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
@@ -8,16 +8,37 @@ 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"
 
+        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)
 

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),

tests/llms/test_gemini.py

@@ -4,6 +4,7 @@ import pytest
 from google.genai import types
 
 from mem0.configs.llms.base import BaseLlmConfig
+from mem0.configs.llms.gemini import GeminiConfig
 from mem0.llms.gemini import GeminiLLM
 
 
@@ -229,3 +230,71 @@ def test_explicit_config_values_passed_to_generation_config(mock_gemini_client:
     assert config_arg.max_output_tokens == 200
     assert "top_p" in config_arg.model_fields_set
     assert config_arg.top_p == 0.9
+
+
+# --- Vertex AI backend initialization (issue #3990, PR #4030) ---
+
+
+def test_init_default_path_uses_api_key(monkeypatch):
+    """Default (non-Vertex) path stays backward compatible: client built with api_key, never vertexai."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    monkeypatch.setenv("GOOGLE_API_KEY", "test-key")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(model="gemini-2.0-flash"))
+    mock_client_class.assert_called_once_with(api_key="test-key")
+
+
+def test_init_backward_compat_with_base_config(monkeypatch):
+    """A legacy BaseLlmConfig still works and uses the API-key path (no missing-attr error)."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    monkeypatch.setenv("GOOGLE_API_KEY", "legacy-key")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(BaseLlmConfig(model="gemini-2.0-flash"))
+    mock_client_class.assert_called_once_with(api_key="legacy-key")
+
+
+def test_init_vertexai_via_explicit_config(monkeypatch):
+    """vertexai=True in GeminiConfig routes to the Vertex AI client with project/location, no api_key."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(vertexai=True, project="my-project", location="europe-west1"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="my-project", location="europe-west1")
+
+
+def test_init_vertexai_via_dict_config(monkeypatch):
+    """The factory hands GeminiLLM a dict; the vertexai key still routes to the Vertex client."""
+    monkeypatch.delenv("GOOGLE_GENAI_USE_VERTEXAI", raising=False)
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM({"vertexai": True, "project": "p", "location": "us-central1"})
+    mock_client_class.assert_called_once_with(vertexai=True, project="p", location="us-central1")
+
+
+def test_init_vertexai_via_env_vars(monkeypatch):
+    """GOOGLE_GENAI_USE_VERTEXAI + project/location env vars enable Vertex (the exact ask in issue #3990)."""
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    monkeypatch.setenv("GOOGLE_CLOUD_PROJECT", "env-project")
+    monkeypatch.setenv("GOOGLE_CLOUD_LOCATION", "asia-south1")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig())
+    mock_client_class.assert_called_once_with(vertexai=True, project="env-project", location="asia-south1")
+
+
+def test_init_vertexai_location_defaults_to_us_central1(monkeypatch):
+    """When Vertex is on but no location is supplied, it defaults to us-central1."""
+    monkeypatch.delenv("GOOGLE_CLOUD_LOCATION", raising=False)
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(GeminiConfig(project="p"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="p", location="us-central1")
+
+
+def test_init_base_config_respects_vertexai_env(monkeypatch):
+    """GOOGLE_GENAI_USE_VERTEXAI is the authoritative global switch (Google's own
+    convention): a legacy BaseLlmConfig is routed to Vertex when the env var is set,
+    even if it carries an api_key. This pins the precedence as intentional."""
+    monkeypatch.setenv("GOOGLE_GENAI_USE_VERTEXAI", "true")
+    monkeypatch.setenv("GOOGLE_CLOUD_PROJECT", "env-project")
+    monkeypatch.setenv("GOOGLE_CLOUD_LOCATION", "us-west1")
+    with patch("mem0.llms.gemini.genai.Client") as mock_client_class:
+        GeminiLLM(BaseLlmConfig(model="gemini-2.0-flash", api_key="ignored-key"))
+    mock_client_class.assert_called_once_with(vertexai=True, project="env-project", location="us-west1")