\"response\",\"created_at\":1762781676,\"status\":\"in_progress\",\"background\":false,\"error\":null,\"incomplete_details\":null,\"instructions\":null,\"max_output_tokens\":null,\"max_tool_calls\":null,\"model\":\"gpt-4.1-nano-2025-04-14\",\"output\":[],\"parallel_tool_calls\":true,\"previous_response_id\":null,\"prompt_cache_key\":null,\"prompt_cache_retention\":null,\"reasoning\":{\"effort\":null,\"summary\":null},\"safety_identifier\":null,\"service_tier\":\"auto\",\"store\":true,\"temperature\":1.0,\"text\":{\"format\":{\"type\":\"text\"},\"verbosity\":\"medium\"},\"tool_choice\":\"auto\",\"tools\":[],\"top_logprobs\":0,\"top_p\":1.0,\"truncation\":\"disabled\",\"usage\":null,\"user\":null,\"metadata\":{}}}\n\nevent:
+        response.in_progress\ndata: {\"type\":\"response.in_progress\",\"sequence_number\":1,\"response\":{\"id\":\"resp_0fef0f8a68937870006911e9ecf124819491634b434678464a\",\"object\":\"response\",\"created_at\":1762781676,\"status\":\"in_progress\",\"background\":false,\"error\":null,\"incomplete_details\":null,\"instructions\":null,\"max_output_tokens\":null,\"max_tool_calls\":null,\"model\":\"gpt-4.1-nano-2025-04-14\",\"output\":[],\"parallel_tool_calls\":true,\"previous_response_id\":null,\"prompt_cache_key\":null,\"prompt_cache_retention\":null,\"reasoning\":{\"effort\":null,\"summary\":null},\"safety_identifier\":null,\"service_tier\":\"auto\",\"store\":true,\"temperature\":1.0,\"text\":{\"format\":{\"type\":\"text\"},\"verbosity\":\"medium\"},\"tool_choice\":\"auto\",\"tools\":[],\"top_logprobs\":0,\"top_p\":1.0,\"truncation\":\"disabled\",\"usage\":null,\"user\":null,\"metadata\":{}}}\n\nevent:
+        response.output_item.added\ndata: {\"type\":\"response.output_item.added\",\"sequence_number\":2,\"output_index\":0,\"item\":{\"id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"type\":\"message\",\"status\":\"in_progress\",\"content\":[],\"role\":\"assistant\"}}\n\nevent:
+        response.content_part.added\ndata: {\"type\":\"response.content_part.added\",\"sequence_number\":3,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"part\":{\"type\":\"output_text\",\"annotations\":[],\"logprobs\":[],\"text\":\"\"}}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":4,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"Once\",\"logprobs\":[],\"obfuscation\":\"lOokrPj8RO6Z\"}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":5,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        upon\",\"logprobs\":[],\"obfuscation\":\"Tv7ebWtX2Ed\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":6,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        a\",\"logprobs\":[],\"obfuscation\":\"8axF0YPU0fXp7h\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":7,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        time\",\"logprobs\":[],\"obfuscation\":\"NXqYzsjEIGb\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":8,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\",\",\"logprobs\":[],\"obfuscation\":\"2Jd3IUgnnP4mLrt\"}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":9,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        in\",\"logprobs\":[],\"obfuscation\":\"Ql8dH38dSglUH\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":10,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        a\",\"logprobs\":[],\"obfuscation\":\"x171oDHxbNc8PD\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":11,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        enchanted\",\"logprobs\":[],\"obfuscation\":\"FZwnwG\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":12,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        forest\",\"logprobs\":[],\"obfuscation\":\"lQ6ALw3qg\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":13,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\",\",\"logprobs\":[],\"obfuscation\":\"F9zXblw4jtPyJU6\"}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":14,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        there\",\"logprobs\":[],\"obfuscation\":\"H7b2VntIit\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":15,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        was\",\"logprobs\":[],\"obfuscation\":\"DH5pCqSugHT6\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":16,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        a\",\"logprobs\":[],\"obfuscation\":\"BQIkM6dCVYtDGL\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":17,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        gentle\",\"logprobs\":[],\"obfuscation\":\"cjZV8s9Z3\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":18,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        unicorn\",\"logprobs\":[],\"obfuscation\":\"pggvktk3\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":19,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        