diff --git a/litellm/llms/mongodb/__init__.py b/litellm/llms/mongodb/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/mongodb/common_utils.py b/litellm/llms/mongodb/common_utils.py
new file mode 100644
index 00000000000..7f26eadb08f
--- /dev/null
+++ b/litellm/llms/mongodb/common_utils.py
@@ -0,0 +1,163 @@
+"""Shared helpers for MongoDB Atlas integrations.
+
+pymongo ships in the optional ``mongodb`` extra, so every import of it is
+deferred to call time and raises an actionable error when it is absent.
+
+Clients are cached per connection because building one costs an SRV lookup, a
+TLS handshake and topology discovery: measured at ~890ms against Atlas versus
+~80ms on a warm client, so a client per search would dominate query latency.
+"""
+
+import asyncio
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Final
+
+if TYPE_CHECKING:
+    from pymongo import AsyncMongoClient, MongoClient
+
+PYMONGO_INSTALL_HINT: Final = (
+    "The MongoDB vector store requires the 'pymongo' package. "
+    "Run 'pip install litellm[mongodb]' (or 'pip install pymongo') to install it."
+)
+
+DEFAULT_CONNECT_TIMEOUT_MS: Final = 10_000
+DEFAULT_SOCKET_TIMEOUT_MS: Final = 30_000
+DEFAULT_SERVER_SELECTION_TIMEOUT_MS: Final = 10_000
+
+_MAX_CACHED_CLIENTS: Final = 32
+
+_APP_NAME: Final = "litellm"
+
+
+@dataclass(frozen=True, slots=True)
+class MongoClientKey:
+    connection_string: str
+    connect_timeout_ms: int
+    socket_timeout_ms: int
+    server_selection_timeout_ms: int
+
+
+_sync_clients: dict[MongoClientKey, "MongoClient"] = {}  # mutable-ok: process-level connection cache, see module docstring
+_async_clients: dict[tuple[MongoClientKey, int], "AsyncMongoClient"] = {}  # mutable-ok: same cache, keyed per event loop
+
+
+def import_sync_mongo_client() -> "type[MongoClient]":
+    try:
+        from pymongo import MongoClient as SyncMongoClient
+    except ImportError as e:
+        raise ValueError(PYMONGO_INSTALL_HINT) from e
+    return SyncMongoClient
+
+
+def import_async_mongo_client() -> "type[AsyncMongoClient]":
+    try:
+        from pymongo import AsyncMongoClient as AsyncMongoClientClass
+    except ImportError as e:
+        raise ValueError(PYMONGO_INSTALL_HINT) from e
+    return AsyncMongoClientClass
+
+
+def _client_kwargs(key: MongoClientKey) -> dict[str, object]:
+    return {  # mutable-ok: pymongo's client constructor takes keyword arguments
+        "connectTimeoutMS": key.connect_timeout_ms,
+        "socketTimeoutMS": key.socket_timeout_ms,
+        "serverSelectionTimeoutMS": key.server_selection_timeout_ms,
+        "appname": _APP_NAME,
+    }
+
+
+def get_sync_client(key: MongoClientKey) -> "MongoClient":
+    cached: Final = _sync_clients.get(key)
+    if cached is not None:
+        return cached
+    client: Final = import_sync_mongo_client()(key.connection_string, **_client_kwargs(key))
+    if len(_sync_clients) < _MAX_CACHED_CLIENTS:
+        _sync_clients[key] = client
+    return client
+
+
+def get_async_client(key: MongoClientKey) -> "AsyncMongoClient":
+    """Async clients bind to the loop that created them, so the cache is keyed per loop."""
+    loop_key: Final = (key, id(asyncio.get_running_loop()))
+    cached: Final = _async_clients.get(loop_key)
+    if cached is not None:
+        return cached
+    client: Final = import_async_mongo_client()(key.connection_string, **_client_kwargs(key))
+    if len(_async_clients) < _MAX_CACHED_CLIENTS:
+        _async_clients[loop_key] = client
+    return client
+
+
+def reset_client_cache() -> None:
+    _sync_clients.clear()
+    _async_clients.clear()
+
+
+_AUTHENTICATION_FAILED_CODE: Final = 18
+_UNAUTHORIZED_CODE: Final = 13
+
+
+def _index_hint(index_name: str, database: str, collection: str) -> str:
+    return (
+        f"No queryable Atlas Vector Search index named '{index_name}' was found on "
+        f"'{database}.{collection}'. Confirm the index exists on that exact collection, that its "
+        "status is READY rather than still building, and that the vector store id matches the index name."
