multi-agent-orchestration

What this skill does

# 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/`](examples/) directory for complete implementations:
  - [`orchestration_patterns.py`](examples/orchestration_patterns.py) - Sequential, parallel, hierarchical, and consensus orchestration
  - [`framework_implementations.py`](examples/framework_implementations.py) - Templates for CrewAI, AutoGen, LangGraph, and Swarm

- **Utilities**: See [`scripts/`](scripts/) directory for helper modules:
  - [`agent_communication.py`](scripts/agent_communication.py) - Message broker, shared memory, and communication protocols
  - [`workflow_management.py`](scripts/workflow_management.py) - Workflow execution, optimization, and monitoring
  - [`benchmarking.py`](scripts/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

```python
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

```python
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

```python
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

```python
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 route_to_billing(reason: str):
    return billing_agent

def route_to_technical(reason: str):
    return technical_agent

# Add tools to triage agent
triage_agent.functions = [route_to_billing, route_to_technical]

# Execute swarm
client = Swarm()
response = client.run(
    agent=triage_agent,
    messages=[{"role": "user", "content": "I have a billing qu