named\",\"logprobs\":[],\"obfuscation\":\"q5Fev4vcn4\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":20,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        Luna\",\"logprobs\":[],\"obfuscation\":\"xnRqm54KfcU\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":21,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        who\",\"logprobs\":[],\"obfuscation\":\"NhtP5tN59aKb\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":22,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        loved\",\"logprobs\":[],\"obfuscation\":\"pBelAdvYUg\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":23,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        to\",\"logprobs\":[],\"obfuscation\":\"eytyHdZMDTepR\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":24,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        weave\",\"logprobs\":[],\"obfuscation\":\"bamP4SqFpY\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":25,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        dreams\",\"logprobs\":[],\"obfuscation\":\"m5j1ARS01\"}\n\nevent: response.output_text.delta\ndata:
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+        One\",\"logprobs\":[],\"obfuscation\":\"1ywa4ORElBut\"}\n\nevent: response.output_text.delta\ndata:
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+        night\",\"logprobs\":[],\"obfuscation\":\"oLIS3h6yKU\"}\n\nevent: response.output_text.delta\ndata:
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+        she\",\"logprobs\":[],\"obfuscation\":\"GQqIbvIglgQA\"}\n\nevent: response.output_text.delta\ndata:
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+        gathered\",\"logprobs\":[],\"obfuscation\":\"DsU1les\"}\n\nevent: response.output_text.delta\ndata:
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+        her\",\"logprobs\":[],\"obfuscation\":\"jqjBNUW9eeS4\"}\n\nevent: response.output_text.delta\ndata:
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+        magic\",\"logprobs\":[],\"obfuscation\":\"4pbvRjCvJ0\"}\n\nevent: response.output_text.delta\ndata:
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+        create\",\"logprobs\":[],\"obfuscation\":\"W66Zbu9G1\"}\n\nevent: response.output_text.delta\ndata:
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+        a\",\"logprobs\":[],\"obfuscation\":\"6hUzY2oVNs4IU3\"}\n\nevent: response.output_text.delta\ndata:
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+        special\",\"logprobs\":[],\"obfuscation\":\"lY6RibHA\"}\n\nevent: response.output_text.delta\ndata:
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+        dream\",\"logprobs\":[],\"obfuscation\":\"rx4NLNJnq9\"}\n\nevent: response.output_text.delta\ndata:
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+        for\",\"logprobs\":[],\"obfuscation\":\"QlLjQageSjYX\"}\n\nevent: response.output_text.delta\ndata:
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+        a\",\"logprobs\":[],\"obfuscation\":\"7YSl9UHcINIVtA\"}\n\nevent: response.output_text.delta\ndata:
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+        little\",\"logprobs\":[],\"obfuscation\":\"UzaH2FnSG\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":49,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        girl\",\"logprobs\":[],\"obfuscation\":\"xndhO0b9Vlj\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":50,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        who\",\"logprobs\":[],\"obfuscation\":\"bHDjWs9pdrer\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":51,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        believed\",\"logprobs\":[],\"obfuscation\":\"1rCGba6\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":52,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        in\",\"logprobs\":[],\"obfuscation\":\"uDqlwMtWRe1AJ\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":53,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        kindness\",\"logprobs\":[],\"obfuscation\":\"Cfb7EDR\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":54,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        and\",\"logprobs\":[],\"obfuscation\":\"RmRl0UcAi117\"}\n\nevent: response.output_text.delta\ndata:
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+        hope\",\"logprobs\":[],\"obfuscation\":\"xMG6A07AbIo\"}\n\nevent: response.output_text.delta\ndata:
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+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":57,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        As\",\"logprobs\":[],\"obfuscation\":\"UJaW762YGDSxP\"}\n\nevent: response.output_text.delta\ndata:
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+        girl\",\"logprobs\":[],\"obfuscation\":\"2RhbeqtrUQH\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":60,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        slept\",\"logprobs\":[],\"obfuscation\":\"TYCNKHPXhe\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":61,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        peacefully\",\"logprobs\":[],\"obfuscation\":\"OmW0M\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":62,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\",\",\"logprobs\":[],\"obfuscation\":\"FEo6K9FLY5vrKzc\"}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":63,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        Luna\",\"logprobs\":[],\"obfuscation\":\"Rdgmkx93Gm1\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":64,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"\u2019s\",\"logprobs\":[],\"obfuscation\":\"IJSP3g05fHk1w2\"}\n\nevent:
+        response.output_text.delta\ndata: {\"type\":\"response.output_text.delta\",\"sequence_number\":65,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        dreams\",\"logprobs\":[],\"obfuscation\":\"YAtM80znq\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":66,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        floated\",\"logprobs\":[],\"obfuscation\":\"52v95Ec5\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":67,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        down\",\"logprobs\":[],\"obfuscation\":\"NuVfTeuBS2o\"}\n\nevent: response.output_text.delta\ndata:
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+        like\",\"logprobs\":[],\"obfuscation\":\"l8YcTwyP4ZE\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":69,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        glowing\",\"logprobs\":[],\"obfuscation\":\"SarF7yss\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":70,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        clouds\",\"logprobs\":[],\"obfuscation\":\"fS70h1tit\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":71,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\",\",\"logprobs\":[],\"obfuscation\":\"piNGFZ5xIeZ25mj\"}\n\nevent:
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+        filling\",\"logprobs\":[],\"obfuscation\":\"9abtqV40\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":73,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        her\",\"logprobs\":[],\"obfuscation\":\"y3XzC0zitOoY\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":74,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        heart\",\"logprobs\":[],\"obfuscation\":\"t22qzqlwP1\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":75,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        with\",\"logprobs\":[],\"obfuscation\":\"a6BRPSqCiiO\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":76,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        joy\",\"logprobs\":[],\"obfuscation\":\"PrpSwMgv297g\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":77,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        and\",\"logprobs\":[],\"obfuscation\":\"25TlFvWI31Wz\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":78,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        wonder\",\"logprobs\":[],\"obfuscation\":\"wS1TFY5fw\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":79,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        until\",\"logprobs\":[],\"obfuscation\":\"aKT3enYjyO\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":80,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\"
+        morning\",\"logprobs\":[],\"obfuscation\":\"PEacn7sS\"}\n\nevent: response.output_text.delta\ndata:
+        {\"type\":\"response.output_text.delta\",\"sequence_number\":81,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"delta\":\".\",\"logprobs\":[],\"obfuscation\":\"Kv5IpzXsfR9pNyj\"}\n\nevent:
+        response.output_text.done\ndata: {\"type\":\"response.output_text.done\",\"sequence_number\":82,\"item_id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"output_index\":0,\"content_index\":0,\"text\":\"Once
+        upon a time, in a enchanted forest, there was a gentle unicorn named Luna
+        who loved to weave dreams into sparkling stardust. One night, she gathered
+        all her shimmering magic to create a special dream for a little girl who believed
+        in kindness and hope. As the girl slept peacefully, Luna\u2019s dreams floated
+        down like glowing clouds, filling her heart with joy and wonder until morning.\",\"logprobs\":[]}\n\nevent:
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+        upon a time, in a enchanted forest, there was a gentle unicorn named Luna
+        who loved to weave dreams into sparkling stardust. One night, she gathered
