Parallel

from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM
from mcp_agent.workflows.parallel.parallel_llm import ParallelLLM

app = MCPApp(name="parallel_example")

async with app.run() as context:
    # Create specialized agents for different tasks
    proofreader = Agent(
        name="proofreader",
        instruction="Review for grammar, spelling, and punctuation errors."
    )

    fact_checker = Agent(
        name="fact_checker",
        instruction="Verify factual consistency and logical coherence."
    )

    style_enforcer = Agent(
        name="style_enforcer",
        instruction="Analyze narrative flow and storytelling quality.",
        server_names=["fetch"]
    )

    # Aggregator agent
    grader = Agent(
        name="grader",
        instruction="Compile feedback into a structured report with final grade."
    )

    # Create parallel LLM with fan-out and fan-in
    parallel = ParallelLLM(
        fan_in_agent=grader,
        fan_out_agents=[proofreader, fact_checker, style_enforcer],
        llm_factory=OpenAIAugmentedLLM,
    )

    # Execute parallel workflow
    result = await parallel.generate_str(
        message="Grade this student's short story submission: [story text]"
    )