Parallel

                ┌────────────────┐
            ┌──▶│ Proofreader    ├───┐
            │   │ Agent          │   │
            │   └────────────────┘   │
┌─────────────┐ │   ┌────────────────┐   │     ┌─────────┐
│ ParallelLLM ├─┼──▶│ Fact Checker   ├───┼────▶│ Grader  │
└─────────────┘ │   │ Agent          │   │     │ Agent   │
            │   └────────────────┘   │     └─────────┘
            │   ┌────────────────┐   │
            └──▶│ Style Enforcer ├───┘
                │ Agent          │
                └────────────────┘

import asyncio
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_grader")

async def run_parallel_grading():
    async with app.run() as context:
        # Create specialized agents for different evaluation criteria
        proofreader = Agent(
            name="proofreader",
            instruction="""Review the short story for grammar, spelling, and punctuation errors.
            Identify any awkward phrasing or structural issues that could improve clarity. 
            Provide detailed feedback on corrections."""
        )

        fact_checker = Agent(
            name="fact_checker",
            instruction="""Verify the factual consistency within the story. Identify any contradictions,
            logical inconsistencies, or inaccuracies in the plot, character actions, or setting. 
            Highlight potential issues with reasoning or coherence."""
        )

        style_enforcer = Agent(
            name="style_enforcer",
            instruction="""Analyze the story for adherence to style guidelines but first fetch APA style guides from
            at https://owl.purdue.edu/owl/research_and_citation/apa_style/apa_formatting_and_style_guide/general_format.html.
            Evaluate the narrative flow, clarity of expression, and tone. Suggest improvements to 
            enhance storytelling, readability, and engagement.""",
            server_names=["fetch"]  # Access to web fetch for style guidelines
        )

        # Aggregator agent to compile all feedback
        grader = Agent(
            name="grader",
            instruction="""Compile the feedback from the Proofreader, Fact Checker, and Style Enforcer
            into a structured report. Summarize key issues and categorize them by type. 
            Provide actionable recommendations for improving the story, 
            and give an overall grade based on the feedback."""
        )

        # Create parallel workflow
        parallel = ParallelLLM(
            fan_in_agent=grader,
            fan_out_agents=[proofreader, fact_checker, style_enforcer],
            llm_factory=OpenAIAugmentedLLM,
        )

        # Sample story for grading
        story = """
        The Battle of Glimmerwood
        
        In the heart of Glimmerwood, a mystical forest knowed for its radiant trees, a small village thrived. 
        The villagers, who were live peacefully, shared their home with the forest's magical creatures, 
        especially the Glimmerfoxes whose fur shimmer like moonlight.
        
        One fateful evening, the peace was shaterred when the infamous Dark Marauders attack. 
        Lead by the cunning Captain Thorn, the bandits aim to steal the precious Glimmerstones which was believed to grant immortality.
        """

        # Execute parallel grading
        result = await parallel.generate_str(
            message=f"Grade this student's short story submission: {story}"
        )
        
        return result

if __name__ == "__main__":
    result = asyncio.run(run_parallel_grading())
    print(result)

# Advanced configuration
parallel = ParallelLLM(
    fan_in_agent=grader,
    fan_out_agents=[proofreader, fact_checker, style_enforcer],
    llm_factory=OpenAIAugmentedLLM,
    max_concurrent_tasks=3,  # Control concurrency
    timeout_seconds=300,     # Task timeout
    retry_failed_tasks=True, # Retry on failure
)

# Execute with custom parameters
from mcp_agent.workflows.llm.augmented_llm import RequestParams

result = await parallel.generate_str(
    message="Grade this assignment",
    request_params=RequestParams(
        model="gpt-4o",
        temperature=0.3,
        max_tokens=2000
    )
)

git clone https://github.com/lastmile-ai/mcp-agent.git
cd mcp-agent/examples/workflows/workflow_parallel

pip install uv
uv sync
uv pip install -r requirements.txt

cp mcp_agent.secrets.yaml.example mcp_agent.secrets.yaml

openai_api_key: "your-openai-api-key"
anthropic_api_key: "your-anthropic-api-key"  # optional

uv run main.py