+    )
+
+
+def translate_mongo_error(error: Exception, index_name: str, database: str, collection: str) -> Exception:
+    """Turn a driver failure into a message that names the misconfiguration, never a silent empty result.
+
+    Returns the exception to raise so callers keep the original as ``__cause__``.
+    """
+    try:
+        from pymongo.errors import (
+            ConfigurationError,
+            ExecutionTimeout,
+            InvalidOperation,
+            NetworkTimeout,
+            OperationFailure,
+            ServerSelectionTimeoutError,
+        )
+    except ImportError:
+        return error
+
+    if isinstance(error, ServerSelectionTimeoutError):
+        return ValueError(
+            "Could not reach the MongoDB deployment before the timeout. On Atlas this is usually the "
+            "project's IP access list not containing this host, or a paused cluster; it can also be an "
+            f"unresolvable hostname. Driver detail: {error}"
+        )
+    if isinstance(error, OperationFailure):
+        code: Final = error.code
+        if code in (_AUTHENTICATION_FAILED_CODE, _UNAUTHORIZED_CODE):
+            return ValueError(
+                "MongoDB rejected the credentials in mongodb_connection_string, or the database user "
+                f"lacks read access to '{database}.{collection}'. Driver detail: {error.details}"
+            )
+        detail: Final = str(error).lower()
+        if "index" in detail and ("not found" in detail or "does not exist" in detail or "unknown" in detail):
+            return ValueError(f"{_index_hint(index_name, database, collection)} Driver detail: {error}")
+        if "dimension" in detail or "numdimensions" in detail or "queryvector" in detail:
+            return ValueError(
+                "The query embedding does not match the vector dimensions the Atlas index was built for. "
+                "litellm_embedding_model must be the same model that produced the stored vectors. "
+                f"Driver detail: {error}"
+            )
+        return ValueError(
+            f"MongoDB rejected the vector search against '{database}.{collection}' using index "
+            f"'{index_name}'. Driver detail: {error}"
+        )
+    if isinstance(error, (NetworkTimeout, ExecutionTimeout)):
+        return ValueError(
+            f"The MongoDB vector search against '{database}.{collection}' timed out before returning. "
+            f"Driver detail: {error}"
+        )
+    if isinstance(error, ConfigurationError):
+        return ValueError(
+            "mongodb_connection_string is not a usable MongoDB connection string. "
+            f"Driver detail: {error}"
+        )
+    if isinstance(error, InvalidOperation):
+        return ValueError(f"The MongoDB client was already closed or is unusable. Driver detail: {error}")
+    return error
diff --git a/litellm/llms/mongodb/vector_stores/__init__.py b/litellm/llms/mongodb/vector_stores/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/litellm/llms/mongodb/vector_stores/transformation.py b/litellm/llms/mongodb/vector_stores/transformation.py
new file mode 100644
index 00000000000..efeff16ae5a
--- /dev/null
+++ b/litellm/llms/mongodb/vector_stores/transformation.py
@@ -0,0 +1,337 @@
+"""MongoDB Atlas vector store provider.
+
+Atlas Vector Search has no HTTP query API (the Data API and HTTPS Endpoints are
+end-of-life), so this config extends BaseDirectVectorStoreConfig and runs the
+``$vectorSearch`` aggregation itself through pymongo instead of shaping an httpx
+request.
+
+``vector_store_id`` is the Atlas Search index name, matching the Valkey provider
+where the id names the index; the database and collection it covers come from
+litellm_params.
