diff --git a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py
index 9ab46f5ba4..fa7684adc0 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py
@@ -452,10 +452,7 @@ def _create_llm_span(
         )
 
         _set_span_attribute(span, GenAIAttributes.GEN_AI_SYSTEM, vendor)
-        _set_span_attribute(span, SpanAttributes.LLM_REQUEST_TYPE, request_type.value)
-        _set_span_attribute(
-            span, GenAIAttributes.GEN_AI_OPERATION_NAME, GenAICustomOperationName.LLM_REQUEST.value
-        )
+        _set_span_attribute(span, GenAIAttributes.GEN_AI_OPERATION_NAME, request_type.value)
 
         # we already have an LLM span by this point,
         # so skip any downstream instrumentation from here
@@ -732,11 +729,11 @@ def on_llm_end(
                 span, GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS, completion_tokens
             )
             _set_span_attribute(
-                span, SpanAttributes.LLM_USAGE_TOTAL_TOKENS, total_tokens
+                span, SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS, total_tokens
             )
 
             # Record token usage metrics
-            vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "Langchain")
+            vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "langchain")
             if prompt_tokens > 0:
                 self.token_histogram.record(
                     prompt_tokens,
@@ -768,7 +765,7 @@ def on_llm_end(
 
         # Record duration before ending span
         duration = time.time() - self.spans[run_id].start_time
-        vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "Langchain")
+        vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "langchain")
         self.duration_histogram.record(
             duration,
             attributes={
diff --git a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/span_utils.py b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/span_utils.py
index f7d71b18ab..fff0e321e2 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/span_utils.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/span_utils.py
@@ -97,23 +97,19 @@ def set_request_params(span, kwargs, span_holder: SpanHolder):
     _set_span_attribute(span, GenAIAttributes.GEN_AI_REQUEST_TOP_P, params.get("top_p"))
 
     tools = kwargs.get("invocation_params", {}).get("tools", [])
-    for i, tool in enumerate(tools):
-        tool_function = tool.get("function", tool)
-        _set_span_attribute(
-            span,
-            f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.{i}.name",
-            tool_function.get("name"),
-        )
-        _set_span_attribute(
-            span,
-            f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.{i}.description",
-            tool_function.get("description"),
-        )
-        _set_span_attribute(
-            span,
-            f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.{i}.parameters",
-            json.dumps(tool_function.get("parameters", tool.get("input_schema"))),
-        )
+    if tools:
+        tool_defs = []
+        for tool in tools:
+            tool_function = tool.get("function", tool)
+            tool_def = {
+                "name": tool_function.get("name"),
+                "description": tool_function.get("description"),
+            }
+            params = tool_function.get("parameters") or tool.get("input_schema")
+            if params is not None:
+                tool_def["parameters"] = params
+            tool_defs.append(tool_def)
+        span.set_attribute(GenAIAttributes.GEN_AI_TOOL_DEFINITIONS, json.dumps(tool_defs))
 
 
 def set_llm_request(
@@ -126,17 +122,8 @@ def set_llm_request(
     set_request_params(span, kwargs, span_holder)
 
     if should_send_prompts():
-        for i, msg in enumerate(prompts):
-            _set_span_attribute(
-                span,
-                f"{GenAIAttributes.GEN_AI_PROMPT}.{i}.role",
-                "user",
-            )
-            _set_span_attribute(
-                span,
-                f"{GenAIAttributes.GEN_AI_PROMPT}.{i}.content",
-                msg,
-            )
+        messages = [{"role": "user", "content": msg} for msg in prompts]
+        span.set_attribute(GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(messages))
 
 
 def set_chat_request(
@@ -148,81 +135,62 @@ def set_chat_request(
 ) -> None:
     set_request_params(span, serialized.get("kwargs", {}), span_holder)
 
+    functions = kwargs.get("invocation_params", {}).get("functions", [])
+    if functions:
+        tool_defs = [
+            {
+                "name": f.get("name"),
+                "description": f.get("description"),
+                "parameters": f.get("parameters"),
+            }
+            for f in functions
+        ]
+        span.set_attribute(GenAIAttributes.GEN_AI_TOOL_DEFINITIONS, json.dumps(tool_defs))
+
     if should_send_prompts():
-        for i, function in enumerate(
-            kwargs.get("invocation_params", {}).get("functions", [])
-        ):
-            prefix = f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.{i}"
-
-            _set_span_attribute(span, f"{prefix}.name", function.get("name"))
-            _set_span_attribute(
-                span, f"{prefix}.description", function.get("description")
-            )
-            _set_span_attribute(
-                span, f"{prefix}.parameters", json.dumps(function.get("parameters"))
-            )
-
-        i = 0
+        input_messages = []
         for message in messages:
             for msg in message:
-                _set_span_attribute(
-                    span,
-                    f"{GenAIAttributes.GEN_AI_PROMPT}.{i}.role",
-                    _message_type_to_role(msg.type),
-                )
+                msg_obj = {"role": _message_type_to_role(msg.type)}
+
                 tool_calls = (
                     msg.tool_calls
                     if hasattr(msg, "tool_calls")
                     else msg.additional_kwargs.get("tool_calls")
                 )
-
                 if tool_calls:
-                    _set_chat_tool_calls(
-                        span, f"{GenAIAttributes.GEN_AI_PROMPT}.{i}", tool_calls
-                    )
+                    msg_obj["tool_calls"] = _build_tool_calls_list(tool_calls)
 
-                # Always set content if it exists, regardless of tool_calls presence
                 content = (
                     msg.content
                     if isinstance(msg.content, str)
                     else json.dumps(msg.content, cls=CallbackFilteredJSONEncoder)
                 )
-                _set_span_attribute(
-                    span,
-                    f"{GenAIAttributes.GEN_AI_PROMPT}.{i}.content",
-                    content,
-                )
+                if content:
+                    msg_obj["content"] = content
 
                 if msg.type == "tool" and hasattr(msg, "tool_call_id"):
-                    _set_span_attribute(
-                        span,
-                        f"{GenAIAttributes.GEN_AI_PROMPT}.{i}.tool_call_id",
-                        msg.tool_call_id,
-                    )
+                    msg_obj["tool_call_id"] = msg.tool_call_id
 
-                i += 1
+                input_messages.append(msg_obj)
+
+        if input_messages:
+            span.set_attribute(GenAIAttributes.GEN_AI_INPUT_MESSAGES, json.dumps(input_messages))
 
 
 def set_chat_response(span: Span, response: LLMResult) -> None:
     if not should_send_prompts():
         return
 
-    i = 0
+    output_messages = []
     for generations in response.generations:
         for generation in generations:
-            prefix = f"{GenAIAttributes.GEN_AI_COMPLETION}.{i}"
-
             if hasattr(generation, "message") and generation.message and hasattr(generation.message, "type"):
                 role = _message_type_to_role(generation.message.type)
             else:
-                # For non-chat completions (Generation objects), default to assistant
                 role = "assistant"
 
-            _set_span_attribute(
-                span,
-                f"{prefix}.role",
-                role,
-            )
+            msg_obj = {"role": role}
 
             # Try to get content from various sources
             content = None
@@ -235,38 +203,19 @@ def set_chat_response(span: Span, response: LLMResult) -> None:
                     content = json.dumps(generation.message.content, cls=CallbackFilteredJSONEncoder)
 
             if content:
-                _set_span_attribute(
-                    span,
-                    f"{prefix}.content",
-                    content,
-                )
+                msg_obj["content"] = content
 
             # Set finish reason if available
             if generation.generation_info and generation.generation_info.get("finish_reason"):
-                _set_span_attribute(
-                    span,
-                    f"{prefix}.finish_reason",
-                    generation.generation_info.get("finish_reason"),
-                )
+                msg_obj["finish_reason"] = generation.generation_info.get("finish_reason")
 
             # Handle tool calls and function calls
             if hasattr(generation, "message") and generation.message:
                 # Handle legacy function_call format (single function call)
                 if generation.message.additional_kwargs.get("function_call"):
-                    _set_span_attribute(
-                        span,
-                        f"{prefix}.tool_calls.0.name",
-                        generation.message.additional_kwargs.get("function_call").get(
-                            "name"
-                        ),
-                    )
-                    _set_span_attribute(
-                        span,
-                        f"{prefix}.tool_calls.0.arguments",
-                        generation.message.additional_kwargs.get("function_call").get(
-                            "arguments"
-                        ),
-                    )
+                    fc = generation.message.additional_kwargs.get("function_call")
+                    msg_obj["role"] = "assistant"
+                    msg_obj["tool_calls"] = [{"name": fc.get("name"), "arguments": fc.get("arguments")}]
 
                 # Handle new tool_calls format (multiple tool calls)
                 tool_calls = (
@@ -275,13 +224,13 @@ def set_chat_response(span: Span, response: LLMResult) -> None:
                     else generation.message.additional_kwargs.get("tool_calls")
                 )
                 if tool_calls and isinstance(tool_calls, list):
-                    _set_span_attribute(
-                        span,
-                        f"{prefix}.role",
-                        "assistant",
-                    )
-                    _set_chat_tool_calls(span, prefix, tool_calls)
-            i += 1
+                    msg_obj["role"] = "assistant"
+                    msg_obj["tool_calls"] = _build_tool_calls_list(tool_calls)
+
+            output_messages.append(msg_obj)
+
+    if output_messages:
+        span.set_attribute(GenAIAttributes.GEN_AI_OUTPUT_MESSAGES, json.dumps(output_messages))
 
 
 def set_chat_response_usage(
@@ -325,9 +274,8 @@ def set_chat_response_usage(
                             "input_token_details", {}
                         )
                         cache_read_tokens += input_token_details.get("cache_read", 0)
-    except Exception as e:
+    except Exception:
         # If there's any issue processing usage metadata, continue without it
-        print(f"DEBUG: Error processing usage metadata: {e}")
         pass
 
     if (
@@ -348,16 +296,16 @@ def set_chat_response_usage(
         )
         _set_span_attribute(
             span,
-            SpanAttributes.LLM_USAGE_TOTAL_TOKENS,
+            SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS,
             total_tokens,
         )
         _set_span_attribute(
             span,
-            SpanAttributes.LLM_USAGE_CACHE_READ_INPUT_TOKENS,
+            SpanAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS,
             cache_read_tokens,
         )
         if record_token_usage:
-            vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "Langchain")
+            vendor = span.attributes.get(GenAIAttributes.GEN_AI_SYSTEM, "langchain")
 
             if input_tokens > 0:
                 token_histogram.record(
@@ -397,11 +345,9 @@ def _extract_model_name_from_association_metadata(metadata: Optional[dict[str, A
     return "unknown"
 
 
-def _set_chat_tool_calls(
-    span: Span, prefix: str, tool_calls: list[dict[str, Any]]
-) -> None:
-    for idx, tool_call in enumerate(tool_calls):
-        tool_call_prefix = f"{prefix}.tool_calls.{idx}"
+def _build_tool_calls_list(tool_calls: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    result = []
+    for tool_call in tool_calls:
         tool_call_dict = dict(tool_call)
         tool_id = tool_call_dict.get("id")
         tool_name = tool_call_dict.get(
@@ -411,14 +357,12 @@ def _set_chat_tool_calls(
             "args", tool_call_dict.get("function", {}).get("arguments")
         )
 
