diff --git a/config/config.yaml b/config/config.yaml
index 1f5b85c21..e724897ee 100644
--- a/config/config.yaml
+++ b/config/config.yaml
@@ -35,6 +35,10 @@ RPM: 10
 #### if zhipuai from `https://open.bigmodel.cn`. You can set here or export API_KEY="YOUR_API_KEY"
 # ZHIPUAI_API_KEY: "YOUR_API_KEY"
 
+#### if Google Gemini from `https://ai.google.dev/` and API_KEY from `https://makersuite.google.com/app/apikey`.
+#### You can set here or export GOOGLE_API_KEY="YOUR_API_KEY"
+# GEMINI_API_KEY: "YOUR_API_KEY"
+
 #### if use self-host open llm model with openai-compatible interface
 #OPEN_LLM_API_BASE: "http://127.0.0.1:8000/v1"
 #OPEN_LLM_API_MODEL: "llama2-13b"
diff --git a/metagpt/config.py b/metagpt/config.py
index 279f929fd..5176a7677 100644
--- a/metagpt/config.py
+++ b/metagpt/config.py
@@ -7,6 +7,7 @@ Provide configuration, singleton
         2. Add the parameter `src_workspace` for the old version project path.
 """
 import os
+import warnings
 from copy import deepcopy
 from enum import Enum
 from pathlib import Path
@@ -17,6 +18,7 @@ import yaml
 from metagpt.const import DEFAULT_WORKSPACE_ROOT, METAGPT_ROOT, OPTIONS
 from metagpt.logs import logger
 from metagpt.tools import SearchEngineType, WebBrowserEngineType
+from metagpt.utils.common import require_python_version
 from metagpt.utils.singleton import Singleton
 
 
@@ -39,6 +41,7 @@ class LLMProviderEnum(Enum):
     ZHIPUAI = "zhipuai"
     FIREWORKS = "fireworks"
     OPEN_LLM = "open_llm"
+    GEMINI = "gemini"
 
 
 class Config(metaclass=Singleton):
@@ -74,10 +77,14 @@ class Config(metaclass=Singleton):
             (self.anthropic_api_key, LLMProviderEnum.ANTHROPIC),
             (self.zhipuai_api_key, LLMProviderEnum.ZHIPUAI),
             (self.fireworks_api_key, LLMProviderEnum.FIREWORKS),
-            (self.open_llm_api_base, LLMProviderEnum.OPEN_LLM),  # reuse logic. but not a key
+            (self.open_llm_api_base, LLMProviderEnum.OPEN_LLM),
+            (self.gemini_api_key, LLMProviderEnum.GEMINI),  # reuse logic. but not a key
         ]:
             if self._is_valid_llm_key(k):
-                if self.openai_api_model:
+                # logger.debug(f"Use LLMProvider: {v.value}")
+                if v == LLMProviderEnum.GEMINI and not require_python_version(req_version=(3, 10)):
+                    warnings.warn("Use Gemini requires Python >= 3.10")
+                if self.openai_api_key and self.openai_api_model:
                     logger.info(f"OpenAI API Model: {self.openai_api_model}")
                 return v
         raise NotConfiguredException("You should config a LLM configuration first")
@@ -87,7 +94,6 @@ class Config(metaclass=Singleton):
         return k and k != "YOUR_API_KEY"
 
     def _update(self):
-        # logger.info("Config loading done.")
         self.global_proxy = self._get("GLOBAL_PROXY")
 
         self.openai_api_key = self._get("OPENAI_API_KEY")
@@ -96,6 +102,7 @@ class Config(metaclass=Singleton):
         self.open_llm_api_base = self._get("OPEN_LLM_API_BASE")
         self.open_llm_api_model = self._get("OPEN_LLM_API_MODEL")
         self.fireworks_api_key = self._get("FIREWORKS_API_KEY")
+        self.gemini_api_key = self._get("GEMINI_API_KEY")
         _ = self.get_default_llm_provider_enum()
 
         self.openai_base_url = self._get("OPENAI_BASE_URL")
diff --git a/metagpt/provider/__init__.py b/metagpt/provider/__init__.py
index 56dc19b4b..a9f46eb03 100644
--- a/metagpt/provider/__init__.py
+++ b/metagpt/provider/__init__.py
@@ -6,7 +6,10 @@
 @File    : __init__.py
 """
 
