diff --git a/newrelic/config.py b/newrelic/config.py
index d3dd127645..8bce65b9e2 100644
--- a/newrelic/config.py
+++ b/newrelic/config.py
@@ -2009,6 +2009,8 @@ def _process_module_builtin_defaults():
     )
     _process_module_definition("openai._base_client", "newrelic.hooks.mlmodel_openai", "instrument_openai_base_client")
 
+    _process_module_definition("google.genai.models", "newrelic.hooks.mlmodel_gemini", "instrument_genai_models")
+
     _process_module_definition(
         "asyncio.base_events", "newrelic.hooks.coroutines_asyncio", "instrument_asyncio_base_events"
     )
diff --git a/newrelic/hooks/mlmodel_gemini.py b/newrelic/hooks/mlmodel_gemini.py
new file mode 100644
index 0000000000..d2be0cb0e5
--- /dev/null
+++ b/newrelic/hooks/mlmodel_gemini.py
@@ -0,0 +1,605 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import sys
+import uuid
+
+import google
+
+from newrelic.api.function_trace import FunctionTrace
+from newrelic.api.time_trace import get_trace_linking_metadata
+from newrelic.api.transaction import current_transaction
+from newrelic.common.object_wrapper import wrap_function_wrapper
+from newrelic.common.package_version_utils import get_package_version
+from newrelic.core.config import global_settings
+
+GEMINI_VERSION = get_package_version("google-genai")
+EXCEPTION_HANDLING_FAILURE_LOG_MESSAGE = (
+    "Exception occurred in Gemini instrumentation: While reporting an exception "
+    "in Gemini, another exception occurred. Report this issue to New Relic "
+    "Support.\n "
+)
+RECORD_EVENTS_FAILURE_LOG_MESSAGE = (
+    "Exception occurred in Gemini instrumentation: Failed to record LLM events. "
+    "Please report this issue to New Relic Support.\n "
+)
+
+_logger = logging.getLogger(__name__)
+
+
+def wrap_embed_content_sync(wrapped, instance, args, kwargs):
+    transaction = current_transaction()
+    if not transaction:
+        return wrapped(*args, **kwargs)
+
+    settings = transaction.settings or global_settings()
+    if not settings.ai_monitoring.enabled:
+        return wrapped(*args, **kwargs)
+
+    # Framework metric also used for entity tagging in the UI
+    transaction.add_ml_model_info("Gemini", GEMINI_VERSION)
+    transaction._add_agent_attribute("llm", True)
+
+    # Obtain attributes to be stored on embedding events regardless of whether we hit an error
+    embedding_id = str(uuid.uuid4())
+
+    ft = FunctionTrace(name=wrapped.__name__, group="Llm/embedding/Gemini")
+    ft.__enter__()
+    linking_metadata = get_trace_linking_metadata()
+    try:
+        response = wrapped(*args, **kwargs)
+    except Exception as exc:
+        _record_embedding_error(transaction, embedding_id, linking_metadata, kwargs, ft, exc)
+        raise
+    ft.__exit__(None, None, None)
+
+    if not response:
+        return response
+
+    _record_embedding_success(transaction, embedding_id, linking_metadata, kwargs, ft)
+    return response
+
+
+async def wrap_embed_content_async(wrapped, instance, args, kwargs):
+    transaction = current_transaction()
+    if not transaction:
+        return await wrapped(*args, **kwargs)
+
+    settings = transaction.settings or global_settings()
+    if not settings.ai_monitoring.enabled:
+        return await wrapped(*args, **kwargs)
+
+    # Framework metric also used for entity tagging in the UI
+    transaction.add_ml_model_info("Gemini", GEMINI_VERSION)
+    transaction._add_agent_attribute("llm", True)
+
+    # Obtain attributes to be stored on embedding events regardless of whether we hit an error
+    embedding_id = str(uuid.uuid4())
+
+    ft = FunctionTrace(name=wrapped.__name__, group="Llm/embedding/Gemini")
+    ft.__enter__()
+    linking_metadata = get_trace_linking_metadata()
+    try:
+        response = await wrapped(*args, **kwargs)
+    except Exception as exc:
+        _record_embedding_error(transaction, embedding_id, linking_metadata, kwargs, ft, exc)
+        raise
+    ft.__exit__(None, None, None)
+
+    if not response:
+        return response
+
+    _record_embedding_success(transaction, embedding_id, linking_metadata, kwargs, ft)
+    return response
+
+
+def _record_embedding_error(transaction, embedding_id, linking_metadata, kwargs, ft, exc):
+    settings = transaction.settings or global_settings()
+    span_id = linking_metadata.get("span.id")
+    trace_id = linking_metadata.get("trace.id")
+
+    notice_error_attributes = {}
+    try:
+        # We key directly into the kwargs dict here so that we can raise a KeyError if "contents" is not available
+        embedding_content = kwargs["contents"]
+        # embedding_content could be a list, so we typecast it to a string
+        embedding_content = str(embedding_content)
+        model = kwargs.get("model")
+
+        notice_error_attributes = {
+            "http.statusCode": getattr(exc, "code", None),
+            "error.message": getattr(exc, "message", None),
+            "error.code": getattr(exc, "status", None),  # ex: 'NOT_FOUND'
+            "embedding_id": embedding_id,
+        }
+    except Exception:
+        _logger.warning(EXCEPTION_HANDLING_FAILURE_LOG_MESSAGE, exc_info=True)
+
+    message = notice_error_attributes.pop("error.message", None)
+    if message:
+        exc._nr_message = message
+
+    ft.notice_error(attributes=notice_error_attributes)
+    # Exit the trace now so that the duration is calculated.
+    ft.__exit__(*sys.exc_info())
+
+    try:
+        error_embedding_dict = {
+            "id": embedding_id,
+            "span_id": span_id,
+            "trace_id": trace_id,
+            "token_count": (
+                settings.ai_monitoring.llm_token_count_callback(model, embedding_content)
+                if settings.ai_monitoring.llm_token_count_callback
+                else None
+            ),
+            "request.model": model,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "duration": ft.duration * 1000,
+            "error": True,
+        }
+        if settings.ai_monitoring.record_content.enabled:
+            error_embedding_dict["input"] = embedding_content
+
+        error_embedding_dict.update(_get_llm_attributes(transaction))
+        transaction.record_custom_event("LlmEmbedding", error_embedding_dict)
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+
+def _record_embedding_success(transaction, embedding_id, linking_metadata, kwargs, ft):
+    settings = transaction.settings or global_settings()
+    span_id = linking_metadata.get("span.id")
+    trace_id = linking_metadata.get("trace.id")
+    try:
+        # We key directly into the kwargs dict here so that we can raise a KeyError if "contents" is not available
+        embedding_content = kwargs["contents"]
+        # embedding_content could be a list, so we typecast it to a string
+        embedding_content = str(embedding_content)
+        request_model = kwargs.get("model")
+
+        full_embedding_response_dict = {
+            "id": embedding_id,
+            "span_id": span_id,
+            "trace_id": trace_id,
+            "token_count": (
+                settings.ai_monitoring.llm_token_count_callback(request_model, embedding_content)
+                if settings.ai_monitoring.llm_token_count_callback
+                else None
+            ),
+            "request.model": request_model,
+            "duration": ft.duration * 1000,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        }
+        if settings.ai_monitoring.record_content.enabled:
+            full_embedding_response_dict["input"] = embedding_content
+
+        full_embedding_response_dict.update(_get_llm_attributes(transaction))
+
+        transaction.record_custom_event("LlmEmbedding", full_embedding_response_dict)
+
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+
+def _get_llm_attributes(transaction):
+    """Returns llm.* custom attributes off of the transaction."""
