diff --git a/letta/jobs/__init__.py b/letta/jobs/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/letta/jobs/helpers.py b/letta/jobs/helpers.py
new file mode 100644
index 00000000..20f4af8f
--- /dev/null
+++ b/letta/jobs/helpers.py
@@ -0,0 +1,25 @@
+from anthropic.types.beta.messages import (
+    BetaMessageBatchCanceledResult,
+    BetaMessageBatchIndividualResponse,
+    BetaMessageBatchSucceededResult,
+)
+
+from letta.schemas.enums import JobStatus
+
+
+def map_anthropic_batch_job_status_to_job_status(anthropic_status: str) -> JobStatus:
+    mapping = {
+        "in_progress": JobStatus.running,
+        "canceling": JobStatus.cancelled,
+        "ended": JobStatus.completed,
+    }
+    return mapping.get(anthropic_status, JobStatus.pending)  # fallback just in case
+
+
+def map_anthropic_individual_batch_item_status_to_job_status(individual_item: BetaMessageBatchIndividualResponse) -> JobStatus:
+    if isinstance(individual_item.result, BetaMessageBatchSucceededResult):
+        return JobStatus.completed
+    elif isinstance(individual_item.result, BetaMessageBatchCanceledResult):
+        return JobStatus.cancelled
+    else:
+        return JobStatus.failed
diff --git a/letta/jobs/llm_batch_job_polling.py b/letta/jobs/llm_batch_job_polling.py
new file mode 100644
index 00000000..79788aa5
--- /dev/null
+++ b/letta/jobs/llm_batch_job_polling.py
@@ -0,0 +1,204 @@
+import asyncio
+import datetime
+from typing import List
+
+from letta.jobs.helpers import map_anthropic_batch_job_status_to_job_status, map_anthropic_individual_batch_item_status_to_job_status
+from letta.jobs.types import BatchId, BatchPollingResult, ItemUpdateInfo
+from letta.log import get_logger
+from letta.schemas.enums import JobStatus, ProviderType
+from letta.schemas.llm_batch_job import LLMBatchJob
+from letta.server.server import SyncServer
+
+logger = get_logger(__name__)
+
+
+class BatchPollingMetrics:
+    """Class to track metrics for batch polling operations."""
+
+    def __init__(self):
+        self.start_time = datetime.datetime.now()
+        self.total_batches = 0
+        self.anthropic_batches = 0
+        self.running_count = 0
+        self.completed_count = 0
+        self.updated_items_count = 0
+
+    def log_summary(self):
+        """Log a summary of the metrics collected during polling."""
+        elapsed = (datetime.datetime.now() - self.start_time).total_seconds()
+        logger.info(f"[Poll BatchJob] Finished poll_running_llm_batches job in {elapsed:.2f}s")
+        logger.info(f"[Poll BatchJob] Found {self.total_batches} running batches total.")
+        logger.info(f"[Poll BatchJob] Found {self.anthropic_batches} Anthropic batch(es) to poll.")
+        logger.info(f"[Poll BatchJob] Final results: {self.completed_count} completed, {self.running_count} still running.")
+        logger.info(f"[Poll BatchJob] Updated {self.updated_items_count} items for newly completed batch(es).")
+
+
+async def fetch_batch_status(server: SyncServer, batch_job: LLMBatchJob) -> BatchPollingResult:
+    """
+    Fetch the current status of a single batch job from the provider.
+
+    Args:
+        server: The SyncServer instance
+        batch_job: The batch job to check status for
+
+    Returns:
+        A tuple containing (batch_id, new_status, polling_response)
+    """
+    batch_id_str = batch_job.create_batch_response.id
+    try:
+        response = await server.anthropic_async_client.beta.messages.batches.retrieve(batch_id_str)
+        new_status = map_anthropic_batch_job_status_to_job_status(response.processing_status)
+        logger.debug(f"[Poll BatchJob] Batch {batch_job.id}: provider={response.processing_status} → internal={new_status}")
+        return (batch_job.id, new_status, response)
+    except Exception as e:
+        logger.warning(f"[Poll BatchJob] Batch {batch_job.id}: failed to retrieve {batch_id_str}: {e}")
+        # We treat a retrieval error as still running to try again next cycle
+        return (batch_job.id, JobStatus.running, None)
+
+
+async def fetch_batch_items(server: SyncServer, batch_id: BatchId, batch_resp_id: str) -> List[ItemUpdateInfo]:
+    """
+    Fetch individual item results for a completed batch.
+
+    Args:
+        server: The SyncServer instance
+        batch_id: The internal batch ID
+        batch_resp_id: The provider's batch response ID
+
+    Returns:
+        A list of item update information tuples
+    """
+    updates = []
+    try:
+        async for item_result in server.anthropic_async_client.beta.messages.batches.results(batch_resp_id):
+            # Here, custom_id should be the agent_id
+            item_status = map_anthropic_individual_batch_item_status_to_job_status(item_result)
+            updates.append((batch_id, item_result.custom_id, item_status, item_result))
+        logger.info(f"[Poll BatchJob] Fetched {len(updates)} item updates for batch {batch_id}.")
+    except Exception as e:
+        logger.error(f"[Poll BatchJob] Error fetching item updates for batch {batch_id}: {e}")
+
+    return updates
+
+
+async def poll_batch_updates(server: SyncServer, batch_jobs: List[LLMBatchJob], metrics: BatchPollingMetrics) -> List[BatchPollingResult]:
+    """
+    Poll for updates to multiple batch jobs concurrently.
+
+    Args:
+        server: The SyncServer instance
+        batch_jobs: List of batch jobs to poll
+        metrics: Metrics collection object
+
+    Returns:
+        List of batch polling results
+    """
+    if not batch_jobs:
+        logger.info("[Poll BatchJob] No Anthropic batches to update; job complete.")
+        return []
+
+    # Create polling tasks for all batch jobs
+    coros = [fetch_batch_status(server, b) for b in batch_jobs]
+    results: List[BatchPollingResult] = await asyncio.gather(*coros)
+
+    # Update the server with batch status changes
+    server.batch_manager.bulk_update_batch_statuses(updates=results)