-        _set_span_attribute(span, f"{tool_call_prefix}.id", tool_id)
-        _set_span_attribute(
-            span,
-            f"{tool_call_prefix}.name",
-            tool_name,
-        )
-        _set_span_attribute(
-            span,
-            f"{tool_call_prefix}.arguments",
-            json.dumps(tool_args, cls=CallbackFilteredJSONEncoder),
-        )
+        call_obj = {}
+        if tool_id:
+            call_obj["id"] = tool_id
+        if tool_name:
+            call_obj["name"] = tool_name
+        if tool_args is not None:
+            call_obj["arguments"] = json.dumps(tool_args, cls=CallbackFilteredJSONEncoder)
+        result.append(call_obj)
+    return result
diff --git a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/vendor_detection.py b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/vendor_detection.py
index 887e174523..0e003b8f25 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/vendor_detection.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/vendor_detection.py
@@ -26,7 +26,7 @@ def _get_vendor_rules() -> List[VendorRule]:
         VendorRule(
             exact_matches={"AzureChatOpenAI", "AzureOpenAI", "AzureOpenAIEmbeddings"},
             patterns=["azure"],
-            vendor_name="Azure"
+            vendor_name="az.ai.openai"
         ),
         VendorRule(
             exact_matches={"ChatOpenAI", "OpenAI", "OpenAIEmbeddings"},
@@ -36,12 +36,12 @@ def _get_vendor_rules() -> List[VendorRule]:
         VendorRule(
             exact_matches={"ChatBedrock", "BedrockEmbeddings", "Bedrock", "BedrockChat"},
             patterns=["bedrock", "aws"],
-            vendor_name="AWS"
+            vendor_name="aws.bedrock"
         ),
         VendorRule(
             exact_matches={"ChatAnthropic", "AnthropicLLM"},
             patterns=["anthropic"],
-            vendor_name="Anthropic"
+            vendor_name="anthropic"
         ),
         VendorRule(
             exact_matches={
@@ -49,12 +49,12 @@ def _get_vendor_rules() -> List[VendorRule]:
                 "GoogleGenerativeAI", "GooglePaLM", "ChatGooglePaLM"
             },
             patterns=["vertex", "google", "palm", "gemini"],
-            vendor_name="Google"
+            vendor_name="gcp.gen_ai"
         ),
         VendorRule(
             exact_matches={"ChatCohere", "CohereEmbeddings", "Cohere"},
             patterns=["cohere"],
-            vendor_name="Cohere"
+            vendor_name="cohere"
         ),
         VendorRule(
             exact_matches={
@@ -62,37 +62,37 @@ def _get_vendor_rules() -> List[VendorRule]:
                 "HuggingFaceEmbeddings", "ChatHuggingFace"
             },
             patterns=["huggingface"],
-            vendor_name="HuggingFace"
+            vendor_name="hugging_face"
         ),
         VendorRule(
             exact_matches={"ChatOllama", "OllamaEmbeddings", "Ollama"},
             patterns=["ollama"],
-            vendor_name="Ollama"
+            vendor_name="ollama"
         ),
         VendorRule(
             exact_matches={"Together", "ChatTogether"},
             patterns=["together"],
-            vendor_name="Together"
+            vendor_name="together_ai"
         ),
         VendorRule(
             exact_matches={"Replicate", "ChatReplicate"},
             patterns=["replicate"],
-            vendor_name="Replicate"
+            vendor_name="replicate"
         ),
         VendorRule(
             exact_matches={"ChatFireworks", "Fireworks"},
             patterns=["fireworks"],
-            vendor_name="Fireworks"
+            vendor_name="fireworks"
         ),
         VendorRule(
             exact_matches={"ChatGroq"},
             patterns=["groq"],
-            vendor_name="Groq"
+            vendor_name="groq"
         ),
         VendorRule(
             exact_matches={"ChatMistralAI", "MistralAI"},
             patterns=["mistral"],
-            vendor_name="MistralAI"
+            vendor_name="mistral_ai"
         ),
     ]
 
@@ -109,7 +109,7 @@ def detect_vendor_from_class(class_name: str) -> str:
         Vendor string, defaults to "Langchain" if no match found
     """
     if not class_name:
-        return "Langchain"
+        return "langchain"
 
     vendor_rules = _get_vendor_rules()
 
@@ -117,4 +117,4 @@ def detect_vendor_from_class(class_name: str) -> str:
         if rule.matches(class_name):
             return rule.vendor_name
 
-    return "Langchain"
+    return "langchain"
diff --git a/packages/opentelemetry-instrumentation-langchain/pyproject.toml b/packages/opentelemetry-instrumentation-langchain/pyproject.toml
index 1725903e45..03459aea43 100644
--- a/packages/opentelemetry-instrumentation-langchain/pyproject.toml
+++ b/packages/opentelemetry-instrumentation-langchain/pyproject.toml
@@ -13,7 +13,7 @@ requires-python = ">=3.10,<4"
 dependencies = [
   "opentelemetry-api>=1.38.0,<2",
   "opentelemetry-instrumentation>=0.59b0",
-  "opentelemetry-semantic-conventions-ai>=0.4.16,<0.5.0",
+  "opentelemetry-semantic-conventions-ai>=0.5.0,<0.6.0",
   "opentelemetry-semantic-conventions>=0.59b0",
 ]
 
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py b/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py
index bc5631ea47..62669b33ca 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py
@@ -127,7 +127,8 @@ def test_sequential_chain(instrument_legacy, span_exporter, log_exporter):
         (openai_span.attributes[GenAIAttributes.GEN_AI_RESPONSE_MODEL])
         == "gpt-3.5-turbo-instruct"
     )
-    assert openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"]
+    input_messages = json.loads(openai_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"]
 
     logs = log_exporter.get_finished_logs()
     assert (
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_generation_role_extraction.py b/packages/opentelemetry-instrumentation-langchain/tests/test_generation_role_extraction.py
index 60068843a3..8c133e2e1b 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_generation_role_extraction.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_generation_role_extraction.py
@@ -6,6 +6,7 @@
 in observability traces.
 """
 
+import json
 import pytest
 from unittest.mock import Mock
 from langchain_core.outputs import LLMResult, ChatGeneration, Generation
@@ -32,98 +33,78 @@ def set_attribute(key, value):
 
     def test_chat_generation_with_ai_message_role(self, mock_span, monkeypatch):
         """Test that ChatGeneration with AIMessage correctly extracts 'assistant' role."""
-        # Mock should_send_prompts to return True
         monkeypatch.setattr(
             "opentelemetry.instrumentation.langchain.span_utils.should_send_prompts",
             lambda: True
         )
 
-        # Create ChatGeneration with AIMessage
         generation = ChatGeneration(message=AIMessage(content="Hello!"))
         llm_result = LLMResult(generations=[[generation]])
 
-        # Call the function
         set_chat_response(mock_span, llm_result)
 
-        # Assert role is 'assistant', not 'unknown'
-        role_key = f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role"
-        assert role_key in mock_span.attributes
-        assert mock_span.attributes[role_key] == "assistant"
+        output_messages = json.loads(mock_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+        assert output_messages[0]["role"] == "assistant"
 
     def test_chat_generation_with_tool_message_role(self, mock_span, monkeypatch):
         """Test that ChatGeneration with ToolMessage correctly extracts 'tool' role."""
-        # Mock should_send_prompts to return True
         monkeypatch.setattr(
             "opentelemetry.instrumentation.langchain.span_utils.should_send_prompts",
             lambda: True
         )
 
-        # Create ChatGeneration with ToolMessage
         generation = ChatGeneration(
             message=ToolMessage(content="Tool result", tool_call_id="123")
         )
         llm_result = LLMResult(generations=[[generation]])
 
-        # Call the function
         set_chat_response(mock_span, llm_result)
 
-        # Assert role is 'tool', not 'unknown'
-        role_key = f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role"
-        assert role_key in mock_span.attributes
-        assert mock_span.attributes[role_key] == "tool"
+        output_messages = json.loads(mock_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+        assert output_messages[0]["role"] == "tool"
 
     def test_generation_without_message_defaults_to_assistant(self, mock_span, monkeypatch):
         """Test that Generation (non-chat) defaults to 'assistant' role."""
-        # Mock should_send_prompts to return True
         monkeypatch.setattr(
             "opentelemetry.instrumentation.langchain.span_utils.should_send_prompts",
             lambda: True
         )
 
-        # Create Generation without message (legacy completion)
         generation = Generation(text="This is a completion")
         llm_result = LLMResult(generations=[[generation]])
 
-        # Call the function
         set_chat_response(mock_span, llm_result)
 
-        # Assert role defaults to 'assistant', not 'unknown'
-        role_key = f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role"
-        assert role_key in mock_span.attributes
-        assert mock_span.attributes[role_key] == "assistant"
+        output_messages = json.loads(mock_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+        assert output_messages[0]["role"] == "assistant"
 
     def test_multiple_generations_with_different_roles(self, mock_span, monkeypatch):
         """Test that multiple generations with different message types are handled correctly."""
-        # Mock should_send_prompts to return True
         monkeypatch.setattr(
             "opentelemetry.instrumentation.langchain.span_utils.should_send_prompts",
             lambda: True
         )
 
-        # Create multiple generations with different message types
         gen1 = ChatGeneration(message=AIMessage(content="AI response"))
         gen2 = ChatGeneration(message=ToolMessage(content="Tool result", tool_call_id="123"))
         gen3 = Generation(text="Legacy completion")
 
         llm_result = LLMResult(generations=[[gen1], [gen2], [gen3]])
 
-        # Call the function
         set_chat_response(mock_span, llm_result)
 
-        # Assert all roles are correctly set
-        assert mock_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role"] == "assistant"
-        assert mock_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.1.role"] == "tool"
-        assert mock_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.2.role"] == "assistant"
+        output_messages = json.loads(mock_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+        assert output_messages[0]["role"] == "assistant"
+        assert output_messages[1]["role"] == "tool"
+        assert output_messages[2]["role"] == "assistant"
 
     def test_generation_type_attribute_is_not_used(self, mock_span, monkeypatch):
         """Test that generation.type (which returns class name) is not used directly."""
-        # Mock should_send_prompts to return True
         monkeypatch.setattr(
             "opentelemetry.instrumentation.langchain.span_utils.should_send_prompts",
             lambda: True
         )
 
-        # Create ChatGeneration - note that generation.type would be "ChatGeneration"
         generation = ChatGeneration(message=AIMessage(content="Test"))
 
         # Verify the bug scenario: generation.type returns class name, not message type
@@ -132,11 +113,7 @@ def test_generation_type_attribute_is_not_used(self, mock_span, monkeypatch):
 
         llm_result = LLMResult(generations=[[generation]])
 
-        # Call the function
         set_chat_response(mock_span, llm_result)
 