+    custom_attrs_dict = transaction._custom_params
+    llm_metadata_dict = {key: value for key, value in custom_attrs_dict.items() if key.startswith("llm.")}
+
+    llm_context_attrs = getattr(transaction, "_llm_context_attrs", None)
+    if llm_context_attrs:
+        llm_metadata_dict.update(llm_context_attrs)
+
+    return llm_metadata_dict
+
+
+def wrap_generate_content_sync(wrapped, instance, args, kwargs):
+    transaction = current_transaction()
+    if not transaction:
+        return wrapped(*args, **kwargs)
+
+    settings = transaction.settings or global_settings()
+    if not settings.ai_monitoring.enabled:
+        return wrapped(*args, **kwargs)
+
+    # Framework metric also used for entity tagging in the UI
+    transaction.add_ml_model_info("Gemini", GEMINI_VERSION)
+    transaction._add_agent_attribute("llm", True)
+
+    completion_id = str(uuid.uuid4())
+
+    ft = FunctionTrace(name=wrapped.__name__, group="Llm/completion/Gemini")
+    ft.__enter__()
+
+    linking_metadata = get_trace_linking_metadata()
+    try:
+        return_val = wrapped(*args, **kwargs)
+    except Exception as exc:
+        _record_generation_error(transaction, settings, linking_metadata, completion_id, kwargs, ft, exc)
+        raise
+
+    ft.__exit__(None, None, None)
+
+    _handle_generation_success(transaction, settings, linking_metadata, completion_id, kwargs, ft, return_val)
+
+    return return_val
+
+
+async def wrap_generate_content_async(wrapped, instance, args, kwargs):
+    transaction = current_transaction()
+    if not transaction:
+        return await wrapped(*args, **kwargs)
+
+    settings = transaction.settings or global_settings()
+    if not settings.ai_monitoring.enabled:
+        return await wrapped(*args, **kwargs)
+
+    # Framework metric also used for entity tagging in the UI
+    transaction.add_ml_model_info("Gemini", GEMINI_VERSION)
+    transaction._add_agent_attribute("llm", True)
+
+    completion_id = str(uuid.uuid4())
+
+    ft = FunctionTrace(name=wrapped.__name__, group="Llm/completion/Gemini")
+    ft.__enter__()
+    linking_metadata = get_trace_linking_metadata()
+    try:
+        return_val = await wrapped(*args, **kwargs)
+    except Exception as exc:
+        _record_generation_error(transaction, settings, linking_metadata, completion_id, kwargs, ft, exc)
+        raise
+
+    ft.__exit__(None, None, None)
+
+    _handle_generation_success(transaction, settings, linking_metadata, completion_id, kwargs, ft, return_val)
+
+    return return_val
+
+
+def _record_generation_error(transaction, settings, linking_metadata, completion_id, kwargs, ft, exc):
+    span_id = linking_metadata.get("span.id")
+    trace_id = linking_metadata.get("trace.id")
+
+    # If generate_content was called directly, "contents" should hold a string with just the user input.
+    # If send_message was called, "contents" should hold a list containing the user input & role.
+    # When send_message is called multiple times within a chat conversation, "contents" will hold chat history with
+    # multiple lists to capture each input to the LLM (only inputs and not responses)
+    messages = kwargs.get("contents")
+    if isinstance(messages, str):
+        input_message = messages
+    else:
+        try:
+            input_message = messages[-1]
+        except Exception:
+            input_message = None
+            _logger.warning(
+                "Unable to parse input message to Gemini LLM. Message content and role will be omitted from "
+                "corresponding LlmChatCompletionMessage event. "
+            )
+
+    # Extract the input message content and role from the input message if it exists
+    input_message_content, input_role = _parse_input_message(input_message) if input_message else (None, None)
+
+    # Extract data from generation config object
+    request_temperature, request_max_tokens = _extract_generation_config(kwargs)
+
+    # Extract model and prompt token count
+    request_model = kwargs.get("model")
+    prompt_tokens = (
+        settings.ai_monitoring.llm_token_count_callback(request_model, input_message_content)
+        if settings.ai_monitoring.llm_token_count_callback and input_message_content
+        else None
+    )
+
+    # Prepare error attributes
+    notice_error_attributes = {
+        "http.statusCode": getattr(exc, "code", None),
+        "error.message": getattr(exc, "message", None),
+        "error.code": getattr(exc, "status", None),  # ex: 'NOT_FOUND'
+        "completion_id": completion_id,
+    }
+
+    # Override the default message if it is not empty.
+    message = notice_error_attributes.pop("error.message", None)
+    if message:
+        exc._nr_message = message
+
+    ft.notice_error(attributes=notice_error_attributes)
+    # Stop the span now so we compute the duration before we create the events.
+    ft.__exit__(*sys.exc_info())
+
+    try:
+        error_chat_completion_dict = {
+            "id": completion_id,
+            "span_id": span_id,
+            "trace_id": trace_id,
+            "response.number_of_messages": len(messages),
+            "request.model": request_model,
+            "request.temperature": request_temperature,
+            "request.max_tokens": request_max_tokens,
+            "response.usage.prompt_tokens": prompt_tokens,
+            # In error cases, we do not have a response to calculate completion tokens on so omit them from the LLM
+            # event and set total tokens to the prompt tokens value
+            "response.usage.total_tokens": prompt_tokens,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "duration": ft.duration * 1000,
+            "error": True,
+        }
+        llm_metadata = _get_llm_attributes(transaction)
+        error_chat_completion_dict.update(llm_metadata)
+        transaction.record_custom_event("LlmChatCompletionSummary", error_chat_completion_dict)
+
+        create_chat_completion_message_event(
+            transaction,
+            input_message_content,
+            input_role,
+            completion_id,
+            span_id,
+            trace_id,
+            # Passing the request model as the response model here since we do not have access to a response model
+            request_model,
+            llm_metadata,
+            # Pass an empty output_message_list since we didn't receive a response from the LLM
+            [],
+        )
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+
+def _handle_generation_success(transaction, settings, linking_metadata, completion_id, kwargs, ft, return_val):
+    if not return_val:
+        return
+
+    try:
+        response = return_val
+        # Response objects are pydantic models so this function call converts the response into a dict
+        if hasattr(response, "model_dump"):
+            response = response.model_dump()
+        _record_generation_success(transaction, settings, linking_metadata, completion_id, kwargs, ft, response)
+
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+
+def _record_generation_success(transaction, settings, linking_metadata, completion_id, kwargs, ft, response):
+    span_id = linking_metadata.get("span.id")
+    trace_id = linking_metadata.get("trace.id")
+    try:
+        # Parse response details
+        if response:
+            response_model = response.get("model_version")
+            # finish_reason is an enum, so grab just the stringified value from it to report
+            finish_reason = response.get("candidates")[0].get("finish_reason").value
+            output_message_list = [response.get("candidates")[0].get("content")]
+            token_usage = response.get("usage_metadata") or {}
+        else:
+            # Set all values to NoneTypes since we cannot access them through kwargs or another method that doesn't
+            # require the response object
+            response_model = None
+            output_message_list = []
+            finish_reason = None
+            token_usage = {}
+
+        # Parse request details
+        request_model = kwargs.get("model")
+
+        # If generate_content was called directly, "contents" should hold a string with just the user input.
+        # If send_message was called, "contents" should hold a list containing the user input & role.
+        # When send_message is called multiple times within a chat conversation, "contents" will hold chat history with
+        # multiple lists to capture each input to the LLM (only inputs and not responses)
+        messages = kwargs.get("contents")
+
+        if isinstance(messages, str):
+            input_message = messages
+        else:
+            try:
+                input_message = messages[-1]
+            except Exception:
+                input_message = None
+                _logger.warning(
+                    "Unable to parse input message to Gemini LLM. Message content and role will be omitted from "
+                    "corresponding LlmChatCompletionMessage event. "
+                )
+         
+        input_message_content, input_role = _parse_input_message(input_message) if input_message else (None, None)
+
+        # Parse output message content
+        # This list should have a length of 1 to represent the output message
+        # Parse the message text out to pass to any registered token counting callback
+        output_message_content = output_message_list[0].get("parts")[0].get("text") if output_message_list else None
+
+        # Extract token counts from response object
+        if token_usage:
+            response_prompt_tokens = token_usage.get("prompt_token_count")
+            response_completion_tokens = token_usage.get("candidates_token_count")
+            response_total_tokens = token_usage.get("total_token_count")
+        else:
+            response_prompt_tokens = None
+            response_completion_tokens = None
+            response_total_tokens = None
+
+        # Calculate token counts by checking if a callback is registered and if we have the necessary content to pass
+        # to it. If not, then we use the token counts provided in the response object
+        prompt_tokens = (
+            settings.ai_monitoring.llm_token_count_callback(request_model, input_message_content)
+            if settings.ai_monitoring.llm_token_count_callback and input_message_content
+            else response_prompt_tokens
+        )
+        completion_tokens = (
+            settings.ai_monitoring.llm_token_count_callback(response_model, output_message_content)
+            if settings.ai_monitoring.llm_token_count_callback and output_message_content
+            else response_completion_tokens
+        )
+        total_tokens = (
+            prompt_tokens + completion_tokens if all([prompt_tokens, completion_tokens]) else response_total_tokens
+        )
+
+        # Extract generation config
+        request_temperature, request_max_tokens = _extract_generation_config(kwargs)
+
+        full_chat_completion_summary_dict = {
+            "id": completion_id,