+    logger.info(f"[Poll BatchJob] Bulk-updated {len(results)} LLM batch(es) in the DB at job level.")
+
+    return results
+
+
+async def process_completed_batches(
+    server: SyncServer, batch_results: List[BatchPollingResult], metrics: BatchPollingMetrics
+) -> List[ItemUpdateInfo]:
+    """
+    Process batches that have completed and fetch their item results.
+
+    Args:
+        server: The SyncServer instance
+        batch_results: Results from polling batch statuses
+        metrics: Metrics collection object
+
+    Returns:
+        List of item updates to apply
+    """
+    item_update_tasks = []
+
+    # Process each top-level polling result
+    for batch_id, new_status, maybe_batch_resp in batch_results:
+        if not maybe_batch_resp:
+            if new_status == JobStatus.running:
+                metrics.running_count += 1
+            logger.warning(f"[Poll BatchJob] Batch {batch_id}: JobStatus was {new_status} and no batch response was found.")
+            continue
+
+        if new_status == JobStatus.completed:
+            metrics.completed_count += 1
+            batch_resp_id = maybe_batch_resp.id  # The Anthropic-assigned batch ID
+            # Queue an async call to fetch item results for this batch
+            item_update_tasks.append(fetch_batch_items(server, batch_id, batch_resp_id))
+        elif new_status == JobStatus.running:
+            metrics.running_count += 1
+
+    # Launch all item update tasks concurrently
+    concurrent_results = await asyncio.gather(*item_update_tasks, return_exceptions=True)
+
+    # Flatten and filter the results
+    item_updates = []
+    for result in concurrent_results:
+        if isinstance(result, Exception):
+            logger.error(f"[Poll BatchJob] A fetch_batch_items task failed with: {result}")
+        elif isinstance(result, list):
+            item_updates.extend(result)
+
+    logger.info(f"[Poll BatchJob] Collected a total of {len(item_updates)} item update(s) from completed batches.")
+
+    return item_updates
+
+
+async def poll_running_llm_batches(server: "SyncServer") -> None:
+    """
+    Cron job to poll all running LLM batch jobs and update their polling responses in bulk.
+
+    Steps:
+      1. Fetch currently running batch jobs
+      2. Filter Anthropic only
+      3. Retrieve updated top-level polling info concurrently
+      4. Bulk update LLMBatchJob statuses
+      5. For each completed batch, call .results(...) to get item-level results
+      6. Bulk update all matching LLMBatchItem records by (batch_id, agent_id)
+      7. Log telemetry about success/fail
+    """
+    # Initialize metrics tracking
+    metrics = BatchPollingMetrics()
+
+    logger.info("[Poll BatchJob] Starting poll_running_llm_batches job")
+
+    try:
+        # 1. Retrieve running batch jobs
+        batches = server.batch_manager.list_running_batches()
+        metrics.total_batches = len(batches)
+
+        # TODO: Expand to more providers
+        # 2. Filter for Anthropic jobs only
+        anthropic_batch_jobs = [b for b in batches if b.llm_provider == ProviderType.anthropic]
+        metrics.anthropic_batches = len(anthropic_batch_jobs)
+
+        # 3-4. Poll for batch updates and bulk update statuses
+        batch_results = await poll_batch_updates(server, anthropic_batch_jobs, metrics)
+
+        # 5. Process completed batches and fetch item results
+        item_updates = await process_completed_batches(server, batch_results, metrics)
+
+        # 6. Bulk update all items for newly completed batch(es)
+        if item_updates:
+            metrics.updated_items_count = len(item_updates)
+            server.batch_manager.bulk_update_batch_items_by_agent(item_updates)
+        else:
+            logger.info("[Poll BatchJob] No item-level updates needed.")
+
+    except Exception as e:
+        logger.exception("[Poll BatchJob] Unhandled error in poll_running_llm_batches", exc_info=e)
+    finally:
+        # 7. Log metrics summary
+        metrics.log_summary()
diff --git a/letta/jobs/scheduler.py b/letta/jobs/scheduler.py
new file mode 100644
index 00000000..efbdacea
--- /dev/null
+++ b/letta/jobs/scheduler.py
@@ -0,0 +1,28 @@
+import datetime
+
+from apscheduler.schedulers.asyncio import AsyncIOScheduler
+from apscheduler.triggers.interval import IntervalTrigger
+
+from letta.jobs.llm_batch_job_polling import poll_running_llm_batches
+from letta.server.server import SyncServer
+from letta.settings import settings
+
+scheduler = AsyncIOScheduler()
+
+
+def start_cron_jobs(server: SyncServer):
+    """Initialize cron jobs"""
+    scheduler.add_job(
+        poll_running_llm_batches,
+        args=[server],
+        trigger=IntervalTrigger(seconds=settings.poll_running_llm_batches_interval_seconds),
+        next_run_time=datetime.datetime.now(datetime.UTC),
+        id="poll_llm_batches",
+        name="Poll LLM API batch jobs and update status",
+        replace_existing=True,
+    )
+    scheduler.start()
+
+
+def shutdown_cron_scheduler():
+    scheduler.shutdown()
diff --git a/letta/jobs/types.py b/letta/jobs/types.py
new file mode 100644
index 00000000..217ee85a
--- /dev/null
+++ b/letta/jobs/types.py
@@ -0,0 +1,10 @@
+from typing import Optional, Tuple
+
+from anthropic.types.beta.messages import BetaMessageBatch, BetaMessageBatchIndividualResponse
+
+from letta.schemas.enums import JobStatus
+
+BatchId = str
+AgentId = str
+BatchPollingResult = Tuple[BatchId, JobStatus, Optional[BetaMessageBatch]]
+ItemUpdateInfo = Tuple[BatchId, AgentId, JobStatus, BetaMessageBatchIndividualResponse]
diff --git a/letta/schemas/enums.py b/letta/schemas/enums.py
index c02e1438..2fd6446f 100644
--- a/letta/schemas/enums.py
+++ b/letta/schemas/enums.py
@@ -32,6 +32,7 @@ class JobStatus(str, Enum):
     completed = "completed"
     failed = "failed"
     pending = "pending"
+    cancelled = "cancelled"
 
