diff --git a/libs/langchain/langchain/chat_models/bedrock.py b/libs/langchain/langchain/chat_models/bedrock.py
index 642ef76cdf4..34bb97f0f79 100644
--- a/libs/langchain/langchain/chat_models/bedrock.py
+++ b/libs/langchain/langchain/chat_models/bedrock.py
@@ -5,6 +5,7 @@ from langchain.callbacks.manager import (
 )
 from langchain.chat_models.anthropic import convert_messages_to_prompt_anthropic
 from langchain.chat_models.base import BaseChatModel
+from langchain.chat_models.meta import convert_messages_to_prompt_llama
 from langchain.llms.bedrock import BedrockBase
 from langchain.pydantic_v1 import Extra
 from langchain.schema.messages import AIMessage, AIMessageChunk, BaseMessage
@@ -26,6 +27,8 @@ class ChatPromptAdapter:
     ) -> str:
         if provider == "anthropic":
             prompt = convert_messages_to_prompt_anthropic(messages=messages)
+        if provider == "meta":
+            prompt = convert_messages_to_prompt_llama(messages=messages)
         else:
             raise NotImplementedError(
                 f"Provider {provider} model does not support chat."
diff --git a/libs/langchain/langchain/chat_models/meta.py b/libs/langchain/langchain/chat_models/meta.py
new file mode 100644
index 00000000000..c087ee2b1d2
--- /dev/null
+++ b/libs/langchain/langchain/chat_models/meta.py
@@ -0,0 +1,29 @@
+from typing import List
+
+from langchain.schema.messages import (
+    AIMessage,
+    BaseMessage,
+    ChatMessage,
+    HumanMessage,
+    SystemMessage,
+)
+
+
+def _convert_one_message_to_text_llama(message: BaseMessage) -> str:
+    if isinstance(message, ChatMessage):
+        message_text = f"\n\n{message.role.capitalize()}: {message.content}"
+    elif isinstance(message, HumanMessage):
+        message_text = f"[INST] {message.content} [/INST]"
+    elif isinstance(message, AIMessage):
+        message_text = f"{message.content}"
+    elif isinstance(message, SystemMessage):
+        message_text = f"<<SYS>> {message.content} <</SYS>>"
+    else:
+        raise ValueError(f"Got unknown type {message}")
+    return message_text
+
+
+def convert_messages_to_prompt_llama(messages: List[BaseMessage]) -> str:
+    return "\n".join(
+        [_convert_one_message_to_text_llama(message) for message in messages]
+    )
diff --git a/libs/langchain/langchain/llms/bedrock.py b/libs/langchain/langchain/llms/bedrock.py
index a7fe68b0421..e21bd4d088b 100644
--- a/libs/langchain/langchain/llms/bedrock.py
+++ b/libs/langchain/langchain/llms/bedrock.py
@@ -72,6 +72,7 @@ class LLMInputOutputAdapter:
         "anthropic": "completion",
         "amazon": "outputText",
         "cohere": "text",
+        "meta": "generation",
     }
 
     @classmethod
@@ -81,7 +82,7 @@ class LLMInputOutputAdapter:
         input_body = {**model_kwargs}
         if provider == "anthropic":
             input_body["prompt"] = _human_assistant_format(prompt)
-        elif provider == "ai21" or provider == "cohere":
+        elif provider in ("ai21", "cohere", "meta"):
             input_body["prompt"] = prompt
         elif provider == "amazon":
             input_body = dict()
@@ -107,6 +108,8 @@ class LLMInputOutputAdapter:
             return response_body.get("completions")[0].get("data").get("text")
         elif provider == "cohere":
             return response_body.get("generations")[0].get("text")
+        elif provider == "meta":
+            return response_body.get("generation")
         else:
             return response_body.get("results")[0].get("outputText")
 
diff --git a/libs/langchain/tests/unit_tests/chat_models/test_bedrock.py b/libs/langchain/tests/unit_tests/chat_models/test_bedrock.py
new file mode 100644
index 00000000000..e92caf5c816
--- /dev/null
+++ b/libs/langchain/tests/unit_tests/chat_models/test_bedrock.py
@@ -0,0 +1,30 @@
+"""Test Anthropic Chat API wrapper."""
+from typing import List
+
+import pytest
+
+from langchain.chat_models.meta import convert_messages_to_prompt_llama
+from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage
+
+
+@pytest.mark.parametrize(
+    ("messages", "expected"),
+    [
+        ([HumanMessage(content="Hello")], "[INST] Hello [/INST]"),
+        (
+            [HumanMessage(content="Hello"), AIMessage(content="Answer:")],
+            "[INST] Hello [/INST]\nAnswer:",
+        ),
+        (
+            [
+                SystemMessage(content="You're an assistant"),
+                HumanMessage(content="Hello"),
+                AIMessage(content="Answer:"),
+            ],
+            "<<SYS>> You're an assistant <</SYS>>\n[INST] Hello [/INST]\nAnswer:",
+        ),
+    ],
+)
+def test_formatting(messages: List[BaseMessage], expected: str) -> None:
+    result = convert_messages_to_prompt_llama(messages)
+    assert result == expected