+from metagpt.provider.fireworks_api import FireWorksGPTAPI
+from metagpt.provider.google_gemini_api import GeminiGPTAPI
+from metagpt.provider.open_llm_api import OpenLLMGPTAPI
 from metagpt.provider.openai_api import OpenAIGPTAPI
+from metagpt.provider.zhipuai_api import ZhiPuAIGPTAPI
 
-
-__all__ = ["OpenAIGPTAPI"]
+__all__ = ["FireWorksGPTAPI", "GeminiGPTAPI", "OpenLLMGPTAPI", "OpenAIGPTAPI", "ZhiPuAIGPTAPI"]
diff --git a/metagpt/provider/google_gemini_api.py b/metagpt/provider/google_gemini_api.py
new file mode 100644
index 000000000..682f7b507
--- /dev/null
+++ b/metagpt/provider/google_gemini_api.py
@@ -0,0 +1,142 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : Google Gemini LLM from https://ai.google.dev/tutorials/python_quickstart
+
+import google.generativeai as genai
+from google.ai import generativelanguage as glm
+from google.generativeai.generative_models import GenerativeModel
+from google.generativeai.types import content_types
+from google.generativeai.types.generation_types import (
+    AsyncGenerateContentResponse,
+    GenerateContentResponse,
+    GenerationConfig,
+)
+from tenacity import (
+    after_log,
+    retry,
+    retry_if_exception_type,
+    stop_after_attempt,
+    wait_random_exponential,
+)
+
+from metagpt.config import CONFIG, LLMProviderEnum
+from metagpt.logs import logger
+from metagpt.provider.base_gpt_api import BaseGPTAPI
+from metagpt.provider.llm_provider_registry import register_provider
+from metagpt.provider.openai_api import CostManager, log_and_reraise
+
+
+class GeminiGenerativeModel(GenerativeModel):
+    """
+    Due to `https://github.com/google/generative-ai-python/pull/123`, inherit a new class.
+    Will use default GenerativeModel if it fixed.
+    """
+
+    def count_tokens(self, contents: content_types.ContentsType) -> glm.CountTokensResponse:
+        contents = content_types.to_contents(contents)
+        return self._client.count_tokens(model=self.model_name, contents=contents)
+
+    async def count_tokens_async(self, contents: content_types.ContentsType) -> glm.CountTokensResponse:
+        contents = content_types.to_contents(contents)
+        return await self._async_client.count_tokens(model=self.model_name, contents=contents)
+
+
+@register_provider(LLMProviderEnum.GEMINI)
+class GeminiGPTAPI(BaseGPTAPI):
+    """
+    Refs to `https://ai.google.dev/tutorials/python_quickstart`
+    """
+
+    def __init__(self):
+        self.use_system_prompt = False  # google gemini has no system prompt when use api
+
+        self.__init_gemini(CONFIG)
+        self.model = "gemini-pro"  # so far only one model
+        self.llm = GeminiGenerativeModel(model_name=self.model)
+        self._cost_manager = CostManager()
+
+    def __init_gemini(self, config: CONFIG):
+        genai.configure(api_key=config.gemini_api_key)
+
+    def _user_msg(self, msg: str) -> dict[str, str]:
+        # Not to change BaseGPTAPI default functions but update with Gemini's conversation format.
+        # You should follow the format.
+        return {"role": "user", "parts": [msg]}
+
+    def _assistant_msg(self, msg: str) -> dict[str, str]:
+        return {"role": "model", "parts": [msg]}
+
+    def _const_kwargs(self, messages: list[dict], stream: bool = False) -> dict:
+        kwargs = {"contents": messages, "generation_config": GenerationConfig(temperature=0.3), "stream": stream}
+        return kwargs
+
+    def _update_costs(self, usage: dict):
+        """update each request's token cost"""
+        if CONFIG.calc_usage:
+            try:
+                prompt_tokens = int(usage.get("prompt_tokens", 0))
+                completion_tokens = int(usage.get("completion_tokens", 0))
+                self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
+            except Exception as e:
+                logger.error(f"google gemini updats costs failed! exp: {e}")
+
+    def get_choice_text(self, resp: GenerateContentResponse) -> str:
+        return resp.text
+