+        all her shimmering magic to create a special dream for a little girl who believed
+        in kindness and hope. As the girl slept peacefully, Luna\u2019s dreams floated
+        down like glowing clouds, filling her heart with joy and wonder until morning.\"}}\n\nevent:
+        response.output_item.done\ndata: {\"type\":\"response.output_item.done\",\"sequence_number\":84,\"output_index\":0,\"item\":{\"id\":\"msg_0fef0f8a68937870006911e9ef0ecc81949d727f28043244eb\",\"type\":\"message\",\"status\":\"completed\",\"content\":[{\"type\":\"output_text\",\"annotations\":[],\"logprobs\":[],\"text\":\"Once
+        upon a time, in a enchanted forest, there was a gentle unicorn named Luna
+        who loved to weave dreams into sparkling stardust. One night, she gathered
+        all her shimmering magic to create a special dream for a little girl who believed
+        in kindness and hope. As the girl slept peacefully, Luna\u2019s dreams floated
+        down like glowing clouds, filling her heart with joy and wonder until morning.\"}],\"role\":\"assistant\"}}\n\nevent:
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+        upon a time, in a enchanted forest, there was a gentle unicorn named Luna
+        who loved to weave dreams into sparkling stardust. One night, she gathered
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diff --git a/packages/opentelemetry-instrumentation-openai/tests/traces/test_responses.py b/packages/opentelemetry-instrumentation-openai/tests/traces/test_responses.py
index 7a0b6d8618..3d329f615c 100644
--- a/packages/opentelemetry-instrumentation-openai/tests/traces/test_responses.py
+++ b/packages/opentelemetry-instrumentation-openai/tests/traces/test_responses.py
@@ -1,13 +1,14 @@
-
 import pytest
 
-from openai import OpenAI
+from openai import AsyncOpenAI, OpenAI
 from opentelemetry.instrumentation.openai.utils import is_reasoning_supported
 from opentelemetry.sdk.trace.export.in_memory_span_exporter import InMemorySpanExporter
 
 
 @pytest.mark.vcr
-def test_responses(instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI):
+def test_responses(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
     _ = openai_client.responses.create(
         model="gpt-4.1-nano",
         input="What is the capital of France?",
@@ -26,7 +27,9 @@ def test_responses(instrument_legacy, span_exporter: InMemorySpanExporter, opena
 
 
 @pytest.mark.vcr
-def test_responses_with_input_history(instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI):
+def test_responses_with_input_history(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
     user_message = "Come up with an adjective in English. Respond with just one word."
     first_response = openai_client.responses.create(
         model="gpt-4.1-nano",
@@ -79,7 +82,9 @@ def test_responses_with_input_history(instrument_legacy, span_exporter: InMemory
 
 
 @pytest.mark.vcr
-def test_responses_tool_calls(instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI):
+def test_responses_tool_calls(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
     tools = [
         {
             "type": "function",
@@ -90,11 +95,11 @@ def test_responses_tool_calls(instrument_legacy, span_exporter: InMemorySpanExpo
                 "properties": {
                     "location": {
                         "type": "string",
-                        "description": "The city and state, e.g. San Francisco, CA"
+                        "description": "The city and state, e.g. San Francisco, CA",
                     }
                 },
-                "required": ["location"]
-            }
+                "required": ["location"],
+            },
         }
     ]
     openai_client.responses.create(
@@ -103,11 +108,11 @@ def test_responses_tool_calls(instrument_legacy, span_exporter: InMemorySpanExpo
             {
                 "type": "message",
                 "role": "user",
-                "content": "What's the weather in London?"
+                "content": "What's the weather in London?",
             }
         ],
         tools=tools,
-        tool_choice="auto"
+        tool_choice="auto",
     )
 
     spans = span_exporter.get_finished_spans()
@@ -145,16 +150,17 @@ def test_responses_tool_calls(instrument_legacy, span_exporter: InMemorySpanExpo
 
 
 @pytest.mark.vcr
-@pytest.mark.skipif(not is_reasoning_supported(),
-                    reason="Reasoning is not supported in older OpenAI library versions")
-def test_responses_reasoning(instrument_legacy, span_exporter: InMemorySpanExporter,
-                             openai_client: OpenAI):
+@pytest.mark.skipif(
+    not is_reasoning_supported(),
+    reason="Reasoning is not supported in older OpenAI library versions",
+)
