feat: support Gemini via Vertex AI as LLM provider (#4030)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

mem0/configs/llms/gemini.py

@@ -0,0 +1,64 @@
+import os
+from typing import Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+
+
+class GeminiConfig(BaseLlmConfig):
+    """
+    Configuration class for Google Gemini LLM.
+
+    Supports both the Gemini Developer API (via API key) and Vertex AI (via GCP credentials).
+    """
+
+    def __init__(
+        self,
+        # Base parameters
+        model: Optional[str] = None,
+        temperature: float = 0.1,
+        api_key: Optional[str] = None,
+        max_tokens: int = 2000,
+        top_p: float = 0.1,
+        top_k: int = 1,
+        enable_vision: bool = False,
+        vision_details: Optional[str] = "auto",
+        http_client_proxies: Optional[dict] = None,
+        # Gemini-specific parameters
+        vertexai: Optional[bool] = None,
+        project: Optional[str] = None,
+        location: Optional[str] = None,
+    ):
+        """
+        Initialize Gemini configuration.
+
+        Args:
+            model: Gemini model to use (e.g., "gemini-2.0-flash"), defaults to None
+            temperature: Controls randomness, defaults to 0.1
+            api_key: Google API key for the Gemini Developer API, defaults to None
+            max_tokens: Maximum tokens to generate, defaults to 2000
+            top_p: Nucleus sampling parameter, defaults to 0.1
+            top_k: Top-k sampling parameter, defaults to 1
+            enable_vision: Enable vision capabilities, defaults to False
+            vision_details: Vision detail level, defaults to "auto"
+            http_client_proxies: HTTP client proxy settings, defaults to None
+            vertexai: Whether to use Vertex AI backend. If None, checks GOOGLE_GENAI_USE_VERTEXAI env var.
+            project: GCP project ID for Vertex AI. If None, checks GOOGLE_CLOUD_PROJECT env var.
+            location: GCP location for Vertex AI. If None, checks GOOGLE_CLOUD_LOCATION env var.
+        """
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            top_k=top_k,
+            enable_vision=enable_vision,
+            vision_details=vision_details,
+            http_client_proxies=http_client_proxies,
+        )
+
+        if vertexai is None:
+            vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() in ("true", "1", "yes")
+        self.vertexai = vertexai
+        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION", "us-central1")

mem0/llms/gemini.py

@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Union
 
 try:
     from google import genai
@@ -8,18 +8,39 @@ except ImportError:
     raise ImportError("The 'google-genai' library is required. Please install it using 'pip install google-genai'.")
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.llms.base import LLMBase
 
 
 class GeminiLLM(LLMBase):
-    def __init__(self, config: Optional[BaseLlmConfig] = None):
+    def __init__(self, config: Optional[Union[BaseLlmConfig, GeminiConfig, Dict]] = None):
+        # Convert to GeminiConfig if needed
+        if config is None:
+            config = GeminiConfig()
+        elif isinstance(config, dict):
+            config = GeminiConfig(**config)
+        elif isinstance(config, BaseLlmConfig) and not isinstance(config, GeminiConfig):
+            config = GeminiConfig(
+                model=config.model,
+                temperature=config.temperature,
+                api_key=config.api_key,
+                max_tokens=config.max_tokens,
+                top_p=config.top_p,
+                top_k=config.top_k,
+                enable_vision=config.enable_vision,
+                vision_details=config.vision_details,
+            )
+
         super().__init__(config)
 
         if not self.config.model:
             self.config.model = "gemini-2.0-flash"
 
-        api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
-        self.client = genai.Client(api_key=api_key)
+        if self.config.vertexai:
+            self.client = genai.Client(vertexai=True, project=self.config.project, location=self.config.location)
+        else:
+            api_key = self.config.api_key or os.getenv("GOOGLE_API_KEY")
+            self.client = genai.Client(api_key=api_key)
 
     def _parse_response(self, response, tools):
         """

mem0/utils/factory.py

@@ -7,6 +7,7 @@ from mem0.configs.llms.aws_bedrock import AWSBedrockConfig
 from mem0.configs.llms.azure import AzureOpenAIConfig
 from mem0.configs.llms.base import BaseLlmConfig
 from mem0.configs.llms.deepseek import DeepSeekConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.configs.llms.lmstudio import LMStudioConfig
 from mem0.configs.llms.minimax import MinimaxConfig
 from mem0.configs.llms.ollama import OllamaConfig
@@ -46,7 +47,7 @@ class LlmFactory:
         "openai_structured": ("mem0.llms.openai_structured.OpenAIStructuredLLM", OpenAIConfig),
         "anthropic": ("mem0.llms.anthropic.AnthropicLLM", AnthropicConfig),
         "azure_openai_structured": ("mem0.llms.azure_openai_structured.AzureOpenAIStructuredLLM", AzureOpenAIConfig),
-        "gemini": ("mem0.llms.gemini.GeminiLLM", BaseLlmConfig),
+        "gemini": ("mem0.llms.gemini.GeminiLLM", GeminiConfig),
         "deepseek": ("mem0.llms.deepseek.DeepSeekLLM", DeepSeekConfig),
         "minimax": ("mem0.llms.minimax.MiniMaxLLM", MinimaxConfig),
         "xai": ("mem0.llms.xai.XAILLM", XAIConfig),