Multi-Agent Orchestration

Design and orchestrate sophisticated multi-agent systems where specialized agents collaborate to solve complex problems, combining different expertise and perspectives.

Quick Start

Get started with multi-agent implementations in the examples and utilities:

• Examples: See examples/ directory for complete implementations:

  • orchestration_patterns.py - Sequential, parallel, hierarchical, and consensus orchestration
  • framework_implementations.py - Templates for CrewAI, AutoGen, LangGraph, and Swarm

• Utilities: See scripts/ directory for helper modules:

  • agent_communication.py - Message broker, shared memory, and communication protocols
  • workflow_management.py - Workflow execution, optimization, and monitoring
  • benchmarking.py - Team performance and agent effectiveness metrics

Overview

Multi-agent systems decompose complex problems into specialized sub-tasks, assigning each to an agent with relevant expertise, then coordinating their work toward a unified goal.

When Multi-Agent Systems Shine

• Complex Workflows: Tasks requiring multiple specialized roles
• Domain-Specific Expertise: Finance, legal, HR, engineering need different knowledge
• Parallel Processing: Multiple agents work on different aspects simultaneously
• Collaborative Reasoning: Agents debate, refine, and improve solutions
• Resilience: Failures in one agent don't break the entire system
• Scalability: Easy to add new specialized agents

Architecture Overview

User Request
    ↓
Orchestrator
    ├→ Agent 1 (Specialist) → Task 1
    ├→ Agent 2 (Specialist) → Task 2
    ├→ Agent 3 (Specialist) → Task 3
    ↓
Result Aggregator
    ↓
Final Response

Core Concepts

Agent Definition

An agent is defined by:

• Role: What responsibility does it have? (e.g., "Financial Analyst")
• Goal: What should it accomplish? (e.g., "Analyze financial risks")
• Expertise: What knowledge/tools does it have?
• Tools: What capabilities can it access?
• Context: What information does it need to work effectively?

Orchestration Patterns

1. Sequential Orchestration

• Agents work one after another
• Each agent uses output from previous agent
• Use Case: Steps must follow order (research → analysis → writing)

2. Parallel Orchestration

• Multiple agents work simultaneously
• Results aggregated at the end
• Use Case: Independent tasks (analyze competitors, market, users)

3. Hierarchical Orchestration

• Senior agent delegates to junior agents
• Manager coordinates flow
• Use Case: Large projects with oversight

4. Consensus-Based Orchestration

• Multiple agents analyze problem
• Debate and refine ideas
• Vote or reach consensus
• Use Case: Complex decisions needing multiple perspectives

5. Tool-Mediated Orchestration

• Agents use shared tools/databases
• Minimal direct communication
• Use Case: Large systems, indirect coordination

Multi-Agent Team Examples

Finance Team

Coordinator Agent
    ├→ Market Analyst Agent
    │   ├ Tools: Market data API, financial news
    │   └ Task: Analyze market conditions
    ├→ Financial Analyst Agent
    │   ├ Tools: Financial statements, ratio calculations
    │   └ Task: Analyze company financials
    ├→ Risk Manager Agent
    │   ├ Tools: Risk models, scenario analysis
    │   └ Task: Assess investment risks
    └→ Report Writer Agent
        ├ Tools: Document generation
        └ Task: Synthesize findings into report

Legal Team

Case Manager Agent (Coordinator)
    ├→ Contract Analyzer Agent
    │   └ Task: Review contract terms
    ├→ Precedent Research Agent
    │   └ Task: Find relevant case law
    ├→ Risk Assessor Agent
    │   └ Task: Identify legal risks
    └→ Document Drafter Agent
        └ Task: Prepare legal documents

Customer Support Team

Support Coordinator
    ├→ Issue Classifier Agent
    │   └ Task: Categorize customer issue
    ├→ Knowledge Base Agent
    │   └ Task: Find relevant documentation
    ├→ Escalation Agent
    │   └ Task: Determine if human escalation needed
    └→ Solution Synthesizer Agent
        └ Task: Prepare comprehensive response

Implementation Frameworks

1. CrewAI

Best For: Teams with clear roles and hierarchical structure

from crewai import Agent, Task, Crew

# Define agents
analyst = Agent(
    role="Financial Analyst",
    goal="Analyze financial data and provide insights",
    backstory="Expert in financial markets with 10+ years experience"
)

researcher = Agent(
    role="Market Researcher",
    goal="Research market trends and competition",
    backstory="Data-driven researcher specializing in market analysis"
)

# Define tasks
analysis_task = Task(
    description="Analyze Q3 financial results for {company}",
    agent=analyst,
    tools=[financial_tool, data_tool]
)

research_task = Task(
    description="Research competitive landscape in {market}",
    agent=researcher,
    tools=[web_search_tool, industry_data_tool]
)

# Create crew and execute
crew = Crew(
    agents=[analyst, researcher],
    tasks=[analysis_task, research_task],
    process=Process.sequential
)

result = crew.kickoff(inputs={"company": "TechCorp", "market": "AI"})

2. AutoGen (Microsoft)

Best For: Complex multi-turn conversations and negotiations

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

# Define agents
analyst = AssistantAgent(
    name="analyst",
    system_message="You are a financial analyst..."
)

researcher = AssistantAgent(
    name="researcher",
    system_message="You are a market researcher..."
)

# Create group chat
groupchat = GroupChat(
    agents=[analyst, researcher],
    messages=[],
    max_round=10,
    speaker_selection_method="auto"
)

# Manage group conversation
manager = GroupChatManager(groupchat=groupchat)

# User proxy to initiate conversation
user = UserProxyAgent(name="user")

# Have conversation
user.initiate_chat(
    manager,
    message="Analyze if Company X should invest in Y market"
)

3. LangGraph

Best For: Complex workflows with state management

from langgraph.graph import Graph, StateGraph
from langgraph.prebuilt import create_agent_executor

# Define state
class AgentState:
    research_findings: str
    analysis: str
    recommendations: str

# Create graph
graph = StateGraph(AgentState)

# Add nodes for each agent
graph.add_node("researcher", research_agent)
graph.add_node("analyst", analyst_agent)
graph.add_node("writer", writer_agent)

# Define edges (workflow)
graph.add_edge("researcher", "analyst")
graph.add_edge("analyst", "writer")

# Set entry/exit points
graph.set_entry_point("researcher")
graph.set_finish_point("writer")

# Compile and run
workflow = graph.compile()
result = workflow.invoke({"topic": "AI trends"})

4. OpenAI Swarm

Best For: Simple agent handoffs and conversational workflows

from swarm import Agent, Swarm

# Define agents
triage_agent = Agent(
    name="Triage Agent",
    instructions="Determine which specialist to route the customer to"
)

billing_agent = Agent(
    name="Billing Specialist",
    instructions="Handle billing and payment questions"
)

technical_agent = Agent(
    name="Technical Support",
    instructions="Handle technical issues"
)

# Define handoff functions
def 
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