+            "span_id": span_id,
+            "trace_id": trace_id,
+            "request.model": request_model,
+            "request.temperature": request_temperature,
+            "request.max_tokens": request_max_tokens,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "duration": ft.duration * 1000,
+            "response.model": response_model,
+            "response.choices.finish_reason": finish_reason,
+            # Adding a 1 to the output_message_list length because we will only ever report the latest, single input
+            # message This value should be 2 in almost all cases since we will report a summary event for each
+            # separate request (every input and output from the LLM)
+            "response.number_of_messages": 1 + len(output_message_list),
+            "response.usage.prompt_tokens": prompt_tokens,
+            "response.usage.completion_tokens": completion_tokens,
+            "response.usage.total_tokens": total_tokens,
+        }
+
+        llm_metadata = _get_llm_attributes(transaction)
+        full_chat_completion_summary_dict.update(llm_metadata)
+        transaction.record_custom_event("LlmChatCompletionSummary", full_chat_completion_summary_dict)
+
+        create_chat_completion_message_event(
+            transaction,
+            input_message_content,
+            input_role,
+            completion_id,
+            span_id,
+            trace_id,
+            response_model,
+            llm_metadata,
+            output_message_list,
+        )
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+        
+def _parse_input_message(input_message):
+    # The input_message will be a string if generate_content was called directly. In this case, we don't have
+    # access to the role, so we default to user since this was an input message
+    if isinstance(input_message, str):
+        return input_message, "user"
+    # The input_message will be a Google Content type if send_message was called, so we parse out the message
+    # text and role (which should be "user")
+    elif isinstance(input_message, google.genai.types.Content):
+        return input_message.parts[0].text, input_message.role
+    else:
+        return None, None
+
+
+def _extract_generation_config(kwargs):
+    generation_config = kwargs.get("config")
+    if generation_config:
+        request_temperature = getattr(generation_config, "temperature", None)
+        request_max_tokens = getattr(generation_config, "max_output_tokens", None)
+    else:
+        request_temperature = None
+        request_max_tokens = None
+
+    return request_temperature, request_max_tokens
+
+
+def create_chat_completion_message_event(
+    transaction,
+    input_message_content,
+    input_role,
+    chat_completion_id,
+    span_id,
+    trace_id,
+    response_model,
+    llm_metadata,
+    output_message_list,
+):
+    try:
+        settings = transaction.settings or global_settings()
+
+        if input_message_content:
+            message_id = str(uuid.uuid4())
+
+            chat_completion_input_message_dict = {
+                "id": message_id,
+                "span_id": span_id,
+                "trace_id": trace_id,
+                "token_count": 0,
+                "role": input_role,
+                "completion_id": chat_completion_id,
+                # The input message will always be the first message in our request/ response sequence so this will
+                # always be 0
+                "sequence": 0,
+                "response.model": response_model,
+                "vendor": "gemini",
+                "ingest_source": "Python",
+            }
+
+            if settings.ai_monitoring.record_content.enabled:
+                chat_completion_input_message_dict["content"] = input_message_content
+
+            chat_completion_input_message_dict.update(llm_metadata)
+
+            transaction.record_custom_event("LlmChatCompletionMessage", chat_completion_input_message_dict)
+
+        if output_message_list:
+            # Loop through all output messages received from the LLM response and emit a custom event for each one
+            # In almost all foreseeable cases, there should only be one item in this output_message_list
+            for index, message in enumerate(output_message_list):
+                message_content = message.get("parts")[0].get("text")
+
+                # Add one to the index to account for the single input message so our sequence value is accurate for
+                # the output message
+                if input_message_content:
+                    index += 1
+
+                message_id = str(uuid.uuid4())
+
+                chat_completion_output_message_dict = {
+                    "id": message_id,
+                    "span_id": span_id,
+                    "trace_id": trace_id,
+                    "token_count": 0,
+                    "role": message.get("role"),
+                    "completion_id": chat_completion_id,
+                    "sequence": index,
+                    "response.model": response_model,
+                    "vendor": "gemini",
+                    "ingest_source": "Python",
+                    "is_response": True,
+                }
+
+                if settings.ai_monitoring.record_content.enabled:
+                    chat_completion_output_message_dict["content"] = message_content
+
+                chat_completion_output_message_dict.update(llm_metadata)
+
+                transaction.record_custom_event("LlmChatCompletionMessage", chat_completion_output_message_dict)
+    except Exception:
+        _logger.warning(RECORD_EVENTS_FAILURE_LOG_MESSAGE, exc_info=True)
+
+
+
+def instrument_genai_models(module):
+    if hasattr(module, "Models"):
+        wrap_function_wrapper(module, "Models.generate_content", wrap_generate_content_sync)
+        wrap_function_wrapper(module, "Models.embed_content", wrap_embed_content_sync)
+
+    if hasattr(module, "AsyncModels"):
+        wrap_function_wrapper(module, "AsyncModels.generate_content", wrap_generate_content_async)
+        wrap_function_wrapper(module, "AsyncModels.embed_content", wrap_embed_content_async)
diff --git a/tests/mlmodel_gemini/_mock_external_gemini_server.py b/tests/mlmodel_gemini/_mock_external_gemini_server.py
new file mode 100644
index 0000000000..753bedb14d
--- /dev/null
+++ b/tests/mlmodel_gemini/_mock_external_gemini_server.py
@@ -0,0 +1,979 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+
+import pytest
+from testing_support.mock_external_http_server import MockExternalHTTPServer
+
+from newrelic.common.package_version_utils import get_package_version_tuple
+
+# This defines an external server test apps can make requests to instead of
+# the real Google Gemini backend. This provides 3 features:
+#
+# 1) This removes dependencies on external websites.
+# 2) Provides a better mechanism for making an external call in a test app than
+#    simple calling another endpoint the test app makes available because this
+#    server will not be instrumented meaning we don't have to sort through
+#    transactions to separate the ones created in the test app and the ones
+#    created by an external call.
+# 3) This app runs on a separate thread meaning it won't block the test app.
+
+RESPONSES = {
+    "Invalid API key.": [
+        {
+            "content-type": "application/json",
+            "x-goog-api-key": "GEMINI_API_KEY",
+            "x-goog-api-client": "google-genai-sdk/1.8.0 gl-python/3.11.4",
+        },
+        400,
+        {
+            "error": {
+                "code": 400,
+                "message": "API key not valid. Please pass a valid API key.",
+                "status": "INVALID_ARGUMENT",
+                "details": [
+                    {
+                        "@type": "type.googleapis.com/google.rpc.ErrorInfo",
+                        "reason": "API_KEY_INVALID",
+                        "domain": "googleapis.com",
+                        "metadata": {"service": "generativelanguage.googleapis.com"},
+                    },
+                    {
+                        "@type": "type.googleapis.com/google.rpc.LocalizedMessage",
+                        "locale": "en-US",
+                        "message": "API key not valid. Please pass a valid API key.",
+                    },
+                ],
+            }
+        },
+    ],
+    "Model does not exist.": [
+        {
+            "content-type": "application/json",
+            "x-goog-api-key": "GEMINI_API_KEY",
+            "x-goog-api-client": "google-genai-sdk/1.8.0 gl-python/3.11.4",
+        },
+        404,
+        {
+            "error": {
+                "code": 404,
+                "message": "models/does-not-exist is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods.",
+                "status": "NOT_FOUND",
+            }
+        },
+    ],
+    "Embedded: Model does not exist.": [
+        {
+            "content-type": "application/json",
+            "x-goog-api-key": "GEMINI_API_KEY",
+            "x-goog-api-client": "google-genai-sdk/1.8.0 gl-python/3.11.4",
+        },
+        404,
+        {
+            "error": {
+                "code": 404,
+                "message": "models/does-not-exist is not found for API version v1beta, or is not supported for embedContent. Call ListModels to see the list of available models and their supported methods.",
+                "status": "NOT_FOUND",
+            }
+        },
+    ],
+    "How many letters are in the word Python?": [
+        {
+            "content-type": "application/json",
+            "x-goog-api-key": "GEMINI_API_KEY",
+            "x-goog-api-client": "google-genai-sdk/1.8.0 gl-python/3.11.4",
+        },
+        200,
+        {
+            "candidates": [
+                {
+                    "content": {
+                        "parts": [{"text": 'There are **6** letters in the word "Python".\n'}],
+                        "role": "model",
+                    },
+                    "finishReason": "STOP",
+                    "avgLogprobs": -0.21492499571580154,
+                }
+            ],
+            "usageMetadata": {
+                "promptTokenCount": 9,
+                "candidatesTokenCount": 13,
+                "totalTokenCount": 22,
+                "promptTokensDetails": [{"modality": "TEXT", "tokenCount": 9}],
+                "candidatesTokensDetails": [{"modality": "TEXT", "tokenCount": 13}],
+            },
+            "modelVersion": "gemini-2.0-flash",
+        },
+    ],
+    "This is an embedding test.": [
+        {
+            "content-type": "application/json",
+            "x-goog-api-key": "GEMINI_API_KEY",
+            "x-goog-api-client": "google-genai-sdk/1.8.0 gl-python/3.11.4",
+        },
+        200,
+        {
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+                    -0.044377174,
+                    0.023601126,
+                    -0.014993059,
+                    -0.03146059,
+                    -0.011369117,