 
 class AgentStepStatus(str, Enum):
diff --git a/letta/server/rest_api/app.py b/letta/server/rest_api/app.py
index ddf66a82..f085f499 100644
--- a/letta/server/rest_api/app.py
+++ b/letta/server/rest_api/app.py
@@ -16,6 +16,7 @@ from starlette.middleware.cors import CORSMiddleware
 from letta.__init__ import __version__
 from letta.constants import ADMIN_PREFIX, API_PREFIX, OPENAI_API_PREFIX
 from letta.errors import BedrockPermissionError, LettaAgentNotFoundError, LettaUserNotFoundError
+from letta.jobs.scheduler import shutdown_cron_scheduler, start_cron_jobs
 from letta.log import get_logger
 from letta.orm.errors import DatabaseTimeoutError, ForeignKeyConstraintViolationError, NoResultFound, UniqueConstraintViolationError
 from letta.schemas.letta_message import create_letta_message_union_schema
@@ -144,6 +145,12 @@ def create_application() -> "FastAPI":
         executor = concurrent.futures.ThreadPoolExecutor(max_workers=settings.event_loop_threadpool_max_workers)
         loop.set_default_executor(executor)
 
+    @app.on_event("startup")
+    def on_startup():
+        global server
+
+        start_cron_jobs(server)
+
     @app.on_event("shutdown")
     def shutdown_mcp_clients():
         global server
@@ -159,6 +166,10 @@ def create_application() -> "FastAPI":
         t.start()
         t.join()
 
+    @app.on_event("shutdown")
+    def shutdown_scheduler():
+        shutdown_cron_scheduler()
+
     @app.exception_handler(Exception)
     async def generic_error_handler(request: Request, exc: Exception):
         # Log the actual error for debugging
diff --git a/letta/server/server.py b/letta/server/server.py
index 3d9aedfe..712a4f93 100644
--- a/letta/server/server.py
+++ b/letta/server/server.py
@@ -8,6 +8,7 @@ from abc import abstractmethod
 from datetime import datetime
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union
 
+from anthropic import AsyncAnthropic
 from composio.client import Composio
 from composio.client.collections import ActionModel, AppModel
 from fastapi import HTTPException
@@ -352,6 +353,9 @@ class SyncServer(Server):
         self._llm_config_cache = {}
         self._embedding_config_cache = {}
 
+        # TODO: Replace this with the Anthropic client we have in house
+        self.anthropic_async_client = AsyncAnthropic()
+
     def load_agent(self, agent_id: str, actor: User, interface: Union[AgentInterface, None] = None) -> Agent:
         """Updated method to load agents from persisted storage"""
         agent_state = self.agent_manager.get_agent_by_id(agent_id=agent_id, actor=actor)
diff --git a/letta/services/llm_batch_manager.py b/letta/services/llm_batch_manager.py
index b538e549..405fd36e 100644
--- a/letta/services/llm_batch_manager.py
+++ b/letta/services/llm_batch_manager.py
@@ -1,13 +1,15 @@
 import datetime
-from typing import Optional
+from typing import List, Optional
 
 from anthropic.types.beta.messages import BetaMessageBatch, BetaMessageBatchIndividualResponse
+from sqlalchemy import tuple_
 
+from letta.jobs.types import BatchPollingResult, ItemUpdateInfo
 from letta.log import get_logger
 from letta.orm.llm_batch_items import LLMBatchItem
 from letta.orm.llm_batch_job import LLMBatchJob
 from letta.schemas.agent import AgentStepState
-from letta.schemas.enums import AgentStepStatus, JobStatus
+from letta.schemas.enums import AgentStepStatus, JobStatus, ProviderType
 from letta.schemas.llm_batch_job import LLMBatchItem as PydanticLLMBatchItem
 from letta.schemas.llm_batch_job import LLMBatchJob as PydanticLLMBatchJob
 from letta.schemas.llm_config import LLMConfig
@@ -28,7 +30,7 @@ class LLMBatchManager:
     @enforce_types
     def create_batch_request(
         self,
-        llm_provider: str,
+        llm_provider: ProviderType,
         create_batch_response: BetaMessageBatch,
         actor: PydanticUser,
         status: JobStatus = JobStatus.created,
@@ -45,7 +47,7 @@ class LLMBatchManager:
             return batch.to_pydantic()
 
