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Multi-Agent Systems

Multi-agent systems allow multiple AI agents to work together, each handling different parts of a complex task. This approach mirrors how human teams collaborate, with each member contributing their specialized skills.

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Why Use Multiple Agents?

Specialization

Each agent can focus on its area of expertise

Complex Tasks

Break down difficult problems into manageable pieces

Scalability

Add more agents as tasks become more complex

Diverse Perspectives

Different agents can approach problems differently

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Basic Multi-Agent Architecture

In this example:
  1. The Research Agent gathers information
  2. The Analysis Agent processes and interprets the data
  3. The Writing Agent creates the final content

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Multi-Agent Patterns

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1. Pipeline Pattern

Agents work in sequence, with each agent handling a specific stage of the process. Example Use Case: Content creation where one agent researches, another drafts, and a third edits.

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2. Expert Panel Pattern

Multiple specialist agents work in parallel on the same problem, then their outputs are combined. Example Use Case: Financial analysis where different experts analyze market trends, economic indicators, and company performance.

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3. Hierarchical Pattern

A manager agent delegates tasks to worker agents and coordinates their efforts. Example Use Case: Project management where a coordinator assigns tasks and integrates results.

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Implementing Multi-Agent Systems in PraisonAI

Here’s a simple example of creating a multi-agent system: 
from praisonaiagents import Agent, PraisonAIAgents

# Create individual agents
research_agent = Agent(
    name="Researcher",
    instructions="Research the latest trends in renewable energy"
)

analysis_agent = Agent(
    name="Analyst",
    instructions="Analyze the research findings and identify key insights"
)

writing_agent = Agent(
    name="Writer", 
    instructions="Create a clear, engaging report based on the analysis"
)

# Create a multi-agent system
agents = PraisonAIAgents(
    agents=[research_agent, analysis_agent, writing_agent]
)

# Start the agents
agents.start()

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Agent Communication

For agents to work together effectively, they need to communicate. This happens through:

Output Passing

One agent’s output becomes another’s input

Shared Memory

Agents can access common information

Message Exchange

Agents can send specific messages to each other

Coordination Protocols

Rules that determine how agents interact

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Challenges in Multi-Agent Systems

Coordination

Ensuring agents work together smoothly

Conflicting Goals

Managing agents with different objectives

Information Transfer

Passing information effectively between agents

Resource Management

Allocating computational resources efficiently

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Designing Effective Multi-Agent Systems

Start simple with just 2-3 agents and a clear workflow before scaling to more complex systems.
Key principles for effective design:
  1. Clear Role Definition: Each agent should have a specific and well-defined role
  2. Minimized Dependencies: Reduce complex interdependencies between agents
  3. Standardized Communication: Use consistent formats for information exchange
  4. Failure Handling: Plan for cases where an agent fails to complete its task
  5. Performance Monitoring: Track how well each agent and the overall system performs
In the next lesson, we’ll explore how to create effective agent workflows using the Process component.