Workflows — DAG orchestration

Compose agents (and any callables) into a dependency graph. Independent steps run in parallel; dependent steps run in order. Conditions skip steps, failures cascade, and steps can retry.

from neuroagent import Agent, Workflow

researcher = Agent(provider="openai", system_prompt="You research topics thoroughly.")
writer     = Agent(provider="openai", system_prompt="You write engaging prose.")

wf = Workflow("content-pipeline")
wf.add_agent_step("research", researcher, lambda ctx: f"Research: {ctx.get('topic')}")
wf.add_agent_step(
    "draft", writer,
    lambda ctx: f"Write a post based on:\n{ctx.result('research')}",
    depends_on=["research"],
)

result = wf.run(inputs={"topic": "AI agent frameworks"})
print(result.success)            # True
print(result["draft"])           # the writer's output

Plain-function steps, conditions, retries, and resume

from neuroagent.workflow import RetryPolicy
wf.add_step("flaky", do_work, retry=RetryPolicy(max_attempts=3, delay=1.0))
wf.run(inputs={...}, checkpoint={"research": "...prior result..."})   # resume