diff --git a/README.md b/README.md
index 918433d510..e2529cac1c 100644
--- a/README.md
+++ b/README.md
@@ -50,6 +50,7 @@ Here is a list of the various API providers and available distributions that can
 |        NVIDIA NIM        | Hosted and Single Node |            |       ✅       |            |            |               |
 |          Chroma          |      Single Node       |            |               |     ✅      |            |               |
 |        PG Vector         |      Single Node       |            |               |     ✅      |            |               |
+|      MongoDB Atlas       |      Hosted            |            |               |     ✅      |            |               |
 |    PyTorch ExecuTorch    |     On-device iOS      |     ✅      |       ✅       |            |            |               |
 |           vLLM           | Hosted and Single Node |            |       ✅       |            |            |               |
 |          OpenAI          |         Hosted         |            |       ✅       |            |            |               |
diff --git a/docs/source/providers/vector_io/mongodb.md b/docs/source/providers/vector_io/mongodb.md
new file mode 100644
index 0000000000..67e4adec00
--- /dev/null
+++ b/docs/source/providers/vector_io/mongodb.md
@@ -0,0 +1,35 @@
+---
+orphan: true
+---
+# MongoDB Atlas
+
+[MongoDB Atlas](https://www.mongodb.com/atlas) is a cloud database service that can be used as a vector store provider for Llama Stack. It supports vector search capabilities through its Atlas Vector Search feature, allowing you to store and query vectors within your MongoDB database.
+
+## Features
+MongoDB Atlas Vector Search supports:
+- Store embeddings and their metadata
+- Vector search with multiple algorithms (cosine similarity, euclidean distance, dot product)
+- Hybrid search (combining vector and keyword search)
+- Metadata filtering
+- Scalable vector indexing
+- Managed cloud infrastructure
+
+## Usage
+
+To use MongoDB Atlas in your Llama Stack project, follow these steps:
+
+1. Create a MongoDB Atlas account and cluster.
+2. Configure your Atlas cluster to enable Vector Search.
+3. Configure your Llama Stack project to use MongoDB Atlas.
+4. Start storing and querying vectors.
+
+## Installation
+
+You can install the MongoDB Python driver using pip:
+
+```bash
+pip install pymongo
+```
+
+## Documentation
+See [MongoDB Atlas Vector Search documentation](https://www.mongodb.com/docs/atlas/atlas-vector-search/) for more details about vector search capabilities in MongoDB Atlas.
diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py
index 93031763d1..ee2d4c706d 100644
--- a/llama_stack/providers/registry/vector_io.py
+++ b/llama_stack/providers/registry/vector_io.py
@@ -110,6 +110,16 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="mongodb",
+                pip_packages=["pymongo"],
+                module="llama_stack.providers.remote.vector_io.mongodb",
+                config_class="llama_stack.providers.remote.vector_io.mongodb.MongoDBVectorIOConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
         remote_provider_spec(
             Api.vector_io,
             AdapterSpec(
diff --git a/llama_stack/providers/remote/vector_io/mongodb/__init__.py b/llama_stack/providers/remote/vector_io/mongodb/__init__.py
new file mode 100644
index 0000000000..fd46551b18
--- /dev/null
+++ b/llama_stack/providers/remote/vector_io/mongodb/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Dict
+
+from llama_stack.providers.datatypes import Api, ProviderSpec
+
+from .config import MongoDBVectorIOConfig
+
+
+async def get_adapter_impl(config: MongoDBVectorIOConfig, deps: Dict[Api, ProviderSpec]):
+    from .mongodb import MongoDBVectorIOAdapter      
+
+    impl = MongoDBVectorIOAdapter(config, deps[Api.inference])
+    await impl.initialize()
+    return impl
diff --git a/llama_stack/providers/remote/vector_io/mongodb/config.py b/llama_stack/providers/remote/vector_io/mongodb/config.py
new file mode 100644
index 0000000000..0ebaad9f6a
--- /dev/null
+++ b/llama_stack/providers/remote/vector_io/mongodb/config.py
@@ -0,0 +1,26 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any, Dict, Optional, List
+
+from pydantic import BaseModel, Field
+
+
+class MongoDBVectorIOConfig(BaseModel):
+    connection_str: str = Field(None, description="Connection string for the MongoDB Atlas collection")
+    namespace: str = Field(None, description="Namespace i.e. db_name.collection_name of the MongoDB Atlas collection")
+    index_name: Optional[str] = Field("default", description="Name of the index in the MongoDB Atlas collection") 
+    filter_fields: Optional[List[str]] = Field([], description="Fields to filter along side vector search in MongoDB Atlas collection")
+    embeddings_key: Optional[str] = Field("embeddings", description="Field name for the embeddings in the MongoDB Atlas collection")