+"""
+
+from collections.abc import Awaitable, Callable, Mapping, Sequence
+from types import MappingProxyType
+from typing import TYPE_CHECKING, Final, NoReturn
+
+import httpx
+from pydantic import BaseModel, ConfigDict
+
+import litellm
+from litellm.llms.base_llm.vector_store.transformation import BaseDirectVectorStoreConfig
+from litellm.llms.mongodb.common_utils import (
+    DEFAULT_CONNECT_TIMEOUT_MS,
+    DEFAULT_SERVER_SELECTION_TIMEOUT_MS,
+    DEFAULT_SOCKET_TIMEOUT_MS,
+    MongoClientKey,
+    get_async_client,
+    get_sync_client,
+    translate_mongo_error,
+)
+from litellm.types.utils import EmbeddingResponse
+from litellm.types.vector_stores import (
+    VectorStoreCreateOptionalRequestParams,
+    VectorStoreResultContent,
+    VectorStoreSearchOptionalRequestParams,
+    VectorStoreSearchResponse,
+    VectorStoreSearchResult,
+)
+
+if TYPE_CHECKING:
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+DEFAULT_EMBEDDING_FIELD_NAME: Final = "embedding"
+DEFAULT_TEXT_FIELD_NAME: Final = "text"
+SCORE_FIELD_NAME: Final = "score"
+
+DEFAULT_MAX_NUM_RESULTS: Final = 10
+MIN_MAX_NUM_RESULTS: Final = 1
+MAX_MAX_NUM_RESULTS: Final = 50
+
+NUM_CANDIDATES_MULTIPLIER: Final = 10
+MIN_NUM_CANDIDATES: Final = 100
+MAX_NUM_CANDIDATES: Final = 10_000
+
+MAX_QUERY_CHARACTERS: Final = 32_000
+
+_EMPTY_EMBEDDING_CONFIG: Final = MappingProxyType({})
+
+_SEARCH_ONLY_MESSAGE: Final = (
+    "MongoDB vector store is search-only. Create the collection and its Atlas Vector Search "
+    "index in MongoDB directly, then register it here by index name."
+)
+
+
+class _MongoDBSearchParams(BaseModel):
+    """Typed view over the vector store's litellm_params; unrelated keys are ignored."""
+
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    litellm_embedding_model: str | None = None
+    litellm_embedding_config: Mapping[str, object] | None = None
+    mongodb_connection_string: str | None = None
+    mongodb_database: str | None = None
+    mongodb_collection: str | None = None
+    mongodb_text_field: str | None = None
+    mongodb_embedding_field: str | None = None
+    mongodb_num_candidates: int | None = None
+
+    @property
+    def text_field(self) -> str:
+        return self.mongodb_text_field or DEFAULT_TEXT_FIELD_NAME
+
+    @property
+    def embedding_field(self) -> str:
+        return self.mongodb_embedding_field or DEFAULT_EMBEDDING_FIELD_NAME
+
+    def require_embedding_model(self) -> str:
+        if not self.litellm_embedding_model:
+            raise ValueError(
+                "litellm_embedding_model is required in litellm_params for the MongoDB vector store. "
+                "It must be the same model that produced the vectors stored in "
+                f"'{self.mongodb_collection or '<collection>'}.{self.embedding_field}', or search results "
+                "will be meaningless. Example: litellm_embedding_model: openai/text-embedding-3-small"
+            )
+        return self.litellm_embedding_model
+
+    def require_connection_string(self) -> str:
+        if not self.mongodb_connection_string:
+            raise ValueError(
+                "mongodb_connection_string is required in litellm_params for the MongoDB vector store. "
+                "Example: mongodb+srv://<user>:<password>@<cluster>.mongodb.net"
+            )
+        scheme: Final = self.mongodb_connection_string.split("://", 1)[0].lower()
+        if scheme not in ("mongodb", "mongodb+srv"):
+            raise ValueError(
+                "mongodb_connection_string must start with 'mongodb://' or 'mongodb+srv://', "
+                f"got '{self.mongodb_connection_string.split('://', 1)[0]}://'"
+            )
+        return self.mongodb_connection_string
+
+    def require_database(self) -> str:
+        if not self.mongodb_database:
+            raise ValueError(
+                "mongodb_database is required in litellm_params for the MongoDB vector store. "
+                "Example: mongodb_database: sample_mflix"
+            )
+        return self.mongodb_database
+
+    def require_collection(self) -> str:
+        if not self.mongodb_collection:
+            raise ValueError(
+                "mongodb_collection is required in litellm_params for the MongoDB vector store. "
+                "Example: mongodb_collection: embedded_movies"
+            )
+        return self.mongodb_collection
+
+
+class MongoDBVectorStoreConfig(BaseDirectVectorStoreConfig):
+    def __init__(
+        self,
+        embedding_fn: Callable[..., EmbeddingResponse] | None = None,
+        aembedding_fn: Callable[..., Awaitable[EmbeddingResponse]] | None = None,
+        sync_client_factory: Callable[[MongoClientKey], object] | None = None,
+        async_client_factory: Callable[[MongoClientKey], object] | None = None,
+    ) -> None:
+        super().__init__()
+        self.embedding_fn = embedding_fn if embedding_fn is not None else litellm.embedding
+        self.aembedding_fn = aembedding_fn if aembedding_fn is not None else litellm.aembedding
+        self.sync_client_factory = sync_client_factory if sync_client_factory is not None else get_sync_client
+        self.async_client_factory = async_client_factory if async_client_factory is not None else get_async_client
+
+    @staticmethod
+    def _query_text(query: str | Sequence[str]) -> str:
+        text: Final = query if isinstance(query, str) else " ".join(query)
+        if not text.strip():
+            raise ValueError("query must not be empty")
+        if len(text) > MAX_QUERY_CHARACTERS:
+            raise ValueError(f"query must be at most {MAX_QUERY_CHARACTERS} characters, got {len(text)}")
+        return text
+
+    @staticmethod
+    def _limit(vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams) -> int:
+        requested: Final = vector_store_search_optional_params.get("max_num_results")
+        if requested is None:
+            return DEFAULT_MAX_NUM_RESULTS
+        if not MIN_MAX_NUM_RESULTS <= requested <= MAX_MAX_NUM_RESULTS:
+            raise ValueError(
+                f"max_num_results must be between {MIN_MAX_NUM_RESULTS} and {MAX_MAX_NUM_RESULTS}, got {requested}"
+            )
+        return requested
+
+    @staticmethod
+    def _num_candidates(limit: int, configured: int | None) -> int:
+        if configured is not None:
+            if not limit <= configured <= MAX_NUM_CANDIDATES:
+                raise ValueError(
+                    f"mongodb_num_candidates must be between max_num_results ({limit}) and "
+                    f"{MAX_NUM_CANDIDATES}, got {configured}"
+                )
+            return configured
+        return min(max(limit * NUM_CANDIDATES_MULTIPLIER, MIN_NUM_CANDIDATES), MAX_NUM_CANDIDATES)
+
+    @staticmethod
+    def _client_key(params: _MongoDBSearchParams, timeout: float | httpx.Timeout | None) -> MongoClientKey:
+        if isinstance(timeout, httpx.Timeout):
+            connect_ms: Final = int((timeout.connect or DEFAULT_CONNECT_TIMEOUT_MS / 1000) * 1000)
+            socket_ms: Final = int((timeout.read or DEFAULT_SOCKET_TIMEOUT_MS / 1000) * 1000)
+        elif timeout is not None:
+            connect_ms = min(int(float(timeout) * 1000), DEFAULT_CONNECT_TIMEOUT_MS)
+            socket_ms = int(float(timeout) * 1000)
+        else:
+            connect_ms = DEFAULT_CONNECT_TIMEOUT_MS
+            socket_ms = DEFAULT_SOCKET_TIMEOUT_MS
+        return MongoClientKey(
+            connection_string=params.require_connection_string(),
+            connect_timeout_ms=connect_ms,
+            socket_timeout_ms=socket_ms,
+            server_selection_timeout_ms=min(connect_ms, DEFAULT_SERVER_SELECTION_TIMEOUT_MS),
+        )
+
+    @classmethod
+    def _pipeline(
+        cls,
+        vector_store_id: str,
+        query_vector: Sequence[float],
+        params: _MongoDBSearchParams,
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+    ) -> list[dict[str, object]]:
+        if vector_store_search_optional_params.get("filters") is not None:
+            raise ValueError(
+                "MongoDB vector store does not support the filters parameter yet. "
+                "Restrict the collection or the Atlas Vector Search index definition instead."