-        # Assert role is 'assistant', not 'unknown'
-        # If the bug existed, passing generation.type directly to _message_type_to_role
-        # would return 'unknown' because "ChatGeneration" doesn't match any message type
-        role_key = f"{GenAIAttributes.GEN_AI_COMPLETION}.0.role"
-        assert mock_span.attributes[role_key] == "assistant"
+        output_messages = json.loads(mock_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+        assert output_messages[0]["role"] == "assistant"
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_langgraph.py b/packages/opentelemetry-instrumentation-langchain/tests/test_langgraph.py
index c912106200..7c84283714 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_langgraph.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_langgraph.py
@@ -56,7 +56,7 @@ def calculate(state: State):
     # agent_id removed per maintainer feedback - rely on agent name only
 
     assert openai_span.parent.span_id == calculate_task_span.context.span_id
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-4o"
     assert (
         openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"]
@@ -76,7 +76,7 @@ def calculate(state: State):
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 24
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 11
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 35
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 35
 
 
 @pytest.mark.vcr
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py b/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py
index fcf8767b75..7c5e3f7408 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py
@@ -147,17 +147,13 @@ def test_custom_llm(instrument_legacy, span_exporter, log_exporter):
         span for span in spans if span.name == "HuggingFaceTextGenInference.completion"
     )
 
-    assert hugging_face_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "completion"
+    assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "completion"
     assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "unknown"
-    assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "HuggingFace"
-    assert (
-        hugging_face_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"]
-        == "System: You are a helpful assistant\nHuman: tell me a short joke"
-    )
-    assert (
-        hugging_face_span.attributes[f"{GenAIAttributes.GEN_AI_COMPLETION}.0.content"]
-        == response
-    )
+    assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "hugging_face"
+    input_messages = json.loads(hugging_face_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == "System: You are a helpful assistant\nHuman: tell me a short joke"
+    output_messages = json.loads(hugging_face_span.attributes[GenAIAttributes.GEN_AI_OUTPUT_MESSAGES])
+    assert output_messages[0]["content"] == response
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 0, (
@@ -192,7 +188,7 @@ def test_custom_llm_with_events_with_content(
         span for span in spans if span.name == "HuggingFaceTextGenInference.completion"
     )
 
-    assert hugging_face_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "completion"
+    assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "completion"
     assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "unknown"
 
     logs = log_exporter.get_finished_logs()
@@ -243,7 +239,7 @@ def test_custom_llm_with_events_with_no_content(
         span for span in spans if span.name == "HuggingFaceTextGenInference.completion"
     )
 
-    assert hugging_face_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "completion"
+    assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "completion"
     assert hugging_face_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "unknown"
 
     logs = log_exporter.get_finished_logs()
@@ -283,20 +279,18 @@ def test_openai(instrument_legacy, span_exporter, log_exporter):
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-4o-mini"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "openai"
-    assert (
-        (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"])
-        == "You are a helpful assistant"
-    )
-    assert (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"]) == "system"
-    assert (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.content"]) == prompt
-    assert (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.role"]) == "user"
+    input_messages = json.loads(openai_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == "You are a helpful assistant"
+    assert input_messages[0]["role"] == "system"
+    assert input_messages[1]["content"] == prompt
+    assert input_messages[1]["role"] == "user"
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 1497
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 1037
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 2534
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 2534
 
     workflow_span = next(
         span for span in spans if span.name == "RunnableSequence.workflow"
@@ -338,12 +332,12 @@ def test_openai_with_events_with_content(
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-4o-mini"
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 1497
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 1037
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 2534
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 2534
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
@@ -389,12 +383,12 @@ def test_openai_with_events_with_no_content(
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-4o-mini"
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 1497
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 1037
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 2534
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 2534
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
@@ -446,33 +440,17 @@ class Joke(BaseModel):
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-3.5-turbo"
-    assert (
-        (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"])
-        == "You are helpful assistant"
-    )
-    assert (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"]) == "system"
-    assert (
-        (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.content"])
-        == "tell me a short joke"
-    )
-    assert (openai_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.role"]) == "user"
-    assert (
-        openai_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"]
-        == "Joke"
-    )
-    assert (
-        openai_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.description"]
-        == "Joke to tell user."
-    )
-    assert (
-        json.loads(
-            openai_span.attributes[
-                f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"
-            ]
-        )
-    ) == {
+    input_messages = json.loads(openai_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == "You are helpful assistant"
+    assert input_messages[0]["role"] == "system"
+    assert input_messages[1]["content"] == "tell me a short joke"
+    assert input_messages[1]["role"] == "user"
+    tool_defs = json.loads(openai_span.attributes[GenAIAttributes.GEN_AI_TOOL_DEFINITIONS])
+    assert tool_defs[0]["name"] == "Joke"
+    assert tool_defs[0]["description"] == "Joke to tell user."
+    assert tool_defs[0]["parameters"] == {
         "type": "object",
         "properties": {
             "setup": {"description": "question to set up a joke", "type": "string"},
@@ -485,7 +463,7 @@ class Joke(BaseModel):
     }
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 76
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 35
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 111
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 111
 
     workflow_span = next(
         span for span in spans if span.name == "RunnableSequence.workflow"
@@ -536,12 +514,12 @@ class Joke(BaseModel):
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-3.5-turbo"
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 76
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 35
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 111
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 111
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
@@ -609,12 +587,12 @@ class Joke(BaseModel):
 
     openai_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert openai_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert openai_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "gpt-3.5-turbo"
 
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 76
     assert openai_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 35
-    assert openai_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 111
+    assert openai_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 111
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
@@ -658,25 +636,18 @@ def test_anthropic(instrument_legacy, span_exporter, log_exporter):
         span for span in spans if span.name == "RunnableSequence.workflow"
     )
 
-    assert anthropic_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert anthropic_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "claude-2.1"
-    assert anthropic_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "Anthropic"
+    assert anthropic_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "anthropic"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.5
-    assert (
-        (anthropic_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"])
-        == "You are a helpful assistant"
-    )
-    assert (
-        (anthropic_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"]) == "system"
-    )
-    assert (
-        (anthropic_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.content"])
-        == "tell me a short joke"
-    )
-    assert (anthropic_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.role"]) == "user"
+    input_messages = json.loads(anthropic_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == "You are a helpful assistant"
+    assert input_messages[0]["role"] == "system"
+    assert input_messages[1]["content"] == "tell me a short joke"
+    assert input_messages[1]["role"] == "user"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 19
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 22
-    assert anthropic_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 41
+    assert anthropic_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 41
     assert (
         anthropic_span.attributes["gen_ai.response.id"]
         == "msg_017fMG9SRDFTBhcD1ibtN1nK"
@@ -725,13 +696,13 @@ def test_anthropic_with_events_with_content(
 
     anthropic_span = next(span for span in spans if span.name == "ChatAnthropic.chat")
 
-    assert anthropic_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert anthropic_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "claude-2.1"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.5
 
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 19
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 22
-    assert anthropic_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 41
+    assert anthropic_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 41
     assert (
         anthropic_span.attributes["gen_ai.response.id"]
         == "msg_017fMG9SRDFTBhcD1ibtN1nK"
@@ -781,13 +752,13 @@ def test_anthropic_with_events_with_no_content(
 
     anthropic_span = next(span for span in spans if span.name == "ChatAnthropic.chat")
 
-    assert anthropic_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert anthropic_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL] == "claude-2.1"
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE] == 0.5
 
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 19
     assert anthropic_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 22
-    assert anthropic_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 41
+    assert anthropic_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 41
     assert (
         anthropic_span.attributes["gen_ai.response.id"]
         == "msg_017fMG9SRDFTBhcD1ibtN1nK"
@@ -843,25 +814,20 @@ def test_bedrock(instrument_legacy, span_exporter, log_exporter):
         span for span in spans if span.name == "RunnableSequence.workflow"
     )
 
-    assert bedrock_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert bedrock_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert (
         bedrock_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
         == "anthropic.claude-3-haiku-20240307-v1:0"
     )
-    assert bedrock_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "AWS"
-    assert (
-        (bedrock_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"])
-        == "You are a helpful assistant"
-    )
-    assert (bedrock_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"]) == "system"
-    assert (
-        (bedrock_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.content"])
-        == "tell me a short joke"
-    )
-    assert (bedrock_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.role"]) == "user"
+    assert bedrock_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "aws.bedrock"
+    input_messages = json.loads(bedrock_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == "You are a helpful assistant"
+    assert input_messages[0]["role"] == "system"
+    assert input_messages[1]["content"] == "tell me a short joke"
+    assert input_messages[1]["role"] == "user"
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 16
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 27
-    assert bedrock_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 43
+    assert bedrock_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 43
     output = json.loads(
         workflow_span.attributes[SpanAttributes.TRACELOOP_ENTITY_OUTPUT]
     )
@@ -914,7 +880,7 @@ def test_bedrock_with_events_with_content(
 
     bedrock_span = next(span for span in spans if span.name == "ChatBedrock.chat")
 
-    assert bedrock_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert bedrock_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert (
         bedrock_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
         == "anthropic.claude-3-haiku-20240307-v1:0"
@@ -922,7 +888,7 @@ def test_bedrock_with_events_with_content(
 
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 16
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 27
-    assert bedrock_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 43
+    assert bedrock_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 43
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
@@ -977,14 +943,14 @@ def test_bedrock_with_events_with_no_content(
 
     bedrock_span = next(span for span in spans if span.name == "ChatBedrock.chat")
 
-    assert bedrock_span.attributes[SpanAttributes.LLM_REQUEST_TYPE] == "chat"
+    assert bedrock_span.attributes[GenAIAttributes.GEN_AI_OPERATION_NAME] == "chat"
     assert (
         bedrock_span.attributes[GenAIAttributes.GEN_AI_REQUEST_MODEL]
         == "anthropic.claude-3-haiku-20240307-v1:0"
     )
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS] == 16
     assert bedrock_span.attributes[GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS] == 27
-    assert bedrock_span.attributes[SpanAttributes.LLM_USAGE_TOTAL_TOKENS] == 43
+    assert bedrock_span.attributes[SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS] == 43
 
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 3
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_semconv_compliance.py b/packages/opentelemetry-instrumentation-langchain/tests/test_semconv_compliance.py
new file mode 100644
index 0000000000..35a01e3380
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_semconv_compliance.py
@@ -0,0 +1,8 @@
+# ruff: noqa: F401, F403
+"""
+Semconv compliance tests re-used from opentelemetry-semantic-conventions-ai.
+
+Ensures the installed semconv package has the expected constant values.
+To add more compliance checks, update _testing.py in that package — not here.
+"""
+from opentelemetry.semconv_ai._testing import *
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_structured_output.py b/packages/opentelemetry-instrumentation-langchain/tests/test_structured_output.py
index 64899bea03..0d2b9e6035 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_structured_output.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_structured_output.py
@@ -1,3 +1,4 @@
+import json
 from typing import List
 
 import pytest
@@ -32,7 +33,8 @@ def test_structured_output(instrument_legacy, span_exporter, log_exporter):
 
     chat_span = next(span for span in spans if span.name == "ChatOpenAI.chat")
 
-    assert chat_span.attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"] == query_text
+    input_messages = json.loads(chat_span.attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+    assert input_messages[0]["content"] == query_text
 
     logs = log_exporter.get_finished_logs()
     assert (
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_tool_call_content.py b/packages/opentelemetry-instrumentation-langchain/tests/test_tool_call_content.py
index c0b447940b..834fc27321 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_tool_call_content.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_tool_call_content.py
@@ -2,10 +2,11 @@
 Test for the fix of the issue where assistant message content is missing
 when tool calls are present in LangGraph/LangChain instrumentation.
 