+                    -0.0068684905,
+                    -0.047642265,
+                    0.023028033,
+                    0.05118852,
+                    0.037967626,
+                    0.03589492,
+                    -0.034851607,
+                    0.05836855,
+                    0.007422612,
+                    -0.0028261798,
+                    -0.084631816,
+                    -0.008416511,
+                    -0.013297727,
+                    0.045844845,
+                    0.0017582007,
+                    0.039870862,
+                    0.038967937,
+                    -0.049584012,
+                    0.013567134,
+                    -0.00683099,
+                    0.01515794,
+                    -0.020968834,
+                    -0.046133906,
+                    0.04676231,
+                    -0.0072222347,
+                    -0.027787503,
+                    -0.0074175396,
+                    0.021458842,
+                    0.0051179165,
+                    -0.024867475,
+                    -0.0020387261,
+                    0.0007908524,
+                    0.031947404,
+                    0.054386783,
+                    0.003670804,
+                    -0.029436346,
+                    0.013517629,
+                    0.03133852,
+                    -0.04301945,
+                    -0.012450221,
+                    -0.011596534,
+                    0.010312541,
+                    -0.004814255,
+                    0.013594315,
+                    -0.00032178903,
+                    -0.008096347,
+                    0.010448234,
+                    0.018018147,
+                    0.012485202,
+                    0.00015640758,
+                    0.056029968,
+                    -0.034684505,
+                    -0.005945102,
+                    0.04540071,
+                    0.03652228,
+                    0.017559635,
+                    -0.0029428585,
+                    0.031903584,
+                    0.003917594,
+                    -0.064691976,
+                    -0.039297286,
+                    0.010467662,
+                    0.0093484605,
+                    -0.014891123,
+                    -0.009175688,
+                    -0.04573362,
+                    0.0035475963,
+                    0.022246609,
+                    0.03532225,
+                    -0.017237678,
+                    -0.034951076,
+                    -0.023324898,
+                    -0.027865818,
+                    -0.010352668,
+                    -0.029556517,
+                    -0.01141666,
+                    -0.023895623,
+                    -0.008008368,
+                    0.034369104,
+                    -0.035263885,
+                    -0.0023880412,
+                    0.0037398383,
+                    -0.043421715,
+                    -0.026140437,
+                    0.017505916,
+                    0.01172007,
+                    -0.0052012587,
+                    -0.010666706,
+                    -0.044013575,
+                    -0.021390941,
+                    0.00925609,
+                    0.04468026,
+                    -0.05654946,
+                    0.05758538,
+                    0.038788065,
+                    -0.06254394,
+                    0.033485573,
+                    -0.03277277,
+                    0.041688804,
+                    -0.016151179,
+                    0.029675728,
+                    -0.052232865,
+                    0.012067895,
+                    -0.039755195,
+                    -0.038339008,
+                    -0.013288515,
+                    0.009655733,
+                    -0.015840251,
+                    -0.017354984,
+                    -0.00071384717,
+                    -0.043791853,
+                    -0.009513481,
+                    0.023261022,
+                    -0.04977938,
+                    0.01587062,
+                    -0.019334927,
+                    -0.0021081585,
+                    0.06377285,
+                    0.026044562,
+                    -0.004681057,
+                    0.02466819,
+                    0.0100608235,
+                    -0.027291354,
+                    0.019331526,
+                    -0.017899454,
+                    -0.036263637,
+                    0.0022282766,
+                    0.05268942,
+                    0.03951839,
+                    -0.018724138,
+                    -0.01991192,
+                    -0.037556514,
+                    0.056540187,
+                    -0.052383814,
+                    0.041265804,
+                    0.059506774,
+                    0.025743607,
+                    -0.045918416,
+                    0.05303863,
+                    -0.013928889,
+                    -0.061523702,
+                    0.02737446,
+                    0.016407,
+                    -0.03208383,
+                    0.047133777,
+                    0.020823127,
+                    -0.049983423,
+                    -0.094918594,
+                    -0.039521795,
+                    0.021127349,
+                    0.0046334304,
+                    -0.088479534,
+                    -0.06331032,
+                    -0.034863796,
+                    -0.0043010265,
+                    -0.09269081,
+                    0.019175887,
+                    0.012120077,
+                    0.02139774,
+                    0.038447183,
+                    -0.06730395,
+                    -0.0045507355,
+                    -0.009664997,
+                    0.032972448,
+                    -0.025034687,
+                    -0.0015198331,
+                    0.082746305,
+                    0.0031048357,
+                    -0.009439897,
+                    -0.113036305,
+                    -0.076955914,
+                    0.042393137,
+                    -0.040755555,
+                ]
+            }
+        },
+    ],
+}
+
+
+@pytest.fixture(scope="session")
+def simple_get(extract_shortened_prompt):
+    def _simple_get(self):
+        content_len = int(self.headers.get("content-length"))
+        content = json.loads(self.rfile.read(content_len).decode("utf-8"))
+
+        prompt = extract_shortened_prompt(content)
+        if not prompt:
+            self.send_response(500)
+            self.end_headers()
+            self.wfile.write("Could not parse prompt.".encode("utf-8"))
+            return
+
+        headers, response = ({}, "")
+
+        for k, v in RESPONSES.items():
+            if prompt.startswith(k):
+                headers, status_code, response = v
+                break
+        else:  # If no matches found
+            self.send_response(500)
+            self.end_headers()
+            self.wfile.write(("Unknown Prompt:\n%s" % prompt).encode("utf-8"))
+            return
+
+        # Send response code
+        self.send_response(status_code)
+
+        # Send headers
+        for k, v in headers.items():
+            self.send_header(k, v)
+        self.end_headers()
+
+        # Send response body
+        self.wfile.write(json.dumps(response).encode("utf-8"))
+        return
+
+    return _simple_get
+
+
+@pytest.fixture(scope="session")
+def extract_shortened_prompt():
+    def _extract_shortened_prompt(content):
+        try:
+            if "requests" in content.keys():
+                prompt = content.get("requests")[0].get("content").get("parts")[0].get("text")
+            else:
+                prompt = content.get("contents")[0].get("parts")[0].get("text")
+
+            prompt = prompt.lstrip().split("\n")[0]
+        except Exception:
+            prompt = ""
+        return prompt
+
+    return _extract_shortened_prompt
+
+
+@pytest.fixture(scope="session")
+def MockExternalGeminiServer(simple_get):
+    class _MockExternalGeminiServer(MockExternalHTTPServer):
+        # To use this class in a test one needs to start and stop this server
+        # before and after making requests to the test app that makes the external
+        # calls.
+
+        def __init__(self, handler=simple_get, port=None, *args, **kwargs):
+            super(_MockExternalGeminiServer, self).__init__(handler=handler, port=port, *args, **kwargs)  # noqa: B026
+
+    return _MockExternalGeminiServer
+
+
+if __name__ == "__main__":
+    with MockExternalGeminiServer() as server:
+        print("MockExternalGeminiServer serving on port %s" % str(server.port))
+        while True:
+            pass  # Serve forever
diff --git a/tests/mlmodel_gemini/conftest.py b/tests/mlmodel_gemini/conftest.py
new file mode 100644
index 0000000000..f30ae555e0
--- /dev/null
+++ b/tests/mlmodel_gemini/conftest.py
@@ -0,0 +1,138 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+
+import google.genai
+import pytest
+from _mock_external_gemini_server import MockExternalGeminiServer, extract_shortened_prompt, simple_get
+from testing_support.fixture.event_loop import event_loop as loop
+from testing_support.fixtures import (
+    collector_agent_registration_fixture,
+    collector_available_fixture,
+    override_application_settings,
+)
+
+from newrelic.common.object_wrapper import wrap_function_wrapper
+from newrelic.common.signature import bind_args
+
+_default_settings = {
+    "package_reporting.enabled": False,  # Turn off package reporting for testing as it causes slow-downs.
+    "transaction_tracer.explain_threshold": 0.0,
+    "transaction_tracer.transaction_threshold": 0.0,
+    "transaction_tracer.stack_trace_threshold": 0.0,
+    "debug.log_data_collector_payloads": True,
+    "debug.record_transaction_failure": True,
+    "ml_insights_events.enabled": True,
+    "ai_monitoring.enabled": True,
+}
+
+collector_agent_registration = collector_agent_registration_fixture(
+    app_name="Python Agent Test (mlmodel_gemini)",
+    default_settings=_default_settings,
+    linked_applications=["Python Agent Test (mlmodel_gemini)"],
+)
+
+
+GEMINI_AUDIT_LOG_FILE = os.path.join(os.path.realpath(os.path.dirname(__file__)), "gemini_audit.log")
+GEMINI_AUDIT_LOG_CONTENTS = {}
+# Intercept outgoing requests and log to file for mocking
+RECORDED_HEADERS = set(["content-type"])
+
+
+@pytest.fixture(scope="session")
+def gemini_clients(MockExternalGeminiServer):  # noqa: F811
+    """
+    This configures the Gemini client and returns it
+    """
+    from newrelic.core.config import _environ_as_bool
+
+    if _environ_as_bool("NEW_RELIC_TESTING_RECORD_GEMINI_RESPONSES", True):
+        with MockExternalGeminiServer() as server:
+            gemini_dev_client = google.genai.Client(
+                api_key="GEMINI_API_KEY",
+                http_options=google.genai.types.HttpOptions(base_url=f"http://localhost:{server.port}"),
+            )
+            yield gemini_dev_client
+    else:
+        google_api_key = os.environ.get("GOOGLE_API_KEY")
+        if not google_api_key:
+            raise RuntimeError("GOOGLE_API_KEY environment variable required.")
