# Multi-Agent Collaboration - Quick Start Guide

## 🚀 Get Started in 3 Minutes

### Step 1: Navigate to the Directory
```bash
cd foundational_design_patterns/7_multi_agent_collaboration
```

### Step 2: Run the Example
```bash
bash run.sh
```

---

## 📖 Understanding Multi-Agent Collaboration in 30 Seconds

**Multi-Agent Collaboration** uses a team of specialized agents to solve complex tasks:

```
Single Agent:                    Multi-Agent Team:
One agent does                   Manager
everything                       ├─ Researcher (web search)
(overloaded,                     ├─ Coder (code execution)
 generalist)                     ├─ Designer (image generation)
                                 └─ Writer (content creation)
                                 (specialized, efficient)
```

Like a company: different roles, coordinated effort!

---

## 🎯 What This Example Does

The example demonstrates **coordinated agent teams**:

1. **Manager/Coordinator** - Delegates tasks to specialists
2. **Research Agent** - Gathers information
3. **Analysis Agent** - Processes data
4. **Writing Agent** - Creates content
5. **QA Agent** - Reviews and validates

---

## 💡 Example Flow

```
Goal: "Create a technical blog post on quantum computing"
    ↓
Manager: Delegates to team
    ↓
Research Agent: Gathers quantum computing facts
    ↓
Analysis Agent: Identifies key concepts
    ↓
Writing Agent: Drafts the blog post
    ↓
QA Agent: Reviews for accuracy and clarity
    ↓
Manager: Combines results → Final blog post
```

---

## 🔧 Key Concepts

### Agent Specialization
Each agent has specific skills and tools.

### Coordination
Manager orchestrates agent interactions.

### Parallel Workstreams
Independent tasks run simultaneously.

### Quality Control
Dedicated reviewers catch errors.

---

## 🎨 When to Use Multi-Agent Collaboration

✅ **Good For:**
- Complex tasks requiring diverse expertise
- Distinct workflow stages (research → draft → edit)
- Tool-specialized roles
- Quality-critical pipelines (with reviewers)
- Reusable agent components

❌ **Not Ideal For:**
- Simple tasks one agent can handle
- Tasks that can't be decomposed
- High coordination overhead scenarios
- When single-agent patterns suffice

---

## 🛠️ Collaboration Patterns

### 1. Sequential Pipeline (Linear)
```
Research Agent → Design Agent → Writing Agent → Package Agent
```
**Best for**: Predictable workflows with clear stages.

### 2. Supervisor/Manager (Hierarchical)
```
        Manager
       ↙   ↓   ↘
   Agent A  B  C
```
**Best for**: Dynamic task allocation and central control.

### 3. Parallel Processing
```
         ┌─ Research A
Goal ────┼─ Research B ──→ Merge → Output
         └─ Research C
```
**Best for**: Independent tasks that can run simultaneously.

### 4. Debate & Consensus
```
Agent A ←→ Agent B ←→ Agent C → Consensus Decision
```
**Best for**: Evaluating options, critical decisions.

### 5. Critic-Reviewer
```
Creator Agent → Critic Agent → Reviser Agent → Final Output
```
**Best for**: High-quality output with validation.

---

## 📊 Multi-Agent Benefits

| Aspect | Single Agent | Multi-Agent |
|--------|-------------|-------------|
| Specialization | Generalist | Experts per domain |
| Quality | Good | Excellent (with QA) |
| Scalability | Limited | High (add agents) |
| Modularity | Low | High (reuse agents) |
| Complexity | Low | Medium-High |
| Speed | Slower | Faster (parallel) |

---

## 💡 Common Multi-Agent Architectures

### Research + Writing Pipeline
```python
research_agent = create_agent(llm, [web_search_tool])
writer_agent = create_agent(llm, [])
editor_agent = create_agent(llm, [])

# Sequential handoff
research = research_agent.invoke(topic)
draft = writer_agent.invoke(research)
final = editor_agent.invoke(draft)
```

### Supervisor Pattern
```python
manager_agent = create_agent(llm, [delegate_tool])
specialist_agents = {
    "research": research_agent,
    "code": code_agent,
    "design": design_agent
}

# Manager delegates
task_type = manager_agent.classify(task)
result = specialist_agents[task_type].invoke(task)
```