-This test reproduces the issue reported in GitHub where gen_ai.prompt.X.content
+This test reproduces the issue reported in GitHub where gen_ai.input.messages
 attributes were missing for assistant messages that contained tool_calls.
 """
 
+import json
 from unittest.mock import Mock
 from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
 from opentelemetry.instrumentation.langchain.span_utils import set_chat_request
@@ -51,48 +52,23 @@ def test_assistant_message_with_tool_calls_includes_content():
     call_args = [call[0] for call in mock_span.set_attribute.call_args_list]
     attributes = {args[0]: args[1] for args in call_args}
 
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"] == "user"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.content" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"]
-        == "what is the current time? First greet me."
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.1.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.role"] == "assistant"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.1.content" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.content"]
-        == "Hello! Let me check the current time for you."
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.1.tool_calls.0.id" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.tool_calls.0.id"]
-        == "call_qU7pH3EdQvzwkPyKPOdpgaKA"
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.1.tool_calls.0.name" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.1.tool_calls.0.name"]
-        == "get_current_time"
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.2.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.2.role"] == "tool"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.2.content" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.2.content"] == "2025-08-15 08:15:21"
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.2.tool_call_id" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.2.tool_call_id"]
-        == "call_qU7pH3EdQvzwkPyKPOdpgaKA"
-    )
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.3.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.3.role"] == "assistant"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.3.content" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.3.content"]
-        == "The current time is 2025-08-15 08:15:21"
-    )
+    assert GenAIAttributes.GEN_AI_INPUT_MESSAGES in attributes
+    input_messages = json.loads(attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+
+    assert input_messages[0]["role"] == "user"
+    assert input_messages[0]["content"] == "what is the current time? First greet me."
+
+    assert input_messages[1]["role"] == "assistant"
+    assert input_messages[1]["content"] == "Hello! Let me check the current time for you."
+    assert input_messages[1]["tool_calls"][0]["id"] == "call_qU7pH3EdQvzwkPyKPOdpgaKA"
+    assert input_messages[1]["tool_calls"][0]["name"] == "get_current_time"
+
+    assert input_messages[2]["role"] == "tool"
+    assert input_messages[2]["content"] == "2025-08-15 08:15:21"
+    assert input_messages[2]["tool_call_id"] == "call_qU7pH3EdQvzwkPyKPOdpgaKA"
+
+    assert input_messages[3]["role"] == "assistant"
+    assert input_messages[3]["content"] == "The current time is 2025-08-15 08:15:21"
 
 
 def test_assistant_message_with_only_tool_calls_no_content():
@@ -121,16 +97,12 @@ def test_assistant_message_with_only_tool_calls_no_content():
     call_args = [call[0] for call in mock_span.set_attribute.call_args_list]
     attributes = {args[0]: args[1] for args in call_args}
 
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"] == "assistant"
-    # Content is being set as empty string, so we expect it to be present
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.content" in attributes
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.tool_calls.0.id" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.tool_calls.0.id"] == "call_123"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.tool_calls.0.name" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.tool_calls.0.name"] == "some_tool"
-    )
+    assert GenAIAttributes.GEN_AI_INPUT_MESSAGES in attributes
+    input_messages = json.loads(attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
+
+    assert input_messages[0]["role"] == "assistant"
+    assert input_messages[0]["tool_calls"][0]["id"] == "call_123"
+    assert input_messages[0]["tool_calls"][0]["name"] == "some_tool"
 
 
 def test_assistant_message_with_only_content_no_tool_calls():
@@ -148,16 +120,11 @@ def test_assistant_message_with_only_content_no_tool_calls():
     set_chat_request(mock_span, {}, messages, {}, mock_span_holder)
 
     call_args = [call[0] for call in mock_span.set_attribute.call_args_list]
-
     attributes = {args[0]: args[1] for args in call_args}
 
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.role" in attributes
-    assert attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.role"] == "assistant"
-    assert f"{GenAIAttributes.GEN_AI_PROMPT}.0.content" in attributes
-    assert (
-        attributes[f"{GenAIAttributes.GEN_AI_PROMPT}.0.content"]
-        == "Just a regular response with no tool calls"
-    )
+    assert GenAIAttributes.GEN_AI_INPUT_MESSAGES in attributes
+    input_messages = json.loads(attributes[GenAIAttributes.GEN_AI_INPUT_MESSAGES])
 
-    tool_call_attributes = [attr for attr in attributes.keys() if "tool_calls" in attr]
-    assert len(tool_call_attributes) == 0
+    assert input_messages[0]["role"] == "assistant"
+    assert input_messages[0]["content"] == "Just a regular response with no tool calls"
+    assert "tool_calls" not in input_messages[0]
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_tool_calls.py b/packages/opentelemetry-instrumentation-langchain/tests/test_tool_calls.py
index 1908535164..3e6d8decf0 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_tool_calls.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_tool_calls.py
@@ -15,7 +15,6 @@
 from opentelemetry.semconv._incubating.attributes import (
     gen_ai_attributes as GenAIAttributes,
 )
-from opentelemetry.semconv_ai import SpanAttributes
 
 
 def food_analysis(
@@ -41,8 +40,8 @@ def test_tool_calls(instrument_legacy, span_exporter, log_exporter):
     #     span for span in spans if span.name == "ChatOpenAI.chat"
     # )
 
-    # assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "food_analysis"
-    # assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
+    # assert chat_span.attributes[f"{GenAIAttributes.GEN_AI_TOOL_DEFINITIONS}.0.name"] == "food_analysis"
+    # assert json.loads(chat_span.attributes[f"{GenAIAttributes.GEN_AI_TOOL_DEFINITIONS}.0.parameters"]) == {
     #     "properties": {
     #         "name": {"type": "string"},
     #         "healthy": {"type": "boolean"},
@@ -192,17 +191,9 @@ def get_weather(location: str) -> str:
     chat_span = spans[0]
     assert chat_span.name == "ChatOpenAI.chat"
 
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "get_weather"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
-        "properties": {
-            "location": {"type": "string"},
-        },
-        "required": ["location"],
-        "type": "object",
-    }
-
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "get_weather"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
+    tool_defs = json.loads(chat_span.attributes[GenAIAttributes.GEN_AI_TOOL_DEFINITIONS])
+    assert tool_defs[0]["name"] == "get_weather"
+    assert tool_defs[0]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
@@ -480,17 +471,17 @@ def get_news(location: str) -> str:
     chat_span = spans[0]
     assert chat_span.name == "ChatAnthropic.chat"
 
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "get_weather"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
+    tool_defs = json.loads(chat_span.attributes[GenAIAttributes.GEN_AI_TOOL_DEFINITIONS])
+    assert tool_defs[0]["name"] == "get_weather"
+    assert tool_defs[0]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
         "required": ["location"],
         "type": "object",
     }
-
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.name"] == "get_news"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.parameters"]) == {
+    assert tool_defs[1]["name"] == "get_news"
+    assert tool_defs[1]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
@@ -697,17 +688,17 @@ def get_news(location: str) -> str:
     chat_span = spans[0]
     assert chat_span.name == "ChatAnthropic.chat"
 
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "get_weather"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
+    tool_defs = json.loads(chat_span.attributes[GenAIAttributes.GEN_AI_TOOL_DEFINITIONS])
+    assert tool_defs[0]["name"] == "get_weather"
+    assert tool_defs[0]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
         "required": ["location"],
         "type": "object",
     }
-
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.name"] == "get_news"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.parameters"]) == {
+    assert tool_defs[1]["name"] == "get_news"
+    assert tool_defs[1]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
@@ -1045,17 +1036,17 @@ def get_news(location: str) -> str:
     chat_span = spans[0]
     assert chat_span.name == "ChatOpenAI.chat"
 
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.name"] == "get_weather"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.0.parameters"]) == {
+    tool_defs = json.loads(chat_span.attributes[GenAIAttributes.GEN_AI_TOOL_DEFINITIONS])
+    assert tool_defs[0]["name"] == "get_weather"
+    assert tool_defs[0]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
         "required": ["location"],
         "type": "object",
     }
-
-    assert chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.name"] == "get_news"
-    assert json.loads(chat_span.attributes[f"{SpanAttributes.LLM_REQUEST_FUNCTIONS}.1.parameters"]) == {
+    assert tool_defs[1]["name"] == "get_news"
+    assert tool_defs[1]["parameters"] == {
         "properties": {
             "location": {"type": "string"},
         },
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_vendor_detection.py b/packages/opentelemetry-instrumentation-langchain/tests/test_vendor_detection.py
new file mode 100644
index 0000000000..af2dac4b9f
--- /dev/null
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_vendor_detection.py
@@ -0,0 +1,59 @@
+import pytest
+from opentelemetry.instrumentation.langchain.vendor_detection import detect_vendor_from_class
+
+
+@pytest.mark.parametrize("class_name,expected", [
+    # Exact matches
+    ("AzureChatOpenAI", "az.ai.openai"),
+    ("AzureOpenAI", "az.ai.openai"),
+    ("AzureOpenAIEmbeddings", "az.ai.openai"),
+    ("ChatOpenAI", "openai"),
+    ("OpenAI", "openai"),
+    ("OpenAIEmbeddings", "openai"),
+    ("ChatBedrock", "aws.bedrock"),
+    ("BedrockEmbeddings", "aws.bedrock"),
+    ("Bedrock", "aws.bedrock"),
+    ("BedrockChat", "aws.bedrock"),
+    ("ChatAnthropic", "anthropic"),
+    ("AnthropicLLM", "anthropic"),
+    ("ChatVertexAI", "gcp.gen_ai"),
+    ("VertexAI", "gcp.gen_ai"),
+    ("ChatGoogleGenerativeAI", "gcp.gen_ai"),
+    ("GoogleGenerativeAI", "gcp.gen_ai"),
+    ("ChatCohere", "cohere"),
+    ("Cohere", "cohere"),
+    ("HuggingFacePipeline", "hugging_face"),
+    ("HuggingFaceTextGenInference", "hugging_face"),
+    ("ChatHuggingFace", "hugging_face"),
+    ("ChatOllama", "ollama"),
+    ("Ollama", "ollama"),
+    ("Together", "together_ai"),
+    ("ChatTogether", "together_ai"),
+    ("Replicate", "replicate"),
+    ("ChatReplicate", "replicate"),
+    ("ChatFireworks", "fireworks"),
+    ("Fireworks", "fireworks"),
+    ("ChatGroq", "groq"),
+    ("ChatMistralAI", "mistral_ai"),
+    ("MistralAI", "mistral_ai"),
+    # Pattern matches
+    ("SomeAzureModel", "az.ai.openai"),
+    ("CustomOpenAIModel", "openai"),
+    ("AwsBedrockModel", "aws.bedrock"),
+    ("AnthropicCustom", "anthropic"),
+    ("VertexCustom", "gcp.gen_ai"),
+    ("GeminiModel", "gcp.gen_ai"),
+    ("CohereCustom", "cohere"),
+    ("OllamaCustom", "ollama"),
+    ("TogetherCustom", "together_ai"),
+    ("ReplicateCustom", "replicate"),
+    ("FireworksCustom", "fireworks"),
+    ("GroqCustom", "groq"),
+    ("MistralCustom", "mistral_ai"),
+    # Default fallback
+    ("UnknownModel", "langchain"),
+    ("", "langchain"),
+    (None, "langchain"),
+])
+def test_detect_vendor_from_class(class_name, expected):
+    assert detect_vendor_from_class(class_name) == expected
diff --git a/packages/opentelemetry-instrumentation-writer/pyproject.toml b/packages/opentelemetry-instrumentation-writer/pyproject.toml
index df9d3e8a7a..fe26396fcd 100644
--- a/packages/opentelemetry-instrumentation-writer/pyproject.toml
+++ b/packages/opentelemetry-instrumentation-writer/pyproject.toml
@@ -12,7 +12,7 @@ requires-python = ">=3.10,<4"
 dependencies = [
   "opentelemetry-api>=1.38.0,<2",
   "opentelemetry-instrumentation>=0.59b0",
-  "opentelemetry-semantic-conventions-ai>=0.4.11",
+  "opentelemetry-semantic-conventions-ai>=0.4.11,<0.5.0",
   "opentelemetry-semantic-conventions>=0.59b0",
 ]
 