+
+        gemini_dev_client = google.genai.Client(api_key=google_api_key)
+        yield gemini_dev_client
+
+
+@pytest.fixture(scope="session")
+def gemini_dev_client(gemini_clients):
+    # Once VertexAI is enabled, gemini_clients() will yield two different clients up that will be unpacked here
+    gemini_dev_client = gemini_clients
+    return gemini_dev_client
+
+
+# @pytest.fixture(scope="session")
+# def vertexai_client(gemini_clients):
+#     # This will eventually also be yielded up in gemini_clients() to test again the Vertex AI API
+#     _, vertexai_client = gemini_clients
+#     return vertexai_client
+
+
+@pytest.fixture(autouse=True, scope="session")
+def gemini_server(gemini_clients, wrap_httpx_client_send):
+    """
+    This fixture will either create a mocked backend for testing purposes, or will
+    set up an audit log file to log responses of the real Gemini backend to a file.
+    The behavior can be controlled by setting NEW_RELIC_TESTING_RECORD_GEMINI_RESPONSES=1 as
+    an environment variable to run using the real Gemini backend. (Default: mocking)
+    """
+    from newrelic.core.config import _environ_as_bool
+
+    if _environ_as_bool("NEW_RELIC_TESTING_RECORD_GEMINI_RESPONSES", True):
+        wrap_function_wrapper("httpx._client", "Client.send", wrap_httpx_client_send)
+        yield  # Run tests
+        # Write responses to audit log
+        with open(GEMINI_AUDIT_LOG_FILE, "w") as audit_log_fp:
+            json.dump(GEMINI_AUDIT_LOG_CONTENTS, fp=audit_log_fp, indent=4)
+    else:
+        # We are mocking responses so we don't need to do anything in this case.
+        yield
+
+
+@pytest.fixture(scope="session")
+def wrap_httpx_client_send(extract_shortened_prompt):  # noqa: F811
+    def _wrap_httpx_client_send(wrapped, instance, args, kwargs):
+        bound_args = bind_args(wrapped, args, kwargs)
+        request = bound_args["request"]
+        if not request:
+            return wrapped(*args, **kwargs)
+
+        params = json.loads(request.content.decode("utf-8"))
+        prompt = extract_shortened_prompt(params)
+
+        # Send request
+        response = wrapped(*args, **kwargs)
+
+        if response.status_code >= 500 or response.status_code < 200:
+            prompt = "error"
+
+        rheaders = response.headers
+        headers = dict(
+            filter(lambda k: k[0].lower() in RECORDED_HEADERS or k[0].lower().startswith("x-goog"), rheaders.items())
+        )
+        body = json.loads(response.content.decode("utf-8"))
+        GEMINI_AUDIT_LOG_CONTENTS[prompt] = headers, response.status_code, body  # Append response data to log
+        return response
+
+    return _wrap_httpx_client_send
diff --git a/tests/mlmodel_gemini/test_embeddings.py b/tests/mlmodel_gemini/test_embeddings.py
new file mode 100644
index 0000000000..0fc92897b6
--- /dev/null
+++ b/tests/mlmodel_gemini/test_embeddings.py
@@ -0,0 +1,218 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import google.genai
+from testing_support.fixtures import override_llm_token_callback_settings, reset_core_stats_engine, validate_attributes
+from testing_support.ml_testing_utils import (
+    add_token_count_to_events,
+    disabled_ai_monitoring_record_content_settings,
+    disabled_ai_monitoring_settings,
+    events_sans_content,
+    llm_token_count_callback,
+    set_trace_info,
+)
+from testing_support.validators.validate_custom_event import validate_custom_event_count
+from testing_support.validators.validate_custom_events import validate_custom_events
+from testing_support.validators.validate_transaction_metrics import validate_transaction_metrics
+
+from newrelic.api.background_task import background_task
+from newrelic.api.transaction import add_custom_attribute
+
+embedding_recorded_events = [
+    (
+        {"type": "LlmEmbedding"},
+        {
+            "id": None,  # UUID that varies with each run
+            "span_id": None,
+            "trace_id": "trace-id",
+            "input": "This is an embedding test.",
+            "llm.conversation_id": "my-awesome-id",
+            "llm.foo": "bar",
+            "duration": None,  # Response time varies each test run
+            "request.model": "text-embedding-004",
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        },
+    )
+]
+
+
+@reset_core_stats_engine()
+@validate_custom_events(embedding_recorded_events)
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_sync",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_sync(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    gemini_dev_client.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+
+
+@reset_core_stats_engine()
+@disabled_ai_monitoring_record_content_settings
+@validate_custom_events(events_sans_content(embedding_recorded_events))
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_sync_no_content",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_sync_no_content(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    gemini_dev_client.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+
+
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_custom_events(add_token_count_to_events(embedding_recorded_events))
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_sync_with_token_count",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_sync_with_token_count(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    gemini_dev_client.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+
+
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+def test_gemini_embedding_sync_outside_txn(gemini_dev_client):
+    gemini_dev_client.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+
+
+@disabled_ai_monitoring_settings
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+@background_task()
+def test_gemini_embedding_sync_disabled_ai_monitoring_events(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    gemini_dev_client.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+
+
+@reset_core_stats_engine()
+@validate_custom_events(embedding_recorded_events)
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_async",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_async(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    loop.run_until_complete(
+        gemini_dev_client.aio.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+    )
+
+
+@reset_core_stats_engine()
+@disabled_ai_monitoring_record_content_settings
+@validate_custom_events(events_sans_content(embedding_recorded_events))
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_async_no_content",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_async_no_content(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    loop.run_until_complete(
+        gemini_dev_client.aio.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+    )
+
+
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_custom_events(add_token_count_to_events(embedding_recorded_events))
+@validate_custom_event_count(count=1)
+@validate_transaction_metrics(
+    name="test_embeddings:test_gemini_embedding_async_with_token_count",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_embedding_async_with_token_count(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+
+    loop.run_until_complete(
+        gemini_dev_client.aio.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+def test_gemini_embedding_async_outside_txn(gemini_dev_client, loop):
+    loop.run_until_complete(
+        gemini_dev_client.aio.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+    )
+
+
+@disabled_ai_monitoring_settings
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+@background_task()
+def test_gemini_embedding_async_disabled_ai_monitoring_events(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    loop.run_until_complete(
+        gemini_dev_client.aio.models.embed_content(contents="This is an embedding test.", model="text-embedding-004")
+    )
diff --git a/tests/mlmodel_gemini/test_embeddings_error.py b/tests/mlmodel_gemini/test_embeddings_error.py
new file mode 100644
index 0000000000..a65a6c2c6f
--- /dev/null
+++ b/tests/mlmodel_gemini/test_embeddings_error.py
@@ -0,0 +1,383 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+
+import google.genai
+import pytest
+from testing_support.fixtures import dt_enabled, override_llm_token_callback_settings, reset_core_stats_engine
+from testing_support.ml_testing_utils import (
+    add_token_count_to_events,
+    disabled_ai_monitoring_record_content_settings,
+    events_sans_content,
+    llm_token_count_callback,
+    set_trace_info,
+)
+from testing_support.validators.validate_custom_event import validate_custom_event_count
+from testing_support.validators.validate_custom_events import validate_custom_events
+from testing_support.validators.validate_error_trace_attributes import validate_error_trace_attributes
+from testing_support.validators.validate_span_events import validate_span_events
+from testing_support.validators.validate_transaction_metrics import validate_transaction_metrics
+
+from newrelic.api.background_task import background_task
+from newrelic.common.object_names import callable_name
+
+embedding_recorded_events = [
+    (
+        {"type": "LlmEmbedding"},
+        {
+            "id": None,  # UUID that varies with each run
+            "span_id": None,
+            "trace_id": "trace-id",
+            "input": "This is an embedding test.",
+            "duration": None,  # Response time varies each test run
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    )
+]
+
+
+def test_embeddings_invalid_request_error_no_model(gemini_dev_client, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "embed_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.embed_content() missing 1 required keyword-only argument: 'model'"
+
+    # No model provided
+    @dt_enabled
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        name="test_embeddings_error:test_embeddings_invalid_request_error_no_model.<locals>._test",
+        scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(embedding_recorded_events)
+    @validate_custom_event_count(count=1)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            gemini_dev_client.models.embed_content(
+                contents="This is an embedding test."
