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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?

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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 Praison Labs

Here’s a simple example of creating a multi-agent system:

from praisonaiagents import Agent, Praison LabsAgents

# 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 = Praison LabsAgents(
    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:

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Challenges in Multi-Agent Systems

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Designing Effective Multi-Agent 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.