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..e97a093711 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/callback_handler.py
@@ -451,11 +451,8 @@ def _create_llm_span(
             _extract_class_name_from_serialized(serialized)
         )
 
-        _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_PROVIDER_NAME, vendor)
+        _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
@@ -732,16 +729,16 @@ 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_PROVIDER_NAME, "langchain")
             if prompt_tokens > 0:
                 self.token_histogram.record(
                     prompt_tokens,
                     attributes={
-                        GenAIAttributes.GEN_AI_SYSTEM: vendor,
+                        GenAIAttributes.GEN_AI_PROVIDER_NAME: vendor,
                         GenAIAttributes.GEN_AI_TOKEN_TYPE: "input",
                         GenAIAttributes.GEN_AI_RESPONSE_MODEL: model_name or "unknown",
                     },
@@ -751,7 +748,7 @@ def on_llm_end(
                 self.token_histogram.record(
                     completion_tokens,
                     attributes={
-                        GenAIAttributes.GEN_AI_SYSTEM: vendor,
+                        GenAIAttributes.GEN_AI_PROVIDER_NAME: vendor,
                         GenAIAttributes.GEN_AI_TOKEN_TYPE: "output",
                         GenAIAttributes.GEN_AI_RESPONSE_MODEL: model_name or "unknown",
                     },
@@ -768,11 +765,11 @@ 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_PROVIDER_NAME, "langchain")
         self.duration_histogram.record(
             duration,
             attributes={
-                GenAIAttributes.GEN_AI_SYSTEM: vendor,
+                GenAIAttributes.GEN_AI_PROVIDER_NAME: vendor,
                 GenAIAttributes.GEN_AI_RESPONSE_MODEL: model_name or "unknown",
             },
         )
diff --git a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/event_emitter.py b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/event_emitter.py
index 76f11b04b3..caf7a5f4cd 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/event_emitter.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/event_emitter.py
@@ -28,7 +28,7 @@ class Roles(Enum):
 VALID_MESSAGE_ROLES = {role.value for role in Roles}
 """The valid roles for naming the message event."""
 
-EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_SYSTEM: "langchain"}
+EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "langchain"}
 """The attributes to be used for the event."""
 
 
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..af375badfd 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 (
@@ -348,22 +296,22 @@ 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_PROVIDER_NAME, "langchain")
 
             if input_tokens > 0:
                 token_histogram.record(
                     input_tokens,
                     attributes={
-                        GenAIAttributes.GEN_AI_SYSTEM: vendor,
+                        GenAIAttributes.GEN_AI_PROVIDER_NAME: vendor,
                         GenAIAttributes.GEN_AI_TOKEN_TYPE: "input",
                         GenAIAttributes.GEN_AI_RESPONSE_MODEL: model_name,
                     },
@@ -373,7 +321,7 @@ def set_chat_response_usage(
                 token_histogram.record(
                     output_tokens,
                     attributes={
-                        GenAIAttributes.GEN_AI_SYSTEM: vendor,
+                        GenAIAttributes.GEN_AI_PROVIDER_NAME: vendor,
                         GenAIAttributes.GEN_AI_TOKEN_TYPE: "output",
                         GenAIAttributes.GEN_AI_RESPONSE_MODEL: model_name,
                     },
@@ -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/utils.py b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/utils.py
index 079ade8bb3..c65a58c810 100644
--- a/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/utils.py
+++ b/packages/opentelemetry-instrumentation-langchain/opentelemetry/instrumentation/langchain/utils.py
@@ -16,7 +16,7 @@
 
 TRACELOOP_TRACE_CONTENT = "TRACELOOP_TRACE_CONTENT"
 
-EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_SYSTEM: "langchain"}
+EVENT_ATTRIBUTES = {GenAIAttributes.GEN_AI_PROVIDER_NAME: "langchain"}
 
 
 class CallbackFilteredJSONEncoder(json.JSONEncoder):
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/metrics/test_langchain_metrics.py b/packages/opentelemetry-instrumentation-langchain/tests/metrics/test_langchain_metrics.py
index 7f596cf4c4..88f4c37231 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/metrics/test_langchain_metrics.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/metrics/test_langchain_metrics.py
@@ -54,7 +54,7 @@ def test_llm_chain_metrics(instrument_legacy, reader, chain):
                         ]
                         assert data_point.sum > 0
                         assert (
-                            data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+                            data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME]
                             == "openai"
                         )
 
@@ -68,7 +68,7 @@ def test_llm_chain_metrics(instrument_legacy, reader, chain):
                     )
                     for data_point in metric.data.data_points:
                         assert (
-                            data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+                            data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME]
                             == "openai"
                         )
 
@@ -106,7 +106,7 @@ def test_llm_chain_streaming_metrics(instrument_legacy, reader, llm):
                         ]
                         assert data_point.sum > 0
                         assert (
-                            data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+                            data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME]
                             == "openai"
                         )
 