     @enforce_types
-    def get_batch_request_by_id(self, batch_id: str, actor: PydanticUser) -> PydanticLLMBatchJob:
+    def get_batch_job_by_id(self, batch_id: str, actor: Optional[PydanticUser] = None) -> PydanticLLMBatchJob:
         """Retrieve a single batch job by ID."""
         with self.session_maker() as session:
             batch = LLMBatchJob.read(db_session=session, identifier=batch_id, actor=actor)
@@ -56,7 +58,7 @@ class LLMBatchManager:
         self,
         batch_id: str,
         status: JobStatus,
-        actor: PydanticUser,
+        actor: Optional[PydanticUser] = None,
         latest_polling_response: Optional[BetaMessageBatch] = None,
     ) -> PydanticLLMBatchJob:
         """Update a batch job’s status and optionally its polling response."""
@@ -65,7 +67,34 @@ class LLMBatchManager:
             batch.status = status
             batch.latest_polling_response = latest_polling_response
             batch.last_polled_at = datetime.datetime.now(datetime.timezone.utc)
-            return batch.update(db_session=session, actor=actor).to_pydantic()
+            batch = batch.update(db_session=session, actor=actor)
+            return batch.to_pydantic()
+
+    def bulk_update_batch_statuses(
+        self,
+        updates: List[BatchPollingResult],
+    ) -> None:
+        """
+        Efficiently update many LLMBatchJob rows. This is used by the cron jobs.
+
+        `updates` = [(batch_id, new_status, polling_response_or_None), …]
+        """
+        now = datetime.datetime.now(datetime.timezone.utc)
+
+        with self.session_maker() as session:
+            mappings = []
+            for batch_id, status, response in updates:
+                mappings.append(
+                    {
+                        "id": batch_id,
+                        "status": status,
+                        "latest_polling_response": response,
+                        "last_polled_at": now,
+                    }
+                )
+
+            session.bulk_update_mappings(LLMBatchJob, mappings)
+            session.commit()
 
     @enforce_types
     def delete_batch_request(self, batch_id: str, actor: PydanticUser) -> None:
@@ -74,6 +103,18 @@ class LLMBatchManager:
             batch = LLMBatchJob.read(db_session=session, identifier=batch_id, actor=actor)
             batch.hard_delete(db_session=session, actor=actor)
 
+    @enforce_types
+    def list_running_batches(self, actor: Optional[PydanticUser] = None) -> List[PydanticLLMBatchJob]:
+        """Return all running LLM batch jobs, optionally filtered by actor's organization."""
+        with self.session_maker() as session:
+            query = session.query(LLMBatchJob).filter(LLMBatchJob.status == JobStatus.running)
+
+            if actor is not None:
+                query = query.filter(LLMBatchJob.organization_id == actor.organization_id)
+
+            results = query.all()
+            return [batch.to_pydantic() for batch in results]
+
     @enforce_types
     def create_batch_item(
         self,
@@ -131,6 +172,56 @@ class LLMBatchManager:
 
             return item.update(db_session=session, actor=actor).to_pydantic()
 
+    def bulk_update_batch_items_by_agent(
+        self,
+        updates: List[ItemUpdateInfo],
+    ) -> None:
+        """
+        Efficiently update LLMBatchItem rows by (batch_id, agent_id).
+
+        Args:
+            updates: List of tuples:
+              (batch_id, agent_id, new_request_status, batch_request_result)
+        """
+        with self.session_maker() as session:
+            # For bulk_update_mappings, we need the primary key of each row
+            # So we must map (batch_id, agent_id) → actual PK (id)
+            # We'll do it in one DB query using the (batch_id, agent_id) sets
+
+            # 1. Gather the pairs
+            key_pairs = [(b_id, a_id) for (b_id, a_id, *_rest) in updates]
+
+            # 2. Query items in a single step
+            items = (
+                session.query(LLMBatchItem.id, LLMBatchItem.batch_id, LLMBatchItem.agent_id)
+                .filter(tuple_(LLMBatchItem.batch_id, LLMBatchItem.agent_id).in_(key_pairs))
+                .all()
+            )
+
+            # Build a map from (batch_id, agent_id) → PK id
+            pair_to_pk = {}
+            for row_id, row_batch_id, row_agent_id in items:
+                pair_to_pk[(row_batch_id, row_agent_id)] = row_id
+
+            # 3. Construct mappings for the PK-based bulk update
+            mappings = []
+            for batch_id, agent_id, new_status, new_result in updates:
+                pk_id = pair_to_pk.get((batch_id, agent_id))
+                if not pk_id:
+                    # Nonexistent or mismatch → skip
+                    continue
+                mappings.append(
+                    {
+                        "id": pk_id,
+                        "request_status": new_status,
+                        "batch_request_result": new_result,
+                    }
+                )
+
+            if mappings:
+                session.bulk_update_mappings(LLMBatchItem, mappings)
+                session.commit()
+
     @enforce_types
     def delete_batch_item(self, item_id: str, actor: PydanticUser) -> None:
         """Hard delete a batch item by ID."""
diff --git a/letta/settings.py b/letta/settings.py
index 7ddec06b..e5186501 100644
--- a/letta/settings.py
+++ b/letta/settings.py
@@ -203,6 +203,9 @@ class Settings(BaseSettings):
     httpx_max_keepalive_connections: int = 500
     httpx_keepalive_expiry: float = 120.0
 
+    # cron job parameters
+    poll_running_llm_batches_interval_seconds: int = 5 * 60
+
     @property
     def letta_pg_uri(self) -> str:
         if self.pg_uri:
diff --git a/poetry.lock b/poetry.lock
index 5132ea81..b0d1277a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -215,6 +215,33 @@ files = [
     {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
 ]
 