+def test_responses_reasoning(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
     openai_client.responses.create(
         model="gpt-5-nano",
         input="Count r's in strawberry",
-        reasoning={
-            "effort": "low", "summary": None
-        },
+        reasoning={"effort": "low", "summary": None},
     )
 
     spans = span_exporter.get_finished_spans()
@@ -172,18 +178,18 @@ def test_responses_reasoning(instrument_legacy, span_exporter: InMemorySpanExpor
 
 
 @pytest.mark.vcr
-@pytest.mark.skipif(not is_reasoning_supported(),
-                    reason="Reasoning is not supported in older OpenAI library versions")
-def test_responses_reasoning_dict_issue(instrument_legacy, span_exporter: InMemorySpanExporter,
-                                        openai_client: OpenAI):
+@pytest.mark.skipif(
+    not is_reasoning_supported(),
+    reason="Reasoning is not supported in older OpenAI library versions",
+)
+def test_responses_reasoning_dict_issue(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
     """Test for issue #3350 - reasoning dict causing invalid type warning"""
     openai_client.responses.create(
         model="gpt-5-nano",
         input="Explain why the sky is blue",
-        reasoning={
-            "effort": "medium",
-            "summary": "auto"
-        },
+        reasoning={"effort": "medium", "summary": "auto"},
     )
 
     spans = span_exporter.get_finished_spans()
@@ -191,14 +197,190 @@ def test_responses_reasoning_dict_issue(instrument_legacy, span_exporter: InMemo
     span = spans[0]
 
     # Verify the reasoning attributes are properly set without causing warnings
-    # assert span.attributes["gen_ai.request.reasoning_effort"] == "medium"
-    # assert span.attributes["gen_ai.request.reasoning_summary"] == "auto"
-    # This should not cause an "Invalid type dict" warning and should contain serialized reasoning
-    assert "gen_ai.completion.0.reasoning" not in span.attributes
+    # The main goal of this test is to ensure that when the API returns reasoning data
+    # as a dict/list, it gets properly serialized as JSON without causing "Invalid type" warnings
+
     # The reasoning should be serialized as JSON since it contains complex data
-    # reasoning_attr = span.attributes["gen_ai.completion.0.reasoning"]
-    # assert isinstance(reasoning_attr, str)
+    reasoning_attr = span.attributes["gen_ai.completion.0.reasoning"]
+    assert isinstance(reasoning_attr, str), "Reasoning should be serialized as a string"
+
     # Should be valid JSON containing reasoning summary data
-    # import json
-    # parsed_reasoning = json.loads(reasoning_attr)
-    # assert isinstance(parsed_reasoning, (dict, list))  # Could be dict or list depending on response structure
+    import json
+
+    parsed_reasoning = json.loads(reasoning_attr)
+    assert isinstance(
+        parsed_reasoning, (dict, list)
+    ), "Reasoning should be a dict or list structure"
+
+
+@pytest.mark.vcr
+def test_responses_streaming(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
+    """Test for streaming responses.create() - reproduces customer issue"""
+    input_text = "Tell me a three sentence bedtime story about a unicorn."
+    stream = openai_client.responses.create(
+        model="gpt-4.1-nano",
+        input=input_text,
+        stream=True,
+    )
+
+    # Consume the stream
+    full_text = ""
+    for item in stream:
+        if hasattr(item, "type") and item.type == "response.output_text.delta":
+            if hasattr(item, "delta") and item.delta:
+                full_text += item.delta
+        elif hasattr(item, "delta") and item.delta:
+            if hasattr(item.delta, "text") and item.delta.text:
+                full_text += item.delta.text
+
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 1, f"Expected 1 span but got {len(spans)}"
+
+    span = spans[0]
+    assert span.name == "openai.response"
+    assert span.attributes["gen_ai.system"] == "openai"
+    assert span.attributes["gen_ai.request.model"] == "gpt-4.1-nano"
+    assert span.attributes["gen_ai.response.model"] == "gpt-4.1-nano-2025-04-14"
+    assert full_text != "", "Should have received streaming content"
+    assert span.attributes["gen_ai.prompt.0.content"] == input_text
+    assert span.attributes["gen_ai.prompt.0.role"] == "user"
+    assert span.attributes["gen_ai.completion.0.role"] == "assistant"
+    assert span.attributes["gen_ai.completion.0.content"] == full_text
+
+
+@pytest.mark.vcr
+@pytest.mark.asyncio
+async def test_responses_streaming_async(
+    instrument_legacy, span_exporter: InMemorySpanExporter, async_openai_client
+):
+    """Test for async streaming responses.create() - reproduces customer issue"""
+    input_text = "Tell me a three sentence bedtime story about a unicorn."
