---
title: "Parallel"
description: "Execute multiple tasks simultaneously with intelligent result aggregation and conflict resolution."
---

import { Card } from "@mintlify/components";

![Parallel Workflow Pattern](/images/parallel-workflow.png)

## Overview

The Parallel Workflow pattern uses a fan-out/fan-in approach where multiple agents work on different aspects of a task simultaneously, then a coordinating agent aggregates their results.

## Quick Example

```python
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]"
    )
```

## Key Features

- **Fan-Out Processing**: Distribute work across multiple specialized agents
- **Fan-In Aggregation**: Intelligent result compilation and synthesis
- **Concurrent Execution**: All fan-out agents work simultaneously
- **Specialized Roles**: Each agent focuses on specific expertise areas
- **Structured Output**: Coordinated final result from aggregator agent

## Use Cases

- **Content Review**: Multiple reviewers checking different aspects
- **Multi-perspective Analysis**: Getting diverse viewpoints on complex topics
- **Quality Assurance**: Parallel validation across different criteria
- **Research Synthesis**: Gathering information from multiple specialized sources

<Card
  title="Full Implementation"
  href="https://github.com/lastmile-ai/mcp-agent/tree/main/examples/workflows/workflow_parallel"
>
  See the complete parallel workflow example with student assignment grading use
  case.
</Card>
