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 
how to use multi-agent-orchestration
CursorClaude CodeClineCodexWindsurfGooseGitHub CopilotVS CodeGemini CLIKiloOpencodeKimi CLIRoo ClineAiderContinueZedBolt.newLovablev0Replit AgentSweepSmol DeveloperDevinCavemanAmpAntigravity
How to use multi-agent-orchestration on Cursor
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1
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
›Cursor installed and configured on your development machine
›Node.js version 16.0+ with npm package manager (verify with node --version)
›Active project directory or workspace where you want to add multi-agent-orchestration
2
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
$npx skills add https://github.com/qodex-ai/ai-agent-skills --skill multi-agent-orchestrationcopy
The skills CLI fetches multi-agent-orchestration from GitHub repository qodex-ai/ai-agent-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│   • Amp
│   • Antigravity
│   • Cline
│   • Codex
│ ●Cursor(selected)
│   • Cursor
│   • Windsurf
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/multi-agent-orchestration
Reload or restart Cursor to activate multi-agent-orchestration. Access the skill through slash commands (e.g., /multi-agent-orchestration) or your agent's skill management interface.
⚠
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
Additional Resources
›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?