+    text_field: Optional[str] = Field("text", description="Field name for the text in the MongoDB Atlas collection")
+
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str, **kwargs: Any) -> Dict[str, Any]:
+        return {
+            "connection_str": "{env.MONGODB_CONNECTION_STR}",
+            "namespace": "{env.MONGODB_NAMESPACE}",
+        }
\ No newline at end of file
diff --git a/llama_stack/providers/remote/vector_io/mongodb/mongodb.py b/llama_stack/providers/remote/vector_io/mongodb/mongodb.py
new file mode 100644
index 0000000000..cc172fc706
--- /dev/null
+++ b/llama_stack/providers/remote/vector_io/mongodb/mongodb.py
@@ -0,0 +1,265 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+import asyncio
+import json
+import logging
+from typing import Any, Dict, List, Optional, Union
+from urllib.parse import urlparse
+
+from pymongo import MongoClient
+from pymongo.operations import InsertOne, SearchIndexModel, UpdateOne
+import certifi
+from numpy.typing import NDArray
+
+from llama_stack.apis.inference import InterleavedContent
+from llama_stack.apis.vector_dbs import VectorDB
+from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
+
+from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
+from llama_stack.providers.utils.memory.vector_store import (
+    EmbeddingIndex,
+    VectorDBWithIndex,
+)
+  
+from .config import MongoDBVectorIOConfig
+
+from time import sleep
+
+log = logging.getLogger(__name__)
+CHUNK_ID_KEY = "_chunk_id"
+
+
+class MongoDBAtlasIndex(EmbeddingIndex):
+
+    def __init__(self, client: MongoClient, namespace: str, embeddings_key: str, embedding_dimension: str, index_name: str, filter_fields: List[str]):
+        self.client = client
+        self.namespace = namespace
+        self.embeddings_key = embeddings_key
+        self.index_name = index_name
+        self.filter_fields = filter_fields
+        self.embedding_dimension = embedding_dimension
+
+    def _get_index_config(self, collection, index_name):
+        idxs = list(collection.list_search_indexes())
+        for ele in idxs:
+            if ele["name"] == index_name:
+                return ele
+
+    def _get_search_index_model(self):
+        index_fields = [
+            {
+                "path": self.embeddings_key,
+                "type": "vector",
+                        "numDimensions": self.embedding_dimension,
+                        "similarity": "cosine"
+            }
+        ]
+
+        if len(self.filter_fields) > 0:
+            for filter_field in self.filter_fields:
+                index_fields.append(
+                    {
+                        "path": filter_field,
+                        "type": "filter"
+                    }
+                )
+
+        return SearchIndexModel(
+            name=self.index_name,
+            type="vectorSearch",
+            definition={
+                "fields": index_fields
+            }
+        )
+
+    def _check_n_create_index(self):
+        client = self.client
+        db, collection = self.namespace.split(".")
+        collection = client[db][collection]
+        index_name = self.index_name
+        idx = self._get_index_config(collection, index_name)
+        if not idx:
+            log.info("Creating search index ...")
+            search_index_model = self._get_search_index_model()
+            collection.create_search_index(search_index_model)
+            while True:
+                idx = self._get_index_config(collection, index_name)
+                if idx and idx["queryable"]:
+                    log.info("Search index created successfully.")
+                    break
+                else:
+                    log.info("Waiting for search index to be created ...")
+                    sleep(5)
+        else:
+            log.info("Search index already exists.")
+
+    async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
+        assert len(chunks) == len(embeddings), (
+            f"Chunk length {len(chunks)} does not match embedding length {len(embeddings)}"
+        )
+
+        # Create a list of operations to perform in bulk
+        operations = []
+        for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
+            chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
+
+            operations.append(
+                UpdateOne(
+                    {CHUNK_ID_KEY: chunk_id},
+                    {
+                        "$set": {
+                            CHUNK_ID_KEY: chunk_id,
+                            "chunk_content": json.loads(chunk.model_dump_json()),
+                            self.embeddings_key: embedding.tolist(),
+                        }
+                    },
+                    upsert=True,
+                )
+            )
+
+        # Perform the bulk operations
+        db, collection_name = self.namespace.split(".")