+    stream = await async_openai_client.responses.create(
+        model="gpt-4.1-nano",
+        input=input_text,
+        stream=True,
+    )
+
+    full_text = ""
+    async for item in stream:
+        if hasattr(item, "type") and item.type == "response.output_text.delta":
+            if hasattr(item, "delta") and item.delta:
+                full_text += item.delta
+        elif hasattr(item, "delta") and item.delta:
+            if hasattr(item.delta, "text") and item.delta.text:
+                full_text += item.delta.text
+
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 1, f"Expected 1 span but got {len(spans)}"
+
+    span = spans[0]
+    assert span.name == "openai.response"
+    assert span.attributes["gen_ai.system"] == "openai"
+    assert span.attributes["gen_ai.request.model"] == "gpt-4.1-nano"
+    assert full_text != "", "Should have received streaming content"
+    assert span.attributes["gen_ai.prompt.0.content"] == input_text
+    assert span.attributes["gen_ai.prompt.0.role"] == "user"
+    assert span.attributes["gen_ai.completion.0.role"] == "assistant"
+    assert span.attributes["gen_ai.completion.0.content"] == full_text
+
+
+@pytest.mark.vcr
+def test_responses_streaming_with_content(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
+    """Test streaming with content tracing - verifies prompts and completions are captured"""
+    input_text = "What is 2+2?"
+    stream = openai_client.responses.create(
+        model="gpt-4.1-nano",
+        input=input_text,
+        stream=True,
+    )
+
+    # Consume the stream
+    full_text = ""
+    for item in stream:
+        if hasattr(item, "type") and item.type == "response.output_text.delta":
+            if hasattr(item, "delta") and item.delta:
+                full_text += item.delta
+
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 1
+
+    span = spans[0]
+    assert span.name == "openai.response"
+    assert span.attributes["gen_ai.system"] == "openai"
+    assert span.attributes["gen_ai.request.model"] == "gpt-4.1-nano"
+    assert full_text != "", "Should have received streaming content"
+    assert span.attributes["gen_ai.prompt.0.content"] == input_text
+    assert span.attributes["gen_ai.prompt.0.role"] == "user"
+    assert span.attributes["gen_ai.completion.0.role"] == "assistant"
+    assert span.attributes["gen_ai.completion.0.content"] == full_text
+
+
+@pytest.mark.vcr
+def test_responses_streaming_with_context_manager(
+    instrument_legacy, span_exporter: InMemorySpanExporter, openai_client: OpenAI
+):
+    """Test streaming responses using context manager (with statement)"""
+    input_text = "Count to 5"
+    full_text = ""
+
+    with openai_client.responses.create(
+        model="gpt-4.1-nano",
+        input=input_text,
+        stream=True,
+    ) as stream:
+        for chunk in stream:
+            if chunk.type == "response.output_text.delta":
+                full_text += chunk.delta
+
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 1
+
+    span = spans[0]
+    assert span.name == "openai.response"
+    assert span.attributes["gen_ai.system"] == "openai"
+    assert span.attributes["gen_ai.request.model"] == "gpt-4.1-nano"
+    assert full_text != "", "Should have received streaming content"
+    assert span.attributes["gen_ai.prompt.0.content"] == input_text
+    assert span.attributes["gen_ai.prompt.0.role"] == "user"
+    assert span.attributes["gen_ai.completion.0.role"] == "assistant"
+    assert span.attributes["gen_ai.completion.0.content"] == full_text
+
+
+@pytest.mark.vcr
+@pytest.mark.asyncio
+async def test_responses_streaming_async_with_context_manager(
+    instrument_legacy,
+    span_exporter: InMemorySpanExporter,
+    async_openai_client: AsyncOpenAI,
+):
+    """Test async streaming responses using context manager (async with statement)"""
+    input_text = "Count to 5"
+    full_text = ""
+
+    stream = await async_openai_client.responses.create(
+        model="gpt-4.1-nano",
+        input=input_text,
+        stream=True,
+    )
+
+    async with stream:
+        async for chunk in stream:
+            if chunk.type == "response.output_text.delta":
+                full_text += chunk.delta
+
+    spans = span_exporter.get_finished_spans()
+    assert len(spans) == 1
+
+    span = spans[0]
+    assert span.name == "openai.response"
+    assert span.attributes["gen_ai.system"] == "openai"
+    assert span.attributes["gen_ai.request.model"] == "gpt-4.1-nano"
+    assert full_text != "", "Should have received streaming content"
+    assert span.attributes["gen_ai.prompt.0.content"] == input_text
+    assert span.attributes["gen_ai.prompt.0.role"] == "user"
+    assert span.attributes["gen_ai.completion.0.role"] == "assistant"
+    assert span.attributes["gen_ai.completion.0.content"] == full_text