+[[package]]
+name = "apscheduler"
+version = "3.11.0"
+description = "In-process task scheduler with Cron-like capabilities"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "APScheduler-3.11.0-py3-none-any.whl", hash = "sha256:fc134ca32e50f5eadcc4938e3a4545ab19131435e851abb40b34d63d5141c6da"},
+    {file = "apscheduler-3.11.0.tar.gz", hash = "sha256:4c622d250b0955a65d5d0eb91c33e6d43fd879834bf541e0a18661ae60460133"},
+]
+
+[package.dependencies]
+tzlocal = ">=3.0"
+
+[package.extras]
+doc = ["packaging", "sphinx", "sphinx-rtd-theme (>=1.3.0)"]
+etcd = ["etcd3", "protobuf (<=3.21.0)"]
+gevent = ["gevent"]
+mongodb = ["pymongo (>=3.0)"]
+redis = ["redis (>=3.0)"]
+rethinkdb = ["rethinkdb (>=2.4.0)"]
+sqlalchemy = ["sqlalchemy (>=1.4)"]
+test = ["APScheduler[etcd,mongodb,redis,rethinkdb,sqlalchemy,tornado,zookeeper]", "PySide6", "anyio (>=4.5.2)", "gevent", "pytest", "pytz", "twisted"]
+tornado = ["tornado (>=4.3)"]
+twisted = ["twisted"]
+zookeeper = ["kazoo"]
+
 [[package]]
 name = "argcomplete"
 version = "3.6.2"
@@ -521,10 +548,6 @@ files = [
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:a37b8f0391212d29b3a91a799c8e4a2855e0576911cdfb2515487e30e322253d"},
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e84799f09591700a4154154cab9787452925578841a94321d5ee8fb9a9a328f0"},
     {file = "Brotli-1.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f66b5337fa213f1da0d9000bc8dc0cb5b896b726eefd9c6046f699b169c41b9e"},
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 pytz = ">=2020.1"
@@ -4424,7 +4409,6 @@ files = [
     {file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
     {file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
     {file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
-    {file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
     {file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
     {file = "psycopg2-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:88138c8dedcbfa96408023ea2b0c369eda40fe5d75002c0964c78f46f11fa442"},
     {file = "psycopg2-2.9.10.tar.gz", hash = "sha256:12ec0b40b0273f95296233e8750441339298e6a572f7039da5b260e3c8b60e11"},
@@ -4484,7 +4468,6 @@ files = [
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:bb89f0a835bcfc1d42ccd5f41f04870c1b936d8507c6df12b7737febc40f0909"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f0c2d907a1e102526dd2986df638343388b94c33860ff3bbe1384130828714b1"},
     {file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567"},
-    {file = "psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-macosx_12_0_x86_64.whl", hash = "sha256:eb09aa7f9cecb45027683bb55aebaaf45a0df8bf6de68801a6afdc7947bb09d4"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b73d6d7f0ccdad7bc43e6d34273f70d587ef62f824d7261c4ae9b8b1b6af90e8"},
     {file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce5ab4bf46a211a8e924d307c1b1fcda82368586a19d0a24f8ae166f5c784864"},
@@ -5237,8 +5220,8 @@ grpcio = ">=1.41.0"
 grpcio-tools = ">=1.41.0"
 httpx = {version = ">=0.20.0", extras = ["http2"]}
 numpy = [
-    {version = ">=1.21", markers = "python_version >= \"3.10\" and python_version < \"3.12\""},
     {version = ">=1.26", markers = "python_version == \"3.12\""},
+    {version = ">=1.21", markers = "python_version >= \"3.10\" and python_version < \"3.12\""},
 ]
 portalocker = ">=2.7.0,<3.0.0"
 pydantic = ">=1.10.8"
@@ -6219,6 +6202,23 @@ files = [
     {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
 ]
 
+[[package]]
+name = "tzlocal"
+version = "5.3.1"
+description = "tzinfo object for the local timezone"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "tzlocal-5.3.1-py3-none-any.whl", hash = "sha256:eb1a66c3ef5847adf7a834f1be0800581b683b5608e74f86ecbcef8ab91bb85d"},
+    {file = "tzlocal-5.3.1.tar.gz", hash = "sha256:cceffc7edecefea1f595541dbd6e990cb1ea3d19bf01b2809f362a03dd7921fd"},
+]
+
+[package.dependencies]
+tzdata = {version = "*", markers = "platform_system == \"Windows\""}
+
+[package.extras]
+devenv = ["check-manifest", "pytest (>=4.3)", "pytest-cov", "pytest-mock (>=3.3)", "zest.releaser"]
+
 [[package]]
 name = "urllib3"
 version = "2.3.0"
@@ -6819,4 +6819,4 @@ tests = ["wikipedia"]
 [metadata]
 lock-version = "2.0"
 python-versions = "<3.14,>=3.10"
-content-hash = "b104f1fe1ae3c5b2e1261105f1e4170d28fee235551c230b3603bb5494054bf1"
+content-hash = "c7532fe22e86ca8602c0b27be85020e0b139ec521cd5b0dc94b180113ede41c4"
diff --git a/pyproject.toml b/pyproject.toml
index a5eccac8..044c499b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,6 +88,7 @@ boto3 = {version = "^1.36.24", optional = true}
 datamodel-code-generator = {extras = ["http"], version = "^0.25.0"}
 mcp = "^1.3.0"
 firecrawl-py = "^1.15.0"
+apscheduler = "^3.11.0"
 