### Parallel Specialists
```python
tasks = [
    research_agent.invoke(query),
    code_agent.invoke(query),
    design_agent.invoke(query)
]

# Run in parallel
results = await asyncio.gather(*tasks)
combined = synthesize(results)
```

---

## 🔧 Customization Tips

### Define Specialized Agents
```python
# Research agent with search tools
research_agent = create_agent(
    model=llm,
    tools=[web_search, wikipedia_search],
    system_prompt="You are a research specialist..."
)

# Code agent with execution tools
code_agent = create_agent(
    model=llm,
    tools=[python_repl, code_validator],
    system_prompt="You are a coding expert..."
)
```

### Create Manager Agent
```python
manager_prompt = """
You are a project manager coordinating specialist agents.

Available agents:
- research_agent: Web search and information gathering
- code_agent: Code generation and execution
- design_agent: Visual design and diagrams

Delegate tasks to appropriate agents and synthesize results.
"""

manager = create_agent(model=llm, tools=[delegate_tool], system_prompt=manager_prompt)
```

### Implement Handoffs
```python
def sequential_pipeline(task):
    # Step 1: Research
    research = research_agent.invoke(task)

    # Step 2: Code based on research
    code = code_agent.invoke({
        "task": task,
        "context": research
    })

    # Step 3: Document the code
    docs = writer_agent.invoke({
        "code": code,
        "research": research
    })

    return {"research": research, "code": code, "docs": docs}
```

---

## 🐛 Common Issues & Solutions

### Issue: Agents Duplicating Work
**Solution**: Clear role definitions and task delegation.

### Issue: Poor Coordination
**Solution**: Use explicit manager agent or state tracking.

### Issue: Information Loss in Handoffs
**Solution**: Pass complete context between agents.

### Issue: Bottleneck at Manager
**Solution**: Allow peer-to-peer communication or parallel execution.

---

## 📚 Real-World Applications

### Software Development
```
Architect → Coder → Tester → Docs Writer → Reviewer
```

### Research Reports
```
Data Collector → Analyst → Statistician → Writer → Editor
```

### Marketing Campaigns
```
Trend Researcher → Designer → Copywriter → QA → Packager
```

### Customer Support
```
Classifier → [Tech Support | Billing | General] → Escalation
```

---

## 🎓 Advanced Techniques

### Multi-Level Hierarchy
```
Executive Manager
├─ Research Lead
│  ├─ Web Researcher
│  └─ Fact Checker
└─ Writing Lead
   ├─ Draft Writer
   └─ Editor
```

### Dynamic Team Formation
Manager creates ad-hoc teams based on task requirements.

### Agent Memory & Context
Agents maintain conversation history and shared knowledge.

### Reputation System
Track agent performance and route tasks accordingly.

---

## 🔒 Best Practices

1. **Clear Role Definitions**: Each agent has specific responsibilities
2. **Explicit Communication**: Use structured messages between agents
3. **Error Handling**: Graceful degradation if agents fail
4. **Observability**: Log agent interactions for debugging
5. **Testing**: Test agents individually and as a team
6. **Resource Limits**: Prevent runaway agent interactions

---

## 📚 Learn More

- **Full Documentation**: See [README.md](./README.md)
- **Main Repository**: See [../../README.md](../../README.md)
- **Related Patterns**:
  - Pattern 3 (Parallelization) - Parallel agent execution
  - Pattern 6 (Planning) - Manager planning for agents
  - Pattern 8 (ReAct) - Agents using ReAct internally

---

## 🎓 Next Steps

1. ✅ Run the multi-agent example
2. ✅ Observe agent interactions
3. ✅ Create a custom specialist agent
4. ✅ Implement a different collaboration pattern
5. ✅ Build a domain-specific agent team

---

**Pattern Type**: Distributed Specialization

**Complexity**: ⭐⭐⭐⭐⭐ (Advanced)

**Best For**: Complex tasks, diverse expertise needs

**Key Benefit**: Modularity, quality, parallel processing

**Trade-off**: Higher complexity, coordination overhead
