Add Grok Support (#2260)

mem0/llms/configs.py

@@ -23,6 +23,7 @@ class LlmConfig(BaseModel):
             "azure_openai_structured",
             "gemini",
             "deepseek",
+            "xai"
         ):
             return v
         else:

mem0/llms/xai.py

@@ -0,0 +1,48 @@
+import os
+from typing import Dict, List, Optional
+
+from openai import OpenAI
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.base import LLMBase
+
+
+class XAILLM(LLMBase):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config)
+
+        if not self.config.model:
+            self.config.model = "grok-2-latest"
+
+        api_key = self.config.api_key or os.getenv("XAI_API_KEY")
+        base_url = self.config.xai_base_url or os.getenv("XAI_API_BASE") or "https://api.x.ai/v1"
+        self.client = OpenAI(api_key=api_key, base_url=base_url)
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None
+    ):
+        """
+        Generate a response based on the given messages using XAI.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Defaults to "text".
+
+        Returns:
+            str: The generated response.
+        """
+        params = {
+            "model": self.config.model,
+            "messages": messages,
+            "temperature": self.config.temperature,
+            "max_tokens": self.config.max_tokens,
+            "top_p": self.config.top_p,
+        }
+
+        if response_format:
+            params["response_format"] = response_format
+
+        response = self.client.chat.completions.create(**params)
+        return response.choices[0].message.content