diff --git a/litellm/proxy/proxy_server.py b/litellm/proxy/proxy_server.py
index f74e53f947..eaf53f0bd7 100644
--- a/litellm/proxy/proxy_server.py
+++ b/litellm/proxy/proxy_server.py
@@ -149,6 +149,7 @@ from fastapi import (
     Request,
     HTTPException,
     status,
+    Path,
     Depends,
     Header,
     Response,
@@ -5179,6 +5180,148 @@ async def create_batch(
             )
 
 
+@router.get(
+    "/v1/batches{batch_id}",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["Batch"],
+)
+@router.get(
+    "/batches{batch_id}",
+    dependencies=[Depends(user_api_key_auth)],
+    tags=["Batch"],
+)
+async def retrieve_batch(
+    request: Request,
+    fastapi_response: Response,
+    user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
+    batch_id: str = Path(
+        title="Batch ID to retrieve", description="The ID of the batch to retrieve"
+    ),
+):
+    """
+    Retrieves a batch.
+    This is the equivalent of GET https://api.openai.com/v1/batches/{batch_id}
+    Supports Identical Params as: https://platform.openai.com/docs/api-reference/batch/retrieve
+
+    Example Curl
+    ```
+    curl http://localhost:4000/v1/batches/batch_abc123 \
+    -H "Authorization: Bearer sk-1234" \
+    -H "Content-Type: application/json" \
+
+    ```
+    """
+    global proxy_logging_obj
+    data: Dict = {}
+    try:
+        # Use orjson to parse JSON data, orjson speeds up requests significantly
+        form_data = await request.form()
+        data = {key: value for key, value in form_data.items() if key != "file"}
+
+        # Include original request and headers in the data
+        data["proxy_server_request"] = {  # type: ignore
+            "url": str(request.url),
+            "method": request.method,
+            "headers": dict(request.headers),
+            "body": copy.copy(data),  # use copy instead of deepcopy
+        }
+
+        if data.get("user", None) is None and user_api_key_dict.user_id is not None:
+            data["user"] = user_api_key_dict.user_id
+
+        if "metadata" not in data:
+            data["metadata"] = {}
+        data["metadata"]["user_api_key"] = user_api_key_dict.api_key
+        data["metadata"]["user_api_key_metadata"] = user_api_key_dict.metadata
+        _headers = dict(request.headers)
+        _headers.pop(
+            "authorization", None
+        )  # do not store the original `sk-..` api key in the db
+        data["metadata"]["headers"] = _headers
+        data["metadata"]["user_api_key_alias"] = getattr(
+            user_api_key_dict, "key_alias", None
+        )
+        data["metadata"]["user_api_key_user_id"] = user_api_key_dict.user_id
+        data["metadata"]["user_api_key_team_id"] = getattr(
+            user_api_key_dict, "team_id", None
+        )
+        data["metadata"]["global_max_parallel_requests"] = general_settings.get(
+            "global_max_parallel_requests", None
+        )
+        data["metadata"]["user_api_key_team_alias"] = getattr(
+            user_api_key_dict, "team_alias", None
+        )
+        data["metadata"]["endpoint"] = str(request.url)
+
+        ### TEAM-SPECIFIC PARAMS ###
+        if user_api_key_dict.team_id is not None:
+            team_config = await proxy_config.load_team_config(
+                team_id=user_api_key_dict.team_id
+            )
+            if len(team_config) == 0:
+                pass
+            else:
+                team_id = team_config.pop("team_id", None)
+                data["metadata"]["team_id"] = team_id
+                data = {
+                    **team_config,
+                    **data,
+                }  # add the team-specific configs to the completion call
+
+        _retrieve_batch_request = RetrieveBatchRequest(
+            batch_id=batch_id,
+        )
+
+        # for now use custom_llm_provider=="openai" -> this will change as LiteLLM adds more providers for acreate_batch
+        response = await litellm.aretrieve_batch(
+            custom_llm_provider="openai", **_retrieve_batch_request
+        )
+
+        ### ALERTING ###
+        data["litellm_status"] = "success"  # used for alerting
+
+        ### RESPONSE HEADERS ###
+        hidden_params = getattr(response, "_hidden_params", {}) or {}
+        model_id = hidden_params.get("model_id", None) or ""
+        cache_key = hidden_params.get("cache_key", None) or ""
+        api_base = hidden_params.get("api_base", None) or ""
+
+        fastapi_response.headers.update(
+            get_custom_headers(
+                user_api_key_dict=user_api_key_dict,
+                model_id=model_id,
+                cache_key=cache_key,
+                api_base=api_base,
+                version=version,
+                model_region=getattr(user_api_key_dict, "allowed_model_region", ""),
+            )
+        )
+
+        return response
+    except Exception as e:
+        data["litellm_status"] = "fail"  # used for alerting
+        await proxy_logging_obj.post_call_failure_hook(
+            user_api_key_dict=user_api_key_dict, original_exception=e, request_data=data
+        )
+        traceback.print_exc()
+        if isinstance(e, HTTPException):
+            raise ProxyException(
+                message=getattr(e, "message", str(e.detail)),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", status.HTTP_400_BAD_REQUEST),
+            )
+        else:
+            error_traceback = traceback.format_exc()
+            error_msg = f"{str(e)}"
+            raise ProxyException(
+                message=getattr(e, "message", error_msg),
+                type=getattr(e, "type", "None"),
+                param=getattr(e, "param", "None"),
+                code=getattr(e, "status_code", 500),
+            )
+
+
 ######################################################################
 
 #            END OF  /v1/batches Endpoints Implementation