+            )
+        limit: Final = cls._limit(vector_store_search_optional_params)
+        return [  # mutable-ok: pymongo's aggregate contract is a list of stage dicts
+            {
+                "$vectorSearch": {
+                    "index": vector_store_id,
+                    "path": params.embedding_field,
+                    "queryVector": list(query_vector),
+                    "numCandidates": cls._num_candidates(limit, params.mongodb_num_candidates),
+                    "limit": limit,
+                }
+            },
+            {"$project": {params.text_field: 1, SCORE_FIELD_NAME: {"$meta": "vectorSearchScore"}}},
+        ]
+
+    @staticmethod
+    def _field_value(document: Mapping[str, object], dotted_path: str) -> str:
+        current: object = document
+        for segment in dotted_path.split("."):
+            if not isinstance(current, Mapping):
+                return ""
+            current = current.get(segment)
+        return "" if current is None else str(current)
+
+    @classmethod
+    def _to_result(cls, document: Mapping[str, object], text_field: str) -> VectorStoreSearchResult:
+        document_id: Final = document.get("_id")
+        identifier: Final = None if document_id is None else str(document_id)
+        content: Final = [  # mutable-ok: VectorStoreSearchResult declares a list of content parts
+            VectorStoreResultContent(text=cls._field_value(document, text_field), type="text")
+        ]
+        raw_score: Final = document.get(SCORE_FIELD_NAME)
+        return VectorStoreSearchResult(
+            score=float(raw_score) if isinstance(raw_score, (int, float)) else None,
+            content=content,
+            file_id=identifier,
+            filename=identifier,
+        )
+
+    @classmethod
+    def _to_response(
+        cls, documents: Sequence[Mapping[str, object]], query_text: str, text_field: str
+    ) -> VectorStoreSearchResponse:
+        return VectorStoreSearchResponse(
+            object="vector_store.search_results.page",
+            search_query=query_text,
+            data=[cls._to_result(document, text_field) for document in documents],
+        )
+
+    @staticmethod
+    def _embedding_vector(embedding_response: EmbeddingResponse) -> Sequence[float]:
+        data: Final = embedding_response.data
+        if not data:
+            raise ValueError(
+                "The embedding model returned no embedding for the search query, so there is nothing "
+                "to search MongoDB with. Check the embedding deployment named by litellm_embedding_model."
+            )
+        return data[0]["embedding"]
+
+    def execute_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        litellm_logging_obj: "LiteLLMLoggingObj",
+        litellm_params: Mapping[str, object],
+        timeout: float | httpx.Timeout | None = None,
+    ) -> VectorStoreSearchResponse:
+        params: Final = _MongoDBSearchParams.model_validate(litellm_params)
+        query_text: Final = self._query_text(query)
+        key: Final = self._client_key(params, timeout)
+        database: Final = params.require_database()
+        collection: Final = params.require_collection()
+
+        embedding_response: Final = self.embedding_fn(
+            model=params.require_embedding_model(),
+            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
+            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        )
+        pipeline: Final = self._pipeline(
+            vector_store_id, self._embedding_vector(embedding_response), params, vector_store_search_optional_params
+        )
+
+        client: Final = self.sync_client_factory(key)
+        try:
+            documents: Final = list(client[database][collection].aggregate(pipeline))  # pyright: ignore[reportIndexIssue]  # factory is typed as returning object so injected doubles are accepted
+        except Exception as e:
+            raise translate_mongo_error(
+                e, index_name=vector_store_id, database=database, collection=collection
+            ) from e
+        return self._to_response(documents, query_text, params.text_field)
+
+    async def aexecute_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        litellm_logging_obj: "LiteLLMLoggingObj",
+        litellm_params: Mapping[str, object],
+        timeout: float | httpx.Timeout | None = None,
+    ) -> VectorStoreSearchResponse:
+        params: Final = _MongoDBSearchParams.model_validate(litellm_params)
+        query_text: Final = self._query_text(query)
+        key: Final = self._client_key(params, timeout)
+        database: Final = params.require_database()
+        collection: Final = params.require_collection()
+
+        embedding_response: Final = await self.aembedding_fn(
+            model=params.require_embedding_model(),
+            input=[query_text],  # mutable-ok: litellm.embedding's input contract is a list