@@ -120,7 +120,7 @@ def test_llm_chain_streaming_metrics(instrument_legacy, reader, llm):
                     )
                     for data_point in metric.data.data_points:
                         assert (
-                            data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+                            data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME]
                             == "openai"
                         )
 
@@ -135,14 +135,14 @@ def verify_token_metrics(data_points):
             "input",
         ]
         assert data_point.sum > 0
-        assert data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "openai"
+        assert data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "openai"
 
 
 def verify_duration_metrics(data_points):
     assert any(data_point.count > 0 for data_point in data_points)
     assert any(data_point.sum > 0 for data_point in data_points)
     for data_point in data_points:
-        assert data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "openai"
+        assert data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "openai"
 
 
 def verify_langchain_metrics(reader):
@@ -237,7 +237,7 @@ def calculate(state: State):
     token_usage_data_point = token_usage_metric.data.data_points[0]
     assert token_usage_data_point.sum > 0
     assert (
-        token_usage_data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "openai"
+        token_usage_data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "openai"
         and token_usage_data_point.attributes[GenAIAttributes.GEN_AI_TOKEN_TYPE] in ["input", "output"]
     )
 
@@ -252,7 +252,7 @@ def calculate(state: State):
     assert duration_metric is not None
     duration_data_point = duration_metric.data.data_points[0]
     assert duration_data_point.sum > 0
-    assert duration_data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM] == "openai"
+    assert duration_data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "openai"
 
     generation_choices_metric = next(
         (
@@ -266,7 +266,7 @@ def calculate(state: State):
     generation_choices_data_points = generation_choices_metric.data.data_points
     for data_point in generation_choices_data_points:
         assert (
-            data_point.attributes[GenAIAttributes.GEN_AI_SYSTEM]
+            data_point.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME]
             == "openai"
         )
         assert data_point.value > 0
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_agents.py b/packages/opentelemetry-instrumentation-langchain/tests/test_agents.py
index 9c8835ef04..077aed9dfc 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_agents.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_agents.py
@@ -197,7 +197,7 @@ def test_agents_with_events_with_no_content(
     logs = log_exporter.get_finished_logs()
     assert len(logs) == 8
     assert all(
-        log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+        log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
         for log in logs
     )
 
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py b/packages/opentelemetry-instrumentation-langchain/tests/test_chains.py
index bc5631ea47..f649dd3fdc 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 (
@@ -818,7 +819,7 @@ async def test_astream_with_events_with_no_content(
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_documents_chains.py b/packages/opentelemetry-instrumentation-langchain/tests/test_documents_chains.py
index 59b272062e..74f02cac1d 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_documents_chains.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_documents_chains.py
@@ -155,7 +155,7 @@ def test_sequential_chain_with_events_with_no_content(
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
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_lcel.py b/packages/opentelemetry-instrumentation-langchain/tests/test_lcel.py
index cf981f9bc1..96c37b3cf1 100644
--- a/packages/opentelemetry-instrumentation-langchain/tests/test_lcel.py
+++ b/packages/opentelemetry-instrumentation-langchain/tests/test_lcel.py
@@ -1003,7 +1003,7 @@ class Joke(BaseModel):
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
diff --git a/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py b/packages/opentelemetry-instrumentation-langchain/tests/test_llms.py
index fcf8767b75..4bd98a7acb 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_PROVIDER_NAME] == "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"
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == "openai"
+    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_PROVIDER_NAME] == "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_PROVIDER_NAME] == "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
@@ -1050,7 +1016,7 @@ def test_trace_propagation(instrument_legacy, span_exporter, log_exporter, LLM):
         VLLMOpenAI: "openai",
         ChatOpenAI: "openai"
     }
-    assert openai_span.attributes[GenAIAttributes.GEN_AI_SYSTEM] == expected_vendors[LLM]
+    assert openai_span.attributes[GenAIAttributes.GEN_AI_PROVIDER_NAME] == expected_vendors[LLM]
 
     args, kwargs = send_spy.mock.call_args
     request = args[0]
@@ -1658,7 +1624,7 @@ async def test_trace_propagation_stream_async_with_events_with_no_content(
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
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..90aeec4032 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 (
@@ -104,7 +106,7 @@ def test_structured_output_with_events_with_no_content(
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
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..72b4a1c6f1 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"},
         },
@@ -1226,7 +1217,7 @@ def get_news(location: str) -> str:
 
 def assert_message_in_logs(log: ReadableLogRecord, event_name: str, expected_content: dict):
     assert log.log_record.event_name == event_name
-    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_SYSTEM) == "langchain"
+    assert log.log_record.attributes.get(GenAIAttributes.GEN_AI_PROVIDER_NAME) == "langchain"
 
     if not expected_content:
         assert not log.log_record.body
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