diff --git a/packages/opentelemetry-semantic-conventions-ai/MIGRATION.md b/packages/opentelemetry-semantic-conventions-ai/MIGRATION.md
new file mode 100644
index 0000000000..dd0426a315
--- /dev/null
+++ b/packages/opentelemetry-semantic-conventions-ai/MIGRATION.md
@@ -0,0 +1,189 @@
+# Migration Guide: opentelemetry-semantic-conventions-ai v0.4.x → v0.5.x
+
+This guide covers breaking changes introduced when aligning the `opentelemetry-semantic-conventions-ai`
+package with the upstream [OTel GenAI semantic conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/).
+
+---
+
+## 1. Removed constants (previously duplicated upstream)
+
+These `SpanAttributes` constants have been **removed**. They are now part of the official
+`opentelemetry-semantic-conventions` package. Import them directly from upstream.
+
+```python
+# Before
+from opentelemetry.semconv_ai import SpanAttributes
+span.set_attribute(SpanAttributes.LLM_SYSTEM, "openai")
+
+# After
+from opentelemetry.semconv._incubating.attributes import gen_ai_attributes as GenAIAttributes
+span.set_attribute(GenAIAttributes.GEN_AI_SYSTEM, "openai")
+```
+
+| Removed constant | Upstream replacement |
+|---|---|
+| `SpanAttributes.LLM_SYSTEM` | `GenAIAttributes.GEN_AI_SYSTEM` |
+| `SpanAttributes.LLM_REQUEST_MODEL` | `GenAIAttributes.GEN_AI_REQUEST_MODEL` |
+| `SpanAttributes.LLM_REQUEST_MAX_TOKENS` | `GenAIAttributes.GEN_AI_REQUEST_MAX_TOKENS` |
+| `SpanAttributes.LLM_REQUEST_TEMPERATURE` | `GenAIAttributes.GEN_AI_REQUEST_TEMPERATURE` |
+| `SpanAttributes.LLM_REQUEST_TOP_P` | `GenAIAttributes.GEN_AI_REQUEST_TOP_P` |
+| `SpanAttributes.LLM_TOP_K` | `GenAIAttributes.GEN_AI_REQUEST_TOP_K` |
+| `SpanAttributes.LLM_CHAT_STOP_SEQUENCES` | `GenAIAttributes.GEN_AI_REQUEST_STOP_SEQUENCES` |
+| `SpanAttributes.LLM_FREQUENCY_PENALTY` | `GenAIAttributes.GEN_AI_REQUEST_FREQUENCY_PENALTY` |
+| `SpanAttributes.LLM_PRESENCE_PENALTY` | `GenAIAttributes.GEN_AI_REQUEST_PRESENCE_PENALTY` |
+| `SpanAttributes.LLM_RESPONSE_MODEL` | `GenAIAttributes.GEN_AI_RESPONSE_MODEL` |
+| `SpanAttributes.LLM_USAGE_COMPLETION_TOKENS` | `GenAIAttributes.GEN_AI_USAGE_OUTPUT_TOKENS` |
+| `SpanAttributes.LLM_USAGE_PROMPT_TOKENS` | `GenAIAttributes.GEN_AI_USAGE_INPUT_TOKENS` |
+| `SpanAttributes.LLM_TOKEN_TYPE` | `GenAIAttributes.GEN_AI_TOKEN_TYPE` |
+| `SpanAttributes.LLM_REQUEST_FUNCTIONS` | `GenAIAttributes.GEN_AI_TOOL_DEFINITIONS` |
+| `SpanAttributes.LLM_PROMPTS` | `GenAIAttributes.GEN_AI_PROMPT` |
+| `SpanAttributes.LLM_COMPLETIONS` | `GenAIAttributes.GEN_AI_COMPLETION` |
+| `SpanAttributes.LLM_OPENAI_RESPONSE_SYSTEM_FINGERPRINT` | `GenAIAttributes.GEN_AI_OPENAI_RESPONSE_SYSTEM_FINGERPRINT` |
+| `SpanAttributes.LLM_REQUEST_TYPE` | `GenAIAttributes.GEN_AI_OPERATION_NAME` |
+
+> **Note on `LLM_REQUEST_TYPE`**: The old `LLMRequestTypeValues` enum is replaced by
+> `GenAiOperationNameValues` from upstream, or by `GenAICustomOperationName` for
+> project-specific operation names.
+
+---
+
+## 2. Renamed constants (stay in `SpanAttributes`, new `GEN_AI_*` prefix)
+
+These constants remain in the `opentelemetry-semantic-conventions-ai` package but their
+Python names have been renamed from `LLM_*` to `GEN_AI_*`.
+
+```python
+# Before
+from opentelemetry.semconv_ai import SpanAttributes
+span.set_attribute(SpanAttributes.LLM_IS_STREAMING, True)
+
+# After
+from opentelemetry.semconv_ai import SpanAttributes
+span.set_attribute(SpanAttributes.GEN_AI_IS_STREAMING, True)
+```
+
+| Old name | New name |
+|---|---|
+| `SpanAttributes.LLM_USAGE_TOTAL_TOKENS` | `SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS` |
+| `SpanAttributes.LLM_USER` | `SpanAttributes.GEN_AI_USER` |
+| `SpanAttributes.LLM_HEADERS` | `SpanAttributes.GEN_AI_HEADERS` |
+| `SpanAttributes.LLM_IS_STREAMING` | `SpanAttributes.GEN_AI_IS_STREAMING` |
+| `SpanAttributes.LLM_REQUEST_REPETITION_PENALTY` | `SpanAttributes.GEN_AI_REQUEST_REPETITION_PENALTY` |
+| `SpanAttributes.LLM_REQUEST_REASONING_EFFORT` | `SpanAttributes.GEN_AI_REQUEST_REASONING_EFFORT` |
+| `SpanAttributes.LLM_REQUEST_REASONING_SUMMARY` | `SpanAttributes.GEN_AI_REQUEST_REASONING_SUMMARY` |
+| `SpanAttributes.LLM_RESPONSE_REASONING_EFFORT` | `SpanAttributes.GEN_AI_RESPONSE_REASONING_EFFORT` |
+| `SpanAttributes.LLM_RESPONSE_FINISH_REASON` | `SpanAttributes.GEN_AI_RESPONSE_FINISH_REASON` |
+| `SpanAttributes.LLM_RESPONSE_STOP_REASON` | `SpanAttributes.GEN_AI_RESPONSE_STOP_REASON` |
+| `SpanAttributes.LLM_CONTENT_COMPLETION_CHUNK` | `SpanAttributes.GEN_AI_CONTENT_COMPLETION_CHUNK` |
+| `SpanAttributes.LLM_USAGE_REASONING_TOKENS` | `SpanAttributes.GEN_AI_USAGE_REASONING_TOKENS` |
+| `SpanAttributes.LLM_USAGE_TOKEN_TYPE` | `SpanAttributes.GEN_AI_USAGE_TOKEN_TYPE` |
+| `SpanAttributes.LLM_USAGE_CACHE_CREATION_INPUT_TOKENS` | `SpanAttributes.GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS` ¹ |
+| `SpanAttributes.LLM_USAGE_CACHE_READ_INPUT_TOKENS` | `SpanAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS` ¹ |
+| `SpanAttributes.LLM_REQUEST_STRUCTURED_OUTPUT_SCHEMA` | `SpanAttributes.GEN_AI_REQUEST_STRUCTURED_OUTPUT_SCHEMA` |
+| `SpanAttributes.LLM_OPENAI_API_BASE` | `SpanAttributes.GEN_AI_OPENAI_API_BASE` |
+| `SpanAttributes.LLM_OPENAI_API_VERSION` | `SpanAttributes.GEN_AI_OPENAI_API_VERSION` |
+| `SpanAttributes.LLM_OPENAI_API_TYPE` | `SpanAttributes.GEN_AI_OPENAI_API_TYPE` |
+| `SpanAttributes.LLM_DECODING_METHOD` | `SpanAttributes.GEN_AI_WATSONX_DECODING_METHOD` |
+| `SpanAttributes.LLM_RANDOM_SEED` | `SpanAttributes.GEN_AI_WATSONX_RANDOM_SEED` |
+| `SpanAttributes.LLM_MAX_NEW_TOKENS` | `SpanAttributes.GEN_AI_WATSONX_MAX_NEW_TOKENS` |
+| `SpanAttributes.LLM_MIN_NEW_TOKENS` | `SpanAttributes.GEN_AI_WATSONX_MIN_NEW_TOKENS` |
+| `SpanAttributes.LLM_REPETITION_PENALTY` | `SpanAttributes.GEN_AI_WATSONX_REPETITION_PENALTY` |
+
+> ¹ The string value of these two cache-token attributes **also changed** — see [section 3](#cache-token-attributes).
+
+---
+
+## 3. Changed string values
+
+Some constants kept their Python name but the underlying **string value** changed.
+
+### Cache token attributes
+
+| Python name | Old string value | New string value |
+|---|---|---|
+| `SpanAttributes.GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS` | `gen_ai.usage.cache_creation_input_tokens` | `gen_ai.usage.cache_creation.input_tokens` |
+| `SpanAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS` | `gen_ai.usage.cache_read_input_tokens` | `gen_ai.usage.cache_read.input_tokens` |
+
+> **Dashboard impact**: Update any Grafana queries, alerts, or OTLP processors that filter on
+> these attribute names.
+
+### `GenAISystem` values
+
+All `GenAISystem` enum values now use the OTel spec canonical form (lowercase / dot-separated).
+
+| Enum member | Old value | New value |
+|---|---|---|
+| `GenAISystem.ANTHROPIC` | `"Anthropic"` | `"anthropic"` |
+| `GenAISystem.COHERE` | `"Cohere"` | `"cohere"` |
+| `GenAISystem.MISTRALAI` | `"MistralAI"` | `"mistral_ai"` |
+| `GenAISystem.OLLAMA` | `"Ollama"` | `"ollama"` |
+| `GenAISystem.GROQ` | `"Groq"` | `"groq"` |
+| `GenAISystem.ALEPH_ALPHA` | `"AlephAlpha"` | `"aleph_alpha"` |
+| `GenAISystem.REPLICATE` | `"Replicate"` | `"replicate"` |
+| `GenAISystem.TOGETHER_AI` | `"TogetherAI"` | `"together_ai"` |
+| `GenAISystem.WATSONX` | `"Watsonx"` | `"ibm.watsonx.ai"` |
+| `GenAISystem.HUGGINGFACE` | `"HuggingFace"` | `"hugging_face"` |
+| `GenAISystem.FIREWORKS` | `"Fireworks"` | `"fireworks"` |
+| `GenAISystem.AZURE` | `"Azure"` | `"az.ai.openai"` |
+| `GenAISystem.AWS` | `"AWS"` | `"aws.bedrock"` |
+| `GenAISystem.GOOGLE` | `"Google"` | `"gcp.gen_ai"` |
+| `GenAISystem.OPENROUTER` | `"OpenRouter"` | `"openrouter"` |
+| `GenAISystem.LANGCHAIN` | `"Langchain"` | `"langchain"` |
+
+> `GenAISystem.OPENAI` (`"openai"`) is unchanged.
+
+> **Dashboard impact**: Update dashboards, alerts, and OTLP processors that filter on
+> `gen_ai.system` to use the new lowercase values shown above.
+
+---
+
+## 4. Tool definitions format change
+
+Tool definitions are now encoded as a **single JSON-array attribute** instead of per-field
+indexed sub-attributes.
+
+```python
+# Before — multiple flat attributes
+span.set_attribute("gen_ai.tool.definitions.0.name", "my_tool")
+span.set_attribute("gen_ai.tool.definitions.0.description", "Does something")
+span.set_attribute("gen_ai.tool.definitions.0.parameters", json.dumps({...}))
+
+# After — one JSON array attribute
+import json
+tool_defs = [
+    {
+        "name": "my_tool",
+        "description": "Does something",
+        "parameters": {...},
+    }
+]
+span.set_attribute(GenAIAttributes.GEN_AI_TOOL_DEFINITIONS, json.dumps(tool_defs))
+```
+
+> **Dashboard impact**: Dashboards that expand `gen_ai.tool.definitions.{i}.name` as individual
+> attributes will no longer find them. Parse the JSON value of `gen_ai.tool.definitions` instead.
+
+---
+
+## 5. Quickstart: minimal import update
+
+```python
+# Before
+from opentelemetry.semconv_ai import SpanAttributes
+
+SpanAttributes.LLM_SYSTEM          # removed
+SpanAttributes.LLM_REQUEST_MODEL   # removed
+SpanAttributes.LLM_REQUEST_TYPE    # removed
+SpanAttributes.LLM_IS_STREAMING    # renamed
+SpanAttributes.LLM_USAGE_TOTAL_TOKENS  # renamed
+
+# After
+from opentelemetry.semconv_ai import SpanAttributes
+from opentelemetry.semconv._incubating.attributes import gen_ai_attributes as GenAIAttributes
+
+GenAIAttributes.GEN_AI_SYSTEM          # upstream
+GenAIAttributes.GEN_AI_REQUEST_MODEL   # upstream
+GenAIAttributes.GEN_AI_OPERATION_NAME  # upstream
+SpanAttributes.GEN_AI_IS_STREAMING     # project semconv (renamed)
+SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS  # project semconv (renamed)
+```
diff --git a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/__init__.py b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/__init__.py
index 8bb286d0e9..1bc3fec034 100644
--- a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/__init__.py
+++ b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/__init__.py
@@ -7,29 +7,31 @@ class GenAISystem(Enum):
     """
     Supported LLM vendor (System) names used across OpenLLMetry instrumentations.
 