+    def get_usage(self, messages: list[dict], resp_text: str) -> dict:
+        req_text = messages[-1]["parts"][0] if messages else ""
+        prompt_resp = self.llm.count_tokens(contents={"role": "user", "parts": [{"text": req_text}]})
+        completion_resp = self.llm.count_tokens(contents={"role": "model", "parts": [{"text": resp_text}]})
+        usage = {"prompt_tokens": prompt_resp.total_tokens, "completion_tokens": completion_resp.total_tokens}
+        return usage
+
+    async def aget_usage(self, messages: list[dict], resp_text: str) -> dict:
+        req_text = messages[-1]["parts"][0] if messages else ""
+        prompt_resp = await self.llm.count_tokens_async(contents={"role": "user", "parts": [{"text": req_text}]})
+        completion_resp = await self.llm.count_tokens_async(contents={"role": "model", "parts": [{"text": resp_text}]})
+        usage = {"prompt_tokens": prompt_resp.total_tokens, "completion_tokens": completion_resp.total_tokens}
+        return usage
+
+    def completion(self, messages: list[dict]) -> "GenerateContentResponse":
+        resp: GenerateContentResponse = self.llm.generate_content(**self._const_kwargs(messages))
+        usage = self.get_usage(messages, resp.text)
+        self._update_costs(usage)
+        return resp
+
+    async def _achat_completion(self, messages: list[dict]) -> "AsyncGenerateContentResponse":
+        resp: AsyncGenerateContentResponse = await self.llm.generate_content_async(**self._const_kwargs(messages))
+        usage = await self.aget_usage(messages, resp.text)
+        self._update_costs(usage)
+        return resp
+
+    async def acompletion(self, messages: list[dict]) -> dict:
+        return await self._achat_completion(messages)
+
+    async def _achat_completion_stream(self, messages: list[dict]) -> str:
+        resp: AsyncGenerateContentResponse = await self.llm.generate_content_async(
+            **self._const_kwargs(messages, stream=True)
+        )
+        collected_content = []
+        async for chunk in resp:
+            content = chunk.text
+            print(content, end="")
+            collected_content.append(content)
+
+        full_content = "".join(collected_content)
+        usage = await self.aget_usage(messages, full_content)
+        self._update_costs(usage)
+        return full_content
+
+    @retry(
+        stop=stop_after_attempt(3),
+        wait=wait_random_exponential(min=1, max=60),
+        after=after_log(logger, logger.level("WARNING").name),
+        retry=retry_if_exception_type(ConnectionError),
+        retry_error_callback=log_and_reraise,
+    )
+    async def acompletion_text(self, messages: list[dict], stream=False) -> str:
+        """response in async with stream or non-stream mode"""
+        if stream:
+            return await self._achat_completion_stream(messages)
+        resp = await self._achat_completion(messages)
+        return self.get_choice_text(resp)
diff --git a/metagpt/provider/zhipuai_api.py b/metagpt/provider/zhipuai_api.py
index eef0e51e1..60d9a0777 100644
--- a/metagpt/provider/zhipuai_api.py
+++ b/metagpt/provider/zhipuai_api.py
@@ -63,7 +63,7 @@ class ZhiPuAIGPTAPI(BaseGPTAPI):
                 completion_tokens = int(usage.get("completion_tokens", 0))
                 self._cost_manager.update_cost(prompt_tokens, completion_tokens, self.model)
             except Exception as e:
-                logger.error("zhipuai updats costs failed!", e)
+                logger.error(f"zhipuai updats costs failed! exp: {e}")
 
     def get_choice_text(self, resp: dict) -> str:
         """get the first text of choice from llm response"""
diff --git a/metagpt/utils/common.py b/metagpt/utils/common.py
index e5d4573e8..8db7a80a1 100644
--- a/metagpt/utils/common.py
+++ b/metagpt/utils/common.py
@@ -19,6 +19,7 @@ import json
 import os
 import platform
 import re
+import sys
 import traceback
 import typing
 from pathlib import Path
@@ -47,6 +48,12 @@ def check_cmd_exists(command) -> int:
     return result
 