+            **(params.litellm_embedding_config or _EMPTY_EMBEDDING_CONFIG),
+        )
+        pipeline: Final = self._pipeline(
+            vector_store_id, self._embedding_vector(embedding_response), params, vector_store_search_optional_params
+        )
+
+        client: Final = self.async_client_factory(key)
+        try:
+            cursor: Final = await client[database][collection].aggregate(pipeline)  # pyright: ignore[reportIndexIssue]  # factory is typed as returning object so injected doubles are accepted
+            documents: Final = [document async for document in cursor]
+        except Exception as e:
+            raise translate_mongo_error(
+                e, index_name=vector_store_id, database=database, collection=collection
+            ) from e
+        return self._to_response(documents, query_text, params.text_field)
+
+    def transform_create_vector_store_request(
+        self,
+        vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams,
+        api_base: str,
+    ) -> NoReturn:
+        raise NotImplementedError(_SEARCH_ONLY_MESSAGE)
+
+    def transform_create_vector_store_response(self, response: httpx.Response) -> NoReturn:
+        raise NotImplementedError(_SEARCH_ONLY_MESSAGE)
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 5783a39b30c..fd98bbf4896 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -3867,6 +3867,7 @@ class LlmProviders(str, Enum):
     PG_VECTOR = "pg_vector"
     S3_VECTORS = "s3_vectors"
     VALKEY = "valkey"
+    MONGODB = "mongodb"
     HELICONE = "helicone"
     HYPERBOLIC = "hyperbolic"
     RECRAFT = "recraft"
diff --git a/litellm/utils.py b/litellm/utils.py
index 252b6756937..2dbfe096a59 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -9022,6 +9022,12 @@ class ProviderConfigManager:
             )
 
             return ValkeyVectorStoreConfig()
+        elif litellm.LlmProviders.MONGODB == provider:
+            from litellm.llms.mongodb.vector_stores.transformation import (
+                MongoDBVectorStoreConfig,
+            )
+
+            return MongoDBVectorStoreConfig()
         return None
 
     @staticmethod
diff --git a/pyproject.toml b/pyproject.toml
index 2866e27e84c..65d8886bc26 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -112,6 +112,12 @@ utils = [
 ]
 caching = ["diskcache>=5.6.3,<6.0"]
 mcp = ["mcp>=1.28.1,<2.0"]
+# Driver for the MongoDB Atlas vector store. Atlas Vector Search has no HTTP query
+# API, so that provider talks to the cluster over the wire protocol. Imported lazily
+# and kept out of the base install, which never needs a MongoDB driver. The floor is
+# 4.17 because that is where dnspython became a core dependency rather than the `srv`
+# extra, and Atlas hands out mongodb+srv:// URIs that do not resolve without it.
+mongodb = ["pymongo>=4.17,<5.0"]
 # SAML SSO for the admin UI. python3-saml pulls in xmlsec/lxml, whose wheels
 # bundle the native libxmlsec1/libxml2 libraries, so no system packages are
 # required. Kept out of the base `proxy` extra so it stays optional.
diff --git a/uv.lock b/uv.lock
index 27be919eea1..e4b23804b4a 100644
--- a/uv.lock
+++ b/uv.lock
@@ -10,7 +10,7 @@ resolution-markers = [
 ]
 
 [options]
-exclude-newer = "2026-08-29T17:58:57.633306Z"
+exclude-newer = "2026-08-30T07:50:56.793842Z"
 exclude-newer-span = "P3D"
 
 [manifest]
@@ -4323,6 +4323,9 @@ mcp = [
 mlflow = [
     { name = "mlflow" },
 ]
+mongodb = [
+    { name = "pymongo" },
+]
 proxy = [
     { name = "apscheduler" },
     { name = "azure-identity" },
@@ -4551,6 +4554,7 @@ requires-dist = [
     { name = "pydantic", specifier = ">=2.10.0,<3.0.0" },
     { name = "pydantic-settings", specifier = ">=2.14.1,<3.0" },
     { name = "pyjwt", marker = "extra == 'proxy'", specifier = ">=2.13.0,<3.0" },
+    { name = "pymongo", marker = "extra == 'mongodb'", specifier = ">=4.17,<5.0" },
     { name = "pynacl", marker = "extra == 'proxy'", specifier = ">=1.6.2,<2.0" },
     { name = "pypdf", marker = "extra == 'proxy-runtime'", specifier = ">=6.12.0,<7.0" },
     { name = "pyroscope-io", marker = "sys_platform != 'win32' and extra == 'proxy'", specifier = ">=0.8.16,<1.0" },
@@ -4577,7 +4581,7 @@ requires-dist = [
     { name = "uvloop", marker = "sys_platform != 'win32' and extra == 'proxy'", specifier = ">=0.21.0,<1.0" },
     { name = "websockets", marker = "extra == 'proxy'", specifier = ">=15.0.1,<16.0" },
 ]
-provides-extras = ["proxy", "cli", "extra-proxy", "utils", "caching", "mcp", "saml", "semantic-router", "mlflow", "grpc", "stt-nvidia-riva", "google", "bedrock-realtime", "proxy-runtime"]
+provides-extras = ["proxy", "cli", "extra-proxy", "utils", "caching", "mcp", "mongodb", "saml", "semantic-router", "mlflow", "grpc", "stt-nvidia-riva", "google", "bedrock-realtime", "proxy-runtime"]
 
 [package.metadata.requires-dev]
 ci = [
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 ]
 
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