+        collection = self.client[db][collection_name]
+        collection.bulk_write(operations)
+        print(f"Added {len(chunks)} chunks to the collection")
+        # Create a search index model
+        print("Creating search index ...")
+        self._check_n_create_index()
+
+    async def query(self, embedding: NDArray, k: int, score_threshold: float) -> QueryChunksResponse:
+        # Perform a query
+        db, collection_name = self.namespace.split(".")
+        collection = self.client[db][collection_name]
+
+        # Create vector search query
+        vs_query = {"$vectorSearch":
+                    {
+                        "index": self.index_name,
+                        "path": self.embeddings_key,
+                        "queryVector": embedding.tolist(),
+                        "numCandidates": k,
+                        "limit": k,
+                    }
+                    }
+        # Add a field to store the score
+        score_add_field_query = {
+            "$addFields": {
+                "score": {"$meta": "vectorSearchScore"}
+            }
+        }
+        if score_threshold is None:
+            score_threshold = 0.01
+        # Filter the results based on the score threshold
+        filter_query = {
+            "$match": {
+                "score": {"$gt": score_threshold}
+            }
+        }
+
+        project_query = {
+            "$project": {
+                CHUNK_ID_KEY: 1,
+                "chunk_content": 1,
+                "score": 1,
+                "_id": 0,
+            }
+        }
+
+        query = [vs_query, score_add_field_query, filter_query, project_query]
+
+        results = collection.aggregate(query)
+
+        # Create the response
+        chunks = []
+        scores = []
+        for result in results:
+            content = result["chunk_content"]
+            chunk = Chunk(
+                metadata=content["metadata"],
+                content=content["content"],
+            )
+            chunks.append(chunk)
+            scores.append(result["score"])
+
+        return QueryChunksResponse(chunks=chunks, scores=scores)
+
+    async def delete(self):
+        db, _ = self.namespace.split(".")
+        self.client.drop_database(db)
+
+
+class MongoDBVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
+    def __init__(self, config: MongoDBVectorIOConfig, inference_api: Api.inference):
+        self.config = config
+        self.inference_api = inference_api
+        self.cache = {}
+
+    async def initialize(self) -> None:
+        self.client = MongoClient(
+            self.config.connection_str,
+            tlsCAFile=certifi.where(),
+        )
+
+    async def shutdown(self) -> None:
+        if not self.client:
+            self.client.close()
+
+    async def register_vector_db(self, vector_db: VectorDB) -> None:
+        index=MongoDBAtlasIndex(
+                client=self.client,
+                namespace=self.config.namespace,
+                embeddings_key=self.config.embeddings_key,
+                embedding_dimension=vector_db.embedding_dimension,
+                index_name=self.config.index_name,
+                filter_fields=self.config.filter_fields,
+            )
+        self.cache[vector_db.identifier] = VectorDBWithIndex(
+            vector_db=vector_db,
+            index=index,
+            inference_api=self.inference_api,
+        )
+
+    async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> VectorDBWithIndex:
+        if vector_db_id in self.cache:
+            return self.cache[vector_db_id]
+        vector_db = await self.vector_db_store.get_vector_db(vector_db_id)
+        self.cache[vector_db_id] = VectorDBWithIndex(
+            vector_db=vector_db_id,
+            index=MongoDBAtlasIndex(
+                client=self.client,
+                namespace=self.config.namespace,
+                embeddings_key=self.config.embeddings_key,
+                embedding_dimension=vector_db.embedding_dimension,
+                index_name=self.config.index_name,
+                filter_fields=self.config.filter_fields,
+            ),
+            inference_api=self.inference_api,
+        )
+        return self.cache[vector_db_id]
+
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
+        await self.cache[vector_db_id].index.delete()
+        del self.cache[vector_db_id]
+
+    async def insert_chunks(self,
+                            vector_db_id: str,
+                            chunks: List[Chunk],
+                            ttl_seconds: Optional[int] = None,
+                            ) -> None:
+        index = await self._get_and_cache_vector_db_index(vector_db_id)
+        if not index:
+            raise ValueError(f"Vector DB {vector_db_id} not found")
+        await index.insert_chunks(chunks)
+
+    async def query_chunks(self,
+                           vector_db_id: str,
+                           query: InterleavedContent,
+                           params: Optional[Dict[str, Any]] = None,
+                           ) -> QueryChunksResponse:
+        index = await self._get_and_cache_vector_db_index(vector_db_id)
+        if not index:
+            raise ValueError(f"Vector DB {vector_db_id} not found")
+        return await index.query_chunks(query, params)