 
+def require_python_version(req_version: tuple[int]) -> bool:
+    if not (2 <= len(req_version) <= 3):
+        raise ValueError("req_version should be (3, 9) or (3, 10, 13)")
+    return True if sys.version_info > req_version else False
+
+
 class OutputParser:
     @classmethod
     def parse_blocks(cls, text: str):
diff --git a/metagpt/utils/token_counter.py b/metagpt/utils/token_counter.py
index af49845be..94b8d76d2 100644
--- a/metagpt/utils/token_counter.py
+++ b/metagpt/utils/token_counter.py
@@ -7,6 +7,7 @@
 ref1: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_count_tokens_with_tiktoken.ipynb
 ref2: https://github.com/Significant-Gravitas/Auto-GPT/blob/master/autogpt/llm/token_counter.py
 ref3: https://github.com/hwchase17/langchain/blob/master/langchain/chat_models/openai.py
+ref4: https://ai.google.dev/models/gemini
 """
 import tiktoken
 
@@ -27,6 +28,7 @@ TOKEN_COSTS = {
     "gpt-4-1106-preview": {"prompt": 0.01, "completion": 0.03},
     "text-embedding-ada-002": {"prompt": 0.0004, "completion": 0.0},
     "chatglm_turbo": {"prompt": 0.0, "completion": 0.00069},  # 32k version, prompt + completion tokens=0.005￥/k-tokens
+    "gemini-pro": {"prompt": 0.00025, "completion": 0.0005},
 }
 
 
@@ -47,6 +49,7 @@ TOKEN_MAX = {
     "gpt-4-1106-preview": 128000,
     "text-embedding-ada-002": 8192,
     "chatglm_turbo": 32768,
+    "gemini-pro": 32768,
 }
 
 
diff --git a/requirements.txt b/requirements.txt
index be7c477bf..e14c6bd3e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -49,3 +49,4 @@ aiofiles==23.2.1
 gitpython==3.1.40
 zhipuai==1.0.7
 gitignore-parser==0.1.9
+google-generativeai==0.3.1
diff --git a/tests/metagpt/provider/test_google_gemini_api.py b/tests/metagpt/provider/test_google_gemini_api.py
new file mode 100644
index 000000000..229d9b9a7
--- /dev/null
+++ b/tests/metagpt/provider/test_google_gemini_api.py
@@ -0,0 +1,41 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : the unittest of google gemini api
+
+from abc import ABC
+from dataclasses import dataclass
+
+import pytest
+
+from metagpt.provider.google_gemini_api import GeminiGPTAPI
+
+messages = [{"role": "user", "content": "who are you"}]
+
+
+@dataclass
+class MockGeminiResponse(ABC):
+    text: str
+
+
+default_resp = MockGeminiResponse(text="I'm gemini from google")
+
+
+def mock_llm_ask(self, messages: list[dict]) -> MockGeminiResponse:
+    return default_resp
+
+
+def test_gemini_completion(mocker):
+    mocker.patch("metagpt.provider.google_gemini_api.GeminiGPTAPI.completion", mock_llm_ask)
+    resp = GeminiGPTAPI().completion(messages)
+    assert resp.text == default_resp.text
+
+
+async def mock_llm_aask(self, messgaes: list[dict]) -> MockGeminiResponse:
+    return default_resp
+
+
+@pytest.mark.asyncio
+async def test_gemini_acompletion(mocker):
+    mocker.patch("metagpt.provider.google_gemini_api.GeminiGPTAPI.acompletion", mock_llm_aask)
+    resp = await GeminiGPTAPI().acompletion(messages)
+    assert resp.text == default_resp.text