-    These values match the actual strings used in span attributes (LLM_SYSTEM)
-    throughout the instrumentation packages.
+    Values that have a counterpart in the official OTel GenAI semantic conventions
+    (opentelemetry.semconv._incubating.attributes.gen_ai_attributes.GenAiSystemValues)
+    use the spec-defined lowercase string. Values without an OTel counterpart use
+    lowercase-with-underscores as a project convention.
     """
 
     OPENAI = "openai"
-    ANTHROPIC = "Anthropic"
-    COHERE = "Cohere"
-    MISTRALAI = "MistralAI"
-    OLLAMA = "Ollama"
-    GROQ = "Groq"
-    ALEPH_ALPHA = "AlephAlpha"
-    REPLICATE = "Replicate"
-    TOGETHER_AI = "TogetherAI"
-    WATSONX = "Watsonx"
-    HUGGINGFACE = "HuggingFace"
-    FIREWORKS = "Fireworks"
-
-    AZURE = "Azure"
-    AWS = "AWS"
-    GOOGLE = "Google"
-    OPENROUTER = "OpenRouter"
-
-    LANGCHAIN = "Langchain"
+    ANTHROPIC = "anthropic"
+    COHERE = "cohere"
+    MISTRALAI = "mistral_ai"
+    OLLAMA = "ollama"
+    GROQ = "groq"
+    ALEPH_ALPHA = "aleph_alpha"
+    REPLICATE = "replicate"
+    TOGETHER_AI = "together_ai"
+    WATSONX = "ibm.watsonx.ai"
+    HUGGINGFACE = "hugging_face"
+    FIREWORKS = "fireworks"
+
+    AZURE = "az.ai.openai"
+    AWS = "aws.bedrock"
+    GOOGLE = "gcp.gen_ai"
+    OPENROUTER = "openrouter"
+
+    LANGCHAIN = "langchain"
     CREWAI = "crewai"
 
 
@@ -62,52 +64,32 @@ class Meters:
 
 
 class SpanAttributes:
-    # GenAI Usage Cache Attributes (missing from incubating semantic conventions)
-    GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS = "gen_ai.usage.cache_creation_input_tokens"
-    GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS = "gen_ai.usage.cache_read_input_tokens"
-
-    # LLM Cache Attributes (legacy naming - keeping for backward compatibility)
-    LLM_SYSTEM = "gen_ai.system"
-    LLM_REQUEST_MODEL = "gen_ai.request.model"
-    LLM_REQUEST_MAX_TOKENS = "gen_ai.request.max_tokens"
-    LLM_REQUEST_TEMPERATURE = "gen_ai.request.temperature"
-    LLM_REQUEST_TOP_P = "gen_ai.request.top_p"
-    LLM_PROMPTS = "gen_ai.prompt"
-    LLM_COMPLETIONS = "gen_ai.completion"
-    LLM_RESPONSE_MODEL = "gen_ai.response.model"
-    LLM_USAGE_COMPLETION_TOKENS = "gen_ai.usage.completion_tokens"
-    LLM_USAGE_PROMPT_TOKENS = "gen_ai.usage.prompt_tokens"
-    LLM_USAGE_CACHE_CREATION_INPUT_TOKENS = "gen_ai.usage.cache_creation_input_tokens"
-    LLM_USAGE_CACHE_READ_INPUT_TOKENS = "gen_ai.usage.cache_read_input_tokens"
-    LLM_TOKEN_TYPE = "gen_ai.token.type"
-    LLM_REQUEST_STRUCTURED_OUTPUT_SCHEMA = "gen_ai.request.structured_output_schema"
-    LLM_REQUEST_REASONING_SUMMARY = "gen_ai.request.reasoning_summary"
-    LLM_RESPONSE_REASONING_EFFORT = "gen_ai.response.reasoning_effort"
-
-    # LLM
-    LLM_REQUEST_TYPE = "llm.request.type"
-    LLM_USAGE_TOTAL_TOKENS = "llm.usage.total_tokens"
-    LLM_USAGE_TOKEN_TYPE = "llm.usage.token_type"
-    LLM_USER = "llm.user"
-    LLM_HEADERS = "llm.headers"
-    LLM_TOP_K = "llm.top_k"
-    LLM_IS_STREAMING = "llm.is_streaming"
-    LLM_FREQUENCY_PENALTY = "llm.frequency_penalty"
-    LLM_PRESENCE_PENALTY = "llm.presence_penalty"
-    LLM_CHAT_STOP_SEQUENCES = "llm.chat.stop_sequences"
-    LLM_REQUEST_FUNCTIONS = "llm.request.functions"
-    LLM_REQUEST_REPETITION_PENALTY = "llm.request.repetition_penalty"
-    LLM_RESPONSE_FINISH_REASON = "llm.response.finish_reason"
-    LLM_RESPONSE_STOP_REASON = "llm.response.stop_reason"
-    LLM_CONTENT_COMPLETION_CHUNK = "llm.content.completion.chunk"
-    LLM_REQUEST_REASONING_EFFORT = "llm.request.reasoning_effort"
-    LLM_USAGE_REASONING_TOKENS = "llm.usage.reasoning_tokens"
+    # GenAI Usage Cache Attributes (not yet in upstream OTel incubating semconv)
+    GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS = "gen_ai.usage.cache_creation.input_tokens"
+    GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS = "gen_ai.usage.cache_read.input_tokens"
+
+    # LLM — project-policy attributes (not in upstream OTel spec)
+    GEN_AI_USAGE_TOTAL_TOKENS = "gen_ai.usage.total_tokens"
+    GEN_AI_USAGE_TOKEN_TYPE = "gen_ai.usage.token_type"
+    GEN_AI_USER = "gen_ai.user"
+    GEN_AI_HEADERS = "gen_ai.headers"
+    GEN_AI_IS_STREAMING = "gen_ai.is_streaming"
+    GEN_AI_REQUEST_REPETITION_PENALTY = "gen_ai.request.repetition_penalty"
+    GEN_AI_RESPONSE_FINISH_REASON = "gen_ai.response.finish_reason"
+    GEN_AI_RESPONSE_STOP_REASON = "gen_ai.response.stop_reason"
+    GEN_AI_CONTENT_COMPLETION_CHUNK = "gen_ai.content.completion.chunk"
+    GEN_AI_REQUEST_REASONING_EFFORT = "gen_ai.request.reasoning_effort"
+    GEN_AI_USAGE_REASONING_TOKENS = "gen_ai.usage.reasoning_tokens"
+    GEN_AI_REQUEST_N = "gen_ai.request.n"
+    GEN_AI_REQUEST_MAX_COMPLETION_TOKENS = "gen_ai.request.max_completion_tokens"
+    GEN_AI_REQUEST_STRUCTURED_OUTPUT_SCHEMA = "gen_ai.request.structured_output_schema"
+    GEN_AI_REQUEST_REASONING_SUMMARY = "gen_ai.request.reasoning_summary"
+    GEN_AI_RESPONSE_REASONING_EFFORT = "gen_ai.response.reasoning_effort"
 