+                # No model
+            )
+
+    _test()
+
+
+def test_embeddings_invalid_request_error_no_model_no_content(gemini_dev_client, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "embed_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.embed_content() missing 1 required keyword-only argument: 'model'"
+
+    @dt_enabled
+    @disabled_ai_monitoring_record_content_settings
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        name="test_embeddings_error:test_embeddings_invalid_request_error_no_model_no_content.<locals>._test",
+        scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_sans_content(embedding_recorded_events))
+    @validate_custom_event_count(count=1)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            gemini_dev_client.models.embed_content(
+                contents="This is an embedding test."
+                # no model provided
+            )
+
+    _test()
+
+
+invalid_model_events = [
+    (
+        {"type": "LlmEmbedding"},
+        {
+            "id": None,  # UUID that varies with each run
+            "span_id": None,
+            "trace_id": "trace-id",
+            "input": "Embedded: Model does not exist.",
+            "duration": None,  # Response time varies each test run
+            "request.model": "does-not-exist",  # No model in this test case
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    )
+]
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for embedContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_invalid_request_error_invalid_model",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(invalid_model_events)
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_invalid_request_error_invalid_model(gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client.models.embed_content(contents="Embedded: Model does not exist.", model="does-not-exist")
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for embedContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_invalid_request_error_invalid_model_with_token_count",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_count_to_events(invalid_model_events))
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_invalid_request_error_invalid_model_with_token_count(gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client.models.embed_content(contents="Embedded: Model does not exist.", model="does-not-exist")
+
+
+embedding_invalid_key_error_events = [
+    (
+        {"type": "LlmEmbedding"},
+        {
+            "id": None,  # UUID that varies with each run
+            "span_id": None,
+            "trace_id": "trace-id",
+            "input": "Invalid API key.",
+            "duration": None,  # Response time varies each test run
+            "request.model": "text-embedding-004",  # No model in this test case
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    )
+]
+
+
+# Wrong api_key provided
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "INVALID_ARGUMENT", "http.statusCode": 400}},
+)
+@validate_span_events(exact_agents={"error.message": "API key not valid. Please pass a valid API key."})
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_wrong_api_key_error",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(embedding_invalid_key_error_events)
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_wrong_api_key_error(monkeypatch, gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client._api_client.api_key = "DEADBEEF"
+        gemini_dev_client.models.embed_content(contents="Invalid API key.", model="text-embedding-004")
+
+
+def test_embeddings_async_invalid_request_error_no_model(gemini_dev_client, loop, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "embed_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.embed_content() missing 1 required keyword-only argument: 'model'"
+
+    @dt_enabled
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        name="test_embeddings_error:test_embeddings_async_invalid_request_error_no_model.<locals>._test",
+        scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(embedding_recorded_events)
+    @validate_custom_event_count(count=1)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            loop.run_until_complete(
+                gemini_dev_client.models.embed_content(
+                    contents="This is an embedding test."
+                    # No model
+                )
+            )
+
+    _test()
+
+
+def test_embeddings_async_invalid_request_error_no_model_no_content(gemini_dev_client, loop, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "embed_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.embed_content() missing 1 required keyword-only argument: 'model'"
+
+    @dt_enabled
+    @disabled_ai_monitoring_record_content_settings
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        name="test_embeddings_error:test_embeddings_async_invalid_request_error_no_model_no_content.<locals>._test",
+        scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+        custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_sans_content(embedding_recorded_events))
+    @validate_custom_event_count(count=1)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            loop.run_until_complete(
+                gemini_dev_client.models.embed_content(
+                    contents="This is an embedding test."
+                    # no model provided
+                )
+            )
+
+    _test()
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for embedContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_async_invalid_request_error_invalid_model",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(invalid_model_events)
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_async_invalid_request_error_invalid_model(gemini_dev_client, loop, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        loop.run_until_complete(
+            gemini_dev_client.models.embed_content(contents="Embedded: Model does not exist.", model="does-not-exist")
+        )
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for embedContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_async_invalid_request_error_invalid_model_with_token_count",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_count_to_events(invalid_model_events))
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_async_invalid_request_error_invalid_model_with_token_count(gemini_dev_client, loop, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        loop.run_until_complete(
+            gemini_dev_client.models.embed_content(contents="Embedded: Model does not exist.", model="does-not-exist")
+        )
+
+
+# Wrong api_key provided
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "INVALID_ARGUMENT", "http.statusCode": 400}},
+)
+@validate_span_events(exact_agents={"error.message": "API key not valid. Please pass a valid API key."})
+@validate_transaction_metrics(
+    name="test_embeddings_error:test_embeddings_async_wrong_api_key_error",
+    scoped_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    rollup_metrics=[("Llm/embedding/Gemini/embed_content", 1)],
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_custom_events(embedding_invalid_key_error_events)
+@validate_custom_event_count(count=1)
+@background_task()
+def test_embeddings_async_wrong_api_key_error(gemini_dev_client, loop, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client._api_client.api_key = "DEADBEEF"
+        loop.run_until_complete(
+            gemini_dev_client.models.embed_content(contents="Invalid API key.", model="text-embedding-004")
+        )
diff --git a/tests/mlmodel_gemini/test_text_generation.py b/tests/mlmodel_gemini/test_text_generation.py
new file mode 100644
index 0000000000..a708393019
--- /dev/null
+++ b/tests/mlmodel_gemini/test_text_generation.py
@@ -0,0 +1,401 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import google.genai
+from testing_support.fixtures import override_llm_token_callback_settings, reset_core_stats_engine, validate_attributes
+from testing_support.ml_testing_utils import (
+    add_token_counts_to_events,
+    disabled_ai_monitoring_record_content_settings,
+    disabled_ai_monitoring_settings,
+    events_sans_content,
+    events_sans_llm_metadata,
+    events_with_context_attrs,
+    llm_token_count_callback,
+    set_trace_info,
+)
+from testing_support.validators.validate_custom_event import validate_custom_event_count
+from testing_support.validators.validate_custom_events import validate_custom_events
+from testing_support.validators.validate_transaction_metrics import validate_transaction_metrics
+
+from newrelic.api.background_task import background_task
+from newrelic.api.llm_custom_attributes import WithLlmCustomAttributes
+from newrelic.api.transaction import add_custom_attribute
+
+text_generation_recorded_events = [
+    (
+        {"type": "LlmChatCompletionSummary"},
+        {
+            "id": None,  # UUID that varies with each run
+            "llm.conversation_id": "my-awesome-id",
+            "llm.foo": "bar",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "duration": None,  # Response time varies each test run
+            "request.model": "gemini-2.0-flash",
+            "response.model": "gemini-2.0-flash",
+            "request.temperature": 0.7,
+            "request.max_tokens": 100,
+            "response.choices.finish_reason": "STOP",
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "response.number_of_messages": 2,
+            "response.usage.prompt_tokens": 9,
+            "response.usage.completion_tokens": 13,
+            "response.usage.total_tokens": 22,
+        },
+    ),
+    (
+        {"type": "LlmChatCompletionMessage"},
+        {
+            "id": None,
+            "llm.conversation_id": "my-awesome-id",
+            "llm.foo": "bar",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "token_count": 0,
+            "content": "How many letters are in the word Python?",
+            "role": "user",
+            "completion_id": None,
+            "sequence": 0,
+            "response.model": "gemini-2.0-flash",
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        },
+    ),
+    (
+        {"type": "LlmChatCompletionMessage"},
+        {
+            "id": None,
+            "llm.conversation_id": "my-awesome-id",
+            "llm.foo": "bar",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "token_count": 0,
+            "content": 'There are **6** letters in the word "Python".\n',
+            "role": "model",
+            "completion_id": None,
+            "sequence": 1,
+            "response.model": "gemini-2.0-flash",
+            "vendor": "gemini",
+            "is_response": True,
+            "ingest_source": "Python",
+        },
+    ),
+]
+
+
+@reset_core_stats_engine()
+# Expect one summary event, one message event for the input, and message event for the output for each send_message_call
+@validate_custom_event_count(count=6)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_multi_text_generation",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_multi_text_generation(gemini_dev_client, set_trace_info):
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+    chat.send_message(
+        message="Who invented the Python programming language?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_with_context_attrs(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_sync_generate_content",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_sync_generate_content(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+    with WithLlmCustomAttributes({"context": "attr"}):
