diff --git a/walledeval/llm/__init__.py b/walledeval/llm/__init__.py
index 0660c36..0268d90 100644
--- a/walledeval/llm/__init__.py
+++ b/walledeval/llm/__init__.py
@@ -26,6 +26,7 @@
     "Claude", "OpenAI",
     "Gemini",
     "AzureOpenAI",
+    "Bedrock",
     "Together", "Anyscale",
     "OctoAI", "Groq",
 ]
diff --git a/walledeval/llm/bedrock.py b/walledeval/llm/bedrock.py
new file mode 100644
index 0000000..4ef8314
--- /dev/null
+++ b/walledeval/llm/bedrock.py
@@ -0,0 +1,56 @@
+import boto3
+
+
+from typing import Optional, Union
+
+from walledeval.types import Messages, LLMType
+from walledeval.util import transform_messages
+from walledeval.llm.core import LLM
+
+__all__ = [
+    "Bedrock"
+]
+
+class Bedrock(LLM):
+    def __init__(self,
+                 model_id: str,
+                 access_key_id:str,
+                 access_key: str,
+                 region_name: str,
+                 system_prompt: str = "",
+                 type: Optional[Union[LLMType, int]] = LLMType.NEITHER):
+        super().__init__(
+            model_id, system_prompt,
+            type
+        )
+        session = boto3.Session(
+            aws_access_key_id=access_key_id,
+            aws_secret_access_key=access_key,
+            region_name=region_name
+        )
+        self.client = session.client('bedrock-runtime', region_name)
+
+    def chat(self,
+             text: Messages,
+             max_new_tokens: int = 1024,
+             temperature: float = 0.1,
+             topP: float = 0.9) -> str:
+        messages = transform_messages(text, self.system_prompt)
+
+        response = self.client.converse(
+            modelId=self.model_id,#"meta.llama2-13b-chat-v1",
+            messages=messages,
+            inferenceConfig={"maxTokens":max_new_tokens,"temperature":temperature,"topP":topP},
+            additionalModelRequestFields={}
+        )
+        # Extract and print the response text.
+        response_text = response["output"]["message"]["content"][0]["text"]
+        return response_text
+
+    def complete(self,
+                 text: str,
+                 max_new_tokens: int = 1024,
+                 temperature: float = 0.1) -> str:
+        text = f"Continue writing: {text}"
+        
+        return self.chat(text, max_new_tokens=max_new_tokens, temperature=temperature)