     # OpenAI
-    LLM_OPENAI_RESPONSE_SYSTEM_FINGERPRINT = "gen_ai.openai.system_fingerprint"
-    LLM_OPENAI_API_BASE = "gen_ai.openai.api_base"
-    LLM_OPENAI_API_VERSION = "gen_ai.openai.api_version"
-    LLM_OPENAI_API_TYPE = "gen_ai.openai.api_type"
+    GEN_AI_OPENAI_API_BASE = "gen_ai.openai.api_base"
+    GEN_AI_OPENAI_API_VERSION = "gen_ai.openai.api_version"
+    GEN_AI_OPENAI_API_TYPE = "gen_ai.openai.api_type"
 
     # Haystack
     HAYSTACK_OPENAI_CHAT = "haystack.openai.chat"
@@ -152,11 +134,11 @@ class SpanAttributes:
     TRACELOOP_CORRELATION_ID = "traceloop.correlation.id"
 
     # Watson/genai LLM
-    LLM_DECODING_METHOD = "llm.watsonx.decoding_method"
-    LLM_RANDOM_SEED = "llm.watsonx.random_seed"
-    LLM_MAX_NEW_TOKENS = "llm.watsonx.max_new_tokens"
-    LLM_MIN_NEW_TOKENS = "llm.watsonx.min_new_tokens"
-    LLM_REPETITION_PENALTY = "llm.watsonx.repetition_penalty"
+    GEN_AI_WATSONX_DECODING_METHOD = "llm.watsonx.decoding_method"
+    GEN_AI_WATSONX_RANDOM_SEED = "llm.watsonx.random_seed"
+    GEN_AI_WATSONX_MAX_NEW_TOKENS = "llm.watsonx.max_new_tokens"
+    GEN_AI_WATSONX_MIN_NEW_TOKENS = "llm.watsonx.min_new_tokens"
+    GEN_AI_WATSONX_REPETITION_PENALTY = "llm.watsonx.repetition_penalty"
 