+        gemini_dev_client.models.generate_content(
+            model="gemini-2.0-flash",
+            contents="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_with_context_attrs(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_sync_with_llm_metadata",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_sync_with_llm_metadata(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+    with WithLlmCustomAttributes({"context": "attr"}):
+        chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+
+
+@reset_core_stats_engine()
+@disabled_ai_monitoring_record_content_settings
+@validate_custom_events(events_sans_content(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_sync_no_content",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_sync_no_content(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_custom_events(add_token_counts_to_events(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_sync_with_token_count",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_sync_with_token_count(gemini_dev_client, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_sans_llm_metadata(text_generation_recorded_events))
+# One summary event, one system message, one user message, and one response message from the assistant
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    "test_text_generation:test_gemini_text_generation_sync_no_llm_metadata",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@background_task()
+def test_gemini_text_generation_sync_no_llm_metadata(gemini_dev_client, set_trace_info):
+    set_trace_info()
+
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+def test_gemini_text_generation_sync_outside_txn(gemini_dev_client):
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@disabled_ai_monitoring_settings
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+@background_task()
+def test_gemini_text_generation_sync_ai_monitoring_disabled(gemini_dev_client):
+    chat = gemini_dev_client.chats.create(model="gemini-2.0-flash")
+    chat.send_message(
+        message="How many letters are in the word Python?",
+        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_with_context_attrs(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_async_generate_content",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_async_generate_content(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+    with WithLlmCustomAttributes({"context": "attr"}):
+        loop.run_until_complete(
+            gemini_dev_client.aio.models.generate_content(
+                model="gemini-2.0-flash",
+                contents="How many letters are in the word Python?",
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+        )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_with_context_attrs(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_async_with_llm_metadata",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_async_with_llm_metadata(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+    add_custom_attribute("non_llm_attr", "python-agent")
+    with WithLlmCustomAttributes({"context": "attr"}):
+        chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+        loop.run_until_complete(
+            chat.send_message(
+                message="How many letters are in the word Python?",
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+        )
+
+
+@reset_core_stats_engine()
+@disabled_ai_monitoring_record_content_settings
+@validate_custom_events(events_sans_content(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_async_no_content",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_async_no_content(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+
+    chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+    loop.run_until_complete(
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+    )
+
+
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_custom_events(add_token_counts_to_events(text_generation_recorded_events))
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    name="test_text_generation:test_gemini_text_generation_async_with_token_count",
+    custom_metrics=[(f"Supportability/Python/ML/Gemini/{google.genai.__version__}", 1)],
+    background_task=True,
+)
+@validate_attributes("agent", ["llm"])
+@background_task()
+def test_gemini_text_generation_async_with_token_count(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+    add_custom_attribute("llm.conversation_id", "my-awesome-id")
+    add_custom_attribute("llm.foo", "bar")
+
+    chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+    loop.run_until_complete(
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_events(events_sans_llm_metadata(text_generation_recorded_events))
+# One summary event, one system message, one user message, and one response message from the assistant
+@validate_custom_event_count(count=3)
+@validate_transaction_metrics(
+    "test_text_generation:test_gemini_text_generation_async_no_llm_metadata",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@background_task()
+def test_gemini_text_generation_async_no_llm_metadata(gemini_dev_client, loop, set_trace_info):
+    set_trace_info()
+
+    chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+    loop.run_until_complete(
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+    )
+
+
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+def test_gemini_text_generation_async_outside_txn(gemini_dev_client, loop):
+    chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+    loop.run_until_complete(
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+    )
+
+
+@disabled_ai_monitoring_settings
+@reset_core_stats_engine()
+@validate_custom_event_count(count=0)
+@background_task()
+def test_gemini_text_generation_async_ai_monitoring_disabled(gemini_dev_client, loop):
+    chat = gemini_dev_client.aio.chats.create(model="gemini-2.0-flash")
+    loop.run_until_complete(
+        chat.send_message(
+            message="How many letters are in the word Python?",
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+    )
diff --git a/tests/mlmodel_gemini/test_text_generation_error.py b/tests/mlmodel_gemini/test_text_generation_error.py
new file mode 100644
index 0000000000..1460430b85
--- /dev/null
+++ b/tests/mlmodel_gemini/test_text_generation_error.py
@@ -0,0 +1,478 @@
+# Copyright 2010 New Relic, Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import sys
+
+import google.genai
+import pytest
+from testing_support.fixtures import dt_enabled, override_llm_token_callback_settings, reset_core_stats_engine
+from testing_support.ml_testing_utils import (
+    add_token_counts_to_events_error,
+    disabled_ai_monitoring_record_content_settings,
+    events_sans_content,
+    events_with_context_attrs,
+    llm_token_count_callback,
+    set_trace_info,
+)
+from testing_support.validators.validate_custom_event import validate_custom_event_count
+from testing_support.validators.validate_custom_events import validate_custom_events
+from testing_support.validators.validate_error_trace_attributes import validate_error_trace_attributes
+from testing_support.validators.validate_span_events import validate_span_events
+from testing_support.validators.validate_transaction_metrics import validate_transaction_metrics
+
+from newrelic.api.background_task import background_task
+from newrelic.api.llm_custom_attributes import WithLlmCustomAttributes
+from newrelic.api.transaction import add_custom_attribute
+from newrelic.common.object_names import callable_name
+
+expected_events_on_no_model_error = [
+    (
+        {"type": "LlmChatCompletionSummary"},
+        {
+            "id": None,  # UUID that varies with each run
+            "llm.conversation_id": "my-awesome-id",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "duration": None,  # Response time varies each test run
+            "request.temperature": 0.7,
+            "request.max_tokens": 100,
+            "response.number_of_messages": 1,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    ),
+    (
+        {"type": "LlmChatCompletionMessage"},
+        {
+            "id": None,
+            "llm.conversation_id": "my-awesome-id",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "token_count": 0,
+            "content": "How many letters are in the word Python?",
+            "role": "user",
+            "completion_id": None,
+            "sequence": 0,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        },
+    ),
+]
+
+
+def test_text_generation_invalid_request_error_no_model(gemini_dev_client, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "generate_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.generate_content() missing 1 required keyword-only argument: 'model'"
+
+    # No model provided
+    @dt_enabled
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        "test_text_generation_error:test_text_generation_invalid_request_error_no_model.<locals>._test",
+        scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_with_context_attrs(expected_events_on_no_model_error))
+    @validate_custom_event_count(count=2)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            add_custom_attribute("llm.conversation_id", "my-awesome-id")
+            with WithLlmCustomAttributes({"context": "attr"}):
+                gemini_dev_client.models.generate_content(
+                    # no model
+                    contents=["How many letters are in the word Python?"],
+                    config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+                )
+
+    _test()
+
+
+def test_text_generation_invalid_request_error_no_model_no_content(gemini_dev_client, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "generate_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.generate_content() missing 1 required keyword-only argument: 'model'"
+
+    @dt_enabled
+    @disabled_ai_monitoring_record_content_settings
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        "test_text_generation_error:test_text_generation_invalid_request_error_no_model_no_content.<locals>._test",
+        scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_sans_content(expected_events_on_no_model_error))
+    @validate_custom_event_count(count=2)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            add_custom_attribute("llm.conversation_id", "my-awesome-id")
+
+            gemini_dev_client.models.generate_content(
+                # no model
+                contents=["How many letters are in the word Python?"],
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+
+    _test()
+
+
+expected_events_on_invalid_model_error = [
+    (
+        {"type": "LlmChatCompletionSummary"},
+        {
+            "id": None,  # UUID that varies with each run
+            "llm.conversation_id": "my-awesome-id",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "duration": None,  # Response time varies each test run
+            "request.model": "does-not-exist",
+            "request.temperature": 0.7,
+            "request.max_tokens": 100,
+            "response.number_of_messages": 1,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    ),
+    (
+        {"type": "LlmChatCompletionMessage"},
+        {
+            "id": None,
+            "llm.conversation_id": "my-awesome-id",
+            "span_id": None,
+            "trace_id": "trace-id",
+            "token_count": 0,
+            "content": "Model does not exist.",
+            "role": "user",
+            "completion_id": None,
+            "response.model": "does-not-exist",
+            "sequence": 0,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        },
+    ),
