from typing import List, Optional from fastapi import APIRouter, Body, Depends, Header, Query from fastapi.exceptions import HTTPException from starlette.requests import Request from letta.agents.letta_agent_batch import LettaAgentBatch from letta.log import get_logger from letta.orm.errors import NoResultFound from letta.schemas.job import BatchJob, JobStatus, JobType, JobUpdate from letta.schemas.letta_request import CreateBatch from letta.schemas.letta_response import LettaBatchMessages from letta.server.rest_api.utils import get_letta_server from letta.server.server import SyncServer from letta.settings import settings router = APIRouter(prefix="/messages", tags=["messages"]) logger = get_logger(__name__) # Batch APIs @router.post( "/batches", response_model=BatchJob, operation_id="create_messages_batch", ) async def create_messages_batch( request: Request, payload: CreateBatch = Body(..., description="Messages and config for all agents"), server: SyncServer = Depends(get_letta_server), actor_id: Optional[str] = Header(None, alias="user_id"), ): """ Submit a batch of agent messages for asynchronous processing. Creates a job that will fan out messages to all listed agents and process them in parallel. """ # Reject requests greater than 256Mbs max_bytes = 256 * 1024 * 1024 content_length = request.headers.get("content-length") if content_length: length = int(content_length) if length > max_bytes: raise HTTPException(status_code=413, detail=f"Request too large ({length} bytes). Max is {max_bytes} bytes.") if not settings.enable_batch_job_polling: logger.warning("Batch job polling is disabled. Enable batch processing by setting LETTA_ENABLE_BATCH_JOB_POLLING to True.") actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id) batch_job = BatchJob( user_id=actor.id, status=JobStatus.running, metadata={ "job_type": "batch_messages", }, callback_url=str(payload.callback_url), ) try: batch_job = await server.job_manager.create_job_async(pydantic_job=batch_job, actor=actor) # create the batch runner batch_runner = LettaAgentBatch( message_manager=server.message_manager, agent_manager=server.agent_manager, block_manager=server.block_manager, passage_manager=server.passage_manager, batch_manager=server.batch_manager, sandbox_config_manager=server.sandbox_config_manager, job_manager=server.job_manager, actor=actor, ) await batch_runner.step_until_request(batch_requests=payload.requests, letta_batch_job_id=batch_job.id) # TODO: update run metadata except Exception as e: import traceback print("Error creating batch job", e) traceback.print_exc() # mark job as failed await server.job_manager.update_job_by_id_async(job_id=batch_job.id, job_update=JobUpdate(status=JobStatus.failed), actor=actor) raise return batch_job @router.get("/batches/{batch_id}", response_model=BatchJob, operation_id="retrieve_batch_run") async def retrieve_batch_run( batch_id: str, actor_id: Optional[str] = Header(None, alias="user_id"), server: "SyncServer" = Depends(get_letta_server), ): """ Get the status of a batch run. """ actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id) try: job = await server.job_manager.get_job_by_id_async(job_id=batch_id, actor=actor) return BatchJob.from_job(job) except NoResultFound: raise HTTPException(status_code=404, detail="Batch not found") @router.get("/batches", response_model=List[BatchJob], operation_id="list_batch_runs") async def list_batch_runs( actor_id: Optional[str] = Header(None, alias="user_id"), server: "SyncServer" = Depends(get_letta_server), ): """ List all batch runs. """ # TODO: filter actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id) jobs = server.job_manager.list_jobs(actor=actor, statuses=[JobStatus.created, JobStatus.running], job_type=JobType.BATCH) return [BatchJob.from_job(job) for job in jobs] @router.get( "/batches/{batch_id}/messages", response_model=LettaBatchMessages, operation_id="list_batch_messages", ) async def list_batch_messages( batch_id: str, limit: int = Query(100, description="Maximum number of messages to return"), cursor: Optional[str] = Query( None, description="Message ID to use as pagination cursor (get messages before/after this ID) depending on sort_descending." ), agent_id: Optional[str] = Query(None, description="Filter messages by agent ID"), sort_descending: bool = Query(True, description="Sort messages by creation time (true=newest first)"), actor_id: Optional[str] = Header(None, alias="user_id"), server: SyncServer = Depends(get_letta_server), ): """ Get messages for a specific batch job. Returns messages associated with the batch in chronological order. Pagination: - For the first page, omit the cursor parameter - For subsequent pages, use the ID of the last message from the previous response as the cursor - Results will include messages before/after the cursor based on sort_descending """ actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id) # First, verify the batch job exists and the user has access to it try: job = await server.job_manager.get_job_by_id_async(job_id=batch_id, actor=actor) BatchJob.from_job(job) except NoResultFound: raise HTTPException(status_code=404, detail="Batch not found") # Get messages directly using our efficient method # We'll need to update the underlying implementation to use message_id as cursor messages = await server.batch_manager.get_messages_for_letta_batch_async( letta_batch_job_id=batch_id, limit=limit, actor=actor, agent_id=agent_id, sort_descending=sort_descending, cursor=cursor ) return LettaBatchMessages(messages=messages) @router.patch("/batches/{batch_id}/cancel", operation_id="cancel_batch_run") async def cancel_batch_run( batch_id: str, server: "SyncServer" = Depends(get_letta_server), actor_id: Optional[str] = Header(None, alias="user_id"), ): """ Cancel a batch run. """ actor = await server.user_manager.get_actor_or_default_async(actor_id=actor_id) try: job = await server.job_manager.get_job_by_id_async(job_id=batch_id, actor=actor) job = await server.job_manager.update_job_by_id_async(job_id=job.id, job_update=JobUpdate(status=JobStatus.cancelled), actor=actor) # Get related llm batch jobs llm_batch_jobs = await server.batch_manager.list_llm_batch_jobs_async(letta_batch_id=job.id, actor=actor) for llm_batch_job in llm_batch_jobs: if llm_batch_job.status in {JobStatus.running, JobStatus.created}: # TODO: Extend to providers beyond anthropic # TODO: For now, we only support anthropic # Cancel the job anthropic_batch_id = llm_batch_job.create_batch_response.id await server.anthropic_async_client.messages.batches.cancel(anthropic_batch_id) # Update all the batch_job statuses await server.batch_manager.update_llm_batch_status_async( llm_batch_id=llm_batch_job.id, status=JobStatus.cancelled, actor=actor ) except NoResultFound: raise HTTPException(status_code=404, detail="Run not found")