Documentation Index
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Workflows chain agents in sequence and support conditional logic via step handlers.
Create and chain
researcher = client.Agent("Researcher", "Finds info", "openai", "gpt-4-turbo", "OPENAI_API_KEY")
summarizer = client.Agent("Summarizer", "Summarizes", "openai", "gpt-3.5-turbo", "OPENAI_API_KEY")
wf = client.Workflow().add(researcher).add(summarizer)
result = wf.run("What are the key differences between nuclear fission and fusion?")
Structured output for final step
from pydantic import BaseModel
class KeyDifferences(BaseModel):
title: str
points: list[str]
summary: str
final = wf.run("Explain differences between fission and fusion", response_model=KeyDifferences)
Conditional workflows
Use a handler that decides to CONTINUE or STOP.
from pydantic import BaseModel
class Triage(BaseModel):
ambiguous: bool
follow_up_details: str
reply_to_user: str
def triage_handler(output: Triage):
return ("STOP", None) if output.ambiguous else ("CONTINUE", output.follow_up_details)
wf = (
client.Workflow()
.add(researcher, response_model=Triage, handler=triage_handler)
.add(summarizer)
)