+]
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_invalid_request_error_invalid_model_with_token_count",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_counts_to_events_error(expected_events_on_invalid_model_error))
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_invalid_request_error_invalid_model_with_token_count(gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        add_custom_attribute("llm.conversation_id", "my-awesome-id")
+        gemini_dev_client.models.generate_content(
+            model="does-not-exist",
+            contents=["Model does not exist."],
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_invalid_request_error_invalid_model_chat",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_counts_to_events_error(expected_events_on_invalid_model_error))
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_invalid_request_error_invalid_model_chat(gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        add_custom_attribute("llm.conversation_id", "my-awesome-id")
+        chat = gemini_dev_client.chats.create(model="does-not-exist")
+        chat.send_message(
+            message=["Model does not exist."],
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+
+
+expected_events_on_wrong_api_key_error = [
+    (
+        {"type": "LlmChatCompletionSummary"},
+        {
+            "id": None,  # UUID that varies with each run
+            "span_id": None,
+            "trace_id": "trace-id",
+            "duration": None,  # Response time varies each test run
+            "request.model": "gemini-flash-2.0",
+            "request.temperature": 0.7,
+            "request.max_tokens": 100,
+            "response.number_of_messages": 1,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+            "error": True,
+        },
+    ),
+    (
+        {"type": "LlmChatCompletionMessage"},
+        {
+            "id": None,
+            "span_id": None,
+            "trace_id": "trace-id",
+            "token_count": 0,
+            "content": "Invalid API key.",
+            "role": "user",
+            "response.model": "gemini-flash-2.0",
+            "completion_id": None,
+            "sequence": 0,
+            "vendor": "gemini",
+            "ingest_source": "Python",
+        },
+    ),
+]
+
+
+# Wrong api_key provided
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "INVALID_ARGUMENT", "http.statusCode": 400}},
+)
+@validate_span_events(exact_agents={"error.message": "API key not valid. Please pass a valid API key."})
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_wrong_api_key_error",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(expected_events_on_wrong_api_key_error)
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_wrong_api_key_error(gemini_dev_client, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client._api_client.api_key = "DEADBEEF"
+        gemini_dev_client.models.generate_content(
+            model="gemini-flash-2.0",
+            contents=["Invalid API key."],
+            config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+        )
+
+
+def test_text_generation_async_invalid_request_error_no_model(gemini_dev_client, loop, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "generate_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.generate_content() missing 1 required keyword-only argument: 'model'"
+
+    # No model provided
+    @dt_enabled
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        "test_text_generation_error:test_text_generation_async_invalid_request_error_no_model.<locals>._test",
+        scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_with_context_attrs(expected_events_on_no_model_error))
+    @validate_custom_event_count(count=2)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            add_custom_attribute("llm.conversation_id", "my-awesome-id")
+            with WithLlmCustomAttributes({"context": "attr"}):
+                loop.run_until_complete(
+                    gemini_dev_client.models.generate_content(
+                        # no model
+                        contents=["How many letters are in the word Python?"],
+                        config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+                    )
+                )
+
+    _test()
+
+
+def test_text_generation_async_invalid_request_error_no_model_no_content(gemini_dev_client, loop, set_trace_info):
+    if sys.version_info < (3, 10):
+        error_message = "generate_content() missing 1 required keyword-only argument: 'model'"
+    else:
+        error_message = "Models.generate_content() missing 1 required keyword-only argument: 'model'"
+
+    @dt_enabled
+    @disabled_ai_monitoring_record_content_settings
+    @reset_core_stats_engine()
+    @validate_error_trace_attributes(callable_name(TypeError), exact_attrs={"agent": {}, "intrinsic": {}, "user": {}})
+    @validate_span_events(exact_agents={"error.message": error_message})
+    @validate_transaction_metrics(
+        "test_text_generation_error:test_text_generation_async_invalid_request_error_no_model_no_content.<locals>._test",
+        scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+        background_task=True,
+    )
+    @validate_custom_events(events_sans_content(expected_events_on_no_model_error))
+    @validate_custom_event_count(count=2)
+    @background_task()
+    def _test():
+        with pytest.raises(TypeError):
+            set_trace_info()
+            add_custom_attribute("llm.conversation_id", "my-awesome-id")
+            loop.run_until_complete(
+                gemini_dev_client.models.generate_content(
+                    # no model
+                    contents=["How many letters are in the word Python?"],
+                    config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+                )
+            )
+
+    _test()
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_async_invalid_request_error_invalid_model_with_token_count",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_counts_to_events_error(expected_events_on_invalid_model_error))
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_async_invalid_request_error_invalid_model_with_token_count(
+    gemini_dev_client, loop, set_trace_info
+):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        add_custom_attribute("llm.conversation_id", "my-awesome-id")
+        loop.run_until_complete(
+            gemini_dev_client.models.generate_content(
+                model="does-not-exist",
+                contents=["Model does not exist."],
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+        )
+
+
+@dt_enabled
+@reset_core_stats_engine()
+@override_llm_token_callback_settings(llm_token_count_callback)
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "NOT_FOUND", "http.statusCode": 404}},
+)
+@validate_span_events(
+    exact_agents={
+        "error.message": "models/does-not-exist is not found for API version v1beta, or is not supported for generateContent. Call ListModels to see the list of available models and their supported methods."
+    }
+)
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_async_invalid_request_error_invalid_model_chat",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(add_token_counts_to_events_error(expected_events_on_invalid_model_error))
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_async_invalid_request_error_invalid_model_chat(gemini_dev_client, loop, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        add_custom_attribute("llm.conversation_id", "my-awesome-id")
+        chat = gemini_dev_client.chats.create(model="does-not-exist")
+        loop.run_until_complete(
+            chat.send_message(
+                message=["Model does not exist."],
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+        )
+
+
+# Wrong api_key provided
+@dt_enabled
+@reset_core_stats_engine()
+@validate_error_trace_attributes(
+    callable_name(google.genai.errors.ClientError),
+    exact_attrs={"agent": {}, "intrinsic": {}, "user": {"error.code": "INVALID_ARGUMENT", "http.statusCode": 400}},
+)
+@validate_span_events(exact_agents={"error.message": "API key not valid. Please pass a valid API key."})
+@validate_transaction_metrics(
+    "test_text_generation_error:test_text_generation_async_wrong_api_key_error",
+    scoped_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    rollup_metrics=[("Llm/completion/Gemini/generate_content", 1)],
+    background_task=True,
+)
+@validate_custom_events(expected_events_on_wrong_api_key_error)
+@validate_custom_event_count(count=2)
+@background_task()
+def test_text_generation_async_wrong_api_key_error(gemini_dev_client, loop, set_trace_info):
+    with pytest.raises(google.genai.errors.ClientError):
+        set_trace_info()
+        gemini_dev_client._api_client.api_key = "DEADBEEF"
+        loop.run_until_complete(
+            gemini_dev_client.models.generate_content(
+                model="gemini-flash-2.0",
+                contents=["Invalid API key."],
+                config=google.genai.types.GenerateContentConfig(max_output_tokens=100, temperature=0.7),
+            )
+        )
diff --git a/tests/testing_support/ml_testing_utils.py b/tests/testing_support/ml_testing_utils.py
index 0e7307bfb0..2450b5333b 100644
--- a/tests/testing_support/ml_testing_utils.py
+++ b/tests/testing_support/ml_testing_utils.py
@@ -37,6 +37,25 @@ def add_token_count_to_events(expected_events):
     return events
 
 
+def add_token_counts_to_events(expected_events):
+    events = copy.deepcopy(expected_events)
+    for event in events:
+        if event[0]["type"] == "LlmChatCompletionSummary":
+            event[1]["response.usage.prompt_tokens"] = 105
+            event[1]["response.usage.completion_tokens"] = 105
+            event[1]["response.usage.total_tokens"] = 210
+    return events
+
+
+def add_token_counts_to_events_error(expected_events):
+    events = copy.deepcopy(expected_events)
+    for event in events:
+        if event[0]["type"] == "LlmChatCompletionSummary":
+            event[1]["response.usage.prompt_tokens"] = 105
+            event[1]["response.usage.total_tokens"] = 105
+    return events
+
+
 def events_sans_content(event):
     new_event = copy.deepcopy(event)
     for _event in new_event:
diff --git a/tox.ini b/tox.ini
index a55832a4d3..66cfc7602b 100644
--- a/tox.ini
+++ b/tox.ini
@@ -152,6 +152,7 @@ envlist =
     python-logger_logging-{py37,py38,py39,py310,py311,py312,py313,pypy310},
     python-logger_loguru-{py37,py38,py39,py310,py311,py312,py313,pypy310}-logurulatest,
     python-logger_structlog-{py37,py38,py39,py310,py311,py312,py313,pypy310}-structloglatest,
+    python-mlmodel_gemini-{py39,py310,py311,py312,py313},
     python-mlmodel_langchain-{py39,py310,py311,py312},
     ;; Package not ready for Python 3.13 (uses an older version of numpy)
     ; python-mlmodel_langchain-py313,
@@ -392,6 +393,7 @@ deps =
     framework_tornado: pycurl
     framework_tornado-tornadolatest: tornado
     framework_tornado-tornadomaster: https://github.com/tornadoweb/tornado/archive/master.zip
+    mlmodel_gemini: google-genai
     mlmodel_openai-openai0: openai[datalib]<1.0
     mlmodel_openai-openai107: openai[datalib]<1.8
     mlmodel_openai-openai107: httpx<0.28
@@ -541,6 +543,7 @@ changedir =
     messagebroker_kafkapython: tests/messagebroker_kafkapython
     messagebroker_kombu: tests/messagebroker_kombu
     messagebroker_pika: tests/messagebroker_pika
+    mlmodel_gemini: tests/mlmodel_gemini
     mlmodel_langchain: tests/mlmodel_langchain
     mlmodel_openai: tests/mlmodel_openai
     mlmodel_sklearn: tests/mlmodel_sklearn