diff --git a/libs/aws/langchain_aws/__init__.py b/libs/aws/langchain_aws/__init__.py
index 29f4bbc6..80d54cb5 100644
--- a/libs/aws/langchain_aws/__init__.py
+++ b/libs/aws/langchain_aws/__init__.py
@@ -14,6 +14,7 @@
     InMemorySemanticCache,
     InMemoryVectorStore,
 )
+from langchain_aws.rerank.rerank import BedrockRerank
 
 __all__ = [
     "BedrockEmbeddings",
@@ -29,4 +30,5 @@
     "NeptuneGraph",
     "InMemoryVectorStore",
     "InMemorySemanticCache",
+    "BedrockRerank"
 ]
diff --git a/libs/aws/langchain_aws/rerank/__init__.py b/libs/aws/langchain_aws/rerank/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libs/aws/langchain_aws/rerank/rerank.py b/libs/aws/langchain_aws/rerank/rerank.py
new file mode 100644
index 00000000..aabed315
--- /dev/null
+++ b/libs/aws/langchain_aws/rerank/rerank.py
@@ -0,0 +1,126 @@
+import json
+from copy import deepcopy
+from typing import Any, Dict, List, Optional, Sequence, Union
+
+import boto3
+from langchain_core.callbacks.manager import Callbacks
+from langchain_core.documents import BaseDocumentCompressor, Document
+from langchain_core.utils import from_env
+from pydantic import ConfigDict, Field, model_validator
+from typing_extensions import Self
+
+
+class BedrockRerank(BaseDocumentCompressor):
+    """Document compressor that uses AWS Bedrock Rerank API."""
+
+    client: Any = None
+    """Bedrock client to use for compressing documents."""
+    top_n: Optional[int] = 3
+    """Number of documents to return."""
+    model: Optional[str] = "amazon.rerank-v1:0"
+    """Model to use for reranking. Default is amazon.rerank-v1:0."""
+    aws_region: str = Field(
+        default_factory=from_env("AWS_DEFAULT_REGION", default="us-west-2")
+    )
+    """AWS region to initialize the Bedrock client."""
+    aws_profile: Optional[str] = Field(
+        default_factory=from_env("AWS_PROFILE", default=None)
+    )
+    """AWS profile for authentication, optional."""
+
+    model_config = ConfigDict(
+        extra="forbid",
+        arbitrary_types_allowed=True,
+    )
+
+    @model_validator(mode="after")
+    def initialize_client(self) -> Self:
+        """Initialize the AWS Bedrock client."""
+        if not self.client:
+            session = (
+                boto3.Session(profile_name=self.aws_profile)
+                if self.aws_profile
+                else boto3.Session()
+            )
+            self.client = session.client("bedrock-runtime", region_name=self.aws_region)
+        return self
+
+    def rerank(
+        self,
+        documents: Sequence[Union[str, Document, dict]],
+        query: str,
+        *,
+        top_n: Optional[int] = None,
+        model: Optional[str] = None,
+    ) -> List[Dict[str, Any]]:
+        """Returns an ordered list of documents based on their relevance to the query.
+
+        Args:
+            query: The query to use for reranking.
+            documents: A sequence of documents to rerank.
+            top_n: The number of top-ranked results to return. Defaults to self.top_n.
+            model: The model to use for reranking. Defaults to self.model.
+
+        Returns:
+            List[Dict[str, Any]]: A list of ranked documents with relevance scores.
+        """
+        if len(documents) == 0:
+            return []
+
+        # Serialize documents for the Bedrock API
+        serialized_documents = [
+            json.dumps(doc)
+            if isinstance(doc, dict)
+            else doc.page_content
+            if isinstance(doc, Document)
+            else doc
+            for doc in documents
+        ]
+
+        body = json.dumps(
+            {
+                "query": query,
+                "documents": serialized_documents,
+                "top_n": top_n or self.top_n,
+            }
+        )
+
+        response = self.client.invoke_model(
+            modelId=model or self.model,
+            accept="application/json",
+            contentType="application/json",
+            body=body,
+        )
+
+        response_body = json.loads(response.get("body").read())
+        results = [
+            {"index": result["index"], "relevance_score": result["relevance_score"]}
+            for result in response_body["results"]
+        ]
+
+        return results
+
+    def compress_documents(
+        self,
+        documents: Sequence[Document],
+        query: str,
+        callbacks: Optional[Callbacks] = None,
+    ) -> Sequence[Document]:
+        """
+        Compress documents using Bedrock's rerank API.
+
+        Args:
+            documents: A sequence of documents to compress.
+            query: The query to use for compressing the documents.
+            callbacks: Callbacks to run during the compression process.
+
+        Returns:
+            A sequence of compressed documents.
+        """
+        compressed = []
+        for res in self.rerank(documents, query):
+            doc = documents[res["index"]]
+            doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
+            doc_copy.metadata["relevance_score"] = res["relevance_score"]
+            compressed.append(doc_copy)
+        return compressed
diff --git a/libs/aws/tests/unit_tests/rerank/__init__.py b/libs/aws/tests/unit_tests/rerank/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/libs/aws/tests/unit_tests/rerank/test_rerank.py b/libs/aws/tests/unit_tests/rerank/test_rerank.py
new file mode 100644
index 00000000..ac99df93
--- /dev/null
+++ b/libs/aws/tests/unit_tests/rerank/test_rerank.py
@@ -0,0 +1,75 @@
+import json
+from unittest.mock import MagicMock
+
+import pytest
+from langchain_core.documents import Document
+
+from langchain_aws import BedrockRerank
+
+
+# Mock setup
+@pytest.fixture
+def mock_bedrock_client():
+    mock_client = MagicMock()
+    mock_client.invoke_model.return_value = {
+        "body": MagicMock(
+            read=MagicMock(
+                return_value=json.dumps(
+                    {
+                        "results": [
+                            {"index": 0, "relevance_score": 0.95},
+                            {"index": 1, "relevance_score": 0.90},
+                        ]
+                    }
+                )
+            )
+        )
+    }
+    return mock_client
+
+
+@pytest.fixture
+def bedrock_rerank(mock_bedrock_client):
+    return BedrockRerank(client=mock_bedrock_client)
+
+
+# Test initialize_client
+def test_initialize_client_with_profile():
+    bedrock_rerank = BedrockRerank(aws_profile="default")
+    bedrock_rerank.initialize_client()
+    assert bedrock_rerank.client is not None
+
+
+def test_initialize_client_without_profile():
+    bedrock_rerank = BedrockRerank()
+    bedrock_rerank.initialize_client()
+    assert bedrock_rerank.client is not None
+
+
+# Test rerank method
+def test_rerank_success(bedrock_rerank):
+    documents = ["doc1", "doc2", "doc3"]
+    query = "Test query"
+    results = bedrock_rerank.rerank(documents, query)
+    assert len(results) == 2
+    assert results[0]["index"] == 0
+    assert results[0]["relevance_score"] == 0.95
+
+
+def test_rerank_empty_documents(bedrock_rerank):
+    results = bedrock_rerank.rerank([], "query")
+    assert results == []
+
+
+# Test compress_documents method
+def test_compress_documents(bedrock_rerank):
+    documents = [
+        Document(page_content="doc1"),
+        Document(page_content="doc2"),
+        Document(page_content="doc3"),
+    ]
+    query = "Test query"
+    compressed = bedrock_rerank.compress_documents(documents, query)
+    assert len(compressed) == 2
+    assert compressed[0].metadata["relevance_score"] == 0.95
+    assert compressed[1].metadata["relevance_score"] == 0.90