diff --git a/mle/cli.py b/mle/cli.py
index e134e9a..f412fc5 100644
--- a/mle/cli.py
+++ b/mle/cli.py
@@ -180,7 +180,7 @@ def new(name):
 
     platform = questionary.select(
         "Which language model platform do you want to use?",
-        choices=['OpenAI', 'Ollama', 'Claude', 'MistralAI', 'DeepSeek']
+        choices=['OpenAI', 'Ollama', 'Claude', 'Gemini', 'MistralAI', 'DeepSeek']
     ).ask()
 
     api_key = None
@@ -208,6 +208,12 @@ def new(name):
             console.log("API key is required. Aborted.")
             return
 
+    elif platform == 'Gemini':
+        api_key = questionary.password("What is your Gemini API key?").ask()
+        if not api_key:
+            console.log("API key is required. Aborted.")
+            return
+
     search_api_key = questionary.password("What is your Tavily API key? (if no, the web search will be disabled)").ask()
     if search_api_key:
         os.environ["SEARCH_API_KEY"] = search_api_key
diff --git a/mle/model/__init__.py b/mle/model/__init__.py
index 629492a..28e4378 100644
--- a/mle/model/__init__.py
+++ b/mle/model/__init__.py
@@ -3,6 +3,7 @@
 from .mistral import *
 from .ollama import *
 from .openai import *
+from .gemini import *
 
 from mle.utils import get_config
 
@@ -12,6 +13,7 @@
 MODEL_CLAUDE = 'Claude'
 MODEL_MISTRAL = 'MistralAI'
 MODEL_DEEPSEEK = 'DeepSeek'
+MODEL_GEMINI = 'Gemini'
 
 
 class ObservableModel:
@@ -64,6 +66,8 @@ def load_model(project_dir: str, model_name: str=None, observable=True):
         model = MistralModel(api_key=config['api_key'], model=model_name)
     if config['platform'] == MODEL_DEEPSEEK:
         model = DeepSeekModel(api_key=config['api_key'], model=model_name)
+    if config['platform'] == MODEL_GEMINI:
+        model = GeminiModel(api_key=config['api_key'], model=model_name)
 
     if observable:
         return ObservableModel(model)
diff --git a/mle/model/gemini.py b/mle/model/gemini.py
new file mode 100644
index 0000000..99465a4
--- /dev/null
+++ b/mle/model/gemini.py
@@ -0,0 +1,124 @@
+import os
+import importlib.util
+import json
+
+from mle.function import SEARCH_FUNCTIONS, get_function, process_function_name
+from mle.model.common import Model
+
+
+class GeminiModel(Model):
+    def __init__(self, api_key, model, temperature=0.7):
+        """
+        Initialize the Gemini model.
+        Args:
+            api_key (str): The Gemini API key.
+            model (str): The model with version.
+            temperature (float): The temperature value.
+        """
+        super().__init__()
+
+        dependency = "google"
+        spec = importlib.util.find_spec(dependency)
+        if spec is not None:
+            self.gemini = importlib.import_module(dependency).generativeai
+        else:
+            raise ImportError(
+                "It seems you didn't install `google-generativeai`. "
+                "In order to enable the Gemini client related features, "
+                "please make sure gemini Python package has been installed. "
+                "More information, please refer to: https://ai.google.dev/gemini-api/docs/quickstart?lang=python"
+            )
+
+        self.model = model if model else 'gemini-1.5-flash-002'
+        self.model_type = 'Gemini'
+        self.temperature = temperature
+        self.func_call_history = []
+
+    @staticmethod
+    def _map_roles_from_openai(chat_history):
+        _map_dict = {
+            "system": "model",
+            "user": "user",
+            "assistant": "model",
+            "content": "parts",
+        }
+        return dict({_map_dict[k]: v for k, v in chat_history.items()})
+
+    def query(self, chat_history, **kwargs):
+        """
+        Query the LLM model.
+
+        Args:
+            chat_history: The context (chat history).
+        """
+        parameters = kwargs
+
+        tools = None
+        if parameters.get("functions") is not None:
+            tools = {'function_declarations': parameters["functions"]}
+            self.gemini.protos.Tool(tools)
+
+        client = self.gemini.GenerativeModel(self.model, tools=tools)
+        chat_handler = client.start_chat(history=chat_history[:-1])
+        generation_config = self.gemini.types.GenerationConfig(
+            max_output_tokens=4096,
+            temperature=self.temperature,
+            response_mime_type='application/json' \
+                if parameters.get("response_format") == {'type': 'json_object'} else None,
+        )
+
+        completion = chat_handler.send_message(
+            chat_history[-1]["parts"],
+            generation_config=generation_config,
+        )
+
+        function_outputs = {}
+        for part in completion.parts:
+            fn = part.function_call
+            if fn:
+                print("[MLE FUNC CALL]: ", fn.name)
+                # avoid the multiple search function calls
+                search_attempts = [item for item in self.func_call_history if item['name'] in SEARCH_FUNCTIONS]
+                if len(search_attempts) > 3:
+                    parameters['functions'] = None
+                result = get_function(fn.name)(**fn.args)
+                function_outputs[fn.name] = result
+
+        if len(function_outputs):
+            response_parts = [
+                self.gemini.protos.Part(
+                    function_response=self.gemini.protos.FunctionResponse(
+                        name=fn, response={"result": val}
+                    )
+                )
+                for fn, val in function_outputs.items()
+            ]
+
+            completion = chat_handler.send_message(
+                response_parts,
+                generation_config=generation_config,
+            )
+
+        return completion.text
+
+    def stream(self, chat_history, **kwargs):
+        """
+        Stream the output from the LLM model.
+        Args:
+            chat_history: The context (chat history).
+        """
+        client = self.gemini.GenerativeModel(self.model)
+        chat_handler = client.start_chat(history=chat_history[:-1])
+        generation_config = self.gemini.types.GenerationConfig(
+            max_output_tokens=4096,
+            temperature=self.temperature,
+        )
+
+        completions = chat_handler.send_message(
+            chat_history[-1]["parts"],
+            generation_config=generation_config,
+            stream=True
+        )
+
+        for chunk in completions:
+            yield chunk.text