     # Chroma db
     CHROMADB_ADD_IDS_COUNT = "db.chroma.add.ids_count"
diff --git a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/_testing.py b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/_testing.py
new file mode 100644
index 0000000000..bf203ab819
--- /dev/null
+++ b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/_testing.py
@@ -0,0 +1,383 @@
+"""
+Shared compliance test classes for opentelemetry-semantic-conventions-ai.
+
+Import these classes in any instrumentation package's test suite to verify
+that the installed semconv constants have the expected values:
+
+    from opentelemetry.semconv_ai._testing import *  # noqa: F401, F403
+
+pytest will discover and run all Test* classes that end up in the module
+namespace, so a single import line is enough.
+"""
+
+import pytest
+from opentelemetry.semconv_ai import GenAISystem, Meters, SpanAttributes
+
+
+# ---------------------------------------------------------------------------
+# SpanAttributes — renamed constants (LLM_* → GEN_AI_*)
+# ---------------------------------------------------------------------------
+
+
+class TestSpanAttributesGENAIRenamed:
+    """Verify all renamed LLM_* → GEN_AI_* constants have the correct string values."""
+
+    def test_gen_ai_usage_total_tokens(self):
+        assert SpanAttributes.GEN_AI_USAGE_TOTAL_TOKENS == "gen_ai.usage.total_tokens"
+
+    def test_gen_ai_usage_token_type(self):
+        assert SpanAttributes.GEN_AI_USAGE_TOKEN_TYPE == "gen_ai.usage.token_type"
+
+    def test_gen_ai_user(self):
+        assert SpanAttributes.GEN_AI_USER == "gen_ai.user"
+
+    def test_gen_ai_headers(self):
+        assert SpanAttributes.GEN_AI_HEADERS == "gen_ai.headers"
+
+    def test_gen_ai_is_streaming(self):
+        assert SpanAttributes.GEN_AI_IS_STREAMING == "gen_ai.is_streaming"
+
+    def test_gen_ai_request_repetition_penalty(self):
+        assert SpanAttributes.GEN_AI_REQUEST_REPETITION_PENALTY == "gen_ai.request.repetition_penalty"
+
+    def test_gen_ai_response_finish_reason(self):
+        assert SpanAttributes.GEN_AI_RESPONSE_FINISH_REASON == "gen_ai.response.finish_reason"
+
+    def test_gen_ai_response_stop_reason(self):
+        assert SpanAttributes.GEN_AI_RESPONSE_STOP_REASON == "gen_ai.response.stop_reason"
+
+    def test_gen_ai_content_completion_chunk(self):
+        assert SpanAttributes.GEN_AI_CONTENT_COMPLETION_CHUNK == "gen_ai.content.completion.chunk"
+
+    def test_gen_ai_request_reasoning_effort(self):
+        assert SpanAttributes.GEN_AI_REQUEST_REASONING_EFFORT == "gen_ai.request.reasoning_effort"
+
+    def test_gen_ai_usage_reasoning_tokens(self):
+        assert SpanAttributes.GEN_AI_USAGE_REASONING_TOKENS == "gen_ai.usage.reasoning_tokens"
+
+    def test_gen_ai_request_n(self):
+        assert SpanAttributes.GEN_AI_REQUEST_N == "gen_ai.request.n"
+
+    def test_gen_ai_request_max_completion_tokens(self):
+        assert SpanAttributes.GEN_AI_REQUEST_MAX_COMPLETION_TOKENS == "gen_ai.request.max_completion_tokens"
+
+    def test_gen_ai_request_structured_output_schema(self):
+        assert SpanAttributes.GEN_AI_REQUEST_STRUCTURED_OUTPUT_SCHEMA == "gen_ai.request.structured_output_schema"
+
+    def test_gen_ai_request_reasoning_summary(self):
+        assert SpanAttributes.GEN_AI_REQUEST_REASONING_SUMMARY == "gen_ai.request.reasoning_summary"
+
+    def test_gen_ai_response_reasoning_effort(self):
+        assert SpanAttributes.GEN_AI_RESPONSE_REASONING_EFFORT == "gen_ai.response.reasoning_effort"
+
+    def test_gen_ai_openai_api_base(self):
+        assert SpanAttributes.GEN_AI_OPENAI_API_BASE == "gen_ai.openai.api_base"
+
+    def test_gen_ai_openai_api_version(self):
+        assert SpanAttributes.GEN_AI_OPENAI_API_VERSION == "gen_ai.openai.api_version"
+
+    def test_gen_ai_openai_api_type(self):
+        assert SpanAttributes.GEN_AI_OPENAI_API_TYPE == "gen_ai.openai.api_type"
+
+
+# ---------------------------------------------------------------------------
+# SpanAttributes — old LLM_* names must be gone
+# ---------------------------------------------------------------------------
+
+
+class TestSpanAttributesOldNamesGone:
+    """Assert that removed LLM_* constants no longer exist on SpanAttributes."""
+
+    @pytest.mark.parametrize(
+        "old_name",
+        [
+            "LLM_SYSTEM",
+            "LLM_REQUEST_MODEL",
+            "LLM_REQUEST_MAX_TOKENS",
+            "LLM_REQUEST_TEMPERATURE",
+            "LLM_REQUEST_TOP_P",
+            "LLM_PROMPTS",
+            "LLM_COMPLETIONS",
+            "LLM_RESPONSE_MODEL",
+            "LLM_USAGE_COMPLETION_TOKENS",
+            "LLM_USAGE_PROMPT_TOKENS",
+            "LLM_USAGE_CACHE_CREATION_INPUT_TOKENS",
+            "LLM_USAGE_CACHE_READ_INPUT_TOKENS",
+            "LLM_TOKEN_TYPE",
+            "LLM_REQUEST_TYPE",
+            "LLM_FREQUENCY_PENALTY",
+            "LLM_PRESENCE_PENALTY",
+            "LLM_CHAT_STOP_SEQUENCES",
+            "LLM_REQUEST_FUNCTIONS",
+            "LLM_TOP_K",
+            "LLM_OPENAI_RESPONSE_SYSTEM_FINGERPRINT",
+        ],
+    )
+    def test_old_name_absent(self, old_name):
+        assert not hasattr(SpanAttributes, old_name), (
+            f"SpanAttributes.{old_name} should have been removed. "
+            "Consumers should import from opentelemetry.semconv._incubating.attributes.gen_ai_attributes directly."
+        )
+
+
+# ---------------------------------------------------------------------------
+# SpanAttributes — cache attributes
+# ---------------------------------------------------------------------------
+
+
+class TestSpanAttributesCacheDotSeparator:
+    """Cache token attributes use dot-separated sub-namespaces (spec update)."""
+
+    def test_gen_ai_usage_cache_read_input_tokens(self):
+        assert SpanAttributes.GEN_AI_USAGE_CACHE_READ_INPUT_TOKENS == "gen_ai.usage.cache_read.input_tokens"
+
+    def test_gen_ai_usage_cache_creation_input_tokens(self):
+        assert SpanAttributes.GEN_AI_USAGE_CACHE_CREATION_INPUT_TOKENS == "gen_ai.usage.cache_creation.input_tokens"
+
+
+# ---------------------------------------------------------------------------
+# SpanAttributes — project-policy attributes use gen_ai namespace
+# ---------------------------------------------------------------------------
+
+
+class TestSpanAttributesProjectPolicy:
+    """Project-policy attributes (not in upstream OTel spec) use gen_ai namespace."""
+
+    def test_is_streaming(self):
+        assert SpanAttributes.GEN_AI_IS_STREAMING == "gen_ai.is_streaming"
+
+    def test_user(self):
+        assert SpanAttributes.GEN_AI_USER == "gen_ai.user"
+
+    def test_headers(self):
+        assert SpanAttributes.GEN_AI_HEADERS == "gen_ai.headers"
+
+
+class TestSpanAttributesOldValuesAbsent:
+    """Regression: old/incorrect string values must not appear anywhere in SpanAttributes."""
+
+    @pytest.mark.parametrize(
+        "old_value",
+        [
+            "llm.usage.total_tokens",
+            "llm.frequency_penalty",
+            "llm.presence_penalty",
+            "llm.is_streaming",
+            "llm.user",
+            "llm.headers",
+            "llm.top_k",
+            "llm.chat.stop_sequences",
+            "llm.request.functions",
+            "llm.request.repetition_penalty",
+            "llm.request.type",
+            "llm.usage.token_type",
+            "llm.response.finish_reason",
+            "llm.response.stop_reason",
+            "llm.content.completion.chunk",
+            "llm.request.reasoning_effort",
+            "llm.usage.reasoning_tokens",
+            "llm.chat_completions.streaming_time_to_generate",
+            "gen_ai.usage.cache_read_input_tokens",    # underscore variant (pre-migration)
+            "gen_ai.usage.cache_creation_input_tokens",  # underscore variant (pre-migration)
+        ],
+    )
+    def test_old_value_not_in_span_attributes(self, old_value):
+        all_values = {
+            name: value
+            for name, value in vars(SpanAttributes).items()
+            if not name.startswith("_") and isinstance(value, str)
+        }
+        assert old_value not in all_values.values(), (
+            f"Old attribute value {old_value!r} is still present in SpanAttributes. "
+            f"It should have been renamed."
+        )
+
+
+class TestSpanAttributesUnchanged:
+    """Constants that should NOT have changed — sanity check."""
+
+    def test_traceloop_span_kind_unchanged(self):
+        assert SpanAttributes.TRACELOOP_SPAN_KIND == "traceloop.span.kind"
+
+
+# ---------------------------------------------------------------------------
+# SpanAttributes — Watsonx vendor-specific attributes (renamed to GEN_AI_WATSONX_*)
+# ---------------------------------------------------------------------------
+
+
+class TestSpanAttributesWatsonxKept:
+    """
+    llm.watsonx.* span attributes are intentionally kept. These use llm.watsonx as a
+    vendor-qualified prefix (analogous to db.chroma.*), not a generic llm.* namespace.
+    The Python names have been renamed to GEN_AI_WATSONX_* prefix.
+    """
+
+    def test_watsonx_decoding_method_kept(self):
+        assert SpanAttributes.GEN_AI_WATSONX_DECODING_METHOD == "llm.watsonx.decoding_method"
+
+    def test_watsonx_random_seed_kept(self):
+        assert SpanAttributes.GEN_AI_WATSONX_RANDOM_SEED == "llm.watsonx.random_seed"
+
+    def test_watsonx_max_new_tokens_kept(self):
+        assert SpanAttributes.GEN_AI_WATSONX_MAX_NEW_TOKENS == "llm.watsonx.max_new_tokens"
+
+    def test_watsonx_min_new_tokens_kept(self):
+        assert SpanAttributes.GEN_AI_WATSONX_MIN_NEW_TOKENS == "llm.watsonx.min_new_tokens"
+
+    def test_watsonx_repetition_penalty_kept(self):
+        assert SpanAttributes.GEN_AI_WATSONX_REPETITION_PENALTY == "llm.watsonx.repetition_penalty"
+
+
+# ---------------------------------------------------------------------------
+# GenAISystem enum — values must match OTel GenAiSystemValues where possible
+# ---------------------------------------------------------------------------
+
+
+class TestGenAISystemOtelAligned:
+    """Enum members that have a counterpart in OTel GenAiSystemValues."""
+
+    def test_openai(self):
+        assert GenAISystem.OPENAI.value == "openai"
+
+    def test_anthropic_lowercase(self):
+        # Was "Anthropic" — must now match OTel GenAiSystemValues.ANTHROPIC
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.ANTHROPIC.value == GenAiSystemValues.ANTHROPIC.value
+        assert GenAISystem.ANTHROPIC.value == "anthropic"
+
+    def test_cohere_lowercase(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.COHERE.value == GenAiSystemValues.COHERE.value
+        assert GenAISystem.COHERE.value == "cohere"
+
+    def test_mistralai_spec_format(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.MISTRALAI.value == GenAiSystemValues.MISTRAL_AI.value
+        assert GenAISystem.MISTRALAI.value == "mistral_ai"
+
+    def test_groq_lowercase(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.GROQ.value == GenAiSystemValues.GROQ.value
+        assert GenAISystem.GROQ.value == "groq"
+
+    def test_watsonx_spec_format(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.WATSONX.value == GenAiSystemValues.IBM_WATSONX_AI.value
+        assert GenAISystem.WATSONX.value == "ibm.watsonx.ai"
+
+    def test_aws_spec_format(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.AWS.value == GenAiSystemValues.AWS_BEDROCK.value
+        assert GenAISystem.AWS.value == "aws.bedrock"
+
+    def test_azure_spec_format(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.AZURE.value == GenAiSystemValues.AZ_AI_OPENAI.value
+        assert GenAISystem.AZURE.value == "az.ai.openai"
+
+    def test_google_spec_format(self):
+        from opentelemetry.semconv._incubating.attributes.gen_ai_attributes import GenAiSystemValues
+
+        assert GenAISystem.GOOGLE.value == GenAiSystemValues.GCP_GEN_AI.value
+        assert GenAISystem.GOOGLE.value == "gcp.gen_ai"
+
+
+class TestGenAISystemProjectValues:
+    """Enum members without an OTel counterpart — project-defined lowercase values."""
+
+    def test_ollama(self):
+        assert GenAISystem.OLLAMA.value == "ollama"
+
+    def test_aleph_alpha(self):
+        assert GenAISystem.ALEPH_ALPHA.value == "aleph_alpha"
+
+    def test_replicate(self):
+        assert GenAISystem.REPLICATE.value == "replicate"
+
+    def test_together_ai(self):
+        assert GenAISystem.TOGETHER_AI.value == "together_ai"
+
+    def test_huggingface(self):
+        assert GenAISystem.HUGGINGFACE.value == "hugging_face"
+
+    def test_fireworks(self):
+        assert GenAISystem.FIREWORKS.value == "fireworks"
+
+    def test_openrouter(self):
+        assert GenAISystem.OPENROUTER.value == "openrouter"
+
+    def test_langchain(self):
+        assert GenAISystem.LANGCHAIN.value == "langchain"
+
+    def test_crewai(self):
+        assert GenAISystem.CREWAI.value == "crewai"
+
+
+class TestGenAISystemNoCaps:
+    """All GenAISystem values must be lowercase (no PascalCase or camelCase)."""
+
+    def test_all_values_lowercase(self):
+        non_lowercase = [
+            member.name
+            for member in GenAISystem
+            if member.value != member.value.lower() and "." not in member.value
+        ]
+        assert non_lowercase == [], (
+            f"GenAISystem members have non-lowercase values: {non_lowercase}. "
+            "Values should use lowercase with dots or underscores."
+        )
+
+
+# ---------------------------------------------------------------------------
+# Meters — metric names must use gen_ai.* namespace
+# ---------------------------------------------------------------------------
+
+
+class TestMetersGenAiNamespace:
+    """Generic metric names must use gen_ai.* namespace."""
+
+    def test_streaming_time_to_generate(self):
+        assert Meters.LLM_STREAMING_TIME_TO_GENERATE == "llm.chat_completions.streaming_time_to_generate"
+
+    def test_core_metrics_unchanged(self):
+        """Core gen_ai.client.* metrics already had the correct namespace."""
+        assert Meters.LLM_GENERATION_CHOICES == "gen_ai.client.generation.choices"
+        assert Meters.LLM_TOKEN_USAGE == "gen_ai.client.token.usage"
+        assert Meters.LLM_OPERATION_DURATION == "gen_ai.client.operation.duration"
+
+
+class TestMetersVendorNamespacesKept:
+    """
+    Vendor-qualified metric names (llm.openai.*, llm.anthropic.*, llm.watsonx.*)
+    are intentionally kept. The llm.<vendor> prefix is a vendor identifier, not the
+    generic llm.* attribute namespace being migrated. These will be renamed in the
+    respective package PRs if/when those vendors adopt the gen_ai namespace.
+    """
+
+    def test_openai_completions_exceptions_kept(self):
+        assert Meters.LLM_COMPLETIONS_EXCEPTIONS == "llm.openai.chat_completions.exceptions"
+
+    def test_openai_embeddings_exceptions_kept(self):
+        assert Meters.LLM_EMBEDDINGS_EXCEPTIONS == "llm.openai.embeddings.exceptions"
+
+    def test_openai_embeddings_vector_size_kept(self):
+        assert Meters.LLM_EMBEDDINGS_VECTOR_SIZE == "llm.openai.embeddings.vector_size"
+
+    def test_openai_image_generations_exceptions_kept(self):
+        assert Meters.LLM_IMAGE_GENERATIONS_EXCEPTIONS == "llm.openai.image_generations.exceptions"
+
+    def test_anthropic_completion_exceptions_kept(self):
+        assert Meters.LLM_ANTHROPIC_COMPLETION_EXCEPTIONS == "llm.anthropic.completion.exceptions"
+
+    def test_watsonx_metrics_kept(self):
+        assert Meters.LLM_WATSONX_COMPLETIONS_DURATION == "llm.watsonx.completions.duration"
+        assert Meters.LLM_WATSONX_COMPLETIONS_EXCEPTIONS == "llm.watsonx.completions.exceptions"
+        assert Meters.LLM_WATSONX_COMPLETIONS_RESPONSES == "llm.watsonx.completions.responses"
+        assert Meters.LLM_WATSONX_COMPLETIONS_TOKENS == "llm.watsonx.completions.tokens"
diff --git a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/version.py b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/version.py
index 6ff6db180c..3d187266f1 100644
--- a/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/version.py
+++ b/packages/opentelemetry-semantic-conventions-ai/opentelemetry/semconv_ai/version.py
@@ -1 +1 @@
-__version__ = "0.4.16"
+__version__ = "0.5.0"
diff --git a/packages/opentelemetry-semantic-conventions-ai/pyproject.toml b/packages/opentelemetry-semantic-conventions-ai/pyproject.toml
index 414f0e296a..581b3b41d1 100644
--- a/packages/opentelemetry-semantic-conventions-ai/pyproject.toml
+++ b/packages/opentelemetry-semantic-conventions-ai/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "opentelemetry-semantic-conventions-ai"
-version = "0.4.16"
+version = "0.5.0"
 description = "OpenTelemetry Semantic Conventions Extension for Large Language Models"
 authors = [
   { name = "Gal Kleinman", email = "gal@traceloop.com" },
diff --git a/packages/opentelemetry-semantic-conventions-ai/tests/test_semconv_compliance.py b/packages/opentelemetry-semantic-conventions-ai/tests/test_semconv_compliance.py
new file mode 100644
index 0000000000..de81d3aa6b
--- /dev/null
+++ b/packages/opentelemetry-semantic-conventions-ai/tests/test_semconv_compliance.py
@@ -0,0 +1,6 @@
+# ruff: noqa: F401, F403
+"""
+Semconv compliance tests re-used from opentelemetry-semantic-conventions-ai.
+To add more compliance checks, update _testing.py in that package — not here.
+"""
+from opentelemetry.semconv_ai._testing import *
diff --git a/packages/opentelemetry-semantic-conventions-ai/tests/test_span_attributes.py b/packages/opentelemetry-semantic-conventions-ai/tests/test_span_attributes.py
new file mode 100644
index 0000000000..eb6f56b70f
--- /dev/null
+++ b/packages/opentelemetry-semantic-conventions-ai/tests/test_span_attributes.py
@@ -0,0 +1,7 @@
+# ruff: noqa: F401, F403
+"""
+Semconv compliance tests — authoritative source lives in
+opentelemetry/semconv_ai/_testing.py so any instrumentation package
+can reuse them with a single import.
+"""
+from opentelemetry.semconv_ai._testing import *
diff --git a/packages/opentelemetry-semantic-conventions-ai/uv.lock b/packages/opentelemetry-semantic-conventions-ai/uv.lock
index 4283fafde2..c755090c04 100644
--- a/packages/opentelemetry-semantic-conventions-ai/uv.lock
+++ b/packages/opentelemetry-semantic-conventions-ai/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.9, <4"
 resolution-markers = [
     "python_full_version >= '3.10'",
@@ -14,18 +14,18 @@ dependencies = [
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