Orchestrator

from distillcore_agents import Orchestrator
 
orch = Orchestrator(
    model="openai:gpt-4o-mini",            # LLM model for agents
    store_path="~/.distillcore/agents.db",  # agent result persistence
    doc_store_path="~/.distillcore/store.db",  # document store path
    openai_api_key=None,                     # or OPENAI_API_KEY env var
    tenant_id=None,                          # tenant isolation
    max_concurrency=3,                       # batch concurrency limit
)

async with Orchestrator() as orch:
    # Agents are initialized, stores are connected
    result = await orch.process_one("doc.pdf")
# Stores are closed on exit

result = await orch.process_one("doc.pdf")
# Returns PipelineResult with triage, processing, qa, and optional research

async for event, result in orch.process_one_stream("doc.pdf"):
    print(event.event_type)  # 'started', 'tool_call', 'tool_result', 'completed', 'error'
    print(event.data)        # event-specific data
# result is the final PipelineResult (available after loop)

batch = await orch.process_batch(
    ["doc1.pdf", "doc2.pdf", "doc3.pdf"],
    batch_id="my-batch",         # optional batch identifier
    on_progress=callback,        # optional progress callback
)
 
print(batch.total)       # 3
print(batch.succeeded)   # 3
print(batch.failed)      # 0

result = await orch.research("What are the key findings?")

class PipelineResult(BaseModel):
    session_id: str
    source: str
    triage: TriageDecision
    processing: ProcessingDecision
    qa: QADecision
    research: ResearchResult | None  # only if QA fails

class BatchOutput(BaseModel):
    session_id: str
    batch_id: str | None
    results: list[PipelineResult]
    failures: list[ItemFailure]
    total: int
    succeeded: int
    failed: int