 
 [tool.poetry.extras]
diff --git a/tests/integration_test_batch_api.py b/tests/integration_test_batch_api.py
new file mode 100644
index 00000000..576a9a6c
--- /dev/null
+++ b/tests/integration_test_batch_api.py
@@ -0,0 +1,331 @@
+import os
+import threading
+import time
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock
+
+import pytest
+from anthropic.types import BetaErrorResponse, BetaRateLimitError
+from anthropic.types.beta import BetaMessage
+from anthropic.types.beta.messages import (
+    BetaMessageBatch,
+    BetaMessageBatchErroredResult,
+    BetaMessageBatchIndividualResponse,
+    BetaMessageBatchRequestCounts,
+    BetaMessageBatchSucceededResult,
+)
+from dotenv import load_dotenv
+from letta_client import Letta
+
+from letta.config import LettaConfig
+from letta.helpers import ToolRulesSolver
+from letta.jobs.llm_batch_job_polling import poll_running_llm_batches
+from letta.orm import Base
+from letta.schemas.agent import AgentStepState
+from letta.schemas.enums import JobStatus, ProviderType
+from letta.schemas.llm_config import LLMConfig
+from letta.schemas.tool_rule import InitToolRule
+from letta.server.db import db_context
+from letta.server.server import SyncServer
+
+# --- Server and Database Management --- #
+
+
+@pytest.fixture(autouse=True)
+def _clear_tables():
+    with db_context() as session:
+        for table in reversed(Base.metadata.sorted_tables):  # Reverse to avoid FK issues
+            if table.name in {"llm_batch_job", "llm_batch_items"}:
+                session.execute(table.delete())  # Truncate table
+        session.commit()
+
+
+def _run_server():
+    """Starts the Letta server in a background thread."""
+    load_dotenv()
+    from letta.server.rest_api.app import start_server
+
+    start_server(debug=True)
+
+
+@pytest.fixture(scope="session")
+def server_url():
+    """Ensures a server is running and returns its base URL."""
+    url = os.getenv("LETTA_SERVER_URL", "http://localhost:8283")
+
+    if not os.getenv("LETTA_SERVER_URL"):
+        thread = threading.Thread(target=_run_server, daemon=True)
+        thread.start()
+        time.sleep(5)  # Allow server startup time
+
+    return url
+
+
+@pytest.fixture(scope="module")
+def server():
+    config = LettaConfig.load()
+    print("CONFIG PATH", config.config_path)
+    config.save()
+    return SyncServer()
+
+
+@pytest.fixture(scope="session")
+def client(server_url):
+    """Creates a REST client for testing."""
+    return Letta(base_url=server_url)
+
+
+# --- Dummy Response Factories --- #
+
+
+def create_batch_response(batch_id: str, processing_status: str = "in_progress") -> BetaMessageBatch:
+    """Create a dummy BetaMessageBatch with the specified ID and status."""
+    now = datetime(2024, 8, 20, 18, 37, 24, 100435, tzinfo=timezone.utc)
+    return BetaMessageBatch(
+        id=batch_id,
+        archived_at=now,
+        cancel_initiated_at=now,
+        created_at=now,
+        ended_at=now,
+        expires_at=now,
+        processing_status=processing_status,
+        request_counts=BetaMessageBatchRequestCounts(
+            canceled=10,
+            errored=30,
+            expired=10,
+            processing=100,
+            succeeded=50,
+        ),
+        results_url=None,
+        type="message_batch",
+    )
+
+
+def create_successful_response(custom_id: str) -> BetaMessageBatchIndividualResponse:
+    """Create a dummy successful batch response."""
+    return BetaMessageBatchIndividualResponse(
+        custom_id=custom_id,
+        result=BetaMessageBatchSucceededResult(
+            type="succeeded",
+            message=BetaMessage(
+                id="msg_abc123",
+                role="assistant",
+                type="message",
+                model="claude-3-5-sonnet-20240620",
+                content=[{"type": "text", "text": "hi!"}],
+                usage={"input_tokens": 5, "output_tokens": 7},
+                stop_reason="end_turn",
+            ),
+        ),
+    )
+
+
+def create_failed_response(custom_id: str) -> BetaMessageBatchIndividualResponse:
+    """Create a dummy failed batch response with a rate limit error."""
+    return BetaMessageBatchIndividualResponse(
+        custom_id=custom_id,
+        result=BetaMessageBatchErroredResult(
+            type="errored",
+            error=BetaErrorResponse(type="error", error=BetaRateLimitError(type="rate_limit_error", message="Rate limit hit.")),
+        ),
+    )
+
+
+# --- Test Setup Helpers --- #
+
+
+def create_test_agent(client, name, model="anthropic/claude-3-5-sonnet-20241022"):
+    """Create a test agent with standardized configuration."""
+    return client.agents.create(
+        name=name,
+        include_base_tools=True,
+        model=model,
+        tags=["test_agents"],
+        embedding="letta/letta-free",
+    )
+
+
+def create_test_batch_job(server, batch_response, default_user):
+    """Create a test batch job with the given batch response."""
+    return server.batch_manager.create_batch_request(
+        llm_provider=ProviderType.anthropic,
+        create_batch_response=batch_response,
+        actor=default_user,
+        status=JobStatus.running,
+    )
+
+
+def create_test_batch_item(server, batch_id, agent_id, default_user):
+    """Create a test batch item for the given batch and agent."""
+    dummy_llm_config = LLMConfig(
+        model="claude-3-7-sonnet-latest",
+        model_endpoint_type="anthropic",
+        model_endpoint="https://api.anthropic.com/v1",
+        context_window=32000,
+        handle=f"anthropic/claude-3-7-sonnet-latest",
+        put_inner_thoughts_in_kwargs=True,
+        max_tokens=4096,
+    )
+
+    common_step_state = AgentStepState(
+        step_number=1, tool_rules_solver=ToolRulesSolver(tool_rules=[InitToolRule(tool_name="send_message")])
+    )
+
+    return server.batch_manager.create_batch_item(
+        batch_id=batch_id,
+        agent_id=agent_id,
+        llm_config=dummy_llm_config,
+        step_state=common_step_state,
+        actor=default_user,
+    )
+
+
+def mock_anthropic_client(server, batch_a_resp, batch_b_resp, agent_b_id, agent_c_id):
+    """Set up mocks for the Anthropic client's retrieve and results methods."""
+
+    # Mock the retrieve method
+    async def dummy_retrieve(batch_resp_id: str) -> BetaMessageBatch:
+        if batch_resp_id == batch_a_resp.id:
+            return batch_a_resp
+        elif batch_resp_id == batch_b_resp.id:
+            return batch_b_resp
+        else:
+            raise ValueError(f"Unknown batch response id: {batch_resp_id}")
+
+    server.anthropic_async_client.beta.messages.batches.retrieve = AsyncMock(side_effect=dummy_retrieve)
+
+    # Mock the results method
+    def dummy_results(batch_resp_id: str):
+        if batch_resp_id == batch_b_resp.id:
+
+            async def generator():
+                yield create_successful_response(agent_b_id)
+                yield create_failed_response(agent_c_id)
+
+            return generator()
+        else:
+            raise RuntimeError("This test should never request the results for batch_a.")
+
+    server.anthropic_async_client.beta.messages.batches.results = dummy_results
+
+
+# -----------------------------
+# End-to-End Test
+# -----------------------------
+@pytest.mark.asyncio
+async def test_polling_mixed_batch_jobs(client, default_user, server):
+    """
+    End-to-end test for polling batch jobs with mixed statuses and idempotency.
+
+    Test scenario:
+      - Create two batch jobs:
+          - Job A: Single agent that remains "in_progress"
+          - Job B: Two agents that complete (one succeeds, one fails)
+      - Poll jobs and verify:
+          - Job A remains in "running" state
+          - Job B moves to "completed" state
+          - Job B's items reflect appropriate individual success/failure statuses
+      - Test idempotency:
+          - Run additional polls and verify:
+              - Completed job B remains unchanged (no status changes or re-polling)
+              - In-progress job A continues to be polled
+              - All batch items maintain their final states
+    """
+    # --- Step 1: Prepare test data ---
+    # Create batch responses with different statuses
+    batch_a_resp = create_batch_response("msgbatch_A", processing_status="in_progress")
+    batch_b_resp = create_batch_response("msgbatch_B", processing_status="ended")
+
+    # Create test agents
+    agent_a = create_test_agent(client, "agent_a")
+    agent_b = create_test_agent(client, "agent_b")
+    agent_c = create_test_agent(client, "agent_c")
+
+    # --- Step 2: Create batch jobs ---
+    job_a = create_test_batch_job(server, batch_a_resp, default_user)
+    job_b = create_test_batch_job(server, batch_b_resp, default_user)
+
+    # --- Step 3: Create batch items ---
+    item_a = create_test_batch_item(server, job_a.id, agent_a.id, default_user)
+    item_b = create_test_batch_item(server, job_b.id, agent_b.id, default_user)
+    item_c = create_test_batch_item(server, job_b.id, agent_c.id, default_user)
+
+    # --- Step 4: Mock the Anthropic client ---
+    mock_anthropic_client(server, batch_a_resp, batch_b_resp, agent_b.id, agent_c.id)
+
+    # --- Step 5: Run the polling job twice (simulating periodic polling) ---
+    await poll_running_llm_batches(server)
+    await poll_running_llm_batches(server)
+
+    # --- Step 6: Verify batch job status updates ---
+    updated_job_a = server.batch_manager.get_batch_job_by_id(batch_id=job_a.id, actor=default_user)
+    updated_job_b = server.batch_manager.get_batch_job_by_id(batch_id=job_b.id, actor=default_user)
+
+    # Job A should remain running since its processing_status is "in_progress"
+    assert updated_job_a.status == JobStatus.running
+    # Job B should be updated to completed
+    assert updated_job_b.status == JobStatus.completed
+
+    # Both jobs should have been polled
+    assert updated_job_a.last_polled_at is not None
+    assert updated_job_b.last_polled_at is not None
+    assert updated_job_b.latest_polling_response is not None
+
+    # --- Step 7: Verify batch item status updates ---
+    # Item A should remain unchanged
+    updated_item_a = server.batch_manager.get_batch_item_by_id(item_a.id, actor=default_user)
+    assert updated_item_a.request_status == JobStatus.created
+    assert updated_item_a.batch_request_result is None
+
+    # Item B should be marked as completed with a successful result
+    updated_item_b = server.batch_manager.get_batch_item_by_id(item_b.id, actor=default_user)
+    assert updated_item_b.request_status == JobStatus.completed
+    assert updated_item_b.batch_request_result is not None
+
+    # Item C should be marked as failed with an error result
+    updated_item_c = server.batch_manager.get_batch_item_by_id(item_c.id, actor=default_user)
+    assert updated_item_c.request_status == JobStatus.failed
+    assert updated_item_c.batch_request_result is not None
+
+    # --- Step 8: Test idempotency by running polls again ---
+    # Save timestamps and response objects to compare later
+    job_a_polled_at = updated_job_a.last_polled_at
+    job_b_polled_at = updated_job_b.last_polled_at
+    job_b_response = updated_job_b.latest_polling_response
+
+    # Save detailed item states
+    item_a_status = updated_item_a.request_status
+    item_b_status = updated_item_b.request_status
+    item_c_status = updated_item_c.request_status
+    item_b_result = updated_item_b.batch_request_result
+    item_c_result = updated_item_c.batch_request_result
+
+    # Run the polling job again multiple times
+    await poll_running_llm_batches(server)
+    await poll_running_llm_batches(server)
+    await poll_running_llm_batches(server)
+
+    # --- Step 9: Verify that nothing changed for completed jobs ---
+    # Refresh all objects
+    final_job_a = server.batch_manager.get_batch_job_by_id(batch_id=job_a.id, actor=default_user)
+    final_job_b = server.batch_manager.get_batch_job_by_id(batch_id=job_b.id, actor=default_user)
+    final_item_a = server.batch_manager.get_batch_item_by_id(item_a.id, actor=default_user)
+    final_item_b = server.batch_manager.get_batch_item_by_id(item_b.id, actor=default_user)
+    final_item_c = server.batch_manager.get_batch_item_by_id(item_c.id, actor=default_user)
+
+    # Job A should still be polling (last_polled_at should update)
+    assert final_job_a.status == JobStatus.running
+    assert final_job_a.last_polled_at > job_a_polled_at
+
+    # Job B should remain completed with no status changes
+    assert final_job_b.status == JobStatus.completed
+    # The completed job should not be polled again
+    assert final_job_b.last_polled_at == job_b_polled_at
+    assert final_job_b.latest_polling_response == job_b_response
+
+    # All items should maintain their final states
+    assert final_item_a.request_status == item_a_status
+    assert final_item_b.request_status == item_b_status
+    assert final_item_c.request_status == item_c_status
+    assert final_item_b.batch_request_result == item_b_result
+    assert final_item_c.batch_request_result == item_c_result
diff --git a/tests/test_managers.py b/tests/test_managers.py
index 49373f5d..83a2a0f3 100644
--- a/tests/test_managers.py
+++ b/tests/test_managers.py
@@ -39,7 +39,7 @@ from letta.schemas.agent import AgentStepState, CreateAgent, UpdateAgent
 from letta.schemas.block import Block as PydanticBlock
 from letta.schemas.block import BlockUpdate, CreateBlock
 from letta.schemas.embedding_config import EmbeddingConfig
-from letta.schemas.enums import AgentStepStatus, JobStatus, MessageRole
+from letta.schemas.enums import AgentStepStatus, JobStatus, MessageRole, ProviderType
 from letta.schemas.environment_variables import SandboxEnvironmentVariableCreate, SandboxEnvironmentVariableUpdate
 from letta.schemas.file import FileMetadata as PydanticFileMetadata
 from letta.schemas.identity import IdentityCreate, IdentityProperty, IdentityPropertyType, IdentityType, IdentityUpdate
@@ -4708,20 +4708,20 @@ def test_list_tags(server: SyncServer, default_user, default_organization):
 
 def test_create_and_get_batch_request(server, default_user, dummy_beta_message_batch):
     batch = server.batch_manager.create_batch_request(
-        llm_provider="anthropic",
+        llm_provider=ProviderType.anthropic,
         status=JobStatus.created,
         create_batch_response=dummy_beta_message_batch,
         actor=default_user,
     )
     assert batch.id.startswith("batch_req-")
     assert batch.create_batch_response == dummy_beta_message_batch
-    fetched = server.batch_manager.get_batch_request_by_id(batch.id, actor=default_user)
+    fetched = server.batch_manager.get_batch_job_by_id(batch.id, actor=default_user)
     assert fetched.id == batch.id
 
 
 def test_update_batch_status(server, default_user, dummy_beta_message_batch):
     batch = server.batch_manager.create_batch_request(
-        llm_provider="anthropic",
+        llm_provider=ProviderType.anthropic,
         status=JobStatus.created,
         create_batch_response=dummy_beta_message_batch,
         actor=default_user,
@@ -4735,7 +4735,7 @@ def test_update_batch_status(server, default_user, dummy_beta_message_batch):
         actor=default_user,
     )
 
-    updated = server.batch_manager.get_batch_request_by_id(batch.id, actor=default_user)
+    updated = server.batch_manager.get_batch_job_by_id(batch.id, actor=default_user)
     assert updated.status == JobStatus.completed
     assert updated.latest_polling_response == dummy_beta_message_batch
     assert updated.last_polled_at >= before
@@ -4743,7 +4743,7 @@ def test_update_batch_status(server, default_user, dummy_beta_message_batch):
 
 def test_create_and_get_batch_item(server, default_user, sarah_agent, dummy_beta_message_batch, dummy_llm_config, dummy_step_state):
     batch = server.batch_manager.create_batch_request(
-        llm_provider="anthropic",
+        llm_provider=ProviderType.anthropic,
         status=JobStatus.created,
         create_batch_response=dummy_beta_message_batch,
         actor=default_user,
@@ -4769,7 +4769,7 @@ def test_update_batch_item(
     server, default_user, sarah_agent, dummy_beta_message_batch, dummy_llm_config, dummy_step_state, dummy_successful_response
 ):
     batch = server.batch_manager.create_batch_request(
-        llm_provider="anthropic",
+        llm_provider=ProviderType.anthropic,
         status=JobStatus.created,
         create_batch_response=dummy_beta_message_batch,
         actor=default_user,
@@ -4801,7 +4801,7 @@ def test_update_batch_item(
 
 def test_delete_batch_item(server, default_user, sarah_agent, dummy_beta_message_batch, dummy_llm_config, dummy_step_state):
     batch = server.batch_manager.create_batch_request(
-        llm_provider="anthropic",
+        llm_provider=ProviderType.anthropic,
         status=JobStatus.created,
         create_batch_response=dummy_beta_message_batch,
         actor=default_user,